{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "import os\n",
    "import linearmodels"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "           size  share\nstk month             \n8   5         1      3\n9   6         2      4",
      "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>size</th>\n      <th>share</th>\n    </tr>\n    <tr>\n      <th>stk</th>\n      <th>month</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <th>5</th>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <th>6</th>\n      <td>2</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF=pd.DataFrame({'stk':[8,9],'month':[5,6],'size':[1,2],'share':[3,4]})\n",
    "DF=DF.set_index('stk')\n",
    "DF=DF.set_index('month',append=True)\n",
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'FamaMacBeth' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_8936\\3597670844.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     27\u001B[0m \u001B[1;31m#     print(i)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     28\u001B[0m     \u001B[1;32mreturn\u001B[0m \u001B[0mlag\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 29\u001B[1;33m \u001B[0mFamaMacBeth_summary\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mDF\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mreg_lst\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mreg_order\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     31\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_8936\\3597670844.py\u001B[0m in \u001B[0;36mFamaMacBeth_summary\u001B[1;34m(DF, reg_lst, reg_order, reg_names, params_format, tvalues_format)\u001B[0m\n\u001B[0;32m     16\u001B[0m         \u001B[0mT\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_level_values\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mnames\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munique\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     17\u001B[0m         \u001B[0mlag\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;36m4\u001B[0m\u001B[1;33m*\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m100\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m**\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m/\u001B[0m\u001B[1;36m9\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 18\u001B[1;33m         \u001B[0mfmb\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mFamaMacBeth\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mreg\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msm\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0madd_constant\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mreg\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     19\u001B[0m         \u001B[0mfmb\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfmb\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcov_type\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'kernel'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mbandwidth\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mlag\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     20\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mNameError\u001B[0m: name 'FamaMacBeth' is not defined"
     ]
    }
   ],
   "source": [
    "reg_lst=['size','share']\n",
    "reg_order=['size','share']\n",
    "def FamaMacBeth_summary(DF,\n",
    "                        reg_lst,\n",
    "                        reg_order,\n",
    "                        reg_names=None,\n",
    "                        params_format='{:.3f}',\n",
    "                        tvalues_format='{:.2f}'):\n",
    "\n",
    "    rows = sum([[var, f'{var}_t'] for var in ['const'] + reg_order], [])##sum的两个输入参数，后面是表示初始的相加对象\n",
    "    if reg_names is None:\n",
    "        reg_names = [f'({i+1})' for i in range(len(reg_lst))]\n",
    "    show = pd.DataFrame(index=rows, columns=reg_names)##以rows为行reg_names为列构造dataframe\n",
    "    for reg, reg_name in zip(reg_lst, reg_names):\n",
    "        df = DF.loc[:, reg].copy()\n",
    "        T = len(df.index.get_level_values(df.index.names[1]).unique())\n",
    "        lag = 4*(2/100)**(2/9)\n",
    "        fmb = FamaMacBeth(df[reg[0]], sm.add_constant(df[reg[1:]]))\n",
    "        fmb = fmb.fit(cov_type='kernel', bandwidth=lag)\n",
    "\n",
    "\n",
    "#zip量不同时输出都有的\n",
    "# list1 = ['a', 'b', 'c']\n",
    "# list2 = [1, 2,3,4]\n",
    "# zipped = zip(list2, list1)\n",
    "# for i in zipped:\n",
    "#     print(i)\n",
    "    return lag\n",
    "FamaMacBeth_summary(DF,reg_lst,reg_order)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'x':[1,2],'y':[3,4],'z':[8,5]})\n",
    "df1=df1.set_index('x')\n",
    "\n",
    "print(df1)\n",
    "df2 = pd.DataFrame({'x':[1,6],'y':[7,8],})\n",
    "df2 = df2.set_index('x')\n",
    "print(df2)\n",
    "df3 = pd.DataFrame({'x':[1,2],'y':[3,4],'z':[8,5]})\n",
    "res1 = pd.concat([df1,df2,df3],axis=1,keys=['z','m','l'])\n",
    "res1\n",
    "factors=pd.merge()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 date       mkt  ten_return\n",
      "0     2010/01/05 0:00  0.005650     -0.0062\n",
      "1     2010/01/06 0:00 -0.002050      0.0318\n",
      "2     2010/01/07 0:00 -0.019750      0.0154\n",
      "3     2010/01/08 0:00  0.006250     -0.0318\n",
      "4     2010/01/11 0:00  0.001650      0.0215\n",
      "...               ...       ...         ...\n",
      "2841  2021/12/27 0:00  0.000832      0.1425\n",
      "2842  2021/12/28 0:00  0.008232      0.0567\n",
      "2843  2021/12/29 0:00 -0.008068      0.1075\n",
      "2844  2021/12/30 0:00  0.009031      0.1670\n",
      "2845  2021/12/31 0:00  0.005332     -0.0086\n",
      "\n",
      "[2846 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": "         code                 date  code_return\n0           1  2010-01-05 00:00:00      -0.0173\n1        2142  2010-01-05 00:00:00       0.0024\n2      600000  2010-01-05 00:00:00       0.0076\n3      600015  2010-01-05 00:00:00       0.0252\n4      600016  2010-01-05 00:00:00       0.0177\n...       ...                  ...          ...\n46006  601328  2010-08-09 00:00:00      -0.0092\n46007  601398  2010-08-09 00:00:00      -0.0047\n46008  601939  2010-08-09 00:00:00      -0.0041\n46009  601988  2010-08-09 00:00:00      -0.0057\n46010  601998  2010-08-09 00:00:00      -0.0034\n\n[46011 rows x 3 columns]",
