pandas_for_fun_and_profit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:powderblue;font-style:italic;line-height:150%;\">\n",
    "    <h4>&nbsp;</h4>\n",
    "    <h1 style=\"text-indent:5%;\"><a href=\"https://www.youtube.com/watch?v=4AXAKUlF_3c&t\">Pandas for Fun and Profit</a>&nbsp;&nbsp; &larr; Click here for video.</h1>\n",
    "    <h2 style=\"line-height:150%;\">\n",
    "       <ul>\n",
    "          <li>This jupyter notebook is intended to accompany the talk<br>that I gave at 2019 PyCon Israel and at 2019 PyLondinium.</li>\n",
    "       </ul>\n",
    "    </h2>\n",
    "</div>    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:lightgreen;font-style:italic;line-height:150%;\">\n",
    "    <h4>&nbsp;</h4>\n",
    "    <h1 style=\"text-indent:5%;\">Conclusions</h1>\n",
    "    <h2 style=\"line-height:150%;\">\n",
    "       <ul>\n",
    "          <li>Pandas makes it easy to explore data, to shape, analyze, and visualize your data!</li>\n",
    "          <li>Know your domain/data</li>\n",
    "          <li>Plan to spend 50% or more of time \"shaping\" your data.</li>\n",
    "        </ul>\n",
    "    </h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pandas version 0.24.2\n"
     ]
    }
   ],
   "source": [
    "# Import pandas, display version, register pandas matplotlib converters:\n",
    "import pandas as pd\n",
    "print(\"Using pandas version\",pd.__version__)\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib version 3.0.3\n",
      "using numpy version 1.16.2\n"
     ]
    }
   ],
   "source": [
    "# Additional imports\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "print(\"Using matplotlib version\",mpl.__version__)\n",
    "import numpy as np\n",
    "print(\"using numpy version\",np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows output from multiple ipython commands in one cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      " Create a Pandas Series and display it:\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-10-04    113.00\n",
       "2016-10-05    113.05\n",
       "2016-10-06    113.89\n",
       "Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"========================================\")\n",
    "print(\" Create a Pandas Series and display it:\")\n",
    "print(\"========================================\")\n",
    "\n",
    "dates  = ['2016-10-04','2016-10-05','2016-10-06']\n",
    "prices = [ 113.0, 113.05, 113.89 ]\n",
    "dtindx = pd.DatetimeIndex(dates,name='Date')\n",
    "myseries = pd.Series(prices,index=dtindx,name='AAPL')\n",
    "myseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "len(myseries)= 3\n",
      "\n",
      "myseries.loc['2016-10-06']= 113.89\n",
      "\n",
      "myseries.iloc[0]= 113.0\n"
     ]
    }
   ],
   "source": [
    "print('\\nlen(myseries)=',len(myseries))\n",
    "print(\"\\nmyseries.loc['2016-10-06']=\",myseries.loc['2016-10-06'])\n",
    "print(\"\\nmyseries.iloc[0]=\",myseries.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================\n",
      " Create a Pandas DataFrame and display it:\n",
      "==========================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>113.00</td>\n",
       "      <td>31.35</td>\n",
       "      <td>57.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-05</th>\n",
       "      <td>113.05</td>\n",
       "      <td>31.59</td>\n",
       "      <td>57.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-06</th>\n",
       "      <td>113.89</td>\n",
       "      <td>31.48</td>\n",
       "      <td>57.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   CSCO   MSFT\n",
       "Date                            \n",
       "2016-10-04  113.00  31.35  57.24\n",
       "2016-10-05  113.05  31.59  57.64\n",
       "2016-10-06  113.89  31.48  57.74"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"==========================================\")\n",
    "print(\" Create a Pandas DataFrame and display it:\")\n",
    "print(\"==========================================\")\n",
    "prices={'AAPL':[ 113.0, 113.05, 113.89 ],\n",
    "        'CSCO':[ 31.35,  31.59,  31.48 ],\n",
    "        'MSFT':[ 57.24,  57.64,  57.74 ] }\n",
    "df = pd.DataFrame(prices,index=dtindx)  # dtindx is from a cell above\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-10-04', '2016-10-05', '2016-10-06'], dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index\n",
    "type(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AAPL', 'CSCO', 'MSFT'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "type(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "type(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113.05"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2016-10-05','AAPL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    113.05\n",
       "CSCO     31.59\n",
       "MSFT     57.64\n",
       "Name: 2016-10-05 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2016-10-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================\n",
      " Create another Pandas DataFrame and concatenate\n",
      " it with the first, creating a Multi-Index:\n",
      "================================================\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>29736800</td>\n",
       "      <td>18460400</td>\n",
       "      <td>20085900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-05</th>\n",
       "      <td>21453100</td>\n",
       "      <td>11808600</td>\n",
       "      <td>16726400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-06</th>\n",
       "      <td>28779000</td>\n",
       "      <td>14077100</td>\n",
       "      <td>16212600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      CSCO      MSFT\n",
       "Date                                    \n",
       "2016-10-04  29736800  18460400  20085900\n",
       "2016-10-05  21453100  11808600  16726400\n",
       "2016-10-06  28779000  14077100  16212600"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"================================================\")\n",
    "print(\" Create another Pandas DataFrame and concatenate\")\n",
    "print(\" it with the first, creating a Multi-Index:\"     )\n",
    "print(\"================================================\")\n",
    "volumes={'AAPL':[ 29736800, 21453100, 28779000 ],\n",
    "         'CSCO':[ 18460400, 11808600, 14077100 ],\n",
    "         'MSFT':[ 20085900, 16726400, 16212600 ] }\n",
    "vdf = pd.DataFrame(volumes,index=dtindx)\n",
    "vdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>113.00</td>\n",
       "      <td>31.35</td>\n",
       "      <td>57.24</td>\n",
       "      <td>29736800</td>\n",
       "      <td>18460400</td>\n",
       "      <td>20085900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-05</th>\n",
       "      <td>113.05</td>\n",
       "      <td>31.59</td>\n",
       "      <td>57.64</td>\n",
       "      <td>21453100</td>\n",
       "      <td>11808600</td>\n",
       "      <td>16726400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-06</th>\n",
       "      <td>113.89</td>\n",
       "      <td>31.48</td>\n",
       "      <td>57.74</td>\n",
       "      <td>28779000</td>\n",
       "      <td>14077100</td>\n",
       "      <td>16212600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price                  Volume                    \n",
       "              AAPL   CSCO   MSFT      AAPL      CSCO      MSFT\n",
       "Date                                                          \n",
       "2016-10-04  113.00  31.35  57.24  29736800  18460400  20085900\n",
       "2016-10-05  113.05  31.59  57.64  21453100  11808600  16726400\n",
       "2016-10-06  113.89  31.48  57.74  28779000  14077100  16212600"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midf = pd.concat([df,vdf], axis=1, keys=['Price','Volume'])\n",
    "midf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">AAPL</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CSCO</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>113.00</td>\n",
       "      <td>29736800</td>\n",
       "      <td>31.35</td>\n",
       "      <td>18460400</td>\n",
       "      <td>57.24</td>\n",
       "      <td>20085900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-05</th>\n",
       "      <td>113.05</td>\n",
       "      <td>21453100</td>\n",
       "      <td>31.59</td>\n",
       "      <td>11808600</td>\n",
       "      <td>57.64</td>\n",
       "      <td>16726400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-06</th>\n",
       "      <td>113.89</td>\n",
       "      <td>28779000</td>\n",
       "      <td>31.48</td>\n",
       "      <td>14077100</td>\n",
       "      <td>57.74</td>\n",
       "      <td>16212600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL             CSCO             MSFT          \n",
       "             Price    Volume  Price    Volume  Price    Volume\n",
       "Date                                                          \n",
       "2016-10-04  113.00  29736800  31.35  18460400  57.24  20085900\n",
       "2016-10-05  113.05  21453100  31.59  11808600  57.64  16726400\n",
       "2016-10-06  113.89  28779000  31.48  14077100  57.74  16212600"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midf.swaplevel(0, 1, axis=1).sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:lightgreen;font-style:italic;line-height:150%;\">    <h1 style=\"text-indent:5%;\">Hypothesis</h1>\n",
    "    <h2 style=\"line-height:150%;\">\n",
    "       <ul>\n",
    "          <li>Low P/E represents a bargain</li>\n",
    "          <li>If so, buying stocks with low P/E shoul result in higher returns</li>\n",
    "          <li>TEST: Compare P/E to RETURNS over time.</li>\n",
    "        </ul>\n",
    "    </h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;border-left: 5px solid #dfb5b4;background-color:#ffe6b3;line-height:150%;\"> \n",
    "    <h1 style=\"text-indent:5%;\">INFORMATION, NOT ADVICE</h1>\n",
    "    <h3 style=\"line-height:150%;text-indent:5%;\">\n",
    "        This content, and any presentation thereof, is for informational purposes only.  Any such information (or its associated material) should not in any way be construed as investment, financial, or other advice. Nothing contained in this presentation or its materials constitutes a solicitation, recommendation, endorsement of, or offer to buy or sell any securities or other financial instruments in this or in in any other\n",
    "jurisdiction.<br>\n",
    "    </h3>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:lightgreen;font-style:italic;line-height:150%;\">     <h1 style=\"text-indent:4%;\">Choose Data to represent the Market:</h1>\n",
    "    <h1 style=\"text-indent:7%;\">The 30 Stocks in the Dow Jones Industrial Average</h1>\n",
    "    <hr width=90%;>\n",
    "    <h1 style=\"text-indent:4%;\">Data we will need for these 30 stocks:</h1>\n",
    "    <h2 style=\"line-height:150%;\">\n",
    "       <ul>\n",
    "          <li>Price/Earnings ratios over time</li>\n",
    "          <li>Calculate RETURN over time</li>\n",
    "             <ul>\n",
    "               <li>Prices over time</li>\n",
    "               <li>Dividends over time</li>\n",
    "           </ul>\n",
    "        </ul>\n",
    "    </h2>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================\n",
      " Read the Price/Earnings csv file \"plain\": \n",
      "===========================================\n"
     ]
    },
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AAPL UW Equity</td>\n",
       "      <td>AXP UN Equity</td>\n",
       "      <td>BA UN Equity</td>\n",
       "      <td>CAT UN Equity</td>\n",
       "      <td>CSCO UW Equity</td>\n",
       "      <td>CVX UN Equity</td>\n",
       "      <td>DIS UN Equity</td>\n",
       "      <td>DWDP UN Equity</td>\n",
       "      <td>GS UN Equity</td>\n",
       "      <td>...</td>\n",
       "      <td>PG UN Equity</td>\n",
       "      <td>TRV UN Equity</td>\n",
       "      <td>UNH UN Equity</td>\n",
       "      <td>UTX UN Equity</td>\n",
       "      <td>V UN Equity</td>\n",
       "      <td>VZ UN Equity</td>\n",
       "      <td>WBA UW Equity</td>\n",
       "      <td>WMT UN Equity</td>\n",
       "      <td>XOM UN Equity</td>\n",
       "      <td>INDU Index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Price Earnings Ratio (P/E)</td>\n",
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       "      <td>Price Earnings Ratio (P/E)</td>\n",
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       "      <td>Price Earnings Ratio (P/E)</td>\n",
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       "      <td>Dates</td>\n",
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       "      <td>PE_RATIO</td>\n",
       "      <td>PE_RATIO</td>\n",
       "      <td>PE_RATIO</td>\n",
       "      <td>PE_RATIO</td>\n",
       "      <td>PE_RATIO</td>\n",
       "      <td>PE_RATIO</td>\n",
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       "      <td>1/31/1989</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0                           1                           2   \\\n",
       "0  Start Date                    1/1/1989                         NaN   \n",
       "1    End Date                  12/31/2018                         NaN   \n",
       "2         NaN                         NaN                         NaN   \n",
       "3         NaN              AAPL UW Equity               AXP UN Equity   \n",
