Aggregate_Monthly_data_into_industries1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdd1638c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#================= Aggregation of firms into industries: aggregation of firm characteristics and aggregation of excess firm-level stock return (Excess industry-level return)========================================================================# \n",
    "#================= Then export aggregate data\n",
    "#==================================================================================================================================================================================================================================================#\n",
    "\n",
    "\n",
    "\n",
    "!pip install dask\n",
    "!pip install numpy\n",
    "!pip install pandas\n",
    "\n",
    "import dask.dataframe as dd\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from sklearn import metrics\n",
    "from timeit import default_timer as timer\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "##### Set the current working directory\n",
    "\n",
    "path = r'C:/Users/Emile Ndoumbe/Dropbox/US/3500/monthly'                     \n",
    "all_files = glob.glob(path + \"/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d46190d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 14 0 1 52 3 13 12 5 0 0 41 2 10 11 5 16 16 23 189 11 14 8 9 24 32 104 135 55 117 17 4 0 15 0 4 13 0 5 48 74 54 26 6 12 5 0 15 25 10 26 27 204 22 32 63 6 7 50 11 0 4 178 0 3 0 5 16 29 0 11 3 0 0 54 1 0 0 0 0 0 0 0 0 19\n"
     ]
    }
   ],
   "source": [
    "##### Group firms into industry based on industry'SIC code\n",
    "\n",
    "A_0=[]\n",
    "A_10=[]\n",
    "A_11=[]\n",
    "A_12=[]\n",
    "A_13=[]\n",
    "A_14=[]\n",
    "A_15=[]\n",
    "A_16=[]\n",
    "A_17=[]\n",
    "A_18=[]\n",
    "A_19=[]\n",
    "A_20=[]\n",
    "A_21=[]\n",
    "A_22=[]\n",
    "A_23=[]\n",
    "A_24=[]\n",
    "A_25=[]\n",
    "A_26=[]\n",
    "A_27=[]\n",
    "A_28=[]\n",
    "A_29=[]\n",
    "A_30=[]\n",
    "A_31=[]\n",
    "A_32=[]\n",
    "A_33=[]\n",
    "A_34=[]\n",
    "A_35=[]\n",
    "A_36=[]\n",
    "A_37=[]\n",
    "A_38=[]\n",
    "A_39=[]\n",
    "A_40=[]\n",
    "A_41=[]\n",
    "A_42=[]\n",
    "A_43=[]\n",
    "A_44=[]\n",
    "A_45=[]\n",
    "A_46=[]\n",
    "A_47=[]\n",
    "A_48=[]\n",
    "A_49=[]\n",
    "A_50=[]\n",
    "A_51=[]\n",
    "A_52=[]\n",
    "A_53=[]\n",
    "A_54=[]\n",
    "A_55=[]\n",
    "A_56=[]\n",
    "A_57=[]\n",
    "A_58=[]\n",
    "A_59=[]\n",
    "A_60=[]\n",
    "A_61=[]\n",
    "A_62=[]\n",
    "A_63=[]\n",
    "A_64=[]\n",
    "A_65=[]\n",
    "A_66=[]\n",
    "A_67=[]\n",
    "A_70=[]\n",
    "A_71=[]\n",
    "A_72=[]\n",
    "A_73=[]\n",
    "A_74=[]\n",
    "A_75=[]\n",
    "A_76=[]\n",
    "A_78=[]\n",
    "A_79=[]\n",
    "A_80=[]\n",
    "A_81=[]\n",
    "A_82=[]\n",
    "A_83=[]\n",
    "A_85=[]\n",
    "A_86=[]\n",
    "A_87=[]\n",
    "A_89=[]\n",
    "A_90=[]\n",
    "A_91=[]\n",
    "A_92=[]\n",
    "A_93=[]\n",
    "A_94=[]\n",
    "A_95=[]\n",
    "A_96=[]\n",
    "A_97=[]\n",
    "A_99=[]\n",
    "\n",
    "for i in range (2026):\n",
    "    file=pd.read_csv(all_files[i])\n",
    "    if file[\"ind_0\"][0]==1:\n",
    "             A_0.append(all_files[i])\n",
    "    if file[\"ind_10\"][0]==1:\n",
    "        A_10.append(all_files[i])\n",
    "    if file[\"ind_11\"][0]==1:\n",
    "        A_11.append(all_files[i])\n",
    "    if file[\"ind_12\"][0]==1:\n",
    "        A_12.append(all_files[i])\n",
    "    if file[\"ind_13\"][0]==1:\n",
    "        A_13.append(all_files[i])\n",
    "    if file[\"ind_14\"][0]==1:\n",
    "        A_14.append(all_files[i])\n",
    "    if file[\"ind_15\"][0]==1:\n",
    "        A_15.append(all_files[i])\n",
    "    if file[\"ind_16\"][0]==1:\n",
    "        A_16.append(all_files[i])\n",
    "    if file[\"ind_17\"][0]==1:\n",
    "        A_17.append(all_files[i])\n",
    "    if file[\"ind_18\"][0]==1:\n",
    "        A_18.append(all_files[i])\n",
    "    if file[\"ind_19\"][0]==1:\n",
    "        A_19.append(all_files[i])\n",
    "    if file[\"ind_20\"][0]==1:\n",
    "        A_20.append(all_files[i])\n",
    "    if file[\"ind_21\"][0]==1:\n",
    "        A_21.append(all_files[i])\n",
    "    if file[\"ind_22\"][0]==1:\n",
    "        A_22.append(all_files[i])\n",
    "    if file[\"ind_23\"][0]==1:\n",
    "        A_23.append(all_files[i])\n",
    "    if file[\"ind_24\"][0]==1:\n",
    "        A_24.append(all_files[i])\n",
    "    if file[\"ind_25\"][0]==1:\n",
    "        A_25.append(all_files[i])\n",
    "    if file[\"ind_26\"][0]==1:\n",
    "        A_26.append(all_files[i])\n",
    "    if file[\"ind_27\"][0]==1:\n",
    "        A_27.append(all_files[i])\n",
    "    if file[\"ind_28\"][0]==1:\n",
    "        A_28.append(all_files[i])\n",
    "    if file[\"ind_29\"][0]==1:\n",
    "        A_29.append(all_files[i])\n",
    "    if file[\"ind_30\"][0]==1:\n",
    "        A_30.append(all_files[i])\n",
    "    if file[\"ind_31\"][0]==1:\n",
    "        A_31.append(all_files[i])\n",
    "    if file[\"ind_32\"][0]==1:\n",
    "        A_32.append(all_files[i])\n",
    "    if file[\"ind_33\"][0]==1:\n",
    "        A_33.append(all_files[i])\n",
    "    if file[\"ind_34\"][0]==1:\n",
    "        A_34.append(all_files[i])\n",
    "    if file[\"ind_35\"][0]==1:\n",
    "        A_35.append(all_files[i])\n",
    "    if file[\"ind_36\"][0]==1:\n",
    "        A_36.append(all_files[i])\n",
    "    if file[\"ind_37\"][0]==1:\n",
    "        A_37.append(all_files[i])\n",
    "    if file[\"ind_38\"][0]==1:\n",
    "        A_38.append(all_files[i])\n",
    "    if file[\"ind_39\"][0]==1:\n",
    "        A_39.append(all_files[i])\n",
    "    if file[\"ind_40\"][0]==1:\n",
    "        A_40.append(all_files[i])\n",
    "    if file[\"ind_41\"][0]==1:\n",
    "        A_41.append(all_files[i])\n",
    "    if file[\"ind_42\"][0]==1:\n",
    "        A_42.append(all_files[i])\n",
    "    if file[\"ind_43\"][0]==1:\n",
    "        A_43.append(all_files[i])\n",
    "    if file[\"ind_44\"][0]==1:\n",
    "        A_44.append(all_files[i])\n",
    "    if file[\"ind_45\"][0]==1:\n",
    "        A_45.append(all_files[i])\n",
    "    if file[\"ind_46\"][0]==1:\n",
    "        A_46.append(all_files[i])\n",
    "    if file[\"ind_47\"][0]==1:\n",
    "        A_47.append(all_files[i])\n",
    "    if file[\"ind_48\"][0]==1:\n",
    "        A_48.append(all_files[i])\n",
    "    if file[\"ind_49\"][0]==1:\n",
    "        A_49.append(all_files[i])\n",
    "    if file[\"ind_50\"][0]==1:\n",
    "        A_50.append(all_files[i])\n",
    "    if file[\"ind_51\"][0]==1:\n",
    "        A_51.append(all_files[i])\n",
    "    if file[\"ind_52\"][0]==1:\n",
    "        A_52.append(all_files[i])\n",
    "    if file[\"ind_53\"][0]==1:\n",
    "        A_53.append(all_files[i])\n",
    "    if file[\"ind_54\"][0]==1:\n",
    "        A_54.append(all_files[i])\n",
    "    if file[\"ind_55\"][0]==1:\n",
    "        A_55.append(all_files[i])\n",
    "    if file[\"ind_56\"][0]==1:\n",
    "        A_56.append(all_files[i])\n",
    "    if file[\"ind_57\"][0]==1:\n",
    "        A_57.append(all_files[i])\n",
    "    if file[\"ind_58\"][0]==1:\n",
    "        A_58.append(all_files[i])\n",
    "    if file[\"ind_59\"][0]==1:\n",
    "        A_59.append(all_files[i])\n",
    "    if file[\"ind_60\"][0]==1:\n",
    "        A_60.append(all_files[i])\n",
    "    if file[\"ind_61\"][0]==1:\n",
    "        A_61.append(all_files[i])\n",
    "    if file[\"ind_62\"][0]==1:\n",
    "        A_62.append(all_files[i])\n",
    "    if file[\"ind_63\"][0]==1:\n",
    "        A_63.append(all_files[i])\n",
    "    if file[\"ind_64\"][0]==1:\n",
    "        A_64.append(all_files[i])\n",
    "    if file[\"ind_65\"][0]==1:\n",
    "        A_65.append(all_files[i])\n",
    "    if file[\"ind_66\"][0]==1:\n",
    "        A_66.append(all_files[i])\n",
    "    if file[\"ind_67\"][0]==1:\n",
    "        A_67.append(all_files[i])\n",
    "    if file[\"ind_70\"][0]==1:\n",
    "        A_70.append(all_files[i])\n",
    "    if file[\"ind_71\"][0]==1:\n",
    "        A_71.append(all_files[i])\n",
    "    if file[\"ind_72\"][0]==1:\n",
    "        A_72.append(all_files[i])\n",
    "    if file[\"ind_73\"][0]==1:\n",
    "        A_73.append(all_files[i])\n",
    "    if file[\"ind_74\"][0]==1:\n",
    "        A_74.append(all_files[i])\n",
    "    if file[\"ind_75\"][0]==1:\n",
    "        A_75.append(all_files[i])\n",
    "    if file[\"ind_76\"][0]==1:\n",
    "        A_76.append(all_files[i])\n",
    "    if file[\"ind_78\"][0]==1:\n",
    "        A_78.append(all_files[i])\n",
    "    if file[\"ind_79\"][0]==1:\n",
    "        A_79.append(all_files[i])\n",
    "    if file[\"ind_80\"][0]==1:\n",
    "        A_80.append(all_files[i])\n",
    "    if file[\"ind_81\"][0]==1:\n",
    "        A_81.append(all_files[i])\n",
    "    if file[\"ind_82\"][0]==1:\n",
    "        A_82.append(all_files[i])\n",
    "    if file[\"ind_83\"][0]==1:\n",
    "        A_83.append(all_files[i])\n",
    "    if file[\"ind_85\"][0]==1:\n",
    "        A_85.append(all_files[i])\n",
    "    if file[\"ind_86\"][0]==1:\n",
    "        A_86.append(all_files[i])\n",
    "    if file[\"ind_87\"][0]==1:\n",
    "        A_87.append(all_files[i])\n",
    "    if file[\"ind_89\"][0]==1:\n",
    "        A_89.append(all_files[i])\n",
    "    if file[\"ind_90\"][0]==1:\n",
    "        A_90.append(all_files[i])\n",
    "    if file[\"ind_91\"][0]==1:\n",
    "        A_91.append(all_files[i])\n",
    "    if file[\"ind_92\"][0]==1:\n",
    "        A_92.append(all_files[i])\n",
    "    if file[\"ind_93\"][0]==1:\n",
    "        A_93.append(all_files[i])\n",
    "    if file[\"ind_94\"][0]==1:\n",
    "        A_94.append(all_files[i])\n",
    "    if file[\"ind_95\"][0]==1:\n",
    "        A_95.append(all_files[i])\n",
    "    if file[\"ind_96\"][0]==1:\n",
    "        A_96.append(all_files[i])\n",
    "    if file[\"ind_97\"][0]==1:\n",
    "        A_97.append(all_files[i])\n",
    "    if file[\"ind_99\"][0]==1:\n",
    "        A_99.append(all_files[i])\n",
    "    \n",
    "##### Determine the number of firms by industry        \n",
    "    \n",
