Final Code & Results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.tseries.offsets import MonthEnd, YearEnd\n",
    "import os\n",
    "os.chdir(\"D:/Quarter3/QAM/Project/My project\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Industry = pd.read_csv('10_Industry_Portfolios.csv',skiprows=11)\n",
    "Industry = Industry.iloc[0:1126,:]\n",
    "\n",
    "FF_mkt = pd.read_csv('F-F_Research_Data_Factors.csv',skiprows =3)\n",
    "FF_mkt = FF_mkt.iloc[0:1126,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Record CRISP unkowns\n",
    "unknowns = [\"-66.0\", \"-77.0\", \"-88.0\", \"-99.0\", \"-99.99\", \"-999\", \"A\", \"B\", \"C\", \"D\", \"E\", \"S\", \"T\", \"P\"]\n",
    "\n",
    "# Create function to convert CRISP unkowns to np.nan\n",
    "convert_unknows = np.vectorize(lambda x: np.nan if x in unknowns else x)\n",
    "\n",
    "# Convert to decimal\n",
    "Industry.iloc[:, 1:] = Industry.iloc[:, 1:].apply(convert_unknows, axis = 0).astype(float).div(100)\n",
    "FF_mkt.iloc[:, 1:] = FF_mkt.iloc[:, 1:].apply(convert_unknows, axis = 0).astype(float).div(100)\n",
    "\n",
    "# Rename data columns\n",
    "Industry.rename(columns = {\"Unnamed: 0\":\"date\"}, inplace = True)\n",
    "FF_mkt.rename(columns = {\"Unnamed: 0\":\"date\"}, inplace = True)\n",
    "\n",
    "# Convert date column\n",
    "Industry['date'] = pd.to_datetime(Industry['date'], format = \"%Y%m\")\n",
    "FF_mkt['date'] = pd.to_datetime(FF_mkt['date'], format = \"%Y%m\")\n",
    "\n",
    "Industry['Year'] = pd.DatetimeIndex(Industry.loc[:,'date']).year\n",
    "Industry['Month'] = pd.DatetimeIndex(Industry.loc[:,'date']).month\n",
    "FF_mkt['Year'] = pd.DatetimeIndex(FF_mkt.loc[:,'date']).year\n",
    "FF_mkt['Month'] = pd.DatetimeIndex(FF_mkt.loc[:,'date']).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Industry = Industry.loc[((Industry['Year']>=1989) & (Industry['Year']<=2019)),:].reset_index(drop=True)\n",
    "Industry = Industry.iloc[8:372,:].reset_index(drop=True)\n",
    "FF_mkt = FF_mkt.loc[((FF_mkt['Year']>=1989) & (FF_mkt['Year']<=2019)),:].reset_index(drop=True)\n",
    "FF_mkt = FF_mkt.iloc[8:372,:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1989-09-01</td>\n",
       "      <td>-0.0076</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.0134</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>1989</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989-10-01</td>\n",
       "      <td>-0.0367</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>-0.0103</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>1989</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989-11-01</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>-0.0124</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>1989</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989-12-01</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>1989</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>-0.0785</td>\n",
       "      <td>-0.0129</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>-0.0258</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>-0.0499</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>-0.0207</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>-0.0186</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  Mkt-RF     SMB     HML      RF  Year  Month\n",
       "0   1989-09-01 -0.0076  0.0028 -0.0134  0.0065  1989      9\n",
       "1   1989-10-01 -0.0367  -0.033 -0.0103  0.0068  1989     10\n",
       "2   1989-11-01  0.0103 -0.0124 -0.0112  0.0069  1989     11\n",
       "3   1989-12-01  0.0116 -0.0241  0.0028  0.0061  1989     12\n",
       "4   1990-01-01 -0.0785 -0.0129  0.0087  0.0057  1990      1\n",
       "..         ...     ...     ...     ...     ...   ...    ...\n",
       "359 2019-08-01 -0.0258 -0.0241 -0.0499  0.0016  2019      8\n",
       "360 2019-09-01  0.0144  -0.009  0.0671  0.0018  2019      9\n",
       "361 2019-10-01  0.0206  0.0025 -0.0207  0.0015  2019     10\n",
       "362 2019-11-01  0.0387  0.0087 -0.0186  0.0012  2019     11\n",
       "363 2019-12-01  0.0277  0.0068  0.0183  0.0014  2019     12\n",
       "\n",
       "[364 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FF_mkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>ExNoDur</th>\n",
       "      <th>ExDurbl</th>\n",
       "      <th>ExManuf</th>\n",
       "      <th>ExEnrgy</th>\n",
       "      <th>ExHiTec</th>\n",
       "      <th>ExTelcm</th>\n",
       "      <th>ExShops</th>\n",
       "      <th>ExHlth</th>\n",
       "      <th>ExUtils</th>\n",
       "      <th>ExOther</th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>Mean_rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1989-09-01</td>\n",
       "      <td>1989</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.0119</td>\n",
       "      <td>-0.0525</td>\n",
       "      <td>-0.0318</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>-0.0336</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>-0.0288</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>-0.0017</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>-0.0076</td>\n",
       "      <td>-0.009427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989-10-01</td>\n",
       "      <td>1989</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>-0.0638</td>\n",