       "4         NaN  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5       Dates                    PE_RATIO                    PE_RATIO   \n",
       "6   1/31/1989                         NaN                         NaN   \n",
       "7   2/28/1989                         NaN                         NaN   \n",
       "\n",
       "                           3                           4   \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3                BA UN Equity               CAT UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                     15.7649                     10.1768   \n",
       "7                     15.3918                      9.6628   \n",
       "\n",
       "                           5                           6   \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3              CSCO UW Equity               CVX UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                         NaN                         NaN   \n",
       "7                         NaN                         NaN   \n",
       "\n",
       "                           7                           8   \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3               DIS UN Equity              DWDP UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                         NaN                         NaN   \n",
       "7                         NaN                         NaN   \n",
       "\n",
       "                           9   ...                          22  \\\n",
       "0                         NaN  ...                         NaN   \n",
       "1                         NaN  ...                         NaN   \n",
       "2                         NaN  ...                         NaN   \n",
       "3                GS UN Equity  ...                PG UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  ...  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO  ...                    PE_RATIO   \n",
       "6                         NaN  ...                         NaN   \n",
       "7                         NaN  ...                         NaN   \n",
       "\n",
       "                           23                          24  \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3               TRV UN Equity               UNH UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                      7.4575                         NaN   \n",
       "7                      7.3416                         NaN   \n",
       "\n",
       "                           25                          26  \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3               UTX UN Equity                 V UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                         NaN                         NaN   \n",
       "7                         NaN                         NaN   \n",
       "\n",
       "                           27                          28  \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3                VZ UN Equity               WBA UW Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                         NaN                         NaN   \n",
       "7                         NaN                         NaN   \n",
       "\n",
       "                           29                          30  \\\n",
       "0                         NaN                         NaN   \n",
       "1                         NaN                         NaN   \n",
       "2                         NaN                         NaN   \n",
       "3               WMT UN Equity               XOM UN Equity   \n",
       "4  Price Earnings Ratio (P/E)  Price Earnings Ratio (P/E)   \n",
       "5                    PE_RATIO                    PE_RATIO   \n",
       "6                         NaN                     11.7405   \n",
       "7                         NaN                     11.1076   \n",
       "\n",
       "                           31  \n",
       "0                         NaN  \n",
       "1                         NaN  \n",
       "2                         NaN  \n",
       "3                  INDU Index  \n",
       "4  Price Earnings Ratio (P/E)  \n",
       "5                    PE_RATIO  \n",
       "6                         NaN  \n",
       "7                         NaN  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('===========================================')\n",
    "print(' Read the Price/Earnings csv file \"plain\": ')\n",
    "print('===========================================')\n",
    "df = pd.read_csv(\"data/indu_pe_1989-2018.csv\",header=None)\n",
    "df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================\n",
      " Re-read the P/E csv file, specifying header, index, etc:\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
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       "      <th>AAPL UW Equity</th>\n",
       "      <th>AXP UN Equity</th>\n",
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       "      <th>1989-02-28</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>11.1076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-03-31</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>7.2932</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>10.2035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-04-28</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>19.2055</td>\n",
       "      <td>10.1030</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>NaN</td>\n",
       "      <td>7.5940</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>10.0291</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL UW Equity  AXP UN Equity  BA UN Equity  CAT UN Equity  \\\n",
       "1989-01-31             NaN            NaN       15.7649        10.1768   \n",
       "1989-02-28             NaN            NaN       15.3918         9.6628   \n",
       "1989-03-31             NaN            NaN       16.3082         9.0531   \n",
       "1989-04-28             NaN            NaN       18.1004         9.5880   \n",
       "1989-05-31             NaN            NaN       19.2055        10.1030   \n",
       "\n",
       "            CSCO UW Equity  CVX UN Equity  DIS UN Equity  DWDP UN Equity  \\\n",
       "1989-01-31             NaN            NaN            NaN             NaN   \n",
       "1989-02-28             NaN            NaN            NaN             NaN   \n",
       "1989-03-31             NaN            NaN            NaN             NaN   \n",
       "1989-04-28             NaN            NaN            NaN             NaN   \n",
       "1989-05-31             NaN            NaN            NaN             NaN   \n",
       "\n",
       "            GS UN Equity  HD UN Equity  ...  PG UN Equity  TRV UN Equity  \\\n",
       "1989-01-31           NaN           NaN  ...           NaN         7.4575   \n",
       "1989-02-28           NaN           NaN  ...           NaN         7.3416   \n",
       "1989-03-31           NaN           NaN  ...           NaN         7.2932   \n",
       "1989-04-28           NaN           NaN  ...           NaN         7.5940   \n",
       "1989-05-31           NaN           NaN  ...           NaN         7.5940   \n",
       "\n",
       "            UNH UN Equity  UTX UN Equity  V UN Equity  VZ UN Equity  \\\n",
       "1989-01-31            NaN            NaN          NaN           NaN   \n",
       "1989-02-28            NaN            NaN          NaN           NaN   \n",
       "1989-03-31            NaN            NaN          NaN           NaN   \n",
       "1989-04-28            NaN            NaN          NaN           NaN   \n",
       "1989-05-31            NaN            NaN          NaN           NaN   \n",
       "\n",
       "            WBA UW Equity  WMT UN Equity  XOM UN Equity  INDU Index  \n",
       "1989-01-31            NaN            NaN        11.7405         NaN  \n",
       "1989-02-28            NaN            NaN        11.1076         NaN  \n",
       "1989-03-31            NaN            NaN        10.2035         NaN  \n",
       "1989-04-28            NaN            NaN        10.0581         NaN  \n",
       "1989-05-31            NaN            NaN        10.0291         NaN  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('==========================================================')\n",
    "print(' Re-read the P/E csv file, specifying header, index, etc:' )\n",
    "print('==========================================================')\n",
    "df = pd.read_csv(\"data/indu_pe_1989-2018.csv\",\n",
    "                 header=3,\n",
    "                 index_col=0,\n",
    "                 parse_dates=True,\n",
    "                 skiprows=[4,5])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================\n",
      " Name the Index:\n",
      "=================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL UW Equity</th>\n",
       "      <th>AXP UN Equity</th>\n",
       "      <th>BA UN Equity</th>\n",
       "      <th>CAT UN Equity</th>\n",
       "      <th>CSCO UW Equity</th>\n",
       "      <th>CVX UN Equity</th>\n",
       "      <th>DIS UN Equity</th>\n",
       "      <th>DWDP UN Equity</th>\n",
       "      <th>GS UN Equity</th>\n",
       "      <th>HD UN Equity</th>\n",
       "      <th>...</th>\n",
       "      <th>PG UN Equity</th>\n",
       "      <th>TRV UN Equity</th>\n",
       "      <th>UNH UN Equity</th>\n",
       "      <th>UTX UN Equity</th>\n",
       "      <th>V UN Equity</th>\n",
       "      <th>VZ UN Equity</th>\n",
       "      <th>WBA UW Equity</th>\n",
       "      <th>WMT UN Equity</th>\n",
       "      <th>XOM UN Equity</th>\n",
       "      <th>INDU Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1989-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.7649</td>\n",
       "      <td>10.1768</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>NaN</td>\n",
       "      <td>7.4575</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>11.7405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.3918</td>\n",
       "      <td>9.6628</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>NaN</td>\n",
       "      <td>7.3416</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>11.1076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3082</td>\n",
       "      <td>9.0531</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>NaN</td>\n",
       "      <td>7.2932</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>10.2035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL UW Equity  AXP UN Equity  BA UN Equity  CAT UN Equity  \\\n",
       "Date                                                                     \n",
       "1989-01-31             NaN            NaN       15.7649        10.1768   \n",
       "1989-02-28             NaN            NaN       15.3918         9.6628   \n",
       "1989-03-31             NaN            NaN       16.3082         9.0531   \n",
       "\n",
       "            CSCO UW Equity  CVX UN Equity  DIS UN Equity  DWDP UN Equity  \\\n",
       "Date                                                                       \n",
       "1989-01-31             NaN            NaN            NaN             NaN   \n",
       "1989-02-28             NaN            NaN            NaN             NaN   \n",
       "1989-03-31             NaN            NaN            NaN             NaN   \n",
       "\n",
       "            GS UN Equity  HD UN Equity  ...  PG UN Equity  TRV UN Equity  \\\n",
       "Date                                    ...                                \n",
       "1989-01-31           NaN           NaN  ...           NaN         7.4575   \n",
       "1989-02-28           NaN           NaN  ...           NaN         7.3416   \n",
       "1989-03-31           NaN           NaN  ...           NaN         7.2932   \n",
       "\n",
       "            UNH UN Equity  UTX UN Equity  V UN Equity  VZ UN Equity  \\\n",
       "Date                                                                  \n",
       "1989-01-31            NaN            NaN          NaN           NaN   \n",
       "1989-02-28            NaN            NaN          NaN           NaN   \n",
       "1989-03-31            NaN            NaN          NaN           NaN   \n",
       "\n",
       "            WBA UW Equity  WMT UN Equity  XOM UN Equity  INDU Index  \n",
       "Date                                                                 \n",
       "1989-01-31            NaN            NaN        11.7405         NaN  \n",
       "1989-02-28            NaN            NaN        11.1076         NaN  \n",
       "1989-03-31            NaN            NaN        10.2035         NaN  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('=================')\n",
    "print(' Name the Index:' )\n",
    "print('=================')\n",
    "df.index.name = 'Date'\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================\n",
      " Take a closer look at the columns, to see\n",
      " how we can clean up or shorten the column names:\n",
      "==========================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['AAPL UW Equity', 'AXP UN Equity', 'BA UN Equity', 'CAT UN Equity',\n",
       "       'CSCO UW Equity', 'CVX UN Equity', 'DIS UN Equity', 'DWDP UN Equity',\n",
       "       'GS UN Equity', 'HD UN Equity', 'IBM UN Equity', 'INTC UW Equity',\n",