      "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>code</th>\n      <th>date</th>\n      <th>code_return</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-01-05 00:00:00</td>\n      <td>-0.0173</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2142</td>\n      <td>2010-01-05 00:00:00</td>\n      <td>0.0024</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>600000</td>\n      <td>2010-01-05 00:00:00</td>\n      <td>0.0076</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>600015</td>\n      <td>2010-01-05 00:00:00</td>\n      <td>0.0252</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>600016</td>\n      <td>2010-01-05 00:00:00</td>\n      <td>0.0177</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>46006</th>\n      <td>601328</td>\n      <td>2010-08-09 00:00:00</td>\n      <td>-0.0092</td>\n    </tr>\n    <tr>\n      <th>46007</th>\n      <td>601398</td>\n      <td>2010-08-09 00:00:00</td>\n      <td>-0.0047</td>\n    </tr>\n    <tr>\n      <th>46008</th>\n      <td>601939</td>\n      <td>2010-08-09 00:00:00</td>\n      <td>-0.0041</td>\n    </tr>\n    <tr>\n      <th>46009</th>\n      <td>601988</td>\n      <td>2010-08-09 00:00:00</td>\n      <td>-0.0057</td>\n    </tr>\n    <tr>\n      <th>46010</th>\n      <td>601998</td>\n      <td>2010-08-09 00:00:00</td>\n      <td>-0.0034</td>\n    </tr>\n  </tbody>\n</table>\n<p>46011 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.regression.rolling import RollingOLS # 滚动窗口回归\n",
    "import statsmodels.api as sm\n",
    "ret=pd.read_csv('汇总1.csv',usecols=[0,1,2])\n",
    "factors=pd.read_csv('factors.csv',usecols=[0,1,2])\n",
    "factornames=['date','mkt','tenreturn']\n",
    "print(factors)\n",
    "res_ts={}\n",
    "ret"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         code       date  code_return\n",
      "0           1 2010-09-02       0.0388\n",
      "1        2142 2010-09-02      -0.0038\n",
      "2      600000 2010-09-02       0.0057\n",
      "3      600015 2010-09-02       0.0017\n",
      "4      600016 2010-09-02       0.0019\n",
      "...       ...        ...          ...\n",
      "40651  601398 2021-12-31      -0.0022\n",
      "40652  601818 2021-12-31       0.0000\n",
      "40653  601939 2021-12-31      -0.0017\n",
      "40654  601988 2021-12-31       0.0000\n",
      "40655  601998 2021-12-31       0.0022\n",
      "\n",
      "[40656 rows x 3 columns]\n",
      "         code       date  code_return\n",
      "0           1 2010-09-02       0.0388\n",
      "1        2142 2010-09-02      -0.0038\n",
      "2      600000 2010-09-02       0.0057\n",
      "3      600015 2010-09-02       0.0017\n",
      "4      600016 2010-09-02       0.0019\n",
      "...       ...        ...          ...\n",
      "40651  601398 2021-12-31      -0.0022\n",
      "40652  601818 2021-12-31       0.0000\n",
      "40653  601939 2021-12-31      -0.0017\n",
      "40654  601988 2021-12-31       0.0000\n",
      "40655  601998 2021-12-31       0.0022\n",
      "\n",
      "[40656 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Passing 'suffixes' which cause duplicate columns {'code_return_x', 'code_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": "           date       mkt  ten_return\n0    2010-09-02  0.018731     -0.0106\n1    2010-09-03  0.005331     -0.0010\n2    2010-09-06 -0.000469     -0.0174\n3    2010-09-07  0.007131     -0.0870\n4    2010-09-08  0.007731     -0.0058\n...         ...       ...         ...\n2536 2021-12-27  0.000832      0.1425\n2537 2021-12-28  0.008232      0.0567\n2538 2021-12-29 -0.008068      0.1075\n2539 2021-12-30  0.009031      0.1670\n2540 2021-12-31  0.005332     -0.0086\n\n[2541 rows x 3 columns]",
      "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>mkt</th>\n      <th>ten_return</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-09-02</td>\n      <td>0.018731</td>\n      <td>-0.0106</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-09-03</td>\n      <td>0.005331</td>\n      <td>-0.0010</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-09-06</td>\n      <td>-0.000469</td>\n      <td>-0.0174</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-09-07</td>\n      <td>0.007131</td>\n      <td>-0.0870</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-09-08</td>\n      <td>0.007731</td>\n      <td>-0.0058</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2536</th>\n      <td>2021-12-27</td>\n      <td>0.000832</td>\n      <td>0.1425</td>\n    </tr>\n    <tr>\n      <th>2537</th>\n      <td>2021-12-28</td>\n      <td>0.008232</td>\n      <td>0.0567</td>\n    </tr>\n    <tr>\n      <th>2538</th>\n      <td>2021-12-29</td>\n      <td>-0.008068</td>\n      <td>0.1075</td>\n    </tr>\n    <tr>\n      <th>2539</th>\n      <td>2021-12-30</td>\n      <td>0.009031</td>\n      <td>0.1670</td>\n    </tr>\n    <tr>\n      <th>2540</th>\n      <td>2021-12-31</td>\n      <td>0.005332</td>\n      <td>-0.0086</td>\n    </tr>\n  </tbody>\n</table>\n<p>2541 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ret)\n",
    "codelist=ret['code'].unique()\n",
    "first=ret[ret['code']==codelist[0]]\n",
    "for i in codelist[1:]:\n",
    "    first=pd.merge(left=first,right=ret[ret['code']==i],how='inner',on='date')\n",
    "first['date']\n",
    "factors['date']=pd.to_datetime(factors['date'],format='%Y/%m/%d %H:%M:%S')\n",
    "first['date']=pd.to_datetime(first['date'],format='%Y-%m-%d %H:%M:%S')\n",
    "ret['date']=pd.to_datetime(ret['date'],format='%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "ret=pd.merge(left=ret,right=first['date'],how='inner',on='date')\n",
    "factors=pd.merge(left=factors,right=first['date'],how='inner',on='date')\n",
    "print(ret)\n",
    "factors"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "1"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "                  mkt  ten_return\ncode                             \n1      0          NaN         NaN\n       1          NaN         NaN\n       2          NaN         NaN\n       3          NaN         NaN\n       4          NaN         NaN\n...               ...         ...\n601998 2536  0.073522   -0.007918\n       2537  0.088027   -0.007224\n       2538  0.098329   -0.007488\n       2539  0.069860   -0.009307\n       2540  0.064703   -0.008807\n\n[40656 rows x 2 columns]",
