answer-keys-pypd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv(\"./data/stock-market-data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 哪些股票的代码中包含\"8\"这个数字？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['600008.SH', '600018.SH', '600028.SH', '600038.SH', '600048.SH'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\\\n",
    "    [data[\"symbol\"].str.contains(\"8\")]\\\n",
    "    [\"symbol\"]\\\n",
    "    .unique()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.  每天上涨和下跌的股票各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      tag\n",
       "20120104  上涨      191\n",
       "          下跌     2007\n",
       "          不变      122\n",
       "20120105  上涨      132\n",
       "          下跌     2071\n",
       "                 ... \n",
       "20120628  下跌     1819\n",
       "          不变      176\n",
       "20120629  上涨     1890\n",
       "          下跌      358\n",
       "          不变      174\n",
       "Name: symbol, Length: 351, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"tag\"] \\\n",
    "    = np.where(\n",
    "        data[\"close\"] - data[\"pre_close\"] > 0,\n",
    "        \"上涨\", \n",
    "        np.where(data[\"close\"] - data[\"pre_close\"] < 0, \n",
    "                \"下跌\", \n",
    "                \"不变\"))\n",
    "data\\\n",
    "    .groupby(['date', 'tag'])\\\n",
    "    ['symbol']\\\n",
    "    .count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 每天每个交易所上涨、下跌的股票各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      exchange\n",
       "20120104  SH           921\n",
       "          SZ          1399\n",
       "20120105  SH           921\n",
       "          SZ          1399\n",
       "20120106  SH           921\n",
       "                      ... \n",
       "20120627  SZ          1484\n",
       "20120628  SH           935\n",
       "          SZ          1487\n",
       "20120629  SH           935\n",
       "          SZ          1487\n",
       "Name: symbol, Length: 234, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"tag\"] \\\n",
    "    = np.where(\n",
    "        data[\"close\"] - data[\"pre_close\"] > 0, \n",
    "        \"上涨\", \n",
    "        np.where(\n",
    "            data[\"close\"] - data[\"pre_close\"] < 0, \n",
    "            \"下跌\", \n",
    "            \"不变\"))\n",
    "data[\"exchange\"] = \\\n",
    "    data['symbol']\\\n",
    "    .str.strip()\\\n",
    "    .str[-2:]\n",
    "\n",
    "data\\\n",
    "    .groupby([\"date\", \"exchange\"])[\"symbol\"]\\\n",
    "    .count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 沪深300成分股中，每天上涨、下跌的股票各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      tag\n",
       "20120104  上涨      20\n",
       "          下跌     275\n",
       "          不变       5\n",
       "20120105  上涨      50\n",
       "          下跌     242\n",
       "                ... \n",
       "20120628  下跌     229\n",
       "          不变      19\n",
       "20120629  上涨     244\n",
       "          下跌      40\n",
       "          不变      16\n",
       "Name: symbol, Length: 350, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"tag\"] \\\n",
    "    = np.where(\n",
    "        data[\"close\"] - data[\"pre_close\"] > 0, \n",
    "        \"上涨\", \n",
    "        np.where(\n",
    "            data[\"close\"] - data[\"pre_close\"] < 0, \n",
    "            \"下跌\", \n",
    "            \"不变\")\n",
    "            )\n",
    "data\\\n",
    "    [data.index_w300 > 0]\\\n",
    "    .groupby(['date', 'tag'])\\\n",
    "    [\"symbol\"]\\\n",
    "    .count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. 每天每个行业各有多少只股票？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      industry\n",
       "20120104  AERODEF      10\n",
       "          AIRLINE      12\n",
       "          AUTO         85\n",
       "          BANKS        16\n",
       "          BEV          30\n",
       "                     ... \n",
       "20120629  REALEST     132\n",
       "          RETAIL       74\n",
       "          SOFTWARE     89\n",
       "          TRDDIST      36\n",
       "          UTILITIE     75\n",
       "Name: symbol, Length: 3744, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\\\n",
    "    .groupby([\"date\", \"industry\"])\\\n",
    "    [\"symbol\"]\\\n",
    "    .count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. 股票数最大的行业和总成交额最大的行业是否总是同一个行业？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>date</th>\n",
       "      <th>industry</th>\n",
       "      <th>amount</th>\n",
       "      <th>stknum</th>\n",
       "      <th>amount_seq</th>\n",
       "      <th>stknum_seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20120104</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>6.878138e+09</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>20120106</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>5.979259e+09</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>20120213</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.460269e+10</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>20120216</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.506463e+10</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>20120217</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.145135e+10</td>\n",
       "      <td>229</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>20120220</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.525181e+10</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>20120221</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.549184e+10</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>20120222</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.990051e+10</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>20120301</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.486525e+10</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>20120302</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.620855e+10</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>20120305</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.980309e+10</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>20120307</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.755056e+10</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>20120308</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.657560e+10</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>20120418</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.466609e+10</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3087</th>\n",
       "      <td>20120531</td>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.312933e+10</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  industry        amount  stknum  amount_seq  stknum_seq\n",
       "15    20120104  HDWRSEMI  6.878138e+09     224           1           1\n",
       "79    20120106  HDWRSEMI  5.979259e+09     224           1           1\n",
       "751   20120213  HDWRSEMI  1.460269e+10     227           1           1\n",
       "847   20120216  HDWRSEMI  1.506463e+10     227           1           1\n",
       "879   20120217  HDWRSEMI  1.145135e+10     229           1           1\n",
       "911   20120220  HDWRSEMI  1.525181e+10     230           1           1\n",
       "943   20120221  HDWRSEMI  1.549184e+10     230           1           1\n",
       "975   20120222  HDWRSEMI  1.990051e+10     230           1           1\n",
       "1167  20120301  HDWRSEMI  1.486525e+10     231           1           1\n",
       "1199  20120302  HDWRSEMI  1.620855e+10     231           1           1\n",
       "1231  20120305  HDWRSEMI  1.980309e+10     231           1           1\n",
       "1295  20120307  HDWRSEMI  1.755056e+10     231           1           1\n",
       "1327  20120308  HDWRSEMI  1.657560e+10     231           1           1\n",
       "2159  20120418  HDWRSEMI  1.466609e+10     237           1           1\n",
       "3087  20120531  HDWRSEMI  1.312933e+10     238           1           1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry = pd.DataFrame({\n",
    "    \"amount\" : data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"amount\"]\\\n",
    "        .sum(), \n",
    "    \"stknum\" : data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"symbol\"]\\\n",
    "        .size()})\\\n",
    "    .reset_index()\n",
    "\n",
    "data_industry[\"amount_seq\"]\\\n",
    "    = data_industry\\\n",
    "        .sort_values(\n",
    "            by = [\"date\", \"amount\"], \n",
    "            ascending = [True, False])\\\n",
    "        .groupby(\"date\")\\\n",
    "        .cumcount()\\\n",
    "        .add(1)\n",
    "\n",
    "data_industry[\"stknum_seq\"] \\\n",
    "    = data_industry\\\n",
    "        .sort_values(\n",
    "            by = [\"date\", \"stknum\"], \n",
    "            ascending = [True, False])\\\n",
    "        .groupby(\"date\")\\\n",
    "        .cumcount()\\\n",
    "        .add(1)\n",
    "\n",
    "data_industry\\\n",
    "    [(data_industry['amount_seq'] == 1 )\\\n",
    "    & (data_industry['stknum_seq'] == 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. 每天涨幅超过5%、跌幅超过5%的股票各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      tag    \n",
       "20120104  down5%+    2303\n",
       "          up5%+        17\n",
       "20120105  down5%+    2310\n",
       "          up5%+        10\n",
       "20120106  down5%+    2271\n",
       "                     ... \n",
       "20120627  up5%+        63\n",
       "20120628  down5%+    2396\n",
       "          up5%+        26\n",
       "20120629  down5%+    2349\n",
       "          up5%+        73\n",
       "Name: symbol, Length: 234, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data[\"tag\"] \\\n",
    "    = np.where(\n",
    "        data[\"ret\"] > 0.05, \n",
    "        \"up5%+\",\n",
    "        np.where(\n",
    "            data[\"ret\"] < 0.05, \n",
    "            \"down5%+\", \n",
    "            \"eq5%\"))\n",
    "data\\\n",
    "    [data[\"tag\"]\n",
    "        .str.contains(\"up5%+|down5%+\")]\\\n",
    "    .groupby([\"date\", \"tag\"])\\\n",
    "    [\"symbol\"]\\\n",
    "    .size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. 每天涨幅前10的股票的总成交额和跌幅前10的股票的总成交额比例是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>0.691766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>0.580306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>0.742506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>0.351822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>1.778087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date     ratio\n",
       "0  20120104  0.691766\n",
       "1  20120105  0.580306\n",
       "2  20120106  0.742506\n",
       "3  20120109  0.351822\n",
       "4  20120110  1.778087"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data_top10amount \\\n",
    "    = data\\\n",
    "        .sort_values(by = [\"date\", \"ret\"])\\\n",
    "        .groupby(\"date\")\\\n",
    "        .head(10)\n",
    "data_bottom10amount \\\n",
    "    = data\\\n",
    "        .sort_values(by = [\"date\", \"ret\"])\\\n",
    "        .groupby(\"date\")\\\n",
    "        .tail(10)\n",
    "data_compare = pd.DataFrame({\n",
    "    'top10amount':data_top10amount\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"amount\"]\\\n",
    "        .sum(),\n",
    "    'bottom10amount':data_bottom10amount\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"amount\"]\\\n",
    "        .sum()})\\\n",
    "    .reset_index()\n",
    "data_compare[\"ratio\"] \\\n",
    "    = data_compare['top10amount']\\\n",
    "        /data_compare['bottom10amount']\n",
    "data_compare.iloc[:5, [0, 3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. 每天开盘涨停的股票与收盘涨停的股票各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      tag       \n",
       "20120104  closelimit     42\n",
       "          openlimit     325\n",
       "20120105  closelimit     55\n",
       "          openlimit      27\n",
       "20120106  closelimit    716\n",
       "                       ... \n",
       "20120627  openlimit      37\n",
       "20120628  closelimit    141\n",
       "          openlimit      75\n",
       "20120629  closelimit    944\n",
       "          openlimit      22\n",
       "Name: symbol, Length: 234, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"tag\"] \\\n",
    "    = np.where(\n",
    "        (data[\"open\"]/data[\"pre_close\"] - 1) > 0.015, \n",
    "        \"openlimit\", \n",
    "        np.where(\n",
    "            (data[\"close\"]/data[\"pre_close\"] - 1) > 0.015, \n",
    "            \"closelimit\", \n",
    "            \"others\"\n",
    "            ))\n",
    "data\\\n",
    "    [data[\"tag\"]\\\n",
    "        .str.contains(\"openlimit|closelimit\")]\\\n",
    "    .groupby([\"date\", \"tag\"])\\\n",
    "    [\"symbol\"]\\\n",
    "    .size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. 每天统计最近3天出现过开盘涨停的股票各有多少只？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20120625</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>20120626</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>20120627</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>20120628</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>20120629</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    tag\n",
       "0    20120104    0.0\n",
       "1    20120105    0.0\n",
       "2    20120106  394.0\n",
       "3    20120109  143.0\n",
       "4    20120110  207.0\n",
       "..        ...    ...\n",
       "112  20120625   88.0\n",
       "113  20120626   70.0\n",
       "114  20120627   79.0\n",
       "115  20120628  116.0\n",
       "116  20120629  117.0\n",
       "\n",
       "[117 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"tag\"] \\\n",
    "    = np.where(\n",
    "        (data[\"open\"]/data[\"pre_close\"] - 1) > 0.015, \n",
    "        1,\n",
    "        0)\n",
    "\n",
    "data_rolling \\\n",
    "    = data\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .rolling(3, on = \"date\")\\\n",
    "        [\"tag\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\n",
    "\n",
    "data_rolling[\"tag\"] \\\n",
    "    = np.where(\n",
    "        data_rolling[\"tag\"] > 1, \n",
    "        1, \n",
    "        data_rolling[\"tag\"])\n",
    "\n",
    "data_rolling\\\n",
    "    .groupby(\"date\")\\\n",
    "    [\"tag\"]\\\n",
    "    .sum()\\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. 股票每天的成交额变化率和收益率的相关性如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22096889206014472"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['amount_lag'] \\\n",
    "    = data\\\n",
    "    .groupby(\"symbol\")\\\n",
    "    ['amount']\\\n",
    "    .shift(periods = 1)\n",
    "\n",
    "data['amount_change'] \\\n",
    "    = data['amount']/data['amount_lag'] - 1\n",
    "\n",
    "data['ret'] \\\n",
    "    = data['close']/data['pre_close'] - 1\n",
    "\n",
    "data\\\n",
    "    [~data.isin([np.nan, np.inf, -np.inf])]\\\n",
    "    ['amount_change']\\\n",
    "    .corr(\n",
    "        data\\\n",
    "            [~data.isin([np.nan, np.inf, -np.inf])]\\\n",
    "            ['ret'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12. 每天每个行业的总成交额变化率和行业收益率的相关性如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>amount_change_industry</th>\n",
       "      <th>ret_industry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120105</th>\n",
       "      <th>amount_change_industry</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_industry</th>\n",
       "      <td>0.313861</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120106</th>\n",
       "      <th>amount_change_industry</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_industry</th>\n",
       "      <td>0.082021</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20120109</th>\n",
       "      <th>amount_change_industry</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20120627</th>\n",
       "      <th>ret_industry</th>\n",
       "      <td>0.260895</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120628</th>\n",
       "      <th>amount_change_industry</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_industry</th>\n",
       "      <td>0.168723</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120629</th>\n",
       "      <th>amount_change_industry</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.606476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_industry</th>\n",
       "      <td>0.606476</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>232 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 amount_change_industry  ret_industry\n",
       "date                                                                 \n",
       "20120105 amount_change_industry                1.000000      0.313861\n",
       "         ret_industry                          0.313861      1.000000\n",
       "20120106 amount_change_industry                1.000000      0.082021\n",
       "         ret_industry                          0.082021      1.000000\n",
       "20120109 amount_change_industry                1.000000      0.114235\n",
       "...                                                 ...           ...\n",
       "20120627 ret_industry                          0.260895      1.000000\n",
       "20120628 amount_change_industry                1.000000      0.168723\n",
       "         ret_industry                          0.168723      1.000000\n",
       "20120629 amount_change_industry                1.000000      0.606476\n",
       "         ret_industry                          0.606476      1.000000\n",
       "\n",
       "[232 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"] - data[\"pre_close\"]\n",
    "data_ind \\\n",
    "    = pd.DataFrame({\n",
    "        'amount_industry': \\\n",
    "            data\\\n",
    "            .groupby([\"date\", \"industry\"])\\\n",
    "            [\"amount\"]\\\n",
    "            .sum(), \n",
    "        'ret_industry': \\\n",
    "            data\\\n",
    "            .groupby([\"date\", \"industry\"])\\\n",
    "            [\"ret\"]\\\n",
    "            .mean()})\\\n",
    "        .reset_index()\\\n",
    "        .sort_values(by = [\"industry\", \"date\"])\n",
    "\n",
    "data_ind['amount_industry_lag'] \\\n",
    "    = data_ind\\\n",
    "        .groupby(\"industry\")\\\n",
    "        ['amount_industry']\\\n",
    "        .shift(periods = 1)\n",
    "\n",
    "data_ind[\"amount_change_industry\"] \\\n",
    "    = data_ind[\"amount_industry\"]\\\n",
    "        /data_ind[\"amount_industry_lag\"] - 1\n",
    "data_ind \\\n",
    "    = data_ind\\\n",
    "        .dropna(subset = ['amount_change_industry'])\n",
    "data_ind\\\n",
    "    .groupby(['date'])\\\n",
    "    [[\"amount_change_industry\", \"ret_industry\"]]\\\n",
    "    .corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. 每天市场的总成交额变化率和市场收益率相关性如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>ret_mkt</th>\n",
       "      <th>amount_mkt_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ret_mkt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.442459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_mkt_change</th>\n",
       "      <td>0.442459</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ret_mkt  amount_mkt_change\n",
       "ret_mkt            1.000000           0.442459\n",
       "amount_mkt_change  0.442459           1.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ret'] = data['close']/data['pre_close'] - 1\n",
    "# 生成关于成交量数据集\n",
    "amount\\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"amount\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"amount\":\"amount_mkt\"})\n",
    "\n",
    "amount[\"amount_mkt_lag\"]\\\n",
    "    = amount[\"amount_mkt\"]\\\n",
    "        .shift(periods = 1)\n",
    "\n",
    "amount[\"amount_mkt_change\"] \\\n",
    "    = amount[\"amount_mkt\"]\\\n",
    "        /amount[\"amount_mkt_lag\"] - 1\n",
    "\n",
    "amount = amount[~amount.isin([np.nan])]\n",
    "\n",
    "# 生成关于收益率数据集\n",
    "data_13 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            data\\\n",
    "            .groupby(\"date\")\\\n",
    "            [\"capt\"]\\\n",
    "            .sum()\\\n",
    "            .reset_index()\\\n",
    "            .rename(\n",
    "                columns = {\"capt\":\"capt_sum\"}), \n",
    "                left_on = \"date\", \n",
    "                right_on=\"date\")\n",
    "\n",
    "data_13[\"capt_ratio\"] \\\n",
    "    = data_13[\"capt\"]/data_13[\"capt_sum\"]\n",
    "data_13[\"ret_mkt\"] \\\n",
    "    = data_13[\"ret\"]*data_13[\"capt_ratio\"]\n",
    "ret\\\n",
    "    = data_13\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ret_mkt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\n",
    "\n",
    "ret\\\n",
    "    .merge(\n",
    "        amount, \n",
    "        left_on = \"date\", \n",
    "        right_on = \"date\")\\\n",
    "    [[\"ret_mkt\", \"amount_mkt_change\"]]\\\n",
    "    .corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14. 每天市场的总成交额的变化率和所有股票收益率的标准差相关性如何？ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ret_std</th>\n",
       "      <th>amount_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ret_std</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.191389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_change</th>\n",
       "      <td>-0.191389</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ret_std  amount_change\n",
       "ret_std        1.000000      -0.191389\n",
       "amount_change -0.191389       1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] \\\n",
    "    = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "ret \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        ['ret']\\\n",
    "        .std()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ret\" : \"ret_std\"})\n",
    "\n",
    "amount \\\n",
    "    = data\\\n",
    "        .groupby('date')\\\n",
    "        ['amount']\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"amount\" : \"amount_sum\"})\n",
    "\n",
    "amount[\"amount_sum_lag\"] \\\n",
    "    = amount[\"amount_sum\"]\\\n",
    "        .shift(periods = 1)\n",
    "\n",
    "amount[\"amount_change\"] \\\n",
    "    = amount[\"amount_sum\"]\\\n",
    "        /amount['amount_sum_lag'] - 1\n",
    "\n",
    "amount = amount[~amount.isin([np.nan])]\n",
    "\n",
    "ret\\\n",
    "    .merge(\n",
    "        amount, \n",
    "        left_on = \"date\", \n",
    "        right_on = \"date\")\\\n",
    "    [['ret_std', 'amount_change']]\\\n",
    "    .corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15. 每天每个行业的总成交额变化率和行业内股票收益率的标准差相关性如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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></th>\n",
       "      <th>ret_std</th>\n",
       "      <th>amount_industry_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120104</th>\n",
       "      <th>ret_std</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_industry_change</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120105</th>\n",
       "      <th>ret_std</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_industry_change</th>\n",
       "      <td>-0.366726</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20120106</th>\n",
       "      <th>ret_std</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.352095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20120627</th>\n",
       "      <th>amount_industry_change</th>\n",
       "      <td>0.561366</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120628</th>\n",
       "      <th>ret_std</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_industry_change</th>\n",
       "      <td>0.089067</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20120629</th>\n",
       "      <th>ret_std</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount_industry_change</th>\n",
       "      <td>-0.032249</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  ret_std  amount_industry_change\n",
       "date                                                             \n",
       "20120104 ret_std                 1.000000                     NaN\n",
       "         amount_industry_change       NaN                     NaN\n",
       "20120105 ret_std                 1.000000               -0.366726\n",
       "         amount_industry_change -0.366726                1.000000\n",
       "20120106 ret_std                 1.000000                0.352095\n",
       "...                                   ...                     ...\n",
       "20120627 amount_industry_change  0.561366                1.000000\n",
       "20120628 ret_std                 1.000000                0.089067\n",
       "         amount_industry_change  0.089067                1.000000\n",
       "20120629 ret_std                 1.000000               -0.032249\n",
       "         amount_industry_change -0.032249                1.000000\n",
       "\n",
       "[234 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_15 = data\n",
    "data_15[\"ret\"] \\\n",
    "    = data_15[\"close\"]\\\n",
    "        /data_15[\"pre_close\"] - 1\n",
    "ret \\\n",
    "    = data_15\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        ['ret']\\\n",
    "        .std()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ret\":\"ret_std\"})\n",
    "\n",
    "amount \\\n",
    "    = data_15\\\n",
    "        .groupby([\"industry\", \"date\"])\\\n",
    "        ['amount']\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"amount\":\"amount_industry\"})\n",
    "\n",
    "amount \\\n",
    "    = amount.merge(\n",
    "        amount\\\n",
    "            .groupby(\"industry\")\\\n",
    "            [\"amount_industry\"]\\\n",
    "            .shift(periods=1)\\\n",
    "            .reset_index()\\\n",
    "            .rename(\n",
    "                columns={\"amount_industry\":\"amount_industry_lag\"}), \n",
    "        left_index=True, \n",
    "        right_index=True)\n",
    "\n",
    "amount['amount_industry_change'] \\\n",
    "    = amount['amount_industry']\\\n",
    "        /amount['amount_industry_lag'] - 1\n",
    "\n",
    "ret_amount \\\n",
    "    = ret.merge(\n",
    "        amount, \n",
    "        left_on = [\"industry\", \"date\"], \n",
    "        right_on = [\"industry\", \"date\"])\n",
    "# ret_amount = ret_amount[~ret_amount[\"amount_industry_change\"].isin([np.nan])]\n",
    "ret_amount\\\n",
    "    .groupby(\"date\")\\\n",
    "    [[\"ret_std\", \"amount_industry_change\"]]\\\n",
    "    .corr()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "16. 上证50、沪深300、中证500指数成分股中，沪股和深股各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index  tag\n",
       "上证50   沪股      54\n",
       "中证500  沪股     297\n",
       "       深股     248\n",
       "沪深300  沪股     205\n",
       "       深股      95\n",
       "Name: symbol, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_16 \\\n",
    "    = data[\n",
    "        (data.index_w50 > 0)\\\n",
    "        |(data.index_w300 > 0)\\\n",
    "        |(data.index_w500 > 0)\n",
    "        ]\n",
    "\n",
    "d_16 = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        \"symbol\":\\\n",
    "            d_16[d_16.index_w50 > 0]\\\n",
    "            [\"symbol\"]\\\n",
    "            .unique()\\\n",
    "            .tolist(), \n",
    "        \"index\" : \"上证50\"}),\n",
    "    pd.DataFrame({\n",
    "        \"symbol\":\\\n",
    "            d_16[d_16.index_w300 > 0]\\\n",
    "            [\"symbol\"]\\\n",
    "            .unique()\\\n",
    "            .tolist(),\n",
    "        \"index\" : \"沪深300\"}),\n",
    "    pd.DataFrame({\n",
    "        \"symbol\":\\\n",
    "            d_16[d_16.index_w500 > 0]\\\n",
    "            [\"symbol\"]\\\n",
    "            .unique()\\\n",
    "            .tolist(),\n",
    "        \"index\" : \"中证500\"\n",
    "    })\n",
    "    ])\n",
    "d_16[\"tag\"] \\\n",
    "    = np.where(\n",
    "        d_16[\"symbol\"]\\\n",
    "            .str.contains(\"SH\"), \n",
    "        \"沪股\", \n",
    "        \"深股\")\n",
    "\n",
    "d_16\\\n",
    "    .groupby([\"index\", \"tag\"])\\\n",
    "    [\"symbol\"]\\\n",
    "    .size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "17. 上证50、沪深300、中证500指数成分股中，行业分布如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index  industry\n",
       "上证50   AIRLINE      1\n",
       "       AUTO         1\n",
       "       BANKS       11\n",
       "       BEV          1\n",
       "       CHEM         1\n",
       "                   ..\n",
       "沪深300  REALEST     13\n",
       "       RETAIL       7\n",
       "       SOFTWARE     5\n",
       "       TRDDIST      5\n",
       "       UTILITIE    12\n",
       "Name: symbol, Length: 79, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_17 \\\n",
    "    = pd.concat([\n",
    "        pd.DataFrame({\n",
    "            \"index\" : \"上证50\",\n",
    "            \"industry\" : \n",
    "                data[data.index_w50 > 0]\\\n",
    "                .groupby(\"symbol\")\\\n",
    "                [\"industry\"]\\\n",
    "                .first()\n",
    "            })\\\n",
    "            .reset_index(),\n",
    "        pd.DataFrame({\n",
    "            \"index\" : \"沪深300\",\n",
    "            \"industry\" : \n",
    "                data[data.index_w300 > 0]\\\n",
    "                .groupby(\"symbol\")\\\n",
    "                [\"industry\"]\\\n",
    "                .first()\n",
    "            })\\\n",
    "            .reset_index(),\n",
    "\n",
    "        pd.DataFrame({\n",
    "            \"index\" : \"中证500\",\n",
    "            \"industry\" : \n",