    "print(len(A_0),len(A_10),len(A_11),len(A_12),len(A_13),len(A_14),len(A_15),len(A_16),len(A_17),len(A_18),\n",
    "     len(A_19),len(A_20),len(A_21),len(A_22),len(A_23),len(A_24),len(A_25),len(A_26),len(A_27),len(A_28),len(A_29),len(A_30),\n",
    "     len(A_31),len(A_32),len(A_33),len(A_34),len(A_35),len(A_36),len(A_37),len(A_38),len(A_39),len(A_40),len(A_41),len(A_42),\n",
    "     len(A_43),len(A_44),len(A_45),len(A_46),len(A_47),len(A_48),len(A_49),len(A_50),len(A_51),len(A_52),len(A_53),len(A_54),\n",
    "     len(A_66),len(A_55),len(A_56),len(A_57),len(A_58),len(A_59),len(A_60),len(A_61),len(A_62),len(A_63),len(A_64),len(A_65),\n",
    "     len(A_67),len(A_70),len(A_71),len(A_72),len(A_73),len(A_74),len(A_75),len(A_76),len(A_78),len(A_79),len(A_80),len(A_81),\n",
    "     len(A_82),len(A_83),len(A_85),len(A_86),len(A_87),len(A_89),len(A_90),len(A_91),len(A_92),len(A_93),len(A_94),len(A_95),\n",
    "     len(A_96),len(A_97),len(A_99))       \n",
    "        \n",
    "#######> Only 63 out of 85 industries contain firms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6cc08ad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### Aggregate the firms in each industry: lag firm characteristics and the size of the firm by one month to avoid any look-ahead bias.\n",
    "############################################ Then, compute the weighted average firm characteristics and the weighted average excess firm-level return as follows:\n",
    "############################################ multiply each lagged  firm characteristic by the corresponding lagged size of the firm and compute the weighted average firm characteristics. Mutiply the excess firm-level return by the lagged size of the firm and compute the weighted average excess firm-level return.\n",
    "\n",
    "#############################################################################################################################################################################################################################################################################################################################\n",
    "\n",
    "\n",
    "## ind_10\n",
    "B_10=[]\n",
    "for i in range(14):\n",
    "    file=pd.read_csv(A_10[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for i in range(87):\n",
    "        df[df.columns[i]]=df.iloc[:,i]*df.iloc[:,10]\n",
    "        B_10.append(df)\n",
    "C_10=[]\n",
    "i=0\n",
    "while i <= 1131:\n",
    "    a=B_10[i]\n",
    "    C_10.append(a)\n",
    "    i=i+87\n",
    "S_10=sum(C_10).fillna(0)\n",
    "S_10=S_10.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_12\n",
    "file=pd.read_csv(A_12[0],index_col='date')\n",
    "file=file.fillna(0)\n",
    "file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "y=file.loc[:,'excess_log_return']\n",
    "x=file.drop(['excess_log_return'],axis=1)\n",
    "x=x.shift(periods=1)\n",
    "C_12=pd.concat([y,x],axis=1)\n",
    "S_12=C_12.fillna(0)\n",
    "S_12=S_12.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_13\n",
    "B_13=[]\n",
    "for i in range(52):\n",
    "    file=pd.read_csv(A_13[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for i in range(87):\n",
    "        df[df.columns[i]]=df.iloc[:,i]*df.iloc[:,10]\n",
    "        B_13.append(df)\n",
    "C_13=[]\n",
    "i=0\n",
    "while i <= 4437:\n",
    "    a=B_13[i]\n",
    "    C_13.append(a)\n",
    "    i=i+87\n",
    "S_13=sum(C_13).fillna(0)\n",
    "S_13=S_13.replace([np.inf, -np.inf], 0)  \n",
    "\n",
    "## ind_14\n",
    "B_14=[]\n",
    "for i in range(3):\n",
    "    file=pd.read_csv(A_14[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_14.append(df)\n",
    "C_14=[]\n",
    "i=0\n",
    "while i <= 174:\n",
    "    a=B_14[i]\n",
    "    C_14.append(a)\n",
    "    i=i+87 \n",
    "S_14=sum(C_14).fillna(0)\n",
    "S_14=S_14.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_15\n",
    "B_15=[]\n",
    "for i in range(13):\n",
    "    file=pd.read_csv(A_15[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_15.append(df)\n",
    "C_15=[]\n",
    "i=0\n",
    "while i <= 1044:\n",
    "    a=B_15[i]\n",
    "    C_15.append(a)\n",
    "    i=i+87 \n",
    "S_15=sum(C_15).fillna(0)\n",
    "S_15=S_15.replace([np.inf, -np.inf], 0) \n",
    "\n",
    "## ind_16\n",
    "B_16=[]\n",
    "for i in range(12):\n",
    "    file=pd.read_csv(A_16[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_16.append(df)\n",
    "C_16=[]\n",
    "i=0\n",
    "while i <= 957:\n",
    "    a=B_16[i]\n",
    "    C_16.append(a)\n",
    "    i=i+87\n",
    "S_16=sum(C_16).fillna(0)\n",
    "S_16=S_16.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_17\n",
    "B_17=[]\n",
    "for i in range(5):\n",
    "    file=pd.read_csv(A_17[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_17.append(df)\n",
    "C_17=[]\n",
    "i=0\n",
    "while i <= 348:\n",
    "    a=B_17[i]\n",
    "    C_17.append(a)\n",
    "    i=i+87\n",
    "S_17=sum(C_17).fillna(0)\n",
    "S_17=S_17.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_20\n",
    "B_20=[]\n",
    "for i in range(41):\n",
    "    file=pd.read_csv(A_20[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_20.append(df)\n",
    "C_20=[]\n",
    "i=0\n",
    "while i <= 3480:\n",
    "    a=B_20[i]\n",
    "    C_20.append(a)\n",
    "    i=i+87\n",
    "S_20=sum(C_20).fillna(0)\n",
    "S_20=S_20.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_21\n",
    "B_21=[]\n",
    "for i in range(2):\n",
    "    file=pd.read_csv(A_21[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_21.append(df)\n",
    "C_21=[]\n",
    "i=0\n",
    "while i <=87:\n",
    "    a=B_21[i]\n",
    "    C_21.append(a)\n",
    "    i=i+87\n",
    "S_21=sum(C_21).fillna(0)\n",
    "S_21=S_21.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_22\n",
    "B_22=[]\n",
    "for i in range(10):\n",
    "    file=pd.read_csv(A_22[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_22.append(df)\n",
    "C_22=[]\n",
    "i=0\n",
    "while i <=783:\n",
    "    a=B_22[i]\n",
    "    C_22.append(a)\n",
    "    i=i+87\n",
    "S_22=sum(C_22).fillna(0)\n",
    "S_22=S_22.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_23\n",
    "B_23=[]\n",
    "for i in range(11):\n",
    "    file=pd.read_csv(A_23[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_23.append(df)\n",
    "C_23=[]\n",
    "i=0\n",
    "while i <=870:\n",
    "    a=B_23[i]\n",
    "    C_23.append(a)\n",
    "    i=i+87\n",
    "S_23=sum(C_23).fillna(0)\n",
    "S_23=S_23.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_24\n",
    "B_24=[]\n",
    "for i in range(5):\n",
    "    file=pd.read_csv(A_24[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_24.append(df)\n",
    "C_24=[]\n",
    "i=0\n",
    "while i <=348:\n",
    "    a=B_24[i]\n",
    "    C_24.append(a)\n",
    "    i=i+87\n",
    "S_24=sum(C_24).fillna(0)\n",
    "S_24=S_24.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_25\n",
    "B_25=[]\n",
    "for i in range(16):\n",
    "    file=pd.read_csv(A_25[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_25.append(df)\n",
    "C_25=[]\n",
    "i=0\n",
    "while i <=1305:\n",
    "    a=B_25[i]\n",
    "    C_25.append(a)\n",
    "    i=i+87\n",
    "S_25=sum(C_25).fillna(0)\n",
    "S_25=S_25.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_26\n",
    "B_26=[]\n",
    "for i in range(16):\n",
    "    file=pd.read_csv(A_26[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_26.append(df)\n",
    "C_26=[]\n",
    "i=0\n",
    "while i <=1305:\n",
    "    a=B_26[i]\n",
    "    C_26.append(a)\n",
    "    i=i+87\n",
    "S_26=sum(C_26).fillna(0)\n",
    "S_26=S_26.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_27\n",
    "B_27=[]\n",
    "for i in range(23):\n",
    "    file=pd.read_csv(A_27[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_27.append(df)\n",
    "C_27=[]\n",
    "i=0\n",
    "while i <=1914:\n",
    "    a=B_27[i]\n",
    "    C_27.append(a)\n",
    "    i=i+87\n",
    "S_27=sum(C_27).fillna(0)\n",
    "S_27=S_27.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_28\n",
    "B_28=[]\n",
    "for i in range(189):\n",
    "    file=pd.read_csv(A_28[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_28.append(df)\n",
    "C_28=[]\n",
    "i=0\n",
    "while i <=16356:\n",
    "    a=B_28[i]\n",
    "    C_28.append(a)\n",
    "    i=i+87\n",
    "S_28=sum(C_28).fillna(0)\n",
    "S_28=S_28.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_29\n",
    "B_29=[]\n",
    "for i in range(11):\n",
    "    file=pd.read_csv(A_29[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_29.append(df)\n",
    "C_29=[]\n",
    "i=0\n",
    "while i <=870:\n",
    "    a=B_29[i]\n",
    "    C_29.append(a)\n",
    "    i=i+87\n",
    "S_29=sum(C_29).fillna(0)\n",
    "S_29=S_29.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_30\n",
    "B_30=[]\n",
    "for i in range(14):\n",
    "    file=pd.read_csv(A_30[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_30.append(df)\n",
    "C_30=[]\n",
    "i=0\n",
    "while i <=1131:\n",
    "    a=B_30[i]\n",
    "    C_30.append(a)\n",
    "    i=i+87\n",
    "S_30=sum(C_30).fillna(0)\n",
    "S_30=S_30.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_31\n",
    "B_31=[]\n",
    "for i in range(8):\n",
    "    file=pd.read_csv(A_31[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_31.append(df)\n",
    "C_31=[]\n",
    "i=0\n",
    "while i <=609:\n",
    "    a=B_31[i]\n",
    "    C_31.append(a)\n",
    "    i=i+87\n",
    "S_31=sum(C_31).fillna(0)\n",
    "S_31=S_31.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_32\n",
    "B_32=[]\n",
    "for i in range(9):\n",
    "    file=pd.read_csv(A_32[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_32.append(df)\n",
    "C_32=[]\n",
    "i=0\n",
    "while i <=696:\n",
    "    a=B_32[i]\n",
    "    C_32.append(a)\n",
    "    i=i+87\n",
    "S_32=sum(C_32).fillna(0)\n",
    "S_32=S_32.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_33\n",
    "B_33=[]\n",
    "for i in range(24):\n",
    "    file=pd.read_csv(A_33[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_33.append(df)\n",
    "C_33=[]\n",
    "i=0\n",
    "while i <=2001:\n",
    "    a=B_33[i]\n",
    "    C_33.append(a)\n",