       "      <td>-0.0504</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>-0.0510</td>\n",
       "      <td>-0.0272</td>\n",
       "      <td>-0.0479</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>-0.0553</td>\n",
       "      <td>-0.0367</td>\n",
       "      <td>-0.033255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989-11-01</td>\n",
       "      <td>1989</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>-0.0187</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>-0.0249</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>-0.0071</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.009918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989-12-01</td>\n",
       "      <td>1989</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>-0.0206</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.0525</td>\n",
       "      <td>-0.0170</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>-0.0211</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.012764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.1000</td>\n",
       "      <td>-0.0441</td>\n",
       "      <td>-0.0686</td>\n",
       "      <td>-0.0471</td>\n",
       "      <td>-0.0185</td>\n",
       "      <td>-0.1397</td>\n",
       "      <td>-0.0681</td>\n",
       "      <td>-0.0794</td>\n",
       "      <td>-0.0592</td>\n",
       "      <td>-0.0940</td>\n",
       "      <td>-0.0785</td>\n",
       "      <td>-0.072473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.0144</td>\n",
       "      <td>-0.0440</td>\n",
       "      <td>-0.0199</td>\n",
       "      <td>-0.0899</td>\n",
       "      <td>-0.0295</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>-0.0455</td>\n",
       "      <td>-0.0258</td>\n",
       "      <td>-0.021727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0308</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>-0.0072</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>0.019164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0699</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>-0.0154</td>\n",
       "      <td>0.0280</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.016445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0499</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>-0.0219</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.026845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0334</td>\n",
       "      <td>0.0501</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0616</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.0319</td>\n",
       "      <td>0.0380</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.030645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  Year  Month  ExNoDur  ExDurbl  ExManuf  ExEnrgy  ExHiTec  \\\n",
       "0   1989-09-01  1989      9  -0.0119  -0.0525  -0.0318   0.0076  -0.0336   \n",
       "1   1989-10-01  1989     10  -0.0317  -0.0638  -0.0504  -0.0022  -0.0510   \n",
       "2   1989-11-01  1989     11   0.0099  -0.0187   0.0237   0.0324  -0.0249   \n",
       "3   1989-12-01  1989     12   0.0075  -0.0206   0.0238   0.0525  -0.0170   \n",
       "4   1990-01-01  1990      1  -0.1000  -0.0441  -0.0686  -0.0471  -0.0185   \n",
       "..         ...   ...    ...      ...      ...      ...      ...      ...   \n",
       "359 2019-08-01  2019      8  -0.0144  -0.0440  -0.0199  -0.0899  -0.0295   \n",
       "360 2019-09-01  2019      9   0.0163   0.0308   0.0384   0.0351   0.0096   \n",
       "361 2019-10-01  2019     10  -0.0014   0.0699  -0.0003  -0.0220   0.0320   \n",
       "362 2019-11-01  2019     11   0.0211   0.0268   0.0323   0.0111   0.0499   \n",
       "363 2019-12-01  2019     12   0.0334   0.0501   0.0129   0.0616   0.0359   \n",
       "\n",
       "     ExTelcm  ExShops  ExHlth  ExUtils  ExOther  Mkt-RF  Mean_rows  \n",
       "0     0.0316  -0.0288  0.0173  -0.0017   0.0077 -0.0076  -0.009427  \n",
       "1    -0.0272  -0.0479 -0.0029   0.0033  -0.0553 -0.0367  -0.033255  \n",
       "2     0.0130   0.0067  0.0384   0.0254  -0.0071  0.0103   0.009918  \n",
       "3     0.0512   0.0063  0.0044   0.0418  -0.0211  0.0116   0.012764  \n",
       "4    -0.1397  -0.0681 -0.0794  -0.0592  -0.0940 -0.0785  -0.072473  \n",
       "..       ...      ...     ...      ...      ...     ...        ...  \n",
       "359   0.0027  -0.0005 -0.0061   0.0339  -0.0455 -0.0258  -0.021727  \n",
       "360   0.0112   0.0068 -0.0072   0.0374   0.0180  0.0144   0.019164  \n",
       "361   0.0208   0.0055  0.0432  -0.0154   0.0280  0.0206   0.016445  \n",
       "362   0.0163   0.0208  0.0518  -0.0219   0.0484  0.0387   0.026845  \n",
       "363   0.0109   0.0122  0.0319   0.0380   0.0225  0.0277   0.030645  \n",
       "\n",
       "[364 rows x 15 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(Industry, FF_mkt, on=[\"date\",\"Year\",\"Month\"])\n",
    "data = data.iloc[:,[0,11,12,1,2,3,4,5,6,7,8,9,10,13,16]]\n",
    "data_means = data.iloc[:,3:15]\n",
    "data_means = data_means.apply(pd.to_numeric)\n",
    "temp = pd.DataFrame({\"ExNoDur\": data_means['NoDur'] - data_means['RF'],\n",
    "                     \"ExDurbl\": data_means['Durbl'] - data_means['RF'], \n",
    "                     \"ExManuf\": data_means['Manuf'] - data_means['RF'],\n",
    "                     \"ExEnrgy\": data_means['Enrgy'] - data_means['RF'], \n",