      "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>mkt</th>\n      <th>ten_return</th>\n    </tr>\n    <tr>\n      <th>code</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">1</th>\n      <th>0</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">601998</th>\n      <th>2536</th>\n      <td>0.073522</td>\n      <td>-0.007918</td>\n    </tr>\n    <tr>\n      <th>2537</th>\n      <td>0.088027</td>\n      <td>-0.007224</td>\n    </tr>\n    <tr>\n      <th>2538</th>\n      <td>0.098329</td>\n      <td>-0.007488</td>\n    </tr>\n    <tr>\n      <th>2539</th>\n      <td>0.069860</td>\n      <td>-0.009307</td>\n    </tr>\n    <tr>\n      <th>2540</th>\n      <td>0.064703</td>\n      <td>-0.008807</td>\n    </tr>\n  </tbody>\n</table>\n<p>40656 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factors=factors[['mkt','ten_return']]\n",
    "for stockcode,stockret in ret.groupby('code'):\n",
    "    trans=stockret['code_return'].tolist()\n",
    "    trans=pd.DataFrame(trans,columns=['code_return'])\n",
    "    mod_ts = RollingOLS(trans,sm.add_constant(factors),window=24).fit(cov_type='HAC',use_t=True)\n",
    "    res_ts[stockcode] = mod_ts.params\n",
    "# 获取因子载荷序列\n",
    "\n",
    "betas = pd.concat({k: pd.DataFrame(v) for k, v in res_ts.items()},axis=0,names=['code']) # 所有的参数估计值。这里的names只能对已经有了的索引进行命名\n",
    "loadings = betas.drop('const',axis=1) # 去掉常数项\n",
    "loadings\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "                   date  code_return\ncode                                \n1      0     2010-09-02       0.0388\n2142   1     2010-09-02      -0.0038\n600000 2     2010-09-02       0.0057\n600015 3     2010-09-02       0.0017\n600016 4     2010-09-02       0.0019\n...                 ...          ...\n601398 40651 2021-12-31      -0.0022\n601818 40652 2021-12-31       0.0000\n601939 40653 2021-12-31      -0.0017\n601988 40654 2021-12-31       0.0000\n601998 40655 2021-12-31       0.0022\n\n[40656 rows x 2 columns]",
      "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>date</th>\n      <th>code_return</th>\n    </tr>\n    <tr>\n      <th>code</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <th>0</th>\n      <td>2010-09-02</td>\n      <td>0.0388</td>\n    </tr>\n    <tr>\n      <th>2142</th>\n      <th>1</th>\n      <td>2010-09-02</td>\n      <td>-0.0038</td>\n    </tr>\n    <tr>\n      <th>600000</th>\n      <th>2</th>\n      <td>2010-09-02</td>\n      <td>0.0057</td>\n    </tr>\n    <tr>\n      <th>600015</th>\n      <th>3</th>\n      <td>2010-09-02</td>\n      <td>0.0017</td>\n    </tr>\n    <tr>\n      <th>600016</th>\n      <th>4</th>\n      <td>2010-09-02</td>\n      <td>0.0019</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>601398</th>\n      <th>40651</th>\n      <td>2021-12-31</td>\n      <td>-0.0022</td>\n    </tr>\n    <tr>\n      <th>601818</th>\n      <th>40652</th>\n      <td>2021-12-31</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>601939</th>\n      <th>40653</th>\n      <td>2021-12-31</td>\n      <td>-0.0017</td>\n    </tr>\n    <tr>\n      <th>601988</th>\n      <th>40654</th>\n      <td>2021-12-31</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>601998</th>\n      <th>40655</th>\n      <td>2021-12-31</td>\n      <td>0.0022</td>\n    </tr>\n  </tbody>\n</table>\n<p>40656 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = ret.set_index('code',append=True).swaplevel()\n",
    "temp"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "                   date  code_return\ncode                                \n1      0            NaT          NaN\n2142   1            NaT          NaN\n600000 2            NaT          NaN\n600015 3            NaT          NaN\n600016 4            NaT          NaN\n...                 ...          ...\n601398 40651 2021-12-30      -0.0043\n601818 40652 2021-12-30       0.0000\n601939 40653 2021-12-30       0.0000\n601988 40654 2021-12-30       0.0000\n601998 40655 2021-12-30      -0.0043\n\n[40656 rows x 2 columns]",
      "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>date</th>\n      <th>code_return</th>\n    </tr>\n    <tr>\n      <th>code</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <th>0</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2142</th>\n      <th>1</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>600000</th>\n      <th>2</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>600015</th>\n      <th>3</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>600016</th>\n      <th>4</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>601398</th>\n      <th>40651</th>\n      <td>2021-12-30</td>\n      <td>-0.0043</td>\n    </tr>\n    <tr>\n      <th>601818</th>\n      <th>40652</th>\n      <td>2021-12-30</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>601939</th>\n      <th>40653</th>\n      <td>2021-12-30</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>601988</th>\n      <th>40654</th>\n      <td>2021-12-30</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>601998</th>\n      <th>40655</th>\n      <td>2021-12-30</td>\n      <td>-0.0043</td>\n    </tr>\n  </tbody>\n</table>\n<p>40656 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = temp.groupby('code').shift(1)\n",
