fundamental_back_testing.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 14,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import time\n",
    "from datetime import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 datasets:\n",
    "\n",
    "S&P 500 daily adjusted price\n",
    "\n",
    "Mean-variance weights table\n",
    "\n",
    "Min-variance weights table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_adj = pd.read_csv(\"final_ratios_oct.csv\")\n",
    "weight_meanv = pd.read_excel('mean_weighted_rf.xlsx')\n",
    "weight_minv = pd.read_excel('minimum_weighted_rf.xlsx')\n",
    "weight_equal=pd.read_excel('equally_weighted_rf.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "       Unnamed: 0        date   gvkey  tic  gsector  adj_close_q  y_return  \\\n",
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       "\n",
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     "execution_count": 16,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 17,
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1602</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>0.045424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  trade_date  gvkey  weights  predicted_return\n",
       "0           0  2001-03-01   1078  0.00662          0.044809\n",
       "1           1  2001-03-01   1230  0.00631          0.019396\n",
       "2           2  2001-03-01   1246  0.00629          0.014087\n",
       "3           3  2001-03-01   1327  0.00628          0.128518\n",
       "4           4  2001-03-01   1602  0.00628          0.045424"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_minv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Unnamed: 0</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>gvkey</th>\n",
       "      <th>weights</th>\n",
       "      <th>predicted_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1078</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.044809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1230</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.019396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1246</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.014087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.128518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1602</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.045424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  trade_date  gvkey   weights  predicted_return\n",
       "0           0  2001-03-01   1078  0.006579          0.044809\n",
       "1           1  2001-03-01   1230  0.006579          0.019396\n",
       "2           2  2001-03-01   1246  0.006579          0.014087\n",
       "3           3  2001-03-01   1327  0.006579          0.128518\n",
       "4           4  2001-03-01   1602  0.006579          0.045424"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_equal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16319 16319 16319\n"
     ]
    }
   ],
   "source": [
    "#the length of the 3 tables should be the same\n",
    "print(len(weight_meanv),\n",
    "len(weight_minv),\n",
    "len(weight_equal))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Get stock name, trade date, daily date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add 20170901 as tradedate_full\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get unique stock name from table\n",
    "#stock names are the same between mean variance and minimum variance\n",
    "#only the weights are different\n",
    "stocks_name = list(set(weight_meanv['gvkey']))\n",
    "stocks_name.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "858"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stocks_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'> <class 'list'>\n"
     ]
    }
   ],
   "source": [
    "#get all daily date\n",
    "#convert to list from pandas.core.series.Series\n",
    "#use set to get unique values\n",
    "daily = list(set(data_adj['date']))\n",
    "#sort it\n",
    "daily.sort()\n",
    "print(type(data_adj['date']),type(daily))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(daily)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get unique trade date from table\n",
    "tradedate = list(set(weight_meanv['trade_date']))\n",
    "tradedate.sort()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2001-03-01',\n",
       " '2001-06-01',\n",
       " '2001-09-01',\n",
       " '2001-12-01',\n",
       " '2002-03-01',\n",
       " '2002-06-01',\n",
       " '2002-09-01',\n",
       " '2002-12-01',\n",
       " '2003-03-01',\n",
       " '2003-06-01',\n",
       " '2003-09-01',\n",
       " '2003-12-01',\n",
       " '2004-03-01',\n",
       " '2004-06-01',\n",
       " '2004-09-01',\n",
       " '2004-12-01',\n",
       " '2005-03-01',\n",
       " '2005-06-01',\n",
       " '2005-09-01',\n",
       " '2005-12-01',\n",
       " '2006-03-01',\n",
       " '2006-06-01',\n",
       " '2006-09-01',\n",
       " '2006-12-01',\n",
       " '2007-03-01',\n",
       " '2007-06-01',\n",
       " '2007-09-01',\n",
       " '2007-12-01',\n",
       " '2008-03-01',\n",
       " '2008-06-01',\n",
       " '2008-09-01',\n",
       " '2008-12-01',\n",
       " '2009-03-01',\n",
       " '2009-06-01',\n",
       " '2009-09-01',\n",
       " '2009-12-01',\n",
       " '2010-03-01',\n",
       " '2010-06-01',\n",
       " '2010-09-01',\n",
       " '2010-12-01',\n",
       " '2011-03-01',\n",
       " '2011-06-01',\n",
       " '2011-09-01',\n",
       " '2011-12-01',\n",
       " '2012-03-01',\n",
       " '2012-06-01',\n",
       " '2012-09-01',\n",
       " '2012-12-01',\n",
       " '2013-03-01',\n",
       " '2013-06-01',\n",
       " '2013-09-01',\n",
       " '2013-12-01',\n",
       " '2014-03-01',\n",
       " '2014-06-01',\n",
       " '2014-09-01',\n",
       " '2014-12-01',\n",
       " '2015-03-01',\n",
       " '2015-06-01',\n",