       "       'JNJ UN Equity', 'JPM UN Equity', 'KO UN Equity', 'MCD UN Equity',\n",
       "       'MMM UN Equity', 'MRK UN Equity', 'MSFT UW Equity', 'NKE UN Equity',\n",
       "       'PFE UN Equity', 'PG UN Equity', 'TRV UN Equity', 'UNH UN Equity',\n",
       "       'UTX UN Equity', 'V UN Equity', 'VZ UN Equity', 'WBA UW Equity',\n",
       "       'WMT UN Equity', 'XOM UN Equity', 'INDU Index'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('==========================================================')\n",
    "print(' Take a closer look at the columns, to see\\n how we can clean up or shorten the column names:')\n",
    "print('==========================================================')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--<div style=\"border:2px;border-style:solid;border-color:black;background-color:lightgreen;font-style:italic;line-height:150%;\"> -->\n",
    "<!--<div style=\"border:1px;border-style:solid;border-color:black;background-color:lightgreen;font-family:monospace;line-height:200%;\"> -->\n",
    "<div style=\"border:2px;border-style:solid;border-color:black;font-family:monospace;background-color:#FEF9E7;line-height:150%;\">\n",
    "    <p>&nbsp;</p>\n",
    "    <p style=\"text-indent:5%;font-weight: bold;\">  Notice, in the above cell (before the cell below has run), the last column says \"INDU Index\" (<i>not</i> \"INDU Equity\").  All of the other column names say \"Equity.\"  That last column contains the P/E ratio for the Dow Jones Industrials \"Index\" itself, not for an individual stock. (Note: \"Equity\" is another word for \"Stock\"). Later we will drop this \"INDU\" column.\n",
    "    </p>\n",
    "&nbsp;\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====================================================================\n",
      " Use a list comprehension to grab the first token (the Ticker Symbol)\n",
      " from each column name, and reassign back to the dataframe columns:  \n",
      "=====================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DIS', 'DWDP', 'GS', 'HD',\n",
       "       'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE',\n",
       "       'PFE', 'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WBA', 'WMT', 'XOM',\n",
       "       'INDU'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('=====================================================================')\n",
    "print(' Use a list comprehension to grab the first token (the Ticker Symbol)') \n",
    "print(' from each column name, and reassign back to the dataframe columns:  ')\n",
    "print('=====================================================================')\n",
    "df.columns = [x.split()[0] for x in df.columns]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
       "      <th>DWDP</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>...</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>INDU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1989-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.7649</td>\n",
       "      <td>10.1768</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>NaN</td>\n",
       "      <td>7.4575</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>11.7405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.3918</td>\n",
       "      <td>9.6628</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>NaN</td>\n",
       "      <td>7.3416</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>11.1076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3082</td>\n",
       "      <td>9.0531</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>NaN</td>\n",
       "      <td>7.2932</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>10.2035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-04-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1004</td>\n",
       "      <td>9.5880</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>NaN</td>\n",
       "      <td>7.5940</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>10.0581</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL  AXP       BA      CAT  CSCO  CVX  DIS  DWDP  GS  HD  ...  \\\n",
       "Date                                                                   ...   \n",
       "1989-01-31   NaN  NaN  15.7649  10.1768   NaN  NaN  NaN   NaN NaN NaN  ...   \n",
       "1989-02-28   NaN  NaN  15.3918   9.6628   NaN  NaN  NaN   NaN NaN NaN  ...   \n",
       "1989-03-31   NaN  NaN  16.3082   9.0531   NaN  NaN  NaN   NaN NaN NaN  ...   \n",
       "1989-04-28   NaN  NaN  18.1004   9.5880   NaN  NaN  NaN   NaN NaN NaN  ...   \n",
       "\n",
       "            PG     TRV  UNH  UTX   V  VZ  WBA  WMT      XOM  INDU  \n",
       "Date                                                               \n",
       "1989-01-31 NaN  7.4575  NaN  NaN NaN NaN  NaN  NaN  11.7405   NaN  \n",
       "1989-02-28 NaN  7.3416  NaN  NaN NaN NaN  NaN  NaN  11.1076   NaN  \n",
       "1989-03-31 NaN  7.2932  NaN  NaN NaN NaN  NaN  NaN  10.2035   NaN  \n",
       "1989-04-28 NaN  7.5940  NaN  NaN NaN NaN  NaN  NaN  10.0581   NaN  \n",
       "\n",
       "[4 rows x 31 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(360, 31)"
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     "execution_count": 21,
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     "output_type": "execute_result"
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    "df.shape"
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1989-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.7649</td>\n",
       "      <td>10.1768</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4575</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>11.7405</td>\n",
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       "      <th>1989-02-28</th>\n",
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       "      <td>9.6628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.3416</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>11.1076</td>\n",
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       "      <th>1989-03-31</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>9.0531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.2035</td>\n",
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       "            AAPL  AXP       BA      CAT  CSCO  CVX  DIS  DWDP  GS  HD  ...  \\\n",
       "Date                                                                   ...   \n",
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       "1989-02-28   NaN  NaN  15.3918   9.6628   NaN  NaN  NaN   NaN NaN NaN  ...   \n",
       "1989-03-31   NaN  NaN  16.3082   9.0531   NaN  NaN  NaN   NaN NaN NaN  ...   \n",
       "\n",
       "            PFE  PG     TRV  UNH  UTX   V  VZ  WBA  WMT      XOM  \n",
       "Date                                                              \n",
       "1989-01-31  NaN NaN  7.4575  NaN  NaN NaN NaN  NaN  NaN  11.7405  \n",
       "1989-02-28  NaN NaN  7.3416  NaN  NaN NaN NaN  NaN  NaN  11.1076  \n",
       "1989-03-31  NaN NaN  7.2932  NaN  NaN NaN NaN  NaN  NaN  10.2035  \n",
       "\n",
       "[3 rows x 30 columns]"
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     },
     "execution_count": 22,
     "metadata": {},
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   "source": [
    "df.drop('INDU',axis=1,inplace=True)\n",
    "df.head(3)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(360, 30)"
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     "data": {
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       "      <th>2018-10-31</th>\n",
       "      <td>18.4960</td>\n",
       "      <td>24.6947</td>\n",
       "      <td>22.5387</td>\n",
       "      <td>11.0997</td>\n",
       "      <td>18.7380</td>\n",
       "      <td>15.6084</td>\n",
       "      <td>16.4935</td>\n",
       "      <td>14.7244</td>\n",
       "      <td>8.7815</td>\n",
       "      <td>18.9754</td>\n",
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       "      <td>18.2799</td>\n",
       "      <td>21.8772</td>\n",
       "      <td>13.7844</td>\n",
       "      <td>22.6631</td>\n",
       "      <td>16.3757</td>\n",
       "      <td>31.5751</td>\n",
       "      <td>12.8348</td>\n",
       "      <td>15.3241</td>\n",
       "      <td>20.7287</td>\n",
       "      <td>18.3425</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>15.0919</td>\n",
       "      <td>26.9880</td>\n",
       "      <td>22.0242</td>\n",
       "      <td>12.4126</td>\n",
       "      <td>19.6063</td>\n",
       "      <td>16.6275</td>\n",
       "      <td>16.5883</td>\n",
       "      <td>15.7976</td>\n",
       "      <td>7.4302</td>\n",
       "      <td>19.4545</td>\n",
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       "      <td>19.6256</td>\n",
       "      <td>23.3154</td>\n",
       "      <td>14.3616</td>\n",
       "      <td>24.3982</td>\n",
       "      <td>16.0633</td>\n",
       "      <td>32.4593</td>\n",
       "      <td>13.5565</td>\n",
       "      <td>15.7086</td>\n",
       "      <td>20.1850</td>\n",
       "      <td>18.3011</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>12.9878</td>\n",
       "      <td>11.2377</td>\n",
       "      <td>18.0140</td>\n",
       "      <td>11.1878</td>\n",
       "      <td>17.7468</td>\n",
       "      <td>12.8033</td>\n",
       "      <td>15.8175</td>\n",
       "      <td>14.5333</td>\n",
       "      <td>6.4857</td>\n",
       "      <td>18.5374</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0197</td>\n",
       "      <td>22.2320</td>\n",
       "      <td>13.4065</td>\n",
       "      <td>20.4364</td>\n",
       "      <td>13.6045</td>\n",
       "      <td>28.9023</td>\n",
       "      <td>11.1576</td>\n",
       "      <td>12.6771</td>\n",
       "      <td>19.2549</td>\n",
       "      <td>13.7177</td>\n",
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       "               AAPL      AXP       BA      CAT     CSCO      CVX      DIS  \\\n",
       "Date                                                                        \n",
       "2018-10-31  18.4960  24.6947  22.5387  11.0997  18.7380  15.6084  16.4935   \n",
       "2018-11-30  15.0919  26.9880  22.0242  12.4126  19.6063  16.6275  16.5883   \n",
       "2018-12-31  12.9878  11.2377  18.0140  11.1878  17.7468  12.8033  15.8175   \n",
       "\n",
       "               DWDP      GS       HD  ...      PFE       PG      TRV      UNH  \\\n",
       "Date                                  ...                                       \n",
       "2018-10-31  14.7244  8.7815  18.9754  ...  18.2799  21.8772  13.7844  22.6631   \n",
       "2018-11-30  15.7976  7.4302  19.4545  ...  19.6256  23.3154  14.3616  24.3982   \n",
       "2018-12-31  14.5333  6.4857  18.5374  ...  27.0197  22.2320  13.4065  20.4364   \n",
       "\n",
       "                UTX        V       VZ      WBA      WMT      XOM  \n",
       "Date                                                              \n",
       "2018-10-31  16.3757  31.5751  12.8348  15.3241  20.7287  18.3425  \n",
       "2018-11-30  16.0633  32.4593  13.5565  15.7086  20.1850  18.3011  \n",
       "2018-12-31  13.6045  28.9023  11.1576  12.6771  19.2549  13.7177  \n",
       "\n",
       "[3 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================================\n",
      " Define the P/E dataframe as the most recent 12 years (144 months) of data:\n",
      "============================================================================\n"
     ]
    },
    {
     "data": {
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       "      <th>2007-01-31</th>\n",
       "      <td>30.9495</td>\n",
       "      <td>20.1453</td>\n",
       "      <td>22.0591</td>\n",
       "      <td>12.1806</td>\n",
       "      <td>23.9550</td>\n",
       "      <td>9.1904</td>\n",
       "      <td>20.4132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.7641</td>\n",
       "      <td>15.4905</td>\n",
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       "      <td>12.7379</td>\n",
       "      <td>23.0854</td>\n",
       "      <td>8.6186</td>\n",
       "      <td>17.3046</td>\n",
       "      <td>18.2849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.4448</td>\n",
       "      <td>11.3130</td>\n",
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       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>30.5451</td>\n",
       "      <td>19.6782</td>\n",
       "      <td>21.4951</td>\n",
       "      <td>12.2471</td>\n",
       "      <td>23.3694</td>\n",
       "      <td>8.6520</td>\n",
       "      <td>19.8850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4648</td>\n",
       "      <td>15.0570</td>\n",
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       "      <td>12.1165</td>\n",
       "      <td>22.5943</td>\n",
       "      <td>8.6034</td>\n",
       "      <td>17.2848</td>\n",
       "      <td>17.6425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.2045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.6552</td>\n",
       "      <td>10.9435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>29.3091</td>\n",
       "      <td>18.4314</td>\n",
       "      <td>20.5335</td>\n",
       "      <td>12.6711</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>9.2566</td>\n",
       "      <td>19.3024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.7009</td>\n",
       "      <td>13.9696</td>\n",
       "      <td>...</td>\n",