    "                data[data.index_w500 > 0]\\\n",
    "                .groupby(\"symbol\")\\\n",
    "                [\"industry\"]\\\n",
    "                .first()\n",
    "            })\\\n",
    "            .reset_index()\n",
    "    ])\n",
    "# d_17[\"industry\"] = pd.Series(d_17[\"industry\"])\n",
    "d_17\\\n",
    "    .groupby([\"index\", \"industry\"])\\\n",
    "    [\"symbol\"]\\\n",
    "    .size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "18. 每天上证50、沪深300、中证500指数成分股的总成交额各是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index  date    \n",
       "上证50   20120104    1.137204e+10\n",
       "       20120105    1.517460e+10\n",
       "       20120106    1.145797e+10\n",
       "       20120109    1.978309e+10\n",
       "       20120110    2.584061e+10\n",
       "                       ...     \n",
       "沪深300  20120625    4.250351e+10\n",
       "       20120626    3.389651e+10\n",
       "       20120627    3.143760e+10\n",
       "       20120628    3.441879e+10\n",
       "       20120629    4.097654e+10\n",
       "Name: amount, Length: 351, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_18 \\\n",
    "    = pd.concat([\n",
    "        pd.DataFrame({\n",
    "            \"index\" : \"上证50\",\n",
    "            \"amount\" : \n",
    "                data[data.index_w50 > 0]\\\n",
    "                    .groupby([\"symbol\", \"date\"])\\\n",
    "                    [\"amount\"]\\\n",
    "                    .first()\n",
    "            })\\\n",
    "            .reset_index(),\n",
    "        pd.DataFrame({\n",
    "            \"index\" : \"沪深300\",\n",
    "            \"amount\" : \n",
    "                data[data.index_w300 > 0]\\\n",
    "                    .groupby([\"symbol\", \"date\"])\\\n",
    "                    [\"amount\"]\\\n",
    "                    .first()\n",
    "            })\\\n",
    "            .reset_index(),\n",
    "        pd.DataFrame({\n",
    "            \"index\" : \"中证500\",\n",
    "            \"amount\" : \n",
    "                data[data.index_w500 > 0]\\\n",
    "                    .groupby([\"symbol\", \"date\"])\\\n",
    "                    [\"amount\"]\\\n",
    "                    .first()\n",
    "            })\\\n",
    "            .reset_index()\n",
    "    ])\n",
    "d_18\\\n",
    "    .groupby([\"index\", \"date\"])\\\n",
    "    [\"amount\"]\\\n",
    "    .sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "19. 上证50、沪深300、中证500指数日收益率的历史波动率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "上证50     0.012342\n",
       "中证500    0.016075\n",
       "沪深300    0.013368\n",
       "Name: index_ret, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "d_19 = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        \"index\" : \"上证50\",\n",
    "        \"ratio\" : \n",
    "            data[data.index_w50 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['index_w50']\\\n",
    "                .first(),\n",
    "        \"ret\" : \n",
    "            data[data.index_w50 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['ret']\\\n",
    "                .first()\n",
    "        })\\\n",
    "        .reset_index(),\n",
    "    pd.DataFrame({\n",
    "        \"index\" : \"沪深300\",\n",
    "        \"ratio\" : \n",
    "            data[data.index_w300 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['index_w300']\\\n",
    "                .first(),\n",
    "        \"ret\" : \n",
    "            data[data.index_w300 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['ret']\\\n",
    "                .first()\n",
    "        })\\\n",
    "        .reset_index(),\n",
    "    pd.DataFrame(\n",
    "        {\n",
    "            \"index\" : \"中证500\",\n",
    "            \"ratio\" : \n",
    "                data[data.index_w500 > 0]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['index_w500']\\\n",
    "                    .first(),\n",
    "            \"ret\" : \n",
    "                data[data.index_w500 > 0]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['ret']\\\n",
    "                    .first()\n",
    "        })\\\n",
    "        .reset_index(),    \n",
    "])\n",
    "d_19[\"ratio_ret\"] = d_19[\"ret\"] * d_19[\"ratio\"]\n",
    "d_19 \\\n",
    "    = d_19\\\n",
    "        .groupby([\"index\", \"date\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\":\"index_ret\"})\n",
    "\n",
    "d_19\\\n",
    "    .groupby(\"index\")\\\n",
    "    [\"index_ret\"]\\\n",
    "    .std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20. 上证50、沪深300、中证500指数日收益率的相关系数矩阵？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>index</th>\n",
       "      <th>上证50</th>\n",
       "      <th>沪深300</th>\n",
       "      <th>中证500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上证50</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976085</td>\n",
       "      <td>0.847698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>0.976085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.933358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中证500</th>\n",
       "      <td>0.847698</td>\n",
       "      <td>0.933358</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "index      上证50     沪深300     中证500\n",
       "index                              \n",
       "上证50   1.000000  0.976085  0.847698\n",
       "沪深300  0.976085  1.000000  0.933358\n",
       "中证500  0.847698  0.933358  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "d_20 = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        \"index\" : \"上证50\",\n",
    "        \"ratio\" : \n",
    "            data[data.index_w50 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['index_w50']\\\n",
    "                .first(),\n",
    "        \"ret\" : \n",
    "            data[data.index_w50 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['ret']\\\n",
    "                .first()\n",
    "        })\\\n",
    "        .reset_index(),\n",
    "    pd.DataFrame({\n",
    "            \"index\" : \"沪深300\",\n",
    "            \"ratio\" : \n",
    "                data[data.index_w300 > 0]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['index_w300']\\\n",
    "                    .first(),\n",
    "            \"ret\" : \n",
    "                data[data.index_w300 > 0]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['ret']\\\n",
    "                    .first()\n",
    "        })\\\n",
    "        .reset_index(),\n",
    "    pd.DataFrame(\n",
    "        {\n",
    "            \"index\" : \"中证500\",\n",
    "            \"ratio\" : \n",
    "                data[data.index_w500 > 0]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['index_w500']\\\n",
    "                    .first(),\n",
    "            \"ret\" : \n",
    "                data[data.index_w500 > 0]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['ret']\\\n",
    "                    .first()\n",
    "        })\\\n",
    "        .reset_index(),    \n",
    "])\n",
    "d_20[\"ratio_ret\"] = d_20[\"ret\"] * d_20[\"ratio\"]\n",
    "\n",
    "d_20 \\\n",
    "    = d_20\\\n",
    "        .groupby([\"index\", \"date\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\":\"index_ret\"})\n",
    "\n",
    "d_20 \\\n",
    "    = d_20\\\n",
    "        .pivot_table(\n",
    "            index = 'date', \n",
    "            columns = 'index', \n",
    "            values = \"index_ret\")\\\n",
    "        .reset_index()\n",
    "\n",
    "d_20[[\"上证50\", \"沪深300\", \"中证500\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "21. 上证50、沪深300、去除上证50的沪深300指数日收益率的相关系数矩阵？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>index</th>\n",
       "      <th>上证50</th>\n",
       "      <th>沪深300</th>\n",
       "      <th>沪深300去除上证50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上证50</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976085</td>\n",
       "      <td>0.929438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>0.976085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300去除上证50</th>\n",
       "      <td>0.929438</td>\n",
       "      <td>0.987405</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "index            上证50     沪深300  沪深300去除上证50\n",
       "index                                       \n",
       "上证50         1.000000  0.976085     0.929438\n",
       "沪深300        0.976085  1.000000     0.987405\n",
       "沪深300去除上证50  0.929438  0.987405     1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_21 = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        \"index\" : \"上证50\",\n",
    "        \"ratio\" : \n",
    "            data[data.index_w50 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['index_w50']\\\n",
    "                .first(),\n",
    "        \"ret\" : \n",
    "            data[data.index_w50 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['ret']\\\n",
    "                .first()\n",
    "        })\\\n",
    "        .reset_index(),\n",
    "    pd.DataFrame({\n",
    "        \"index\" : \"沪深300\",\n",
    "        \"ratio\" : \n",
    "            data[data.index_w300 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['index_w300']\\\n",
    "                .first(),\n",
    "        \"ret\" : \n",
    "            data[data.index_w300 > 0]\\\n",
    "                .groupby([\"date\", \"symbol\"])\\\n",
    "                ['ret']\\\n",
    "                .first()\n",
    "        })\\\n",
    "        .reset_index(),\n",
    "    pd.DataFrame(\n",
    "        {\n",
    "            \"index\" : \"沪深300去除上证50\",\n",
    "            \"ratio\" : \n",
    "                data[(data.index_w300 > 0) \\\n",
    "                    & (data.index_w50 == 0)]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['index_w300']\\\n",
    "                    .first(),\n",
    "            \"ret\" : \n",
    "                data[(data.index_w300 > 0) \\\n",
    "                    & (data.index_w50 == 0)]\\\n",
    "                    .groupby([\"date\", \"symbol\"])\\\n",
    "                    ['ret']\\\n",
    "                    .first()\n",
    "        })\\\n",
    "        .reset_index(),    \n",
    "])\n",
    "d_21[\"ratio_ret\"] = d_21[\"ret\"] * d_21[\"ratio\"]\n",
    "d_21 \\\n",
    "    = d_21\\\n",
    "        .groupby([\"index\", \"date\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\":\"index_ret\"})\n",
    "\n",
    "d_21 \\\n",
    "    = d_21\\\n",
    "        .pivot_table(\n",
    "            index = 'date', \n",
    "            columns = 'index', \n",
    "            values = \"index_ret\")\\\n",
    "        .reset_index()\n",
    "\n",
    "d_21[[\"上证50\", \"沪深300\", \"沪深300去除上证50\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "22. 每天沪深300指数成分占比最大的10只股票是哪些？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>symbol</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99884</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>600016.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>600036.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>601328.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49679</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95531</th>\n",
       "      <td>601088.SH</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49563</th>\n",
       "      <td>600519.SH</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108533</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>600030.SH</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1170 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      date\n",
       "99884   601318.SH  20120629\n",
       "1403    600016.SH  20120629\n",
       "3158    600036.SH  20120629\n",
       "100001  601328.SH  20120629\n",
       "49679   600519.SH  20120629\n",
       "...           ...       ...\n",
       "0       600000.SH  20120104\n",
       "95531   601088.SH  20120104\n",
       "49563   600519.SH  20120104\n",
       "108533  000002.SZ  20120104\n",
       "2574    600030.SH  20120104\n",
       "\n",
       "[1170 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\\\n",
    "    [data.index_w300 > 0]\\\n",
    "    .sort_values(\n",
    "        by = [\"date\", \"index_w300\"],\n",
    "        ascending = False)\\\n",
    "    .groupby(\"date\")\\\n",
    "    [['symbol', 'date']]\\\n",
    "    .head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "23. 各个行业的平均每日股票数量从大到小排序是什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>20120629</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>CHEM</td>\n",
       "      <td>20120629</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>MACH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>HEALTH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>ELECEQP</td>\n",
       "      <td>20120629</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>BANKS</td>\n",
       "      <td>20120104</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2106</th>\n",
       "      <td>INDCONG</td>\n",
       "      <td>20120104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>AIRLINE</td>\n",
       "      <td>20120104</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>MARINE</td>\n",
       "      <td>20120104</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      industry      date  symbol\n",
       "1871  HDWRSEMI  20120629     242\n",
       "818       CHEM  20120629     225\n",
       "2456      MACH  20120629     208\n",
       "1988    HEALTH  20120629     183\n",
       "1520   ELECEQP  20120629     138\n",
       "...        ...       ...     ...\n",
       "351      BANKS  20120104      16\n",
       "2106   INDCONG  20120104      13\n",
       "117    AIRLINE  20120104      12\n",
       "2457    MARINE  20120104      12\n",
       "0      AERODEF  20120104      10\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_23 \\\n",
    "    = data\\\n",
    "        .groupby([\"industry\", \"date\"])\\\n",
    "        [\"symbol\"]\\\n",
    "        .count()\\\n",
    "        .reset_index()\n",
    "d_23\\\n",
    "    .sort_values(\n",
    "        by = [\"date\", \"symbol\"], \n",
    "        ascending = False\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "24. 每个行业每天成交额最大的一只股票代码是什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>industry</th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "      <td>000768.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120105</td>\n",
       "      <td>000768.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120106</td>\n",
       "      <td>000768.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120109</td>\n",
       "      <td>600316.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120110</td>\n",
       "      <td>000768.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120625</td>\n",
       "      <td>600011.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120626</td>\n",
       "      <td>300262.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120627</td>\n",
       "      <td>600011.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120628</td>\n",
       "      <td>600011.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120629</td>\n",
       "      <td>600011.SH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      industry      date     symbol\n",
       "0      AERODEF  20120104  000768.SZ\n",
       "1      AERODEF  20120105  000768.SZ\n",
       "2      AERODEF  20120106  000768.SZ\n",
       "3      AERODEF  20120109  600316.SH\n",
       "4      AERODEF  20120110  000768.SZ\n",
       "...        ...       ...        ...\n",
       "3739  UTILITIE  20120625  600011.SH\n",
       "3740  UTILITIE  20120626  300262.SZ\n",
       "3741  UTILITIE  20120627  600011.SH\n",
       "3742  UTILITIE  20120628  600011.SH\n",
       "3743  UTILITIE  20120629  600011.SH\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\\\n",
    "    .sort_values(\n",
    "        by = [\"industry\", \"date\", \"amount\"],\n",
    "        ascending = False)\\\n",
    "    .groupby([\"industry\", \"date\"])\\\n",
    "    ['symbol']\\\n",
    "    .first()\\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "25. 每个行业每天最大成交额是最小成交额的几倍？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>industry</th>\n",
       "      <th>date</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "      <td>159432638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120105</td>\n",
       "      <td>129651459.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120106</td>\n",
       "      <td>184793300.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120109</td>\n",
       "      <td>90420423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120110</td>\n",
       "      <td>152842538.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120625</td>\n",
       "      <td>358829878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120626</td>\n",
       "      <td>193623101.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120627</td>\n",
       "      <td>172117879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120628</td>\n",
       "      <td>217489778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120629</td>\n",
       "      <td>365654611.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      industry      date       amount\n",
       "0      AERODEF  20120104  159432638.0\n",
       "1      AERODEF  20120105  129651459.1\n",
       "2      AERODEF  20120106  184793300.7\n",
       "3      AERODEF  20120109   90420423.0\n",
       "4      AERODEF  20120110  152842538.4\n",
       "...        ...       ...          ...\n",
       "3739  UTILITIE  20120625  358829878.0\n",
       "3740  UTILITIE  20120626  193623101.5\n",
       "3741  UTILITIE  20120627  172117879.0\n",
       "3742  UTILITIE  20120628  217489778.0\n",
       "3743  UTILITIE  20120629  365654611.0\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\\\n",
    "    .groupby([\"industry\", \"date\"])\\\n",
    "    [\"amount\"]\\\n",
    "    .agg(np.ptp)\\\n",
    "    .reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "26. 每个行业每天成交额最大的5只股票的成交额总和是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>industry</th>\n",
       "      <th>date</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "      <td>3.847582e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120105</td>\n",
       "      <td>4.422500e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120106</td>\n",
       "      <td>5.428553e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120109</td>\n",
       "      <td>3.834715e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120110</td>\n",
       "      <td>6.540624e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120625</td>\n",
       "      <td>1.158774e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120626</td>\n",
       "      <td>6.363266e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120627</td>\n",
       "      <td>7.098671e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120628</td>\n",
       "      <td>7.635796e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120629</td>\n",
       "      <td>8.490405e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      industry      date        amount\n",
       "0      AERODEF  20120104  3.847582e+08\n",
       "1      AERODEF  20120105  4.422500e+08\n",
       "2      AERODEF  20120106  5.428553e+08\n",
       "3      AERODEF  20120109  3.834715e+08\n",
       "4      AERODEF  20120110  6.540624e+08\n",
       "...        ...       ...           ...\n",
       "3739  UTILITIE  20120625  1.158774e+09\n",
       "3740  UTILITIE  20120626  6.363266e+08\n",
       "3741  UTILITIE  20120627  7.098671e+08\n",
       "3742  UTILITIE  20120628  7.635796e+08\n",
       "3743  UTILITIE  20120629  8.490405e+08\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_26 \\\n",
    "    = data\\\n",
    "        .sort_values(\n",
    "            by = [\"industry\", \"date\", \"amount\"],\n",
    "            ascending = False)\\\n",
    "            .groupby(['industry', 'date'])\\\n",
    "            .head(5)\n",
    "d_26\\\n",
    "    .groupby([\"industry\", \"date\"])\\\n",
    "    ['amount']\\\n",
    "    .sum()\\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "27. 每个行业每天成交额超过该行业中股票成交额80%分位数的股票的平均收益率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>industry</th>\n",
       "      <th>date</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "      <td>0.005714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120105</td>\n",
       "      <td>0.006090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120106</td>\n",
       "      <td>-0.050832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120109</td>\n",
       "      <td>0.022119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120110</td>\n",
       "      <td>0.053002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120625</td>\n",
       "      <td>-0.002189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120626</td>\n",
       "      <td>-0.008984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120627</td>\n",
       "      <td>0.003968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120628</td>\n",
       "      <td>-0.006003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.007404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      industry      date       ret\n",
       "0      AERODEF  20120104  0.005714\n",
       "1      AERODEF  20120105  0.006090\n",
       "2      AERODEF  20120106 -0.050832\n",
       "3      AERODEF  20120109  0.022119\n",
       "4      AERODEF  20120110  0.053002\n",
       "...        ...       ...       ...\n",
       "3739  UTILITIE  20120625 -0.002189\n",
       "3740  UTILITIE  20120626 -0.008984\n",
       "3741  UTILITIE  20120627  0.003968\n",
       "3742  UTILITIE  20120628 -0.006003\n",
       "3743  UTILITIE  20120629  0.007404\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data\\\n",
    "    [data.amount >\\\n",
    "        data.groupby([\"industry\", \"date\"])\\\n",
    "            .amount\\\n",
    "            .transform('quantile', 0.8)]\\\n",
    "    .groupby([\"industry\", \"date\"])\\\n",
    "    [\"ret\"]\\\n",
    "    .mean()\\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "28. 每天成交额最大的10%的股票的平均收益率和成交额最小的10%的股票的平均收益率的相关系数是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3658878486773265"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_28 = pd.DataFrame({\n",
    "    \"amount_max\": \n",
    "        data\\\n",
    "            [data.amount >= data.groupby([\"date\"])\\\n",
    "            .amount.transform('quantile', 0.9)]\\\n",
    "            .groupby([\"industry\", \"date\"])\\\n",
    "            [\"ret\"]\\\n",
    "            .mean(),\n",
    "    \"amount_min\": \n",
    "        data\\\n",
    "            [data.amount <= data.groupby([\"date\"])\\\n",
    "            .amount.transform('quantile', 0.1)]\\\n",
    "            .groupby([\"industry\", \"date\"])\\\n",
    "            [\"ret\"]\\\n",
    "            .mean()\n",
    "    })\\\n",
    "    .reset_index()\n",
    "\n",
    "d_28[\"amount_min\"].corr(d_28[\"amount_max\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "29. 每天哪些行业的平均成交额高于全市场平均成交额？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>industry</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>BANKS</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>BEV</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>CONMAT</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>DVFININS</td>\n",
       "      <td>20120104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>3730</td>\n",
       "      <td>INDCONG</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1409</th>\n",
       "      <td>3733</td>\n",
       "      <td>MARINE</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>3735</td>\n",
       "      <td>MEDIA</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>3736</td>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>3739</td>\n",
       "      <td>REALEST</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1413 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  industry      date\n",
       "0         0   AERODEF  20120104\n",
       "1         3     BANKS  20120104\n",
       "2         4       BEV  20120104\n",
       "3         9    CONMAT  20120104\n",
       "4        11  DVFININS  20120104\n",
       "...     ...       ...       ...\n",
       "1408   3730   INDCONG  20120629\n",
       "1409   3733    MARINE  20120629\n",
       "1410   3735     MEDIA  20120629\n",
       "1411   3736    MTLMIN  20120629\n",
       "1412   3739   REALEST  20120629\n",
       "\n",
       "[1413 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_29 = pd.merge(\n",
    "    data\\\n",
    "        .groupby([\"industry\", \"date\"])\\\n",
    "        .amount.mean()\\\n",
    "        .reset_index(),\n",
    "    data\\\n",
    "        .groupby(\"date\")\\\n",
    "        .amount.mean()\\\n",
    "        .reset_index(),\n",
    "    on = 'date'\n",
    ")\n",
    "\n",
    "d_29\\\n",
    "    [d_29.amount_x > d_29.amount_y]\\\n",
    "    [[\"industry\", \"date\"]]\\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30. 每天每个股票对市场的超额收益率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>abnr_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.015698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120105</td>\n",
       "      <td>0.026020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120106</td>\n",
       "      <td>-0.006935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120109</td>\n",
       "      <td>0.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120110</td>\n",
       "      <td>-0.020913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276895</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120625</td>\n",
       "      <td>-0.033429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276896</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120626</td>\n",
       "      <td>-0.017837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276897</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120627</td>\n",
       "      <td>0.004242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276898</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120628</td>\n",
       "      <td>-0.005762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276899</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.010693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276900 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      date  abnr_ret\n",
       "0       000001.SZ  20120104 -0.015698\n",
       "1       000001.SZ  20120105  0.026020\n",
       "2       000001.SZ  20120106 -0.006935\n",
       "3       000001.SZ  20120109  0.006368\n",
       "4       000001.SZ  20120110 -0.020913\n",
       "...           ...       ...       ...\n",
       "276895  603366.SH  20120625 -0.033429\n",
       "276896  603366.SH  20120626 -0.017837\n",
       "276897  603366.SH  20120627  0.004242\n",
       "276898  603366.SH  20120628 -0.005762\n",
       "276899  603366.SH  20120629  0.010693\n",
       "\n",
       "[276900 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data cleaning\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_30, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\")\n",
    "\n",
    "d_30[\"capt_ratio\"] = d_30[\"capt\"]/d_30[\"mkt_capt\"]\n",
    "d_30[\"ratio_ret\"] = d_30[\"capt_ratio\"]*d_30[\"stkcd_ret\"]\n",
    "d_30 \\\n",
    "    = d_30\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        [[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_30, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\")\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "\n",
    "d_30 \\\n",
    "    = d_30\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .apply(regress, 'stkcd_ret', ['mkt_ret'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"mkt_ret\":\"beta\"})\\\n",
    "        .merge(\n",
    "            d_30, \n",
    "            right_on = \"symbol\", \n",
    "            left_on = \"symbol\")\n",
    "\n",
    "d_30['abnr_ret'] \\\n",
    "    = d_30[\"stkcd_ret\"] \\\n",
    "        - d_30[\"intercept\"] \\\n",
    "        - d_30[\"mkt_ret\"]*d_30[\"beta\"]\n",
    "\n",
    "d_30[[\"symbol\", \"date\", \"abnr_ret\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "31. 每天每个股票对市场去除自身的超额收益率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>abnr_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.027643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120105</td>\n",
       "      <td>0.015143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120106</td>\n",
       "      <td>-0.002595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120109</td>\n",
       "      <td>0.028236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120110</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276895</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120625</td>\n",
       "      <td>-0.052794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276896</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120626</td>\n",
       "      <td>-0.018613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276897</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120627</td>\n",
       "      <td>0.002372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276898</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120628</td>\n",
       "      <td>-0.014576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276899</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.025313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276900 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      date  abnr_ret\n",
       "0       000001.SZ  20120104 -0.027643\n",