       " '2015-09-01',\n",
       " '2015-12-01',\n",
       " '2016-03-01',\n",
       " '2016-06-01',\n",
       " '2016-09-01',\n",
       " '2016-12-01',\n",
       " '2017-03-01',\n",
       " '2017-06-01',\n",
       " '2017-09-01',\n",
       " '2017-12-01',\n",
       " '2018-03-01',\n",
       " '2018-06-01',\n",
       " '2018-09-01',\n",
       " '2018-12-01',\n",
       " '2019-03-01',\n",
       " '2019-06-01',\n",
       " '2019-09-01',\n",
       " '2019-12-01',\n",
       " '2020-03-01',\n",
       " '2020-06-01',\n",
       " '2020-09-01',\n",
       " '2020-12-01',\n",
       " '2021-03-01',\n",
       " '2021-06-01',\n",
       " '2021-09-01',\n",
       " '2021-12-01',\n",
       " '2022-03-01',\n",
       " '2022-06-01',\n",
       " '2022-09-01',\n",
       " '2022-12-01']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tradedate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tradedate_full = [x for x in tradedate if x > '2018-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2018-03-01',\n",
       " '2018-06-01',\n",
       " '2018-09-01',\n",
       " '2018-12-01',\n",
       " '2019-03-01',\n",
       " '2019-06-01',\n",
       " '2019-09-01',\n",
       " '2019-12-01',\n",
       " '2020-03-01',\n",
       " '2020-06-01',\n",
       " '2020-09-01',\n",
       " '2020-12-01',\n",
       " '2021-03-01',\n",
       " '2021-06-01',\n",
       " '2021-09-01',\n",
       " '2021-12-01',\n",
       " '2022-03-01',\n",
       " '2022-06-01',\n",
       " '2022-09-01',\n",
       " '2022-12-01']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tradedate_full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Get the daily price matrix based on stock_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.190996885299683\n"
     ]
    }
   ],
   "source": [
    "#30mins running time\n",
    "##################match current all stocks to daily adjusted price table#######################\n",
    "# backtest daily price matrix:\n",
    "# covert original table to matrix\n",
    "# rows = all stocks: 971, columns = all dates:7155 days\n",
    "#\n",
    "start_time = time.time()\n",
    "\n",
    "balance_daily = pd.DataFrame(index = stocks_name, columns = daily)\n",
    "balance_daily = balance_daily.fillna(np.nan)\n",
    "\n",
    "for i in stocks_name:\n",
    "    ind = np.where(data_adj['gvkey'] == i)[0]\n",
    "    tmp = data_adj.loc[ind, :]\n",
    "    tmp.index = range(tmp.shape[0])\n",
    "    for j in daily:\n",
    "        ind2 = np.where(tmp['date'] == j)[0]\n",
    "        if(len(ind2) != 0):\n",
    "            balance_daily.loc[i,j] = tmp.loc[ind2[0], 'adj_close_q']\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print(elapsed_time)\n",
    "writer2 = pd.ExcelWriter('balance_daily.xlsx')\n",
    "writer2 = pd.ExcelWriter('balance_daily_user8.xlsx')\n",
    "\n",
    "balance_daily.to_excel(writer2,'Sheet1')\n",
    "writer2.save()\n",
    "###############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "balance_daily = pd.read_excel('balance_daily_user8.xlsx', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>1996-03-01</th>\n",
       "      <th>1996-06-01</th>\n",
       "      <th>1996-09-01</th>\n",
       "      <th>1996-12-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-06-01</th>\n",
       "      <th>1997-09-01</th>\n",
       "      <th>1997-12-01</th>\n",
       "      <th>1998-03-01</th>\n",
       "      <th>1998-06-01</th>\n",
       "      <th>...</th>\n",
       "      <th>2020-09-01</th>\n",
       "      <th>2020-12-01</th>\n",
       "      <th>2021-03-01</th>\n",
       "      <th>2021-06-01</th>\n",
       "      <th>2021-09-01</th>\n",
       "      <th>2021-12-01</th>\n",
       "      <th>2022-03-01</th>\n",
       "      <th>2022-06-01</th>\n",
       "      <th>2022-09-01</th>\n",
       "      <th>2022-12-01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>NaN</td>\n",
       "      <td>44.7500</td>\n",
       "      <td>45.5000</td>\n",
       "      <td>39.81250</td>\n",
       "      <td>44.0625</td>\n",
       "      <td>41.25000</td>\n",
       "      <td>46.25000</td>\n",
       "      <td>55.34350</td>\n",
       "      <td>64.2500</td>\n",
       "      <td>71.59350</td>\n",
       "      <td>...</td>\n",
       "      <td>13.07</td>\n",
       "      <td>12.29</td>\n",
       "      <td>15.77</td>\n",
       "      <td>23.90</td>\n",
       "      <td>21.21</td>\n",
       "      <td>20.52</td>\n",
       "      <td>17.96</td>\n",
       "      <td>18.25</td>\n",
       "      <td>12.68</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.8750</td>\n",
       "      <td>30.3750</td>\n",
       "      <td>29.62500</td>\n",
       "      <td>31.7500</td>\n",
       "      <td>30.12500</td>\n",
       "      <td>30.06200</td>\n",
       "      <td>33.62500</td>\n",
       "      <td>42.3750</td>\n",
       "      <td>44.43700</td>\n",
       "      <td>...</td>\n",
       "      <td>73.29</td>\n",
       "      <td>74.55</td>\n",
       "      <td>79.95</td>\n",
       "      <td>81.35</td>\n",
       "      <td>81.97</td>\n",
       "      <td>72.36</td>\n",
       "      <td>70.59</td>\n",
       "      <td>78.10</td>\n",
       "      <td>73.12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.3750</td>\n",
       "      <td>21.7500</td>\n",
       "      <td>24.62500</td>\n",
       "      <td>25.3750</td>\n",
       "      <td>28.06250</td>\n",
       "      <td>33.37500</td>\n",
       "      <td>31.96850</td>\n",
       "      <td>32.7500</td>\n",
       "      <td>37.65600</td>\n",
       "      <td>...</td>\n",
       "      <td>91.43</td>\n",
       "      <td>108.83</td>\n",
       "      <td>109.49</td>\n",
       "      <td>119.84</td>\n",
       "      <td>115.93</td>\n",
       "      <td>118.13</td>\n",
       "      <td>140.74</td>\n",
       "      <td>118.36</td>\n",
       "      <td>108.65</td>\n",