    "                     \"ExHiTec\": data_means['HiTec'] - data_means['RF'],\n",
    "                     \"ExTelcm\": data_means['Telcm'] - data_means['RF'], \n",
    "                     \"ExShops\": data_means['Shops'] - data_means['RF'],\n",
    "                     \"ExHlth\": data_means['Hlth '] - data_means['RF'], \n",
    "                     \"ExUtils\": data_means['Utils'] - data_means['RF'],\n",
    "                     \"ExOther\": data_means['Other'] - data_means['RF'], \n",
    "                     \"Mkt-RF\":data_means['Mkt-RF']})\n",
    "\n",
    "temp['Mean_rows'] = temp.mean(axis=1)\n",
    "data = pd.concat([data.loc[:,[\"date\",\"Year\",\"Month\"]], temp],axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_info(data, M):\n",
    "    \n",
    "    # define X\n",
    "    X = data.iloc[:,3:15]\n",
    "    #X = data.loc[:,  data.columns != \"date\"]\n",
    "    \n",
    "    # define N\n",
    "    N = len(X.columns)\n",
    "    \n",
    "    # Define table\n",
    "    table = pd.DataFrame(0, columns = ['mean', 'std', 'Sharpe', 'ceq', 'turn_over'], index = ['ew', 'vw', 'mve_in', 'mve_out', 'shrink_mve_out', 'rp'])\n",
    "    \n",
    "    # ==== Equal weighting ====\n",
    "    table.loc['ew', 'mean'] = X.apply(np.mean, axis = 1).mean()\n",
    "    table.loc['ew', 'std'] = X.apply(np.mean, axis = 1).std()\n",
    "    table.loc['ew', 'Sharpe'] = table.loc['ew', 'mean']/table.loc['ew', 'std']\n",
    "    table.loc['ew', 'ceq'] = table.loc['ew', 'mean'] - 0.5 * table.loc['ew', 'std']**2\n",
    "\n",
    "    # Get turn_over\n",
    "    turn_over = np.zeros(len(X))\n",
    "    for i in range(len(X)):\n",
    "        if i == 0:\n",
    "            turn_over[i] = 1\n",
    "        else:\n",
    "            R = 1 + X.iloc[i - 1, :]\n",
    "            turn_over[i] = 1/N * np.absolute(1 - R/R.mean()).sum()\n",
    "    \n",
    "    table.loc['ew', 'turn_over'] = turn_over.mean()\n",
    "    \n",
    "    \n",
    "    # === Value weighting ===\n",
    "    table.loc['vw', 'mean'] = X['Mkt-RF'].mean()\n",
    "    table.loc['vw', 'std'] = X['Mkt-RF'].std()\n",
    "    table.loc['vw', 'Sharpe'] = table.loc['vw', 'mean']/table.loc['vw', 'std']\n",
    "    table.loc['vw', 'turn_over'] = 0\n",
    "    table.loc['vw', 'ceq'] = table.loc['vw', 'mean'] - 0.5 * table.loc['vw', 'std']**2\n",
    "    \n",
    "    # === MVE in-sample === \n",
    "    Sigma = X.cov()\n",
    "    mu = X.mean()\n",
    "    w = np.linalg.pinv(Sigma) @ mu/(np.ones(N).T @ np.linalg.pinv(Sigma) @ mu)\n",
    "    \n",
    "    if w @ mu > 0:\n",
    "        table.loc['mve_in', 'mean'] = w @ mu\n",
    "        table.loc['mve_in', 'std'] = np.sqrt(w.T @ Sigma @ w)\n",
    "        table.loc['mve_in', 'Sharpe'] = w @ mu/np.sqrt(w @ Sigma @ w.T)\n",
    "    else:\n",
    "        table.loc['mve_in', 'mean'] = -w @ mu\n",
    "        table.loc['mve_in', 'std'] = np.sqrt(w.T @ Sigma @ w)\n",
    "        table.loc['mve_in', 'Sharpe'] = -w @ mu/np.sqrt(w @ Sigma @ w.T)\n",
    "            \n",
    "    table.loc['mve_in', 'turn_over'] = np.nan\n",
    "    table.loc['mve_in', 'ceq'] = table.loc['mve_in', 'mean'] - 0.5 * table.loc['mve_in', 'std']**2\n",
    "    \n",
    "    # === MVE out-sample ===\n",
    "    outsample = np.zeros(len(X) - M)\n",
    "    turn_over = np.zeros(len(X) - M)\n",
    "    w = np.zeros(N)\n",
    "\n",
    "    for i in range(M, len(X)):\n",
    "        Sigma = X.iloc[(i - M):i, :].cov()\n",
    "        mu = X.iloc[(i - M):i, :].mean()\n",
    "        w_new = np.linalg.pinv(Sigma) @ mu/(np.ones(N).T @ np.linalg.pinv(Sigma) @ mu)\n",
    "        R = 1 + X.iloc[i - 1, :]\n",
    "        turn_over[i - M] = np.sum(np.absolute(w_new - (w * R)/(w @ R)))\n",
    "        w = w_new\n",
    "        if w @ mu > 0:\n",
    "            outsample[i - M] = X.iloc[i, :] @ w\n",
    "        else:\n",
    "            outsample[i - M] = -X.iloc[i, :] @ w\n",
    "    \n",
    "    table.loc['mve_out', 'mean'] = np.mean(outsample)\n",
    "    table.loc['mve_out', 'std'] = np.std(outsample)\n",
    "    table.loc['mve_out', 'Sharpe'] = np.mean(outsample)/np.std(outsample)\n",
    "    table.loc['mve_out', 'turn_over'] = turn_over.mean()\n",
    "    table.loc['mve_out', 'ceq'] = table.loc['mve_out', 'mean'] - 0.5 * table.loc['mve_out', 'std']**2\n",
    "    \n",
    "    # === Shrink MVE out-of-sample === \n",
    "    outsample = np.zeros(len(X) - M)\n",
    "    turn_over = np.zeros(len(X) - M)\n",
    "    w = np.zeros(N)\n",
    "\n",
    "    for i in range(M, len(X)):     \n",
    "        # Record sample covariance matrix\n",
    "        S = X.iloc[(i - M):i, :].cov()\n",
    "        \n",
    "        # Define target matrix\n",
    "        target = np.mean(np.diag(S)) * np.eye(N)\n",
    "        \n",
    "        # Define function to help compute omega2\n",
    "        f = lambda row: ((X.iloc[row, :] @ X.iloc[row, :].T - S)**2).sum()\n",
    "  \n",
    "        # Compute non-idiosyncratic variance of variance\n",
    "        omega2 = np.nanmean([f(x) for x in range(M + 1)])/(M - 1)\n",
    "  \n",
    "        # Calculate total variation of variance\n",
    "        total_var = ((S - target)**2).sum().sum()\n",
    "  \n",
    "        # Calculate idiosyncratic variance of variance\n",
    "        delta2 = total_var - omega2\n",
    "  \n",