    "temp"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'date'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_8936\\2619771353.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mfmdata\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmerge\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtemp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdropna\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreset_index\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mloadings\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreset_index\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mon\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'code'\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;34m'date'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      2\u001B[0m \u001B[0mfmdata\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mE:\\Python37\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001B[0m in \u001B[0;36mmerge\u001B[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[0;32m    117\u001B[0m         \u001B[0mcopy\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mcopy\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    118\u001B[0m         \u001B[0mindicator\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mindicator\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 119\u001B[1;33m         \u001B[0mvalidate\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mvalidate\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    120\u001B[0m     )\n\u001B[0;32m    121\u001B[0m     \u001B[1;32mreturn\u001B[0m \u001B[0mop\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_result\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mE:\\Python37\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[0;32m    697\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mright_join_keys\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    698\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mjoin_names\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 699\u001B[1;33m         ) = self._get_merge_keys()\n\u001B[0m\u001B[0;32m    700\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    701\u001B[0m         \u001B[1;31m# validate the merge keys dtypes. We may need to coerce\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mE:\\Python37\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001B[0m in \u001B[0;36m_get_merge_keys\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m   1094\u001B[0m                     \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mis_rkey\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrk\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1095\u001B[0m                         \u001B[1;32mif\u001B[0m \u001B[0mrk\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1096\u001B[1;33m                             \u001B[0mright_keys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mright\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_get_label_or_level_values\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrk\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1097\u001B[0m                         \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1098\u001B[0m                             \u001B[1;31m# work-around for merge_asof(right_index=True)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mE:\\Python37\\lib\\site-packages\\pandas\\core\\generic.py\u001B[0m in \u001B[0;36m_get_label_or_level_values\u001B[1;34m(self, key, axis)\u001B[0m\n\u001B[0;32m   1777\u001B[0m             \u001B[0mvalues\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maxes\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0maxis\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_level_values\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_values\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1778\u001B[0m         \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1779\u001B[1;33m             \u001B[1;32mraise\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1780\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1781\u001B[0m         \u001B[1;31m# Check for duplicates\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'date'"
     ]
    }
   ],
   "source": [
    "fmdata = pd.merge(temp.dropna().reset_index(),loadings.reset_index(),on=['code','date'])\n",
    "fmdata"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "        stkcode    date  monret          size        ep\n0             1  200001  0.0619  1.985739e+10  0.019309\n1             1  200002 -0.0113  1.963234e+10  0.019531\n2             1  200003  0.0027  1.968592e+10  0.019478\n3             1  200004  0.0370  2.041464e+10  0.017718\n4             1  200005 -0.0551  1.928942e+10  0.018751\n...         ...     ...     ...           ...       ...\n592718   900957  202108 -0.0589  1.116880e+08  0.018720\n592719   900957  202109  0.0906  1.218080e+08  0.017120\n592720   900957  202110 -0.0408  1.168400e+08  0.015647\n592721   900957  202111 -0.0409  1.120560e+08  0.016345\n592722   900957  202112  0.0361  1.161040e+08  0.015783\n\n[571479 rows x 5 columns]",
      "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>stkcode</th>\n      <th>date</th>\n      <th>monret</th>\n      <th>size</th>\n      <th>ep</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>200001</td>\n      <td>0.0619</td>\n      <td>1.985739e+10</td>\n      <td>0.019309</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>200002</td>\n      <td>-0.0113</td>\n      <td>1.963234e+10</td>\n      <td>0.019531</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>200003</td>\n      <td>0.0027</td>\n      <td>1.968592e+10</td>\n      <td>0.019478</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>200004</td>\n      <td>0.0370</td>\n      <td>2.041464e+10</td>\n      <td>0.017718</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>200005</td>\n      <td>-0.0551</td>\n      <td>1.928942e+10</td>\n      <td>0.018751</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>592718</th>\n      <td>900957</td>\n      <td>202108</td>\n      <td>-0.0589</td>\n      <td>1.116880e+08</td>\n      <td>0.018720</td>\n    </tr>\n    <tr>\n      <th>592719</th>\n      <td>900957</td>\n      <td>202109</td>\n      <td>0.0906</td>\n      <td>1.218080e+08</td>\n      <td>0.017120</td>\n    </tr>\n    <tr>\n      <th>592720</th>\n      <td>900957</td>\n      <td>202110</td>\n      <td>-0.0408</td>\n      <td>1.168400e+08</td>\n      <td>0.015647</td>\n    </tr>\n    <tr>\n      <th>592721</th>\n      <td>900957</td>\n      <td>202111</td>\n      <td>-0.0409</td>\n      <td>1.120560e+08</td>\n      <td>0.016345</td>\n    </tr>\n    <tr>\n      <th>592722</th>\n      <td>900957</td>\n      <td>202112</td>\n      <td>0.0361</td>\n      <td>1.161040e+08</td>\n      <td>0.015783</td>\n    </tr>\n  </tbody>\n</table>\n<p>571479 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monret=pd.read_csv('RESSET_MRESSTK_all.csv',usecols=[0,2,3,4,5,6],encoding='utf-8')\n",