       "      <td>96.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6875</td>\n",
       "      <td>6.8125</td>\n",
       "      <td>7.37500</td>\n",
       "      <td>12.8750</td>\n",
       "      <td>20.75000</td>\n",
       "      <td>17.93750</td>\n",
       "      <td>16.28100</td>\n",
       "      <td>8.8750</td>\n",
       "      <td>14.53100</td>\n",
       "      <td>...</td>\n",
       "      <td>52.61</td>\n",
       "      <td>81.99</td>\n",
       "      <td>91.71</td>\n",
       "      <td>78.50</td>\n",
       "      <td>93.93</td>\n",
       "      <td>102.90</td>\n",
       "      <td>143.90</td>\n",
       "      <td>109.34</td>\n",
       "      <td>76.47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.8750</td>\n",
       "      <td>17.8750</td>\n",
       "      <td>17.59375</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>21.46875</td>\n",
       "      <td>25.59375</td>\n",
       "      <td>20.35925</td>\n",
       "      <td>17.6405</td>\n",
       "      <td>20.85925</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270281</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.03</td>\n",
       "      <td>4.06</td>\n",
       "      <td>3.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11.13</td>\n",
       "      <td>9.84</td>\n",
       "      <td>12.91</td>\n",
       "      <td>12.82</td>\n",
       "      <td>14.62</td>\n",
       "      <td>14.35</td>\n",
       "      <td>12.89</td>\n",
       "      <td>18.60</td>\n",
       "      <td>10.86</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294524</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>65.72</td>\n",
       "      <td>70.49</td>\n",
       "      <td>91.66</td>\n",
       "      <td>104.05</td>\n",
       "      <td>102.87</td>\n",
       "      <td>93.85</td>\n",
       "      <td>92.23</td>\n",
       "      <td>102.82</td>\n",
       "      <td>87.46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.98</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.22</td>\n",
       "      <td>7.15</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.37</td>\n",
       "      <td>7.07</td>\n",
       "      <td>8.80</td>\n",
       "      <td>8.86</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316056</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>102.22</td>\n",
       "      <td>98.91</td>\n",
       "      <td>116.38</td>\n",
       "      <td>125.62</td>\n",
       "      <td>139.30</td>\n",
       "      <td>132.18</td>\n",
       "      <td>132.44</td>\n",
       "      <td>109.78</td>\n",
       "      <td>97.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>858 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1996-03-01  1996-06-01  1996-09-01  1996-12-01  1997-03-01  \\\n",
       "1045           NaN     44.7500     45.5000    39.81250     44.0625   \n",
       "1075           NaN     28.8750     30.3750    29.62500     31.7500   \n",
       "1078           NaN     20.3750     21.7500    24.62500     25.3750   \n",
       "1161           NaN      8.6875      6.8125     7.37500     12.8750   \n",
       "1177           NaN     18.8750     17.8750    17.59375     20.0000   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "270281         NaN         NaN         NaN         NaN         NaN   \n",
       "287882         NaN         NaN         NaN         NaN         NaN   \n",
       "294524         NaN         NaN         NaN         NaN         NaN   \n",
       "312009         NaN         NaN         NaN         NaN         NaN   \n",
       "316056         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        1997-06-01  1997-09-01  1997-12-01  1998-03-01  1998-06-01  ...  \\\n",
       "1045      41.25000    46.25000    55.34350     64.2500    71.59350  ...   \n",
       "1075      30.12500    30.06200    33.62500     42.3750    44.43700  ...   \n",
       "1078      28.06250    33.37500    31.96850     32.7500    37.65600  ...   \n",
       "1161      20.75000    17.93750    16.28100      8.8750    14.53100  ...   \n",
       "1177      21.46875    25.59375    20.35925     17.6405    20.85925  ...   \n",
       "...            ...         ...         ...         ...         ...  ...   \n",
       "270281         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "287882         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "294524         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "312009         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "316056         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "\n",
       "        2020-09-01  2020-12-01  2021-03-01  2021-06-01  2021-09-01  \\\n",
       "1045         13.07       12.29       15.77       23.90       21.21   \n",
       "1075         73.29       74.55       79.95       81.35       81.97   \n",
       "1078         91.43      108.83      109.49      119.84      115.93   \n",
       "1161         52.61       81.99       91.71       78.50       93.93   \n",
       "1177           NaN         NaN         NaN         NaN         NaN   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "270281        1.72        1.51        2.05        1.73        2.03   \n",
       "287882       11.13        9.84       12.91       12.82       14.62   \n",
       "294524       65.72       70.49       91.66      104.05      102.87   \n",
       "312009        6.98        6.50        6.22        7.15        9.00   \n",
       "316056      102.22       98.91      116.38      125.62      139.30   \n",
       "\n",
       "        2021-12-01  2022-03-01  2022-06-01  2022-09-01  2022-12-01  \n",
       "1045         20.52       17.96       18.25       12.68       12.04  \n",
       "1075         72.36       70.59       78.10       73.12         NaN  \n",
       "1078        118.13      140.74      118.36      108.65       96.76  \n",
       "1161        102.90      143.90      109.34       76.47         NaN  \n",