    "    i=i+87\n",
    "S_33=sum(C_33).fillna(0)\n",
    "S_33=S_33.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_34\n",
    "B_34=[]\n",
    "for i in range(32):\n",
    "    file=pd.read_csv(A_34[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_34.append(df)\n",
    "C_34=[]\n",
    "i=0\n",
    "while i <=2697:\n",
    "    a=B_34[i]\n",
    "    C_34.append(a)\n",
    "    i=i+87\n",
    "S_34=sum(C_34).fillna(0)\n",
    "S_34=S_34.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_35\n",
    "B_35=[]\n",
    "for i in range(104):\n",
    "    file=pd.read_csv(A_35[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_35.append(df)\n",
    "C_35=[]\n",
    "i=0\n",
    "while i <=8961:\n",
    "    a=B_35[i]\n",
    "    C_35.append(a)\n",
    "    i=i+87\n",
    "S_35=sum(C_35).fillna(0)\n",
    "S_35=S_35.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_36\n",
    "B_36=[]\n",
    "for i in range(135):\n",
    "    file=pd.read_csv(A_36[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_36.append(df)\n",
    "C_36=[]\n",
    "i=0\n",
    "while i <=11658:\n",
    "    a=B_36[i]\n",
    "    C_36.append(a)\n",
    "    i=i+87\n",
    "S_36=sum(C_36).fillna(0)\n",
    "S_36=S_36.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_37\n",
    "B_37=[]\n",
    "for i in range(55):\n",
    "    file=pd.read_csv(A_37[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_37.append(df)\n",
    "C_37=[]\n",
    "i=0\n",
    "while i <=4698:\n",
    "    a=B_37[i]\n",
    "    C_37.append(a)\n",
    "    i=i+87\n",
    "S_37=sum(C_37).fillna(0)\n",
    "S_37=S_37.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_38\n",
    "B_38=[]\n",
    "for i in range(117):\n",
    "    file=pd.read_csv(A_38[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_38.append(df)\n",
    "C_38=[]\n",
    "i=0\n",
    "while i <=10092:\n",
    "    a=B_38[i]\n",
    "    C_38.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_38=sum(C_38).fillna(0)\n",
    "S_38=S_38.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_39\n",
    "B_39=[]\n",
    "for i in range(17):\n",
    "    file=pd.read_csv(A_39[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_39.append(df)\n",
    "C_39=[]\n",
    "i=0\n",
    "while i <=1392:\n",
    "    a=B_39[i]\n",
    "    C_39.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_39=sum(C_39).fillna(0)\n",
    "S_39=S_39.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_40\n",
    "B_40=[]\n",
    "for i in range(4):\n",
    "    file=pd.read_csv(A_40[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_40.append(df)\n",
    "C_40=[]\n",
    "i=0\n",
    "while i <=261:\n",
    "    a=B_40[i]\n",
    "    C_40.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_40=sum(C_40).fillna(0)\n",
    "S_40=S_40.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_42\n",
    "B_42=[]\n",
    "for i in range(15):\n",
    "    file=pd.read_csv(A_42[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_42.append(df)\n",
    "C_42=[]\n",
    "i=0\n",
    "while i <= 1218:\n",
    "    a=B_42[i]\n",
    "    C_42.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_42=sum(C_42).fillna(0)\n",
    "S_42=S_42.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_44\n",
    "B_44=[]\n",
    "for i in range(4):\n",
    "    file=pd.read_csv(A_44[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_44.append(df)\n",
    "C_44=[]\n",
    "i=0\n",
    "while i <= 261:\n",
    "    a=B_44[i]\n",
    "    C_44.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_44=sum(C_44).fillna(0)\n",
    "S_44=S_44.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_45\n",
    "B_45=[]\n",
    "for i in range(13):\n",
    "    file=pd.read_csv(A_45[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_45.append(df)\n",
    "C_45=[]\n",
    "i=0\n",
    "while i <= 1044:\n",
    "    a=B_45[i]\n",
    "    C_45.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_45=sum(C_45).fillna(0)\n",
    "S_45=S_45.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_47\n",
    "B_47=[]\n",
    "for i in range(5):\n",
    "    file=pd.read_csv(A_47[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_47.append(df)\n",
    "C_47=[]\n",
    "i=0\n",
    "while i <= 348:\n",
    "    a=B_47[i]\n",
    "    C_47.append(a)\n",
    "    i=i+87\n",
    "    \n",
    "S_47=sum(C_47).fillna(0)\n",
    "S_47=S_47.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_48\n",
    "B_48=[]\n",
    "for i in range(48):\n",
    "    file=pd.read_csv(A_48[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_48.append(df)\n",
    "C_48=[]\n",
    "i=0\n",
    "while i <= 4089:\n",
    "    a=B_48[i]\n",
    "    C_48.append(a)\n",
    "    i=i+87\n",
    "    \n",
    "S_48=sum(C_48).fillna(0)\n",
    "S_48=S_48.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_49\n",
    "B_49=[]\n",
    "for i in range(74):\n",
    "    file=pd.read_csv(A_49[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_49.append(df)\n",
    "C_49=[]\n",
    "i=0\n",
    "while i <= 6351:\n",
    "    a=B_49[i]\n",
    "    C_49.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_49=sum(C_49).fillna(0)\n",
    "S_49=S_49.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_50\n",
    "B_50=[]\n",
    "for i in range(54):\n",
    "    file=pd.read_csv(A_50[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_50.append(df)\n",
    "C_50=[]\n",
    "i=0\n",
    "while i <= 4611:\n",
    "    a=B_50[i]\n",
    "    C_50.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_50=sum(C_50).fillna(0)\n",
    "S_50=S_50.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_51\n",
    "B_51=[]\n",
    "for i in range(26):\n",
    "    file=pd.read_csv(A_51[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_51.append(df)\n",
    "C_51=[]\n",
    "i=0\n",
    "while i <= 2175:\n",
    "    a=B_51[i]\n",
    "    C_51.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_51=sum(C_51).fillna(0)\n",
    "S_51=S_51.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_52\n",
    "B_52=[]\n",
    "for i in range(6):\n",
    "    file=pd.read_csv(A_52[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_52.append(df)\n",
    "C_52=[]\n",
    "i=0\n",
    "while i <= 435:\n",
    "    a=B_52[i]\n",
    "    C_52.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_52=sum(C_52).fillna(0)\n",
    "S_52=S_52.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_53\n",
    "B_53=[]\n",
    "for i in range(12):\n",
    "    file=pd.read_csv(A_53[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_53.append(df)\n",
    "C_53=[]\n",
    "i=0\n",
    "while i <= 957:\n",
    "    a=B_53[i]\n",
    "    C_53.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_53=sum(C_53).fillna(0)\n",
    "S_53=S_53.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_54\n",
    "B_54=[]\n",
    "for i in range(5):\n",
    "    file=pd.read_csv(A_54[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_54.append(df)\n",
    "C_54=[]\n",
    "i=0\n",
    "while i <= 348:\n",
    "    a=B_54[i]\n",
    "    C_54.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_54=sum(C_54).fillna(0)\n",
    "S_54=S_54.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_55\n",
    "B_55=[]\n",
    "for i in range(15):\n",
    "    file=pd.read_csv(A_55[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_55.append(df)\n",
    "C_55=[]\n",
    "i=0\n",
    "while i <= 1218:\n",
    "    a=B_55[i]\n",
    "    C_55.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_55=sum(C_55).fillna(0)\n",
    "S_55=S_55.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_56\n",
    "B_56=[]\n",
    "for i in range(25):\n",
    "    file=pd.read_csv(A_56[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_56.append(df)\n",
    "C_56=[]\n",
    "i=0\n",
    "while i <= 2088:\n",
    "    a=B_56[i]\n",
    "    C_56.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_56=sum(C_56).fillna(0)\n",
    "S_56=S_56.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_57\n",
    "B_57=[]\n",
    "for i in range(10):\n",
    "    file=pd.read_csv(A_57[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_57.append(df)\n",
    "C_57=[]\n",
    "i=0\n",
    "while i <= 783:\n",
    "    a=B_57[i]\n",
    "    C_57.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_57=sum(C_57).fillna(0)\n",
    "S_57=S_57.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_58\n",
    "B_58=[]\n",
    "for i in range(26):\n",
    "    file=pd.read_csv(A_58[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_58.append(df)\n",
    "C_58=[]\n",
    "i=0\n",
    "while i <= 2175:\n",
    "    a=B_58[i]\n",
    "    C_58.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_58=sum(C_58).fillna(0)\n",
    "S_58=S_58.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_59\n",
    "B_59=[]\n",
    "for i in range(27):\n",
    "    file=pd.read_csv(A_59[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_59.append(df)\n",
    "C_59=[]\n",
    "i=0\n",
    "while i <= 2262:\n",
    "    a=B_59[i]\n",
    "    C_59.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_59=sum(C_59).fillna(0)\n",
    "S_59=S_59.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_60\n",
    "B_60=[]\n",
    "for i in range(204):\n",
    "    file=pd.read_csv(A_60[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_60.append(df)\n",
    "C_60=[]\n",
    "i=0\n",
    "while i <= 17661:\n",
    "    a=B_60[i]\n",
    "    C_60.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_60=sum(C_60).fillna(0)\n",
    "S_60=S_60.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_61\n",
    "B_61=[]\n",
    "for i in range(22):\n",
    "    file=pd.read_csv(A_61[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_61.append(df)\n",
    "C_61=[]\n",
    "i=0\n",
    "while i <= 1827:\n",
    "    a=B_61[i]\n",
    "    C_61.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_61=sum(C_61).fillna(0)\n",
    "S_61=S_61.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_62\n",
    "B_62=[]\n",
    "for i in range(32):\n",
    "    file=pd.read_csv(A_62[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_62.append(df)\n",
    "C_62=[]\n",
    "i=0\n",
    "while i <= 2697:\n",
    "    a=B_62[i]\n",
    "    C_62.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_62=sum(C_62).fillna(0)\n",