    "beta=pd.read_csv('RESSET_SMONRETBETA_BFDT12_all.csv',encoding='utf-8')\n",
    "monret.columns=['stkcode','date','close','tshare','monret','pe']\n",
    "beta.columns=['stkcode','date','beta']\n",
    "monret['date']=pd.to_datetime(monret['date'],format='%Y/%m/%d')\n",
    "monret['date']=monret['date'].dt.strftime('%Y%m').astype(int)\n",
    "monret['size']=monret['close']*monret['tshare']\n",
    "monret['ep']=1/monret['pe']\n",
    "monret=monret[['stkcode','date','monret','size','ep']]\n",
    "monret.dropna(inplace=True)\n",
    "monret"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "        stkcode    date    beta\n0             1  200001  1.3269\n1             1  200002  1.2640\n2             1  200003  1.2676\n3             1  200004  1.2784\n4             1  200005  1.1890\n...         ...     ...     ...\n535164   900957  202108 -0.6000\n535165   900957  202109 -0.9615\n535166   900957  202110 -0.7221\n535167   900957  202111 -1.4672\n535168   900957  202112 -1.3773\n\n[535169 rows x 3 columns]",
      "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>stkcode</th>\n      <th>date</th>\n      <th>beta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>200001</td>\n      <td>1.3269</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>200002</td>\n      <td>1.2640</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>200003</td>\n      <td>1.2676</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>200004</td>\n      <td>1.2784</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>200005</td>\n      <td>1.1890</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>535164</th>\n      <td>900957</td>\n      <td>202108</td>\n      <td>-0.6000</td>\n    </tr>\n    <tr>\n      <th>535165</th>\n      <td>900957</td>\n      <td>202109</td>\n      <td>-0.9615</td>\n    </tr>\n    <tr>\n      <th>535166</th>\n      <td>900957</td>\n      <td>202110</td>\n      <td>-0.7221</td>\n    </tr>\n    <tr>\n      <th>535167</th>\n      <td>900957</td>\n      <td>202111</td>\n      <td>-1.4672</td>\n    </tr>\n    <tr>\n      <th>535168</th>\n      <td>900957</td>\n      <td>202112</td>\n      <td>-1.3773</td>\n    </tr>\n  </tbody>\n</table>\n<p>535169 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta['date']=pd.to_datetime(beta['date'],format='%Y/%m/%d')\n",
    "beta['date']=beta['date'].dt.strftime('%Y%m').astype(int)\n",
    "beta.dropna()\n",
    "beta"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "        stkcode    date  monret          size        ep    beta\n0             1  200001  0.0619  1.985739e+10  0.019309  1.3269\n1             1  200002 -0.0113  1.963234e+10  0.019531  1.2640\n2             1  200003  0.0027  1.968592e+10  0.019478  1.2676\n3             1  200004  0.0370  2.041464e+10  0.017718  1.2784\n4             1  200005 -0.0551  1.928942e+10  0.018751  1.1890\n...         ...     ...     ...           ...       ...     ...\n534365   900957  202108 -0.0589  1.116880e+08  0.018720 -0.6000\n534366   900957  202109  0.0906  1.218080e+08  0.017120 -0.9615\n534367   900957  202110 -0.0408  1.168400e+08  0.015647 -0.7221\n534368   900957  202111 -0.0409  1.120560e+08  0.016345 -1.4672\n534369   900957  202112  0.0361  1.161040e+08  0.015783 -1.3773\n\n[534370 rows x 6 columns]",
      "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>stkcode</th>\n      <th>date</th>\n      <th>monret</th>\n      <th>size</th>\n      <th>ep</th>\n      <th>beta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>200001</td>\n      <td>0.0619</td>\n      <td>1.985739e+10</td>\n      <td>0.019309</td>\n      <td>1.3269</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>200002</td>\n      <td>-0.0113</td>\n      <td>1.963234e+10</td>\n      <td>0.019531</td>\n      <td>1.2640</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>200003</td>\n      <td>0.0027</td>\n      <td>1.968592e+10</td>\n      <td>0.019478</td>\n      <td>1.2676</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>200004</td>\n      <td>0.0370</td>\n      <td>2.041464e+10</td>\n      <td>0.017718</td>\n      <td>1.2784</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>200005</td>\n      <td>-0.0551</td>\n      <td>1.928942e+10</td>\n      <td>0.018751</td>\n      <td>1.1890</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>534365</th>\n      <td>900957</td>\n      <td>202108</td>\n      <td>-0.0589</td>\n      <td>1.116880e+08</td>\n      <td>0.018720</td>\n      <td>-0.6000</td>\n    </tr>\n    <tr>\n      <th>534366</th>\n      <td>900957</td>\n      <td>202109</td>\n      <td>0.0906</td>\n      <td>1.218080e+08</td>\n      <td>0.017120</td>\n      <td>-0.9615</td>\n    </tr>\n    <tr>\n      <th>534367</th>\n      <td>900957</td>\n      <td>202110</td>\n      <td>-0.0408</td>\n      <td>1.168400e+08</td>\n      <td>0.015647</td>\n      <td>-0.7221</td>\n    </tr>\n    <tr>\n      <th>534368</th>\n      <td>900957</td>\n      <td>202111</td>\n      <td>-0.0409</td>\n      <td>1.120560e+08</td>\n      <td>0.016345</td>\n      <td>-1.4672</td>\n    </tr>\n    <tr>\n      <th>534369</th>\n      <td>900957</td>\n      <td>202112</td>\n      <td>0.0361</td>\n      <td>1.161040e+08</td>\n      <td>0.015783</td>\n      <td>-1.3773</td>\n    </tr>\n  </tbody>\n</table>\n<p>534370 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel=pd.merge(left=monret,right=beta,on=['stkcode','date'],how='inner')\n",
    "panel"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "        const          size        ep      beta\n0   -0.028908  2.706794e-11  0.151357  0.150815\n1    0.088102  1.410143e-11 -0.044479  0.018328\n2    0.177329 -1.374837e-11 -0.306896 -0.054933\n3    0.037395  1.351040e-11  0.155326 -0.042211\n4    0.069028 -2.060338e-11  0.136973  0.018272\n..        ...           ...       ...       ...\n259 -0.021313  1.262353e-13 -0.013304 -0.003581\n260  0.083763 -2.534541e-13 -0.025108  0.021680\n261  0.038268 -3.333640e-14 -0.071991  0.005694\n262  0.057655 -1.050947e-13  0.107436  0.099658\n263  0.111225 -1.832814e-13 -0.389525 -0.007620\n\n[264 rows x 4 columns]",