       "1177           NaN         NaN         NaN         NaN         NaN  \n",
       "...            ...         ...         ...         ...         ...  \n",
       "270281        4.06        3.18         NaN         NaN         NaN  \n",
       "287882       14.35       12.89       18.60       10.86         NaN  \n",
       "294524       93.85       92.23      102.82       87.46         NaN  \n",
       "312009        9.37        7.07        8.80        8.86         NaN  \n",
       "316056      132.18      132.44      109.78       97.90         NaN  \n",
       "\n",
       "[858 rows x 108 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance_daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Function: calculate the rebalance matrix:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. shares table\n",
    "2. cost table\n",
    "3. cash table\n",
    "4. portfolio table\n",
    "5. portfolio_cost table\n",
    "6. portfolio_return table\n",
    "7. portfolio_cumsum table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set initial capital to 1 million and transaction percet to 0.1%\n",
    "\n",
    "capital = 1000000\n",
    "transaction_percent = 1/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16319, 5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_meanv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#construct an empty matrix\n",
    "#rows are stocks: 971 unique stocks\n",
    "#columns are trade dates\n",
    "#name the index as stock names\n",
    "\n",
    "def cal_portfolio(stocks_name,tradedate, weight_table, capital, transaction_percent):\n",
    "\n",
    "\n",
    "    balance_weight = pd.DataFrame(index = stocks_name, columns = tradedate)\n",
    "    balance_weight = balance_weight.fillna(0)\n",
    "    \n",
    "    #loop through the weights table to fill the matrix\n",
    "    #need weight tabele\n",
    "    #0. weights table\n",
    "    for i in range(weight_table.shape[0]):\n",
    "        tics = weight_table.loc[i, 'gvkey']\n",
    "        date = weight_table.loc[i, 'trade_date']\n",
    "        w = weight_table.loc[i, 'weights']\n",
    "        balance_weight.loc[tics, date] = w\n",
    "        \n",
    "    #1. shares table\n",
    "    balance_share = pd.DataFrame(index = stocks_name, columns = tradedate)\n",
    "    balance_share['initial'] = 0\n",
    "    balance_share = balance_share.fillna(0)\n",
    "    #2. costs table\n",
    "    balance_cost = pd.DataFrame(index = stocks_name, columns = tradedate)\n",
    "    balance_cost = balance_cost.fillna(0)\n",
    "    #3. cash table\n",
    "    balance_cash = pd.DataFrame(index = stocks_name, columns = tradedate)\n",
    "    balance_cash = balance_cash.fillna(0)\n",
    "\n",
    "    #4. portfolio without transaction costs table \n",
    "    portfolio = pd.DataFrame(index = range(1), columns = tradedate_full)\n",
    "    portfolio.loc[0,tradedate[0]] = capital\n",
    "    \n",
    "    \n",
    "    # calculate portfolio value without cost\n",
    "    balance_price_full = []\n",
    "    balance_price_full = balance_daily[tradedate_full]\n",
    "    balance_price = balance_daily[tradedate]\n",
    "    balance_price.replace([0, np.inf, -np.inf], np.nan, inplace=True)\n",
    "    for j in range(len(tradedate) - 1):\n",
    "        i = tradedate[j]\n",
    "        #current capital\n",
    "        cap = portfolio.loc[0, i]\n",
    "        #\n",
    "        balance_cash[i] = cap * balance_weight[i]\n",
    "        balance_share[i] = list(np.array(balance_cash[i])\n",
    "                                      / np.array(balance_price[i]))\n",
    "        balance_share[i] = balance_share[i].fillna(0)\n",
    "        balance_share[i] = balance_share[i].astype(int)\n",
    "        #next quarter price * current share to calculate next quarter total account value\n",
    "        a = balance_price_full[tradedate_full[j+1]].fillna(0)\n",
    "        portfolio.loc[0, tradedate_full[j+1]] = np.dot(balance_share[i], a)\n",
    "\n",
    "    balance_cost = abs(np.diff(balance_share, axis = 1))\n",
    "    balance_cost = balance_cost * balance_price\n",
    "    balance_cost = np.sum(balance_cost, axis = 0) * transaction_percent\n",
    "    \n",
    "    #5. portfolio with transaction cost table\n",
    "    #calcualte portolio value with transaction cost\n",
    "    balance_cost.index = tradedate_full\n",
    "    portfolio_cost = []\n",
    "    portfolio_cost = portfolio.loc[0,] - balance_cost\n",
    "    portfolio_cost.index = tradedate_full\n",
    "    portfolio_cost_value = portfolio_cost.copy()\n",
    "    \n",
    "    #6. portfolio return P&L table\n",
    "    portfolio_cumsum = portfolio_cost_value.copy()\n",
    "    portfolio_cumsum.index = range(len(portfolio_cumsum))\n",
    "    tmp = portfolio_cumsum.shift(1)\n",
    "    tmp[0] = 0\n",
    "    tmp.index = tradedate_full\n",
    "\n",
    "    portfolio_cumsum = portfolio_cost_value- tmp\n",
    "    portfolio_cumsum.index = range(len(portfolio_cumsum))\n",
    "    tmp.index = range(len(tmp))\n",
    "    for i in range(len(portfolio_cumsum)):\n",
    "        if(tmp[i] != 0):\n",
    "            portfolio_cumsum[i] = portfolio_cumsum[i]/tmp[i]\n",
    "\n",
    "    portfolio_cumsum.index = range(len(portfolio_cumsum))\n",
    "    portfolio_cumsum[0] = 0\n",
    "    portfolio_return=portfolio_cumsum.copy()\n",
    "\n",
    "\n",
    "\n",
    "    # 7. portfolio accumulative return table\n",
    "    initial = 0\n",
    "    for i in range(portfolio_cumsum.shape[0]):\n",
    "        portfolio_cumsum[i] = initial + portfolio_cumsum[i]\n",
    "        initial = portfolio_cumsum[i]\n",
    "    \n",
    "    #1. shares table\n",
    "    #2. costs table\n",
    "    #3. cash table\n",
    "    #4. portfolio without transaction costs table \n",
    "    #5. portfolio with transaction cost table\n",