    "S_62=S_62.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_63\n",
    "B_63=[]\n",
    "for i in range(63):\n",
    "    file=pd.read_csv(A_63[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_63.append(df)\n",
    "C_63=[]\n",
    "i=0\n",
    "while i <= 5394:\n",
    "    a=B_63[i]\n",
    "    C_63.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_63=sum(C_63).fillna(0)\n",
    "S_63=S_63.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_64\n",
    "B_64=[]\n",
    "for i in range(6):\n",
    "    file=pd.read_csv(A_64[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_64.append(df)\n",
    "C_64=[]\n",
    "i=0\n",
    "while i <= 435:\n",
    "    a=B_64[i]\n",
    "    C_64.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_64=sum(C_64).fillna(0)\n",
    "S_64=S_64.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_65\n",
    "B_65=[]\n",
    "for i in range(7):\n",
    "    file=pd.read_csv(A_65[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_65.append(df)\n",
    "C_65=[]\n",
    "i=0\n",
    "while i <= 522:\n",
    "    a=B_65[i]\n",
    "    C_65.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_65=sum(C_65).fillna(0)\n",
    "S_65=S_65.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_67\n",
    "B_67=[]\n",
    "for i in range(50):\n",
    "    file=pd.read_csv(A_67[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_67.append(df)\n",
    "C_67=[]\n",
    "i=0\n",
    "while i <= 4263:\n",
    "    a=B_67[i]\n",
    "    C_67.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_67=sum(C_67).fillna(0)\n",
    "S_67=S_67.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_70\n",
    "B_70=[]\n",
    "for i in range(11):\n",
    "    file=pd.read_csv(A_70[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_70.append(df)\n",
    "C_70=[]\n",
    "i=0\n",
    "while i <= 870:\n",
    "    a=B_70[i]\n",
    "    C_70.append(a)\n",
    "    i=i+87\n",
    "S_70=sum(C_70).fillna(0)\n",
    "S_70=S_70.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_72\n",
    "B_72=[]\n",
    "for i in range(4):\n",
    "    file=pd.read_csv(A_72[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_72.append(df)\n",
    "C_72=[]\n",
    "i=0\n",
    "while i <= 261:\n",
    "    a=B_72[i]\n",
    "    C_72.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_72=sum(C_72).fillna(0)\n",
    "S_72=S_72.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_73\n",
    "B_73=[]\n",
    "for i in range(178):\n",
    "    file=pd.read_csv(A_73[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_73.append(df)\n",
    "C_73=[]\n",
    "i=0\n",
    "while i <= 15399:\n",
    "    a=B_73[i]\n",
    "    C_73.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_73=sum(C_73).fillna(0)\n",
    "S_73=S_73.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_75\n",
    "B_75=[]\n",
    "for i in range(3):\n",
    "    file=pd.read_csv(A_75[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_75.append(df)\n",
    "C_75=[]\n",
    "i=0\n",
    "while i <= 174:\n",
    "    a=B_75[i]\n",
    "    C_75.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_75=sum(C_75).fillna(0)\n",
    "S_75=S_75.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_78\n",
    "B_78=[]\n",
    "for i in range(5):\n",
    "    file=pd.read_csv(A_78[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_78.append(df)\n",
    "C_78=[]\n",
    "i=0\n",
    "while i <= 348:\n",
    "    a=B_78[i]\n",
    "    C_78.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_78=sum(C_78).fillna(0)\n",
    "S_78=S_78.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_79\n",
    "B_79=[]\n",
    "for i in range(16):\n",
    "    file=pd.read_csv(A_79[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_79.append(df)\n",
    "C_79=[]\n",
    "i=0\n",
    "while i <= 1305:\n",
    "    a=B_79[i]\n",
    "    C_79.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_79=sum(C_79).fillna(0)\n",
    "S_79=S_79.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_80\n",
    "B_80=[]\n",
    "for i in range(29):\n",
    "    file=pd.read_csv(A_80[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_80.append(df)\n",
    "C_80=[]\n",
    "i=0\n",
    "while i <= 2436:\n",
    "    a=B_80[i]\n",
    "    C_80.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_80=sum(C_80).fillna(0)\n",
    "S_80=S_80.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_82\n",
    "B_82=[]\n",
    "for i in range(11):\n",
    "    file=pd.read_csv(A_82[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_82.append(df)\n",
    "C_82=[]\n",
    "i=0\n",
    "while i <= 870:\n",
    "    a=B_82[i]\n",
    "    C_82.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_82=sum(C_82).fillna(0)\n",
    "S_82=S_82.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_83\n",
    "B_83=[]\n",
    "for i in range(3):\n",
    "    file=pd.read_csv(A_83[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_83.append(df)\n",
    "C_83=[]\n",
    "i=0\n",
    "while i <= 174:\n",
    "    a=B_83[i]\n",
    "    C_83.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_83=sum(C_83).fillna(0)\n",
    "S_83=S_83.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_87\n",
    "B_87=[]\n",
    "for i in range(54):\n",
    "    file=pd.read_csv(A_87[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_87.append(df)\n",
    "C_87=[]\n",
    "i=0\n",
    "while i <= 4611:\n",
    "    a=B_87[i]\n",
    "    C_87.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_87=sum(C_87).fillna(0)\n",
    "S_87=S_87.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_89\n",
    "file=pd.read_csv(A_89[0],index_col='date')\n",
    "file=file.fillna(0)\n",
    "file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "y=file.loc[:,'excess_log_return']\n",
    "x=file.drop(['excess_log_return'],axis=1)\n",
    "x=x.shift(periods=1)\n",
    "C_89=pd.concat([y,x],axis=1)\n",
    "S_89=C_89.fillna(0)\n",
    "S_89=S_89.replace([np.inf, -np.inf], 0)\n",
    "\n",
    "## ind_99\n",
    "B_99=[]\n",
    "for i in range(19):\n",
    "    file=pd.read_csv(A_99[i],index_col='date')\n",
    "    file=file.fillna(0)\n",
    "    file=file.drop(file.loc[:,'ind_0':'ind_Z'],axis=1)\n",
    "    y=file.loc[:,'excess_log_return']\n",
    "    x=file.drop(['excess_log_return'],axis=1)\n",
    "    x=x.shift(periods=1)\n",
    "    df=pd.concat([y,x],axis=1)\n",
    "    df=df.fillna(0)\n",
    "    for j in range(87):\n",
    "        df[df.columns[j]]=df.iloc[:,j]*df.iloc[:,10]\n",
    "        B_99.append(df)\n",
    "C_99=[]\n",
    "i=0\n",
    "while i <= 1566:\n",
    "    a=B_99[i]\n",
    "    C_99.append(a)\n",
    "    i=i+87\n",
    "\n",
    "S_99=sum(C_99).fillna(0)\n",
    "S_99=S_99.replace([np.inf, -np.inf], 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2eba4725",
   "metadata": {},
   "outputs": [],
   "source": [
    "############################################################ In each industry, check wether the aggregated firm characteristics are stationary##################################################################################################################################################################################\n",
    "\n",
    " \n",
    "########## ADF test function to check for stationary data:\n",
    "\n",
    "def Augmented_Dickey_Fuller_Test_func(series,column_name):\n",
    "    print(f'Results of Dickey-Fuller Test for column:{column_name}')\n",
    "    df_concattest=adfuller(series,autolag='AIC')\n",
    "    df_concatoutput=pd.Series(df_concattest[0:4],index=['Test Statistic','p-value','No Lags Used','Number of Observations Used'])\n",
    "    for key, value in df_concattest[4].items():\n",
    "        df_concatoutput['Critical Value (%s)'%key]=value\n",
    "        print(df_concatoutput)\n",
    "        if df_concattest[1]<=0.05:\n",
    "            print(\"Conclusion:====>\")\n",
    "            print(\"Reject the null hypothesis\")\n",
    "            print(\"Data is stationary\")\n",
    "        else:\n",
    "            print(\"Conclusion====>\")\n",
    "            print(\"Fail to reject the null hypothesis\")\n",
    "            print(\"Data is non-stationary\")\n",
    "            \n",
    "\n",
    "###### Put the industries in the list to facilate the test\n",
    "\n",
    "list=[S_10,S_12,S_13,S_14,S_15,S_16,S_17,S_20,S_21,S_22,S_23,S_24,S_25,S_26,S_27,S_28,S_29,\n",
    "     S_30,S_31,S_32,S_33,S_34,S_35,S_36,S_37,S_38,S_39,S_40,S_42,S_44,S_45,S_47,S_48,S_49,S_50,\n",
    "     S_51,S_52,S_53,S_54,S_55,S_56,S_57,S_58,S_59,S_60,S_61,S_62,S_63,S_64,S_65,S_67,S_70,S_72,\n",
    "     S_73,S_75,S_78,S_79,S_80,S_82,S_83,S_87,S_89,S_99]            \n",
    "\n",
    "######## ADF test\n",
    "\n",
    "#>\n",
    "a=[list[0],list[1],list[2],list[3],list[4],list[5],list[6],list[7],list[8],list[9]]\n",
    "\n",
    "for i in range(10):\n",
    "    for name, column in a[i][a[i].columns].iteritems():\n",
    "        Augmented_Dickey_Fuller_Test_func(a[i][name],name)\n",
    "        print('\\n')\n",
    "\n",
    "# For each industry, get non-stationary variables\n",
    "\n",
    "a_0=['turn','RF','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','coskew','size','max','log_Mkt-RF_lag1','log_Mkt-RF_lag2',\n",
    "    'log_Mkt-RF_lag3','log_Mkt-RF_lag4','r_sq','aeavol','bm','cash','rd_mve','chinv','depr','gma','lev','quick','sp','acc','stdacc',\n",
    "    'stdcf','agr','oa','ol','noa','chatq','chcshoq','ibq_adj','currat','dy','operprof','saleinv','rd_sale','roaq','roavol','salecash',\n",
    "    'salerec','tang','O-score','sfe','disp']\n",
    "a_1=['beta','idiovol','size','max','r_sq','bm','rd_mve','cashpr','ep','lev','acc','stdacc','stdcf','cfp','chcapx','ol','noa',\n",
    "    'cfpq','chcshoq','dy','operprof','saleinv','rd_sale','roaq','roavol','rsup','salerec','O-score','sue','sfe','chnanalyst']\n",
    "a_2=['turn','baspread','beta','idiovol','size','max','r_sq','aeavol','bm','cash','rd_mve','depr','gma','lev','quick','pchquick',\n",
    "    'sp','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','ibq_adj','currat','dy','pchcurrat','saleinv','rd_sale','roavol',\n",
    "    'salerec','tang','chnanalyst','disp']\n",
    "a_3=['illiq','RF','beta','idiovol','coskew','mom','chmom','r_sq','gammaps','cashpr','depr','lev','sp','stdacc','stdcf','chcapx',\n",