       "1       000001.SZ  20120105  0.015143\n",
       "2       000001.SZ  20120106 -0.002595\n",
       "3       000001.SZ  20120109  0.028236\n",
       "4       000001.SZ  20120110  0.000082\n",
       "...           ...       ...       ...\n",
       "276895  603366.SH  20120625 -0.052794\n",
       "276896  603366.SH  20120626 -0.018613\n",
       "276897  603366.SH  20120627  0.002372\n",
       "276898  603366.SH  20120628 -0.014576\n",
       "276899  603366.SH  20120629  0.025313\n",
       "\n",
       "[276900 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data cleaning\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_31 = data\n",
    "d_31_group = d_31.groupby(\"date\")\n",
    "d_31_list = []\n",
    "for name, group in d_31_group:\n",
    "    for i in range(0, len(group)):\n",
    "        a = group\\\n",
    "            .drop(group.index[i])[\"capt\"]\\\n",
    "            /(group.drop(group.index[i])[\"capt\"].sum())\\\n",
    "            .tolist()\n",
    "        b = group.drop(group.index[i])[\"stkcd_ret\"].tolist()\n",
    "        # print(b[:5])\n",
    "        d_31_list.append(sum(a*b))\n",
    "d_31[\"mkt_ret\"] = d_31_list\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "d_31 \\\n",
    "    = d_31\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .apply(regress, 'stkcd_ret', ['mkt_ret'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"mkt_ret\":\"beta\"})\\\n",
    "        .merge(\n",
    "            d_31, \n",
    "            right_on = \"symbol\", \n",
    "            left_on = \"symbol\")\n",
    "\n",
    "d_31['abnr_ret'] \\\n",
    "    = d_31[\"stkcd_ret\"] \\\n",
    "        - d_31[\"intercept\"] \\\n",
    "        - d_31[\"mkt_ret\"]*d_31[\"beta\"]\n",
    "\n",
    "d_31[[\"symbol\", \"date\", \"abnr_ret\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.4, 4.5, 5.333333333333333, 1.4545454545454546, 1.8000000000000003, 2.4444444444444446]\n"
     ]
    }
   ],
   "source": [
    "d = pd.DataFrame({\"A\":[1, 2, 3, 4, 5, 6], \"B\":[\"a\", 'a', 'a', 'b', 'b', 'b'], \"C\":[6, 5, 4, 3, 2, 1]})\n",
    "d_group =d.groupby(\"B\")\n",
    "list = []\n",
    "# for name, group in d_group:\n",
    "for name, group in d_group:\n",
    "    for i in range(len(group)):\n",
    "        a = group.drop(group.index[i])[\"A\"]/(group.drop(group.index[i])[\"A\"].sum()).tolist()\n",
    "        b = group.drop(group.index[i])[\"C\"].tolist()\n",
    "        list.append(sum(a*b))\n",
    "    # print(d[\"C\"])\n",
    "print(list)\n",
    "d[\"D\"] = list\n",
    "# d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "32. 每天每个股票对行业的超额收益率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>abnr_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.023570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.019238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.021616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.024270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.009228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860795</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.017301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860796</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.015986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860797</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.012686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860798</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.018352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860799</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.021975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8860800 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            symbol      date  abnr_ret\n",
       "0        000001.SZ  20120104 -0.023570\n",
       "1        000001.SZ  20120104 -0.019238\n",
       "2        000001.SZ  20120104 -0.021616\n",
       "3        000001.SZ  20120104 -0.024270\n",
       "4        000001.SZ  20120104 -0.009228\n",
       "...            ...       ...       ...\n",
       "8860795  603366.SH  20120629  0.017301\n",
       "8860796  603366.SH  20120629  0.015986\n",
       "8860797  603366.SH  20120629  0.012686\n",
       "8860798  603366.SH  20120629  0.018352\n",
       "8860799  603366.SH  20120629  0.021975\n",
       "\n",
       "[8860800 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data cleaning\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_32\\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"industry_capt\"})\n",
    "\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_32, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"])\n",
    "\n",
    "d_32[\"capt_ratio\"] = d_32[\"capt\"]/d_32[\"industry_capt\"]\n",
    "d_32[\"ratio_ret\"] = d_32[\"capt_ratio\"]*d_32[\"stkcd_ret\"]\n",
    "d_32 \\\n",
    "    = d_32\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"industry_ret\"})\n",
    "\n",
    "d_32 \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_32, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\"\n",
    "            )\n",
    "\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "\n",
    "d_32\\\n",
    "    = d_32\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .apply(regress, 'stkcd_ret', ['industry_ret'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"industry_ret\":\"beta\"})\\\n",
    "        .merge(d_32, right_on = \"symbol\", left_on = \"symbol\")\n",
    "\n",
    "d_32['abnr_ret'] \\\n",
    "    = d_32[\"stkcd_ret\"] \\\n",
    "        - d_32[\"intercept\"] \\\n",
    "        - d_32[\"industry_ret\"]*d_32[\"beta\"]\n",
    "\n",
    "d_32[[\"symbol\", \"date\", \"abnr_ret\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "33. 每天每个股票对行业去除自身的超额收益率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>abnr_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-0.027896</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120105</td>\n",
       "      <td>0.014859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120106</td>\n",
       "      <td>-0.002913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120109</td>\n",
       "      <td>0.027698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20120110</td>\n",
       "      <td>-0.000314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276895</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120625</td>\n",
       "      <td>-0.039445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276896</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120626</td>\n",
       "      <td>-0.018809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276897</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120627</td>\n",
       "      <td>0.004924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276898</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120628</td>\n",
       "      <td>-0.013138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276899</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.024224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276900 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      date  abnr_ret\n",
       "0       000001.SZ  20120104 -0.027896\n",
       "1       000001.SZ  20120105  0.014859\n",
       "2       000001.SZ  20120106 -0.002913\n",
       "3       000001.SZ  20120109  0.027698\n",
       "4       000001.SZ  20120110 -0.000314\n",
       "...           ...       ...       ...\n",
       "276895  603366.SH  20120625 -0.039445\n",
       "276896  603366.SH  20120626 -0.018809\n",
       "276897  603366.SH  20120627  0.004924\n",
       "276898  603366.SH  20120628 -0.013138\n",
       "276899  603366.SH  20120629  0.024224\n",
       "\n",
       "[276900 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data cleaning\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_33 = data\n",
    "d_33_group = d_33.groupby([\"date\", \"industry\"])\n",
    "d_33_list = []\n",
    "for name, group in d_33_group:\n",
    "    for i in range(len(group)):\n",
    "        a \\\n",
    "            = group\\\n",
    "                .drop(group.index[i])[\"capt\"]\\\n",
    "                    /(group\\\n",
    "                        .drop(group.index[i])[\"capt\"]\\\n",
    "                        .sum())\\\n",
    "                        .tolist()\n",
    "        \n",
    "        b \\\n",
    "            = group\\\n",
    "                .drop(group.index[i])[\"stkcd_ret\"]\\\n",
    "                .tolist()\n",
    "        \n",
    "        # print(b[:5])\n",
    "        d_33_list.append(sum(a*b))\n",
    "\n",
    "d_33[\"industry_ret\"] = d_33_list\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "d_33 \\\n",
    "    = d_33\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .apply(regress, 'stkcd_ret', ['industry_ret'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"industry_ret\":\"beta\"})\\\n",
    "        .merge(d_33, right_on = \"symbol\", left_on = \"symbol\")\n",
    "d_33['abnr_ret'] \\\n",
    "    = d_33[\"stkcd_ret\"] \\\n",
    "        - d_33[\"intercept\"] \\\n",
    "        - d_33[\"industry_ret\"]*d_33[\"beta\"]\n",
    "\n",
    "d_33[[\"symbol\", \"date\", \"abnr_ret\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "34. 每个股票每天对市场的超额收益率与对行业的超额收益率的相关系数如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9899083518464112"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对市场超额收益率\n",
    "# data cleaning\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_30\\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_30, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\"\n",
    "            )\n",
    "\n",
    "d_30[\"capt_ratio\"] = d_30[\"capt\"]/d_30[\"mkt_capt\"]\n",
    "d_30[\"ratio_ret\"] = d_30[\"capt_ratio\"]*d_30[\"stkcd_ret\"]\n",
    "d_30 \\\n",
    "    = d_30\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "d_30 \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_30, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\"\n",
    "            )\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "d_30 \\\n",
    "    = d_30\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .apply(regress, 'stkcd_ret', ['mkt_ret'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"mkt_ret\":\"beta\"})\\\n",
    "        .merge(d_30, right_on = \"symbol\", left_on = \"symbol\")\n",
    "\n",
    "d_30['abnr_ret'] \\\n",
    "    = d_30[\"stkcd_ret\"] - d_30[\"intercept\"] - d_30[\"mkt_ret\"]*d_30[\"beta\"]\n",
    "\n",
    "# 对行业超额收益率\n",
    "# data cleaning\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"industry_capt\"})\n",
    "\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_32, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_32[\"capt_ratio\"] = d_32[\"capt\"]/d_32[\"industry_capt\"]\n",
    "d_32[\"ratio_ret\"] = d_32[\"capt_ratio\"]*d_32[\"stkcd_ret\"]\n",
    "d_32 \\\n",
    "    = d_32\\\n",
    "        .groupby([\"date\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"industry_ret\"})\n",
    "\n",
    "d_32 \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(d_32, right_on = \"date\", left_on = \"date\")\n",
    "\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "d_32 \\\n",
    "    = d_32\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        .apply(regress, 'stkcd_ret', ['industry_ret'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"industry_ret\":\"beta\"})\\\n",
    "        .merge(d_32, right_on = \"symbol\", left_on = \"symbol\")\n",
    "\n",
    "d_32['abnr_ret'] \\\n",
    "    = d_32[\"stkcd_ret\"] \\\n",
    "        - d_32[\"intercept\"] \\\n",
    "            - d_32[\"industry_ret\"]*d_32[\"beta\"]\n",
    "\n",
    "d_30['abnr_ret'].corr(d_32['abnr_ret'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "35. 每天有哪些行业的平均收益率超过市场的平均收益率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>industry</th>\n",
       "      <th>date</th>\n",
       "      <th>industry_ret</th>\n",
       "      <th>mkt_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.008403</td>\n",
       "      <td>-0.015816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUTO</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.013058</td>\n",
       "      <td>-0.015816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKS</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.006737</td>\n",
       "      <td>-0.015816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENERGY</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.003714</td>\n",
       "      <td>-0.015816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HOUSEDUR</td>\n",
       "      <td>20120104</td>\n",
       "      <td>-0.011340</td>\n",
       "      <td>-0.015816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3728</th>\n",
       "      <td>HEALTH</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.018305</td>\n",
       "      <td>0.013166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3730</th>\n",
       "      <td>INDCONG</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.020436</td>\n",
       "      <td>0.013166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3735</th>\n",
       "      <td>MEDIA</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.017133</td>\n",
       "      <td>0.013166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3736</th>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.014446</td>\n",
       "      <td>0.013166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>SOFTWARE</td>\n",
       "      <td>20120629</td>\n",
       "      <td>0.016304</td>\n",
       "      <td>0.013166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1849 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      industry      date  industry_ret   mkt_ret\n",
       "0      AERODEF  20120104     -0.008403 -0.015816\n",
       "2         AUTO  20120104     -0.013058 -0.015816\n",
       "3        BANKS  20120104     -0.006737 -0.015816\n",
       "13      ENERGY  20120104     -0.003714 -0.015816\n",
       "17    HOUSEDUR  20120104     -0.011340 -0.015816\n",
       "...        ...       ...           ...       ...\n",
       "3728    HEALTH  20120629      0.018305  0.013166\n",
       "3730   INDCONG  20120629      0.020436  0.013166\n",
       "3735     MEDIA  20120629      0.017133  0.013166\n",
       "3736    MTLMIN  20120629      0.014446  0.013166\n",
       "3741  SOFTWARE  20120629      0.016304  0.013166\n",
       "\n",
       "[1849 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "# 市场收益率\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .merge(d_30, left_on = \"date\", right_on = \"date\")\n",
    "\n",
    "d_30[\"capt_ratio\"] = d_30[\"capt\"]/d_30[\"mkt_capt\"]\n",
    "d_30[\"ratio_ret\"] = d_30[\"capt_ratio\"]*d_30[\"stkcd_ret\"]\n",
    "\n",
    "d_30 \\\n",
    "    = d_30\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "# 行业收益率\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"industry_capt\"})\n",
    "\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_32, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"])\n",
    "\n",
    "d_32[\"capt_ratio\"] = d_32[\"capt\"]/d_32[\"industry_capt\"]\n",
    "d_32[\"ratio_ret\"] = d_32[\"capt_ratio\"]*d_32[\"stkcd_ret\"]\n",
    "\n",
    "d_32 \\\n",
    "    = d_32\\\n",
    "        .groupby([\"industry\", \"date\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"industry_ret\"})\n",
    "\n",
    "d_35 = d_32.merge(d_30, right_on = \"date\", left_on = \"date\")\n",
    "d_35[d_35.industry_ret > d_35.mkt_ret]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "36. 每天每个行业对市场的超额收益率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>industry</th>\n",
       "      <th>abnr_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.010403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>-0.008207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>-0.027298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>-0.004859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.006747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>20120625</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.013292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>20120626</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>-0.008601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>20120627</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.001710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>20120628</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.002332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>20120629</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>-0.009000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  industry  abnr_ret\n",
       "0     20120104   AERODEF  0.010403\n",
       "1     20120105   AERODEF -0.008207\n",
       "2     20120106   AERODEF -0.027298\n",
       "3     20120109   AERODEF -0.004859\n",
       "4     20120110   AERODEF  0.006747\n",
       "...        ...       ...       ...\n",
       "3739  20120625  UTILITIE  0.013292\n",
       "3740  20120626  UTILITIE -0.008601\n",
       "3741  20120627  UTILITIE  0.001710\n",
       "3742  20120628  UTILITIE  0.002332\n",
       "3743  20120629  UTILITIE -0.009000\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "# 市场收益率\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_30 \\\n",
    "    = data\\\n",
    "        .merge(d_30, left_on = \"date\", right_on = \"date\")\n",
    "\n",
    "d_30[\"capt_ratio\"] = d_30[\"capt\"]/d_30[\"mkt_capt\"]\n",
    "d_30[\"ratio_ret\"] = d_30[\"capt_ratio\"]*d_30[\"stkcd_ret\"]\n",
    "d_30 \\\n",
    "    = d_30\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "# 行业收益率\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"industry_capt\"})\n",
    "\n",
    "d_32 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_32, \n",
    "            left_on = [\"date\", \"industry\"],\n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_32[\"capt_ratio\"] = d_32[\"capt\"]/d_32[\"industry_capt\"]\n",
    "d_32[\"ratio_ret\"] = d_32[\"capt_ratio\"]*d_32[\"stkcd_ret\"]\n",
    "\n",
    "d_32 \\\n",
    "    = d_32\\\n",
    "        .groupby([\"industry\", \"date\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"industry_ret\"})\n",
    "\n",
    "d_36 = d_32.merge(d_30, right_on = \"date\", left_on = \"date\")\n",
    "\n",
    "# linear model OLS\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "d_36 \\\n",
    "    = d_36\\\n",
    "        .merge(\n",
    "            d_36\\\n",
    "                .groupby(\"industry\")\\\n",
    "                .apply(regress, \"industry_ret\", [\"mkt_ret\"])\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"mkt_ret\" : 'beta'}), \n",
    "            right_on = ['industry'], \n",
    "            left_on = ['industry']\n",
    "            )\n",
    "\n",
    "d_36[\"abnr_ret\"] \\\n",
    "    = d_36[\"industry_ret\"] \\\n",
    "        - d_36[\"mkt_ret\"]*d_36[\"beta\"] \\\n",
    "            - d_36[\"intercept\"]\n",
    "\n",
    "d_36[['date', 'industry', 'abnr_ret']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "37. 每天每个行业对去除本行业后的市场超额收益是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rayst\\AppData\\Local\\Temp\\ipykernel_2660\\4268839576.py:13: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for name, group in d_37_group:\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>industry</th>\n",
       "      <th>abnr_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.010400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>-0.008307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>-0.027422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>-0.004773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.006892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>20120625</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.013526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>20120626</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>-0.008831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>20120627</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.001740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>20120628</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.002334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>20120629</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>-0.009157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  industry  abnr_ret\n",
       "0     20120104   AERODEF  0.010400\n",
       "1     20120105   AERODEF -0.008307\n",
       "2     20120106   AERODEF -0.027422\n",
       "3     20120109   AERODEF -0.004773\n",
       "4     20120110   AERODEF  0.006892\n",
       "...        ...       ...       ...\n",
       "3739  20120625  UTILITIE  0.013526\n",
       "3740  20120626  UTILITIE -0.008831\n",
       "3741  20120627  UTILITIE  0.001740\n",
       "3742  20120628  UTILITIE  0.002334\n",
       "3743  20120629  UTILITIE -0.009157\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 行业收益率\n",
    "d_37 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "\n",
    "d_37 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_37, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_37[\"capt_ratio\"] = d_37[\"capt\"]/d_37[\"ind_capt\"]\n",
    "d_37[\"ratio_ret\"] = d_37[\"capt_ratio\"] * d_37[\"stkcd_ret\"]\n",
    "\n",
    "d_37 \\\n",
    "    = d_37\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\" : \"ind_ret\"})\\\n",
    "        .merge(\n",
    "            d_37\\\n",
    "                .groupby([\"date\", \"industry\"])\\\n",
    "                [\"ind_capt\"]\\\n",
    "                .first(), \n",
    "            right_on = [\"industry\", \"date\"], \n",
    "            left_on = ['industry', \"date\"]\n",
    "            )\n",
    "\n",
    "# 去除本行业的市场收益率\n",
    "d_37_group = d_37.groupby([\"date\"])\n",
    "d_37_list = []\n",
    "for name, group in d_37_group:\n",
    "    for i in range(len(group)):\n",
    "        a \\\n",
    "            = group\\\n",
    "                .drop(group.index[i])[\"ind_capt\"]\\\n",
    "                    /(group.drop(group.index[i])[\"ind_capt\"].sum())\\\n",
    "                .tolist()\n",
    "        b \\\n",
    "            = group\\\n",
    "                .drop(group.index[i])[\"ind_ret\"]\\\n",
    "                .tolist()\n",
    "        # print(b[:5])\n",
    "        d_37_list.append(sum(a*b))\n",
    "d_37[\"mkt_ret\"] = d_37_list\n",
    "\n",
    "# OLS回归\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "d_37 \\\n",
    "    = d_37\\\n",
    "        .merge(\n",
    "            d_37\\\n",
    "                .groupby(\"industry\")\\\n",
    "                .apply(regress, \"ind_ret\", [\"mkt_ret\"])\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"mkt_ret\" : 'beta'}), \n",
    "            right_on = ['industry'], \n",
    "            left_on = ['industry']\n",
    "            )\n",
    "\n",
    "d_37[\"abnr_ret\"] \\\n",
    "    = d_37[\"ind_ret\"] \\\n",
    "        - d_37[\"mkt_ret\"]*d_37[\"beta\"] \\\n",
    "            - d_37[\"intercept\"]\n",
    "d_37[['date', 'industry', 'abnr_ret']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "38. 每天分别有多少股票是最近连续3个交易日上涨、下跌的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>date</th>\n",
       "      <th>up3day</th>\n",
       "      <th>down3day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>16</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>71</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>1401</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20120625</td>\n",
       "      <td>26</td>\n",
       "      <td>1110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>20120626</td>\n",
       "      <td>30</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>20120627</td>\n",
       "      <td>48</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>20120628</td>\n",
       "      <td>90</td>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>20120629</td>\n",
       "      <td>156</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  up3day  down3day\n",
       "0    20120104       0         0\n",
       "1    20120105       0         0\n",
       "2    20120106      16       747\n",
       "3    20120109      71        98\n",
       "4    20120110    1401        74\n",
       "..        ...     ...       ...\n",
       "112  20120625      26      1110\n",
       "113  20120626      30       943\n",
       "114  20120627      48       791\n",
       "115  20120628      90       723\n",
       "116  20120629     156       298\n",
       "\n",
       "[117 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data[\"tag\"] = np.where(data[\"stkcd_ret\"] > 0, 1, -1)\n",
    "d_38 \\\n",
    "    = data\\\n",
    "        .groupby(\"symbol\")[\"tag\"]\\\n",
    "        .rolling(3)\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        [[\"symbol\", \"tag\"]]\\\n",
    "        .merge(\n",
    "            data\\\n",
    "                .groupby([\"symbol\", \"date\"])\\\n",
    "                [\"stkcd_ret\"]\\\n",
    "                .first()\\\n",
    "                .reset_index(), \n",
    "            right_index = True, \n",
    "            left_index = True\n",
    "            )\n",
    "d_38[\"up3day\"] = np.where(d_38.tag == 3, 1, 0)\n",
    "d_38[\"down3day\"] = np.where(d_38.tag == -3, 1, 0)\n",
    "d_38\\\n",
    "    .groupby([\"date\"])\\\n",
    "    [\"up3day\"]\\\n",
    "    .sum()\\\n",
    "    .reset_index()\\\n",
    "    .merge(\n",
    "        d_38\\\n",
    "            .groupby([\"date\"])\\\n",
    "            [\"down3day\"]\\\n",
    "            .sum()\\\n",
    "            .reset_index(), \n",
    "        left_on = \"date\", \n",
    "        right_on = \"date\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "39. 每天分别有多少股票是最近连续3个交易日收益率超过当天市场平均收益率？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>date</th>\n",
       "      <th>above_mkt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20120625</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>20120626</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>20120627</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>20120628</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>20120629</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  above_mkt\n",
       "0    20120104          0\n",
       "1    20120105          0\n",
       "2    20120106         81\n",
       "3    20120109         95\n",
       "4    20120110        785\n",
       "..        ...        ...\n",
       "112  20120625        251\n",
       "113  20120626        258\n",
       "114  20120627        242\n",
       "115  20120628        275\n",
       "116  20120629        239\n",
       "\n",
       "[117 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['stkcd_ret'] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 市场收益率\n",
    "d_39 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_39 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_39, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\"\n",
    "            )\n",
    "\n",
    "d_39[\"capt_ratio\"] = d_39[\"capt\"]/d_39[\"mkt_capt\"]\n",
    "d_39[\"ratio_ret\"] = d_39[\"capt_ratio\"]*d_39[\"stkcd_ret\"]\n",
    "d_39 \\\n",
    "    = d_39\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "# 合并两个收益率\n",
    "d_39 \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_39, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\")\\\n",
    "        .sort_values(by = [\"symbol\", \"date\"])\n",
    "\n",
    "# 做标记\n",
    "d_39[\"tag\"] = np.where(d_39.stkcd_ret > d_39.mkt_ret, 1, 0)\n",
    "d_39 \\\n",
    "    = d_39\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        [\"tag\"]\\\n",
    "        .rolling(3)\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        [[\"symbol\", \"tag\"]]\\\n",
    "        .merge(\n",
    "            data\\\n",
    "                .sort_values(by = [\"symbol\", \"date\"])\\\n",
    "                .groupby([\"symbol\", \"date\"])\\\n",
    "                [\"stkcd_ret\"]\\\n",
    "                .first()\\\n",
    "                .reset_index(),\n",
    "            right_index = True, \n",
    "            left_index = True\n",
    ")\n",
    "d_39[\"above_mkt\"] = np.where(d_39.tag == 3, 1, 0)\n",
    "d_39.groupby([\"date\"])[\"above_mkt\"].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "40. 每天分别有多少股票是最新5个交易日中至少有4个交易日的收益率超过当天市场平均收益率？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>date</th>\n",
       "      <th>5day_4above</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120105</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120110</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20120625</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>20120626</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>20120627</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>20120628</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>20120629</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  5day_4above\n",
       "0    20120104            0\n",
       "1    20120105            0\n",
       "2    20120106            0\n",
       "3    20120109            0\n",
       "4    20120110          235\n",
       "..        ...          ...\n",
       "112  20120625          405\n",
       "113  20120626          338\n",
       "114  20120627          365\n",
       "115  20120628          375\n",
       "116  20120629          360\n",
       "\n",
       "[117 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 市场收益率\n",
    "d_40 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_40 = data.merge(d_40, left_on = \"date\", right_on = \"date\")\n",
    "d_40[\"capt_ratio\"] = d_40[\"capt\"]/d_40[\"mkt_capt\"]\n",
    "d_40[\"ratio_ret\"] = d_40[\"capt_ratio\"]*d_40[\"stkcd_ret\"]\n",
    "\n",
    "d_40 \\\n",