    "    #6. portfolio return P&L table\n",
    "    #7. portfolio accumulative return table\n",
    "\n",
    "    return(balance_share,\n",
    "           balance_cost,\n",
    "           balance_cash,\n",
    "           portfolio,\n",
    "           portfolio_cost,\n",
    "           portfolio_return,\n",
    "           portfolio_cumsum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3642/1845856610.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  balance_price.replace([0, np.inf, -np.inf], np.nan, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#calculate mean variance allocation portolio\n",
    "(balance_share_meanv,\n",
    " balance_cost_meanv,\n",
    " balance_cash_meanv,\n",
    " portfolio_meanv,\n",
    " portfolio_cost_meanv,\n",
    " portfolio_return_meanv,\n",
    " portfolio_cumsum_meanv)=cal_portfolio(stocks_name,tradedate_full, weight_meanv, capital, transaction_percent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6256100382905669"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(portfolio_return_meanv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3642/1845856610.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  balance_price.replace([0, np.inf, -np.inf], np.nan, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#calculate minimum variance allocation portolio\n",
    "(balance_share_minv,\n",
    " balance_cost_minv,\n",
    " balance_cash_minv,\n",
    " portfolio_minv,\n",
    " portfolio_cost_minv,\n",
    " portfolio_return_minv,\n",
    " portfolio_cumsum_minv)=cal_portfolio(stocks_name,tradedate_full, weight_minv, capital, transaction_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3642/1845856610.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  balance_price.replace([0, np.inf, -np.inf], np.nan, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#calculate equally weighted allocation portolio\n",
    "(balance_share_equal,\n",
    " balance_cost_equal,\n",
    " balance_cash_equal,\n",
    " portfolio_equal,\n",
    " portfolio_cost_equal,\n",
    " portfolio_return_equal,\n",
    " portfolio_cumsum_equal)=cal_portfolio(stocks_name,tradedate_full, weight_equal, capital, transaction_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6039422883904094"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(portfolio_return_equal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Calculate SPX Portfolio Value, Return and Cumulative Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "dates = pd.date_range('1999-09-01', '2022-09-01')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx = pd.read_csv(\"SPX.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx.date = pd.to_datetime(spx.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx = spx.set_index(\"date\").reindex(dates, method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2677.669922\n",
       "1     2734.620117\n",
       "2     2901.520020\n",
       "3     2760.169922\n",
       "4     2803.689941\n",
       "5     2752.060059\n",
       "6     2926.459961\n",
       "7     3140.979980\n",
       "8     2954.219971\n",
       "9     3055.729980\n",
       "10    3526.649902\n",
       "11    3662.449951\n",
       "12    3901.820068\n",
       "13    4202.040039\n",
       "14    4524.089844\n",
       "15    4513.040039\n",
       "16    4306.259766\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "tradedate = tradedate[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "spx_daily = list(set(spx.index))\n",
    "spx_daily.sort()\n",
    "spx = spx['close']\n",
    "spx.index = spx.index.astype(str)\n",
    "spx = spx[tradedate]\n",
    "spx.index = range(len(spx))\n",
    "spx_return = (spx - spx.shift(1))/spx\n",
    "spx_return[0] = 0\n",
    "\n",
    "spx_return.index = tradedate\n",
    "spx_return.index = range(len(spx))\n",
    "spx_cumsum=spx_return.copy()\n",
    "\n",
    "spx_value = spx_return.copy()\n",
    "initial = 0\n",
    "for i in range(len(spx_cumsum)):\n",
    "    spx_cumsum[i] = initial + spx_cumsum[i]\n",
    "    initial = spx_cumsum[i]\n",
    "\n",
    "initial = 1000000\n",
    "for i in range(len(spx_value)):\n",
    "    spx_value[i] = initial * (1 + spx_value[i])\n",
    "    initial = spx_value[i]\n",
    "\n",
    "spx_cumsum.index = tradedate\n",
    "spx_value.index = tradedate"
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  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8455213218848242"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Plot the Portfolio Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'time_ind' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/wenbiaolin/ML4StockRec_using_gvkey/fundamental_back_testing.ipynb Cell 42\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2B10.26.1.22/home/wenbiaolin/ML4StockRec_using_gvkey/fundamental_back_testing.ipynb#X56sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m time_ind\u001b[39m.\u001b[39mhead()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'time_ind' is not defined"
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   ],
   "source": [
    "time_ind.head()"
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  {
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   "metadata": {},
   "source": [
    "## 4.1 Plot Portfolio Value with Transaction Cost"
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  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_ind = tradedate_full[:-1]"
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  },
  {
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   "source": [
    "trace0 = go.Scatter(x = time_ind, y = portfolio_cost_meanv, mode = 'lines', name = 'Mean-Variance')\n",
    "trace1 = go.Scatter(x = time_ind, y = portfolio_cost_equal, mode = 'lines', name = 'Equally Weighted')\n",
    "trace2 = go.Scatter(x = time_ind, y = portfolio_cost_minv, mode = 'lines', name = 'Min-Variance')\n",