       "      <td>11.7488</td>\n",
       "      <td>21.6301</td>\n",
       "      <td>8.5712</td>\n",
       "      <td>17.1981</td>\n",
       "      <td>16.8831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.9466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.1897</td>\n",
       "      <td>11.0956</td>\n",
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      ],
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       "               AAPL      AXP       BA      CAT     CSCO     CVX      DIS  \\\n",
       "Date                                                                       \n",
       "2007-01-31  30.9495  20.1453  22.0591  12.1806  23.9550  9.1904  20.4132   \n",
       "2007-02-28  30.5451  19.6782  21.4951  12.2471  23.3694  8.6520  19.8850   \n",
       "2007-03-30  29.3091  18.4314  20.5335  12.6711  23.0000  9.2566  19.3024   \n",
       "\n",
       "            DWDP       GS       HD  ...      PFE       PG     TRV      UNH  \\\n",
       "Date                                ...                                      \n",
       "2007-01-31   NaN  10.7641  15.4905  ...  12.7379  23.0854  8.6186  17.3046   \n",
       "2007-02-28   NaN   9.4648  15.0570  ...  12.1165  22.5943  8.6034  17.2848   \n",
       "2007-03-30   NaN   9.7009  13.9696  ...  11.7488  21.6301  8.5712  17.1981   \n",
       "\n",
       "                UTX   V       VZ  WBA      WMT      XOM  \n",
       "Date                                                     \n",
       "2007-01-31  18.2849 NaN  15.6472  NaN  16.4448  11.3130  \n",
       "2007-02-28  17.6425 NaN  15.2045  NaN  16.6552  10.9435  \n",
       "2007-03-30  16.8831 NaN  14.9466  NaN  16.1897  11.0956  \n",
       "\n",
       "[3 rows x 30 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
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       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>18.4960</td>\n",
       "      <td>24.6947</td>\n",
       "      <td>22.5387</td>\n",
       "      <td>11.0997</td>\n",
       "      <td>18.7380</td>\n",
       "      <td>15.6084</td>\n",
       "      <td>16.4935</td>\n",
       "      <td>14.7244</td>\n",
       "      <td>8.7815</td>\n",
       "      <td>18.9754</td>\n",
       "      <td>...</td>\n",
       "      <td>18.2799</td>\n",
       "      <td>21.8772</td>\n",
       "      <td>13.7844</td>\n",
       "      <td>22.6631</td>\n",
       "      <td>16.3757</td>\n",
       "      <td>31.5751</td>\n",
       "      <td>12.8348</td>\n",
       "      <td>15.3241</td>\n",
       "      <td>20.7287</td>\n",
       "      <td>18.3425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>15.0919</td>\n",
       "      <td>26.9880</td>\n",
       "      <td>22.0242</td>\n",
       "      <td>12.4126</td>\n",
       "      <td>19.6063</td>\n",
       "      <td>16.6275</td>\n",
       "      <td>16.5883</td>\n",
       "      <td>15.7976</td>\n",
       "      <td>7.4302</td>\n",
       "      <td>19.4545</td>\n",
       "      <td>...</td>\n",
       "      <td>19.6256</td>\n",
       "      <td>23.3154</td>\n",
       "      <td>14.3616</td>\n",
       "      <td>24.3982</td>\n",
       "      <td>16.0633</td>\n",
       "      <td>32.4593</td>\n",
       "      <td>13.5565</td>\n",
       "      <td>15.7086</td>\n",
       "      <td>20.1850</td>\n",
       "      <td>18.3011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>12.9878</td>\n",
       "      <td>11.2377</td>\n",
       "      <td>18.0140</td>\n",
       "      <td>11.1878</td>\n",
       "      <td>17.7468</td>\n",
       "      <td>12.8033</td>\n",
       "      <td>15.8175</td>\n",
       "      <td>14.5333</td>\n",
       "      <td>6.4857</td>\n",
       "      <td>18.5374</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0197</td>\n",
       "      <td>22.2320</td>\n",
       "      <td>13.4065</td>\n",
       "      <td>20.4364</td>\n",
       "      <td>13.6045</td>\n",
       "      <td>28.9023</td>\n",
       "      <td>11.1576</td>\n",
       "      <td>12.6771</td>\n",
       "      <td>19.2549</td>\n",
       "      <td>13.7177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL      AXP       BA      CAT     CSCO      CVX      DIS  \\\n",
       "Date                                                                        \n",
       "2018-10-31  18.4960  24.6947  22.5387  11.0997  18.7380  15.6084  16.4935   \n",
       "2018-11-30  15.0919  26.9880  22.0242  12.4126  19.6063  16.6275  16.5883   \n",
       "2018-12-31  12.9878  11.2377  18.0140  11.1878  17.7468  12.8033  15.8175   \n",
       "\n",
       "               DWDP      GS       HD  ...      PFE       PG      TRV      UNH  \\\n",
       "Date                                  ...                                       \n",
       "2018-10-31  14.7244  8.7815  18.9754  ...  18.2799  21.8772  13.7844  22.6631   \n",
       "2018-11-30  15.7976  7.4302  19.4545  ...  19.6256  23.3154  14.3616  24.3982   \n",
       "2018-12-31  14.5333  6.4857  18.5374  ...  27.0197  22.2320  13.4065  20.4364   \n",
       "\n",
       "                UTX        V       VZ      WBA      WMT      XOM  \n",
       "Date                                                              \n",
       "2018-10-31  16.3757  31.5751  12.8348  15.3241  20.7287  18.3425  \n",
       "2018-11-30  16.0633  32.4593  13.5565  15.7086  20.1850  18.3011  \n",
       "2018-12-31  13.6045  28.9023  11.1576  12.6771  19.2549  13.7177  \n",
       "\n",
       "[3 rows x 30 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('============================================================================')\n",
    "print(' Define the P/E dataframe as the most recent 12 years (144 months) of data:')\n",
    "print('============================================================================')\n",
    "pedf = df.iloc[-144:]\n",
    "pedf.head(3)\n",
    "pedf.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 30)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pedf.iloc[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pedf.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
       "      <th>DWDP</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>...</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
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       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.135728</td>\n",
       "      <td>17.018117</td>\n",
       "      <td>16.015704</td>\n",
       "      <td>16.456440</td>\n",
       "      <td>16.156933</td>\n",
       "      <td>19.837657</td>\n",
       "      <td>17.450703</td>\n",
       "      <td>21.571120</td>\n",
       "      <td>11.331267</td>\n",
       "      <td>19.472149</td>\n",
       "      <td>...</td>\n",
       "      <td>14.156956</td>\n",
       "      <td>19.279000</td>\n",
       "      <td>10.717726</td>\n",
       "      <td>15.036556</td>\n",
       "      <td>16.131736</td>\n",
       "      <td>28.520182</td>\n",
       "      <td>14.946363</td>\n",
       "      <td>18.445402</td>\n",
       "      <td>15.593640</td>\n",
       "      <td>15.667203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.746895</td>\n",
       "      <td>5.919784</td>\n",
       "      <td>5.201220</td>\n",
       "      <td>6.298471</td>\n",
       "      <td>3.595349</td>\n",
       "      <td>21.918411</td>\n",
       "      <td>2.943092</td>\n",
       "      <td>3.231646</td>\n",
       "      <td>4.243340</td>\n",
       "      <td>4.199639</td>\n",
       "      <td>...</td>\n",
       "      <td>3.964105</td>\n",
       "      <td>2.551134</td>\n",
       "      <td>3.089557</td>\n",
       "      <td>5.527816</td>\n",
       "      <td>2.350672</td>\n",
       "      <td>4.073837</td>\n",
       "      <td>2.888870</td>\n",
       "      <td>4.039751</td>\n",
       "      <td>2.210513</td>\n",
       "      <td>7.428544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.892100</td>\n",
       "      <td>4.385500</td>\n",
       "      <td>5.071000</td>\n",
       "      <td>4.348100</td>\n",
       "      <td>10.523200</td>\n",
       "      <td>5.339500</td>\n",
       "      <td>8.220600</td>\n",
       "      <td>14.533300</td>\n",
       "      <td>5.562200</td>\n",
       "      <td>10.779600</td>\n",
       "      <td>...</td>\n",
       "      <td>5.065800</td>\n",
       "      <td>13.609800</td>\n",
       "      <td>6.626000</td>\n",
       "      <td>6.638500</td>\n",
       "      <td>8.215300</td>\n",
       "      <td>21.306400</td>\n",
       "      <td>10.589200</td>\n",
       "      <td>11.682700</td>\n",
       "      <td>12.010900</td>\n",
       "      <td>8.054600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.410625</td>\n",
       "      <td>13.583175</td>\n",
       "      <td>13.242975</td>\n",
       "      <td>12.230475</td>\n",
       "      <td>13.746100</td>\n",
       "      <td>9.240050</td>\n",
       "      <td>15.816575</td>\n",
       "      <td>20.032800</td>\n",
       "      <td>9.153650</td>\n",
       "      <td>15.939675</td>\n",
       "      <td>...</td>\n",
       "      <td>11.256825</td>\n",
       "      <td>17.061875</td>\n",
       "      <td>8.564500</td>\n",
       "      <td>10.228200</td>\n",
       "      <td>14.849125</td>\n",
       "      <td>25.502925</td>\n",
       "      <td>12.722400</td>\n",
       "      <td>15.708600</td>\n",
       "      <td>14.086300</td>\n",
       "      <td>11.153650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.073100</td>\n",
       "      <td>15.723900</td>\n",
       "      <td>16.221900</td>\n",
       "      <td>14.615900</td>\n",
       "      <td>15.089900</td>\n",
       "      <td>10.961150</td>\n",
       "      <td>17.393800</td>\n",
       "      <td>22.938500</td>\n",
       "      <td>10.170800</td>\n",
       "      <td>20.480050</td>\n",
       "      <td>...</td>\n",
       "      <td>15.222450</td>\n",
       "      <td>19.396750</td>\n",
       "      <td>9.879950</td>\n",
       "      <td>14.152400</td>\n",
       "      <td>16.665650</td>\n",
       "      <td>28.371700</td>\n",
       "      <td>14.335050</td>\n",
       "      <td>18.112200</td>\n",
       "      <td>15.242100</td>\n",
       "      <td>12.385700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.941825</td>\n",
       "      <td>18.207275</td>\n",
       "      <td>19.624000</td>\n",
       "      <td>21.062150</td>\n",
       "      <td>18.247000</td>\n",
       "      <td>17.215850</td>\n",
       "      <td>19.389800</td>\n",
       "      <td>23.799000</td>\n",
       "      <td>11.789150</td>\n",
       "      <td>22.991000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.236500</td>\n",
       "      <td>21.126150</td>\n",
       "      <td>11.935225</td>\n",
       "      <td>20.152425</td>\n",
       "      <td>17.935200</td>\n",
       "      <td>30.946725</td>\n",
       "      <td>17.133075</td>\n",
       "      <td>20.807300</td>\n",
       "      <td>16.586975</td>\n",
       "      <td>17.869400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.456600</td>\n",
       "      <td>34.743000</td>\n",
       "      <td>29.465000</td>\n",
       "      <td>33.187000</td>\n",
       "      <td>26.488000</td>\n",
       "      <td>110.849700</td>\n",
       "      <td>24.862600</td>\n",
       "      <td>25.368200</td>\n",
       "      <td>28.398300</td>\n",
       "      <td>27.189700</td>\n",
       "      <td>...</td>\n",
       "      <td>27.019700</td>\n",
       "      <td>24.203400</td>\n",
       "      <td>19.786300</td>\n",
       "      <td>26.077000</td>\n",
       "      <td>20.309900</td>\n",
       "      <td>39.353300</td>\n",
       "      <td>23.669100</td>\n",
       "      <td>27.894500</td>\n",
       "      <td>24.172300</td>\n",
       "      <td>40.794400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             AAPL         AXP          BA         CAT        CSCO         CVX  \\\n",
       "count  144.000000  144.000000  144.000000  144.000000  144.000000  144.000000   \n",
       "mean    18.135728   17.018117   16.015704   16.456440   16.156933   19.837657   \n",
       "std      7.746895    5.919784    5.201220    6.298471    3.595349   21.918411   \n",
       "min      9.892100    4.385500    5.071000    4.348100   10.523200    5.339500   \n",
       "25%     13.410625   13.583175   13.242975   12.230475   13.746100    9.240050   \n",
       "50%     16.073100   15.723900   16.221900   14.615900   15.089900   10.961150   \n",
       "75%     18.941825   18.207275   19.624000   21.062150   18.247000   17.215850   \n",
       "max     48.456600   34.743000   29.465000   33.187000   26.488000  110.849700   \n",
       "\n",
       "              DIS       DWDP          GS          HD  ...         PFE  \\\n",
       "count  144.000000  25.000000  144.000000  144.000000  ...  144.000000   \n",
       "mean    17.450703  21.571120   11.331267   19.472149  ...   14.156956   \n",
       "std      2.943092   3.231646    4.243340    4.199639  ...    3.964105   \n",
       "min      8.220600  14.533300    5.562200   10.779600  ...    5.065800   \n",
       "25%     15.816575  20.032800    9.153650   15.939675  ...   11.256825   \n",
       "50%     17.393800  22.938500   10.170800   20.480050  ...   15.222450   \n",
       "75%     19.389800  23.799000   11.789150   22.991000  ...   17.236500   \n",
       "max     24.862600  25.368200   28.398300   27.189700  ...   27.019700   \n",