    "    = d_40\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "# 合并两个收益率\n",
    "d_40 \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_40, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\")\\\n",
    "        .sort_values(by = [\"symbol\", \"date\"])\n",
    "\n",
    "# 做标记\n",
    "d_40[\"tag\"] = np.where(d_40.stkcd_ret > d_40.mkt_ret, 1, 0)\n",
    "d_40 \\\n",
    "    = d_40\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        [\"tag\"]\\\n",
    "        .rolling(5)\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        [[\"symbol\", \"tag\"]]\\\n",
    "        .merge(\n",
    "            data\\\n",
    "                .sort_values(by = [\"symbol\", \"date\"])\\\n",
    "                .groupby([\"symbol\", \"date\"])\\\n",
    "                [\"stkcd_ret\"]\\\n",
    "                .first()\\\n",
    "                .reset_index(),\n",
    "            right_index = True,\n",
    "            left_index = True\n",
    ")\n",
    "\n",
    "# 出结果\n",
    "d_40[\"5day_4above\"] = np.where(d_40.tag >= 4, 1, 0)\n",
    "d_40.groupby([\"date\"])[\"5day_4above\"].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "41. 每个月中，个股月收益超过市场月收益1倍以上的股票有哪些？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000026.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000028.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000062.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000063.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000151.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>600998.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601318.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601566.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601991.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601996.SH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1862 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year month     symbol\n",
       "0     2012    01  000026.SZ\n",
       "1     2012    01  000028.SZ\n",
       "2     2012    01  000062.SZ\n",
       "3     2012    01  000063.SZ\n",
       "4     2012    01  000151.SZ\n",
       "...    ...   ...        ...\n",
       "1857  2012    06  600998.SH\n",
       "1858  2012    06  601318.SH\n",
       "1859  2012    06  601566.SH\n",
       "1860  2012    06  601991.SH\n",
       "1861  2012    06  601996.SH\n",
       "\n",
       "[1862 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"date\"] = data[\"date\"].astype(str)\n",
    "data[\"year\"] = data[\"date\"].str.slice(stop = 4)\n",
    "data[\"month\"] = data[\"date\"].str.slice(start = 4, stop = 6)\n",
    "\n",
    "# 股票月收益率\n",
    "d_41_1 \\\n",
    "    = data\\\n",
    "        .groupby([\"symbol\", \"year\", \"month\"])\\\n",
    "        [\"close\"]\\\n",
    "        .first()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"close\" : \"month_start\"})\\\n",
    "        .merge(\n",
    "            data\\\n",
    "                .groupby([\"symbol\", \"year\", \"month\"])\\\n",
    "                [\"close\"]\\\n",
    "                .last()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"close\" : \"month_end\"}), \n",
    "            right_on = [\"symbol\", \"year\", \"month\"], \n",
    "            left_on = [\"symbol\", \"year\", \"month\"]\n",
    "            )\n",
    "d_41_1[\"stkcd_ret_monthly\"] = d_41_1[\"month_start\"]/d_41_1[\"month_end\"] - 1\n",
    "\n",
    "# 市场月收益率\n",
    "d_41_2 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_41_2 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_41_2, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\"\n",
    "            )\n",
    "d_41_2[\"capt_ratio\"] = d_41_2[\"capt\"]/d_41_2[\"mkt_capt\"]\n",
    "d_41_2[\"ratio_close\"] = d_41_2[\"capt_ratio\"]*d_41_2[\"close\"]\n",
    "d_41_2 \\\n",
    "    = d_41_2\\\n",
    "        .groupby([\"date\", \"year\", \"month\"])\\\n",
    "        [\"ratio_close\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_close\": \"mkt_close\"})\n",
    "\n",
    "d_41_2 \\\n",
    "    = d_41_2\\\n",
    "        .groupby([\"year\", \"month\"])\\\n",
    "        [\"mkt_close\"]\\\n",
    "        .first()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"mkt_close\" : \"mkt_month_start\"})\\\n",
    "        .merge(\n",
    "            d_41_2\\\n",
    "                .groupby([\"year\", \"month\"])\\\n",
    "                [\"mkt_close\"]\\\n",
    "                .last()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"mkt_close\":\"mkt_month_end\"}),\n",
    "            right_on = [\"year\", \"month\"],\n",
    "            left_on = [\"year\", \"month\"]\n",
    "            )\n",
    "d_41_2[\"mkt_ret_monthly\"] = d_41_2[\"mkt_month_end\"]/d_41_2[\"mkt_month_start\"] - 1\n",
    "\n",
    "# 合并数据集\n",
    "d_41 \\\n",
    "    = d_41_1\\\n",
    "        .merge(\n",
    "            d_41_2, \n",
    "            how = \"left\", \n",
    "            left_on = [\"year\", \"month\"], \n",
    "            right_on = [\"year\", \"month\"])\\\n",
    "        [[\"year\", \"month\", \"symbol\", \"stkcd_ret_monthly\", \"mkt_ret_monthly\"]]\n",
    "\n",
    "d_41[\"tag\"] = np.where(d_41.stkcd_ret_monthly/d_41.mkt_ret_monthly >= 2, 1, 0)\n",
    "\n",
    "d_41\\\n",
    "    [d_41.tag == 1]\\\n",
    "    .sort_values(by = [\"year\", \"month\"])\\\n",
    "    .reset_index()\\\n",
    "    [[\"year\", \"month\", \"symbol\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "42. 每个月中，个股月收益超过行业月收益1倍以上的股票有哪些？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000019.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000033.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000034.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000065.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>000069.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601599.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601688.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601788.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>601991.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>603123.SH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1648 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year month     symbol\n",
       "0     2012    01  000019.SZ\n",
       "1     2012    01  000033.SZ\n",
       "2     2012    01  000034.SZ\n",
       "3     2012    01  000065.SZ\n",
       "4     2012    01  000069.SZ\n",
       "...    ...   ...        ...\n",
       "1643  2012    06  601599.SH\n",
       "1644  2012    06  601688.SH\n",
       "1645  2012    06  601788.SH\n",
       "1646  2012    06  601991.SH\n",
       "1647  2012    06  603123.SH\n",
       "\n",
       "[1648 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"date\"] = data[\"date\"].astype(str)\n",
    "data[\"year\"] = data[\"date\"].str.slice(stop = 4)\n",
    "data[\"month\"] = data[\"date\"].str.slice(start = 4, stop = 6)\n",
    "\n",
    "# 股票月收益率\n",
    "d_42_1 \\\n",
    "    = data\\\n",
    "        .groupby([\"symbol\", \"industry\", \"year\", \"month\"])\\\n",
    "        [\"close\"]\\\n",
    "        .first()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"close\" : \"month_start\"})\\\n",
    "        .merge(\n",
    "            data\\\n",
    "                .groupby([\"symbol\", \"industry\", \"year\", \"month\"])\\\n",
    "                [\"close\"]\\\n",
    "                .last()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"close\" : \"month_end\"}), \n",
    "            right_on = [\"symbol\", \"industry\", \"year\", \"month\"], \n",
    "            left_on = [\"symbol\", \"industry\", \"year\", \"month\"]\n",
    "            )\n",
    "d_42_1[\"stkcd_ret_monthly\"] = d_42_1[\"month_start\"]/d_42_1[\"month_end\"] - 1\n",
    "\n",
    "# 行业月收益率\n",
    "d_42_2 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"industry_capt\"})\n",
    "\n",
    "d_42_2 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_42_2, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_42_2[\"capt_ratio\"] = d_42_2[\"capt\"]/d_42_2[\"industry_capt\"]\n",
    "d_42_2[\"ratio_close\"] = d_42_2[\"capt_ratio\"]*d_42_2[\"close\"]\n",
    "\n",
    "d_42_2 \\\n",
    "    = d_42_2\\\n",
    "        .groupby([\"date\", \"year\", \"month\", \"industry\"])\\\n",
    "        [\"ratio_close\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_close\": \"ind_close\"})\n",
    "\n",
    "d_42_2 \\\n",
    "    = d_42_2\\\n",
    "        .groupby([\"year\", \"month\", \"industry\"])\\\n",
    "        [\"ind_close\"]\\\n",
    "        .first()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ind_close\" : \"ind_month_start\"})\\\n",
    "        .merge(\n",
    "            d_42_2\\\n",
    "                .groupby([\"year\", \"month\", \"industry\"])\\\n",
    "                [\"ind_close\"]\\\n",
    "                .last()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"ind_close\":\"ind_month_end\"}),\n",
    "            right_on = [\"year\", \"month\", \"industry\"],\n",
    "            left_on = [\"year\", \"month\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_42_2[\"ind_ret_monthly\"] = d_42_2[\"ind_month_end\"]/d_42_2[\"ind_month_start\"] - 1\n",
    "\n",
    "# 合并数据集\n",
    "d_42 \\\n",
    "    = d_42_1\\\n",
    "        .merge(\n",
    "            d_42_2, \n",
    "            how = \"left\", \n",
    "            left_on = [\"year\", \"month\", \"industry\"], \n",
    "            right_on = [\"year\", \"month\", \"industry\"]\n",
    "            )\\\n",
    "            [[\"year\", \"month\", \"symbol\", \"stkcd_ret_monthly\", \"ind_ret_monthly\"]]\n",
    "\n",
    "d_42[\"tag\"] = np.where(d_42.stkcd_ret_monthly/d_42.ind_ret_monthly >= 2, 1, 0)\n",
    "\n",
    "d_42\\\n",
    "    [d_42.tag == 1]\\\n",
    "    .sort_values(by = [\"year\", \"month\"])\\\n",
    "    .reset_index()\\\n",
    "    [[\"year\", \"month\", \"symbol\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "43. 每个股票的收益率对市场收益率的相关系数最高的10个股票是哪些？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>symbol</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>300331.SZ</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>600508.SH</td>\n",
       "      <td>0.927955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2310</th>\n",
       "      <td>601101.SH</td>\n",
       "      <td>0.909776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>000685.SZ</td>\n",
       "      <td>0.897915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>601666.SH</td>\n",
       "      <td>0.893953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>002082.SZ</td>\n",
       "      <td>0.893250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>002212.SZ</td>\n",
       "      <td>0.891920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>601001.SH</td>\n",
       "      <td>0.891893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>000089.SZ</td>\n",
       "      <td>0.891447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>601168.SH</td>\n",
       "      <td>0.888806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         symbol  correlation\n",
       "1484  300331.SZ     1.000000\n",
       "1902  600508.SH     0.927955\n",
       "2310  601101.SH     0.909776\n",
       "245   000685.SZ     0.897915\n",
       "2370  601666.SH     0.893953\n",
       "553   002082.SZ     0.893250\n",
       "683   002212.SZ     0.891920\n",
       "2292  601001.SH     0.891893\n",
       "59    000089.SZ     0.891447\n",
       "2323  601168.SH     0.888806"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 市场收益率\n",
    "d_43 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_43 = data.merge(d_43, left_on = \"date\", right_on = \"date\")\n",
    "d_43[\"capt_ratio\"] = d_43[\"capt\"]/d_43[\"mkt_capt\"]\n",
    "d_43[\"ratio_ret\"] = d_43[\"capt_ratio\"]*d_43[\"stkcd_ret\"]\n",
    "d_43 \\\n",
    "    = d_43\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "# 与股票收益率合并\n",
    "d_43 = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "    .merge(\n",
    "        d_43, \n",
    "        how = \"left\", \n",
    "        left_on = \"date\", \n",
    "        right_on = \"date\"\n",
    "        )\n",
    "\n",
    "# 求相关系数\n",
    "d_43 \\\n",
    "    = d_43\\\n",
    "        .groupby(\"symbol\")\\\n",
    "        [[\"stkcd_ret\", \"mkt_ret\"]]\\\n",
    "        .corr()\\\n",
    "        .reset_index()\n",
    "\n",
    "d_43\\\n",
    "    .groupby(\"symbol\")\\\n",
    "    [\"stkcd_ret\"]\\\n",
    "    .last()\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns = {\"stkcd_ret\" : \"correlation\"})\\\n",
    "    .sort_values(by = [\"correlation\"], ascending = False)\\\n",
    "    .head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "44. 每个行业日收益率的历史波动率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>industry</th>\n",
       "      <th>industry_standard_deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.017500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AIRLINE</td>\n",
       "      <td>0.017127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUTO</td>\n",
       "      <td>0.015097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKS</td>\n",
       "      <td>0.008106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEV</td>\n",
       "      <td>0.016408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLDPROD</td>\n",
       "      <td>0.016608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEM</td>\n",
       "      <td>0.017266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CNSTENG</td>\n",
       "      <td>0.015310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COMSERV</td>\n",
       "      <td>0.017257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CONMAT</td>\n",
       "      <td>0.021500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CONSSERV</td>\n",
       "      <td>0.014880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DVFININS</td>\n",
       "      <td>0.018947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ELECEQP</td>\n",
       "      <td>0.016273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENERGY</td>\n",
       "      <td>0.010658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FOODPROD</td>\n",
       "      <td>0.015785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>0.015934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HEALTH</td>\n",
       "      <td>0.015500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HOUSEDUR</td>\n",
       "      <td>0.015175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>INDCONG</td>\n",
       "      <td>0.019694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LEISLUX</td>\n",
       "      <td>0.015416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MACH</td>\n",
       "      <td>0.016836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MARINE</td>\n",
       "      <td>0.018242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MATERIAL</td>\n",
       "      <td>0.015680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MEDIA</td>\n",
       "      <td>0.018032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>0.017472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PERSPRD</td>\n",
       "      <td>0.015277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RDRLTRAN</td>\n",
       "      <td>0.009676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REALEST</td>\n",
       "      <td>0.017361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RETAIL</td>\n",
       "      <td>0.015780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SOFTWARE</td>\n",
       "      <td>0.018210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TRDDIST</td>\n",
       "      <td>0.017190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.011773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    industry  industry_standard_deviation\n",
       "0    AERODEF                     0.017500\n",
       "1    AIRLINE                     0.017127\n",
       "2       AUTO                     0.015097\n",
       "3      BANKS                     0.008106\n",
       "4        BEV                     0.016408\n",
       "5    BLDPROD                     0.016608\n",
       "6       CHEM                     0.017266\n",
       "7    CNSTENG                     0.015310\n",
       "8    COMSERV                     0.017257\n",
       "9     CONMAT                     0.021500\n",
       "10  CONSSERV                     0.014880\n",
       "11  DVFININS                     0.018947\n",
       "12   ELECEQP                     0.016273\n",
       "13    ENERGY                     0.010658\n",
       "14  FOODPROD                     0.015785\n",
       "15  HDWRSEMI                     0.015934\n",
       "16    HEALTH                     0.015500\n",
       "17  HOUSEDUR                     0.015175\n",
       "18   INDCONG                     0.019694\n",
       "19   LEISLUX                     0.015416\n",
       "20      MACH                     0.016836\n",
       "21    MARINE                     0.018242\n",
       "22  MATERIAL                     0.015680\n",
       "23     MEDIA                     0.018032\n",
       "24    MTLMIN                     0.017472\n",
       "25   PERSPRD                     0.015277\n",
       "26  RDRLTRAN                     0.009676\n",
       "27   REALEST                     0.017361\n",
       "28    RETAIL                     0.015780\n",
       "29  SOFTWARE                     0.018210\n",
       "30   TRDDIST                     0.017190\n",
       "31  UTILITIE                     0.011773"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 行业收益率\n",
    "d_44\\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "\n",
    "d_44 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_44, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_44[\"capt_ratio\"] = d_44[\"capt\"]/d_44[\"ind_capt\"]\n",
    "d_44[\"ratio_ret\"] = d_44[\"capt_ratio\"] * d_44[\"stkcd_ret\"]\n",
    "d_44 \\\n",
    "    = d_44\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\" : \"ind_ret\"})\\\n",
    "        .merge(\n",
    "            d_44\\\n",
    "                .groupby([\"date\", \"industry\"])\\\n",
    "                [\"ind_capt\"]\\\n",
    "                .first(), \n",
    "            right_on = [\"industry\", \"date\"], \n",
    "            left_on = ['industry', \"date\"]\n",
    "            )\n",
    "\n",
    "d_44\\\n",
    "    .groupby([\"industry\"])\\\n",
    "    [\"ind_ret\"]\\\n",
    "    .std()\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns = {\"ind_ret\":\"industry_standard_deviation\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "45. 各个行业的日收益率的相关系数矩阵如何？哪两个行业相关性最高、最低？"
   ]
  },
  {
   "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>industry</th>\n",
       "      <th>AERODEF</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>AUTO</th>\n",
       "      <th>BANKS</th>\n",
       "      <th>BEV</th>\n",
       "      <th>BLDPROD</th>\n",
       "      <th>CHEM</th>\n",
       "      <th>CNSTENG</th>\n",
       "      <th>COMSERV</th>\n",
       "      <th>CONMAT</th>\n",
       "      <th>...</th>\n",
       "      <th>MATERIAL</th>\n",
       "      <th>MEDIA</th>\n",
       "      <th>MTLMIN</th>\n",
       "      <th>PERSPRD</th>\n",
       "      <th>RDRLTRAN</th>\n",
       "      <th>REALEST</th>\n",
       "      <th>RETAIL</th>\n",
       "      <th>SOFTWARE</th>\n",
       "      <th>TRDDIST</th>\n",
       "      <th>UTILITIE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AERODEF</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715879</td>\n",
       "      <td>0.800998</td>\n",
       "      <td>0.465783</td>\n",
       "      <td>0.471870</td>\n",
       "      <td>0.764393</td>\n",
       "      <td>0.800025</td>\n",
       "      <td>0.792307</td>\n",
       "      <td>0.801909</td>\n",
       "      <td>0.739309</td>\n",
       "      <td>...</td>\n",
       "      <td>0.758858</td>\n",
       "      <td>0.729190</td>\n",
       "      <td>0.720849</td>\n",
       "      <td>0.770809</td>\n",
       "      <td>0.696612</td>\n",
       "      <td>0.691247</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.783704</td>\n",
       "      <td>0.786471</td>\n",
       "      <td>0.681218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIRLINE</th>\n",
       "      <td>0.715879</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818696</td>\n",
       "      <td>0.654099</td>\n",
       "      <td>0.540875</td>\n",
       "      <td>0.721121</td>\n",
       "      <td>0.811375</td>\n",
       "      <td>0.829383</td>\n",
       "      <td>0.723600</td>\n",
       "      <td>0.767873</td>\n",
       "      <td>...</td>\n",
       "      <td>0.733148</td>\n",
       "      <td>0.710533</td>\n",
       "      <td>0.796893</td>\n",
       "      <td>0.732164</td>\n",
       "      <td>0.850983</td>\n",
       "      <td>0.734250</td>\n",
       "      <td>0.767441</td>\n",
       "      <td>0.733805</td>\n",
       "      <td>0.804745</td>\n",
       "      <td>0.775823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUTO</th>\n",
       "      <td>0.800998</td>\n",
       "      <td>0.818696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.606801</td>\n",
       "      <td>0.533968</td>\n",
       "      <td>0.853847</td>\n",
       "      <td>0.879147</td>\n",
       "      <td>0.897974</td>\n",
       "      <td>0.849278</td>\n",
       "      <td>0.838886</td>\n",
       "      <td>...</td>\n",
       "      <td>0.802517</td>\n",
       "      <td>0.811508</td>\n",
       "      <td>0.823657</td>\n",
       "      <td>0.837697</td>\n",
       "      <td>0.874041</td>\n",
       "      <td>0.828531</td>\n",
       "      <td>0.862460</td>\n",
       "      <td>0.822306</td>\n",
       "      <td>0.878464</td>\n",
       "      <td>0.825702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANKS</th>\n",
       "      <td>0.465783</td>\n",
       "      <td>0.654099</td>\n",
       "      <td>0.606801</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383583</td>\n",
       "      <td>0.492436</td>\n",
       "      <td>0.560457</td>\n",
       "      <td>0.627839</td>\n",
       "      <td>0.493130</td>\n",
       "      <td>0.572595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.481197</td>\n",
       "      <td>0.519133</td>\n",
       "      <td>0.626276</td>\n",
       "      <td>0.529474</td>\n",
       "      <td>0.718676</td>\n",
       "      <td>0.585352</td>\n",
       "      <td>0.559214</td>\n",
       "      <td>0.478655</td>\n",
       "      <td>0.630844</td>\n",
       "      <td>0.608247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEV</th>\n",
       "      <td>0.471870</td>\n",
       "      <td>0.540875</td>\n",
       "      <td>0.533968</td>\n",
       "      <td>0.383583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.584932</td>\n",
       "      <td>0.631810</td>\n",
       "      <td>0.590255</td>\n",
       "      <td>0.605060</td>\n",
       "      <td>0.525400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573882</td>\n",
       "      <td>0.622516</td>\n",
       "      <td>0.548910</td>\n",
       "      <td>0.647726</td>\n",
       "      <td>0.493790</td>\n",
       "      <td>0.490988</td>\n",
       "      <td>0.655867</td>\n",
       "      <td>0.613208</td>\n",
       "      <td>0.589354</td>\n",
       "      <td>0.596221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLDPROD</th>\n",
       "      <td>0.764393</td>\n",
       "      <td>0.721121</td>\n",
       "      <td>0.853847</td>\n",
       "      <td>0.492436</td>\n",
       "      <td>0.584932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924797</td>\n",
       "      <td>0.876360</td>\n",
       "      <td>0.908562</td>\n",
       "      <td>0.819237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.865592</td>\n",
       "      <td>0.853001</td>\n",
       "      <td>0.803298</td>\n",
       "      <td>0.892116</td>\n",
       "      <td>0.775380</td>\n",
       "      <td>0.794407</td>\n",
       "      <td>0.876892</td>\n",
       "      <td>0.884670</td>\n",
       "      <td>0.881484</td>\n",
       "      <td>0.797645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEM</th>\n",
       "      <td>0.800025</td>\n",
       "      <td>0.811375</td>\n",
       "      <td>0.879147</td>\n",
       "      <td>0.560457</td>\n",
       "      <td>0.631810</td>\n",
       "      <td>0.924797</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.903770</td>\n",
       "      <td>0.900213</td>\n",
       "      <td>0.851568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.894377</td>\n",
       "      <td>0.886290</td>\n",
       "      <td>0.907845</td>\n",
       "      <td>0.899530</td>\n",
       "      <td>0.842615</td>\n",
       "      <td>0.785785</td>\n",
       "      <td>0.903980</td>\n",
       "      <td>0.907963</td>\n",
       "      <td>0.943814</td>\n",
       "      <td>0.848540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTENG</th>\n",
       "      <td>0.792307</td>\n",
       "      <td>0.829383</td>\n",
       "      <td>0.897974</td>\n",
       "      <td>0.627839</td>\n",
       "      <td>0.590255</td>\n",
       "      <td>0.876360</td>\n",
       "      <td>0.903770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.868062</td>\n",
       "      <td>0.916646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833593</td>\n",
       "      <td>0.815147</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.849566</td>\n",
       "      <td>0.862651</td>\n",
       "      <td>0.855420</td>\n",
       "      <td>0.873918</td>\n",
       "      <td>0.829065</td>\n",
       "      <td>0.912860</td>\n",
       "      <td>0.835865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMSERV</th>\n",
       "      <td>0.801909</td>\n",
       "      <td>0.723600</td>\n",
       "      <td>0.849278</td>\n",
       "      <td>0.493130</td>\n",
       "      <td>0.605060</td>\n",
       "      <td>0.908562</td>\n",
       "      <td>0.900213</td>\n",
       "      <td>0.868062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.788934</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867183</td>\n",
       "      <td>0.861177</td>\n",
       "      <td>0.762735</td>\n",
       "      <td>0.893613</td>\n",
       "      <td>0.771725</td>\n",
       "      <td>0.784265</td>\n",
       "      <td>0.889649</td>\n",
       "      <td>0.915850</td>\n",
       "      <td>0.867255</td>\n",
       "      <td>0.826870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONMAT</th>\n",
       "      <td>0.739309</td>\n",
       "      <td>0.767873</td>\n",
       "      <td>0.838886</td>\n",
       "      <td>0.572595</td>\n",
       "      <td>0.525400</td>\n",
       "      <td>0.819237</td>\n",
       "      <td>0.851568</td>\n",
       "      <td>0.916646</td>\n",
       "      <td>0.788934</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.794888</td>\n",
       "      <td>0.732443</td>\n",
       "      <td>0.824352</td>\n",
       "      <td>0.750224</td>\n",
       "      <td>0.815910</td>\n",
       "      <td>0.839655</td>\n",
       "      <td>0.786332</td>\n",
       "      <td>0.754734</td>\n",
       "      <td>0.851671</td>\n",
       "      <td>0.746566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSSERV</th>\n",
       "      <td>0.801341</td>\n",
       "      <td>0.753063</td>\n",
       "      <td>0.853259</td>\n",
       "      <td>0.522336</td>\n",
       "      <td>0.611753</td>\n",
       "      <td>0.897564</td>\n",
       "      <td>0.923518</td>\n",
       "      <td>0.890114</td>\n",
       "      <td>0.921697</td>\n",
       "      <td>0.837624</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889770</td>\n",
       "      <td>0.875717</td>\n",
       "      <td>0.808085</td>\n",
       "      <td>0.899692</td>\n",
       "      <td>0.789400</td>\n",
       "      <td>0.837292</td>\n",
       "      <td>0.907748</td>\n",
       "      <td>0.900991</td>\n",
       "      <td>0.903728</td>\n",
       "      <td>0.824086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DVFININS</th>\n",
       "      <td>0.667329</td>\n",
       "      <td>0.813496</td>\n",
       "      <td>0.799564</td>\n",
       "      <td>0.701848</td>\n",
       "      <td>0.513520</td>\n",
       "      <td>0.716910</td>\n",
       "      <td>0.779492</td>\n",
       "      <td>0.808860</td>\n",
       "      <td>0.715979</td>\n",
       "      <td>0.776073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.695263</td>\n",
       "      <td>0.693629</td>\n",
       "      <td>0.771522</td>\n",
       "      <td>0.730530</td>\n",
       "      <td>0.829757</td>\n",
       "      <td>0.750546</td>\n",
       "      <td>0.783327</td>\n",
       "      <td>0.722164</td>\n",
       "      <td>0.813018</td>\n",
       "      <td>0.741397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELECEQP</th>\n",
       "      <td>0.803280</td>\n",
       "      <td>0.799166</td>\n",
       "      <td>0.879306</td>\n",
       "      <td>0.593090</td>\n",
       "      <td>0.628727</td>\n",
       "      <td>0.930992</td>\n",
       "      <td>0.964927</td>\n",
       "      <td>0.906210</td>\n",
       "      <td>0.923465</td>\n",
       "      <td>0.845613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.899821</td>\n",
       "      <td>0.904211</td>\n",
       "      <td>0.877598</td>\n",
       "      <td>0.912340</td>\n",
       "      <td>0.838604</td>\n",
       "      <td>0.796434</td>\n",
       "      <td>0.913369</td>\n",
       "      <td>0.929265</td>\n",
       "      <td>0.929624</td>\n",
       "      <td>0.848510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENERGY</th>\n",