    "        # Compute shrinkage parameter\n",
    "        beta = np.max([delta2/total_var, 0])\n",
    "  \n",
    "        # Get Sigma_hat\n",
    "        Sigma = (1 - beta) * target + beta * S\n",
    "        \n",
    "        # Record sample mean\n",
    "        m = X.iloc[(i - M):i, :].mean()\n",
    "        \n",
    "        # Record target\n",
    "        target = np.mean(m)\n",
    "        \n",
    "        # Calculate variance of mean estimate\n",
    "        omega2 = (X.iloc[(i - M):i, :].var()/M).mean()\n",
    "        \n",
    "        # Calculate total variance\n",
    "        total_var = np.mean(m.sub(target)**2)\n",
    "        \n",
    "        # Calculate idiosyncratic variance\n",
    "        delta2 = total_var - omega2\n",
    "        \n",
    "        # Compute shrinkage parameter\n",
    "        beta = np.max([delta2/total_var, 0])\n",
    "        \n",
    "        # Compute mu estimate\n",
    "        mu = (1 - beta) * target + beta * m\n",
    "    \n",
    "        w_new = np.linalg.pinv(Sigma) @ mu/(np.ones(N).T @ np.linalg.pinv(Sigma) @ mu)\n",
    "        R = 1 + X.iloc[i - 1, :]\n",
    "        turn_over[i - M] = np.sum(np.absolute(w_new - (w * R)/(w @ R)))\n",
    "        \n",
    "        w = w_new\n",
    "        if w @ mu > 0:\n",
    "            outsample[i - M] = X.iloc[i, :] @ w\n",
    "        else:\n",
    "            outsample[i - M] = -X.iloc[i, :] @ w\n",
    "    \n",
    "    table.loc['shrink_mve_out', 'mean'] = np.mean(outsample)\n",
    "    table.loc['shrink_mve_out', 'std'] = np.std(outsample)\n",
    "    table.loc['shrink_mve_out', 'Sharpe'] = np.mean(outsample)/np.std(outsample)\n",
    "    table.loc['shrink_mve_out', 'turn_over'] = turn_over.mean()\n",
    "    table.loc['shrink_mve_out', 'ceq'] = table.loc['shrink_mve_out', 'mean'] - 0.5 * table.loc['shrink_mve_out', 'std']**2\n",
    "      \n",
    "    # === RP ===\n",
    "    returns = np.zeros(len(X) - M)\n",
    "    turn_over = np.zeros(len(X) - M)\n",
    "    w = np.zeros(N)\n",
    "    \n",
    "    for i in range(M, len(X)):\n",
    "        sigma = np.sqrt(np.diag(X.iloc[(i - M):i, :].cov()))\n",
    "        w_new = (1/sigma)/np.sum(1/sigma)\n",
    "        returns[i - M] = w_new.T @ X.iloc[i, :]\n",
    "        R = 1 + X.iloc[i - 1, :]\n",
    "        turn_over[i - M] = np.sum(np.absolute(w_new - (w * R)/(w @ R)))\n",
    "        w = w_new\n",
    "        \n",
    "    table.loc['rp', 'mean'] = np.mean(returns)\n",
    "    table.loc['rp', 'std'] = np.std(returns)\n",
    "    table.loc['rp', 'Sharpe'] = np.mean(returns)/np.std(returns)\n",
    "    table.loc['rp', 'turn_over'] = turn_over.mean()\n",
    "    table.loc['rp', 'ceq'] = table.loc['rp', 'mean'] - 0.5 * table.loc['rp', 'std']**2\n",
    "    \n",
    "    return(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_Industry = get_info(data, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>ceq</th>\n",
       "      <th>turn_over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.039300</td>\n",
       "      <td>0.173520</td>\n",
       "      <td>0.006047</td>\n",
       "      <td>0.022884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.044141</td>\n",
       "      <td>0.288708</td>\n",
       "      <td>0.011770</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.018310</td>\n",
       "      <td>0.368839</td>\n",
       "      <td>0.049642</td>\n",
       "      <td>-0.049711</td>\n",
       "      <td>38.960477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.036177</td>\n",
       "      <td>0.152930</td>\n",
       "      <td>0.004878</td>\n",
       "      <td>0.057253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.007290</td>\n",
       "      <td>0.038231</td>\n",
       "      <td>0.190672</td>\n",
       "      <td>0.006559</td>\n",
       "      <td>0.021706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std    Sharpe       ceq  turn_over\n",
       "ew              0.006819  0.039300  0.173520  0.006047   0.022884\n",
       "vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       "mve_in          0.012744  0.044141  0.288708  0.011770        NaN\n",
       "mve_out         0.018310  0.368839  0.049642 -0.049711  38.960477\n",
       "shrink_mve_out  0.005533  0.036177  0.152930  0.004878   0.057253\n",
       "rp              0.007290  0.038231  0.190672  0.006559   0.021706"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_Industry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Charles Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"D:/Quarter3/QAM/Project\")\n",
    "# FF25 portfolio\n",
    "FF25 = pd.read_csv(\"FF_25_Portfolios.csv\", nrows = 1127)\n",
    "\n",
    "## Fama-French 3 factor\n",
    "FF3 = pd.read_csv(\"FF3.csv\")\n",
    "\n",
    "## Fama-French mom\n",
    "FFmom = pd.read_csv(\"FF_Mom.csv\")\n",
    "\n",
    "\n",
    "# Record CRISP unkowns\n",
    "unknowns = [\"-66.0\", \"-77.0\", \"-88.0\", \"-99.0\", \"-99.99\", \"-999\", \"A\", \"B\", \"C\", \"D\", \"E\", \"S\", \"T\", \"P\"]\n",
    "\n",
    "# Create function to convert CRISP unkowns to np.nan\n",
    "convert_unknows = np.vectorize(lambda x: np.nan if x in unknowns else x)\n",
    "\n",
    "# Convert to decimal\n",
    "FF25.iloc[:, 1:] = FF25.iloc[:, 1:].apply(convert_unknows, axis = 0).astype(float).div(100)\n",