       "\n",
       "               PG         TRV         UNH         UTX           V          VZ  \\\n",
       "count  144.000000  144.000000  144.000000  144.000000  124.000000  144.000000   \n",
       "mean    19.279000   10.717726   15.036556   16.131736   28.520182   14.946363   \n",
       "std      2.551134    3.089557    5.527816    2.350672    4.073837    2.888870   \n",
       "min     13.609800    6.626000    6.638500    8.215300   21.306400   10.589200   \n",
       "25%     17.061875    8.564500   10.228200   14.849125   25.502925   12.722400   \n",
       "50%     19.396750    9.879950   14.152400   16.665650   28.371700   14.335050   \n",
       "75%     21.126150   11.935225   20.152425   17.935200   30.946725   17.133075   \n",
       "max     24.203400   19.786300   26.077000   20.309900   39.353300   23.669100   \n",
       "\n",
       "             WBA         WMT         XOM  \n",
       "count  49.000000  144.000000  144.000000  \n",
       "mean   18.445402   15.593640   15.667203  \n",
       "std     4.039751    2.210513    7.428544  \n",
       "min    11.682700   12.010900    8.054600  \n",
       "25%    15.708600   14.086300   11.153650  \n",
       "50%    18.112200   15.242100   12.385700  \n",
       "75%    20.807300   16.586975   17.869400  \n",
       "max    27.894500   24.172300   40.794400  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    144\n",
       "AXP     144\n",
       "BA      144\n",
       "CAT     144\n",
       "CSCO    144\n",
       "CVX     144\n",
       "DIS     144\n",
       "DWDP     25\n",
       "GS      144\n",
       "HD      144\n",
       "IBM     144\n",
       "INTC    144\n",
       "JNJ     144\n",
       "JPM     144\n",
       "KO      144\n",
       "MCD     144\n",
       "MMM     144\n",
       "MRK     144\n",
       "MSFT    144\n",
       "NKE     144\n",
       "PFE     144\n",
       "PG      144\n",
       "TRV     144\n",
       "UNH     144\n",
       "UTX     144\n",
       "V       124\n",
       "VZ      144\n",
       "WBA      49\n",
       "WMT     144\n",
       "XOM     144\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedf.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    False\n",
       "AXP     False\n",
       "BA      False\n",
       "CAT     False\n",
       "CSCO    False\n",
       "CVX     False\n",
       "DIS     False\n",
       "DWDP     True\n",
       "GS      False\n",
       "HD      False\n",
       "IBM     False\n",
       "INTC    False\n",
       "JNJ     False\n",
       "JPM     False\n",
       "KO      False\n",
       "MCD     False\n",
       "MMM     False\n",
       "MRK     False\n",
       "MSFT    False\n",
       "NKE     False\n",
       "PFE     False\n",
       "PG      False\n",
       "TRV     False\n",
       "UNH     False\n",
       "UTX     False\n",
       "V        True\n",
       "VZ      False\n",
       "WBA      True\n",
       "WMT     False\n",
       "XOM     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedf.count() < 144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DWDP', 'V', 'WBA'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countLT144 = pedf.count() < 144\n",
    "cols_with_less_than_144 = pedf.columns[countLT144]\n",
    "cols_with_less_than_144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>30.9495</td>\n",
       "      <td>20.1453</td>\n",
       "      <td>22.0591</td>\n",
       "      <td>12.1806</td>\n",
       "      <td>23.9550</td>\n",
       "      <td>9.1904</td>\n",
       "      <td>20.4132</td>\n",
       "      <td>10.7641</td>\n",
       "      <td>15.4905</td>\n",
       "      <td>16.2808</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0894</td>\n",
       "      <td>18.8210</td>\n",
       "      <td>12.7379</td>\n",
       "      <td>23.0854</td>\n",
       "      <td>8.6186</td>\n",
       "      <td>17.3046</td>\n",
       "      <td>18.2849</td>\n",
       "      <td>15.6472</td>\n",
       "      <td>16.4448</td>\n",
       "      <td>11.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>30.5451</td>\n",
       "      <td>19.6782</td>\n",
       "      <td>21.4951</td>\n",
       "      <td>12.2471</td>\n",
       "      <td>23.3694</td>\n",
       "      <td>8.6520</td>\n",
       "      <td>19.8850</td>\n",
       "      <td>9.4648</td>\n",
       "      <td>15.0570</td>\n",
       "      <td>15.2726</td>\n",
       "      <td>...</td>\n",
       "      <td>22.9024</td>\n",
       "      <td>22.2751</td>\n",
       "      <td>12.1165</td>\n",
       "      <td>22.5943</td>\n",
       "      <td>8.6034</td>\n",
       "      <td>17.2848</td>\n",
       "      <td>17.6425</td>\n",
       "      <td>15.2045</td>\n",
       "      <td>16.6552</td>\n",
       "      <td>10.9435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>29.3091</td>\n",
       "      <td>18.4314</td>\n",
       "      <td>20.5335</td>\n",
       "      <td>12.6711</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>9.2566</td>\n",
       "      <td>19.3024</td>\n",
       "      <td>9.7009</td>\n",
       "      <td>13.9696</td>\n",
       "      <td>15.1543</td>\n",
       "      <td>...</td>\n",
       "      <td>19.9071</td>\n",
       "      <td>22.6567</td>\n",
       "      <td>11.7488</td>\n",
       "      <td>21.6301</td>\n",
       "      <td>8.5712</td>\n",
       "      <td>17.1981</td>\n",
       "      <td>16.8831</td>\n",
       "      <td>14.9466</td>\n",
       "      <td>16.1897</td>\n",
       "      <td>11.0956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL      AXP       BA      CAT     CSCO     CVX      DIS  \\\n",
       "Date                                                                       \n",
       "2007-01-31  30.9495  20.1453  22.0591  12.1806  23.9550  9.1904  20.4132   \n",
       "2007-02-28  30.5451  19.6782  21.4951  12.2471  23.3694  8.6520  19.8850   \n",
       "2007-03-30  29.3091  18.4314  20.5335  12.6711  23.0000  9.2566  19.3024   \n",
       "\n",
       "                 GS       HD      IBM  ...     MSFT      NKE      PFE  \\\n",
       "Date                                   ...                              \n",
       "2007-01-31  10.7641  15.4905  16.2808  ...  25.0894  18.8210  12.7379   \n",
       "2007-02-28   9.4648  15.0570  15.2726  ...  22.9024  22.2751  12.1165   \n",
       "2007-03-30   9.7009  13.9696  15.1543  ...  19.9071  22.6567  11.7488   \n",
       "\n",
       "                 PG     TRV      UNH      UTX       VZ      WMT      XOM  \n",
       "Date                                                                      \n",
       "2007-01-31  23.0854  8.6186  17.3046  18.2849  15.6472  16.4448  11.3130  \n",
       "2007-02-28  22.5943  8.6034  17.2848  17.6425  15.2045  16.6552  10.9435  \n",
       "2007-03-30  21.6301  8.5712  17.1981  16.8831  14.9466  16.1897  11.0956  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pedf = pedf.drop(cols_with_less_than_144,axis=1)\n",
    "pedf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    18.135728\n",
       "AXP     17.018117\n",
       "BA      16.015704\n",
       "CAT     16.456440\n",
       "CSCO    16.156933\n",
       "CVX     19.837657\n",
       "DIS     17.450703\n",
       "GS      11.331267\n",
       "HD      19.472149\n",
       "IBM     12.283763\n",
       "INTC    14.682974\n",
       "JNJ     16.184714\n",
       "JPM     12.702983\n",
       "KO      19.749747\n",
       "MCD     19.223497\n",
       "MMM     18.294041\n",
       "MRK     18.522142\n",
       "MSFT    16.939769\n",
       "NKE     22.799566\n",
       "PFE     14.156956\n",
       "PG      19.279000\n",
       "TRV     10.717726\n",
       "UNH     15.036556\n",
       "UTX     16.131736\n",
       "VZ      14.946363\n",
       "WMT     15.593640\n",
       "XOM     15.667203\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedf.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a0de8d7f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIGSIZE = (11, 7)\n",
    "COLORS  = ['m','tab:orange','y','g','c','b']\n",
    "pedf.mean().plot.bar(color=COLORS,figsize=FIGSIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a0db5c3c8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pedf.std().plot.bar(color=COLORS,figsize=FIGSIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a0da3d0b8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pedf.plot(y='CVX',color=COLORS,figsize=FIGSIZE)  # Chevron Corp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>30.9495</td>\n",
       "      <td>20.1453</td>\n",
       "      <td>22.0591</td>\n",
       "      <td>12.1806</td>\n",
       "      <td>23.9550</td>\n",
       "      <td>20.4132</td>\n",
       "      <td>10.7641</td>\n",
       "      <td>15.4905</td>\n",
       "      <td>16.2808</td>\n",
       "      <td>23.8182</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0894</td>\n",
       "      <td>18.8210</td>\n",
       "      <td>12.7379</td>\n",
       "      <td>23.0854</td>\n",
       "      <td>8.6186</td>\n",
       "      <td>17.3046</td>\n",
       "      <td>18.2849</td>\n",
       "      <td>15.6472</td>\n",
       "      <td>16.4448</td>\n",
       "      <td>11.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>30.5451</td>\n",
       "      <td>19.6782</td>\n",
       "      <td>21.4951</td>\n",
       "      <td>12.2471</td>\n",
       "      <td>23.3694</td>\n",
       "      <td>19.8850</td>\n",
       "      <td>9.4648</td>\n",
       "      <td>15.0570</td>\n",
       "      <td>15.2726</td>\n",
       "      <td>22.5568</td>\n",
       "      <td>...</td>\n",
       "      <td>22.9024</td>\n",
       "      <td>22.2751</td>\n",
       "      <td>12.1165</td>\n",
       "      <td>22.5943</td>\n",
       "      <td>8.6034</td>\n",
       "      <td>17.2848</td>\n",
       "      <td>17.6425</td>\n",
       "      <td>15.2045</td>\n",
       "      <td>16.6552</td>\n",
       "      <td>10.9435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>29.3091</td>\n",
       "      <td>18.4314</td>\n",
       "      <td>20.5335</td>\n",
       "      <td>12.6711</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>19.3024</td>\n",
       "      <td>9.7009</td>\n",
       "      <td>13.9696</td>\n",
       "      <td>15.1543</td>\n",
       "      <td>21.4944</td>\n",
       "      <td>...</td>\n",
       "      <td>19.9071</td>\n",
       "      <td>22.6567</td>\n",
       "      <td>11.7488</td>\n",
       "      <td>21.6301</td>\n",
       "      <td>8.5712</td>\n",
       "      <td>17.1981</td>\n",
       "      <td>16.8831</td>\n",
       "      <td>14.9466</td>\n",
       "      <td>16.1897</td>\n",
       "      <td>11.0956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL      AXP       BA      CAT     CSCO      DIS       GS  \\\n",
       "Date                                                                        \n",
       "2007-01-31  30.9495  20.1453  22.0591  12.1806  23.9550  20.4132  10.7641   \n",
       "2007-02-28  30.5451  19.6782  21.4951  12.2471  23.3694  19.8850   9.4648   \n",
       "2007-03-30  29.3091  18.4314  20.5335  12.6711  23.0000  19.3024   9.7009   \n",
       "\n",
       "                 HD      IBM     INTC  ...     MSFT      NKE      PFE  \\\n",
       "Date                                   ...                              \n",
       "2007-01-31  15.4905  16.2808  23.8182  ...  25.0894  18.8210  12.7379   \n",
       "2007-02-28  15.0570  15.2726  22.5568  ...  22.9024  22.2751  12.1165   \n",
       "2007-03-30  13.9696  15.1543  21.4944  ...  19.9071  22.6567  11.7488   \n",
       "\n",
       "                 PG     TRV      UNH      UTX       VZ      WMT      XOM  \n",
       "Date                                                                      \n",
       "2007-01-31  23.0854  8.6186  17.3046  18.2849  15.6472  16.4448  11.3130  \n",
       "2007-02-28  22.5943  8.6034  17.2848  17.6425  15.2045  16.6552  10.9435  \n",
       "2007-03-30  21.6301  8.5712  17.1981  16.8831  14.9466  16.1897  11.0956  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedf = pedf.drop('CVX',axis=1)\n",
    "pedf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a0d9d16d8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pedf.std().plot.bar(color=COLORS,figsize=FIGSIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:white;font-style:italic;line-height:150%;\">\n",
    "    <h1 style=\"text-indent:3%;\">How do we decide if the P/E is low?\n",
    "    <h1 style=\"text-indent:7%;\">P/E that is <i>below average</i> for the market place.</h1>\n",
    "    <h2 style=\"text-indent:7%;\">Measure this by calculating the Number of Standard Deviations the P/E, for a given stock, is above or below the average P/E for all the stocks:</h2>\n",
    "    <hr width=90%;>\n",
    "    <h1 style=\"text-indent:7%;\">Z-Score = (Value - Mean) / StandardDeviation<br></h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>18.028723</td>\n",
       "      <td>5.033794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>17.640954</td>\n",
       "      <td>4.987392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>17.060388</td>\n",
       "      <td>4.702940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-30</th>\n",
       "      <td>18.031981</td>\n",
       "      <td>4.953115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-31</th>\n",
       "      <td>18.832308</td>\n",