      "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>const</th>\n      <th>size</th>\n      <th>ep</th>\n      <th>beta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.028908</td>\n      <td>2.706794e-11</td>\n      <td>0.151357</td>\n      <td>0.150815</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.088102</td>\n      <td>1.410143e-11</td>\n      <td>-0.044479</td>\n      <td>0.018328</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.177329</td>\n      <td>-1.374837e-11</td>\n      <td>-0.306896</td>\n      <td>-0.054933</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.037395</td>\n      <td>1.351040e-11</td>\n      <td>0.155326</td>\n      <td>-0.042211</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.069028</td>\n      <td>-2.060338e-11</td>\n      <td>0.136973</td>\n      <td>0.018272</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>259</th>\n      <td>-0.021313</td>\n      <td>1.262353e-13</td>\n      <td>-0.013304</td>\n      <td>-0.003581</td>\n    </tr>\n    <tr>\n      <th>260</th>\n      <td>0.083763</td>\n      <td>-2.534541e-13</td>\n      <td>-0.025108</td>\n      <td>0.021680</td>\n    </tr>\n    <tr>\n      <th>261</th>\n      <td>0.038268</td>\n      <td>-3.333640e-14</td>\n      <td>-0.071991</td>\n      <td>0.005694</td>\n    </tr>\n    <tr>\n      <th>262</th>\n      <td>0.057655</td>\n      <td>-1.050947e-13</td>\n      <td>0.107436</td>\n      <td>0.099658</td>\n    </tr>\n    <tr>\n      <th>263</th>\n      <td>0.111225</td>\n      <td>-1.832814e-13</td>\n      <td>-0.389525</td>\n      <td>-0.007620</td>\n    </tr>\n  </tbody>\n</table>\n<p>264 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "coefficient=[]\n",
    "for date in panel['date'].unique():\n",
    "    panel_date=panel[panel['date']==date]\n",
    "    X=panel_date[['size','ep','beta']]\n",
    "    X=sm.add_constant(X)\n",
    "    Y=panel_date['monret']\n",
    "    result=sm.OLS(Y,X).fit()\n",
    "    coefficient.append(result.params)\n",
    "coefficient=pd.DataFrame(coefficient,columns=['const','size','ep','beta'])\n",
    "coefficient"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "['const:  t_statistic:0.7631101972892858   p_value:0.4460816261704841',\n 'size:  t_statistic:1.9299761192647984   p_value:0.05468514305777287',\n 'ep:  t_statistic:-0.8640830459728995   p_value:0.3883296475253617',\n 'beta:  t_statistic:2.0395581163718233   p_value:0.0423937550458561']"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "result=[]\n",
    "for factor in coefficient.columns:\n",
    "    t_test_result, p_value=stats.ttest_1samp(coefficient[factor],0)\n",
    "    result.append(factor+':  t_statistic:'+str(t_test_result)+'   p_value:'+str(p_value))\n",
    "result"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "data": {
      "text/plain": "        stkcode    date  monret    beta\n0             1  200001  0.0619  1.3269\n1             1  200002 -0.0113  1.2640\n2             1  200003  0.0027  1.2676\n3             1  200004  0.0370  1.2784\n4             1  200005 -0.0551  1.1890\n...         ...     ...     ...     ...\n535164   900957  202108 -0.0589 -0.6000\n535165   900957  202109  0.0906 -0.9615\n535166   900957  202110 -0.0408 -0.7221\n535167   900957  202111 -0.0409 -1.4672\n535168   900957  202112  0.0361 -1.3773\n\n[535169 rows x 4 columns]",
      "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>stkcode</th>\n      <th>date</th>\n      <th>monret</th>\n      <th>beta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>200001</td>\n      <td>0.0619</td>\n      <td>1.3269</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>200002</td>\n      <td>-0.0113</td>\n      <td>1.2640</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>200003</td>\n      <td>0.0027</td>\n      <td>1.2676</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>200004</td>\n      <td>0.0370</td>\n      <td>1.2784</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>200005</td>\n      <td>-0.0551</td>\n      <td>1.1890</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>535164</th>\n      <td>900957</td>\n      <td>202108</td>\n      <td>-0.0589</td>\n      <td>-0.6000</td>\n    </tr>\n    <tr>\n      <th>535165</th>\n      <td>900957</td>\n      <td>202109</td>\n      <td>0.0906</td>\n      <td>-0.9615</td>\n    </tr>\n    <tr>\n      <th>535166</th>\n      <td>900957</td>\n      <td>202110</td>\n      <td>-0.0408</td>\n      <td>-0.7221</td>\n    </tr>\n    <tr>\n      <th>535167</th>\n      <td>900957</td>\n      <td>202111</td>\n      <td>-0.0409</td>\n      <td>-1.4672</td>\n    </tr>\n    <tr>\n      <th>535168</th>\n      <td>900957</td>\n      <td>202112</td>\n      <td>0.0361</td>\n      <td>-1.3773</td>\n    </tr>\n  </tbody>\n</table>\n<p>535169 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [
    {
     "data": {
      "text/plain": "        stkcode    date  monret    beta\n0             1  200001  0.0619  1.3269\n1             1  200002 -0.0113  1.2640\n2             1  200003  0.0027  1.2676\n3             1  200004  0.0370  1.2784\n4             1  200005 -0.0551  1.1890\n...         ...     ...     ...     ...\n535164   900957  202108 -0.0589 -0.6000\n535165   900957  202109  0.0906 -0.9615\n535166   900957  202110 -0.0408 -0.7221\n535167   900957  202111 -0.0409 -1.4672\n535168   900957  202112  0.0361 -1.3773\n\n[535169 rows x 4 columns]",
      "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>stkcode</th>\n      <th>date</th>\n      <th>monret</th>\n      <th>beta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>200001</td>\n      <td>0.0619</td>\n      <td>1.3269</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>200002</td>\n      <td>-0.0113</td>\n      <td>1.2640</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>200003</td>\n      <td>0.0027</td>\n      <td>1.2676</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>200004</td>\n      <td>0.0370</td>\n      <td>1.2784</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>200005</td>\n      <td>-0.0551</td>\n      <td>1.1890</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>535164</th>\n      <td>900957</td>\n      <td>202108</td>\n      <td>-0.0589</td>\n      <td>-0.6000</td>\n    </tr>\n    <tr>\n      <th>535165</th>\n      <td>900957</td>\n      <td>202109</td>\n      <td>0.0906</td>\n      <td>-0.9615</td>\n    </tr>\n    <tr>\n      <th>535166</th>\n      <td>900957</td>\n      <td>202110</td>\n      <td>-0.0408</td>\n      <td>-0.7221</td>\n    </tr>\n    <tr>\n      <th>535167</th>\n      <td>900957</td>\n      <td>202111</td>\n      <td>-0.0409</td>\n      <td>-1.4672</td>\n    </tr>\n    <tr>\n      <th>535168</th>\n      <td>900957</td>\n      <td>202112</td>\n      <td>0.0361</td>\n      <td>-1.3773</td>\n    </tr>\n  </tbody>\n</table>\n<p>535169 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel=merge\n",