    "FF3.iloc[:, 1:] =FF3.iloc[:, 1:].apply(convert_unknows, axis = 0).astype(float).div(100)\n",
    "FFmom.iloc[:, 1] = FFmom.iloc[:, 1].apply(convert_unknows).div(100)\n",
    "\n",
    "# Rename data columns\n",
    "FF25.rename(columns = {\"Unnamed: 0\":\"date\"}, inplace = True)\n",
    "FF3.rename(columns = {\"Unnamed: 0\":\"date\"}, inplace = True)\n",
    "FFmom.rename(columns = {\"Unnamed: 0\":\"date\"}, inplace = True)\n",
    "\n",
    "# Convert date column\n",
    "FF25['date'] = pd.to_datetime(FF25['date'], format = \"%Y%m\")\n",
    "FF3['date'] = pd.to_datetime(FF3['date'], format = \"%Y%m\") \n",
    "FFmom['date'] = pd.to_datetime(FFmom['date'], format = \"%Y%m\") \n",
    "\n",
    "# Remove the top 5 ME quintiles\n",
    "ME = []\n",
    "for i in range(len(FF25.columns)):  \n",
    "    if FF25.columns[i][:3] != 'ME5':  \n",
    "        ME.append(i)\n",
    "\n",
    "FF25 = FF25.iloc[:, ME]\n",
    "FF25.drop(['BIG LoBM', 'BIG HiBM'], axis = 1, inplace = True)\n",
    "\n",
    "col4 = FF3.drop('RF', axis = 1)\n",
    "col5 = FF25.merge(FF3[['date', 'Mkt-RF', 'RF']], on = 'date')\n",
    "col6 = FF25.merge(FF3, on = 'date' ).merge(FFmom, on = 'date')\n",
    "\n",
    "col5.iloc[:, 1:21] = col5.iloc[:, 1:21].sub(col5['RF'], axis = 0)\n",
    "col6.iloc[:, 1:21] = col6.iloc[:, 1:21].sub(col6['RF'], axis = 0)\n",
    "    \n",
    "col5.drop('RF', axis = 1, inplace = True)\n",
    "col6.drop('RF', axis = 1, inplace = True)\n",
    "\n",
    "start = pd.to_datetime(\"19899\", format = \"%Y%m\")\n",
    "end = pd.to_datetime(\"201912\", format = \"%Y%m\")\n",
    "\n",
    "col4 = col4.loc[(col4.date >= start) & (col4.date <= end)]\n",
    "col5 = col5.loc[(col5.date >= start) & (col5.date <= end)]\n",
    "col6 = col6.loc[(col6.date >= start) & (col6.date <= end)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_mkt_smb_hml = get_info(col4, 60)\n",
    "result_ff1 = get_info(col5, 60)\n",
    "result_ff4 = get_info(col6, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>ceq</th>\n",
       "      <th>turn_over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.039300</td>\n",
       "      <td>0.173520</td>\n",
       "      <td>0.006047</td>\n",
       "      <td>0.022884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.044141</td>\n",
       "      <td>0.288708</td>\n",
       "      <td>0.011770</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.018310</td>\n",
       "      <td>0.368839</td>\n",
       "      <td>0.049642</td>\n",
       "      <td>-0.049711</td>\n",
       "      <td>38.960477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.036177</td>\n",
       "      <td>0.152930</td>\n",
       "      <td>0.004878</td>\n",
       "      <td>0.057253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.007290</td>\n",
       "      <td>0.038231</td>\n",
       "      <td>0.190672</td>\n",
       "      <td>0.006559</td>\n",
       "      <td>0.021706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std    Sharpe       ceq  turn_over\n",
       "ew              0.006819  0.039300  0.173520  0.006047   0.022884\n",
       "vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       "mve_in          0.012744  0.044141  0.288708  0.011770        NaN\n",
       "mve_out         0.018310  0.368839  0.049642 -0.049711  38.960477\n",
       "shrink_mve_out  0.005533  0.036177  0.152930  0.004878   0.057253\n",
       "rp              0.007290  0.038231  0.190672  0.006559   0.021706"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>std</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>ceq</th>\n",
       "      <th>turn_over</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>0.002909</td>\n",
       "      <td>0.019409</td>\n",
       "      <td>0.149891</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.023718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.004402</td>\n",
       "      <td>0.025830</td>\n",
       "      <td>0.170433</td>\n",
       "      <td>0.004069</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.210896</td>\n",
       "      <td>0.015748</td>\n",
       "      <td>-0.018917</td>\n",
       "      <td>34.568402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.027915</td>\n",
       "      <td>0.138826</td>\n",
       "      <td>0.003486</td>\n",
       "      <td>0.150042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.002368</td>\n",
       "      <td>0.018359</td>\n",
       "      <td>0.128997</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.024765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std    Sharpe       ceq  turn_over\n",
       "ew              0.002909  0.019409  0.149891  0.002721   0.023718\n",
       "vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       "mve_in          0.004402  0.025830  0.170433  0.004069        NaN\n",
       "mve_out         0.003321  0.210896  0.015748 -0.018917  34.568402\n",
       "shrink_mve_out  0.003875  0.027915  0.138826  0.003486   0.150042\n",
       "rp              0.002368  0.018359  0.128997  0.002200   0.024765"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_mkt_smb_hml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>std</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>ceq</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>0.007769</td>\n",
       "      <td>0.050617</td>\n",
       "      <td>0.153490</td>\n",