       "      <td>5.769437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean       std\n",
       "Date                           \n",
       "2007-01-31  18.028723  5.033794\n",
       "2007-02-28  17.640954  4.987392\n",
       "2007-03-30  17.060388  4.702940\n",
       "2007-04-30  18.031981  4.953115\n",
       "2007-05-31  18.832308  5.769437"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pestats = pd.DataFrame()            # construct emppty dataframe\n",
    "pestats['mean'] = pedf.mean(axis=1) # set 'mean' column values\n",
    "pestats['std']  = pedf.std(axis=1)  # set 'std' column values\n",
    "\n",
    "pestats.head()                      # display the first few rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a0d9d1b00>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hhJiwtqWXoWmdwWV2eZNdx7IrswJ3ZweSonxH5X56nSLC14WCqpFlKpvaTfw7tYhr54US6NF3h5/LZwbg7+405gU7ElQKIYQQYsL69GQZ/u7OACTnVtltHBaLxs7MCi6f6Y+Tw+iFT9F+biPOVH5yooSWDjN3XRJp9XFHvY4b5oexM7OcmuaOET3XQCSoFEIIIcSEVNvcwf4z1dyWFM40fzf22jGoPFlcT2VjO2vjg0b1vlF+bhRUtww4tW+xaNS3Gvt9/K1DhcwIdGdhpHe/51w/PwyjWeupXh8LElQKIYQQYkLanl6O2aJx1ZwQlsX6c+BMNR2msS026c/OjHJ0ClbHBY7qfaP9XWk1mqlstL7rzdGztdz43D6WPLmD3Iq+0/+ZZQ0cK6zj9sURdO5XY11CqCc+ro7sza0etbFfSIJKIYQQQkxIm0+VEu7jQkKoJ8ti/WnpMHOssM4uY9mRUcGiKB983JxG9b5RPW2Feq+rrGhs47vvHOfGv+2jpK4VR72OpzZn9Ln+7cOFOOl13LQwfMDn0ekUS6f7sze3aswKngZtfi6EEEIIMd7qW43sza3i/mUxKKW4bLofOtW5rnJJzOgUytiqpK6V9NIGntg4a9TvHd3VVuiNgwXsza2isc1EfauRrWlltJvMPLxyOo+tieX1AwX8+tNMknOqWD7DH4A2o5kPjhazLiEIXxuC3WWx/nxyspQzVc1MD3Af9dcimUohhBBCTDg7M8oxmjU2JgYD4OXiyNxwb7usq9yZUQ7AFaO8nhIgzNsFPzcnPjxWwp925vBOSiH7TlexLNaPbd9eyQ83zsLd2YH7l0UT4evC//skHXPXto5b08qoazFyx2LrBToXWhbrBzBmX0PJVAohhBBiwvn0VBkhXgbmhZ8rPlke689zn52moc2Ip8FxwOstFg2lGHCdoa12ZFQQ7efK9AC3Ed/rQg56Hck/WEOH2YK7s0PP9ooXcnbQ88TGeB594whvHy7krksieftwIRG+Liyd7mfTc0X6uhLu48Le3Cq+fFn0KL6KTpKpFEIIIcSE0tRu4rPsSjYkBqM7L8haFuuP2aJx8EyN1evqW43852gxj7yeSsLPt/KbLbbvwlPX0kFmWUNPFrBbc7uJ/aeruSI+aFQCVGtcnPR4uTj2G1B225gYzJJoX57ZnsWp4nr2na7m9qSIXl+jgSilWDbdn/2nq/u8ToCff3iKh/6ZMqzXAJKpFEIIIcQEsyuzgg6ThavmhPQ6vjDKGxdHPXtzq7hy9rmp6PKGNp54/yRf5FRiNGsEeDgT5efKy8l5fPmyKEK9++7TXdHQxjPbs8kubySvqpnals6WPesTgvjb3Yt6ArwvcqroMFtGvZXQcCil+Mk18Vz3l73ct+kwOkWvfb5tsWyGP2+nFHKquJ55EeeywGX1bbxx8CyeLgNngAcimUohhBBCTChbTpUS4OHMokifXsedHfQsifHt1QS9sc3IfZsOc+BMNV9ZFsN7jyzl4BNX8OK9SWhoPLsrt8/9NU3jB++d4P2jxTg76Nk4J4SfXB3PwyunszWtnF9+nN5TIb0zoxxPgwNJ0T597mMPc8O9uWlBGFVN7ayZFUiwV98ddAbSPVV+YSP5f+7Px2TRqGnuoN1kHtbYJFMphBBCiAmhsKaFT06WsiuzglsXWZ/WXR7rz5ObMyitb8XPzZlHXj9CTnkjL923mJUzz+3JHe7jyp1LInnz4FkeXjmtp3UPwOaTZezOquQnV8fz4Ippve7fYbLw8t48wn1cuH9ZDLsyK1gVF4ijfuLk4b6/YRZpJQ19xm4Lf3dnZgV7sO90FV9fHQtAa4eZNw+dxeCoo81ooaKhnQhf1yHfe0oGlZWN7Xi7Ok6o/wBCCCGE6Ku4rpXNJ0r5+GQpx7t6UM6L8ObBFTFWz18W29lOJzmnin2nq0nOreLpW+f1Cii7PbY6lrcPF/KnHTk8c/t8ABrajPziv2kkhHpy39LoPtf85Op4SutbeXJzBhWN7VQ3d7B2tv2nvs8X7GVg67cvH/b1y2L9ee1AAW1GMwZHPe8dKaKuxcjja2J5dlcu5Q1twwoqp1zU1dhmZPXTe3jhizP2HooQQgghrCirb+Ol5Dxu+ttelv16F09uzsBi0fjhxll88f3VfPj1Zb0yi+ebFeyBv7sTT27O4IOjxXxv3UxuWWS98Xegp4F7l0bzwbFicsobAfjtlkyqm9p56qY5OFhJPul0ij/cPp+FkT48//kZHHTKasA6mS2P9afDZCG1oBaLRePl5Dzmhnv1rGEtb7C+u89gplymcm9uFU3tJpJzqnh0Vay9hyOEEEKI8/xlVw6/356NpsHsEE/+Z30cV88JIdrftnY93TvDfHS8hLsuieyZwu3Pwyun88aBAv6wI5sHlk/jjYNnuW9pNHPD+98n2+Co54UvJ3Hr3/cxLcAdrxEUr0xES2J8cdAp9uZW0W4yc6aqmT/dMZ9gz871mWUNbcO675QLKndnVgJwrLAOk9li9beQ4ShvaON0ZRNLp/uPyv2EEEKIi43RbOHF5DyWTvfj/65PHPauLo+viSUu2IOvXT5t0DY/vm5OPLA8hj/vyuV4YT3Bnga+uy5u0OfwdXNi27dXYrLYZ6/xseTm7MCCyM5G8seL6gj2NHDVnBAcdAonBx3lwwwqp9T0t6Zp7M6qwMPgQEuHmYzSxlG7928+zeRLLx0a9hdaCCGEuNjtO11NXYuR+5bGjGibwBlBHnx9dazNiaMHVkzDy8WR4rpW/ve6BNydbcup6XUKZwf9sMc5kS2d7s+J4nr25lbz5aVROOp1KKUI9jRIUAmQVtJARWM7X7u8sxoqpcB6c9ShMpkt7MqqwGzR+Hdq0ajcUwghhLjYfHKiBHdnB1bMGN9ZPy8XR35z81y+e+VM1iUEj+tzT1TLZ/ijaeDiqOeuJee2eQzydKasXoJKdmdWAHDHkkhCvQyk5NeOyn1TC2qpazHi7uzAvw6fxWKlC70QQggxlZktGg+/lsptf9/PX3fncqq4vuf9sMNk4XhhHa/uy+fJT9JpbDP2ud5otrAtvZwrZwdhcBz/7N+GxGAev2LGuD/vRDU/whtfNyduXxyBt6tTz/EgTwMVjVKow+6sCuaFe+Hv7kxStC8H86rRNG3E2yrtyCjHSa/jiatm8eMPTrHvdDXLx/m3LCGEEMKe/v7ZabaklTE9wI3fbc3id1uz8Hd3JtzHhfTSBjpM59Yeujo58O0rZ/a6vnvq+8JdcoR9OOp17PjOSjwMvUPBIE8DOzMqhhU/TZlMZU1zB0cL61g9KxCApGgfyhvaKaptHfG9d2ZUcOl0P25eGI63qyP/Onx2xPcUQgghJotTxfX8YXs2V88NYcd3VnL4x2t55rZ5LJ3uh7ODjnsvi+Kvdy1k3w/XsCEhmJf35lHf2jtbaa+pb9E/XzenPj29gz0NtBrNNLSZhny/KZOp/Dy7Ek2D1XGdQeWiqM7tlFILaofVwLPb6comzlQ1c9+yaAyOem5cEMYbB85S09yBr5vT4DcQQgghJrHWDjPf/NdR/N2defKGRJRSBHg4c9PCcG5a2Lc/5DfXzmBLWhkvJ+f1ZCu7p77XxgfaZepb2C6oa9vHioa2IbdSmjKZyl2ZFfi7OzEnzAuAWcGeuDs7cDh/ZMU6OzPKAbiiayP5O5dE0mG28P4RKdgRQggx9f360wxOVzbz9K3zeq296098iCcbE3tnK7unvq+eGzrWwxUjFOThDAyvV+WUCCrNFo3PsitZOTOwZ59QvU6xINKb1IKRFevsSK8gPsSTMG8XAGYGebAw0pu3Dp3t2WxeCCGEmIo+y67k1f0F3L8seki1BN+4YgaNbSZeTs4DZOp7MgnuylQOpwJ8SgSVR8/WUt9qZPWs3tsoJUX5klXe2Gddh61qmztIKajhyvjAXsfvWBLJ6cpmUkYYsAohhBATVX2rkf959zgzg9z5wYZZQ7r2/GxldVO7TH1PIkFdu+oMpwJ8SgSVu7Mq0OsUK2ZcEFRG+6BpnUHncO9r0c5NfXe7Zm4I7s4OvHVICnaEEEJMTX//7DQVje38/tb5wwoGu7OVj7x+RKa+JxGDox4vF8eLN1O5O7OSRVE+fRaUzo/wRq9Tw+5XuTOjgkAP5551mt1cnRy4bn4om0+WDjsLKoQQQkxUZfVtbNqbxw3zQ5kT7jX4BVZ0ZysP5dfI1PckE+xpmDprKi1DWKtYVt9GemkDa2YF9nnMzdmB2SGew9pZp8Nk4bPsSq6IP7dO83y3JUXQZrT0FPIIIYQQU8WfdmZjtmg27ZE9kG90NRuXqe/JJcjLQMVUCSorGmyfx0/OrQJg5cwAq48vivLhWGEdRvPAG8I3tZt6nXMwr5qmdhNrL5j67jY3rLPJ+p6sSpvHKoQQQkx0uRVNvJNSxN2XRI2oJR90Zitf/HIS3x/imkxhX0EezsPKVE7IPpVVTe3kVjQSG+gx6LmH8qrxcnEkLsj6uUnRPryyL5/0kgbmRXhbPWdHejmPvJGKTilmh3oyN8yLvOoWDI46lsVaT9frdIpVcQFsTy/HZLbYvKm9EEIIMZE9vTULF0c9j6+JHZX7rZ1tPTkjJq5gLwOVje2YBknIXWhCRkI6pfjZh2k2tew5nF/L4mgfq1PU0FkB3nme9Snw44V1PP7WUeKCPfjyZVE46nW8m1rE59mVrJo5cLp+dVwg9a1GjhXW2fCqhBBCCPs5lFdDSd3Au8wdOVvLlrQyHrp8Gn7uzuM0MjHRBHkasGhQ3dwxpOsmZKYyyMvAvtPVfHKylGsGqBaraGwjr6qZO5dE9HtOsJeBcB8XtqWXc/viCDwM54p5zla38MCrh/Fzd2LTfUsI6Gr4abZo5FU1E+Q58DfU8hn+6HWK3VkVJEX7DvFVCiGEEOOjvtXIHc/vx8lBx+NrZvDgihicHXonTTRN49efZuLv7swDy2PsNFIxEXS3FRpqBfiEzFT6uTmREOrJLz9Op6m9/70nD+d1VnUvHiSgu+fSKA7l1bD66T28degsZotGbXMH971yCKNZ45X7zwWU0Nk4PTbQvVcAao2XiyNJUT7sypR1lUIIISaunPJGLBrE+Lvzu61ZbPjjF+zJqsBktpBe0sDrBwr4xr+OcSivhm9eEYub84TMOYlxEtwVVJYPcV3lhP1f88sbErnpb/t4dmcOT1wVb/Wcw/k1uDjqSQwbuN3Bwyunc9k0P375cTpPvH+SV/fl4+yop6imldcfvITYQPdhj3P1rEB+/WkmZfVtPV3ohRBC2Ne2tDIqm9q5+5Iom685WVSPm7OeaQHDf0+YqLLLmwB4/kuLOFPVzC8+SuO+TYcxOOpoM3aum/Nzc+KWReHcsSTSnkMVE0CQV2eibcoElQsjfbgtKZyXkvO4ZVE4M6wU4hzKq2FhlDeONhTJzIvw5t2HL2PzyTKe+jSDzLJGnr1zAUtiRjZtvTquM6jck1Uh34hCCDEBmC0aP/nPKSoa2/EwOHLdvMGbbmuaxoP/PIybswPbv70SfT/r9Cer7PJGXJ30hHm7EOHrypZvreC1/QUU1bayINKbBRE+RPi6oNTUet1iePzcnNHr1JArwAcNKpVSLwPXABWapiV2HXsb6G5e5Q3UaZo238q1+UAjYAZMmqYlDWVwP9gwiy2nynhmezbP3bOo12MNbUYyyhr4ZlcPLFsopbh6bghXxAdSUtc6Kr+NzgxyJ9TLwG4JKoUQYkLYm1tFRWM7/u5O/PC9E8QHe1hNTJwvp6KJ8oZ2oJ2PT5Rw/fyw8RnsOMmpaGRGoHtPUauzg54HV0yz86jERKXXKQI9nLu+J2xny5rKV4AN5x/QNO12TdPmdwWS7wHvD3D96q5zhxRQAvi5O3PHkki2p5dT0dg7Wk7Nr0XTYMkwCmQMjqM3vaGUYtWsQJJzqmg3mUflnkIIIYbvg6PFeBoc+ODRZbg66Xn49dQB1+cDfJHT2fM4zNuFP+3MwWyxfROOySC7vMmmNn1CdAv0NAx5+nvQoFLTtM8Bq/14VGee/DbgrSE96xDcsTgCk0Xj3ZSiXscP5dfgoFMsiPQZq6e22Zq4QJo7zMPeDlIIIcToaG43seVUGVfPDSXC15Vn71xIfnULP/j3iQHb1O3aJiyaAAAgAElEQVTNrSLG340fXx3PmcpmPj5RMo6jHlt1LR1UNrYzM2jqrRUVYyfY03ncq79XAOWapuX087gGbFNKpSqlHhroRkqph5RSKUqplMrKc9XU0wLcuWyaH/86fBbLeb85Hs6rYU64Fy5O9t/2aWmsH056HbszK+w9FCGEuKhtOVVGq9HMTQs7p68vm+7H99fH8cnJUl7em2/1mg6ThQNnqlkW68eGhGDigjz48xTKVuZUdBbpzBxkCYAQ5wsei0zlIO5k4CzlMk3TFgIbga8rpS7v70RN057XNC1J07SkgIDeWy7edUkkhTWtfNG1JWOb0czxorphTX2PBVcnBy6Z5svuLAkqhRDCnj44WkyErwtJUedmsR66fBrrE4J4anMGRbUtfa45eraWlg4zy2MD0OkU37hiBqdtzFaWN7TRYRrariPjLbu8EYAZkqkUQxDoaaChbeBlIxcadlCplHIAbgLe7u8cTdNKuv6uAD4AlgznudYnBOPn5sSbBwsAuvby1kZcuT2aVscFcrqymbPVfX9gCSGEGHtl9W3sPV3FjfPDelUxK6X42bUJmDWNdw4X9rlub24VOtWZ1QTYmBjMzCB3nt2V22+2sryhjSfeP8FlT+3k/lcOYexnO7t9p6u4f9MhmgdZ03m+91KLSPjZFh58NYV3DhdS1TS0YokL5ZQ34dZV+S2Erbp7VQ7FSDKVa4FMTdOKrD2olHJTSnl0fwysA04N54mcHHTcsiicHRkVVDS0cTivBqXObcE4EayZFQgg2UohhLCTD48Vo2lw48LwPo+FebuwamYAb6cU9tnP+IvcKuZFeOPl0rnhRXe2MreiiU9OlvY6t7HNyO+3ZbHqd3v4d2oRa+OD2JtbzU8+ONVnzeaxwjoefDWF3VmVHD1r+3a+n2VXopQio7SB7793gsVP7uDm5/bx5sGzgxYcWZNd3khskIe0CxJDMpze27a0FHoLWAX4K6WKgJ9rmvYScAcXTH0rpUKBFzVNuwoIAj7o+k/sALypadqWIY+wyx1LIvnH52d4J6WQQ/k1xAV54OU68I434yna340AD2dOFdfbeyhCCHHR0TSN948UsyDSmxh/N6vn3HVJFF/9Zwo7MytYnxAMdG5feLywjq+vju117lWJIcwIzOHHH5zk2Z05mDUNs0WjpqmDxnYT18wN4X/WxxHl58bvt2Xx7K5cov3deGTVdKBzB5v7Nh3Cx9WJlo5WTpXUs3yGv02vJa2knkun+fHClxeRXtrA9vRyNp8s5UcfnOTJT9K5bn4Ydy2JZE74wBt/dMsub2J1XMDgJwpxnsG2qrZm0KBS07Q7+zl+n5VjJcBVXR+fAeYNeUT9iPF3Y+l0P946VEhdSwc3WflN1N6m+buRV9Vs72EIIcRFJ720gazyRn55fUK/56yOCyDY08Bbh872BJUHzlRj0WB5bO+AT6dT/L8bEtm0Nx+lOvv26XUKF0c9dy6JZF6Ed8+537lyJvnVLfxmSyZRfq7MCfPinpcO4qTX8dZXL+WuFw9w0saEQ0uHiTNVzVwzNxSlFAmhXiSEevHNK2Zw5Gwdbx06ywdHi3jr0Fm+t24mj60ZuFdzbXMHVU3tsp5SDFnQMKa/J+yOOtbcdUkkj715FGBCrafsNi3AjW1p5fYehhBCTFj1rUZyKxpZNITlS28cLODTk2U8/+VFuDpZf9v64EgxjnrFNXP73z3HQa/jtsURPLsrh8KaFiJ8XUnOqcLVSW+1Pd0l0/y4ZJrfoONTSvG7W+ZSUtfKt98+RoCHM60dZt55+DIi/VxJDPUizcagMqO0EU2DhFDPPs+xKMqHRVE+/PSa2Tzyeir/3F/AI6tiB9z951yRjlR+i6HxMDjiNsQOOyOt/h5X62Z3FuzAxAwqY/zdqG7uoL7FaO+hCCHEhHOquJ5rnv2Cm5/bz1929deJrrcOk4U/bM8hObeKn32YZvWcqqZ2/nOsmFVxgfh0vUf05/bFESjgnZTOgp3k3CouneaHk8PI3g4Njnqe/9IigjwNVDd1sOn+JcwK7gwME8M8ya9uoaFt8PeG9JLO4DMhrP+pbS8XR+5YEklFYzuH8622ke6RLe2ExAgMNVs5qYJKJwcdj6yazqq4gGGlZcdajH/n9EJetUyBCyEmlzajmZeS80Zcadyftw+f5abn9mEya6yND+Lpbdk8sy1rwIbkAFvTyqhqamd5rD//Ti3ivdTetaFN7Sbu33SYpnYTj12wLtKaMG8XVsUF8vbhQgqqm8mramZZrG1rHQfj5+7Mf76+jC3fWsGi81oaJXYFiGnFDYPeI62kAW9XR0IHKZJYGx+Ii6Oe/x4fuO1Rbnkj7s4Og95PCGumdFAJ8OCKabxy/7A6E425GH9XAPKqmuw8kotba4eZp7dmDatKUoiL1Tsphfzy43TufuEgNc0do3bfNqOZ7//7OD947yRLon35+PHl/ONLi7gtKZw/78rl11syBwwsX9tfQISvC5vuX8yl03z5yX9OkduVfWs3mXn4tVTSSxv4290Le61zHMhdXVm+//tvOgArbCygsYWvmxNRfr0LhXqCypLBp8DTSxtICPUctFLb1cmBtbOD2HyytN92RtC9PaO7VH6LYRlqBfikCyonsghfV3QK8iolU2lPOzPL+cvuXLanl9l7KEJMCmaLxsvJeUT5uZJf3czdLx6krmXkgWVjm5G7XjjAOylFPLY6lle/sgQ/d2f0OsWvb5rLPZdG8o/PzvC//023GlhmljVwKL+Gey6JwlGv4093LMDVSc9jbx6hpcPEd985TnJuFb+5eS5rZgXZPK5VXQU7OzMrCPJ0Zkbg2Bax+Ls7E+JlGLRYx2i2kFnWyOwQzwHP63bt3BBqW4zs7doYxJqcikbZnlEMW+AQK8AlqBxFzg56wn1cOSMV4HZ1vLCzH9xQ+sIJcTHbmVFOfnUL318/ixfvTeJ0ZRP3vHRwROvDm9tNfOWVwxwvqudvdy/ke+vjehWU6HSKX16fyFeWxfDKvnz+siu3zz1eP1CAk4OO25IigM6puGdun09mWSPr/vA5H58o5YcbZ3HLoqF1A3HQ67h9cec9l8X6j0sWLyHUa9CWc6crm+gwWUgIta1V0Mq4ADwMDvz3eKnVx2uaO6hq6pD1lGLYhtoAXYLKURYjbYXs7lhXUNn9txBiYC8l5xHm7cL6hCBWzAjgH19aRHZZE19++aBNxSUXau0w8+CrKaQW1PKnO+Zz1ZwQq+cppfjpNfHcMD+UZ3Zkk5xzLuPW2GbkgyPFXDs3tFfxzcqZATyyajpFta08uDyGr10+begvGLhjSQQeBgeuSrQ+ttE2J8yLM1XNAy7L6V5zeWHld3+cHfSsTwhmW1oZbUZzn8e7K79jxzgTK6auof5CIkHlKIvxdyO/qnnQxedibJjMFk4W16PXKdJLGqz+oBVCnHOyqJ6DeTXcvywaB33nW8LquED+dvdC0koa+O2WzCHdr81o5qHXUjiQV80zt80fsMUPdAaWv7ppDrEB7nzjX0cprW8F4D9Hi2nuMPOly6L6XPO9dXH85+vL+NFV8cPOMoZ4uXDi5+tYO9v2afORSAzzRNMgo7T/Yp20kgYMjjqmBdgeBF47L5TGdhOfZVf2eSynK6iUTKUYrqEWsUlQOcqmBbjR3GGmsnFsKijFwLLLm2gzWtiQGIzJotm0MF6Ii9lLyWdwd3bgtq7p4G5rZwexITGYLafK+t3/uqi2hUdeT+Whf6bw6BupPP7WUW7/x36+yKnitzfP5YYFYTaNwdXJgefuWUS70cxjbx6lw2Thn/sLmBvuxXwrxTd6nWJ+hDe6Afoz2mI8i1fmdBXrnCzq/2dSWkk9s4I9B+w7eaFl0/3wdXPiIytV4NnlTXg4OxAild9inEhQOcq6tweTdZX20T3lff/SaEDWVQoxkLL6Nj4+UcptSRF4Gvpue7s+IZiqpg5SC2qtXv/a/gK2pZdztqaF7PImThbVUd9q5Le3zOXWpAir1/QnNtCdX988l9SCWu7bdIiciibuubRvlnKyCvQ0dG7l288vupqm9VR+D4WDXsdVc4LZmVFO8wVT6517fkvltxg/k2pHncmgO6jMq2rmUht2YhCj63hhHT6ujiyK8iHM20XWVQoxgFf352PRNO5fFm318dWzAnHS69iaVtZnwwmLRePDYyWsmhnAS/ctHpXxXDsvlNSCWl7Zl4+XiyPXDjJ1PtnMCeu/WKewppXGNpPNRTrnu3ZuKK8fOMuOjHKun38uO5xb0cTa+PGZ3hcCJFM56kK9XHBy0Emxjp0cK6xjXoQ3SnVOj0mmUgjrWjpMvHnwLBsSg4nwdbV6jruzA8tn+LPlVFmfdeIH8qopa2jjehunuG31o6viuXpOCN+5ciYuQ9wibqJLDPUkt6KJ1o6+a727l+oMNVMJsDjalyBPZzbtzWdrWhkF1c1UNrZT3dwhe36LcSWZylGm0yli/Nw4I70qx11Tu4nsikY2zgkGYEGkN5+cLKWysZ0Aj6H12hJiqnsvtYj6ViMPLB+4enpDQjC7MitIK2noaeIN8OHREtyc9Fw5ypkwJwcdf7174ajec6JICPPConU2OD9/xx3oPKbXKeKCh15Uo9Mpvnb5dP7v43S+9loqAE5dRVdSpCPGkwSVYyDa37Vnxwcxfk4W1aNp9Oyq0b3A/1hhHVeOU4WnEJOBxaKxaV8+8yK8+wQ3F7oiPhCdgm1pZT1BZZvRzOZTpaxPDJ5y2cSxNOe8nXUu/LqnlTQwPcANg+Pwvp5fWR7D7YsjyC5vJKuskcyyRupbjSRFD/zvK8RokqByDMT4u7MrswKT2dLTokOMveNFnVPd88I7g8nEMC8cdIpjhbUSVApxns9zKjlT2cyf7pg/6Ll+7s4sjvZlS1oZ31kXB8DuzAoa20zcOMpT31NdiJcBXzcnqxXgaSX1LJ0+su0i3ZwdWBDpw4JICSSFfUjEMwam+bthNGsU17XaeygXlWNn64jyc8W3q1GywVFPfIinrKsUU96Jojru33SoT/Vvf17Zl0+ghzMbbWz8vSExmOzyJs5Uds7A/OdYMQEeziMOgi42SikSw7w4VdK7V2VVUzvlDe3DWk8pxEQiQeUYiAmQtkL2cLyoridL2W1+hDcniur77bMnxFTw5CcZ7M6qZFdmxaDnnq5sYk9WJfdcGoWTg21vAesSOtcpb00rp77FyO7MSq6bFzqkfoqiU2KoJznljb02ZkjrCjJnS1ApJjkJKsdAT1shKdYZN+UNbZTWt/VplDw/wpumdhOnK2WNq5iaDpyp5mBeDQBb08oGPf/Vffk46XXcuSTS5ucI83ZhbrgXW9PK2HyqlA6zhRvmy9T3cMwJ88Jk0cgqa+w51lP5HTL0dkJCTCSypnIM+Lk54WFwkLZC46i7H+W8C4LKBZGdnx89WytVkGJK+tOOHAI8nFke68/29HLaTWacHawXezS0Gfl3ahHXzgsdckeE9QnB/G5rFs3tJqYFuJEYJlm14egudrrzhQOEersQ6u3C2epmwn1c8HLt24BeiMlEMpVjQCnFNH83CSrH0fHCOhx0qs+apBh/N7xcHKUJupiSDp6pZv+Zah5eOZ3r5oXS1G5iX251v+e/c7iQlg5zv83OB7I+obPYLaeiiRvnh8kuLcMU4evK72+dxx2LI4kNcKeupYOmdjMbE4PtPTQhRkwylWMkxt+Nw/nWtzYTQ9PaYcbgqBvwTexYYR3xIZ592nEopZgnTdDFFPWnnTn4uztz9yWRKNXZrHxrWhmrZwX2Odds0fjn/gIWR/v06jdpq9hAD6YHuHG6srnXri1i6G5eFM7Ni+w9CiFGn2Qqx0iMvzsl9a29FmOLoSuqbWHhL7dz69/3s/+09QyMxaJxoqi+z3rKbgsivMkub7S5MlaIyeBwfg37Tlfz8MppGBz1ODvoWRUXwI6McquFabsyKzhb08J9S2OG/ZxfXx3LV5bFEOlnfQceIcTFTYLKMRIT4IamQUF1i72HMql9eKyEVqOZszUt3PnCAe558SBHz/bOAJ+ubKKp3dRnPWW3+ZHeWDQ4YaU3nBATXXlDG6/tz2fLqdJevxj9aUcO/u5O3H1JVM+x9QnBVDV1cOSC7xFN0/jHZ6cJ8TL0TGMPx00Lw/nZtbOHfb0QYmqT6e8xEuPXVQFe1TSsbbdE5xvhf44WkxTlw+sPXsLrBwp4bs9pbvzbPqb5uxHibSDI00BjW+cbbX+ZyvldbYaOFtZy2XS/cRu/EMPV2Gbk01NlfHismH2nq+nedtvJQcey6X7MCfMiObeKH18V32tHm1VxATjpdWw9VcbiaN+e4++kFJJSUMtTN82RDRmEEGNGgsoxEu3fOT0kvSqHL720gZyKJn55QyIGRz0PrpjGnUsief1AAccK6yhraOPA6WoqGtuJ8HVhWlcrpwv5uDkRF+TB59mVPLoqdpxfhRBDk1HawC3P7aO5w0yUnyvfWDODa+eFUtnYzo6Mcranl7M7q7IzS3lp77ZAHgZHlsb6sTW9jB9fHY9SiorGNp78JIMl0b7cnhRhp1clhLgYSFA5RjwMjgR4OEuvyhH48FgJDjrFNXPO7frh5uzA11ZO73WexaJh0TR0