       "      <td>0.665367</td>\n",
       "      <td>0.796409</td>\n",
       "      <td>0.804894</td>\n",
       "      <td>0.760872</td>\n",
       "      <td>0.554911</td>\n",
       "      <td>0.730419</td>\n",
       "      <td>0.805139</td>\n",
       "      <td>0.839552</td>\n",
       "      <td>0.692627</td>\n",
       "      <td>0.774167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697471</td>\n",
       "      <td>0.740022</td>\n",
       "      <td>0.848265</td>\n",
       "      <td>0.740646</td>\n",
       "      <td>0.856754</td>\n",
       "      <td>0.742121</td>\n",
       "      <td>0.781511</td>\n",
       "      <td>0.680883</td>\n",
       "      <td>0.844507</td>\n",
       "      <td>0.756578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOODPROD</th>\n",
       "      <td>0.738297</td>\n",
       "      <td>0.749015</td>\n",
       "      <td>0.811961</td>\n",
       "      <td>0.506564</td>\n",
       "      <td>0.744934</td>\n",
       "      <td>0.880963</td>\n",
       "      <td>0.917984</td>\n",
       "      <td>0.853865</td>\n",
       "      <td>0.893140</td>\n",
       "      <td>0.766511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.840773</td>\n",
       "      <td>0.858302</td>\n",
       "      <td>0.801223</td>\n",
       "      <td>0.930906</td>\n",
       "      <td>0.752622</td>\n",
       "      <td>0.725734</td>\n",
       "      <td>0.919056</td>\n",
       "      <td>0.900352</td>\n",
       "      <td>0.874258</td>\n",
       "      <td>0.814810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDWRSEMI</th>\n",
       "      <td>0.818807</td>\n",
       "      <td>0.792379</td>\n",
       "      <td>0.877243</td>\n",
       "      <td>0.558363</td>\n",
       "      <td>0.593514</td>\n",
       "      <td>0.925031</td>\n",
       "      <td>0.956528</td>\n",
       "      <td>0.885712</td>\n",
       "      <td>0.932473</td>\n",
       "      <td>0.809606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881984</td>\n",
       "      <td>0.903730</td>\n",
       "      <td>0.848576</td>\n",
       "      <td>0.921828</td>\n",
       "      <td>0.821953</td>\n",
       "      <td>0.766616</td>\n",
       "      <td>0.908287</td>\n",
       "      <td>0.955865</td>\n",
       "      <td>0.914960</td>\n",
       "      <td>0.838115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH</th>\n",
       "      <td>0.704243</td>\n",
       "      <td>0.688595</td>\n",
       "      <td>0.744090</td>\n",
       "      <td>0.473658</td>\n",
       "      <td>0.678333</td>\n",
       "      <td>0.825389</td>\n",
       "      <td>0.840891</td>\n",
       "      <td>0.793003</td>\n",
       "      <td>0.857275</td>\n",
       "      <td>0.682142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.769390</td>\n",
       "      <td>0.811848</td>\n",
       "      <td>0.707666</td>\n",
       "      <td>0.895213</td>\n",
       "      <td>0.676100</td>\n",
       "      <td>0.653898</td>\n",
       "      <td>0.842773</td>\n",
       "      <td>0.847162</td>\n",
       "      <td>0.782932</td>\n",
       "      <td>0.797065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSEDUR</th>\n",
       "      <td>0.726259</td>\n",
       "      <td>0.769219</td>\n",
       "      <td>0.855163</td>\n",
       "      <td>0.572934</td>\n",
       "      <td>0.579734</td>\n",
       "      <td>0.846976</td>\n",
       "      <td>0.863345</td>\n",
       "      <td>0.816348</td>\n",
       "      <td>0.814721</td>\n",
       "      <td>0.775264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.828149</td>\n",
       "      <td>0.796235</td>\n",
       "      <td>0.774076</td>\n",
       "      <td>0.835697</td>\n",
       "      <td>0.784760</td>\n",
       "      <td>0.796468</td>\n",
       "      <td>0.840651</td>\n",
       "      <td>0.806720</td>\n",
       "      <td>0.833871</td>\n",
       "      <td>0.789948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDCONG</th>\n",
       "      <td>0.716674</td>\n",
       "      <td>0.742431</td>\n",
       "      <td>0.820294</td>\n",
       "      <td>0.504700</td>\n",
       "      <td>0.595567</td>\n",
       "      <td>0.859329</td>\n",
       "      <td>0.904536</td>\n",
       "      <td>0.864876</td>\n",
       "      <td>0.849497</td>\n",
       "      <td>0.823928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.832469</td>\n",
       "      <td>0.805257</td>\n",
       "      <td>0.817896</td>\n",
       "      <td>0.814293</td>\n",
       "      <td>0.775722</td>\n",
       "      <td>0.800310</td>\n",
       "      <td>0.852942</td>\n",
       "      <td>0.834709</td>\n",
       "      <td>0.902494</td>\n",
       "      <td>0.773835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEISLUX</th>\n",
       "      <td>0.815937</td>\n",
       "      <td>0.796376</td>\n",
       "      <td>0.871725</td>\n",
       "      <td>0.544504</td>\n",
       "      <td>0.648506</td>\n",
       "      <td>0.925965</td>\n",
       "      <td>0.962381</td>\n",
       "      <td>0.902461</td>\n",
       "      <td>0.944455</td>\n",
       "      <td>0.829256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896507</td>\n",
       "      <td>0.894074</td>\n",
       "      <td>0.851209</td>\n",
       "      <td>0.922456</td>\n",
       "      <td>0.837324</td>\n",
       "      <td>0.811534</td>\n",
       "      <td>0.924022</td>\n",
       "      <td>0.925557</td>\n",
       "      <td>0.927751</td>\n",
       "      <td>0.865146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACH</th>\n",
       "      <td>0.838329</td>\n",
       "      <td>0.843994</td>\n",
       "      <td>0.903551</td>\n",
       "      <td>0.600120</td>\n",
       "      <td>0.604298</td>\n",
       "      <td>0.885429</td>\n",
       "      <td>0.930396</td>\n",
       "      <td>0.956560</td>\n",
       "      <td>0.884493</td>\n",
       "      <td>0.921058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.871810</td>\n",
       "      <td>0.832276</td>\n",
       "      <td>0.871930</td>\n",
       "      <td>0.872875</td>\n",
       "      <td>0.855312</td>\n",
       "      <td>0.831989</td>\n",
       "      <td>0.890043</td>\n",
       "      <td>0.871498</td>\n",
       "      <td>0.921690</td>\n",
       "      <td>0.828181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARINE</th>\n",
       "      <td>0.691683</td>\n",
       "      <td>0.778977</td>\n",
       "      <td>0.809432</td>\n",
       "      <td>0.581819</td>\n",
       "      <td>0.476195</td>\n",
       "      <td>0.780425</td>\n",
       "      <td>0.845392</td>\n",
       "      <td>0.841675</td>\n",
       "      <td>0.741415</td>\n",
       "      <td>0.797809</td>\n",
       "      <td>...</td>\n",
       "      <td>0.777308</td>\n",
       "      <td>0.778952</td>\n",
       "      <td>0.822583</td>\n",
       "      <td>0.740172</td>\n",
       "      <td>0.846844</td>\n",
       "      <td>0.726297</td>\n",
       "      <td>0.768532</td>\n",
       "      <td>0.749610</td>\n",
       "      <td>0.852030</td>\n",
       "      <td>0.770974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATERIAL</th>\n",
       "      <td>0.758858</td>\n",
       "      <td>0.733148</td>\n",
       "      <td>0.802517</td>\n",
       "      <td>0.481197</td>\n",
       "      <td>0.573882</td>\n",
       "      <td>0.865592</td>\n",
       "      <td>0.894377</td>\n",
       "      <td>0.833593</td>\n",
       "      <td>0.867183</td>\n",
       "      <td>0.794888</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.823658</td>\n",
       "      <td>0.790778</td>\n",
       "      <td>0.834067</td>\n",
       "      <td>0.770055</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>0.844322</td>\n",
       "      <td>0.837999</td>\n",
       "      <td>0.859931</td>\n",
       "      <td>0.750584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDIA</th>\n",
       "      <td>0.729190</td>\n",
       "      <td>0.710533</td>\n",
       "      <td>0.811508</td>\n",
       "      <td>0.519133</td>\n",
       "      <td>0.622516</td>\n",
       "      <td>0.853001</td>\n",
       "      <td>0.886290</td>\n",
       "      <td>0.815147</td>\n",
       "      <td>0.861177</td>\n",
       "      <td>0.732443</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790843</td>\n",
       "      <td>0.853583</td>\n",
       "      <td>0.776697</td>\n",
       "      <td>0.724874</td>\n",
       "      <td>0.840314</td>\n",
       "      <td>0.880056</td>\n",
       "      <td>0.848279</td>\n",
       "      <td>0.803142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTLMIN</th>\n",
       "      <td>0.720849</td>\n",
       "      <td>0.796893</td>\n",
       "      <td>0.823657</td>\n",
       "      <td>0.626276</td>\n",
       "      <td>0.548910</td>\n",
       "      <td>0.803298</td>\n",
       "      <td>0.907845</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.762735</td>\n",
       "      <td>0.824352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.790778</td>\n",
       "      <td>0.790843</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790274</td>\n",
       "      <td>0.831253</td>\n",
       "      <td>0.709485</td>\n",
       "      <td>0.795802</td>\n",
       "      <td>0.778547</td>\n",
       "      <td>0.915207</td>\n",
       "      <td>0.786093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSPRD</th>\n",
       "      <td>0.770809</td>\n",
       "      <td>0.732164</td>\n",
       "      <td>0.837697</td>\n",
       "      <td>0.529474</td>\n",
       "      <td>0.647726</td>\n",
       "      <td>0.892116</td>\n",
       "      <td>0.899530</td>\n",
       "      <td>0.849566</td>\n",
       "      <td>0.893613</td>\n",
       "      <td>0.750224</td>\n",
       "      <td>...</td>\n",
       "      <td>0.834067</td>\n",
       "      <td>0.853583</td>\n",
       "      <td>0.790274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.766715</td>\n",
       "      <td>0.723175</td>\n",
       "      <td>0.920144</td>\n",
       "      <td>0.895815</td>\n",
       "      <td>0.874387</td>\n",
       "      <td>0.811728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RDRLTRAN</th>\n",
       "      <td>0.696612</td>\n",
       "      <td>0.850983</td>\n",
       "      <td>0.874041</td>\n",
       "      <td>0.718676</td>\n",
       "      <td>0.493790</td>\n",
       "      <td>0.775380</td>\n",
       "      <td>0.842615</td>\n",
       "      <td>0.862651</td>\n",
       "      <td>0.771725</td>\n",
       "      <td>0.815910</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770055</td>\n",
       "      <td>0.776697</td>\n",
       "      <td>0.831253</td>\n",
       "      <td>0.766715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.794202</td>\n",
       "      <td>0.825533</td>\n",
       "      <td>0.730350</td>\n",
       "      <td>0.882893</td>\n",
       "      <td>0.819050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REALEST</th>\n",
       "      <td>0.691247</td>\n",
       "      <td>0.734250</td>\n",
       "      <td>0.828531</td>\n",
       "      <td>0.585352</td>\n",
       "      <td>0.490988</td>\n",
       "      <td>0.794407</td>\n",
       "      <td>0.785785</td>\n",
       "      <td>0.855420</td>\n",
       "      <td>0.784265</td>\n",
       "      <td>0.839655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>0.724874</td>\n",
       "      <td>0.709485</td>\n",
       "      <td>0.723175</td>\n",
       "      <td>0.794202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771229</td>\n",
       "      <td>0.701977</td>\n",
       "      <td>0.823417</td>\n",
       "      <td>0.744441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETAIL</th>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.767441</td>\n",
       "      <td>0.862460</td>\n",
       "      <td>0.559214</td>\n",
       "      <td>0.655867</td>\n",
       "      <td>0.876892</td>\n",
       "      <td>0.903980</td>\n",
       "      <td>0.873918</td>\n",
       "      <td>0.889649</td>\n",
       "      <td>0.786332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844322</td>\n",
       "      <td>0.840314</td>\n",
       "      <td>0.795802</td>\n",
       "      <td>0.920144</td>\n",
       "      <td>0.825533</td>\n",
       "      <td>0.771229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867108</td>\n",
       "      <td>0.907546</td>\n",
       "      <td>0.816468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOFTWARE</th>\n",
       "      <td>0.783704</td>\n",
       "      <td>0.733805</td>\n",
       "      <td>0.822306</td>\n",
       "      <td>0.478655</td>\n",
       "      <td>0.613208</td>\n",
       "      <td>0.884670</td>\n",
       "      <td>0.907963</td>\n",
       "      <td>0.829065</td>\n",
       "      <td>0.915850</td>\n",
       "      <td>0.754734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.837999</td>\n",
       "      <td>0.880056</td>\n",
       "      <td>0.778547</td>\n",
       "      <td>0.895815</td>\n",
       "      <td>0.730350</td>\n",
       "      <td>0.701977</td>\n",
       "      <td>0.867108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.845122</td>\n",
       "      <td>0.783975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRDDIST</th>\n",
       "      <td>0.786471</td>\n",
       "      <td>0.804745</td>\n",
       "      <td>0.878464</td>\n",
       "      <td>0.630844</td>\n",
       "      <td>0.589354</td>\n",
       "      <td>0.881484</td>\n",
       "      <td>0.943814</td>\n",
       "      <td>0.912860</td>\n",
       "      <td>0.867255</td>\n",
       "      <td>0.851671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.859931</td>\n",
       "      <td>0.848279</td>\n",
       "      <td>0.915207</td>\n",
       "      <td>0.874387</td>\n",
       "      <td>0.882893</td>\n",
       "      <td>0.823417</td>\n",
       "      <td>0.907546</td>\n",
       "      <td>0.845122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.842137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTILITIE</th>\n",
       "      <td>0.681218</td>\n",
       "      <td>0.775823</td>\n",
       "      <td>0.825702</td>\n",
       "      <td>0.608247</td>\n",
       "      <td>0.596221</td>\n",
       "      <td>0.797645</td>\n",
       "      <td>0.848540</td>\n",
       "      <td>0.835865</td>\n",
       "      <td>0.826870</td>\n",
       "      <td>0.746566</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750584</td>\n",
       "      <td>0.803142</td>\n",
       "      <td>0.786093</td>\n",
       "      <td>0.811728</td>\n",
       "      <td>0.819050</td>\n",
       "      <td>0.744441</td>\n",
       "      <td>0.816468</td>\n",
       "      <td>0.783975</td>\n",
       "      <td>0.842137</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "industry   AERODEF   AIRLINE      AUTO     BANKS       BEV   BLDPROD  \\\n",
       "industry                                                               \n",
       "AERODEF   1.000000  0.715879  0.800998  0.465783  0.471870  0.764393   \n",
       "AIRLINE   0.715879  1.000000  0.818696  0.654099  0.540875  0.721121   \n",
       "AUTO      0.800998  0.818696  1.000000  0.606801  0.533968  0.853847   \n",
       "BANKS     0.465783  0.654099  0.606801  1.000000  0.383583  0.492436   \n",
       "BEV       0.471870  0.540875  0.533968  0.383583  1.000000  0.584932   \n",
       "BLDPROD   0.764393  0.721121  0.853847  0.492436  0.584932  1.000000   \n",
       "CHEM      0.800025  0.811375  0.879147  0.560457  0.631810  0.924797   \n",
       "CNSTENG   0.792307  0.829383  0.897974  0.627839  0.590255  0.876360   \n",
       "COMSERV   0.801909  0.723600  0.849278  0.493130  0.605060  0.908562   \n",
       "CONMAT    0.739309  0.767873  0.838886  0.572595  0.525400  0.819237   \n",
       "CONSSERV  0.801341  0.753063  0.853259  0.522336  0.611753  0.897564   \n",
       "DVFININS  0.667329  0.813496  0.799564  0.701848  0.513520  0.716910   \n",
       "ELECEQP   0.803280  0.799166  0.879306  0.593090  0.628727  0.930992   \n",
       "ENERGY    0.665367  0.796409  0.804894  0.760872  0.554911  0.730419   \n",
       "FOODPROD  0.738297  0.749015  0.811961  0.506564  0.744934  0.880963   \n",
       "HDWRSEMI  0.818807  0.792379  0.877243  0.558363  0.593514  0.925031   \n",
       "HEALTH    0.704243  0.688595  0.744090  0.473658  0.678333  0.825389   \n",
       "HOUSEDUR  0.726259  0.769219  0.855163  0.572934  0.579734  0.846976   \n",
       "INDCONG   0.716674  0.742431  0.820294  0.504700  0.595567  0.859329   \n",
       "LEISLUX   0.815937  0.796376  0.871725  0.544504  0.648506  0.925965   \n",
       "MACH      0.838329  0.843994  0.903551  0.600120  0.604298  0.885429   \n",
       "MARINE    0.691683  0.778977  0.809432  0.581819  0.476195  0.780425   \n",
       "MATERIAL  0.758858  0.733148  0.802517  0.481197  0.573882  0.865592   \n",
       "MEDIA     0.729190  0.710533  0.811508  0.519133  0.622516  0.853001   \n",
       "MTLMIN    0.720849  0.796893  0.823657  0.626276  0.548910  0.803298   \n",
       "PERSPRD   0.770809  0.732164  0.837697  0.529474  0.647726  0.892116   \n",
       "RDRLTRAN  0.696612  0.850983  0.874041  0.718676  0.493790  0.775380   \n",
       "REALEST   0.691247  0.734250  0.828531  0.585352  0.490988  0.794407   \n",
       "RETAIL    0.764706  0.767441  0.862460  0.559214  0.655867  0.876892   \n",
       "SOFTWARE  0.783704  0.733805  0.822306  0.478655  0.613208  0.884670   \n",
       "TRDDIST   0.786471  0.804745  0.878464  0.630844  0.589354  0.881484   \n",
       "UTILITIE  0.681218  0.775823  0.825702  0.608247  0.596221  0.797645   \n",
       "\n",
       "industry      CHEM   CNSTENG   COMSERV    CONMAT  ...  MATERIAL     MEDIA  \\\n",
       "industry                                          ...                       \n",
       "AERODEF   0.800025  0.792307  0.801909  0.739309  ...  0.758858  0.729190   \n",
       "AIRLINE   0.811375  0.829383  0.723600  0.767873  ...  0.733148  0.710533   \n",
       "AUTO      0.879147  0.897974  0.849278  0.838886  ...  0.802517  0.811508   \n",
       "BANKS     0.560457  0.627839  0.493130  0.572595  ...  0.481197  0.519133   \n",
       "BEV       0.631810  0.590255  0.605060  0.525400  ...  0.573882  0.622516   \n",
       "BLDPROD   0.924797  0.876360  0.908562  0.819237  ...  0.865592  0.853001   \n",
       "CHEM      1.000000  0.903770  0.900213  0.851568  ...  0.894377  0.886290   \n",
       "CNSTENG   0.903770  1.000000  0.868062  0.916646  ...  0.833593  0.815147   \n",
       "COMSERV   0.900213  0.868062  1.000000  0.788934  ...  0.867183  0.861177   \n",
       "CONMAT    0.851568  0.916646  0.788934  1.000000  ...  0.794888  0.732443   \n",
       "CONSSERV  0.923518  0.890114  0.921697  0.837624  ...  0.889770  0.875717   \n",
       "DVFININS  0.779492  0.808860  0.715979  0.776073  ...  0.695263  0.693629   \n",
       "ELECEQP   0.964927  0.906210  0.923465  0.845613  ...  0.899821  0.904211   \n",
       "ENERGY    0.805139  0.839552  0.692627  0.774167  ...  0.697471  0.740022   \n",
       "FOODPROD  0.917984  0.853865  0.893140  0.766511  ...  0.840773  0.858302   \n",
       "HDWRSEMI  0.956528  0.885712  0.932473  0.809606  ...  0.881984  0.903730   \n",
       "HEALTH    0.840891  0.793003  0.857275  0.682142  ...  0.769390  0.811848   \n",
       "HOUSEDUR  0.863345  0.816348  0.814721  0.775264  ...  0.828149  0.796235   \n",
       "INDCONG   0.904536  0.864876  0.849497  0.823928  ...  0.832469  0.805257   \n",
       "LEISLUX   0.962381  0.902461  0.944455  0.829256  ...  0.896507  0.894074   \n",
       "MACH      0.930396  0.956560  0.884493  0.921058  ...  0.871810  0.832276   \n",
       "MARINE    0.845392  0.841675  0.741415  0.797809  ...  0.777308  0.778952   \n",
       "MATERIAL  0.894377  0.833593  0.867183  0.794888  ...  1.000000  0.823658   \n",
       "MEDIA     0.886290  0.815147  0.861177  0.732443  ...  0.823658  1.000000   \n",
       "MTLMIN    0.907845  0.864339  0.762735  0.824352  ...  0.790778  0.790843   \n",
       "PERSPRD   0.899530  0.849566  0.893613  0.750224  ...  0.834067  0.853583   \n",
       "RDRLTRAN  0.842615  0.862651  0.771725  0.815910  ...  0.770055  0.776697   \n",
       "REALEST   0.785785  0.855420  0.784265  0.839655  ...  0.775337  0.724874   \n",
       "RETAIL    0.903980  0.873918  0.889649  0.786332  ...  0.844322  0.840314   \n",
       "SOFTWARE  0.907963  0.829065  0.915850  0.754734  ...  0.837999  0.880056   \n",
       "TRDDIST   0.943814  0.912860  0.867255  0.851671  ...  0.859931  0.848279   \n",
       "UTILITIE  0.848540  0.835865  0.826870  0.746566  ...  0.750584  0.803142   \n",
       "\n",
       "industry    MTLMIN   PERSPRD  RDRLTRAN   REALEST    RETAIL  SOFTWARE  \\\n",
       "industry                                                               \n",
       "AERODEF   0.720849  0.770809  0.696612  0.691247  0.764706  0.783704   \n",
       "AIRLINE   0.796893  0.732164  0.850983  0.734250  0.767441  0.733805   \n",
       "AUTO      0.823657  0.837697  0.874041  0.828531  0.862460  0.822306   \n",
       "BANKS     0.626276  0.529474  0.718676  0.585352  0.559214  0.478655   \n",
       "BEV       0.548910  0.647726  0.493790  0.490988  0.655867  0.613208   \n",
       "BLDPROD   0.803298  0.892116  0.775380  0.794407  0.876892  0.884670   \n",
       "CHEM      0.907845  0.899530  0.842615  0.785785  0.903980  0.907963   \n",
       "CNSTENG   0.864339  0.849566  0.862651  0.855420  0.873918  0.829065   \n",
       "COMSERV   0.762735  0.893613  0.771725  0.784265  0.889649  0.915850   \n",
       "CONMAT    0.824352  0.750224  0.815910  0.839655  0.786332  0.754734   \n",
       "CONSSERV  0.808085  0.899692  0.789400  0.837292  0.907748  0.900991   \n",
       "DVFININS  0.771522  0.730530  0.829757  0.750546  0.783327  0.722164   \n",
       "ELECEQP   0.877598  0.912340  0.838604  0.796434  0.913369  0.929265   \n",
       "ENERGY    0.848265  0.740646  0.856754  0.742121  0.781511  0.680883   \n",
       "FOODPROD  0.801223  0.930906  0.752622  0.725734  0.919056  0.900352   \n",
       "HDWRSEMI  0.848576  0.921828  0.821953  0.766616  0.908287  0.955865   \n",
       "HEALTH    0.707666  0.895213  0.676100  0.653898  0.842773  0.847162   \n",
       "HOUSEDUR  0.774076  0.835697  0.784760  0.796468  0.840651  0.806720   \n",
       "INDCONG   0.817896  0.814293  0.775722  0.800310  0.852942  0.834709   \n",
       "LEISLUX   0.851209  0.922456  0.837324  0.811534  0.924022  0.925557   \n",
       "MACH      0.871930  0.872875  0.855312  0.831989  0.890043  0.871498   \n",
       "MARINE    0.822583  0.740172  0.846844  0.726297  0.768532  0.749610   \n",
       "MATERIAL  0.790778  0.834067  0.770055  0.775337  0.844322  0.837999   \n",
       "MEDIA     0.790843  0.853583  0.776697  0.724874  0.840314  0.880056   \n",
       "MTLMIN    1.000000  0.790274  0.831253  0.709485  0.795802  0.778547   \n",
       "PERSPRD   0.790274  1.000000  0.766715  0.723175  0.920144  0.895815   \n",
       "RDRLTRAN  0.831253  0.766715  1.000000  0.794202  0.825533  0.730350   \n",
       "REALEST   0.709485  0.723175  0.794202  1.000000  0.771229  0.701977   \n",
       "RETAIL    0.795802  0.920144  0.825533  0.771229  1.000000  0.867108   \n",
       "SOFTWARE  0.778547  0.895815  0.730350  0.701977  0.867108  1.000000   \n",
       "TRDDIST   0.915207  0.874387  0.882893  0.823417  0.907546  0.845122   \n",
       "UTILITIE  0.786093  0.811728  0.819050  0.744441  0.816468  0.783975   \n",
       "\n",
       "industry   TRDDIST  UTILITIE  \n",
       "industry                      \n",
       "AERODEF   0.786471  0.681218  \n",
       "AIRLINE   0.804745  0.775823  \n",
       "AUTO      0.878464  0.825702  \n",
       "BANKS     0.630844  0.608247  \n",
       "BEV       0.589354  0.596221  \n",
       "BLDPROD   0.881484  0.797645  \n",
       "CHEM      0.943814  0.848540  \n",
       "CNSTENG   0.912860  0.835865  \n",
       "COMSERV   0.867255  0.826870  \n",
       "CONMAT    0.851671  0.746566  \n",
       "CONSSERV  0.903728  0.824086  \n",
       "DVFININS  0.813018  0.741397  \n",
       "ELECEQP   0.929624  0.848510  \n",
       "ENERGY    0.844507  0.756578  \n",
       "FOODPROD  0.874258  0.814810  \n",
       "HDWRSEMI  0.914960  0.838115  \n",
       "HEALTH    0.782932  0.797065  \n",
       "HOUSEDUR  0.833871  0.789948  \n",
       "INDCONG   0.902494  0.773835  \n",
       "LEISLUX   0.927751  0.865146  \n",
       "MACH      0.921690  0.828181  \n",
       "MARINE    0.852030  0.770974  \n",
       "MATERIAL  0.859931  0.750584  \n",
       "MEDIA     0.848279  0.803142  \n",
       "MTLMIN    0.915207  0.786093  \n",
       "PERSPRD   0.874387  0.811728  \n",
       "RDRLTRAN  0.882893  0.819050  \n",
       "REALEST   0.823417  0.744441  \n",
       "RETAIL    0.907546  0.816468  \n",
       "SOFTWARE  0.845122  0.783975  \n",
       "TRDDIST   1.000000  0.842137  \n",
       "UTILITIE  0.842137  1.000000  \n",
       "\n",
       "[32 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 行业收益率\n",
    "d_45 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "\n",
    "d_45 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_45, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_45[\"capt_ratio\"] = d_45[\"capt\"]/d_45[\"ind_capt\"]\n",
    "d_45[\"ratio_ret\"] = d_45[\"capt_ratio\"] * d_45[\"stkcd_ret\"]\n",
    "\n",
    "d_45 \\\n",
    "    = d_45\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\" : \"ind_ret\"})\\\n",
    "        .merge(\n",
    "            d_45\\\n",
    "                .groupby([\"date\", \"industry\"])\\\n",
    "                [\"ind_capt\"]\\\n",
    "                .first(), \n",
    "            right_on = [\"industry\", \"date\"], \n",
    "            left_on = ['industry', \"date\"]\n",
    "            )\\\n",
    "        [[\"date\", \"industry\", \"ind_ret\"]]\n",
    "\n",
    "d_45 \\\n",
    "    = d_45\\\n",
    "        .pivot_table(\n",
    "            index = [\"date\"],\n",
    "            columns = \"industry\",\n",
    "            values = \"ind_ret\"\n",
    "            )\n",
    "\n",
    "d_45.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ELECEQP', 'HDWRSEMI')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_45_coef = d_45.corr()\n",
    "d_45_coef.stack()[d_45_coef.stack() != 1].index[np.argmax(d_45_coef.values[d_45_coef.values != 1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('BANKS', 'BEV')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_45_coef.stack().index[np.argmin(d_45_coef.values)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "46. 各个行业的收益率对市场收益率的相关系数由高到低排列如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>industry</th>\n",
       "      <th>industry_standard_deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CONMAT</td>\n",
       "      <td>0.021500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>INDCONG</td>\n",
       "      <td>0.019694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DVFININS</td>\n",
       "      <td>0.018947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MARINE</td>\n",
       "      <td>0.018242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SOFTWARE</td>\n",
       "      <td>0.018210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MEDIA</td>\n",
       "      <td>0.018032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.017500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>0.017472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REALEST</td>\n",
       "      <td>0.017361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEM</td>\n",
       "      <td>0.017266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COMSERV</td>\n",
       "      <td>0.017257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TRDDIST</td>\n",
       "      <td>0.017190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AIRLINE</td>\n",
       "      <td>0.017127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MACH</td>\n",
       "      <td>0.016836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLDPROD</td>\n",
       "      <td>0.016608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEV</td>\n",
       "      <td>0.016408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ELECEQP</td>\n",
       "      <td>0.016273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>0.015934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FOODPROD</td>\n",
       "      <td>0.015785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RETAIL</td>\n",
       "      <td>0.015780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MATERIAL</td>\n",
       "      <td>0.015680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HEALTH</td>\n",
       "      <td>0.015500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LEISLUX</td>\n",
       "      <td>0.015416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CNSTENG</td>\n",
       "      <td>0.015310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PERSPRD</td>\n",
       "      <td>0.015277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HOUSEDUR</td>\n",
       "      <td>0.015175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUTO</td>\n",
       "      <td>0.015097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CONSSERV</td>\n",
       "      <td>0.014880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.011773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENERGY</td>\n",
       "      <td>0.010658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RDRLTRAN</td>\n",
       "      <td>0.009676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKS</td>\n",
       "      <td>0.008106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    industry  industry_standard_deviation\n",