    "    'pctacc','dy','roavol','rsup','chfeps','chnanalyst']\n",
    "a_4=['turn','baspread','beta','idiovol','size','mom','chmom','max','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','chatoia',\n",
    "    'depr','ep','gma','quick','pchquick','sp','acc','stdacc','stdcf','cfp','agr','oa','ol','noa','chatq','cfpq','currat','operprof',\n",
    "    'pchcurrat','saleinv','rd_sale','roaq','roavol','rsup','salecash','chsaleq','tang','O-score','sfe']\n",
    "a_5=['RF','beta','idiovol','size','mom','r_sq','aeavol','bm','cash','rd_mve','cashdebt','cashpr','chinv','depr','gma','lev',\n",
    "    'quick','sp','acc','stdacc','stdcf','cfp','oa','ol','noa','chatq','cfpq','currat','pchgm_pchsale','saleinv','rd_sale','roavol',\n",
    "    'rsup','salecash','salerec','tang','chfeps','sfe','chnanalyst']\n",
    "a_6=['turn','beta','idiovol','coskew','size','rev2','rev3','rev4','mom','mom1m','chmom','r_sq','gammaps','cash','rd_mve','chatoia',\n",
    "    'depr','ep','gma','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','dy','operprof','pchgm_pchsale','pchsale_pchinvt',\n",
    "    'pchsale_pchrect','pchsale_pchxsga','saleinv','pchsaleinv','rd_sale','salecash','salerec','chsaleq','chfeps','sfe']\n",
    "a_7=['turn','RF','baspread','beta','idiovol','size','mom','mom1m','chmom','max','r_sq','aeavol','bm','cash','rd_mve','cashdebt','cashpr',\n",
    "    'chatoia','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','chcshoq','currat','dy','operprof',\n",
    "    'rd_sale','roaq','roavol','rsup','chsaleq','tang','O-score']\n",
    "a_8=['turn','RF','beta','idiovol','coskew','mom','chmom','r_sq','gammaps','aeavol','bm','lev','quick','sp','stdacc','stdcf','cfp','ol','noa',\n",
    "    'cfpq','chcshoq','saleinv','roavol','rsup','chfeps','disp']\n",
    "a_9=['turn','baspread','beta','idiovol','coskew','size','mom','chmom','max','r_sq','gammaps','bm','cash','cashdebt','cashpr','chatoia','gma','lev','lgr',\n",
    "    'sp','stdacc','stdcf','cfp','chcapx','oa','ol','noa','grnoa','cfpq','operprof','saleinv','roavol','salecash','salerec','chsaleq','tang',\n",
    "    'O-score','sfe']\n",
    "\n",
    "#>\n",
    "a=[list[10],list[11],list[12],list[13],list[14],list[15],list[16],list[17],list[18],list[19]]\n",
    "\n",
    "for i in range(10):\n",
    "    for name, column in a[i][a[i].columns].iteritems():\n",
    "        Augmented_Dickey_Fuller_Test_func(a[i][name],name)\n",
    "        print('\\n')\n",
    "\n",
    "# For each industry, get non-stationary variables\n",
    "        \n",
    "a_10=['turn','baspread','beta','idiovol','size','mom','max','r_sq','aeavol','bm','cash','rd_mve','cashdebt','depr','gma','lev','quick',\n",
    "     'sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','egr','operprof','pchsale_pchxsga','saleinv','rd_sale','roaq','roavol',\n",
    "     'roeq','salerec','chsaleq','tang','O-score']\n",
    "a_11=['illiq','baspread','beta','idiovol','coskew','size','mom','chmom','max','r_sq','gammaps','bm','rd_mve','cashdebt','cashpr','chatoia','gma','acc',\n",
    "     'stdacc','stdcf','cfp','cfpq','dy','pchgm_pchsale','saleinv','pchsaleinv','rd_sale','roaq','rsup','salerec','sue','chfeps','sfe']\n",
    "a_12=['turn','RF','baspread','beta','idiovol','log_Mkt-RF^2','coskew','size','mom','mom1m','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr','chatoia','chinv',\n",
    "     'depr','ep','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','pchsale_pchinvt','pchsale_pchxsga',\n",
    "     'saleinv','pchsaleinv','rd_sale','roaq','roavol','rsup','salecash','salerec','chsaleq','tang','O-score','sue','chfeps','sfe','chnanalyst','disp']\n",
    "a_13=['turn','RF','baspread','beta','idiovol','log_Mkt-RF^2','coskew','size','mom','mom1m','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr',\n",
    "     'chatoia','depr','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','currat','dy','egr','operprof','saleinv','rd_sale','roavol','roeq',\n",
    "     'rsup','salecash','salerec','tang','O-score','sfe']\n",
    "a_14=['turn','baspread','beta','idiovol','size','mom','max','r_sq','aeavol','bm','cash','rd_mve','cashdebt','depr','gma','lev','quick','sp','acc','stdacc','stdcf',\n",
    "     'cfp','oa','ol','noa','cfpq','currat','dy','pchsale_pchxsga','saleinv','rd_sale','roaq','roavol','salecash','salerec','tang','O-score','disp']\n",
    "a_15=['illiq','turn','baspread','beta','idiovol','size','max','r_sq','aeavol','bm','cash','rd_mve','cashdebt','cashpr','depr','ep','gma','lev','lgr','quick',\n",
    "     'sp','acc','stdacc','stdcf','cfp','agr','chcapx','oa','ol','noa','chatq','cfpq','ibq_adj','chtx','invest','currat','dy','pchsale_pchinvt','rd_sale',\n",
    "     'roaq','roavol','salecash','salerec','tang','O-score','disp']\n",
    "a_16=['turn','beta','idiovol','coskew','size','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashpr','depr','gma','lev','quick','sp',\n",
    "     'acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','operprof','saleinv','roaq','roavol','salecash','salerec','tang','O-score','sfe','disp']\n",
    "a_17=['turn','RF','baspread','beta','idiovol','size','mom','max','r_sq','gammaps','bm','cash','depr','gma','lev','quick','sp','acc','stdacc',\n",
    "     'stdcf','oa','ol','noa','currat','saleinv','pchsaleinv','rd_sale','roavol','tang','sue','chfeps']\n",
    "a_18=['beta','idiovol','coskew','mom','chmom','r_sq','gammaps','bm','rd_mve','cashpr','chatoia','ep','gma','lev','quick','sp','stdacc',\n",
    "     'stdacc','oa','ol','noa','currat','dy','operprof','saleinv','rd_sale','rsup','salecash','salerec','O-score','chfeps']\n",
    "a_19=['RF','beta','idiovol','size','mom','max','r_sq','bm','cash','rd_mve','cashpr','depr','gma','lev','quick','acc','stdacc','cfp','oa','ol','noa','cfpq','currat','saleinv',\n",
    "     'rd_sale','roavol','salerec','tang','chfeps','sfe']\n",
    "\n",
    "#> \n",
    "a=[list[20],list[21],list[22],list[23],list[24],list[25],list[26],list[27],list[28],list[29]]\n",
    "  \n",
    "\n",
    "for i in range(10):\n",
    "    for name, column in a[i][a[i].columns].iteritems():\n",
    "        Augmented_Dickey_Fuller_Test_func(a[i][name],name)\n",
    "        print('\\n')\n",
    "\n",
    "# In each industry, get non-stationary variables\n",
    "\n",
    "a_20=['turn','RF','beta','idiovol','coskew','size','mom','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','depr','gma','lev','quick','sp','stdacc','stdcf','chcapx','oa',\n",
    "     'ol','noa','pctacc','currat','dy','saleinv','rd_sale','roavol','salecash','salerec','tang','O-score']\n",
    "a_21=['turn','beta','idiovol','mom','max','r_sq','cash','cashdebt','depr','gma','quick','stdacc','stdcf','cfp','oa','ol','noa','cfpq','dy','pchsale_pchrect','pchsale_pchxsga','roaq','roavol','roeq',\n",
    "     'salecash','tang','sue','disp']\n",
    "a_22=['turn','beta','idiovol','size','mom','max','r_sq','aeavol','bm','cash','rd_mve','cashdebt','cashpr','depr','gma','lev','quick','sp','acc','stdcf','cfp','chcapx','oa','ol','cfpq','invest','currat','dy','saleinv','rd_sale','roaq','roavol','salerec','tang','O-score','chnanalyst','disp']\n",
    "a_23=['turn','baspread','beta','idiovol','size','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','depr','gma','lev','lgr','quick','pchquick','sp','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','operprof','pchcurrat','saleinv','pchsaleinv',\n",
    "     'roavol','tang','O-score','chnanalyst','disp']\n",
    "a_24=['turn','RF','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','max','r_sq','aeavol','bm','cash','rd_mve','cashdebt','cashpr','chinv','depr','ep','gma','lev','lgr','quick','sp',\n",
    "     'acc','stdacc','stdcf','cfp','agr','chcapx','oa','ol','noa','chatq','cfpq','chcshoq','invest','currat','dy','pchgm_pchsale','pchsale_pchinvt','pchsale_pchrect','pchsale_pchxsga',\n",
    "     'saleinv','rd_sale','roaq','roavol','salecash','salerec','chsaleq','tang','O-score','chfeps','sfe','chnanalyst','disp']\n",
    "a_25=['turn','baspread','beta','idiovol','log_Mkt-RF^2','size','max','log_Mkt-RF_lag4','r_sq','aeavol','bm','cash','rd_mve','cashdebt','cashpr','chatoia','depr','ep','gma',\n",
    "     'lev','lgr','quick','sp','acc','stdacc','stdcf','cfp','agr','oa','ol','noa','chatq','cfpq','currat','dy','pchcurrat','saleinv','rd_sale','roavol','salerec','tang','O-score','disp']\n",
    "a_26=['beta','idiovol','size','mom','max','r_sq','bm','cash','depr','gma','quick','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','currat','dy','operprof','pchdepr','saleinv','rd_sale','roavol','salerec','tang',\n",
    "     'sfe']\n",
    "a_27=['turn','beta','idiovol','coskew','mom','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','chinv','gma','lev','acc','stdacc','stdcf','cfp','agr','chcapx','oa','grnoa','pctacc','chatq','cfpq','invest','currat','dy','rd_sale','roaq','roavol','roeq',\n",
    "     'rsup','salecash','salerec','O-score','chfeps']\n",
    "a_28=['turn','baspread','beta','idiovol','coskew','mom','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','ep','gma','quick','acc','stdacc','stdcf','cfp','ol','noa','cfpq','currat','pchsale_pchinvt','rd_sale','roaq','roeq','tang']\n",
    "a_29=['illiq','turn','beta','idiovol','coskew','size','mom','mom1m','r_sq','gammaps','bm','rd_mve','cashdebt','cashpr','stdacc','stdcf','chcapx','ol','noa','dy','saleinv','rd_sale','roaq','roavol','rsup','salerec','sfe','chnanalyst']\n",
    "\n",
    "#>\n",
    "\n",
    "a=[list[30],list[31],list[32],list[33],list[34],list[35],list[36],list[37],list[38],list[39],list[40],list[41],list[42]]\n",
    "  \n",
    "\n",
    "for i in range(13):\n",
    "    for name, column in a[i][a[i].columns].iteritems():\n",
    "        Augmented_Dickey_Fuller_Test_func(a[i][name],name)\n",
    "        print('\\n')\n",
    "        \n",
    "# In each industry, get non-stationary variables\n",
    "\n",
    "a_30=['illiq','turn','baspread','beta','idiovol','size','mom','chmom','max','r_sq','gammaps','cash','rd_mve','chatoia','depr','gma','lev','quick','pchquick','sp','acc','chcapx','oa','ol','noa','ibq_adj','invest','currat','dy',\n",
    "     'egr','operprof','pchcurrat','pchsale_pchinvt','pchsaleinv','rd_sale','rsup','salerec','chsaleq','tang','O-score']\n",