    "panel\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [
    {
     "data": {
      "text/plain": "        stkcode       date  monret    beta\n0             1 2000-01-01  0.0619  1.3269\n1             1 2000-02-01 -0.0113  1.2640\n2             1 2000-03-01  0.0027  1.2676\n3             1 2000-04-01  0.0370  1.2784\n4             1 2000-05-01 -0.0551  1.1890\n...         ...        ...     ...     ...\n535164   900957 2021-08-01 -0.0589 -0.6000\n535165   900957 2021-09-01  0.0906 -0.9615\n535166   900957 2021-10-01 -0.0408 -0.7221\n535167   900957 2021-11-01 -0.0409 -1.4672\n535168   900957 2021-12-01  0.0361 -1.3773\n\n[535169 rows x 4 columns]",
      "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>stkcode</th>\n      <th>date</th>\n      <th>monret</th>\n      <th>beta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2000-01-01</td>\n      <td>0.0619</td>\n      <td>1.3269</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2000-02-01</td>\n      <td>-0.0113</td>\n      <td>1.2640</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2000-03-01</td>\n      <td>0.0027</td>\n      <td>1.2676</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>2000-04-01</td>\n      <td>0.0370</td>\n      <td>1.2784</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2000-05-01</td>\n      <td>-0.0551</td>\n      <td>1.1890</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>535164</th>\n      <td>900957</td>\n      <td>2021-08-01</td>\n      <td>-0.0589</td>\n      <td>-0.6000</td>\n    </tr>\n    <tr>\n      <th>535165</th>\n      <td>900957</td>\n      <td>2021-09-01</td>\n      <td>0.0906</td>\n      <td>-0.9615</td>\n    </tr>\n    <tr>\n      <th>535166</th>\n      <td>900957</td>\n      <td>2021-10-01</td>\n      <td>-0.0408</td>\n      <td>-0.7221</td>\n    </tr>\n    <tr>\n      <th>535167</th>\n      <td>900957</td>\n      <td>2021-11-01</td>\n      <td>-0.0409</td>\n      <td>-1.4672</td>\n    </tr>\n    <tr>\n      <th>535168</th>\n      <td>900957</td>\n      <td>2021-12-01</td>\n      <td>0.0361</td>\n      <td>-1.3773</td>\n    </tr>\n  </tbody>\n</table>\n<p>535169 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel['date']=pd.to_datetime(panel['date'],format='%Y%m')##format不要忘了！！！！！！！\n",
    "panel"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "outputs": [
    {
     "data": {
      "text/plain": "                    monret    beta\nstkcode date                      \n1       2000-01-01  0.0619  1.3269\n        2000-02-01 -0.0113  1.2640\n        2000-03-01  0.0027  1.2676\n        2000-04-01  0.0370  1.2784\n        2000-05-01 -0.0551  1.1890\n...                    ...     ...\n900957  2021-08-01 -0.0589 -0.6000\n        2021-09-01  0.0906 -0.9615\n        2021-10-01 -0.0408 -0.7221\n        2021-11-01 -0.0409 -1.4672\n        2021-12-01  0.0361 -1.3773\n\n[535169 rows x 2 columns]",
      "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>monret</th>\n      <th>beta</th>\n    </tr>\n    <tr>\n      <th>stkcode</th>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">1</th>\n      <th>2000-01-01</th>\n      <td>0.0619</td>\n      <td>1.3269</td>\n    </tr>\n    <tr>\n      <th>2000-02-01</th>\n      <td>-0.0113</td>\n      <td>1.2640</td>\n    </tr>\n    <tr>\n      <th>2000-03-01</th>\n      <td>0.0027</td>\n      <td>1.2676</td>\n    </tr>\n    <tr>\n      <th>2000-04-01</th>\n      <td>0.0370</td>\n      <td>1.2784</td>\n    </tr>\n    <tr>\n      <th>2000-05-01</th>\n      <td>-0.0551</td>\n      <td>1.1890</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">900957</th>\n      <th>2021-08-01</th>\n      <td>-0.0589</td>\n      <td>-0.6000</td>\n    </tr>\n    <tr>\n      <th>2021-09-01</th>\n      <td>0.0906</td>\n      <td>-0.9615</td>\n    </tr>\n    <tr>\n      <th>2021-10-01</th>\n      <td>-0.0408</td>\n      <td>-0.7221</td>\n    </tr>\n    <tr>\n      <th>2021-11-01</th>\n      <td>-0.0409</td>\n      <td>-1.4672</td>\n    </tr>\n    <tr>\n      <th>2021-12-01</th>\n      <td>0.0361</td>\n      <td>-1.3773</td>\n    </tr>\n  </tbody>\n</table>\n<p>535169 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel=panel.set_index(['stkcode','date'])\n",
    "panel"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [
    {
     "data": {
      "text/plain": "stkcode  date      \n1        2000-01-01    0.0619\n         2000-02-01   -0.0113\n         2000-03-01    0.0027\n         2000-04-01    0.0370\n         2000-05-01   -0.0551\n                        ...  \n900957   2021-08-01   -0.0589\n         2021-09-01    0.0906\n         2021-10-01   -0.0408\n         2021-11-01   -0.0409\n         2021-12-01    0.0361\nName: monret, Length: 535169, dtype: float64"
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newmonret=panel['monret']\n",
    "newmonret"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [
    {
     "data": {
      "text/plain": "stkcode  date      \n1        2000-01-01    1.3269\n         2000-02-01    1.2640\n         2000-03-01    1.2676\n         2000-04-01    1.2784\n         2000-05-01    1.1890\n                        ...  \n900957   2021-08-01   -0.6000\n         2021-09-01   -0.9615\n         2021-10-01   -0.7221\n         2021-11-01   -1.4672\n         2021-12-01   -1.3773\nName: beta, Length: 535169, dtype: float64"
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newbeta=panel['beta']\n",
    "newbeta"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Python37\\lib\\site-packages\\linearmodels\\shared\\exceptions.py:37: MissingValueWarning: \n",