    "trace3 = go.Scatter(x = time_ind, y = spx_value, mode = 'lines', name = 'SPX')\n",
    "\n",
    "data = [trace0, trace1, trace2, trace3]\n",
    "layout = dict(title = \"Portfolio Value Performance Quarterly with Transaction cost\", \n",
    "              xaxis = dict(title = 'Quarterly Trade Date'), \n",
    "              yaxis = dict(title = 'Portfolio Value'))\n",
    "fig = dict(data = data, layout = layout)\n",
    "\n",
    "plotly.offline.iplot(fig)\n",
    "\n",
    "#portfolio_tmp = portfolio_cost_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Plot Portfolio Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
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   "source": [
    "trace_return1 = go.Scatter(x = time_ind, y = portfolio_return_meanv, mode = 'lines', name = 'Mean-Variance')\n",
    "trace_return2 = go.Scatter(x = time_ind, y = portfolio_return_minv, mode = 'lines', name = 'Minimum-Variance')\n",
    "trace_return3 = go.Scatter(x = time_ind, y = portfolio_return_equal, mode = 'lines', name = 'Equally Weighted')\n",
    "trace_return4 = go.Scatter(x = time_ind, y = spx_return, mode = 'lines', name = 'SPX')\n",
    "\n",
    "data = [trace_return1,trace_return2,trace_return3, trace_return4]\n",
    "layout = dict(title = \"Portfolio Return Quarterly\", \n",
    "              xaxis = dict(title = 'Quarterly Trade Date'), \n",
    "              yaxis = dict(title = 'Portfolio Value'))\n",
    "fig = dict(data = data, layout = layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Plot Portfolio Cumulative Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "trace_return1 = go.Scatter(x = time_ind, y = portfolio_cumsum_meanv, mode = 'lines', name = 'Mean-Variance')\n",
    "trace_return2 = go.Scatter(x = time_ind, y = portfolio_cumsum_minv, mode = 'lines', name = 'Minimum-Variance')\n",
    "trace_return3 = go.Scatter(x = time_ind, y = portfolio_cumsum_equal, mode = 'lines', name = 'Equally Weighted')\n",
    "trace_return4 = go.Scatter(x = time_ind, y = spx_cumsum, mode = 'lines', name = 'SPX')\n",
    "\n",
    "data = [trace_return1,trace_return2,trace_return3, trace_return4]\n",
    "layout = dict(title = \"Portfolio Cumulative Return Quarterly\", \n",
    "              xaxis = dict(title = 'Quarterly Trade Date'), \n",
    "              yaxis = dict(title = 'Portfolio Value'))\n",
    "fig = dict(data = data, layout = layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Get all Quarterly Return and Calcualte Annulized return, standard deviation and Sharpe ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "quarter_return = pd.DataFrame(index = range(len(portfolio_return_equal)), columns = ['Mean-Var', 'Equally', 'Min-Var'])\n",
    "quarter_return['SPX'] = spx_return\n",
    "quarter_return['Mean-Var'] = portfolio_return_meanv\n",
    "quarter_return['Equally'] = portfolio_return_equal\n",
    "quarter_return['Min-Var'] = portfolio_return_minv\n",
    "\n",
    "#writer_return = pd.ExcelWriter('quarter_return_user8.xlsx')\n",
    "quarter_return.index=tradedate_full\n",
    "#quarter_return.to_excel(writer_return,'Sheet1')\n",
    "#writer_return.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Mean-Var</th>\n",
       "      <th>Equally</th>\n",
       "      <th>Min-Var</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>0.02529</td>\n",
       "      <td>-0.01857</td>\n",
       "      <td>-0.006528</td>\n",
       "      <td>0.015420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>-0.018328</td>\n",
       "      <td>0.018824</td>\n",
       "      <td>0.016548</td>\n",
       "      <td>-0.112114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.026002</td>\n",
       "      <td>0.024862</td>\n",
       "      <td>0.005152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>-0.214458</td>\n",
       "      <td>-0.16628</td>\n",
       "      <td>-0.164245</td>\n",
       "      <td>-0.006777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            Mean-Var   Equally   Min-Var       SPX\n",
       "2018-03-01         0         0         0  0.000000\n",
       "2018-06-01   0.02529  -0.01857 -0.006528  0.015420\n",
       "2018-09-01 -0.018328  0.018824  0.016548 -0.112114\n",
       "2018-12-01  0.026316  0.026002  0.024862  0.005152\n",
       "2019-03-01 -0.214458  -0.16628 -0.164245 -0.006777"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarter_return.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "annulizedR_spx=quarter_return['SPX'].mean()*4\n",
    "annulizedR_meanv=quarter_return['Mean-Var'].mean()*4\n",
    "annulizedR_equal=quarter_return['Equally'].mean()*4\n",
    "annulizedR_minv=quarter_return['Min-Var'].mean()*4\n",
    "annulizedS_spx=quarter_return['SPX'].std()*2\n",
    "annulizedS_meanv=quarter_return['Mean-Var'].std()*2\n",
    "annulizedS_equal=quarter_return['Equally'].std()*2\n",
    "annulizedS_minv=quarter_return['Min-Var'].std()*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPX -0.00522854196153727 0.13913436271090593\n",
      "Mean-Var -0.12512200765811338 0.5267684626050293\n",
      "Equally -0.12078845767808188 0.4858170592420743\n",
      "Min-Var -0.12281867413974365 0.4877126592890122\n"
     ]
    }
   ],
   "source": [
    "print(\"SPX\",annulizedR_spx,annulizedS_spx)\n",
    "print(\"Mean-Var\",annulizedR_meanv,annulizedS_meanv)\n",
    "print(\"Equally\",annulizedR_equal,annulizedS_equal)\n",
    "print(\"Min-Var\",annulizedR_minv,annulizedS_minv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "risk_free=0.015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_spx=(annulizedR_spx-risk_free)/annulizedS_spx\n",
    "sharpe_meanv=(annulizedR_meanv-risk_free)/annulizedS_meanv\n",
    "sharpe_equal=(annulizedR_equal-risk_free)/annulizedS_equal\n",
    "sharpe_minv=(annulizedR_minv-risk_free)/annulizedS_minv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPX Sharpe -0.1453885407415006\n",
      "Mean-Var Sharpe -0.2660030309429834\n",