    "a_31=['illiq','turn','baspread','beta','idiovol','coskew','mom','r_sq','gammaps','cash','rd_mve','cashdebt','chinv','gma','quick','sp','stdacc','stdcf','cfp','oa','ol','noa','cfpq','invest','currat','dy','operprof','pchgm_pchsale','rd_sale',\n",
    "     'salerec','sfe','disp']\n",
    "a_32=['turn','baspread','beta','idiovol','size','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr','depr','gma','lev','quick',\n",
    "     'sp','acc','stdacc','stdcf','cfp','agr','oa','ol','noa','chatq','cfpq','currat','dy','saleinv','roavol','salecash','salerec','tang','O-score','disp']\n",
    "a_33=['RF','beta','idiovol','size','mom','chmom','max','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','depr','gma','lev','quick','sp','acc','stdacc',\n",
    "     'stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','saleinv','rd_sale','roavol','salecash','salerec','tang','chfeps']\n",
    "a_34=['illiq','turn','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','rev1','rev2','mom','mom1m','max','log_Mkt-RF_lag1','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt',\n",
    "     'cashpr','chatoia','chinv','depr','ep','gma','lev','quick','pchquick','sp','acc','stdacc','stdcf','cfp','agr','chcapx','oa','ol','noa',\n",
    "     'chatq','cfpq','ibq_adj','invest','currat','dy','operprof','pchsale_pchxsga','saleinv','rd_sale','roaq','roavol','salecash','salerec','chsaleq','tang','O-score','sue','sfe','disp']\n",
    "a_35=['turn','baspread','beta','idiovol','coskew','size','mom','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','depr','ep','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','chcapx','oa',\n",
    "     'ol','noa','cfpq','currat','dy','egr','operprof','pchcurrat','pchsale_pchxsga','saleinv','rd_sale','roaq','roavol','roeq','salecash','salerec','tang',\n",
    "     'O-score','sue','disp']\n",
    "a_36=['RF','baspread','beta','idiovol','mom','chmom','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr','pchquick','acc','stdacc','stdcf','cfp','chcapx','ol','cfpq','chcshoq','ibq_adj','dy','operprof','pchcurrat','pchsale_pchinvt','pchsale_pchrect','roaq',\n",
    "     'roavol','roeq','rsup','salecash','salerec','sue','chfeps','sfe']\n",
    "a_37=['illiq','turn','RF','beta','idiovol','coskew','size','mom','mom1m','chmom','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt',\n",
    "     'cashpr','chatoia','chinv','depr','ep','gma','lev','quick','sp','acc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat',\n",
    "     'operprof','pchdepr','pchgm_pchsale','saleinv','rd_sale','roavol','rsup','salecash','salerec','tang','O-score','chfeps']\n",
    "a_38=['illiq','turn','beta','idiovol','coskew','mom','chmom','r_sq','gammaps','cash','rd_mve','cashpr','depr','ep','gma','lgr','quick','sp','stdcf','cfp','agr','chcapx',\n",
    "     'ol','noa','grnoa','chatq','cfpq','currat','dy','operprof','pchdepr','saleinv','rd_sale','salerec','chfeps','sfe','disp']\n",
    "\n",
    "a_39=['turn','baspread','beta','idiovol','mom','chmom','max','r_sq','gammaps','bm','cash','rd_mve','chatoia','depr','ep','gma','lev','sp','acc','stdacc','stdcf','oa',\n",
    "     'ol','noa','pctacc','egr','operprof','pchgm_pchsale','pchsale_pchinvt','saleinv','pchsaleinv','rd_sale','roaq','roavol','roeq','rsup','salerec','chsaleq',\n",
    "     'tang','O-score','chfeps','sfe']\n",
    "a_40=['illiq','turn','RF','beta','idiovol','log_Mkt-RF^2','coskew','size','mom','mom1m','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr','chatoia','chinv','depr','ep','gma',\n",
    "     'lev','quick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','grnoa','pctacc','cfpq','currat','dy','saleinv','rd_sale','roaq','roavol','salecash','salerec',\n",
    "     'chsaleq','tang','O-score','sue','sfe']\n",
    "a_41=['baspread','beta','idiovol','size','mom','chmom','max','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','depr','quick','acc','stdacc','stdcf','chcapx','oa','ol','noa','currat','dy',\n",
    "     'egr','operprof','saleinv','rd_sale','roaq','roavol','roeq','salecash','salerec','chsaleq','tang','sue','chnanalyst']\n",
    "a_42=['turn','baspread','beta','idiovol','log_Mkt-RF^2','coskew','size','mom','chmom','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','depr','ep','gma','lev','quick',\n",
    "     'pchquick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','grnoa','cfpq','currat','dy','pchcurrat','saleinv','rd_sale','roaq','roavol','salecash','salerec','tang','O-score','sfe']\n",
    "\n",
    "#>\n",
    "\n",
    "a=[list[43],list[44],list[45],list[46],list[47],list[48],list[49],list[50],list[51],list[52],list[53],list[54],list[55]]\n",
    "  \n",
    "\n",
    "for i in range(13):\n",
    "    for name, column in a[i][a[i].columns].iteritems():\n",
    "        Augmented_Dickey_Fuller_Test_func(a[i][name],name)\n",
    "        print('\\n')\n",
    "\n",
    "# In each industry, get non-stationary variables\n",
    "\n",
    "a_43=['RF','beta','idiovol','coskew','size','mom','chmom','max','r_sq','gammaps','aeavol','bm','cash','cashpr','chinv','depr','lev','sp','acc','stdacc','stdcf','oa','ol','noa',\n",
    "     'currat','dy','pchsale_pchinvt','pchsale_pchxsga','saleinv','rd_sale','roavol','chsaleq','tang']\n",
    "a_44=['turn','baspread','beta','idiovol','size','mom','max','r_sq','gammaps','aeavol','bm','cash','cashdebt','cashpr','chatoia','depr','ep','gma','lev','lgr','quick','sp','acc','stdcf','cfp','agr','chcapx','oa','ol','chatq',\n",
    "     'cfpq','chcshoq','invest','currat','dy','saleinv','roaq','roavol','rsup','salecash','salerec','tang','O-score','chfeps','chnanalyst','disp']\n",
    "a_45=['turn','RF','baspread','beta','idiovol','log_Mkt-RF^2','size','mom','chmom','max','r_sq','gammaps','bm','cash','cashdebt','cashpr','chatoia','depr','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','ibq_adj',\n",
    "     'currat','pchsale_pchxsga','saleinv','roaq','roavol','salecash','salerec','chsaleq','tang','chfeps','sfe','disp']\n",
    "a_46=['illiq','turn','RF','baspread','beta','idiovol','size','mom','chmom','max','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','depr','ep','gma','lev','lgr','quick','pchquick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','operprof',\n",
    "     'pchcurrat','saleinv','rd_sale','roaq','roavol','roeq','salecash','salerec','tang','O-score','sfe','chnanalyst','disp']\n",
    "a_47=['turn','RF','beta','idiovol','coskew','size','mom','chmom','max','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','depr','gma','lev','lgr','quick','sp','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','currat','dy','operprof','saleinv','rd_sale','roavol','rsup',\n",
    "     'salecash','salerec','tang','sfe','chnanalyst']\n",
    "\n",
    "\n",
    "a_48=['turn','beta','idiovol','coskew','size','mom','chmom','r_sq','cash','rd_mve','cashpr','depr','quick','pchquick','sp','stdacc','stdcf','cfp','chcapx','ol','noa','pctacc','cfpq','currat','dy','operprof','pchcurrat','pchsale_pchxsga','saleinv','rd_sale','roaq',\n",
    "     'roavol','salecash','salerec','tang','sue']\n",
    "a_49=['turn','baspread','beta','idiovol','size','mom','r_sq','gammaps','rd_mve','quick','acc','stdacc','stdcf','oa','ol','noa','currat','operprof','saleinv','pchsaleinv','roavol','rsup','chfeps','disp']\n",
    "a_50=['turn','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','rev2','rev3','mom','mom1m','max','log_Mkt-RF_lag1','log_Mkt-RF_lag2','r_sq','aeavol','bm','cash','rd_mve','cashpr','depr','gma','lev','quick','sp','acc','stdacc','stdcf','cfp',\n",
    "     'chcapx','oa','ol','noa','pctacc','cfpq','currat','dy','operprof','saleinv','rd_sale','roavol','rsup','salecash','salerec','tang','O-score','sfe','disp']\n",
    "a_51=['RF','baspread','beta','idiovol','log_Mkt-RF^2','coskew','size','mom','chmom','max','r_sq','gammaps','bm','cash','rd_mve','cashdebt','cashpr','chatoia','depr','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','operprof','pchcurrat',\n",
    "     'saleinv','rd_sale','roaq','roavol','roeq','rsup','salecash','salerec','chsaleq','tang','chfeps','sfe','disp']\n",
    "a_52=['beta','idiovol','mom','r_sq','gammaps','bm','rd_mve','depr','gma','quick','sp','acc','stdcf','cfp','cfpq','invest','dy','rd_sale','roaq','rsup','chfeps','sfe']\n",
    "a_53=['illiq','turn','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','rev1','mom','max','r_sq','aeavol','bm','cash','rd_mve','cashpr','depr','gma','lev','lgr','quick','sp','acc','stdacc','cfp','agr','oa','ol','noa','pctacc','chatq','cfpq','chcshoq','currat','dy','saleinv','rd_sale','roavol','salecash','salerec','tang','O-score','disp']\n",
    "a_54=['turn','baspread','beta','idiovol','mom','mom1m','chmom','r_sq','gammaps','aeavol','bm','rd_mve','cashdebt','depr','ep','gma','lev','quick','acc','stdacc','stdcf','cfp','chcapx','oa','noa','cfpq','dy','pchsale_pchrect',\n",
    "     'saleinv','rd_sale','roaq','roavol','rsup','salerec','O-score','disp']\n",
    "a_55=['turn','baspread','beta','idiovol','size','max','r_sq','gammaps','aeavol','bm','cash','cashpr','depr','gma','lev','quick','sp','acc','stdacc','stdcf','cfp','oa','ol','noa','cfpq','currat','dy','saleinv','rd_sale','roavol','rsup','salecash','salerec','tang','O-score','disp']\n",
    "\n",
    "#> list[56]:\n",
    "\n",
    "for name, column in list[56][list[56].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[56][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "# Get non-stationary variables\n",
    "\n",
    "a_56=['turn','beta','idiovol','coskew','mom','max','r_sq','gammaps','bm','cashpr','chatoia','depr','acc','stdacc','stdcf','cfp','oa','ol','noa','pctacc','cfpq','ibq_adj','dy','pchdepr',\n",
    "     'pchsale_pchrect','saleinv','pchsaleinv','salecash','sfe']\n",
    "\n",
    "#> list[57]:\n",
    "\n",
    "for name, column in list[57][list[57].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[57][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "# Get non-stationary variables\n",
    "\n",
    "a_57=['turn','RF','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','max','log_Mkt-RF_lag1','log_Mkt-RF_lag2','log_Mkt-RF_lag3','log_Mkt-RF_lag4','r_sq','aeavol','bm','cash','rd_mve','cashpr','chatoia','depr','ep','gma',\n",
    "     'lev','quick','sp','acc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','operprof','saleinv','rd_sale','roavol','rsup','salecash','salerec','tang','O-score','sue','disp']\n",