       "      <td>0.006488</td>\n",
       "      <td>0.018814</td>\n",
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       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.025654</td>\n",
       "      <td>0.049555</td>\n",
       "      <td>0.517681</td>\n",
       "      <td>0.024426</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.023020</td>\n",
       "      <td>0.161758</td>\n",
       "      <td>0.142310</td>\n",
       "      <td>0.009937</td>\n",
       "      <td>12.840058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.047239</td>\n",
       "      <td>0.155231</td>\n",
       "      <td>0.006217</td>\n",
       "      <td>0.084851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.008547</td>\n",
       "      <td>0.050516</td>\n",
       "      <td>0.169190</td>\n",
       "      <td>0.007271</td>\n",
       "      <td>0.017888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std    Sharpe       ceq  turn_over\n",
       "ew              0.007769  0.050617  0.153490  0.006488   0.018814\n",
       "vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       "mve_in          0.025654  0.049555  0.517681  0.024426        NaN\n",
       "mve_out         0.023020  0.161758  0.142310  0.009937  12.840058\n",
       "shrink_mve_out  0.007333  0.047239  0.155231  0.006217   0.084851\n",
       "rp              0.008547  0.050516  0.169190  0.007271   0.017888"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_ff1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>0.007105</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>0.159727</td>\n",
       "      <td>0.006116</td>\n",
       "      <td>0.022231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.006833</td>\n",
       "      <td>0.012363</td>\n",
       "      <td>0.552708</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.105233</td>\n",
       "      <td>1.733706</td>\n",
       "      <td>0.060698</td>\n",
       "      <td>-1.397635</td>\n",
       "      <td>46.158496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.005015</td>\n",
       "      <td>0.034197</td>\n",
       "      <td>0.146642</td>\n",
       "      <td>0.004430</td>\n",
       "      <td>0.057106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.007191</td>\n",
       "      <td>0.041462</td>\n",
       "      <td>0.173446</td>\n",
       "      <td>0.006332</td>\n",
       "      <td>0.023530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std    Sharpe       ceq  turn_over\n",
       "ew              0.007105  0.044485  0.159727  0.006116   0.022231\n",
       "vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       "mve_in          0.006833  0.012363  0.552708  0.006757        NaN\n",
       "mve_out         0.105233  1.733706  0.060698 -1.397635  46.158496\n",
       "shrink_mve_out  0.005015  0.034197  0.146642  0.004430   0.057106\n",
       "rp              0.007191  0.041462  0.173446  0.006332   0.023530"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_ff4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pd.DataFrame(0, columns = ['Industry Portfolios', 'Mkt/SMB/HML', 'FF 1-factor', 'FF 4-factor'], index = ['1/N', 'mve_in', 'mve_out', 'shrink_mve_out', 'vw','rp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = [result_Industry, result_mkt_smb_hml, result_ff1, result_ff4 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                    mean       std    Sharpe       ceq  turn_over\n",
       " ew              0.006819  0.039300  0.173520  0.006047   0.022884\n",
       " vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       " mve_in          0.012744  0.044141  0.288708  0.011770        NaN\n",
       " mve_out         0.018310  0.368839  0.049642 -0.049711  38.960477\n",
       " shrink_mve_out  0.005533  0.036177  0.152930  0.004878   0.057253\n",
       " rp              0.007290  0.038231  0.190672  0.006559   0.021706,\n",
       "                     mean       std    Sharpe       ceq  turn_over\n",
       " ew              0.002909  0.019409  0.149891  0.002721   0.023718\n",
       " vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       " mve_in          0.004402  0.025830  0.170433  0.004069        NaN\n",
       " mve_out         0.003321  0.210896  0.015748 -0.018917  34.568402\n",
       " shrink_mve_out  0.003875  0.027915  0.138826  0.003486   0.150042\n",
       " rp              0.002368  0.018359  0.128997  0.002200   0.024765,\n",
       "                     mean       std    Sharpe       ceq  turn_over\n",
       " ew              0.007769  0.050617  0.153490  0.006488   0.018814\n",
       " vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       " mve_in          0.025654  0.049555  0.517681  0.024426        NaN\n",
       " mve_out         0.023020  0.161758  0.142310  0.009937  12.840058\n",
       " shrink_mve_out  0.007333  0.047239  0.155231  0.006217   0.084851\n",
       " rp              0.008547  0.050516  0.169190  0.007271   0.017888,\n",
       "                     mean       std    Sharpe       ceq  turn_over\n",
       " ew              0.007105  0.044485  0.159727  0.006116   0.022231\n",
       " vw              0.006558  0.042175  0.155488  0.005668   0.000000\n",
       " mve_in          0.006833  0.012363  0.552708  0.006757        NaN\n",
       " mve_out         0.105233  1.733706  0.060698 -1.397635  46.158496\n",