       "9     CONMAT                     0.021500\n",
       "18   INDCONG                     0.019694\n",
       "11  DVFININS                     0.018947\n",
       "21    MARINE                     0.018242\n",
       "29  SOFTWARE                     0.018210\n",
       "23     MEDIA                     0.018032\n",
       "0    AERODEF                     0.017500\n",
       "24    MTLMIN                     0.017472\n",
       "27   REALEST                     0.017361\n",
       "6       CHEM                     0.017266\n",
       "8    COMSERV                     0.017257\n",
       "30   TRDDIST                     0.017190\n",
       "1    AIRLINE                     0.017127\n",
       "20      MACH                     0.016836\n",
       "5    BLDPROD                     0.016608\n",
       "4        BEV                     0.016408\n",
       "12   ELECEQP                     0.016273\n",
       "15  HDWRSEMI                     0.015934\n",
       "14  FOODPROD                     0.015785\n",
       "28    RETAIL                     0.015780\n",
       "22  MATERIAL                     0.015680\n",
       "16    HEALTH                     0.015500\n",
       "19   LEISLUX                     0.015416\n",
       "7    CNSTENG                     0.015310\n",
       "25   PERSPRD                     0.015277\n",
       "17  HOUSEDUR                     0.015175\n",
       "2       AUTO                     0.015097\n",
       "10  CONSSERV                     0.014880\n",
       "31  UTILITIE                     0.011773\n",
       "13    ENERGY                     0.010658\n",
       "26  RDRLTRAN                     0.009676\n",
       "3      BANKS                     0.008106"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 行业收益率\n",
    "d_46 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "\n",
    "d_46 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_46, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_46[\"capt_ratio\"] = d_46[\"capt\"]/d_46[\"ind_capt\"]\n",
    "d_46[\"ratio_ret\"] = d_46[\"capt_ratio\"] * d_46[\"stkcd_ret\"]\n",
    "\n",
    "d_46 \\\n",
    "    = d_46\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\" : \"ind_ret\"})\\\n",
    "        .merge(\n",
    "            d_46\\\n",
    "                .groupby([\"date\", \"industry\"])\\\n",
    "                [\"ind_capt\"]\\\n",
    "                .first(), \n",
    "            right_on = [\"industry\", \"date\"], \n",
    "            left_on = ['industry', \"date\"]\n",
    "            )\n",
    "\n",
    "d_46\\\n",
    "    .groupby([\"industry\"])\\\n",
    "    [\"ind_ret\"]\\\n",
    "    .std()\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns = {\"ind_ret\":\"industry_standard_deviation\"})\\\n",
    "    .sort_values(by = [\"industry_standard_deviation\"], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "47. 每个月总成交额比上个月下降幅度最大的行业是哪个？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>industry</th>\n",
       "      <th>amount_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>MARINE</td>\n",
       "      <td>4.090901e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>2012</td>\n",
       "      <td>03</td>\n",
       "      <td>CONMAT</td>\n",
       "      <td>-2.175255e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>147</td>\n",
       "      <td>2012</td>\n",
       "      <td>04</td>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>-1.657756e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>166</td>\n",
       "      <td>2012</td>\n",
       "      <td>05</td>\n",
       "      <td>REALEST</td>\n",
       "      <td>-4.528376e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>-1.232720e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  year month industry  amount_change\n",
       "0    127  2012    02   MARINE   4.090901e+09\n",
       "1     56  2012    03   CONMAT  -2.175255e+10\n",
       "2    147  2012    04   MTLMIN  -1.657756e+11\n",
       "3    166  2012    05  REALEST  -4.528376e+09\n",
       "4    149  2012    06   MTLMIN  -1.232720e+11"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"date\"] = data[\"date\"].astype(str)\n",
    "data[\"year\"] = data[\"date\"].str.slice(stop = 4)\n",
    "data[\"month\"] = data[\"date\"].str.slice(start = 4, stop = 6)\n",
    "\n",
    "d_47 \\\n",
    "    = data\\\n",
    "        .groupby([\"industry\", \"year\", \"month\"])\\\n",
    "        [\"amount\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\n",
    "\n",
    "d_47[\"amount_lag\"] = d_47.groupby([\"industry\"])[\"amount\"].shift(1)\n",
    "d_47[\"amount_change\"] = d_47[\"amount\"] - d_47[\"amount_lag\"] \n",
    "d_47 \\\n",
    "    = d_47\\\n",
    "        .groupby([\"year\", \"month\"])\\\n",
    "        [\"amount_change\"]\\\n",
    "        .min()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"amount_change\" : \"amount_change_min\"})\\\n",
    "        .merge(\n",
    "            d_47, \n",
    "            how = \"right\", \n",
    "            right_on = [\"year\", \"month\"], \n",
    "            left_on = [\"year\", \"month\"]\n",
    "            )\n",
    "\n",
    "d_47\\\n",
    "    [d_47.amount_change_min == d_47.amount_change]\\\n",
    "    [[\"year\", \"month\", \"industry\", \"amount_change\"]]\\\n",
    "    .sort_values(by = [\"year\", \"month\"])\\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "48. 数据当中各个股票的最大回撤幅度是多少？（最大回撤是从一个高点到低点的降幅的最大值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>symbol</th>\n",
       "      <th>max_retreat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>603002.SH</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2421</th>\n",
       "      <td>603123.SH</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>603128.SH</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2423</th>\n",
       "      <td>603333.SH</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2425 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symbol  max_retreat\n",
       "0     000001.SZ         3.33\n",
       "1     000002.SZ         0.92\n",
       "2     000004.SZ         2.12\n",
       "3     000005.SZ         1.74\n",
       "4     000006.SZ         1.47\n",
       "...         ...          ...\n",
       "2420  603002.SH         2.27\n",
       "2421  603123.SH         4.53\n",
       "2422  603128.SH         1.78\n",
       "2423  603333.SH         2.99\n",
       "2424  603366.SH         3.94\n",
       "\n",
       "[2425 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_48 = data.sort_values(by = [\"symbol\", \"date\"])\n",
    "d_48_group = d_48.groupby('symbol')\n",
    "\n",
    "d_48_list = []\n",
    "for name, group in d_48_group:\n",
    "    for i in range(len(group)):\n",
    "        # a = group.iloc[i,:][\"high\"]\n",
    "        # b = group.iloc[i:(len(group) + 1), :][\"low\"]\n",
    "        # c = a - b\n",
    "        d_48_list\\\n",
    "            .append(\n",
    "                max(group.iloc[i,:][\"high\"] \\\n",
    "                    - group.iloc[i:(len(group) + 1), :][\"low\"]))\n",
    "d_48[\"max_retreat\"] = d_48_list\n",
    "d_48.groupby([\"symbol\"])[\"max_retreat\"].max().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "48. 每只股票的胜率是多少？（胜率是每天收益率为正数的概率）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>symbol</th>\n",
       "      <th>gain_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>0.418803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>0.470085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>603002.SH</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2421</th>\n",
       "      <td>603123.SH</td>\n",
       "      <td>0.317073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>603128.SH</td>\n",
       "      <td>0.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2423</th>\n",
       "      <td>603333.SH</td>\n",
       "      <td>0.358974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>0.379310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2425 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symbol  gain_ratio\n",
       "0     000001.SZ    0.418803\n",
       "1     000002.SZ    0.470085\n",
       "2     000004.SZ    0.512821\n",
       "3     000005.SZ    0.153846\n",
       "4     000006.SZ    0.512821\n",
       "...         ...         ...\n",
       "2420  603002.SH    0.433333\n",
       "2421  603123.SH    0.317073\n",
       "2422  603128.SH    0.478261\n",
       "2423  603333.SH    0.358974\n",
       "2424  603366.SH    0.379310\n",
       "\n",
       "[2425 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data[\"tag\"] = np.where(data.stkcd_ret > 0, 1, 0)\n",
    "d_48 \\\n",
    "    = data\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        [\"tag\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .merge(\n",
    "            data\\\n",
    "                .groupby([\"symbol\"])\\\n",
    "                [\"date\"]\\\n",
    "                .count()\\\n",
    "                .reset_index(),\n",
    "            right_on = \"symbol\",\n",
    "            left_on = \"symbol\"\n",
    "            )\n",
    "\n",
    "d_48[\"gain_ratio\"] = d_48[\"tag\"]/d_48[\"date\"]\n",
    "d_48[[\"symbol\", \"gain_ratio\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "50. 每只股票的盈亏比是多少？（盈亏比是正收益之和与负收益之和的比值的绝对值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>symbol</th>\n",
       "      <th>gl_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>0.971861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>1.279686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>1.082869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>0.553243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>1.371993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>603002.SH</td>\n",
       "      <td>3.327521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>603123.SH</td>\n",
       "      <td>1.415443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>603128.SH</td>\n",
       "      <td>1.266408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>603333.SH</td>\n",
       "      <td>1.020868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>0.716711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2393 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symbol  gl_ratio\n",
       "0     000001.SZ  0.971861\n",
       "1     000002.SZ  1.279686\n",
       "2     000004.SZ  1.082869\n",
       "3     000005.SZ  0.553243\n",
       "4     000006.SZ  1.371993\n",
       "...         ...       ...\n",
       "2388  603002.SH  3.327521\n",
       "2389  603123.SH  1.415443\n",
       "2390  603128.SH  1.266408\n",
       "2391  603333.SH  1.020868\n",
       "2392  603366.SH  0.716711\n",
       "\n",
       "[2393 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "d_50 \\\n",
    "    = data[data.stkcd_ret > 0]\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        [\"stkcd_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"stkcd_ret\": \"gain\"})\\\n",
    "        .merge(\n",
    "            data[data.stkcd_ret < 0]\\\n",
    "                .groupby([\"symbol\"])\\\n",
    "                [\"stkcd_ret\"]\\\n",
    "                .sum()\\\n",
    "                .abs()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"stkcd_ret\": \"loss\"}),\n",
    "            right_on = \"symbol\",\n",
    "            left_on = \"symbol\"\n",
    "            )\n",
    "\n",
    "d_50[\"gl_ratio\"] = d_50[\"gain\"]/d_50[\"loss\"]\n",
    "d_50[[\"symbol\", \"gl_ratio\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "51. 市场的胜率是多少？（市场收益率为正的概率）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48717948717948717"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 市场收益率\n",
    "d_51 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "d_51 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_51, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\"\n",
    "            )\n",
    "\n",
    "d_51[\"capt_ratio\"] = d_51[\"capt\"]/d_51[\"mkt_capt\"]\n",
    "d_51[\"ratio_ret\"] = d_51[\"capt_ratio\"]*d_51[\"stkcd_ret\"]\n",
    "\n",
    "d_51 \\\n",
    "    = d_51\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "d_51\\\n",
    "    [d_51.mkt_ret > 0][\"date\"].count()\\\n",
    "        /d_51[\"date\"].count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "52. 市场的盈亏比是多少？（市场中每个股票的市值加权正收益和市值加权负收益比）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1473732397390033"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 市场收益率\n",
    "d_52 \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_52 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_52, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\"\n",
    "            )\n",
    "\n",
    "d_52[\"capt_ratio\"] = d_52[\"capt\"]/d_52[\"mkt_capt\"]\n",
    "d_52[\"ratio_ret\"] = d_52[\"capt_ratio\"]*d_52[\"stkcd_ret\"]\n",
    "d_52 \\\n",
    "    = d_52\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "d_52[d_52.mkt_ret > 0][\"mkt_ret\"].sum()\\\n",
    "    /d_52[d_52.mkt_ret < 0][\"mkt_ret\"].abs().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "53. 每个行业的胜率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>industry</th>\n",
       "      <th>gain_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AIRLINE</td>\n",
       "      <td>0.478632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUTO</td>\n",
       "      <td>0.470085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKS</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEV</td>\n",
       "      <td>0.547009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLDPROD</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEM</td>\n",
       "      <td>0.495726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CNSTENG</td>\n",
       "      <td>0.547009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COMSERV</td>\n",
       "      <td>0.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CONMAT</td>\n",
       "      <td>0.529915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CONSSERV</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DVFININS</td>\n",
       "      <td>0.470085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ELECEQP</td>\n",
       "      <td>0.495726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENERGY</td>\n",
       "      <td>0.452991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FOODPROD</td>\n",
       "      <td>0.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>0.495726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HEALTH</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HOUSEDUR</td>\n",
       "      <td>0.478632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>INDCONG</td>\n",
       "      <td>0.547009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LEISLUX</td>\n",
       "      <td>0.478632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MACH</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MARINE</td>\n",
       "      <td>0.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MATERIAL</td>\n",
       "      <td>0.495726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MEDIA</td>\n",
       "      <td>0.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>0.470085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PERSPRD</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RDRLTRAN</td>\n",
       "      <td>0.478632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REALEST</td>\n",
       "      <td>0.529915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RETAIL</td>\n",
       "      <td>0.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SOFTWARE</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TRDDIST</td>\n",
       "      <td>0.547009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.547009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    industry  gain_ratio\n",
       "0    AERODEF    0.521368\n",
       "1    AIRLINE    0.478632\n",
       "2       AUTO    0.470085\n",
       "3      BANKS    0.461538\n",
       "4        BEV    0.547009\n",
       "5    BLDPROD    0.538462\n",
       "6       CHEM    0.495726\n",
       "7    CNSTENG    0.547009\n",
       "8    COMSERV    0.521368\n",
       "9     CONMAT    0.529915\n",
       "10  CONSSERV    0.512821\n",
       "11  DVFININS    0.470085\n",
       "12   ELECEQP    0.495726\n",
       "13    ENERGY    0.452991\n",
       "14  FOODPROD    0.521368\n",
       "15  HDWRSEMI    0.495726\n",
       "16    HEALTH    0.555556\n",
       "17  HOUSEDUR    0.478632\n",
       "18   INDCONG    0.547009\n",
       "19   LEISLUX    0.478632\n",
       "20      MACH    0.512821\n",
       "21    MARINE    0.521368\n",
       "22  MATERIAL    0.495726\n",
       "23     MEDIA    0.521368\n",
       "24    MTLMIN    0.470085\n",
       "25   PERSPRD    0.555556\n",
       "26  RDRLTRAN    0.478632\n",
       "27   REALEST    0.529915\n",
       "28    RETAIL    0.521368\n",
       "29  SOFTWARE    0.555556\n",
       "30   TRDDIST    0.547009\n",
       "31  UTILITIE    0.547009"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 行业收益率\n",
    "d_53 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "\n",
    "d_53 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_53, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_53[\"capt_ratio\"] = d_53[\"capt\"]/d_53[\"ind_capt\"]\n",
    "d_53[\"ratio_ret\"] = d_53[\"capt_ratio\"] * d_53[\"stkcd_ret\"]\n",
    "d_53 \\\n",
    "    = d_53\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\" : \"ind_ret\"})\\\n",
    "        .merge(\n",
    "            d_53\\\n",
    "                .groupby([\"date\", \"industry\"])\\\n",
    "                [\"ind_capt\"]\\\n",
    "                .first(), \n",
    "            right_on = [\"industry\", \"date\"], \n",
    "            left_on = ['industry', \"date\"]\n",
    "            )\n",
    "\n",
    "d_53 \\\n",
    "    = d_53\\\n",
    "        [d_53.ind_ret > 0]\\\n",
    "        .groupby([\"industry\"])\\\n",
    "        [\"date\"]\\\n",
    "        .count()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"date\": \"gain_amount\"})\\\n",
    "        .merge(\n",
    "            d_53\\\n",
    "                .groupby([\"industry\"])\\\n",
    "                [\"date\"]\\\n",
    "                .count()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"date\": \"amount\"}),\n",
    "            right_on = \"industry\",\n",
    "            left_on = \"industry\"\n",
    "            )\n",
    "\n",
    "d_53[\"gain_ratio\"] = d_53[\"gain_amount\"]/d_53[\"amount\"]\n",
    "d_53[[\"industry\", \"gain_ratio\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "54. 每个行业的盈亏比是多少？（行业盈亏比是行业内每个股票的市值加权的正收益率和市值加权的负收益率之比）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>industry</th>\n",
       "      <th>gl_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.997362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AIRLINE</td>\n",
       "      <td>1.071538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUTO</td>\n",
       "      <td>1.120609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKS</td>\n",
       "      <td>1.015873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEV</td>\n",
       "      <td>1.281267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLDPROD</td>\n",
       "      <td>1.269879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEM</td>\n",
       "      <td>1.114915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CNSTENG</td>\n",
       "      <td>1.241538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COMSERV</td>\n",
       "      <td>1.199034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CONMAT</td>\n",
       "      <td>1.027472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CONSSERV</td>\n",
       "      <td>1.268893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DVFININS</td>\n",
       "      <td>1.293645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ELECEQP</td>\n",
       "      <td>0.965409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENERGY</td>\n",
       "      <td>0.924195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FOODPROD</td>\n",
       "      <td>1.094845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HDWRSEMI</td>\n",
       "      <td>1.052315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HEALTH</td>\n",
       "      <td>1.269041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HOUSEDUR</td>\n",
       "      <td>1.235703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>INDCONG</td>\n",
       "      <td>1.310078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LEISLUX</td>\n",
       "      <td>1.109010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MACH</td>\n",
       "      <td>1.123400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MARINE</td>\n",
       "      <td>1.014531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MATERIAL</td>\n",
       "      <td>1.196789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MEDIA</td>\n",
       "      <td>1.051200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MTLMIN</td>\n",
       "      <td>1.146891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PERSPRD</td>\n",
       "      <td>1.199735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RDRLTRAN</td>\n",
       "      <td>1.070569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REALEST</td>\n",
       "      <td>1.366244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RETAIL</td>\n",
       "      <td>1.103894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SOFTWARE</td>\n",
       "      <td>1.071894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TRDDIST</td>\n",
       "      <td>1.201983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>1.266518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    industry  gl_ratio\n",
       "0    AERODEF  0.997362\n",
       "1    AIRLINE  1.071538\n",
       "2       AUTO  1.120609\n",
       "3      BANKS  1.015873\n",
       "4        BEV  1.281267\n",
       "5    BLDPROD  1.269879\n",
       "6       CHEM  1.114915\n",
       "7    CNSTENG  1.241538\n",
       "8    COMSERV  1.199034\n",
       "9     CONMAT  1.027472\n",
       "10  CONSSERV  1.268893\n",
       "11  DVFININS  1.293645\n",
       "12   ELECEQP  0.965409\n",
       "13    ENERGY  0.924195\n",
       "14  FOODPROD  1.094845\n",
       "15  HDWRSEMI  1.052315\n",
       "16    HEALTH  1.269041\n",
       "17  HOUSEDUR  1.235703\n",
       "18   INDCONG  1.310078\n",
       "19   LEISLUX  1.109010\n",
       "20      MACH  1.123400\n",
       "21    MARINE  1.014531\n",
       "22  MATERIAL  1.196789\n",
       "23     MEDIA  1.051200\n",
       "24    MTLMIN  1.146891\n",
       "25   PERSPRD  1.199735\n",
       "26  RDRLTRAN  1.070569\n",
       "27   REALEST  1.366244\n",
       "28    RETAIL  1.103894\n",
       "29  SOFTWARE  1.071894\n",
       "30   TRDDIST  1.201983\n",
       "31  UTILITIE  1.266518"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "# 行业收益率\n",
    "d_54 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "\n",
    "d_54 \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_54, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_54[\"capt_ratio\"] = d_54[\"capt\"]/d_54[\"ind_capt\"]\n",
    "d_54[\"ratio_ret\"] = d_54[\"capt_ratio\"] * d_54[\"stkcd_ret\"]\n",
    "\n",
    "d_54 \\\n",
    "    = d_54\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\" : \"ind_ret\"})\\\n",
    "        .merge(\n",
    "            d_54\\\n",
    "                .groupby([\"date\", \"industry\"])\\\n",
    "                [\"ind_capt\"]\\\n",
    "                .first(), \n",
    "            right_on = [\"industry\", \"date\"], \n",
    "            left_on = ['industry', \"date\"]\n",
    "            )\n",
    "\n",
    "d_54 \\\n",
    "    = d_54\\\n",
    "        [d_54.ind_ret > 0]\\\n",
    "        .groupby([\"industry\"])\\\n",
    "        [\"ind_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ind_ret\": \"gain\"})\\\n",
    "        .merge(\n",
    "            d_54\\\n",
    "                [d_54.ind_ret < 0]\\\n",
    "                .groupby([\"industry\"])\\\n",
    "                [\"ind_ret\"]\\\n",
    "                .sum()\\\n",
    "                .abs()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"ind_ret\": \"loss\"}),\n",
    "            right_on = \"industry\",\n",
    "            left_on = \"industry\"\n",
    "            )\n",
    "\n",
    "d_54[\"gl_ratio\"] = d_54[\"gain\"]/d_54[\"loss\"]\n",
    "d_54[[\"industry\", \"gl_ratio\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "55. 是否存在股票的月成交额超过所在行业当月中某一天总成交额的情况？"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>industry</th>\n",
       "      <th>symbol</th>\n",
       "      <th>stkcd_amount</th>\n",
       "      <th>ind_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>000768.SZ</td>\n",
       "      <td>1.460111e+09</td>\n",
       "      <td>4.933312e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>000768.SZ</td>\n",
       "      <td>1.460111e+09</td>\n",
       "      <td>5.488054e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>000768.SZ</td>\n",
       "      <td>1.460111e+09</td>\n",
       "      <td>6.273671e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>000768.SZ</td>\n",
       "      <td>1.460111e+09</td>\n",
       "      <td>5.059227e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>000768.SZ</td>\n",
       "      <td>1.460111e+09</td>\n",
       "      <td>8.318035e+08</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>278024</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>600795.SH</td>\n",
       "      <td>3.541810e+09</td>\n",
       "      <td>3.527167e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278025</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>600795.SH</td>\n",
       "      <td>3.541810e+09</td>\n",
       "      <td>2.805190e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278026</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>600795.SH</td>\n",
       "      <td>3.541810e+09</td>\n",
       "      <td>2.673393e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278027</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>600795.SH</td>\n",
       "      <td>3.541810e+09</td>\n",
       "      <td>2.899220e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278028</th>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>600795.SH</td>\n",
       "      <td>3.541810e+09</td>\n",
       "      <td>2.929382e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15813 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year month  industry     symbol  stkcd_amount    ind_amount\n",
       "0       2012    01   AERODEF  000768.SZ  1.460111e+09  4.933312e+08\n",
       "1       2012    01   AERODEF  000768.SZ  1.460111e+09  5.488054e+08\n",
       "2       2012    01   AERODEF  000768.SZ  1.460111e+09  6.273671e+08\n",
       "3       2012    01   AERODEF  000768.SZ  1.460111e+09  5.059227e+08\n",
       "4       2012    01   AERODEF  000768.SZ  1.460111e+09  8.318035e+08\n",
       "...      ...   ...       ...        ...           ...           ...\n",
       "278024  2012    06  UTILITIE  600795.SH  3.541810e+09  3.527167e+09\n",
       "278025  2012    06  UTILITIE  600795.SH  3.541810e+09  2.805190e+09\n",
       "278026  2012    06  UTILITIE  600795.SH  3.541810e+09  2.673393e+09\n",
       "278027  2012    06  UTILITIE  600795.SH  3.541810e+09  2.899220e+09\n",
       "278028  2012    06  UTILITIE  600795.SH  3.541810e+09  2.929382e+09\n",
       "\n",
       "[15813 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"date\"] = data[\"date\"].astype(str)\n",
    "data[\"year\"] = data[\"date\"].str.slice(stop = 4)\n",
    "data[\"month\"] = data[\"date\"].str.slice(start = 4, stop = 6)\n",
    "\n",
    "# 股票的月成交额\n",
    "d_55 \\\n",
    "    = data\\\n",
    "        .groupby([\"year\", \"month\", \"industry\", \"symbol\"])\\\n",
    "        [\"amount\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"amount\" : \"stkcd_amount\"})\\\n",
    "        .merge(\n",
    "            # 行业的日交易额\n",
    "            data\\\n",
    "                .groupby([\"date\", \"year\", \"month\", \"industry\"])\\\n",
    "                [\"amount\"]\\\n",
    "                .sum()\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"amount\" : \"ind_amount\"}),\n",
    "            how = \"inner\",\n",
    "            left_on = [\"year\", \"month\", \"industry\"],\n",
    "            right_on = [\"year\", \"month\", \"industry\"]\n",
    ")\n",
    "\n",
    "d_55\\\n",
    "    [d_55.stkcd_amount > d_55.ind_amount]\\\n",
    "    [[\"year\", \"month\", \"industry\", \"symbol\", \"stkcd_amount\", \"ind_amount\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "56. 每天每个行业编入、编出的股票各有多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date</th>\n",
       "      <th>industry</th>\n",
       "      <th>in_symbol</th>\n",
       "      <th>out_symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20120105</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20120106</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20120109</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>20120110</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>20120625</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>20120626</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>20120627</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3711</th>\n",
       "      <td>20120628</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>20120629</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  industry  in_symbol  out_symbol\n",
       "0     20120104   AERODEF          0           0\n",
       "32    20120105   AERODEF          0           0\n",