    " \n",
    "#> list[58]:\n",
    "\n",
    "for name, column in list[58][list[58].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[58][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "#Get non-stationary variables\n",
    "\n",
    "a_58=['turn','RF','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','max','log_Mkt-RF_lag3','log_Mkt-RF_lag4','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr',\n",
    "     'depr','gma','lev','lgr','quick','sp','acc','stdacc','stdcf','cfp','chcapx','oa','ol','noa','cfpq','currat','dy','pchdepr','saleinv','rd_sale','roaq',\n",
    "     'roavol','rsup','salecash','salerec','tang','O-score','sfe','chnanalyst','disp']\n",
    "\n",
    "#> list[59]:\n",
    "\n",
    "for name, column in list[59][list[59].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[59][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "# Get non-stationary variables\n",
    "a_59=['baspread','beta','idiovol','size','mom','chmom','r_sq','gammaps','rd_mve','cashpr','depr','gma','lev','sp','acc','stdacc','stdcf','chcapx','oa','ol','noa','operprof','saleinv',\n",
    "     'rd_sale','roavol','salerec','O-score']\n",
    "\n",
    "#> list[60]:\n",
    "for name, column in list[60][list[60].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[60][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "# Get non-stationary variables\n",
    "\n",
    "a_60=['illiq','turn','RF','log_Mkt-RF','baspread','beta','idiovol','log_Mkt-RF^2','size','max','log_Mkt-RF_lag1','log_Mkt-RF_lag2','log_Mkt-RF_lag3','log_Mkt-RF_lag4','r_sq','gammaps','aeavol','bm','cash','rd_mve','cashdebt','cashpr','chatoia','depr','ep','gma','lev','lgr',\n",
    "     'quick','sp','stdacc','stdcf','cfp','oa','ol','noa','cfpq','chcshoq','ibq_adj','invest','currat','dy','egr','saleinv','rd_sale','roavol','salecash','tang','O-score','chfeps','disp']\n",
    "\n",
    "#> list[61]:\n",
    "\n",
    "for name, column in list[61][list[61].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[61][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "# Get non-stationary variables\n",
    "a_61=['turn','baspread','beta','idiovol','mom','r_sq','gammaps','bm','cash','rd_mve','cashdebt','chatoia','ep','gma','sp','acc','stdcf','cfp','oa','noa',\n",
    "     'pctacc','cfpq','invest','dy','egr','operprof','pchgm_pchsale','saleinv','rd_sale','roaq','roavol','roeq','rsup','salerec','chsaleq','tang','O-score','sfe','disp']\n",
    "\n",
    "#> list[62]:\n",
    "for name, column in list[62][list[62].columns].iteritems():\n",
    "    Augmented_Dickey_Fuller_Test_func(list[62][name],name)\n",
    "    print('\\n')\n",
    "\n",
    "# Get non-stationary variables\n",
    "a_62=['turn','RF','beta','idiovol','size','mom','mom1m','max','r_sq','gammaps','cash','rd_mve','cashdebt','depr','lev','quick','sp','stdacc',\n",
    "     'stdcf','cfp','oa','ol','noa','cfpq','invest','currat','dy','pchgm_pchsale','saleinv','rd_sale','roavol','salecash','tang','O-score','sfe','chnanalyst']   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b4751e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Use the diffence approach to stationarize the non-stationary variables.\n",
    "\n",
    "\n",
    "x0=list[0][a_0]\n",
    "y0=list[0].drop(x0.columns,axis=1)\n",
    "x0_diff=x0.diff()\n",
    "x0_diff=x0_diff.fillna(0)\n",
    "S_0_diff=pd.concat([y0,x0_diff],axis=1)\n",
    "\n",
    "x1=list[1][a_1]\n",
    "y1=list[1].drop(x1.columns,axis=1)\n",
    "x1_diff=x1.diff()\n",
    "x1_diff=x1_diff.fillna(0)\n",
    "S_1_diff=pd.concat([y1,x1_diff],axis=1)\n",
    "\n",
    "x2=list[2][a_2]\n",
    "y2=list[2].drop(x2.columns,axis=1)\n",
    "x2_diff=x2.diff()\n",
    "x2_diff=x2_diff.fillna(0)\n",
    "S_2_diff=pd.concat([y2,x2_diff],axis=1)\n",
    "\n",
    "x3=list[3][a_3]\n",
    "y3=list[3].drop(x3.columns,axis=1)\n",
    "x3_diff=x3.diff()\n",
    "x3_diff=x3_diff.fillna(0)\n",
    "S_3_diff=pd.concat([y3,x3_diff],axis=1)\n",
    "\n",
    "x4=list[4][a_4]\n",
    "y4=list[4].drop(x4.columns,axis=1)\n",
    "x4_diff=x4.diff()\n",
    "x4_diff=x4_diff.fillna(0)\n",
    "S_4_diff=pd.concat([y4,x4_diff],axis=1)\n",
    "\n",
    "x5=list[5][a_5]\n",
    "y5=list[5].drop(x5.columns,axis=1)\n",
    "x5_diff=x5.diff()\n",
    "x5_diff=x5_diff.fillna(0)\n",
    "S_5_diff=pd.concat([y5,x5_diff],axis=1)\n",
    "\n",
    "\n",
    "\n",
    "x6=list[6][a_6]\n",
    "y6=list[6].drop(x6.columns,axis=1)\n",
    "x6_diff=x6.diff()\n",
    "x6_diff=x6_diff.fillna(0)\n",
    "S_6_diff=pd.concat([y6,x6_diff],axis=1)\n",
    "\n",
    "x7=list[7][a_7]\n",
    "y7=list[7].drop(x7.columns,axis=1)\n",
    "x7_diff=x7.diff()\n",
    "x7_diff=x7_diff.fillna(0)\n",
    "S_7_diff=pd.concat([y7,x7_diff],axis=1)\n",
    "\n",
    "x8=list[8][a_8]\n",
    "y8=list[8].drop(x8.columns,axis=1)\n",
    "x8_diff=x8.diff()\n",
    "x8_diff=x8_diff.fillna(0)\n",
    "S_8_diff=pd.concat([y8,x8_diff],axis=1)\n",
    "\n",
    "x9=list[9][a_9]\n",
    "y9=list[9].drop(x9.columns,axis=1)\n",
    "x9_diff=x9.diff()\n",
    "x9_diff=x9_diff.fillna(0)\n",
    "S_9_diff=pd.concat([y9,x9_diff],axis=1)\n",
    "\n",
    "x10=list[10][a_10]\n",
    "y10=list[10].drop(x10.columns,axis=1)\n",
    "x10_diff=x10.diff()\n",
    "x10_diff=x10_diff.fillna(0)\n",
    "S_10_diff=pd.concat([y10,x10_diff],axis=1)\n",
    "\n",
    "x11=list[11][a_11]\n",
    "y11=list[11].drop(x11.columns,axis=1)\n",
    "x11_diff=x11.diff()\n",
    "x11_diff=x11_diff.fillna(0)\n",
    "S_11_diff=pd.concat([y11,x11_diff],axis=1)\n",
    "\n",
    "x12=list[12][a_12]\n",
    "y12=list[12].drop(x12.columns,axis=1)\n",
    "x12_diff=x12.diff()\n",
    "x12_diff=x12_diff.fillna(0)\n",
    "S_12_diff=pd.concat([y12,x12_diff],axis=1)\n",
    "\n",
    "x13=list[13][a_13]\n",
    "y13=list[13].drop(x13.columns,axis=1)\n",
    "x13_diff=x13.diff()\n",
    "x13_diff=x13_diff.fillna(0)\n",
    "S_13_diff=pd.concat([y13,x13_diff],axis=1)\n",
    "\n",
    "x14=list[14][a_14]\n",
    "y14=list[14].drop(x14.columns,axis=1)\n",
    "x14_diff=x14.diff()\n",
    "x14_diff=x14_diff.fillna(0)\n",
    "S_14_diff=pd.concat([y14,x14_diff],axis=1)\n",
    "\n",
    "x15=list[15][a_15]\n",
    "y15=list[15].drop(x15.columns,axis=1)\n",
    "x15_diff=x15.diff()\n",
    "x15_diff=x15_diff.fillna(0)\n",
    "S_15_diff=pd.concat([y15,x15_diff],axis=1)\n",
    "\n",
    "x16=list[16][a_16]\n",
    "y16=list[16].drop(x16.columns,axis=1)\n",
    "x16_diff=x16.diff()\n",
    "x16_diff=x16_diff.fillna(0)\n",
    "S_16_diff=pd.concat([y16,x16_diff],axis=1)\n",
    "\n",
    "x17=list[17][a_17]\n",
    "y17=list[17].drop(x17.columns,axis=1)\n",
    "x17_diff=x17.diff()\n",
    "x17_diff=x17_diff.fillna(0)\n",
    "S_17_diff=pd.concat([y17,x17_diff],axis=1)\n",
    "\n",
    "x18=list[18][a_18]\n",
    "y18=list[18].drop(x18.columns,axis=1)\n",
    "x18_diff=x18.diff()\n",
    "x18_diff=x18_diff.fillna(0)\n",
    "S_18_diff=pd.concat([y18,x18_diff],axis=1)\n",
    "\n",
    "x19=list[19][a_19]\n",
    "y19=list[19].drop(x19.columns,axis=1)\n",
    "x19_diff=x19.diff()\n",
    "x19_diff=x19_diff.fillna(0)\n",
    "S_19_diff=pd.concat([y19,x19_diff],axis=1)\n",
    "\n",
    "x20=list[20][a_20]\n",
    "y20=list[20].drop(x20.columns,axis=1)\n",
    "x20_diff=x20.diff()\n",
    "x20_diff=x20_diff.fillna(0)\n",
    "S_20_diff=pd.concat([y20,x20_diff],axis=1)\n",
    "\n",
    "x21=list[21][a_21]\n",
    "y21=list[21].drop(x21.columns,axis=1)\n",
    "x21_diff=x21.diff()\n",
    "x21_diff=x21_diff.fillna(0)\n",
    "S_21_diff=pd.concat([y21,x21_diff],axis=1)\n",
    "\n",
    "x22=list[22][a_22]\n",
    "y22=list[22].drop(x22.columns,axis=1)\n",
    "x22_diff=x22.diff()\n",
    "x22_diff=x22_diff.fillna(0)\n",
    "S_22_diff=pd.concat([y22,x22_diff],axis=1)\n",
    "\n",
    "x23=list[23][a_23]\n",
    "y23=list[23].drop(x23.columns,axis=1)\n",
    "x23_diff=x23.diff()\n",
    "x23_diff=x23_diff.fillna(0)\n",
    "S_23_diff=pd.concat([y23,x23_diff],axis=1)\n",
    "\n",
    "x24=list[24][a_24]\n",
    "y24=list[24].drop(x24.columns,axis=1)\n",
    "x24_diff=x24.diff()\n",
    "x24_diff=x24_diff.fillna(0)\n",
    "S_24_diff=pd.concat([y24,x24_diff],axis=1)\n",
    "\n",
    "x25=list[25][a_25]\n",
    "y25=list[25].drop(x25.columns,axis=1)\n",
    "x25_diff=x25.diff()\n",
    "x25_diff=x25_diff.fillna(0)\n",
    "S_25_diff=pd.concat([y25,x25_diff],axis=1)\n",
    "\n",
    "\n",
    "\n",
    "x26=list[26][a_26]\n",
    "y26=list[26].drop(x26.columns,axis=1)\n",
    "x26_diff=x26.diff()\n",
    "x26_diff=x26_diff.fillna(0)\n",
    "S_26_diff=pd.concat([y26,x26_diff],axis=1)\n",
    "\n",
    "x27=list[27][a_27]\n",
    "y27=list[27].drop(x27.columns,axis=1)\n",
    "x27_diff=x27.diff()\n",
    "x27_diff=x27_diff.fillna(0)\n",
    "S_27_diff=pd.concat([y27,x27_diff],axis=1)\n",
    "\n",
    "x28=list[28][a_28]\n",
    "y28=list[28].drop(x28.columns,axis=1)\n",
    "x28_diff=x28.diff()\n",
    "x28_diff=x28_diff.fillna(0)\n",
    "S_28_diff=pd.concat([y28,x28_diff],axis=1)\n",
    "\n",
    "x29=list[29][a_29]\n",
    "y29=list[29].drop(x29.columns,axis=1)\n",
    "x29_diff=x29.diff()\n",
    "x29_diff=x29_diff.fillna(0)\n",
    "S_29_diff=pd.concat([y29,x29_diff],axis=1)\n",
    "\n",
    "\n",
    "x30=list[30][a_30]\n",
    "y30=list[30].drop(x30.columns,axis=1)\n",
    "x30_diff=x30.diff()\n",
    "x30_diff=x30_diff.fillna(0)\n",
    "S_30_diff=pd.concat([y30,x30_diff],axis=1)\n",
    "\n",
    "x31=list[31][a_31]\n",
    "y31=list[31].drop(x31.columns,axis=1)\n",
    "x31_diff=x31.diff()\n",
    "x31_diff=x31_diff.fillna(0)\n",
    "S_31_diff=pd.concat([y31,x31_diff],axis=1)\n",
    "\n",
    "x32=list[32][a_32]\n",
    "y32=list[32].drop(x32.columns,axis=1)\n",
    "x32_diff=x32.diff()\n",
    "x32_diff=x32_diff.fillna(0)\n",
    "S_32_diff=pd.concat([y32,x32_diff],axis=1)\n",
    "\n",
    "x33=list[33][a_33]\n",
    "y33=list[33].drop(x33.columns,axis=1)\n",
    "x33_diff=x33.diff()\n",
    "x33_diff=x33_diff.fillna(0)\n",
    "S_33_diff=pd.concat([y33,x33_diff],axis=1)\n",
    "\n",
    "x34=list[34][a_34]\n",
    "y34=list[34].drop(x34.columns,axis=1)\n",
    "x34_diff=x34.diff()\n",
    "x34_diff=x34_diff.fillna(0)\n",
    "S_34_diff=pd.concat([y34,x34_diff],axis=1)\n",
    "\n",
    "x35=list[35][a_35]\n",
    "y35=list[35].drop(x35.columns,axis=1)\n",
    "x35_diff=x35.diff()\n",