       " shrink_mve_out  0.005015  0.034197  0.146642  0.004430   0.057106\n",
       " rp              0.007191  0.041462  0.173446  0.006332   0.023530]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Industry Portfolios</th>\n",
       "      <th>Mkt/SMB/HML</th>\n",
       "      <th>FF 1-factor</th>\n",
       "      <th>FF 4-factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/N</th>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.002909</td>\n",
       "      <td>0.007769</td>\n",
       "      <td>0.007105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.006558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.004402</td>\n",
       "      <td>0.025654</td>\n",
       "      <td>0.006833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.018310</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.023020</td>\n",
       "      <td>0.105233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.005015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.007290</td>\n",
       "      <td>0.002368</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>0.007191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Industry Portfolios  Mkt/SMB/HML  FF 1-factor  FF 4-factor\n",
       "1/N                        0.006819     0.002909     0.007769     0.007105\n",
       "mve_in                     0.006558     0.006558     0.006558     0.006558\n",
       "mve_out                    0.012744     0.004402     0.025654     0.006833\n",
       "shrink_mve_out             0.018310     0.003321     0.023020     0.105233\n",
       "vw                         0.005533     0.003875     0.007333     0.005015\n",
       "rp                         0.007290     0.002368     0.008547     0.007191"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_returns = pd.DataFrame([result_Industry.iloc[:,0] , result_mkt_smb_hml.iloc[:,0],  result_ff1.iloc[:,0],  result_ff4.iloc[:,0]]).T\n",
    "expected_returns.index = ['1/N', 'mve_in', 'mve_out', 'shrink_mve_out', 'vw','rp']\n",
    "expected_returns.columns = ['Industry Portfolios', 'Mkt/SMB/HML', 'FF 1-factor', 'FF 4-factor']\n",
    "expected_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Industry Portfolios</th>\n",
       "      <th>Mkt/SMB/HML</th>\n",
       "      <th>FF 1-factor</th>\n",
       "      <th>FF 4-factor</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/N</th>\n",
       "      <td>0.039300</td>\n",
       "      <td>0.019409</td>\n",
       "      <td>0.050617</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.042175</td>\n",
       "      <td>0.042175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.044141</td>\n",
       "      <td>0.025830</td>\n",
       "      <td>0.049555</td>\n",
       "      <td>0.012363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.368839</td>\n",
       "      <td>0.210896</td>\n",
       "      <td>0.161758</td>\n",
       "      <td>1.733706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.036177</td>\n",
       "      <td>0.027915</td>\n",
       "      <td>0.047239</td>\n",
       "      <td>0.034197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.038231</td>\n",
       "      <td>0.018359</td>\n",
       "      <td>0.050516</td>\n",
       "      <td>0.041462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Industry Portfolios  Mkt/SMB/HML  FF 1-factor  FF 4-factor\n",
       "1/N                        0.039300     0.019409     0.050617     0.044485\n",
       "mve_in                     0.042175     0.042175     0.042175     0.042175\n",
       "mve_out                    0.044141     0.025830     0.049555     0.012363\n",
       "shrink_mve_out             0.368839     0.210896     0.161758     1.733706\n",
       "vw                         0.036177     0.027915     0.047239     0.034197\n",
       "rp                         0.038231     0.018359     0.050516     0.041462"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev = pd.DataFrame([result_Industry.iloc[:,1] , result_mkt_smb_hml.iloc[:,1],  result_ff1.iloc[:,1],  result_ff4.iloc[:,1]]).T\n",
    "std_dev.index = ['1/N', 'mve_in', 'mve_out', 'shrink_mve_out', 'vw','rp']\n",
    "std_dev.columns = ['Industry Portfolios', 'Mkt/SMB/HML', 'FF 1-factor', 'FF 4-factor']\n",
    "std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Industry Portfolios</th>\n",
       "      <th>Mkt/SMB/HML</th>\n",
       "      <th>FF 1-factor</th>\n",
       "      <th>FF 4-factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/N</th>\n",
       "      <td>0.173520</td>\n",
       "      <td>0.149891</td>\n",
       "      <td>0.153490</td>\n",
       "      <td>0.159727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.155488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.288708</td>\n",
       "      <td>0.170433</td>\n",
       "      <td>0.517681</td>\n",
       "      <td>0.552708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>0.049642</td>\n",
       "      <td>0.015748</td>\n",
       "      <td>0.142310</td>\n",
       "      <td>0.060698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.152930</td>\n",
       "      <td>0.138826</td>\n",
       "      <td>0.155231</td>\n",
       "      <td>0.146642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.190672</td>\n",
       "      <td>0.128997</td>\n",
       "      <td>0.169190</td>\n",