      "Equally Sharpe -0.27950533044254594\n",
      "Min-Var Sharpe -0.2825817036216689\n"
     ]
    }
   ],
   "source": [
    "print(\"SPX Sharpe\",sharpe_spx)\n",
    "print(\"Mean-Var Sharpe\",sharpe_meanv)\n",
    "print(\"Equally Sharpe\",sharpe_equal)\n",
    "print(\"Min-Var Sharpe\",sharpe_minv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "insample_date=tradedate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2001-03-01',\n",
       " '2001-06-01',\n",
       " '2001-09-01',\n",
       " '2001-12-01',\n",
       " '2002-03-01',\n",
       " '2002-06-01',\n",
       " '2002-09-01',\n",
       " '2002-12-01',\n",
       " '2003-03-01',\n",
       " '2003-06-01',\n",
       " '2003-09-01',\n",
       " '2003-12-01',\n",
       " '2004-03-01',\n",
       " '2004-06-01',\n",
       " '2004-09-01',\n",
       " '2004-12-01',\n",
       " '2005-03-01',\n",
       " '2005-06-01',\n",
       " '2005-09-01',\n",
       " '2005-12-01',\n",
       " '2006-03-01',\n",
       " '2006-06-01',\n",
       " '2006-09-01',\n",
       " '2006-12-01',\n",
       " '2007-03-01',\n",
       " '2007-06-01',\n",
       " '2007-09-01',\n",
       " '2007-12-01',\n",
       " '2008-03-01',\n",
       " '2008-06-01',\n",
       " '2008-09-01',\n",
       " '2008-12-01',\n",
       " '2009-03-01',\n",
       " '2009-06-01',\n",
       " '2009-09-01',\n",
       " '2009-12-01',\n",
       " '2010-03-01',\n",
       " '2010-06-01',\n",
       " '2010-09-01',\n",
       " '2010-12-01',\n",
       " '2011-03-01',\n",
       " '2011-06-01',\n",
       " '2011-09-01',\n",
       " '2011-12-01',\n",
       " '2012-03-01',\n",
       " '2012-06-01',\n",
       " '2012-09-01',\n",
       " '2012-12-01',\n",
       " '2013-03-01',\n",
       " '2013-06-01',\n",
       " '2013-09-01',\n",
       " '2013-12-01',\n",
       " '2014-03-01',\n",
       " '2014-06-01',\n",
       " '2014-09-01',\n",
       " '2014-12-01',\n",
       " '2015-03-01',\n",
       " '2015-06-01',\n",
       " '2015-09-01',\n",
       " '2015-12-01',\n",
       " '2016-03-01',\n",
       " '2016-06-01',\n",
       " '2016-09-01',\n",
       " '2016-12-01',\n",
       " '2017-03-01',\n",
       " '2017-06-01',\n",
       " '2017-09-01',\n",
       " '2017-12-01',\n",
       " '2018-03-01',\n",
       " '2018-06-01',\n",
       " '2018-09-01',\n",
       " '2018-12-01',\n",
       " '2019-03-01',\n",
       " '2019-06-01',\n",
       " '2019-09-01',\n",
       " '2019-12-01',\n",
       " '2020-03-01',\n",
       " '2020-06-01',\n",
       " '2020-09-01',\n",
       " '2020-12-01',\n",
       " '2021-03-01',\n",
       " '2021-06-01',\n",
       " '2021-09-01',\n",
       " '2021-12-01',\n",
       " '2022-03-01',\n",
       " '2022-06-01',\n",
       " '2022-09-01']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['2001-03-01', '2001-06-01', '2001-09-01', '2001-12-01', '2002-03-01', '2002-06-01', '2002-09-01', '2002-12-01', '2003-03-01', '2003-06-01', '2003-09-01', '2003-12-01', '2004-03-01', '2004-06-01', '2004-09-01', '2004-12-01', '2005-03-01', '2005-06-01', '2005-09-01', '2005-12-01', '2006-03-01', '2006-06-01', '2006-09-01', '2006-12-01', '2007-03-01', '2007-06-01', '2007-09-01', '2007-12-01', '2008-03-01', '2008-06-01', '2008-09-01', '2008-12-01', '2009-03-01', '2009-06-01', '2009-09-01', '2009-12-01', '2010-03-01', '2010-06-01', '2010-09-01', '2010-12-01', '2011-03-01', '2011-06-01', '2011-09-01', '2011-12-01', '2012-03-01', '2012-06-01', '2012-09-01', '2012-12-01', '2013-03-01', '2013-06-01', '2013-09-01', '2013-12-01', '2014-03-01', '2014-06-01', '2014-09-01', '2014-12-01', '2015-03-01', '2015-06-01', '2015-09-01', '2015-12-01', '2016-03-01', '2016-06-01', '2016-09-01', '2016-12-01', '2017-03-01', '2017-06-01', '2017-09-01', '2017-12-01'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/wenbiaolin/20221117/fundamental_back_testing.ipynb Cell 65\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2B10.26.1.22/home/wenbiaolin/20221117/fundamental_back_testing.ipynb#Y116sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m quarter_return[\u001b[39m'\u001b[39;49m\u001b[39mSPX\u001b[39;49m\u001b[39m'\u001b[39;49m][insample_date]\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/series.py:984\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    981\u001b[0m     key \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray(key, dtype\u001b[39m=\u001b[39m\u001b[39mbool\u001b[39m)\n\u001b[1;32m    982\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_values(key)\n\u001b[0;32m--> 984\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_with(key)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/series.py:1024\u001b[0m, in \u001b[0;36mSeries._get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1021\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39miloc[key]\n\u001b[1;32m   1023\u001b[0m \u001b[39m# handle the dup indexing case GH#4246\u001b[39;00m\n\u001b[0;32m-> 1024\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloc[key]\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexing.py:967\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    964\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39mor\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[1;32m    966\u001b[0m maybe_callable \u001b[39m=\u001b[39m com\u001b[39m.