    "x35_diff=x35_diff.fillna(0)\n",
    "S_35_diff=pd.concat([y35,x35_diff],axis=1)\n",
    "\n",
    "\n",
    "\n",
    "x36=list[36][a_36]\n",
    "y36=list[36].drop(x36.columns,axis=1)\n",
    "x36_diff=x36.diff()\n",
    "x36_diff=x36_diff.fillna(0)\n",
    "S_36_diff=pd.concat([y36,x36_diff],axis=1)\n",
    "\n",
    "x37=list[37][a_37]\n",
    "y37=list[37].drop(x37.columns,axis=1)\n",
    "x37_diff=x37.diff()\n",
    "x37_diff=x37_diff.fillna(0)\n",
    "S_37_diff=pd.concat([y37,x37_diff],axis=1)\n",
    "\n",
    "x38=list[38][a_38]\n",
    "y38=list[38].drop(x38.columns,axis=1)\n",
    "x38_diff=x38.diff()\n",
    "x38_diff=x38_diff.fillna(0)\n",
    "S_38_diff=pd.concat([y38,x38_diff],axis=1)\n",
    "\n",
    "x39=list[39][a_39]\n",
    "y39=list[39].drop(x39.columns,axis=1)\n",
    "x39_diff=x39.diff()\n",
    "x39_diff=x39_diff.fillna(0)\n",
    "S_39_diff=pd.concat([y39,x39_diff],axis=1)\n",
    "\n",
    "x40=list[40][a_40]\n",
    "y40=list[40].drop(x40.columns,axis=1)\n",
    "x40_diff=x40.diff()\n",
    "x40_diff=x40_diff.fillna(0)\n",
    "S_40_diff=pd.concat([y40,x40_diff],axis=1)\n",
    "\n",
    "x41=list[41][a_41]\n",
    "y41=list[41].drop(x41.columns,axis=1)\n",
    "x41_diff=x41.diff()\n",
    "x41_diff=x41_diff.fillna(0)\n",
    "S_41_diff=pd.concat([y41,x41_diff],axis=1)\n",
    "\n",
    "x42=list[42][a_42]\n",
    "y42=list[42].drop(x42.columns,axis=1)\n",
    "x42_diff=x42.diff()\n",
    "x42_diff=x42_diff.fillna(0)\n",
    "S_42_diff=pd.concat([y42,x42_diff],axis=1)\n",
    "\n",
    "\n",
    "x43=list[43][a_43]\n",
    "y43=list[43].drop(x43.columns,axis=1)\n",
    "x43_diff=x43.diff()\n",
    "x43_diff=x43_diff.fillna(0)\n",
    "S_43_diff=pd.concat([y43,x43_diff],axis=1)\n",
    "\n",
    "x44=list[44][a_44]\n",
    "y44=list[44].drop(x44.columns,axis=1)\n",
    "x44_diff=x44.diff()\n",
    "x44_diff=x44_diff.fillna(0)\n",
    "S_44_diff=pd.concat([y44,x44_diff],axis=1)\n",
    "\n",
    "x45=list[45][a_45]\n",
    "y45=list[45].drop(x45.columns,axis=1)\n",
    "x45_diff=x45.diff()\n",
    "x45_diff=x45_diff.fillna(0)\n",
    "S_45_diff=pd.concat([y45,x45_diff],axis=1)\n",
    "\n",
    "x46=list[46][a_46]\n",
    "y46=list[46].drop(x46.columns,axis=1)\n",
    "x46_diff=x46.diff()\n",
    "x46_diff=x46_diff.fillna(0)\n",
    "S_46_diff=pd.concat([y46,x46_diff],axis=1)\n",
    "\n",
    "x47=list[47][a_47]\n",
    "y47=list[47].drop(x47.columns,axis=1)\n",
    "x47_diff=x47.diff()\n",
    "x47_diff=x47_diff.fillna(0)\n",
    "S_47_diff=pd.concat([y47,x47_diff],axis=1)\n",
    "\n",
    "x48=list[48][a_48]\n",
    "y48=list[48].drop(x48.columns,axis=1)\n",
    "x48_diff=x48.diff()\n",
    "x48_diff=x48_diff.fillna(0)\n",
    "S_48_diff=pd.concat([y48,x48_diff],axis=1)\n",
    "\n",
    "\n",
    "\n",
    "x49=list[49][a_49]\n",
    "y49=list[49].drop(x49.columns,axis=1)\n",
    "x49_diff=x49.diff()\n",
    "x49_diff=x49_diff.fillna(0)\n",
    "S_49_diff=pd.concat([y49,x49_diff],axis=1)\n",
    "\n",
    "x50=list[50][a_50]\n",
    "y50=list[50].drop(x50.columns,axis=1)\n",
    "x50_diff=x50.diff()\n",
    "x50_diff=x50_diff.fillna(0)\n",
    "S_50_diff=pd.concat([y50,x50_diff],axis=1)\n",
    "\n",
    "x51=list[51][a_51]\n",
    "y51=list[51].drop(x51.columns,axis=1)\n",
    "x51_diff=x51.diff()\n",
    "x51_diff=x51_diff.fillna(0)\n",
    "S_51_diff=pd.concat([y51,x51_diff],axis=1)\n",
    "\n",
    "x52=list[52][a_52]\n",
    "y52=list[52].drop(x52.columns,axis=1)\n",
    "x52_diff=x52.diff()\n",
    "x52_diff=x52_diff.fillna(0)\n",
    "S_52_diff=pd.concat([y52,x52_diff],axis=1)\n",
    "\n",
    "x53=list[53][a_53]\n",
    "y53=list[53].drop(x53.columns,axis=1)\n",
    "x53_diff=x53.diff()\n",
    "x53_diff=x53_diff.fillna(0)\n",
    "S_53_diff=pd.concat([y53,x53_diff],axis=1)\n",
    "\n",
    "x54=list[54][a_54]\n",
    "y54=list[54].drop(x54.columns,axis=1)\n",
    "x54_diff=x54.diff()\n",
    "x54_diff=x54_diff.fillna(0)\n",
    "S_54_diff=pd.concat([y54,x54_diff],axis=1)\n",
    "\n",
    "x55=list[55][a_55]\n",
    "y55=list[55].drop(x55.columns,axis=1)\n",
    "x55_diff=x55.diff()\n",
    "x55_diff=x55_diff.fillna(0)\n",
    "S_55_diff=pd.concat([y55,x55_diff],axis=1)\n",
    "\n",
    "x56=list[56][a_56]\n",
    "y56=list[56].drop(x56.columns,axis=1)\n",
    "x56_diff=x56.diff()\n",
    "x56_diff=x56_diff.fillna(0)\n",
    "S_56_diff=pd.concat([y56,x56_diff],axis=1)\n",
    "\n",
    "x57=list[57][a_57]\n",
    "y57=list[57].drop(x57.columns,axis=1)\n",
    "x57_diff=x57.diff()\n",
    "x57_diff=x57_diff.fillna(0)\n",
    "S_57_diff=pd.concat([y57,x57_diff],axis=1)\n",
    "\n",
    "x58=list[58][a_58]\n",
    "y58=list[58].drop(x58.columns,axis=1)\n",
    "x58_diff=x58.diff()\n",
    "x58_diff=x58_diff.fillna(0)\n",
    "S_58_diff=pd.concat([y58,x58_diff],axis=1)\n",
    "\n",
    "x59=list[59][a_59]\n",
    "y59=list[59].drop(x59.columns,axis=1)\n",
    "x59_diff=x59.diff()\n",
    "x59_diff=x59_diff.fillna(0)\n",
    "S_59_diff=pd.concat([y59,x59_diff],axis=1)\n",
    "\n",
    "x60=list[60][a_60]\n",
    "y60=list[60].drop(x60.columns,axis=1)\n",
    "x60_diff=x60.diff()\n",
    "x60_diff=x60_diff.fillna(0)\n",
    "S_60_diff=pd.concat([y60,x60_diff],axis=1)\n",
    "\n",
    "x61=list[61][a_61]\n",
    "y61=list[61].drop(x61.columns,axis=1)\n",
    "x61_diff=x61.diff()\n",
    "x61_diff=x61_diff.fillna(0)\n",
    "S_61_diff=pd.concat([y61,x61_diff],axis=1)\n",
    "    \n",
    "x62=list[62][a_62]\n",
    "y62=list[62].drop(x62.columns,axis=1)\n",
    "x62_diff=x62.diff()\n",
    "x62_diff=x62_diff.fillna(0)\n",
    "S_62_diff=pd.concat([y62,x62_diff],axis=1)\n",
    "    \n",
    "\n",
    "\n",
    "lis=[S_0_diff,S_1_diff,S_2_diff,S_3_diff,S_4_diff,S_5_diff,S_6_diff,S_7_diff,S_8_diff,\n",
    "       S_9_diff,S_10_diff,S_11_diff,S_12_diff,S_13_diff,S_14_diff,S_15_diff,S_16_diff,\n",
    "     S_17_diff,S_18_diff,S_19_diff,S_20_diff,S_21_diff,S_22_diff,S_23_diff,\n",
    "       S_24_diff,S_25_diff,S_26_diff,S_27_diff,S_28_diff,S_29_diff,S_30_diff,S_31_diff,S_32_diff,S_33_diff,S_34_diff,\n",
    "     S_35_diff,S_36_diff,S_37_diff,S_38_diff,S_39_diff,S_40_diff,S_41_diff,S_42_diff,S_43_diff,S_44_diff,\n",
    "       S_45_diff,S_46_diff,S_47_diff,S_48_diff,S_49_diff,S_50_diff,S_51_diff,S_52_diff,\n",
    "     S_53_diff,S_54_diff,S_55_diff,S_56_diff,S_57_diff,S_58_diff,S_59_diff,S_60_diff,S_61_diff,S_62_diff]    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "872f1c15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c637d779",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Export industry files as csv\n",
    "\n",
    "S_0_diff.to_csv('Dropbox/filemonth/file_0.csv')\n",
    "S_1_diff.to_csv('Dropbox/filemonth/file_1.csv')\n",
    "S_2_diff.to_csv('Dropbox/filemonth/file_2.csv')\n",
    "S_3_diff.to_csv('Dropbox/filemonth/file_3.csv')\n",
    "S_4_diff.to_csv('Dropbox/filemonth/file_4.csv')\n",
    "S_5_diff.to_csv('Dropbox/filemonth/file_5.csv')\n",
    "S_6_diff.to_csv('Dropbox/filemonth/file_6.csv')\n",
    "S_7_diff.to_csv('Dropbox/filemonth/file_7.csv')\n",
    "S_8_diff.to_csv('Dropbox/filemonth/file_8.csv')\n",
    "S_9_diff.to_csv('Dropbox/filemonth/file_9.csv')\n",
    "S_10_diff.to_csv('Dropbox/filemonth/file_10.csv')\n",
    "S_11_diff.to_csv('Dropbox/filemonth/file_11.csv')\n",
    "S_12_diff.to_csv('Dropbox/filemonth/file_12.csv')\n",
    "S_13_diff.to_csv('Dropbox/filemonth/file_13.csv')\n",
    "S_14_diff.to_csv('Dropbox/filemonth/file_14.csv')\n",
    "S_15_diff.to_csv('Dropbox/filemonth/file_15.csv')\n",
    "S_16_diff.to_csv('Dropbox/filemonth/file_16.csv')\n",
    "S_17_diff.to_csv('Dropbox/filemonth/file_17.csv')\n",
    "S_18_diff.to_csv('Dropbox/filemonth/file_18.csv')\n",
    "S_19_diff.to_csv('Dropbox/filemonth/file_19.csv')\n",
    "S_20_diff.to_csv('Dropbox/filemonth/file_20.csv')\n",
    "S_21_diff.to_csv('Dropbox/filemonth/file_21.csv')\n",
    "S_22_diff.to_csv('Dropbox/filemonth/file_22.csv')\n",
    "S_23_diff.to_csv('Dropbox/filemonth/file_23.csv')\n",
    "S_24_diff.to_csv('Dropbox/filemonth/file_24.csv')\n",
    "S_25_diff.to_csv('Dropbox/filemonth/file_25.csv')\n",
    "S_26_diff.to_csv('Dropbox/filemonth/file_26.csv')\n",
    "S_27_diff.to_csv('Dropbox/filemonth/file_27.csv')\n",
    "S_28_diff.to_csv('Dropbox/filemonth/file_28.csv')\n",
    "S_29_diff.to_csv('Dropbox/filemonth/file_29.csv')\n",
    "S_30_diff.to_csv('Dropbox/filemonth/file_30.csv')\n",
    "S_31_diff.to_csv('Dropbox/filemonth/file_31.csv')\n",
    "S_32_diff.to_csv('Dropbox/filemonth/file_32.csv')\n",
    "S_33_diff.to_csv('Dropbox/filemonth/file_33.csv')\n",
    "S_34_diff.to_csv('Dropbox/filemonth/file_34.csv')\n",
    "S_35_diff.to_csv('Dropbox/filemonth/file_35.csv')\n",
    "S_36_diff.to_csv('Dropbox/filemonth/file_36.csv')\n",
    "S_37_diff.to_csv('Dropbox/filemonth/file_37.csv')\n",
    "S_38_diff.to_csv('Dropbox/filemonth/file_38.csv')\n",
    "S_39_diff.to_csv('Dropbox/filemonth/file_39.csv')\n",
    "S_40_diff.to_csv('Dropbox/filemonth/file_40.csv')\n",
    "S_41_diff.to_csv('Dropbox/filemonth/file_41.csv')\n",
    "S_42_diff.to_csv('Dropbox/filemonth/file_42.csv')\n",
    "S_43_diff.to_csv('Dropbox/filemonth/file_43.csv')\n",
    "S_44_diff.to_csv('Dropbox/filemonth/file_44.csv')\n",
    "S_45_diff.to_csv('Dropbox/filemonth/file_45.csv')\n",
    "S_46_diff.to_csv('Dropbox/filemonth/file_46.csv')\n",
    "S_47_diff.to_csv('Dropbox/filemonth/file_47.csv')\n",
    "S_48_diff.to_csv('Dropbox/filemonth/file_48.csv')\n",
    "S_49_diff.to_csv('Dropbox/filemonth/file_49.csv')\n",
    "S_50_diff.to_csv('Dropbox/filemonth/file_50.csv')\n",
    "S_51_diff.to_csv('Dropbox/filemonth/file_51.csv')\n",
    "S_52_diff.to_csv('Dropbox/filemonth/file_52.csv')\n",
    "S_53_diff.to_csv('Dropbox/filemonth/file_53.csv')\n",
    "S_54_diff.to_csv('Dropbox/filemonth/file_54.csv')\n",
    "S_55_diff.to_csv('Dropbox/filemonth/file_55.csv')\n",
    "S_56_diff.to_csv('Dropbox/filemonth/file_56.csv')\n",
    "S_57_diff.to_csv('Dropbox/filemonth/file_57.csv')\n",
    "S_58_diff.to_csv('Dropbox/filemonth/file_58.csv')\n",
    "S_59_diff.to_csv('Dropbox/filemonth/file_59.csv')\n",
    "S_60_diff.to_csv('Dropbox/filemonth/file_60.csv')\n",
    "S_61_diff.to_csv('Dropbox/filemonth/file_61.csv')\n",
    "S_62_diff.to_csv('Dropbox/filemonth/file_62.csv')\n",
    "\n"
   ]
  }
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