       "      <td>0.173446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Industry Portfolios  Mkt/SMB/HML  FF 1-factor  FF 4-factor\n",
       "1/N                        0.173520     0.149891     0.153490     0.159727\n",
       "mve_in                     0.155488     0.155488     0.155488     0.155488\n",
       "mve_out                    0.288708     0.170433     0.517681     0.552708\n",
       "shrink_mve_out             0.049642     0.015748     0.142310     0.060698\n",
       "vw                         0.152930     0.138826     0.155231     0.146642\n",
       "rp                         0.190672     0.128997     0.169190     0.173446"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe = pd.DataFrame([result_Industry.iloc[:,2] , result_mkt_smb_hml.iloc[:,2],  result_ff1.iloc[:,2],  result_ff4.iloc[:,2]]).T\n",
    "sharpe.index = ['1/N', 'mve_in', 'mve_out', 'shrink_mve_out', 'vw','rp']\n",
    "sharpe.columns = ['Industry Portfolios', 'Mkt/SMB/HML', 'FF 1-factor', 'FF 4-factor']\n",
    "sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Industry Portfolios</th>\n",
       "      <th>Mkt/SMB/HML</th>\n",
       "      <th>FF 1-factor</th>\n",
       "      <th>FF 4-factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/N</th>\n",
       "      <td>0.006047</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.006488</td>\n",
       "      <td>0.006116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.005668</td>\n",
       "      <td>0.005668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>0.011770</td>\n",
       "      <td>0.004069</td>\n",
       "      <td>0.024426</td>\n",
       "      <td>0.006757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>-0.049711</td>\n",
       "      <td>-0.018917</td>\n",
       "      <td>0.009937</td>\n",
       "      <td>-1.397635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.004878</td>\n",
       "      <td>0.003486</td>\n",
       "      <td>0.006217</td>\n",
       "      <td>0.004430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.006559</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.007271</td>\n",
       "      <td>0.006332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Industry Portfolios  Mkt/SMB/HML  FF 1-factor  FF 4-factor\n",
       "1/N                        0.006047     0.002721     0.006488     0.006116\n",
       "mve_in                     0.005668     0.005668     0.005668     0.005668\n",
       "mve_out                    0.011770     0.004069     0.024426     0.006757\n",
       "shrink_mve_out            -0.049711    -0.018917     0.009937    -1.397635\n",
       "vw                         0.004878     0.003486     0.006217     0.004430\n",
       "rp                         0.006559     0.002200     0.007271     0.006332"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceq = pd.DataFrame([result_Industry.iloc[:,3] , result_mkt_smb_hml.iloc[:,3],  result_ff1.iloc[:,3],  result_ff4.iloc[:,3]]).T\n",
    "ceq.index = ['1/N', 'mve_in', 'mve_out', 'shrink_mve_out', 'vw','rp']\n",
    "ceq.columns = ['Industry Portfolios', 'Mkt/SMB/HML', 'FF 1-factor', 'FF 4-factor']\n",
    "ceq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Industry Portfolios</th>\n",
       "      <th>Mkt/SMB/HML</th>\n",
       "      <th>FF 1-factor</th>\n",
       "      <th>FF 4-factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/N</th>\n",
       "      <td>0.022884</td>\n",
       "      <td>0.023718</td>\n",
       "      <td>0.018814</td>\n",
       "      <td>0.022231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_in</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mve_out</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shrink_mve_out</th>\n",
       "      <td>38.960477</td>\n",
       "      <td>34.568402</td>\n",
       "      <td>12.840058</td>\n",
       "      <td>46.158496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vw</th>\n",
       "      <td>0.057253</td>\n",
       "      <td>0.150042</td>\n",
       "      <td>0.084851</td>\n",
       "      <td>0.057106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>0.021706</td>\n",
       "      <td>0.024765</td>\n",
       "      <td>0.017888</td>\n",
       "      <td>0.023530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Industry Portfolios  Mkt/SMB/HML  FF 1-factor  FF 4-factor\n",
       "1/N                        0.022884     0.023718     0.018814     0.022231\n",
       "mve_in                     0.000000     0.000000     0.000000     0.000000\n",
       "mve_out                         NaN          NaN          NaN          NaN\n",
       "shrink_mve_out            38.960477    34.568402    12.840058    46.158496\n",
       "vw                         0.057253     0.150042     0.084851     0.057106\n",
       "rp                         0.021706     0.024765     0.017888     0.023530"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover = pd.DataFrame([result_Industry.iloc[:,4] , result_mkt_smb_hml.iloc[:,4],  result_ff1.iloc[:,4],  result_ff4.iloc[:,4]]).T\n",
    "turnover.index = ['1/N', 'mve_in', 'mve_out', 'shrink_mve_out', 'vw','rp']\n",
    "turnover.columns = ['Industry Portfolios', 'Mkt/SMB/HML', 'FF 1-factor', 'FF 4-factor']\n",
    "turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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