       "64    20120106   AERODEF          0           0\n",
       "96    20120109   AERODEF          0           0\n",
       "128   20120110   AERODEF          0           0\n",
       "...        ...       ...        ...         ...\n",
       "3615  20120625  UTILITIE          0           0\n",
       "3647  20120626  UTILITIE          0           0\n",
       "3679  20120627  UTILITIE          0           0\n",
       "3711  20120628  UTILITIE          0           0\n",
       "3743  20120629  UTILITIE          0           0\n",
       "\n",
       "[3744 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_56 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        .agg({'symbol': lambda x: list(x)})\\\n",
    "        .reset_index()\\\n",
    "        .sort_values(by = [\"industry\", \"date\"])\n",
    "\n",
    "d_56_group = d_56.groupby([\"industry\"])\n",
    "d_56_list1 = []\n",
    "d_56_list2 = []\n",
    "for name, group in d_56_group:\n",
    "    for i in range(0, len(group)):\n",
    "        if i == 0:\n",
    "            d_56_list1.append(0)\n",
    "            d_56_list2.append(0)\n",
    "        else:\n",
    "            a = set(group.iloc[i, :][\"symbol\"])\n",
    "            b = set(group.iloc[(i-1), :][\"symbol\"])\n",
    "            c = a & b\n",
    "            a1 = len(a - c)\n",
    "            b1 = len(b - c)\n",
    "            d_56_list1.append(a1)\n",
    "            d_56_list2.append(b1)\n",
    "            \n",
    "d_56[\"in_symbol\"] = d_56_list1\n",
    "d_56[\"out_symbol\"] = d_56_list2\n",
    "d_56[[\"date\", \"industry\", \"in_symbol\", \"out_symbol\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "57. 每天每个行业内股票收益率的标准差是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date</th>\n",
       "      <th>industry</th>\n",
       "      <th>stkcd_ret_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120104</td>\n",
       "      <td>AERODEF</td>\n",
       "      <td>0.028369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20120104</td>\n",
       "      <td>AIRLINE</td>\n",
       "      <td>0.011949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20120104</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>0.024044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20120104</td>\n",
       "      <td>BANKS</td>\n",
       "      <td>0.008446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20120104</td>\n",
       "      <td>BEV</td>\n",
       "      <td>0.025690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>20120629</td>\n",
       "      <td>REALEST</td>\n",
       "      <td>0.018059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>20120629</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>0.015018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>20120629</td>\n",
       "      <td>SOFTWARE</td>\n",
       "      <td>0.023618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>20120629</td>\n",
       "      <td>TRDDIST</td>\n",
       "      <td>0.024542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>20120629</td>\n",
       "      <td>UTILITIE</td>\n",
       "      <td>0.012882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3744 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  industry  stkcd_ret_std\n",
       "0     20120104   AERODEF       0.028369\n",
       "1     20120104   AIRLINE       0.011949\n",
       "2     20120104      AUTO       0.024044\n",
       "3     20120104     BANKS       0.008446\n",
       "4     20120104       BEV       0.025690\n",
       "...        ...       ...            ...\n",
       "3739  20120629   REALEST       0.018059\n",
       "3740  20120629    RETAIL       0.015018\n",
       "3741  20120629  SOFTWARE       0.023618\n",
       "3742  20120629   TRDDIST       0.024542\n",
       "3743  20120629  UTILITIE       0.012882\n",
       "\n",
       "[3744 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data\\\n",
    "    .groupby([\"date\", \"industry\"])\\\n",
    "    [\"stkcd_ret\"]\\\n",
    "    .std()\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns = {\"stkcd_ret\": \"stkcd_ret_std\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "58. 每天每个行业内股票收益率的标准差的相关性如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>industry</th>\n",
       "      <th>AERODEF</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>AUTO</th>\n",
       "      <th>BANKS</th>\n",
       "      <th>BEV</th>\n",
       "      <th>BLDPROD</th>\n",
       "      <th>CHEM</th>\n",
       "      <th>CNSTENG</th>\n",
       "      <th>COMSERV</th>\n",
       "      <th>CONMAT</th>\n",
       "      <th>...</th>\n",
       "      <th>MATERIAL</th>\n",
       "      <th>MEDIA</th>\n",
       "      <th>MTLMIN</th>\n",
       "      <th>PERSPRD</th>\n",
       "      <th>RDRLTRAN</th>\n",
       "      <th>REALEST</th>\n",
       "      <th>RETAIL</th>\n",
       "      <th>SOFTWARE</th>\n",
       "      <th>TRDDIST</th>\n",
       "      <th>UTILITIE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AERODEF</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115230</td>\n",
       "      <td>0.331956</td>\n",
       "      <td>0.080215</td>\n",
       "      <td>0.024979</td>\n",
       "      <td>0.235179</td>\n",
       "      <td>0.188611</td>\n",
       "      <td>0.216828</td>\n",
       "      <td>0.257316</td>\n",
       "      <td>0.390574</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039475</td>\n",
       "      <td>0.055022</td>\n",
       "      <td>0.237747</td>\n",
       "      <td>0.073751</td>\n",
       "      <td>0.234620</td>\n",
       "      <td>0.240815</td>\n",
       "      <td>0.197279</td>\n",
       "      <td>0.461433</td>\n",
       "      <td>0.128383</td>\n",
       "      <td>0.257165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIRLINE</th>\n",
       "      <td>0.115230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222355</td>\n",
       "      <td>0.024462</td>\n",
       "      <td>0.022503</td>\n",
       "      <td>0.119220</td>\n",
       "      <td>0.167030</td>\n",
       "      <td>0.301270</td>\n",
       "      <td>0.125237</td>\n",
       "      <td>0.170596</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.065845</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>0.153300</td>\n",
       "      <td>0.207793</td>\n",
       "      <td>0.238502</td>\n",
       "      <td>0.234945</td>\n",
       "      <td>0.275787</td>\n",
       "      <td>0.009784</td>\n",
       "      <td>0.320399</td>\n",
       "      <td>0.132329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUTO</th>\n",
       "      <td>0.331956</td>\n",
       "      <td>0.222355</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072840</td>\n",
       "      <td>0.263742</td>\n",
       "      <td>0.520673</td>\n",
       "      <td>0.369540</td>\n",
       "      <td>0.324969</td>\n",
       "      <td>0.574584</td>\n",
       "      <td>0.289746</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120343</td>\n",
       "      <td>0.101366</td>\n",
       "      <td>0.346575</td>\n",
       "      <td>0.402236</td>\n",
       "      <td>0.460378</td>\n",
       "      <td>0.459369</td>\n",
       "      <td>0.428962</td>\n",
       "      <td>0.208919</td>\n",
       "      <td>0.363675</td>\n",
       "      <td>0.322791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANKS</th>\n",
       "      <td>0.080215</td>\n",
       "      <td>0.024462</td>\n",
       "      <td>0.072840</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060737</td>\n",
       "      <td>0.160753</td>\n",
       "      <td>0.157011</td>\n",
       "      <td>-0.010451</td>\n",
       "      <td>0.017724</td>\n",
       "      <td>0.203366</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038268</td>\n",
       "      <td>-0.038437</td>\n",
       "      <td>0.137094</td>\n",
       "      <td>0.149524</td>\n",
       "      <td>0.132156</td>\n",
       "      <td>0.192628</td>\n",
       "      <td>0.116732</td>\n",
       "      <td>-0.042062</td>\n",
       "      <td>0.072882</td>\n",
       "      <td>0.263463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEV</th>\n",
       "      <td>0.024979</td>\n",
       "      <td>0.022503</td>\n",
       "      <td>0.263742</td>\n",
       "      <td>0.060737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.352289</td>\n",
       "      <td>0.114483</td>\n",
       "      <td>0.211685</td>\n",
       "      <td>0.139490</td>\n",
       "      <td>0.118357</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101116</td>\n",
       "      <td>0.154699</td>\n",
       "      <td>0.367896</td>\n",
       "      <td>0.198618</td>\n",
       "      <td>0.053848</td>\n",
       "      <td>0.079432</td>\n",
       "      <td>0.205947</td>\n",
       "      <td>0.122781</td>\n",
       "      <td>0.162351</td>\n",
       "      <td>0.173413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLDPROD</th>\n",
       "      <td>0.235179</td>\n",
       "      <td>0.119220</td>\n",
       "      <td>0.520673</td>\n",
       "      <td>0.160753</td>\n",
       "      <td>0.352289</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414073</td>\n",
       "      <td>0.313940</td>\n",
       "      <td>0.365990</td>\n",
       "      <td>0.210249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044679</td>\n",
       "      <td>0.002654</td>\n",
       "      <td>0.384144</td>\n",
       "      <td>0.277338</td>\n",
       "      <td>0.405853</td>\n",
       "      <td>0.321022</td>\n",
       "      <td>0.275536</td>\n",
       "      <td>0.222124</td>\n",
       "      <td>0.272960</td>\n",
       "      <td>0.349810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEM</th>\n",
       "      <td>0.188611</td>\n",
       "      <td>0.167030</td>\n",
       "      <td>0.369540</td>\n",
       "      <td>0.157011</td>\n",
       "      <td>0.114483</td>\n",
       "      <td>0.414073</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161459</td>\n",
       "      <td>0.128857</td>\n",
       "      <td>0.146228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025712</td>\n",
       "      <td>-0.002682</td>\n",
       "      <td>0.338836</td>\n",
       "      <td>0.189411</td>\n",
       "      <td>0.542983</td>\n",
       "      <td>0.364189</td>\n",
       "      <td>0.309889</td>\n",
       "      <td>0.149510</td>\n",
       "      <td>0.421167</td>\n",
       "      <td>0.236075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTENG</th>\n",
       "      <td>0.216828</td>\n",
       "      <td>0.301270</td>\n",
       "      <td>0.324969</td>\n",
       "      <td>-0.010451</td>\n",
       "      <td>0.211685</td>\n",
       "      <td>0.313940</td>\n",
       "      <td>0.161459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201579</td>\n",
       "      <td>0.234814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008943</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.218982</td>\n",
       "      <td>0.243457</td>\n",
       "      <td>0.255077</td>\n",
       "      <td>0.214717</td>\n",
       "      <td>0.292674</td>\n",
       "      <td>0.169888</td>\n",
       "      <td>0.279617</td>\n",
       "      <td>0.239629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMSERV</th>\n",
       "      <td>0.257316</td>\n",
       "      <td>0.125237</td>\n",
       "      <td>0.574584</td>\n",
       "      <td>0.017724</td>\n",
       "      <td>0.139490</td>\n",
       "      <td>0.365990</td>\n",
       "      <td>0.128857</td>\n",
       "      <td>0.201579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288058</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033835</td>\n",
       "      <td>0.064871</td>\n",
       "      <td>0.320102</td>\n",
       "      <td>0.293915</td>\n",
       "      <td>0.319976</td>\n",
       "      <td>0.248270</td>\n",
       "      <td>0.164136</td>\n",
       "      <td>0.169654</td>\n",
       "      <td>0.212449</td>\n",
       "      <td>0.320810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONMAT</th>\n",
       "      <td>0.390574</td>\n",
       "      <td>0.170596</td>\n",
       "      <td>0.289746</td>\n",
       "      <td>0.203366</td>\n",
       "      <td>0.118357</td>\n",
       "      <td>0.210249</td>\n",
       "      <td>0.146228</td>\n",
       "      <td>0.234814</td>\n",
       "      <td>0.288058</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.103200</td>\n",
       "      <td>0.049189</td>\n",
       "      <td>0.414449</td>\n",
       "      <td>0.220347</td>\n",
       "      <td>0.200052</td>\n",
       "      <td>0.349023</td>\n",
       "      <td>0.201671</td>\n",
       "      <td>0.036888</td>\n",
       "      <td>0.267208</td>\n",
       "      <td>0.282961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSSERV</th>\n",
       "      <td>0.112971</td>\n",
       "      <td>0.087638</td>\n",
       "      <td>0.319193</td>\n",
       "      <td>0.111789</td>\n",
       "      <td>0.176560</td>\n",
       "      <td>0.155815</td>\n",
       "      <td>0.268650</td>\n",
       "      <td>0.145106</td>\n",
       "      <td>0.236574</td>\n",
       "      <td>0.144130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161869</td>\n",
       "      <td>-0.032764</td>\n",
       "      <td>0.331730</td>\n",
       "      <td>0.249852</td>\n",
       "      <td>0.269201</td>\n",
       "      <td>0.168423</td>\n",
       "      <td>0.166150</td>\n",
       "      <td>0.153877</td>\n",
       "      <td>0.230209</td>\n",
       "      <td>0.286851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DVFININS</th>\n",
       "      <td>0.013695</td>\n",
       "      <td>0.192232</td>\n",
       "      <td>0.214957</td>\n",
       "      <td>0.053598</td>\n",
       "      <td>0.130485</td>\n",
       "      <td>0.064916</td>\n",
       "      <td>0.094325</td>\n",
       "      <td>0.086317</td>\n",
       "      <td>0.048544</td>\n",
       "      <td>0.063296</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051507</td>\n",
       "      <td>-0.040323</td>\n",
       "      <td>0.143136</td>\n",
       "      <td>0.110229</td>\n",
       "      <td>0.121807</td>\n",
       "      <td>0.074021</td>\n",
       "      <td>0.734900</td>\n",
       "      <td>-0.042095</td>\n",
       "      <td>0.188584</td>\n",
       "      <td>0.205193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELECEQP</th>\n",
       "      <td>0.301966</td>\n",
       "      <td>0.176012</td>\n",
       "      <td>0.576932</td>\n",
       "      <td>0.086506</td>\n",
       "      <td>0.325258</td>\n",
       "      <td>0.446684</td>\n",
       "      <td>0.358987</td>\n",
       "      <td>0.232307</td>\n",
       "      <td>0.505355</td>\n",
       "      <td>0.345226</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047908</td>\n",
       "      <td>0.063065</td>\n",
       "      <td>0.399325</td>\n",
       "      <td>0.432429</td>\n",
       "      <td>0.424786</td>\n",
       "      <td>0.378284</td>\n",
       "      <td>0.286297</td>\n",
       "      <td>0.143322</td>\n",
       "      <td>0.342246</td>\n",
       "      <td>0.457004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENERGY</th>\n",
       "      <td>0.178778</td>\n",
       "      <td>0.162216</td>\n",
       "      <td>0.299223</td>\n",
       "      <td>0.094106</td>\n",
       "      <td>0.175524</td>\n",
       "      <td>0.152216</td>\n",
       "      <td>0.098007</td>\n",
       "      <td>0.085793</td>\n",
       "      <td>0.079359</td>\n",
       "      <td>0.234726</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034098</td>\n",
       "      <td>0.044128</td>\n",
       "      <td>0.298089</td>\n",
       "      <td>0.092908</td>\n",
       "      <td>0.096474</td>\n",
       "      <td>0.119741</td>\n",
       "      <td>0.114812</td>\n",
       "      <td>0.207162</td>\n",
       "      <td>0.233825</td>\n",
       "      <td>0.323743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOODPROD</th>\n",
       "      <td>0.307628</td>\n",
       "      <td>0.099552</td>\n",
       "      <td>0.501944</td>\n",
       "      <td>0.295439</td>\n",
       "      <td>0.368608</td>\n",
       "      <td>0.357125</td>\n",
       "      <td>0.289945</td>\n",
       "      <td>0.219454</td>\n",
       "      <td>0.419157</td>\n",
       "      <td>0.401250</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.101384</td>\n",
       "      <td>0.153969</td>\n",
       "      <td>0.423245</td>\n",
       "      <td>0.306769</td>\n",
       "      <td>0.339467</td>\n",
       "      <td>0.305784</td>\n",
       "      <td>0.412060</td>\n",
       "      <td>0.358767</td>\n",
       "      <td>0.417571</td>\n",
       "      <td>0.554736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDWRSEMI</th>\n",
       "      <td>0.330626</td>\n",
       "      <td>0.074747</td>\n",
       "      <td>0.300444</td>\n",
       "      <td>-0.004924</td>\n",
       "      <td>0.181870</td>\n",
       "      <td>0.347009</td>\n",
       "      <td>0.264482</td>\n",
       "      <td>0.273154</td>\n",
       "      <td>0.265875</td>\n",
       "      <td>0.254507</td>\n",
       "      <td>...</td>\n",
       "      <td>0.297424</td>\n",
       "      <td>0.067494</td>\n",
       "      <td>0.239789</td>\n",
       "      <td>0.258471</td>\n",
       "      <td>0.253383</td>\n",
       "      <td>0.278162</td>\n",
       "      <td>0.286762</td>\n",
       "      <td>0.428784</td>\n",
       "      <td>0.258023</td>\n",
       "      <td>0.276219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH</th>\n",
       "      <td>0.239202</td>\n",
       "      <td>0.069270</td>\n",
       "      <td>0.231050</td>\n",
       "      <td>0.131421</td>\n",
       "      <td>0.088106</td>\n",
       "      <td>0.230649</td>\n",
       "      <td>0.112739</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.257718</td>\n",
       "      <td>0.198140</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.076335</td>\n",
       "      <td>0.081821</td>\n",
       "      <td>0.216183</td>\n",
       "      <td>0.097742</td>\n",
       "      <td>0.200296</td>\n",
       "      <td>0.126441</td>\n",
       "      <td>0.208428</td>\n",
       "      <td>0.227604</td>\n",
       "      <td>0.183662</td>\n",
       "      <td>0.418128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSEDUR</th>\n",
       "      <td>0.247572</td>\n",
       "      <td>0.183494</td>\n",
       "      <td>0.389186</td>\n",
       "      <td>0.209887</td>\n",
       "      <td>0.190613</td>\n",
       "      <td>0.302236</td>\n",
       "      <td>0.397810</td>\n",
       "      <td>0.261188</td>\n",
       "      <td>0.289250</td>\n",
       "      <td>0.292689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021125</td>\n",
       "      <td>0.089544</td>\n",
       "      <td>0.365058</td>\n",
       "      <td>0.337329</td>\n",
       "      <td>0.338366</td>\n",
       "      <td>0.445039</td>\n",
       "      <td>0.357138</td>\n",
       "      <td>0.239228</td>\n",
       "      <td>0.351184</td>\n",
       "      <td>0.492923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDCONG</th>\n",
       "      <td>0.279267</td>\n",
       "      <td>0.314192</td>\n",
       "      <td>0.301559</td>\n",
       "      <td>0.058520</td>\n",
       "      <td>0.233059</td>\n",
       "      <td>0.305690</td>\n",
       "      <td>0.265355</td>\n",
       "      <td>0.261523</td>\n",
       "      <td>0.263941</td>\n",
       "      <td>0.266069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080175</td>\n",
       "      <td>0.094028</td>\n",
       "      <td>0.155547</td>\n",
       "      <td>0.298958</td>\n",
       "      <td>0.185453</td>\n",
       "      <td>0.407283</td>\n",
       "      <td>0.397488</td>\n",
       "      <td>0.161583</td>\n",
       "      <td>0.378023</td>\n",
       "      <td>0.319275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEISLUX</th>\n",
       "      <td>0.152759</td>\n",
       "      <td>0.126433</td>\n",
       "      <td>0.328734</td>\n",
       "      <td>0.191776</td>\n",
       "      <td>0.155075</td>\n",
       "      <td>0.283086</td>\n",
       "      <td>0.265262</td>\n",
       "      <td>0.198186</td>\n",
       "      <td>0.213476</td>\n",
       "      <td>0.207454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005469</td>\n",
       "      <td>0.112526</td>\n",
       "      <td>0.246047</td>\n",
       "      <td>0.277964</td>\n",
       "      <td>0.219908</td>\n",
       "      <td>0.303149</td>\n",
       "      <td>0.260278</td>\n",
       "      <td>0.141356</td>\n",
       "      <td>0.193347</td>\n",
       "      <td>0.377272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACH</th>\n",
       "      <td>0.487703</td>\n",
       "      <td>0.294601</td>\n",
       "      <td>0.557100</td>\n",
       "      <td>0.136458</td>\n",
       "      <td>0.225041</td>\n",
       "      <td>0.456650</td>\n",
       "      <td>0.376592</td>\n",
       "      <td>0.323047</td>\n",
       "      <td>0.441918</td>\n",
       "      <td>0.453480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052597</td>\n",
       "      <td>0.077862</td>\n",
       "      <td>0.516981</td>\n",
       "      <td>0.375663</td>\n",
       "      <td>0.504160</td>\n",
       "      <td>0.425704</td>\n",
       "      <td>0.347487</td>\n",
       "      <td>0.236721</td>\n",
       "      <td>0.407695</td>\n",
       "      <td>0.431980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARINE</th>\n",
       "      <td>0.139021</td>\n",
       "      <td>0.269741</td>\n",
       "      <td>0.398957</td>\n",
       "      <td>0.138152</td>\n",
       "      <td>0.091399</td>\n",
       "      <td>0.157740</td>\n",
       "      <td>0.362684</td>\n",
       "      <td>0.178241</td>\n",
       "      <td>0.275018</td>\n",
       "      <td>0.371161</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005328</td>\n",
       "      <td>0.044119</td>\n",
       "      <td>0.184248</td>\n",
       "      <td>0.225455</td>\n",
       "      <td>0.336982</td>\n",
       "      <td>0.344272</td>\n",
       "      <td>0.291913</td>\n",
       "      <td>0.059437</td>\n",
       "      <td>0.311701</td>\n",
       "      <td>0.240108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATERIAL</th>\n",
       "      <td>-0.039475</td>\n",
       "      <td>-0.065845</td>\n",
       "      <td>0.120343</td>\n",
       "      <td>-0.038268</td>\n",
       "      <td>0.101116</td>\n",
       "      <td>0.044679</td>\n",
       "      <td>0.025712</td>\n",
       "      <td>0.008943</td>\n",
       "      <td>-0.033835</td>\n",
       "      <td>-0.103200</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>-0.079358</td>\n",
       "      <td>-0.001043</td>\n",
       "      <td>-0.000864</td>\n",
       "      <td>0.052188</td>\n",
       "      <td>-0.048973</td>\n",
       "      <td>0.048226</td>\n",
       "      <td>-0.081350</td>\n",
       "      <td>0.032338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDIA</th>\n",
       "      <td>0.055022</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>0.101366</td>\n",
       "      <td>-0.038437</td>\n",
       "      <td>0.154699</td>\n",
       "      <td>0.002654</td>\n",
       "      <td>-0.002682</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.064871</td>\n",
       "      <td>0.049189</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063296</td>\n",
       "      <td>0.043118</td>\n",
       "      <td>-0.041206</td>\n",
       "      <td>0.070418</td>\n",
       "      <td>0.056686</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.095836</td>\n",
       "      <td>0.063469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTLMIN</th>\n",
       "      <td>0.237747</td>\n",
       "      <td>0.153300</td>\n",
       "      <td>0.346575</td>\n",
       "      <td>0.137094</td>\n",
       "      <td>0.367896</td>\n",
       "      <td>0.384144</td>\n",
       "      <td>0.338836</td>\n",
       "      <td>0.218982</td>\n",
       "      <td>0.320102</td>\n",
       "      <td>0.414449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.079358</td>\n",
       "      <td>0.063296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.281528</td>\n",
       "      <td>0.347636</td>\n",
       "      <td>0.253021</td>\n",
       "      <td>0.316990</td>\n",
       "      <td>0.181768</td>\n",
       "      <td>0.364046</td>\n",
       "      <td>0.471838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSPRD</th>\n",
       "      <td>0.073751</td>\n",
       "      <td>0.207793</td>\n",
       "      <td>0.402236</td>\n",
       "      <td>0.149524</td>\n",
       "      <td>0.198618</td>\n",
       "      <td>0.277338</td>\n",
       "      <td>0.189411</td>\n",
       "      <td>0.243457</td>\n",
       "      <td>0.293915</td>\n",
       "      <td>0.220347</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001043</td>\n",
       "      <td>0.043118</td>\n",
       "      <td>0.281528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336614</td>\n",
       "      <td>0.394072</td>\n",
       "      <td>0.360933</td>\n",
       "      <td>0.205372</td>\n",
       "      <td>0.336784</td>\n",
       "      <td>0.286892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RDRLTRAN</th>\n",
       "      <td>0.234620</td>\n",
       "      <td>0.238502</td>\n",
       "      <td>0.460378</td>\n",
       "      <td>0.132156</td>\n",
       "      <td>0.053848</td>\n",
       "      <td>0.405853</td>\n",
       "      <td>0.542983</td>\n",
       "      <td>0.255077</td>\n",
       "      <td>0.319976</td>\n",
       "      <td>0.200052</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000864</td>\n",
       "      <td>-0.041206</td>\n",
       "      <td>0.347636</td>\n",
       "      <td>0.336614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.492157</td>\n",
       "      <td>0.306438</td>\n",
       "      <td>0.085293</td>\n",
       "      <td>0.500018</td>\n",
       "      <td>0.330821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REALEST</th>\n",
       "      <td>0.240815</td>\n",
       "      <td>0.234945</td>\n",
       "      <td>0.459369</td>\n",
       "      <td>0.192628</td>\n",
       "      <td>0.079432</td>\n",
       "      <td>0.321022</td>\n",
       "      <td>0.364189</td>\n",
       "      <td>0.214717</td>\n",
       "      <td>0.248270</td>\n",
       "      <td>0.349023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052188</td>\n",
       "      <td>0.070418</td>\n",
       "      <td>0.253021</td>\n",
       "      <td>0.394072</td>\n",
       "      <td>0.492157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.388840</td>\n",
       "      <td>0.108763</td>\n",
       "      <td>0.498729</td>\n",
       "      <td>0.357547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETAIL</th>\n",
       "      <td>0.197279</td>\n",
       "      <td>0.275787</td>\n",
       "      <td>0.428962</td>\n",
       "      <td>0.116732</td>\n",
       "      <td>0.205947</td>\n",
       "      <td>0.275536</td>\n",
       "      <td>0.309889</td>\n",
       "      <td>0.292674</td>\n",
       "      <td>0.164136</td>\n",
       "      <td>0.201671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048973</td>\n",
       "      <td>0.056686</td>\n",
       "      <td>0.316990</td>\n",
       "      <td>0.360933</td>\n",
       "      <td>0.306438</td>\n",
       "      <td>0.388840</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182027</td>\n",
       "      <td>0.381375</td>\n",
       "      <td>0.385845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOFTWARE</th>\n",
       "      <td>0.461433</td>\n",
       "      <td>0.009784</td>\n",
       "      <td>0.208919</td>\n",
       "      <td>-0.042062</td>\n",
       "      <td>0.122781</td>\n",
       "      <td>0.222124</td>\n",
       "      <td>0.149510</td>\n",
       "      <td>0.169888</td>\n",
       "      <td>0.169654</td>\n",
       "      <td>0.036888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048226</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.181768</td>\n",
       "      <td>0.205372</td>\n",
       "      <td>0.085293</td>\n",
       "      <td>0.108763</td>\n",
       "      <td>0.182027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.167822</td>\n",
       "      <td>0.267398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRDDIST</th>\n",
       "      <td>0.128383</td>\n",
       "      <td>0.320399</td>\n",
       "      <td>0.363675</td>\n",
       "      <td>0.072882</td>\n",
       "      <td>0.162351</td>\n",
       "      <td>0.272960</td>\n",
       "      <td>0.421167</td>\n",
       "      <td>0.279617</td>\n",
       "      <td>0.212449</td>\n",
       "      <td>0.267208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081350</td>\n",
       "      <td>0.095836</td>\n",
       "      <td>0.364046</td>\n",
       "      <td>0.336784</td>\n",
       "      <td>0.500018</td>\n",
       "      <td>0.498729</td>\n",
       "      <td>0.381375</td>\n",
       "      <td>0.167822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.360980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTILITIE</th>\n",
       "      <td>0.257165</td>\n",
       "      <td>0.132329</td>\n",
       "      <td>0.322791</td>\n",
       "      <td>0.263463</td>\n",
       "      <td>0.173413</td>\n",
       "      <td>0.349810</td>\n",
       "      <td>0.236075</td>\n",
       "      <td>0.239629</td>\n",
       "      <td>0.320810</td>\n",
       "      <td>0.282961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032338</td>\n",
       "      <td>0.063469</td>\n",