AzRiXpcQxF9351Lb3IFP1447Q1Hb3MGPPjjJrGBPvrl2xpCvF8JWf//sNEop3ntkKQsjvXsK1GID3blsuh8/uTqe7PImDI46XJ36/ghfnxDME++fJKO0kdmhnvzvf9NpM1p46uY5A36PCCHESMk8yBiK8XcbUaZS0zTOVrdclI27zRaND48VsyouYNAgUKdTg07prZsdjEWDnTbsOHKhzLIGrvtrMp+eKuO9I0VDvl4IW5XWt/LJiVJuXxzBoigfqx0PlFLEBXsQ5Wc9M782PgilYFt6GTszyvnkRCmPr4lleoD7WA9fCHGRk0zlGJof4c2mvXk0tBnxNNjW1La0vpWPj5eSUlBDakEdVU3tuDjqOf7zdTZvqTYVHDxTTXlDOz+5enRalySGeRLiZWBbWhm3LArv83hyThXfevsYl8/w58aFYSyd7o9ep/j0ZCnfffc4HgYHrpkbwscnSof072kvh/JqCPBw7tndSUwOr+4rwKJp3Lc0etj3CPBwJinKh49PlPLO4ULigjz6ZPeFEGIsXDxRih2sTwjGaNbYlWFbdkzTNO59+RBPbs4go7SRFTP8uWF+KK1GMyV1rWM82onlP8eKcXd2YG388CtVz6eUYt3sID7PqaS1o2+bp7/uzqXDZGZ7RjlfeukQlz21k4dfS+WRN44QF+zBfx9bzs1dwWh61z69E9XJonrufvEAP/vwlL2HIoagud3EmwcL2JAYTITvyFr2rE8IJreiidKGNp66ec5F9QupEMJ+5CfNGFoQ4U2QpzOfniq16fyciiayy5v43+sS+Pz7q/nD7fO5o2t/3qLaiyeobDOa+fRkGesTgntVto7UlbODaTNa+CKnstfxzLIG9p+p5tHVsRz+8Vqeu3sh8yO8+Sy7ktuSwnnrq5cS6Gno2a0nbQIHlU3tJh5/6whGs8bBvBqrAbSYmN47UkRDm4kHlk8b8b3WJwSjU/DlS6NYGOkzCqMTQojByfT3GNLpFOsTgnknpZCWDpPVRfXn+/RkGUrBxjnntusK93EBoLD24ul3uSuzgsZ2EzcsCB3V+14yzRcPgwPb0stZl3Dua/zqvnwMjjruWByBwVHPxjkhbJwTgqZpvda0BXoYCPBwJq1k4va7/PmHaZytaeHrq6fz192nOZhXzaq4vruriInFbNF4OTmP+RHeLIoaeRAY4evK1m9dLssfhBDjSjKVY2xDYmd27LOsykHP3ZJWRlKUD4Eehp5jwZ4GHHSKoosoqPzP0WICPJxZOt1/VO/rqNdxxaxAdmaUYzJbgM6q7g+OFnPjgjC8XXsXBFkrkkgI9Zyw09//OVrMe0eKeGzNDB5fMwNnBx2fZ1fZe1jCBjszysmvbuHBFcPf7eZCM4I8pCelEGJcyU+cMbYk2hdfNyc+PVU24HkF1c1klDaw/rwMGoCDXkeIt4HCmotj+ruupYM9WZVcNy8U/Ri0P1mXEExti5HUgs4dR95OKaTNaOFeGwsjEkI9yalomnDbbxZUN/PjD06yONqHb6yJxeCoZ0mML5/nDP7LjLC/l5LzCPN2YcMF3/9CCDGZSFA5xhz0OtbNDmJXZgXtpv4DkS1dQeeFQSVAhI/rRZOp3JFRQYfZwvXzR3fqu9vlMwNwctCxLb0zW/na/gIum+bHrGBPm65PCPXCbNHIKZ84bZ4a24w8/tZRHPQ6/njHgp7s1OUzAsitaLJa5GWxaCTnVGHsytgK+zlVXM/BvBruWxotmUUhxKQmP8HGwfrEYJraTSTn9D8VuSWtjDlhXlarPsN9XCi8SAp1MksbcHbQkRDqNSb3d3d2YHmsP9vSy9ieXk5xXSv3LYu2+fpzxToTY13lscI6rvrzF6SVNPDbW+YS5u3S89jlMwMA+hQmAbx1+Cz3vHSQB15Noem8/aTF+HspOQ83Jz23L5HdboQQk5sEleNg2XR/PAwO/U6Bl9a3cvRsHRsSrU99Rfi4UtnYPuGmXMfC6compgW4j8nUd7crZwdRWNPKk5szCPN2GVLboggfVzycHexeAW6xaPz9s9Pc8tw+LBZ452uX9slyzwxyJ9jT0GddpcWi8eIXeQR7GtibW8Vtf99PeUPbeA5fdCmrb+O/x0u4fXHkhO99KoQQg5Ggchw4OehYGx/E9vRyq9ON29LKAetT30BP9vJiaCuUW9nE9ICxrVi9Ij4QpTq/nvcujRpSAKvTKeJDPO2aqaxr6eDeTYf49aeZXDk7iM3fWMGiKN8+5ymlWDHDn+TcKswWref4joxy8qqa+ck18bx0bxIF1c3c+Ne9ZJc3jufLEMCr+/OxaBr3DyFbLoQQE9WgQaVS6mWlVIVS6tR5x36hlCpWSh3r+nNVP9duUEplKaVylVI/HM2BTzYbEoOpbzVy8ExNn8c+PVXKjEB3YgOtb6N2sbQVajOaKaptHfPt5AI9DCyM9MHgqOO2pKFPOc4O9SSjtLFXoDae/vH5GfadruZXN87hb3cvxMu1/wzX5TMDqG81cryorufYC1+cIdynsyhkVVwgb3/tMowWjZuf28fxwrp+7yVGV0uHiTcPnmV9wsibnQshxERgS6byFWCDleN/0DRtftefzRc+qJTSA38FNgKzgTuVUrNHMtjJbOXMAFyd9H0aoVc3tXMor6bfqW+4eDKVeVXNaBr9Btej6ZfXJ/KPLyX1aSNki4RQT1qNZvJGsK/7cGmaxpZTZSyd7sddl0RabXt0vuWx/igFn2d3rqs8craWw/m1PLA8pqcoJDHMiw8eXYqzg46/7ckd89cwHIU1LWxPL+dve3L5ztvHuPm5ffxqcwZnqyfvL1rvpRZR32rkgeWj10ZICCHsadCgUtO0z4G+6bXBLQFyNU07o2laB/Av4Pph3GdKMDjqWR0XyNa0cvLPC0a2p5dj0RgwqAxwd8bJQUdRzeR9A7VFbkVnRfVYZyqhM9u4squQZai6i4jsMQWeU9FEXlVzr+btA/Fxc2JumBdfdBWJvfjFGTwNDn0ytOE+rlw9J4TPsitp6ZhYhTufnChlxW9389V/pvDbLVnsO12Npmm8lJzHyqd38+Crh/k8uxKLnTLHw2GxaLy8N595o9TsXAghJoKRrKl8TCl1omt63NpPxTCg8LzPi7qOWaWUekgplaKUSqmsnJq99e5YEkFNczurnt7D9X/dy8vJeXxwtJgIXxdmh/Tf0kanU4R7u0z5TOXpyiaUgmljvKZypGYEueOk15FeOv7FOltOde66tH627cVFl88M4FhhHaeK69lyqox7Lo3Czbnv7k4bEkNsbtQ/XswWjWe2ZxEX5MEHjy7lxC/WceBHV/D+o8tI/sFqHl8dy7HCOr788iGeeP+kvYdrs12ZFeRVNfPg8phBs81CCDFZDDeofA6YDswHSoHfWznH2k/KflMJmqY9r2lakqZpSQEBw8sgTXQrZgSQ/IM1/OiqWRhNFv7v43QO5tWwISF40DeWMB+XKb+m8nRlM+E+LhgcR2+/77HgqNcxM9jdLjvrbE0rY2GkD4GehsFP7nL5zADMFo3H3zqKXqe4r59G74ujfWxq1D+ePj1VyunKZr5xxQwWRPr0qpAO8XLhO+vi2PvDNVw/P5SPjpcM2At2Inkx+QyhXgY2DjBDIYQQk82wgkpN08o1TTNrmmYBXqBzqvtCRcD5c2zhQMlwnm8qCfV24aHLp7P5myvY/u3L+cnV8Tx0+fRBr4vwdZ36mcqKpnGZ+h4NCSFepJU0oGnjN+VaWNNCWknDkHddmR/hjYezA3lVzdwwP6zfgNTWRv3jxWLR+MuuXKYHuA24PMTZQc9180JpNZpJya8dxxEOz6nieg6cqeG+ZdLsXAgxtQzrJ5pSKuS8T28ETlk57TAwQykVo5RyAu4APhrO801VM4I8eHDFNAI8nAc9N9zHhZrmDpqnaKNqi0XjTNUkCirDPKlp7qBsHPs7bk3rf9elgTjqdVw23Q+Ar14+bcBzN9jQqH+87MysILOska+vjh207dNl0/1w0uv4LHviTN1b09hm5Jnt2Z3NzhdH2ns4QggxqmxpKfQWsB+IU0oVKaUeAH6rlDqplDoBrAa+3XVuqFJqM4CmaSbgMWArkAG8o2la2hi9jikvwqezAnyqToEX17XSZrSMS+X3aOheA5tWPH5T4FtOlREf4kmk39Dbz3z7ypn85uY5zAzyGPC8pV2N+rfYeQpc0zT+siuHCF8Xrps3+Jadrk4OLInxnVDrQc/XYbLwyt48Vv1uD7syK3h0dSxeLtLsXAgxtfRdrX8BTdPutHL4pX7OLQGuOu/zzUCfdkNi6HraCtW02rxP9WRyunL8Kr9HQ3yIJ0pBWkkDa4dQNDNcFY1tpIeJHK0AACAASURBVJ6t5VtXzBzW9fEhnsQPUAzWradRf0Zno35HO03PfpFTxfGiep66aY7NU8QrZwbw5OYMSupaCT1vu0p7+/RkKU99msnZmhYum+bHDzfOYl6Et72HJYQQo04W9EwSU70B+rl2QhO78rubm7MDMX5upJeOT1uh7enlaIO0nhotGxKDqWux3qh/vPxlVy4hXgZuWthvw4g+VsZ1Fvh9PoGmwE8V1/PIG0dwddLzyv2LefOrl0hAKYSYsiSonCT83JxwcdRP2WKd05XN+Lg64uc++PrSiWJ2qOe47QG+5VQZMf5uzAwa+0xuf436x8vBM9Ucyq/h4ZXTcXawvRPAjEB3QrwME2pd5YfHinHUK/710KWsiguU9kFCiClNgspJQilFuI8LhVO0AfrpyslTpNMtIdSLotpW6luNo3bP2uYO1v3hMx589TCpBZ2ZwvpWI/tPV7PehtZTo+H8Rv322IryL7tz8Xd35vbFQ9tCUynFqrgAknOqMJotYzQ625ktGh8dL2HlzMBh7dwkhBCTjQSVk4itbYUKa1o4cnbit1Y535lJGFTOCukseskqaxy1e/5qcwanK5tJKajl5uf2c+vf9/HMtixMFo31CWO/drPbhsRgqprax/3/0cmier7IqeKB5THD6le6cmYAje0mjk2APcwP5dVQ3tDO9fMHLzQSQoipQILKSSTcxgbo3377GF955fCk2baurqWDqqaOSVP53S2+q2Aqq2x0psD35VbxbmoRD10+jX0/XMPPrplNcW0rr+4vINjTwLzw8VuLt3pWIE4OOj49Ob5V4H//7DQeBgfuuXR47XaWxvqj1yn2ZFUM+dqsskb+e7xk1HqPfnS8GFcnPWvjx++XASGEsCcJKieRCB9XGttMA063niquJ6WglroWI2eqmsZxdMPXU/kdODmKdLoFeTrj5eJIxihkKtuMZn70wUmi/Fz55hUzcHVy4CvLY/js+6v5850L+OMd89EN0qtxNLk7O7A6LoD/z959h0ddZX0A/970QhLSIAUSAoTei/QmvYjYK4oN9VVXXXfVte5adi2r61pWRFGxK4odpEiX3ltIIEBIIA0SUkm/7x9nQtpMMslMMjOZ7+d58iT5zW9mbjKZyZl7zzn3213JSG+hXpzHM/Ox7GAq5g6Php9X09rt+Hu5Y3BUYKPzKuNSc3Hte1vwwJd7cO9nuy1OaSguK8eyA2mY2jsM3h72vUMUEZG1MKh0IBcrwOvJq/x480m4GYKPXUmOsQSemFEAwHHaCVVSSqFHmB+OWGEP8LfXHMPJc4V4cU7fGsu+7q4umN0/AsM7B1t8H431+PSeKCmvwKPf7m+RnYMWbjgOD1cX3DYqxqLbGdc9FAdP5yIzr9is8xMz8zF30Tb4eLjiwYmxWB2XjplvbrRoCX1DwlnkXCjFbC59E5ETYVDpQC72qjSxBH4uvxg/7TuD64Z2RFsfd4cJKo9l5sPDzQUdAhvf1NvWeob7Iz4tz6JUg/i0PCxYn4grB0VidGyIFUdnmZgQX/xtek+sT8jEl9uTm/W+0nKK8N3uFFw7pKNZO0zVZ1w3aS208WjDs5Up2YW4+YNtAIDP7hyGhyd3w5J7RkBr4JoFm7Fo04kmBdQ/7j2NIF8PjO5qP48nEVFzY1DpQCp31TFVrPPVjmSUlFVg3shOGBQViN2nbF+sYI7EjHx0DvFtcCs+e9QjzA8FJeVNbvVUUaHx+NL98Pd2x1Mze1l5dJabOzwao7oG44VfD+PUuebrPLBo03FUaGB+A9tImqNXuD9C2nhiXQO762TkFuGmD7ahoLgMn9w+7OJM+cCoQCz70xiM794Oz/9yGIs2nWjU/ecXl8lsZ99wmzWPJyKyBb7iORB/bzf4eboZXf4uK6/AZ1uTMKprMGLb+2FwdCCOZeTjfGGJDUbaOI7YTqhS9zCpAD/SxGKdlYfTsOfUeTw5oyeCfO2v7YyLi8KrV/eHq1L4y5J9zdJi6HxhCT7fdgqX9Qu/OBtvCRcXhbHdQrD2SAYWrE/E0fS8i7ONWmvsTT6PF345jJlvbUJmXjEW334JekXU3G0owMcdC+cOxvQ+YfjnsjizZj0rrTqchqLSClZ9E5HTYVDpQJRS6GCirdDKw+lIzSnCvJGSjzYoKhAAsMfOZyuLSstxKqsQXRys8rtSt/Z+UAo40sRinZWH09HWx92uA5CItt54dnZvbD+ZhUWbjlv99j/ZkoTCknLcM76L1W5z/tjO6Bjkg5eWH8Hk/2zA2FfX4s9f78WYV9Zizjt/YPGWk+jfIQCf3TkMAw3PldqUUvj3Nf3Rrb0f7v9iD5LOFZh13z/uPYPItt4Xn4NERM6CQaWDMdVW6OPNJ9Eh0BuX9mgHAOjfMQCuLsru+1UmnStEhXac7Rlr8/V0Q3SQj8mZyi+2ncLOk8a3Oyyv0FgXn4kJ3duZvb+1rVw1KBKTe7XHv1cm4Mx5y3d1Ki4rx/IDqbjj4x347+9HMbFHO6vuad8jzB/LHhyDzY9fihev6IPu7f2wJj4DXULb4NWr+2HnU5Pxwa1DGwz8fD3dsHDuECgF3PXJTuQXl9V7/tn8Ymw8ehazB0S0aLU+EZE9cLP1AKhxOgb64I9jZ6G1vri7SlxqLrafyMITM3pczEv08XBDz3A/uy/WudhOyEGXvwEJYI6k1p2pzCksxdM/HkSfyAD8eN+oOpfvOZWNrIISTOzZriWGaRGlFJ69rBfGvboOH28+iSdm9GzU9cvKK3DibAEOp+Zi58ls/Lz/DM4XliLM3wt3j+2MO8dYnktpTERbb9w0LBo3DYtu8m1EBfvgnRsH4ZYPt+ORb/bi+cv7IDn7AlKyC5GcVYjkrAtIzi5EcnYhUs8XobxCY3Z/+515JiJqLgwqHUyHQG8UlpQjq6AEQb4eSMm+gLfXHIOXuwuuHVJzW7vBUYFYsisFZeUVdjsTlpjh+EFl9zA/rDychgsl5TV6Eq5LyEB5hca+5PNG80ZXx2XAzUVhrKFa2d51CPTBjL7h+GLbKdx/aVf4m9FLctvxc3hxWRzi0/JQXCZbJ3q6uWByr/a4enAHjIkNdYgCrVFdQ/DEjJ54/pfDWHEovcZlIW080THIGwM7BmJ2f2/0iQhAz3DrzboSETkKBpUOprKQYd5HO5B0rgC5RbIcd/uomDr7Cw+KDsTiLUmIT89D74iAFh9rQw6k5GD5wTREtvV26AbRPcP9UKGBoxl56Fdt15vVcRkI8HZHXlEpvt99Gn+Z2r3G9dYcScclMUFmBWf24q4xMfh53xl8vT0Zd5lRqf31jmQczyzA3OHR6BXhj14R/ugS2sYhq6JvH9UJwb4eyLlQio5B3ugY6IMOgT4O/bdLRGRNDCodTL8OAYhs6w0XBczqH4E+EQHoHeGPvpF1g8bKfLHdSdl2E1RqrbE58RzeXZeITcfOws/TDU/Psr9WOo1RmQt4JLUqqCwtr8C6+AxM7xOG9NxifL/nNP48udvFPLvkrEIkpOfjuqFN247QVvp1aIthMUH46I8TmDeqU4PB4f7TORgWE4SnHPwxBiQFYM7ASFsPg4jIbjGodDDt/b3wx+OXmnVuh0BvtPPzxK6kbMwd0al5B2aGigqNWz/ajo1HzyLUzxOPT++BG4dFOdRMnTFRQT7wdndFXLVinR0nspBXVIaJPdujqLQcD361F9tPZl3cGWd1nCyhTuxh//mUtc0f2xl3LN6JZQdScfkA00FWfnEZEjPzMatfeAuOjoiIbIVBZSumlLKrJuh7krOx8ehZ3D+hK+6/tGuN7QgdmYuLQrcwP8RXayu0Oi4DHm4uGBMbAgUFXw9XLN2dcjGo/D0uA11CfdEpxPGq3id0b4fOob54f+NxzO4fcbFgrLaDp3OgNdC/WkoAERG1Xo6X2ESNMjg6EKeyCpGRV2TroeDX/WnwcHXB3eM6t5qAslLPMD/EpeZCaw2tNVbHpWNUl2D4eLjB28MV0/uGY9mBNBSVliOvqBTbTpzDpJ7tbT3sJnFxUbhrTGccPJ2LLcfPmTxvf4q8menbwT5SL4iIqHkxqGzlBkVX5lXadrayokJj+cFUjO0WAj8HX+42pkeYH7ILS5GZV4xjGfk4lVWIidWCxisHRSK/uAwrD6dj49GzKC3XNS53NFcMjERIGw+8v8F0M/T9KTmIbOuNkDaW7eVNRESOgUFlK9cn0h8eri7YY+Mm6HuSzyM1pwgz+rbO/LoehhYycWl5WB2XAQA1+k8OjwlGRIAXvt+dgtVx6QjwdsegKMddFvZyd8Xc4Z2wNj4TR9ON7ya0PyXHaAEZERG1TgwqWzlPN1f0ifS3eRP05QdS4eHqgkm9HHd2rj49DHuAx6flYnVcOvpE+iM8wPvi5S4uUjm84ehZrD6cjgndQ+22d6i5bh4eBTcXhaV7Tte57HxhCU5lFaJfRwaVRETOwrH/q5FZBkcHYv/pHBSXldvk/rXWWH4wDWNiQxy+0tuUtj4eCPP3wh/HzmH3qWxM7FE3eL5yUCTKKzRyDVXhji64jSeGdw7GykNpdS7bn5IDAOgX6bizsURE1DgMKp3AgI6BKCmrwNH0fJvc/97k8zh9/gKmt9Kl70o9wv2wPiETWsNoEU7Xdn7o1yHAoXbRaciU3u2RmFmAYxk1/7YOnJagksvfRETOg0GlE+jSTtrWHD9bYJP7X3YgFe6uCpNb6dJ3pe6GJfD2/p7oE2l8m76nZ/XC83P6IMC7dczYVgbPqw7X3Lpwf8p5dAr2QYBP6/g5iYioYQwqnUB0kASVJ20QVGqtsexAGkZ3DWk1gZQpPQ0760zs2d5k78ahnYJwwyWOtYtOfSLaeqNvZABWHq65BL4/JafGlpVERNT6Mah0At4erggP8LJJULk/JQenz19otVXf1Q2ODoSXuwsu7x9h66G0qCm92mPPqfPIyJVeqBl5RUjNKUI/9qckInIqDCqdREyIL06ca/mgctmBVLi5KEzpFdbi993SOgb54PA/pmGYYdccZzGltzy2qwxbTx6oLNLhTCURkVNhUOkkOoX44kQLz1RqrfHrgVSM6hriNLl1Li7Gl71bs27t2yA62OdiXuX+lBy4KKB3hPG8UiIiap0YVDqJmGBfnC8sxfnCkha7z0NncpGSfQEznWDp25kppTClV3tsPnYOeUWl2J9yHl3btYGvp5uth0ZERC2IQaWT6BQixTotOVu5z7D388iuzrUc7Iwm9wpDSXkF1sVn4sDpHPRlf0oiIqfDoNJJxBiCypMtmFeZkJaHNp5uiGzr3fDJ5NAGRwciyNcDizefxNn8EvTnTjpERE6HQaWTiArygYsCTmS2XFAZn56Hbu3bmGyvQ62Hq4vCpJ7tsNOwHSibnhMROR8GlU7Cw80FkYHeOHGusEXuT2uN+LS8iw3BqfWrrPB3c1HoGc4iHSIiZ8NMeifSKdi3xXpVns0vQXZhKbq1Z1DpLEbHhsDb3RWdQ33h5e5q6+EQEVELY1DpRGJCfPH97tPQWjf7knRCeh4AoDuDSqfh5e6KJ2f2RJCvh62HQkRENsCg0onEhPgir7gM5wpKENLGs1nvKz5NgspuXP52KjcPj7b1EIiIyEYazKlUSn2olMpQSh2sduxVpdQRpdR+pdT3Simj/UOUUieVUgeUUnuVUjutOXBqvJZsKxSflodgX49mD16JiIjIPphTqPMxgGm1jq0C0Edr3Q9AAoC/1XP9CVrrAVrrIU0bIllLTHALBpXpecynJCIiciINBpVa6w0AsmodW6m1LjN8uxVAh2YYG1lZh0BvuLmoZi/WqajQOJrOym8iIiJnYo2WQrcDWG7iMg1gpVJql1Jqfn03opSar5TaqZTamZmZaYVhUW1uri6ICvJp9gbop89fQEFJOWcqiYiInIhFQaVS6kkAZQA+N3HKKK31IADTAdynlBpr6ra01gu11kO01kNCQ0MtGRbVo1OIL443cwP0i5XfYW2a9X6IiIjIfjQ5qFRK3QpgFoCbtNba2Dla6zOGzxkAvgdwSVPvj6yjU7Avks4VwsRDdlFmXjF+3Hu6SfcRbwgqYzlTSURE5DSaFFQqpaYBeAzAbK210S1alFK+Sim/yq8BTAFw0Ni51HJiQnxwobQc6bnF9Z737rpEPPjVXqRkN34HnoS0PEQEeMHfy72pwyQiIiIHY05LoS8BbAHQXSmVopS6A8DbAPwArDK0C1pgODdCKbXMcNX2ADYppfYB2A7gV631b83yU5DZYkJkSbqhCvB1CRkAgN2nzjf6PuLT89mfkoiIyMk02Pxca32DkcOLTJx7BsAMw9fHAfS3aHRkdZ1CfAAAJ88VYESXYKPnJGcVXsy73J2Ujdn9I8y+/bLyCiRm5GNsbIjlgyUiIiKHYY3qb3IgEQHe8HBzqXemcl28zFJGtvXGnuTGzVSePFeIkvIKVn4TERE5GQaVTsbFRSE6yKeBoDITUUE+uKx/BA6fyUFRabnZt19V+c2gkoiIyJkwqHRCMSG+JhugF5WWY3PiOYzvHopBUW1RWq5x8HSO2bcdn5YHpYCu7dhOiIiIyJkwqHRCMSG+SMoqRHlF3bZCO05m4UJpOcZ3D8XAqEAAwJ5GFOskpOehU7AvvNxdrTZeIiIisn8MKp1QpxBflJRV4Mz5C3UuWxefCQ83F4zoHIJQP090DPLG7lPZZt+27PnNWUoiIiJnw6DSCXUK9gUAo9s1rovPwLCYIHh7yEzjoKhAs2cqi0rLcfJsAbqzSIeIiMjpMKh0Qp1DJajcn1IzVzI5qxCJmQUY373dxWMDO7ZFWm6R0VnN2hIz81GhwR6VRERETohBpRNq5+eJkV2C8ebvR2sU4VS2EhrfvWrv9UHRkldpzhL4xcpvzlQSERE5HQaVTkgphTdvGIhgXw/c/ekuZBWUAJB8yo5B3ugc4nvx3J7h/vB0czFrCTw+LR/urgqdql2fiIiInAODSicV0sYTC+YORmZ+MR74cjcKS8qklVC3dlBKXTzP3dUF/ToEmDVTeTQ9D51D2sDdlX9WREREzob//Z1Yvw5t8eKcPvjj2DnM+2jHxVZCtQ2KCsSh07koLqu/CXpCRh5iWflNRETklBhUOrlrhnTE3OHR2H4iS1oJGdkPfGBUW5SUV+DQmVyTt1NYUobkrAuIbcd8SiIiImfkZusBkO09PasXEjPzEdzGEz4edf8kBhmaoO9Oyr74dW2JGdKeiD0qiYiInBODSoKHmws+v3NYjVzK6tr5eyGyrTf2JJsu1qms/I5l5TcREZFT4vI3AYDJgLLSwKi22JNkulgnISMP7q4K0cE+1h4aEREROQAGlWSWQVGBOJNThLScIqOXH0vPZ+U3ERGRE2MEQGZpqAk6K7+JiIicG4NKMkuvcH94ubtg+4msOpcVlpQhJZuV30RERM6MQSWZxcPNBQM7BmLHybpBZWJGAbRm5TcREZEzY1BJZhsaE4S41FzkFZXWOM7KbyIiImJQSWYbFhOECg3sqlUFfjQjn5XfRERETo5BJZltYFRbuLmoOkvg3PObiIiIGAWQ2Xw83NA7MgA7TtScqUzIyENX5lMSERE5NQaV1CiXdArE3pTzKCotB1BV+d2Nld9EREROjUElNcrQTkEoKavA/pQcAKz8JiIiIsGgkhplaKcgALiYV3k0o7Lym0ElERGRM2NQSY0S6OuB2HZtLjZBT0ivrPz2tfHIiIiIyJYYVFKjDY0Jwu6kbJRXaFZ+ExEREQAGldQEw2KCkFdchrjUXBzNyGflNxERETGopMarzKvccDQTydmFrPwmIiIiBpXUeBFtvRHZ1htf70iG1izSISIiIgaV1ESXxAQh6VwhALYTIiIiIgaV1ESVS+Cs/CYiIiKAQSU10SUxgQCAmBBfVn4TERERg0pqmi6hbRDq54me4f62HgoRERHZATdbD4Ack1IKX9w5DAE+7rYeChEREdkBBpXUZLHt2UqIiIiIhFnL30qpD5VSGUqpg9WOBSmlVimljho+B5q47q2Gc44qpW611sCJiIiIyH6Ym1P5MYBptY49DuB3rXUsgN8N39eglAoC8CyAYQAuAfCsqeCTiIiIiByXWUGl1noDgKxahy8HsNjw9WIAc4xcdSqAVVrrLK11NoBVqBucEhEREZGDs6T6u73WOhUADJ/bGTknEkByte9TDMfqUErNV0rtVErtzMzMtGBYRERERNTSmrulkDJyTBs7UWu9UGs9RGs9JDQ0tJmHRURERETWZElQma6UCgcAw+cMI+ekAOhY7fsOAM5YcJ9EREREZIcsCSp/AlBZzX0rgB+NnLMCwBSlVKChQGeK4RgRERERtSLmthT6EsAWAN2VUilKqTsAvARgslLqKIDJhu+hlBqilPoAALTWWQCeB7DD8PGc4RgRERERtSJKa6MpjjY1ZMgQvXPnTlsPg4iIiMipKaV2aa2HmHMu9/4mIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoNNeFbCDtoK1HQURERGSXGFSaa/2rwMJxDCyJiIiIjGhyUKmU6q6U2lvtI1cp9VCtc8YrpXKqnfOM5UO2kfSDQEUZ8NP9QHmZrUfjXLS29QiIiIioAU0OKrXW8VrrAVrrAQAGAygE8L2RUzdWnqe1fq6p92dzmUeAgCjgzB5g6/9sPRrnkZcOvBIDHFtt65EQERFRPay1/D0RQKLWOslKt2dfCrOA/HRg6B1Aj1nA2heBc4m2HpVziF8m+axn9th6JERERFQPawWV1wP40sRlI5RS+5RSy5VSvU3dgFJqvlJqp1JqZ2ZmppWGZSVnE+Rzu57AjH8Drp7AT38CKipsOy5nEL9cPuectu04iIiIqF4WB5VKKQ8AswEsMXLxbgDRWuv+AN4C8IOp29FaL9RaD9FaDwkNDbV0WNaVeUQ+h3YH/MOBqS8ASZuA3R/bdFitXkkBcHydfJ17xqZDISIiovpZY6ZyOoDdWuv02hdorXO11vmGr5cBcFdKhVjhPltWZjzg5i05lQAwcC4QMxZY+QyQl2bbsbVmiWuB8mLAOxDI5UwlERGRPbNGUHkDTCx9K6XClFLK8PUlhvs7Z4X7bFmZR4DQboCL4delFDDrDaC0ANj6rm3H1polLAc8A4CelzGoJCIisnMWBZVKKR8AkwEsrXbsHqXUPYZvrwZwUCm1D8CbAK7X2gH7