\u001b[39mapply_if_callable(key, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj)\n\u001b[0;32m--> 967\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_axis(maybe_callable, axis\u001b[39m=\u001b[39;49maxis)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1191\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1188\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(key, \u001b[39m\"\u001b[39m\u001b[39mndim\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m key\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m   1189\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mCannot index with multidimensional key\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 1191\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_iterable(key, axis\u001b[39m=\u001b[39;49maxis)\n\u001b[1;32m   1193\u001b[0m \u001b[39m# nested tuple slicing\u001b[39;00m\n\u001b[1;32m   1194\u001b[0m \u001b[39mif\u001b[39;00m is_nested_tuple(key, labels):\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1132\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1129\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m   1131\u001b[0m \u001b[39m# A collection of keys\u001b[39;00m\n\u001b[0;32m-> 1132\u001b[0m keyarr, indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_listlike_indexer(key, axis)\n\u001b[1;32m   1133\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39m_reindex_with_indexers(\n\u001b[1;32m   1134\u001b[0m     {axis: [keyarr, indexer]}, copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, allow_dups\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m\n\u001b[1;32m   1135\u001b[0m )\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1327\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1324\u001b[0m ax \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39m_get_axis(axis)\n\u001b[1;32m   1325\u001b[0m axis_name \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39m_get_axis_name(axis)\n\u001b[0;32m-> 1327\u001b[0m keyarr, indexer \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39;49m_get_indexer_strict(key, axis_name)\n\u001b[1;32m   1329\u001b[0m \u001b[39mreturn\u001b[39;00m keyarr, indexer\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py:5782\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   5779\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   5780\u001b[0m     keyarr, indexer, new_indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 5782\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[1;32m   5784\u001b[0m keyarr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtake(indexer)\n\u001b[1;32m   5785\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, Index):\n\u001b[1;32m   5786\u001b[0m     \u001b[39m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py:5845\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   5842\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNone of [\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m] are in the [\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   5844\u001b[0m not_found \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[39m.\u001b[39mnonzero()[\u001b[39m0\u001b[39m]]\u001b[39m.\u001b[39munique())\n\u001b[0;32m-> 5845\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mnot_found\u001b[39m}\u001b[39;00m\u001b[39m not in index\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['2001-03-01', '2001-06-01', '2001-09-01', '2001-12-01', '2002-03-01', '2002-06-01', '2002-09-01', '2002-12-01', '2003-03-01', '2003-06-01', '2003-09-01', '2003-12-01', '2004-03-01', '2004-06-01', '2004-09-01', '2004-12-01', '2005-03-01', '2005-06-01', '2005-09-01', '2005-12-01', '2006-03-01', '2006-06-01', '2006-09-01', '2006-12-01', '2007-03-01', '2007-06-01', '2007-09-01', '2007-12-01', '2008-03-01', '2008-06-01', '2008-09-01', '2008-12-01', '2009-03-01', '2009-06-01', '2009-09-01', '2009-12-01', '2010-03-01', '2010-06-01', '2010-09-01', '2010-12-01', '2011-03-01', '2011-06-01', '2011-09-01', '2011-12-01', '2012-03-01', '2012-06-01', '2012-09-01', '2012-12-01', '2013-03-01', '2013-06-01', '2013-09-01', '2013-12-01', '2014-03-01', '2014-06-01', '2014-09-01', '2014-12-01', '2015-03-01', '2015-06-01', '2015-09-01', '2015-12-01', '2016-03-01', '2016-06-01', '2016-09-01', '2016-12-01', '2017-03-01', '2017-06-01', '2017-09-01', '2017-12-01'] not in index\""
     ]
    }
   ],
   "source": [
    "quarter_return['SPX'][insample_date]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPX 0.001531531093822985 0.21955274735546973\n",
      "Mean-Var 0.2565791830967157 0.29155309241664823\n",
      "Equally 0.26616405706243595 0.3011567507349648\n",
      "Min-Var 0.26844700713719916 0.29823334992044453\n",
      "SPX Sharpe -0.061345025595925314\n",
      "Mean-Var Sharpe 0.828594137329483\n",
      "Equally Sharpe 0.8339977651155983\n",
      "Min-Var Sharpe 0.8498278519314074\n"
     ]
    }
   ],
   "source": [
    "annulizedR_spx_insample=quarter_return['SPX'][insample_date].mean()*4\n",
    "annulizedR_meanv_insample=quarter_return['Mean-Var'][insample_date].mean()*4\n",
    "annulizedR_equal_insample=quarter_return['Equally'][insample_date].mean()*4\n",
    "annulizedR_minv_insample=quarter_return['Min-Var'][insample_date].mean()*4\n",
    "annulizedS_spx_insample=quarter_return['SPX'][insample_date].std()*2\n",
    "annulizedS_meanv_insample=quarter_return['Mean-Var'][insample_date].std()*2\n",
    "annulizedS_equal_insample=quarter_return['Equally'][insample_date].std()*2\n",
    "annulizedS_minv_insample=quarter_return['Min-Var'][insample_date].std()*2\n",
    "print(\"SPX\",annulizedR_spx_insample,annulizedS_spx_insample)\n",
    "print(\"Mean-Var\",annulizedR_meanv_insample,annulizedS_meanv_insample)\n",
    "print(\"Equally\",annulizedR_equal_insample,annulizedS_equal_insample)\n",
    "print(\"Min-Var\",annulizedR_minv_insample,annulizedS_minv_insample)\n",
    "sharpe_spx_insample=(annulizedR_spx_insample-risk_free)/annulizedS_spx_insample\n",
    "sharpe_meanv_insample=(annulizedR_meanv_insample-risk_free)/annulizedS_meanv_insample\n",
    "sharpe_equal_insample=(annulizedR_equal_insample-risk_free)/annulizedS_equal_insample\n",
    "sharpe_minv_insample=(annulizedR_minv_insample-risk_free)/annulizedS_minv_insample\n",
    "print(\"SPX Sharpe\",sharpe_spx_insample)\n",
    "print(\"Mean-Var Sharpe\",sharpe_meanv_insample)\n",
    "print(\"Equally Sharpe\",sharpe_equal_insample)\n",
    "print(\"Min-Var Sharpe\",sharpe_minv_insample)"
   ]
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