       "      <td>0.471838</td>\n",
       "      <td>0.286892</td>\n",
       "      <td>0.330821</td>\n",
       "      <td>0.357547</td>\n",
       "      <td>0.385845</td>\n",
       "      <td>0.267398</td>\n",
       "      <td>0.360980</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "industry   AERODEF   AIRLINE      AUTO     BANKS       BEV   BLDPROD  \\\n",
       "industry                                                               \n",
       "AERODEF   1.000000  0.115230  0.331956  0.080215  0.024979  0.235179   \n",
       "AIRLINE   0.115230  1.000000  0.222355  0.024462  0.022503  0.119220   \n",
       "AUTO      0.331956  0.222355  1.000000  0.072840  0.263742  0.520673   \n",
       "BANKS     0.080215  0.024462  0.072840  1.000000  0.060737  0.160753   \n",
       "BEV       0.024979  0.022503  0.263742  0.060737  1.000000  0.352289   \n",
       "BLDPROD   0.235179  0.119220  0.520673  0.160753  0.352289  1.000000   \n",
       "CHEM      0.188611  0.167030  0.369540  0.157011  0.114483  0.414073   \n",
       "CNSTENG   0.216828  0.301270  0.324969 -0.010451  0.211685  0.313940   \n",
       "COMSERV   0.257316  0.125237  0.574584  0.017724  0.139490  0.365990   \n",
       "CONMAT    0.390574  0.170596  0.289746  0.203366  0.118357  0.210249   \n",
       "CONSSERV  0.112971  0.087638  0.319193  0.111789  0.176560  0.155815   \n",
       "DVFININS  0.013695  0.192232  0.214957  0.053598  0.130485  0.064916   \n",
       "ELECEQP   0.301966  0.176012  0.576932  0.086506  0.325258  0.446684   \n",
       "ENERGY    0.178778  0.162216  0.299223  0.094106  0.175524  0.152216   \n",
       "FOODPROD  0.307628  0.099552  0.501944  0.295439  0.368608  0.357125   \n",
       "HDWRSEMI  0.330626  0.074747  0.300444 -0.004924  0.181870  0.347009   \n",
       "HEALTH    0.239202  0.069270  0.231050  0.131421  0.088106  0.230649   \n",
       "HOUSEDUR  0.247572  0.183494  0.389186  0.209887  0.190613  0.302236   \n",
       "INDCONG   0.279267  0.314192  0.301559  0.058520  0.233059  0.305690   \n",
       "LEISLUX   0.152759  0.126433  0.328734  0.191776  0.155075  0.283086   \n",
       "MACH      0.487703  0.294601  0.557100  0.136458  0.225041  0.456650   \n",
       "MARINE    0.139021  0.269741  0.398957  0.138152  0.091399  0.157740   \n",
       "MATERIAL -0.039475 -0.065845  0.120343 -0.038268  0.101116  0.044679   \n",
       "MEDIA     0.055022  0.004517  0.101366 -0.038437  0.154699  0.002654   \n",
       "MTLMIN    0.237747  0.153300  0.346575  0.137094  0.367896  0.384144   \n",
       "PERSPRD   0.073751  0.207793  0.402236  0.149524  0.198618  0.277338   \n",
       "RDRLTRAN  0.234620  0.238502  0.460378  0.132156  0.053848  0.405853   \n",
       "REALEST   0.240815  0.234945  0.459369  0.192628  0.079432  0.321022   \n",
       "RETAIL    0.197279  0.275787  0.428962  0.116732  0.205947  0.275536   \n",
       "SOFTWARE  0.461433  0.009784  0.208919 -0.042062  0.122781  0.222124   \n",
       "TRDDIST   0.128383  0.320399  0.363675  0.072882  0.162351  0.272960   \n",
       "UTILITIE  0.257165  0.132329  0.322791  0.263463  0.173413  0.349810   \n",
       "\n",
       "industry      CHEM   CNSTENG   COMSERV    CONMAT  ...  MATERIAL     MEDIA  \\\n",
       "industry                                          ...                       \n",
       "AERODEF   0.188611  0.216828  0.257316  0.390574  ... -0.039475  0.055022   \n",
       "AIRLINE   0.167030  0.301270  0.125237  0.170596  ... -0.065845  0.004517   \n",
       "AUTO      0.369540  0.324969  0.574584  0.289746  ...  0.120343  0.101366   \n",
       "BANKS     0.157011 -0.010451  0.017724  0.203366  ... -0.038268 -0.038437   \n",
       "BEV       0.114483  0.211685  0.139490  0.118357  ...  0.101116  0.154699   \n",
       "BLDPROD   0.414073  0.313940  0.365990  0.210249  ...  0.044679  0.002654   \n",
       "CHEM      1.000000  0.161459  0.128857  0.146228  ...  0.025712 -0.002682   \n",
       "CNSTENG   0.161459  1.000000  0.201579  0.234814  ...  0.008943  0.020133   \n",
       "COMSERV   0.128857  0.201579  1.000000  0.288058  ... -0.033835  0.064871   \n",
       "CONMAT    0.146228  0.234814  0.288058  1.000000  ... -0.103200  0.049189   \n",
       "CONSSERV  0.268650  0.145106  0.236574  0.144130  ...  0.161869 -0.032764   \n",
       "DVFININS  0.094325  0.086317  0.048544  0.063296  ... -0.051507 -0.040323   \n",
       "ELECEQP   0.358987  0.232307  0.505355  0.345226  ... -0.047908  0.063065   \n",
       "ENERGY    0.098007  0.085793  0.079359  0.234726  ... -0.034098  0.044128   \n",
       "FOODPROD  0.289945  0.219454  0.419157  0.401250  ... -0.101384  0.153969   \n",
       "HDWRSEMI  0.264482  0.273154  0.265875  0.254507  ...  0.297424  0.067494   \n",
       "HEALTH    0.112739  0.138424  0.257718  0.198140  ... -0.076335  0.081821   \n",
       "HOUSEDUR  0.397810  0.261188  0.289250  0.292689  ...  0.021125  0.089544   \n",
       "INDCONG   0.265355  0.261523  0.263941  0.266069  ... -0.080175  0.094028   \n",
       "LEISLUX   0.265262  0.198186  0.213476  0.207454  ...  0.005469  0.112526   \n",
       "MACH      0.376592  0.323047  0.441918  0.453480  ...  0.052597  0.077862   \n",
       "MARINE    0.362684  0.178241  0.275018  0.371161  ... -0.005328  0.044119   \n",
       "MATERIAL  0.025712  0.008943 -0.033835 -0.103200  ...  1.000000 -0.006598   \n",
       "MEDIA    -0.002682  0.020133  0.064871  0.049189  ... -0.006598  1.000000   \n",
       "MTLMIN    0.338836  0.218982  0.320102  0.414449  ... -0.079358  0.063296   \n",
       "PERSPRD   0.189411  0.243457  0.293915  0.220347  ... -0.001043  0.043118   \n",
       "RDRLTRAN  0.542983  0.255077  0.319976  0.200052  ... -0.000864 -0.041206   \n",
       "REALEST   0.364189  0.214717  0.248270  0.349023  ...  0.052188  0.070418   \n",
       "RETAIL    0.309889  0.292674  0.164136  0.201671  ... -0.048973  0.056686   \n",
       "SOFTWARE  0.149510  0.169888  0.169654  0.036888  ...  0.048226  0.016342   \n",
       "TRDDIST   0.421167  0.279617  0.212449  0.267208  ... -0.081350  0.095836   \n",
       "UTILITIE  0.236075  0.239629  0.320810  0.282961  ...  0.032338  0.063469   \n",
       "\n",
       "industry    MTLMIN   PERSPRD  RDRLTRAN   REALEST    RETAIL  SOFTWARE  \\\n",
       "industry                                                               \n",
       "AERODEF   0.237747  0.073751  0.234620  0.240815  0.197279  0.461433   \n",
       "AIRLINE   0.153300  0.207793  0.238502  0.234945  0.275787  0.009784   \n",
       "AUTO      0.346575  0.402236  0.460378  0.459369  0.428962  0.208919   \n",
       "BANKS     0.137094  0.149524  0.132156  0.192628  0.116732 -0.042062   \n",
       "BEV       0.367896  0.198618  0.053848  0.079432  0.205947  0.122781   \n",
       "BLDPROD   0.384144  0.277338  0.405853  0.321022  0.275536  0.222124   \n",
       "CHEM      0.338836  0.189411  0.542983  0.364189  0.309889  0.149510   \n",
       "CNSTENG   0.218982  0.243457  0.255077  0.214717  0.292674  0.169888   \n",
       "COMSERV   0.320102  0.293915  0.319976  0.248270  0.164136  0.169654   \n",
       "CONMAT    0.414449  0.220347  0.200052  0.349023  0.201671  0.036888   \n",
       "CONSSERV  0.331730  0.249852  0.269201  0.168423  0.166150  0.153877   \n",
       "DVFININS  0.143136  0.110229  0.121807  0.074021  0.734900 -0.042095   \n",
       "ELECEQP   0.399325  0.432429  0.424786  0.378284  0.286297  0.143322   \n",
       "ENERGY    0.298089  0.092908  0.096474  0.119741  0.114812  0.207162   \n",
       "FOODPROD  0.423245  0.306769  0.339467  0.305784  0.412060  0.358767   \n",
       "HDWRSEMI  0.239789  0.258471  0.253383  0.278162  0.286762  0.428784   \n",
       "HEALTH    0.216183  0.097742  0.200296  0.126441  0.208428  0.227604   \n",
       "HOUSEDUR  0.365058  0.337329  0.338366  0.445039  0.357138  0.239228   \n",
       "INDCONG   0.155547  0.298958  0.185453  0.407283  0.397488  0.161583   \n",
       "LEISLUX   0.246047  0.277964  0.219908  0.303149  0.260278  0.141356   \n",
       "MACH      0.516981  0.375663  0.504160  0.425704  0.347487  0.236721   \n",
       "MARINE    0.184248  0.225455  0.336982  0.344272  0.291913  0.059437   \n",
       "MATERIAL -0.079358 -0.001043 -0.000864  0.052188 -0.048973  0.048226   \n",
       "MEDIA     0.063296  0.043118 -0.041206  0.070418  0.056686  0.016342   \n",
       "MTLMIN    1.000000  0.281528  0.347636  0.253021  0.316990  0.181768   \n",
       "PERSPRD   0.281528  1.000000  0.336614  0.394072  0.360933  0.205372   \n",
       "RDRLTRAN  0.347636  0.336614  1.000000  0.492157  0.306438  0.085293   \n",
       "REALEST   0.253021  0.394072  0.492157  1.000000  0.388840  0.108763   \n",
       "RETAIL    0.316990  0.360933  0.306438  0.388840  1.000000  0.182027   \n",
       "SOFTWARE  0.181768  0.205372  0.085293  0.108763  0.182027  1.000000   \n",
       "TRDDIST   0.364046  0.336784  0.500018  0.498729  0.381375  0.167822   \n",
       "UTILITIE  0.471838  0.286892  0.330821  0.357547  0.385845  0.267398   \n",
       "\n",
       "industry   TRDDIST  UTILITIE  \n",
       "industry                      \n",
       "AERODEF   0.128383  0.257165  \n",
       "AIRLINE   0.320399  0.132329  \n",
       "AUTO      0.363675  0.322791  \n",
       "BANKS     0.072882  0.263463  \n",
       "BEV       0.162351  0.173413  \n",
       "BLDPROD   0.272960  0.349810  \n",
       "CHEM      0.421167  0.236075  \n",
       "CNSTENG   0.279617  0.239629  \n",
       "COMSERV   0.212449  0.320810  \n",
       "CONMAT    0.267208  0.282961  \n",
       "CONSSERV  0.230209  0.286851  \n",
       "DVFININS  0.188584  0.205193  \n",
       "ELECEQP   0.342246  0.457004  \n",
       "ENERGY    0.233825  0.323743  \n",
       "FOODPROD  0.417571  0.554736  \n",
       "HDWRSEMI  0.258023  0.276219  \n",
       "HEALTH    0.183662  0.418128  \n",
       "HOUSEDUR  0.351184  0.492923  \n",
       "INDCONG   0.378023  0.319275  \n",
       "LEISLUX   0.193347  0.377272  \n",
       "MACH      0.407695  0.431980  \n",
       "MARINE    0.311701  0.240108  \n",
       "MATERIAL -0.081350  0.032338  \n",
       "MEDIA     0.095836  0.063469  \n",
       "MTLMIN    0.364046  0.471838  \n",
       "PERSPRD   0.336784  0.286892  \n",
       "RDRLTRAN  0.500018  0.330821  \n",
       "REALEST   0.498729  0.357547  \n",
       "RETAIL    0.381375  0.385845  \n",
       "SOFTWARE  0.167822  0.267398  \n",
       "TRDDIST   1.000000  0.360980  \n",
       "UTILITIE  0.360980  1.000000  \n",
       "\n",
       "[32 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_58 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"stkcd_ret\"]\\\n",
    "        .std()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"stkcd_ret\": \"stkcd_ret_std\"})\n",
    "\n",
    "d_58 \\\n",
    "    = d_58\\\n",
    "        .pivot_table(\n",
    "            index = [\"date\"],\n",
    "            columns = \"industry\",\n",
    "            values = \"stkcd_ret_std\"\n",
    "            )\n",
    "\n",
    "d_58.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "59. 每天计算出成交额的z-score（减去均值除以标准差），该指标能解释下一天个股超额收益率的多少比例？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\anaconda\\Lib\\site-packages\\statsmodels\\regression\\linear_model.py:1781: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return 1 - self.ssr/self.centered_tss\n",
      "d:\\software\\anaconda\\Lib\\site-packages\\statsmodels\\regression\\linear_model.py:1781: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return 1 - self.ssr/self.centered_tss\n",
      "d:\\software\\anaconda\\Lib\\site-packages\\statsmodels\\regression\\linear_model.py:1781: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  return 1 - self.ssr/self.centered_tss\n"
     ]
    },
    {
     "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>symbol</th>\n",
       "      <th>r_squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>0.017435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>0.009520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>0.000672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>0.082448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>0.039915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>603002.SH</td>\n",
       "      <td>0.057117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2421</th>\n",
       "      <td>603123.SH</td>\n",
       "      <td>0.328365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>603128.SH</td>\n",
       "      <td>0.003183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2423</th>\n",
       "      <td>603333.SH</td>\n",
       "      <td>0.096465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>603366.SH</td>\n",
       "      <td>0.032684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2425 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symbol  r_squared\n",
       "0     000001.SZ   0.017435\n",
       "1     000002.SZ   0.009520\n",
       "2     000004.SZ   0.000672\n",
       "3     000005.SZ   0.082448\n",
       "4     000006.SZ   0.039915\n",
       "...         ...        ...\n",
       "2420  603002.SH   0.057117\n",
       "2421  603123.SH   0.328365\n",
       "2422  603128.SH   0.003183\n",
       "2423  603333.SH   0.096465\n",
       "2424  603366.SH   0.032684\n",
       "\n",
       "[2425 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. z-score计算\n",
    "d_59 \\\n",
    "    = data\\\n",
    "        .groupby([\"date\"])\\\n",
    "        .agg({\"amount\":[\"mean\", \"std\"]})\\\n",
    "        .reset_index()\n",
    "\n",
    "d_59.columns = ['date', 'amount_mean', \"amount_std\"]\n",
    "d_59 \\\n",
    "    = data\\\n",
    "        [[\"date\", \"symbol\", \"amount\"]]\\\n",
    "        .merge(\n",
    "            d_59, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\", \n",
    "            how = \"left\"\n",
    "            )\n",
    "\n",
    "d_59[\"z_score\"] = (d_59[\"amount\"]-d_59[\"amount_mean\"])/d_59[\"amount_std\"]\n",
    "d_59 = d_59[[\"date\", \"symbol\", \"z_score\"]]\n",
    "\n",
    "# 2. 每只股票的超额收益率\n",
    "# 2.1 市场收益率\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_59_ret \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_59_ret = data.merge(d_59_ret, left_on = \"date\", right_on = \"date\")\n",
    "d_59_ret[\"capt_ratio\"] = d_59_ret[\"capt\"]/d_59_ret[\"mkt_capt\"]\n",
    "d_59_ret[\"ratio_ret\"] = d_59_ret[\"capt_ratio\"]*d_59_ret[\"stkcd_ret\"]\n",
    "\n",
    "d_59_ret \\\n",
    "    = d_59_ret\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "d_59_ret \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(d_59_ret, how = \"left\", right_on = \"date\", left_on = \"date\")\n",
    "\n",
    "# 2.2 循环回归\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "\n",
    "d_59_ret \\\n",
    "    = d_59_ret\\\n",
    "        .merge(\n",
    "            d_59_ret\\\n",
    "                .groupby(\"symbol\")\\\n",
    "                .apply(regress, \"stkcd_ret\", [\"mkt_ret\"])\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"mkt_ret\" : 'beta'}), \n",
    "            right_on = ['symbol'], \n",
    "            left_on = ['symbol']\n",
    "            )\n",
    "\n",
    "d_59_ret[\"abnr_ret\"] = d_59_ret[\"stkcd_ret\"] - d_59_ret[\"mkt_ret\"]*d_59_ret[\"beta\"] - d_59_ret[\"intercept\"]\n",
    "d_59_ret = d_59_ret[['date', 'symbol', 'abnr_ret']]\n",
    "\n",
    "# 3. 合并做两个回归\n",
    "d_59 \\\n",
    "    = d_59_ret\\\n",
    "        .merge(\n",
    "            d_59, \n",
    "            right_on = [\"date\", \"symbol\"], \n",
    "            left_on = [\"date\", \"symbol\"]\n",
    "            )\n",
    "\n",
    "d_59[\"abnr_ret_lag\"] = d_59.groupby([\"symbol\"])[\"abnr_ret\"].shift(-1)\n",
    "d_59 = d_59[~np.isnan(d_59.abnr_ret_lag)]\n",
    "\n",
    "def regress_sq(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.rsquared\n",
    "\n",
    "d_59\\\n",
    "    .groupby(\"symbol\")\\\n",
    "    .apply(regress_sq, \"z_score\", [\"abnr_ret_lag\"])\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns = {0 : \"r_squared\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "60. 每个股票的收益率和300、500指数收益率可以回归出一个截距项和2个beta，这两个beta的分布如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_w500_ret</th>\n",
       "      <th>index_w300_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.122131</td>\n",
       "      <td>-0.154989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.851576</td>\n",
       "      <td>0.882951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index_w500_ret  index_w300_ret\n",
       "mean        1.122131       -0.154989\n",
       "std         0.851576        0.882951"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算300和500指数收益率\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "data[\"index_w500_ret\"] = data[\"stkcd_ret\"]*data[\"index_w500\"]\n",
    "data[\"index_w300_ret\"] = data[\"stkcd_ret\"]*data[\"index_w300\"]\n",
    "d_60_index \\\n",
    "    = data\\\n",
    "        .groupby([\"date\"])\\\n",
    "        .agg({\"index_w500_ret\":\"sum\", \"index_w300_ret\":\"sum\"})\\\n",
    "        .reset_index()\n",
    "d_60 \\\n",
    "    = data[[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_60_index, \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\", \n",
    "            how = \"left\"\n",
    "            )\n",
    "\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "\n",
    "d_60 \\\n",
    "    = d_60\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        .apply(regress, \"stkcd_ret\", [\"index_w500_ret\", \"index_w300_ret\"])\\\n",
    "        .reset_index()\n",
    "d_60 \\\n",
    "    .agg({\n",
    "        \"index_w500_ret\": [\"mean\", \"std\"], \n",
    "        \"index_w300_ret\": [\"mean\", \"std\"]\n",
    "        })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "61. 每天开盘后到最高价涨幅最大的100只股票同样也是全天（昨收到今收）涨幅最大的100只股票的比例是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"open_high\"] = data[\"high\"]/data[\"open\"] - 1\n",
    "data[\"close_close\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "\n",
    "d_61_oh \\\n",
    "    = data\\\n",
    "        .sort_values(by = [\"date\", \"open_high\"])\\\n",
    "        .groupby(\"date\")[[\"symbol\", \"date\"]]\\\n",
    "        .tail(100)\\\n",
    "        .reset_index()\\\n",
    "        [[\"symbol\", \"date\"]]\\\n",
    "        .rename(columns = {\"symbol\": \"open_high_symbol\"})\n",
    "\n",
    "d_61_cc \\\n",
    "    = data\\\n",
    "        .sort_values(by = [\"date\", \"close_close\"])\\\n",
    "        .groupby(\"date\")[[\"symbol\", \"date\"]]\\\n",
    "        .tail(100)\\\n",
    "        .reset_index()\\\n",
    "        [[\"symbol\", \"date\"]]\\\n",
    "        .rename(columns = {\"symbol\": \"close_close_symbol\"})\n",
    "\n",
    "d_61 \\\n",
    "    = pd\\\n",
    "        .merge(\n",
    "            d_61_cc, \n",
    "            d_61_oh, \n",
    "            left_index = True, \n",
    "            right_index = True, \n",
    "            how = \"left\")\\\n",
    "        .drop(columns = [\"date_x\"])\\\n",
    "        .rename(columns = {\"date_y\": \"date\"})\n",
    "\n",
    "# 每天设定成一个list\n",
    "d_61 \\\n",
    "    = d_61\\\n",
    "        .groupby([\"date\"])\\\n",
    "        .agg({\n",
    "            'open_high_symbol': lambda x: list(x), \n",
    "            'close_close_symbol':lambda x: list(x)})\\\n",
    "        .reset_index()\n",
    "\n",
    "# 计算交集\n",
    "d_61[\"intersection_number\"] \\\n",
    "    = [len(set(a) & set(b)) for a, b in zip(d_61.open_high_symbol, d_61.close_close_symbol)]\n",
    "\n",
    "# 计算比例\n",
    "d_61[d_61.intersection_number > 100][\"intersection_number\"].count()\\\n",
    "    /d_61[\"intersection_number\"].count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "62. 每天计算最近三天每天对市场的超额收益率都排进当天前100的股票有哪些？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>symbol</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190867</th>\n",
       "      <td>600228.SH</td>\n",
       "      <td>20120106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212668</th>\n",
       "      <td>600462.SH</td>\n",
       "      <td>20120109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190868</th>\n",
       "      <td>600228.SH</td>\n",
       "      <td>20120109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37327</th>\n",
       "      <td>000791.SZ</td>\n",
       "      <td>20120110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241335</th>\n",
       "      <td>600740.SH</td>\n",
       "      <td>20120111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110681</th>\n",
       "      <td>002475.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237117</th>\n",
       "      <td>600702.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155159</th>\n",
       "      <td>300191.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235479</th>\n",
       "      <td>600687.SH</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131364</th>\n",
       "      <td>002653.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      date\n",
       "190867  600228.SH  20120106\n",
       "212668  600462.SH  20120109\n",
       "190868  600228.SH  20120109\n",
       "37327   000791.SZ  20120110\n",
       "241335  600740.SH  20120111\n",
       "...           ...       ...\n",
       "110681  002475.SZ  20120629\n",
       "237117  600702.SH  20120629\n",
       "155159  300191.SZ  20120629\n",
       "235479  600687.SH  20120629\n",
       "131364  002653.SZ  20120629\n",
       "\n",
       "[312 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 每只股票的超额收益率\n",
    "# 1.1 市场收益率\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_62_ret \\\n",
    "    = data\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"mkt_capt\"})\n",
    "\n",
    "d_62_ret \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_62_ret, \n",
    "            left_on = \"date\", \n",
    "            right_on = \"date\"\n",
    "            )\n",
    "\n",
    "d_62_ret[\"capt_ratio\"] = d_62_ret[\"capt\"]/d_62_ret[\"mkt_capt\"]\n",
    "d_62_ret[\"ratio_ret\"] = d_62_ret[\"capt_ratio\"]*d_62_ret[\"stkcd_ret\"]\n",
    "d_62_ret \\\n",
    "    = d_62_ret\\\n",
    "        .groupby(\"date\")\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"mkt_ret\"})\n",
    "\n",
    "d_62_ret \\\n",
    "    = data\\\n",
    "        [[\"date\", \"symbol\", \"stkcd_ret\"]]\\\n",
    "        .merge(\n",
    "            d_62_ret, \n",
    "            how = \"left\", \n",
    "            right_on = \"date\", \n",
    "            left_on = \"date\"\n",
    "            )\n",
    "\n",
    "# 1.2 循环回归\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "\n",
    "d_62_ret \\\n",
    "    = d_62_ret\\\n",
    "        .merge(\n",
    "            d_62_ret\\\n",
    "                .groupby(\"symbol\")\\\n",
    "                .apply(regress, \"stkcd_ret\", [\"mkt_ret\"])\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"mkt_ret\" : 'beta'}),\n",
    "            right_on = ['symbol'], \n",
    "            left_on = ['symbol']\n",
    "            )\n",
    "\n",
    "d_62_ret[\"abnr_ret\"] \\\n",
    "    = d_62_ret[\"stkcd_ret\"] \\\n",
    "        - d_62_ret[\"mkt_ret\"]*d_62_ret[\"beta\"] \\\n",
    "            - d_62_ret[\"intercept\"]\n",
    "\n",
    "d_62_ret \\\n",
    "    = d_62_ret\\\n",
    "        [['date', 'symbol', 'abnr_ret']]\\\n",
    "            .sort_values(\n",
    "                by = [\"date\", \"abnr_ret\"], \n",
    "                ascending = [True, False]\n",
    "                )\n",
    "\n",
    "# 2. 设定tag进行rolling\n",
    "d_62_ret[\"rank\"] = d_62_ret.groupby([\"date\"]).cumcount()+1\n",
    "d_62_ret = d_62_ret.sort_values(by = [\"symbol\", \"date\"])\n",
    "d_62_ret[\"tag\"] = np.where(d_62_ret[\"rank\"] <= 100, 1, 0)\n",
    "\n",
    "d_62_ret \\\n",
    "    = d_62_ret\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        .rolling(3)\\\n",
    "        .agg({\"tag\": \"sum\", \"date\": \"max\"})\\\n",
    "        .reset_index()\n",
    "\n",
    "d_62_ret = d_62_ret[~ np.isnan(d_62_ret.tag)]\n",
    "d_62_ret[\"date\"] = d_62_ret[\"date\"].astype(\"int\")\n",
    "d_62_ret[d_62_ret.tag == 3][[\"symbol\", \"date\"]].sort_values(by = [\"date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "63. 每天计算最近三天每天对行业的超额收益率都排进当天行业前30%的股票有哪些？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>symbol</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248469</th>\n",
       "      <td>600807.SH</td>\n",
       "      <td>20120106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215943</th>\n",
       "      <td>600499.SH</td>\n",
       "      <td>20120106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30071</th>\n",
       "      <td>000701.SZ</td>\n",
       "      <td>20120106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236652</th>\n",
       "      <td>600698.SH</td>\n",
       "      <td>20120106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250692</th>\n",
       "      <td>600827.SH</td>\n",
       "      <td>20120106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143108</th>\n",
       "      <td>300088.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77219</th>\n",
       "      <td>002188.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166881</th>\n",
       "      <td>300293.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166142</th>\n",
       "      <td>300285.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>000026.SZ</td>\n",
       "      <td>20120629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7535 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      date\n",
       "248469  600807.SH  20120106\n",
       "215943  600499.SH  20120106\n",
       "30071   000701.SZ  20120106\n",
       "236652  600698.SH  20120106\n",
       "250692  600827.SH  20120106\n",
       "...           ...       ...\n",
       "143108  300088.SZ  20120629\n",
       "77219   002188.SZ  20120629\n",
       "166881  300293.SZ  20120629\n",
       "166142  300285.SZ  20120629\n",
       "2690    000026.SZ  20120629\n",
       "\n",
       "[7535 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 每只股票的超额收益率\n",
    "# 1.1 行业收益率\n",
    "data[\"stkcd_ret\"] = data[\"close\"]/data[\"pre_close\"] - 1\n",
    "d_63_ret \\\n",
    "    = data\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"capt\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"capt\": \"ind_capt\"})\n",
    "d_63_ret \\\n",
    "    = data\\\n",
    "        .merge(\n",
    "            d_63_ret, \n",
    "            left_on = [\"date\", \"industry\"], \n",
    "            right_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "d_63_ret[\"capt_ratio\"] = d_63_ret[\"capt\"]/d_63_ret[\"ind_capt\"]\n",
    "d_63_ret[\"ratio_ret\"] = d_63_ret[\"capt_ratio\"]*d_63_ret[\"stkcd_ret\"]\n",
    "\n",
    "d_63_ret \\\n",
    "    = d_63_ret\\\n",
    "        .groupby([\"date\", \"industry\"])\\\n",
    "        [\"ratio_ret\"]\\\n",
    "        .sum()\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns = {\"ratio_ret\": \"ind_ret\"})\n",
    "\n",
    "d_63_ret \\\n",
    "    = data\\\n",
    "        [[\"date\", \"symbol\", \"stkcd_ret\", \"industry\"]]\\\n",
    "        .merge(\n",
    "            d_63_ret, \n",
    "            how = \"left\", \n",
    "            right_on = [\"date\", \"industry\"], \n",
    "            left_on = [\"date\", \"industry\"]\n",
    "            )\n",
    "\n",
    "# 1.2 循环回归\n",
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params\n",
    "\n",
    "d_63_ret \\\n",
    "    = d_63_ret\\\n",
    "        .merge(\n",
    "            d_63_ret\\\n",
    "                .groupby(\"symbol\")\\\n",
    "                .apply(regress, \"stkcd_ret\", [\"ind_ret\"])\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"ind_ret\" : 'beta'}), \n",
    "            right_on = ['symbol'], \n",
    "            left_on = ['symbol']\n",
    "            )\n",
    "\n",
    "d_63_ret[\"abnr_ret\"] \\\n",
    "    = d_63_ret[\"stkcd_ret\"] \\\n",
    "        - d_63_ret[\"ind_ret\"]*d_63_ret[\"beta\"] \\\n",
    "            - d_63_ret[\"intercept\"]\n",
    "\n",
    "d_63_ret \\\n",
    "    = d_63_ret\\\n",
    "        [['date', 'symbol', 'industry', 'abnr_ret']]\\\n",
    "        .sort_values(\n",
    "            by = [\"date\", \"industry\", \"abnr_ret\"], \n",
    "            ascending = [True, True, False]\n",
    "            )\n",
    "\n",
    "# 2. 设定rolling tag\n",
    "d_63_ret[\"rank\"] = d_63_ret.groupby([\"industry\", \"date\"]).cumcount() + 1\n",
    "d_63_ret \\\n",
    "    = d_63_ret\\\n",
    "        .merge(\n",
    "            d_63_ret\\\n",
    "                .groupby([\"industry\", \"date\"])\\\n",
    "                [\"rank\"]\\\n",
    "                .agg(lambda x: x.max()*0.3)\\\n",
    "                .reset_index()\\\n",
    "                .rename(columns = {\"rank\": \"rank_30p\"}),\n",
    "            right_on = [\"industry\", \"date\"],\n",
    "            left_on = [\"industry\", \"date\"]\n",
    "            )\n",
    "\n",
    "d_63_ret = d_63_ret.sort_values([\"symbol\", \"date\"])\n",
    "d_63_ret[\"rank\"] = d_63_ret[\"rank\"].astype(float)\n",
    "d_63_ret[\"tag\"] = np.where(d_63_ret[\"rank\"] <= d_63_ret[\"rank_30p\"], 1, 0)\n",
    "\n",
    "d_63_ret\\\n",
    "    = d_63_ret\\\n",
    "        .groupby([\"symbol\"])\\\n",
    "        .rolling(3)\\\n",
    "        .agg({\"tag\": \"sum\", \"date\": \"max\"})\\\n",
    "        .reset_index()\n",
    "\n",
    "d_63_ret = d_63_ret[~ np.isnan(d_63_ret.tag)]\n",
    "d_63_ret[\"date\"] = d_63_ret[\"date\"].astype(\"int\")\n",
    "d_63_ret[d_63_ret.tag == 3][[\"symbol\", \"date\"]].sort_values(by = [\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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