      "Inputs contain missing values. Dropping rows with missing observations.\n",
      "  warnings.warn(missing_value_warning_msg, MissingValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<class 'linearmodels.compat.statsmodels.Summary'>\n\"\"\"\n                             FamaMacBeth Estimation Summary                            \n=======================================================================================\nDep. Variable:                        monret   R-squared:                        0.0007\nEstimator:                       FamaMacBeth   R-squared (Between):              0.0008\nNo. Observations:                     535157   R-squared (Within):               0.0005\nDate:                       Mon, Apr 17 2023   R-squared (Overall):              0.0007\nTime:                               14:36:40   Log-likelihood                 2.489e+05\nCov. Estimator:    Fama-MacBeth Standard Cov                                           \n                                               F-statistic:                      369.58\nEntities:                               4075   P-value                           0.0000\nAvg Obs:                              131.33   Distribution:                F(1,535155)\nMin Obs:                              1.0000                                           \nMax Obs:                              264.00   F-statistic (robust):             3.7894\n                                               P-value                           0.0516\nTime periods:                            264   Distribution:                F(1,535155)\nAvg Obs:                              2027.1                                           \nMin Obs:                              840.00                                           \nMax Obs:                              4008.0                                           \n                                                                                       \n                             Parameter Estimates                              \n==============================================================================\n            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n------------------------------------------------------------------------------\nconst          0.0039     0.0037     1.0558     0.2910     -0.0034      0.0113\nbeta           0.0096     0.0049     1.9466     0.0516   -6.59e-05      0.0193\n==============================================================================\n\"\"\"",
      "text/html": "<table class=\"simpletable\">\n<caption>FamaMacBeth Estimation Summary</caption>\n<tr>\n  <th>Dep. Variable:</th>             <td>monret</td>           <th>  R-squared:         </th>     <td>0.0007</td>   \n</tr>\n<tr>\n  <th>Estimator:</th>               <td>FamaMacBeth</td>        <th>  R-squared (Between):</th>    <td>0.0008</td>   \n</tr>\n<tr>\n  <th>No. Observations:</th>          <td>535157</td>           <th>  R-squared (Within):</th>     <td>0.0005</td>   \n</tr>\n<tr>\n  <th>Date:</th>                 <td>Mon, Apr 17 2023</td>      <th>  R-squared (Overall):</th>    <td>0.0007</td>   \n</tr>\n<tr>\n  <th>Time:</th>                     <td>14:36:40</td>          <th>  Log-likelihood     </th>    <td>2.489e+05</td> \n</tr>\n<tr>\n  <th>Cov. Estimator:</th>   <td>Fama-MacBeth Standard Cov</td> <th>                     </th>        <td></td>      \n</tr>\n<tr>\n  <th></th>                              <td></td>              <th>  F-statistic:       </th>     <td>369.58</td>   \n</tr>\n<tr>\n  <th>Entities:</th>                   <td>4075</td>            <th>  P-value            </th>     <td>0.0000</td>   \n</tr>\n<tr>\n  <th>Avg Obs:</th>                   <td>131.33</td>           <th>  Distribution:      </th>   <td>F(1,535155)</td>\n</tr>\n<tr>\n  <th>Min Obs:</th>                   <td>1.0000</td>           <th>                     </th>        <td></td>      \n</tr>\n<tr>\n  <th>Max Obs:</th>                   <td>264.00</td>           <th>  F-statistic (robust):</th>   <td>3.7894</td>   \n</tr>\n<tr>\n  <th></th>                              <td></td>              <th>  P-value            </th>     <td>0.0516</td>   \n</tr>\n<tr>\n  <th>Time periods:</th>                <td>264</td>            <th>  Distribution:      </th>   <td>F(1,535155)</td>\n</tr>\n<tr>\n  <th>Avg Obs:</th>                   <td>2027.1</td>           <th>                     </th>        <td></td>      \n</tr>\n<tr>\n  <th>Min Obs:</th>                   <td>840.00</td>           <th>                     </th>        <td></td>      \n</tr>\n<tr>\n  <th>Max Obs:</th>                   <td>4008.0</td>           <th>                     </th>        <td></td>      \n</tr>\n<tr>\n  <th></th>                              <td></td>              <th>                     </th>        <td></td>      \n</tr>\n</table>\n<table class=\"simpletable\">\n<caption>Parameter Estimates</caption>\n<tr>\n    <td></td>    <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th> <th>P-value</th> <th>Lower CI</th>  <th>Upper CI</th>\n</tr>\n<tr>\n  <th>const</th>  <td>0.0039</td>    <td>0.0037</td>   <td>1.0558</td> <td>0.2910</td>   <td>-0.0034</td>   <td>0.0113</td> \n</tr>\n<tr>\n  <th>beta</th>   <td>0.0096</td>    <td>0.0049</td>   <td>1.9466</td> <td>0.0516</td>  <td>-6.59e-05</td>  <td>0.0193</td> \n</tr>\n</table>"
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod=FamaMacBeth(newmonret,sm.add_constant(newbeta))\n",
    "res=mod.fit()\n",
    "res.summary\n",
    "?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [],
   "source": [
    "?FamaMacBeth"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   a\n0  1\n1  2\n2  3",
      "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>a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "m=pd.DataFrame({'a':[1,2,3],'b':[1,2,3]})\n",
    "l=m.loc[:,['a']]\n",
    "l"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n",
      "(2, 2)\n",
      "(3, 3)\n"
     ]
    }
   ],
   "source": [
    "a=[1,2,3]\n",
    "b=[1,2,3]\n",
    "l=zip(a,b)\n",
    "l\n",
    "for m,p in l:\n",
    "    print(m,p)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}