w2TGA6E9ah4L7gL0mgPs/AgozrPNuFqzinIg/jcgdjLQNlqKdUoKbT0qIiIiMsGioFJrXai1DtZa51Q7tkBrvcDw9dta695a6/5a6+Fa682WDrjFFeXKLFlo97qXjXwAKM4Bdn/a8uNq7U7vAgrPAt2nAwEd5BjzKomIiOwWd9RpSGXld+2ZSgCIHAREj5a+leWlLTuu1i5+GeDiBnSdBPhHyDEugRMREdktBpUNuVj5bSSoBGS2MicZOPxjy43JGcQvB6JHAd5tAf9IOcagkoiIyG4xqGxI5hHpSxnYyfjlsVOAkG7A5je5naC1ZB2X33v3GfI9ZyqJiIjsHoPKhmTGS9Do4mr8chcXYMT9QOo+4OTGlh1baxX/m3zuPk0+u3sDPsFsgE5ERGTHGFQ2JPOI8SKd6vpdB/iGApvfapkxtXbxy4B2vWrODvtHsFCHiIjIjjGorE9JAXD+lOl8ykruXsAldwNHVwIZcS0zttbqQjaQtFmqvqvz78DlbyIiIjvGoLI+Fyu/G5ipBIChdwAu7sD+r5t3TI7u0PfA+WTTlx/7HdDlQLfaQWUEg0oiIiI7xqCyPpnx8rmhmUoA8AmSfoo5Kc07JkeWcxpYMg/Y+G/T55zcKLvoRA6qeTwgkg3QiYiI7BiDyvpkxMnsY1CMeef7hQO5qc07JkeWYCjAOfmH6XOSNgNRw+sWRl1sK8S8SiIiInvEoLI+mfFAcFfA1d288/3DgTwGlSbFL5fP544Ceel1L8/PlJSD6BF1L2NbISIiIrvGoLI+5lR+V+dnCCrZr7Ku4nzgxHqg43D5PsnIbOWpLfI5elTdy9gAnYiIyK4xqDSl9AKQfdK8fMpKfmFAaSFQnNtsw3JYx9cC5SXA+McBd1/TQaWbNxA+oO5lnKkkIiKyawwqTTl7FIAG2jUmqAyXz8yrrCt+OeDVFug0GogaZjyvMukPoMMQwM2j7mVsgE5ERGTXGFSa0pjK70qVQSXzKmuqKAcSVgCxkyU/tdNoIDMOKDhXdU5RLpB2AIgeafp22ACdiMi+FeUAa14AivNsPRKyAQaVpmQeAZQrENTF/Ov4M6g0KmUnUHi2qqF59Gj5XH0JPHk7oCsaCCrZAJ2IyK7t+QzY8Cp3mHNSbrYegF0oOAd8doXk84V2k72+T2wAgrsYX4o1hTOVxsUvA1zcgK6T5PuIgfK7TvoD6DVbjiX9Ied0GGr6dvwjgOStzT9eIiJqmgPfyuet7wLD7wW8A207HmpRnKkEgJ2LgNR98vWRZcDKp4CU7UBY38bdjru35A3mpVl/jI4s4Tep6PYKkO/dPICOlwAnN1Wdc2qLFOh4+Jq+HTZAJyKyX+cSgTO7gX7XS8Hqlv/ZekTUwjhTWVoEbF8IxE4BbloixwqzgHPHpEdlY/mFM++vunOJkkow+LaaxzuNBtb+U37X7j7A6V3ADR1UlQAAIABJREFUsLvrv63qDdBDGvnYlJcBygVwseB91M4PATcvYMCNTb8NIqLW6uB38nni00DZharZSp+gmudVVEg3EHevlh8jNSvOVB5YAhRkAiPuqzrmEyQzabWfCObwC+NMZXWVu+h0n1bzePQoAFpmKE/vkhcYY/0pq2tKr0qtgf3fAK90Bja8Yv71aistAlY+A6x6Vl4QiYhau/wM4I1+wKEfGj5Xa1n6jhopWxaPewwoyQO21pqtzM8A3hsLfDiFr6WtkHMHlVoDW94B2vcFYsZZ5zb9I5hTWV38cqBdLyCwU83jkYMBV09pLZS0WY51HFb/bTW2V+WFbOC7O4CldwGlBZJA3tTG9Im/ywtkQQaQsqNpt0FE5EjilwHnk4Af7zO02atH+kHgbDzQ9yr5vn1voNccYOsCWZECgPPJwIfTgIxDknJ25OfmHT+1OOcOKhN/l9Y2I+4DlLLObVbOVPIdmAR1SZurqr6rc/eSopykTcCpzUC73g3PDDcmqDyxAXh3FHD4R+DSp4FZ/wFykiXfpykOfS85oS7ufCEkIueQsAJoEwa4eQLf3FJ/PvuBb6VjSq85VcfGPQaU5ANb3pZUqI+my8rgrb9IZ5UN/+YOdK2McweVm9+WJ0yfq6x3m37hgC6XJ46zO7FRfhexU4xf3mmU9KY8tdX4ft+1mdsA/dRWYPFsOf+OVcDYvwA9Zkl1uTnLOLWVFgHxvwE9LwNixgJxv/CFkIjMc2y1LCE3Ji2qOF8KGW3Z67H0AnB8nXTouPJ9ICMO+PXPxl/7tAYOLgW6XAr4hlQdb98L6D0H2PaeBJSlhcCtP8tr/+iHgbT9wNFVLfYjUfNz3qAy/ZBsHThsfuPaBjXE2m2FysskB8URndktgZyxbRcBKdbRFfJCU19/yurMaYC+/mV5YZu/DogcJMd8goDO42XmsrEBYeXSd+8rgJ6zgOwTQMbhxt0GETmfinJgxVOyhLznM/Ovt/rvwMczgZeigQ8mAav/IasvLflm9uQmeW3uNhXoOhEY9yiw70tg9yd1z03eDuScAvpeXfeycY8BJQVSKDlvGRBh+H/Q7zogoCOwkbOVrYnzBpVb3pGq49pVyZaydgP0tS8Abw2Wd42O5sxeyac0VeHXYSjgagjoo8wNKjvUH1Se3gUkrpGUBk+/mpf1ulxe3FP31r1eWbFhZtXIi9uhH6TXWsw4oPtMAAo48qt54yUi53XgW0mx8g6UoNKctKgL2cDez4HYqcDohwAoYPObwOLLpFNJS0n4DXD3rdqsYtxj8sZ82V9lQ4vqDiyRzhg9Zta9nXY9ZXbyrjU1tz128wBGPQgkb6vZXo7sS0Zco053zqAyL00qggfc1LQK7/pYc6ayKBfYsUj6fZ1uYi6grWgNnNkjjc5NcfeWwDKoc1Uw3hD/CCA3xfTlG1+X3Mchd9S9rMcsyfk5/GPdy1Y9AyyeJS/m1ZUWSbFRj5myxaRfexlzHPMqiage5aXAun9Kv+Op/5IVjlObG77e7k9khnDi08DEZ4A7VwGPJcnmEav/IcUuxvz2N+D9S+X/hqW0lnzKLhOqJgVcXIErP5BVoA8mAd/cCqTul9W0wz/IjGbtN/KVYsZU5cRXN/BmwLed7MBD9qWsWNr+LRjTqKu1zqAyZWf9T6wdi4CKMumfZW2+7WSaP9cKQeWezySgBKT1jiPJPgkUna8/qASAy98Grvu8/nOqq68BekYccOQX4JK7AS//upf7BElOZO0l8PTDwPb3ZdZ0xRM1c58S18jSd68rqo71nCW5QNlJ5o+biJzLnk/ldfDSZ2SVxNMf2P1p/dcpLwO2LQQ6jam5+YZnG2Dm6wC0zBTWXlHZ85m07jm9C/jhXsuXk9MPSWFjt6k1j7cJBeavlxnUY78D740BFk2WGoI+Rpa+G+LuDYx8ADixvu7sJ9nOqW0STK5/WdK+GqH1BZUF54BFU4Dljxm/vKwY2PWRPFmCG7Gvt7lc3SSwtHSmsqIc2PYuEDVCto1M3mad8bWUM3vkc4SJfMpKQZ0lmdtc1Rug17bxdVmuqe/NQu85QNZxKRAC5MV3+aMShM77Vf4+fn2k6kX58A+yS1Lnai2nesySz1wCJyJjSi8A61+VNmmxkwEPH8k3PPwjUJRj+npHfpaVGGOvYYHRwIQngITlQNxPVcdT98trVsw4YPLz8sZ6038sG39lf2FjRZZtQoFJfwcePghMeEoCZ59g0wWZDRlyu6QHbPh3EwfrADLjJRXC1l1hKiqkCr8+G18HPpwqf8M3fQdc9X6j7qL1BZWJv0vF8YFvjC8THP5R3lVdclfzjcEvzPKg8sgvwPlTwPD/A6KGS1Bp6z/IxjizR2b+2jUiYDSHqQboWSeAg98CQ26rP6Wh9hL44R+BkxuBS5+Shvfj/ya/+8M/SIAZv1yu4+pedRvBXeTnOvKLdX82ImoddiwC8s7I8nVlu7qBN8suM5V7Yxuz9V3p6dttmvHLh90LhPUDlj0qwemF89LqxzsIuGqRzPr1vhJY87yssjRVwgpZZfILM32Od1tg3F+Bhw8B921v+u44nm3k/1zCcuCnB6yzfG9PinKAz66WnskfzwDOHmv5MRRmAX+8Cbw1EHhrkPydGXN8PfD7czL58n9bgNhJjb6r1hdUHl0FeAbITFPtTv6AJDoHdwU6X9p8Y/CPsHxXnS3/A9pGSy5fx+Hyh5l5xDrjawln9kjzWzdP696uqV6Vf7whleYj7q//+r4hUnV++AdZQl/5lDS/ryzYGnG/VKsv+6u8+BfnyhOsth6zJCWh4KzlPxMRtR7FecCm14HOE+S1plLEIOnHu8fEEvjpXTJ5MOweyV80xtUNmP2mbMKw+u/AD/8ny9TXLpYZRKWA2W8BId2Bb++QiYnGKjgrGzx0M9Jf2BgPn5pthJpi1ENStLP7U+kvfGKDZbdnT5Y/JrPPYx+VriHvjgQ2vSGpDs0tO0n+Rl7vCax6GvCLkNSKFU9KYWp1hVnA93dLfHT5OxLsN0HrCiorKmSmsvs0WWrYtbiqkz8gxS4pO4Chd1m2B3RDLJ2pTNkFJG+VJRAXV5mpBOSYI6iokCWZhvIpm8LYTGVOCrD3C5kJMKfgp9flsrf793fLC/KMV6pexF3d5Al1IRv4+UEp+jG221KPmdIOKX655T8TEbUem98CCs9JoU11SgGD5sob7rSDda+3dQHg4ScFpPWJGCgzljs/BOJ/Baa8IKsslTzbANd/LnUDX94g+eIJKyR3/EK2dOXYsUh2yfnfSOCrmyRtrNLRVQB03XzK5uTmAUx+Drh9hbwGL75MgjFH7HpS3aEfpA3TmL8Alz4J3LcD6DYFWP2s5KJaOiubuh8oKzF+WV66FJ8e+h4YcCNwzx/A7cuB67+QtLMl86pWc7WWWeKCs8DViwAP3yYPqXUFlWf2yJO562R511NaIE+oStvfBzzayC+4OflFyDjKipt2/a3vSFL3wJvl+6DOgG+oJM86guwTQHFO8wSV7l6Sv3NwqSwpvNEX+E8fyUEd9aB5t9HzMimmivtJkstr98gM6wOM/jNQUSozksb6mIb3BwKiuARORFWSdwAbX5PXlcjBdS/vd52kBdXuWZmbChxaKq/5xooMa5vwhMxG9r9BZjZrC+4CXPWB5Dsu+wvwxbXAuyOAlzsBC8dJE/P45UCbdhJELhxX1WEk4TfpYhLev7E/veWihgH3bJJiy20LZLa1orzlx2ENuanALw/JDPW4R+WYX3vgus+Ay/8nfZwtycvf8YEUSn1yec3JM0Ca539xrQSJ836RHeXC+shlXv4SWJYVA9/MlQ4nuz6S/2WT/m7x4+5m0bXtzbFVAJShq3+w9Pna/p7kmZQWAge/k3eK5jxpLVGZh5KXJsnVjXE+Wd7dDL+3qj2DUpLw7SgV4BeLdJohqASAyCFSLahcpb3PgJvkMa+9v7gpbdoB0aNkuWnyc8bPGfsXWfoedKvxy5WS2cqdH8qT09rL/ERke+Vl8jpgTuu5C+eB726XFJ2Zrxk/xycI6D4D2P+V5Fvmp0vhxL4vJHgaNt+8cXm2kZw3U8vkgMw0Pp4s95GTLEvhuWekg0bkYEmvUkper7+eK3tyT39JcjF7X2G9rYsby8NXVo+CuwLL/yozljNetd14mkJrmQkuLQKuXFgzJx+QNwO/PycB/IAbGn/7iWskrzZ8AHB6pxQn37QECIqRv9kl86RDyQ1fGX9zE9oNuPI94KsbgSW3Si5ll0slt9VCrSuoPLpKfoG+wfL96IeBj6bJu8KSPKC8GLjEzCetJao3QG9sULl9IQANDLu75vGoEfJOIi+t/uRpe3BmD+DqCYT2aPjcprjxa3nSWpLCcPnbMpscEGn8cjdPYPrL9d9G1DCp0M+Ia7jKnYgcS24q8PXNQPpByVPsd63pc7WWWamc07KE693W9LmD5kpO9786SFHpxeO3yKqUueoLKC+e4yL/j/zDay6RVxcxUNoEfXc78MvDcsxUoVBLGjZfdunZ/BbQtqP5K1H2YNt7koo38zUgJLbu5S4usgx+6AdZvm7Mrn6Z8cA38+T/67xfpJPJlzdI79AbvwZ2L5YJtllv1J/C0GOmNLRf/zLgEwLMWWCVtMDWE1QWnJOZp/GPVx2LHiEzfJvfkvy3mHFAaPfmH0tTG6CXl0luYI+ZQNuompdV5lWe2mq8cMSenNkjPdZqvzuzFqUsf9ca2Mn8mU1TwvrJ57T9DCqJWpPk7RJQFufLjjBL75Ln+aR/GA/mdn8iuWsTnwU6Dq3/tjtPkMIUaCA4VmbkgrtaXuxiCd9g4OalwNoXZRass5E8cluY9JwE6quekXx6Y9tA2pOiHBnrro+lxZKxTTgqdZsmfzentpj/+y44J8vabp7AjV/Jamb0SODO1cDnV8uMpS4Hxv5VOqE0ZNzjUuAaM06W5q2g9QSViWsAaMmnrG7UQ8BXhunlGa+0zFgqg8rGNkA/uREoPAv0NfKOOKyfbINl70FlRQWQuk+m91u7wBjJ0a3seUlEjm/XYun7GBAJzP1BZpp+e1wmJ9IPSyGDd2DV+RlHZIm283hDsNgAF1dg8j+aa/RN5+IqS/ITn7H1SKq4uABz3pUl/B/ulf+tnUbZelTGHV0lxZ15qZJyN+HJ+ic/YsbJil7CCvOCyrJieaOTmwrctqzmxFNILHDHannzE9xV7tscLi5V+Z5W0nqCymOrpICjdh5ft2lAaE8p2mmpKX3vQPljaexM5aGlEqTETq57mZuH5BK2VAX42WPS93HcY42bFTx3DCjJb758Snvi4gK07yMVeETkWC6cl36O+enyZliXSzugpD8kv+yqRVW5lDNfk9WXX/8iO40ExUhLspIC6Ufp2Qa4YmHzdhVxVu5eUtzy/gRpo3TnKluPqKayEkkb2PuZLElf+wnQYUjD1/NsI9tXJvwGTPtn/ecWZkk/0lObgas/NH77bUKBW35o2s9gRa3jGVBRIVtGdZlY90nt4gLc/C1wy0/m5aBYg1KNbytUXir7SXefLltXGRM1TAKYkgLrjLM++74A1v1L+mo1RnMX6dib8H6Sc+VIjemJnF1hllTN7vpY3kCfT5IiltJCeSN907d1i3MGz5MctrZREkh4+QMhXaXw5oavrbZ8SEb4BEnl/OmddSudbW3LWxJQjn4YuHuDeQFlpW7TgKzE+huiZ8QBC8dLSsYVC4E+V1k85OZk8UylUuokgDwA5QDKtNZDal2uAPwXwAwAhQDmaa13W3q/NaTukWVjYzN8ABDQwap3Z5bGNkA/vl56iPW+0vQ5HYfLu+mUnc2f85J1Qj6f2ChNzM11Zg/g7iNbSzqDsH5AyUJpo9Qc234SkXUVnJOA8my8tFZpTD/GqOGy9EgtL3aKFJUkrjE/t/LMXiCgY1XxrrVlnwTWvwL0nC3teBqrcmvLhN+AECMbd8QvB767U1Ywb1vWuIDVRqw1UzlBaz2gdkBpMB1ArOFjPgAT+wNZ4OhqSCuhiVa/6SbzCzO+P7Uph5ZKb8qu9fwMHYcCUC2zD3jWcfl8cmP959V2sUin9WRW1Cusr3xO4xI4kV3Z8g6wdD6wf0nV7FZ+BvDxTODcUWm30pINvskyEQNlO8qjZi5/H18HvH+pbI3YHFs/ai07r7m4AdNeatptBEbLlr+Ve61Xt+09qeoOiQXmr3WIgBJomeXvywF8osVWAG2VUvVve3I+SfamTNoi1XcNObYKiBzUfO9GmsLPMFOpdcPnlpUAcb9I1Xd9/Q69A6US8VQDeZVpB2Tms6m0rpqpPLnR/OazFeWGSmgnWfoG5PFwcWNeJZE92fw2sOIJSSlaeifwahfpw/jhNPn/cuM39b+BJ/vj4gp0nQQcW91wulHWCenVGBAJnD0qBSwN/R+rqJD2g28OBA7/2PB44n4Gjq4Exv/NdGs6c3SbKhXgF85XHTu2Woq/us8AbltetT2xA7BGUKkBrFRK7VJKGWsCGQkgudr3KYZjphXnSbXdR9Okl9fKp0yfW5gly8G1q75tzS9MioOKzXiHlLhGdqCpb+m7Usdhklth6gkS/5v0q/ryhqbv6HMhW8YTPkBaJJhb3Xw2QXKSnCmodDP04+RMJZF92PUxsPJJoNcc4PFTwJ2/yzZ5JQWy7d/N39lPyxxqnNjJkuqWusf0OcV58v9Pa+CWH6XfcMJv0mzclDN7gA+nSMPy7CRZ0q5vQqgyRmnf1/iORo3RbZpsqZm4Rr7PPik7CbXvDVz1vukaCztljaBylNZ6EGSZ+z6l1NhalxsrHa7zaCml5iuldiqldma6tAceiZfk577XSCuHHYvq3kpFhbwbhZb9vu1J5TsLc/IqDy0FvNpKS4qGRI2QRu77v6n7R7/3S+mQ7xUgAW3SH40dtahc+h5s2E3G3CVwZyvSqRTWj22FyHnZ0/7MB74Ffn5IJhmufF965XYYIvsu37MReCSu7ras5Di6TASgDClvRlRUAN/fIxMc13wszeQvuQsYcjvwxxvAvq+rztUaSD8kldsLJ0gwOWcBMOt1Kb6s7//nupeA3NNyrqWpXh2GyipkwgrpKPD1zQA0cN2nFu3BbSsWB5Va6zOGzxkAvgdQu21/CoCO1b7vAKBOsqHWeqHWeojWekhoaKjM9HWfBlyxQJJZlz8KnNxU/QrAir/JZu0TnrK/QObiVo0NVICXFgFHlgE9TewxXVvsZOlD9cM9su9n3M/yRNr8lhzrNFo2jnfzAhJWNm3slUvfUSOlOe+JDeZd79hqwMNPxudMwvpKW5K8dFuPhKhlHfgWeDlGCvpsLWEF8P3dEjRe+0njdikhx+AbLLvmHTXxv239S7Lz3NQXgS4Tqo5PfwXoNAb46QHZWveXh4H/9AHeHSl9SYfdAzywU7ZM7HutBHnb3jN+H2kHJD1v8DzTuxQ1hourxDhHV0qfy7SD0s6qMbsr2RGLgkqllK9Syq/yawBTABysddpPAG5RYjiAHK21+b12XFyBqz6QRtPf3CLvJgCpAtu2ABh+n+zTbG/MbYB+bLXMPJqz9A1Ia4X/2yoNYUsK5F3Nf/tJikCvy2X/zzah8gQy9cRrSNZxAEp2nIkZAyRtlpZH9TmzV/ZWH3pHy7VushfhlTvrcLaSHJw5OeyVSouAVc8CZReAn+5vmVZnpsbx+3Oy5BnWVwpwPHxsMxZqfrGTZfe8gnM1jx/7XeKCATfVXZJ2dQeuWSyTPb88LDOWEQOAy94EHj4ke557Bci5Hj6yZeaRX4HzyTVvp7xMAlPvQNk9yVq6TQUuZAEHvgEmPGG6k40DsHSmsj2ATUqpfQC2A/hVa/2bUuoepVTlo7oMwHEAxwC8D6DxO5Z7BcgLRXmZLO9u+o/0UBxwEzDlBfvcaN7cmcpDS6WiLaZ21kA9XN2BATcC9+2QvlVebeVJdPVHVYU+3aZK/6tziY0fe/YJWb5395JxleRL0GiK1hLUegcBY/7c+PtzdBcrwPfZdhxETaG1/EP+9ErgX5HA6n+Y13d1+3tAbgow/gnJA1vzQrMPtY5TW4EFo4GNrwH9r5cdcLz8W34c1HJiJwPQsrd2peJ8SXsI7grMfN14TOAbLEUvt/4MPHYCuP5zSfHyN1I3PPROuY+dtdLutr0raV4zXqnbx9QSXSbK6mK36ZL/68AsSgbQWh8H0N/I8QXVvtYA7rPkfgBIk9lrPgQ+v0a66veYJe8y7HUHAw9fwDOg/qCyKEcKa/pd07R9sl3dgP7XyUdtle90jq4Egu9t3O1mHa+aeu80Rj6f3GB6T9ujKyXvcvorVe/2nIlXANA22vhMZeJaaZbMHpZkrvJSmSXpMat5W3OVlwL7v5bWOxmHgTZhsgy36XUgJwW4/B3TS8gXsiWQ6zoZGP8YUJAhS4K95sgmDcYU5Uqe2okNcv2ZrzU9Z6ysWN7Ibn9f+hDevJTV3M4ifCDgEyKthfoZtjRe8wKQcwq47TeZDDElINK8Su22UVJ5vWuxNMN395YJmjUvynFzVxbN5d0W+L8tgH8H+41pzORYo+86SQLJgTdLzoG990L0D68/qFz3klRLD7nd+vcd2AkI6d60JfCs43J9APANkT5apnKmysuAVc8AQV2AwWZsYN9ahfer21bo7DHgs6uAxbPln6ilykoknSLtAJCZYPntkX3a/Caw5FZgz6fNdx8VFcC3t0u1q3KRAoWHDkirnYnPyDLcZ1fWbHNS3cbXJUic9Hf5ftLfJbj78T5Zjq5UlAP88V/g/YnAy52AL6+XnLb9X0tBRVN3olr1LLB9ITDsbvlnzIDSebi4VGstVA4k75BUuKF3AtEjrHc/w+6WJemD38ls/s8PyuTPzNeaZ3U0qHOryAN2rKASAAbNlXfQ9b0bsRd+YbIsZKw1QUacJAIPngeE15nstY7YyVLc1Jg8qeI8oCCzZpJwzFhZZjLWomjPp0DmEfmn0gqeEE0W1k/SDYrzqo6teV7SEfLT5AXJnJ6ltVXm8PwrCnghFHi9hyz3/W8Ye2O2RnlpwIbX5Out/2u+7T/XvwTE/QRM+gdwzyYpUHDzkH+WYx6RtJpTW6WvY2U3iErnk+W1q//1QFgfOebpB8z+rzQVX/cvKVpb/Xcphlj1jJwz5s+GpcckYPLzcv/rm9A0OnGNLENeMl/axXi2sehXQQ4odrIEfKe2Sj6vf6R1cxwBWaVr10v+1nd9LKtxU553qJ6RtuB4QaUj6TpJZpW2vF3zeGUnfk8/mRVoLrFTgPIS86u3garK76CYqmOdxkgy/uldNc8tzgfW/lO2j+x5meXjdWRhhmKd9EPyOWUXcPgHYOSfgEufkma6uz9p3G1WVMjMz+5PpBPChCclX+jqjyRRfPljTQtUyX79/pw8Z8f/TdqiHDPROiXrOHDoe+D0bunV25i/g4NLDQUNNwOjHjQ+69L/OunnmHsaeHuovLHJPimXrfsXAC0FBdV1uRQYOFdmWt/oKzOUXScC89cDd/0uz4OYsTIhMOI+uf/1L8tMkLkKs4Af/k+2gZ30D/OvR61Ll0tlhn3pXTKpMes/1s+lVUreuKTtl9faTmOAQbda9z5aITtfP3Zww++Txuwrn5YE4u7T5fjhH+Rdz4x/WzfZt7aoEdLi5+gKoMcM866TXRlUVpup7DQKgJIl8Oo93ja+JrlUN3xpn8VSLamyAjx1vzSoX/2s5P2MvB9w95Xcyt8el8cktNq+6Cm7gONrJEenet6l1sCvDwP7v5KWWeP+WvP+inKAXx6SQq8+VzX/z0fN7/RuYO/n8kZkzCOSz7XlbaDblJrnlRQAn8yRnWEqeQYAEf2Bqz+uf2exM3skKOs4XHrs1fe87TwOuG+bFEbu+hjY+4XkTB78ToLCtlF1rzPlBck9C4mVgNVULrFScv/njsl4AmNkV7T6aA38+oispLDC27n5BAGRQ4CU7dICqPZzxFr6XSuv5WXFwGX/5f85M3Cmsjm5uEjrn/D+sil82kH5h7DiSakYbo5cyurcPIAu4yWh2dyZjMqlrsBqM5XegTLeyhnPrBPAF9dLQn/faxxmT9Jm5RcO+ATLu9rE3+VNw7hHZTbaxQW44j2p7vvudnmBSlwLLL4M+OBSSTJ/e4jkmJ1LlMfqt7/JP/LRfzbeMmvQLTI7uvIZ461ctG6+pVOyvsrH3DcUGPtXyd0adjdwYn3dArA1L0pAedUi4PovgKn/lGK/pC3A8r8av31Alta/vFHypK/7rP4tYSv5RwAzXgUe3AcMuUP64nr5S9BrjHdb4PblwOw3Gy5Oc/OUcfiGSlePnJT6zz+wRN5Ejf+btIMh59bvWsnjbeq+2+bw8JUm6jd8xWJLMylth8tnQ4YM0Tt37rT1MKwn94xsbO/iJrkgOz+UKjVrJhWbsvsTWbq6d7Ns+9SQn/4klaeP1mpFtOJJSYwf+SdptO7qLlVxw+5x7lzK6j6ZI1uIacj2nPfvrPm7iV8uhQpt2kuz9DZhMpPZY6bsGLVjkSx9dhgCJG8Dht0LTPuX6XfHSZuBj6YDYx+VHUMuHt8CLJ0vDfWn/atZf2SykgPfAt/dIYWIlTtZXcgGXu8t/WeveFeOJe8AFk2WN6SzXq95G+tfBda+AFz7KdBrds3LivPkTUxmAnDHiqo2WI2Vly6pMJWFfNaQdlD+jj39gLnfA6Hd655z/hTw7migXU/gtmXO1wuXjNOas4ctQCm1S2tt1uwRZypbgn+EzCgUnJWAst91LRNQAlV7oiesMO/86u2EqosZKwHPxn/LP6z7dwKj/sSAsrqwvjKrlH4AuPTpur+b7tOB0Q9LP8/L/gs8tB8Y+YD8vqe+KLNBw++VJfQht9cfUAKSitDnKslhy06SmcmNrwEfz5T2Gvu+Mr1HPNmPkkKpZg7rK50tKnkHyvcHlsgsY1mx5Nj6R1ZVXVc3+iFZFfn1zzUbQ5cWyUxg6n7g6kVNDygBwK+9dQNKQIrDF51jAAARUElEQVR95v0qLY4+nAokb6+6rKIC2PMZsHA8oMtlhzUGlFSJAaXd4UxlS4r7RZLXr/3EeMPV5rJgDODRRpalGvJ6b8mhvHJhzeNlxcDaF4HYqYYcS6qjcrYprC8wf0PT+42Vl5rftzQnBXhriOx8VFEuS+99rgJixgE//6nlZsSpacpLpWnz3s+AecvqPreyjgNvDpLlZqWADa8CN30HxE4yfnvph4D3xknh3DUfSfeAJbfK1nVXGCq27VXWCeDTKySAvnaxvBn/9RGZta/MATVntYWIrKoxM5Us1GlJPWfJR0vrNlX6yuVnyhaOppQWSbWnsZlKN09g8nPNN8bWIGqE5FZO/ZdlDWwb0wg/oIO0aln7IuDqCcx6Q9pUFefKjFXCcgaV9qooB1gyT1rkjPmL8TdrQZ0lPWL7Qulp2/8G0wElIEHXuMdkGbzX5dKn9sgvwLSX7TugBKTjxB0rgc+vli0XoWW29vL/yc/t4E2hiZwBg0pn0PdaYNMbEmRc+4npJYPzSQB0zSIdMl9AJPDIkZa/35EPyIxXr9lVS5teAUD0KMnj5JsB+5N9EvjiOql+nv2WFF6ZMuJ+CQx920lRTkNGPwQc+VlmzSvKgHGPA8Pvafh69qBNO1kK/+lPUvg24Ynm7ZBBRFbFt37OILSb9IiL+0l2sjAly0g7IbJ/7t5SqFM7V677DOl12JT938m6yktlWTd1v/SXfH+ifD/3+/oDSgCIGi7P32s/MS/AcnWXrhOunlLsNf5x6/wMLcXTT5buZzZzyzUisjrOVDqLkQ9Isc6yv0qBh7Eec5XthII4U9kqdJ8G/PYYkPCb9BWklncuUZZza+9KE9QZuHEJENK14dtQStoMNUb73sCjxx1j5zEiajUYVDoLF1epnHx3lPRDvPXnulWU2ScAT39ZdiLHF9hJthmLX86g0hZKCoCvb5bWQOMNPSgrP8L7N3/zbgaURNTCGFQ6k8Bo2Sv3x/8DtrwjLYGqyzous5Rs09B6dJsmHQcuZEvRA7UMrYFfHgYy4mS7w64TbT0iIqJmx5xKZzPgRqDHLGDN89J0uLqsEyzSaW26z5D+fkdN7CFNlinKrdkTstKODyR/ecITDCiJyGkwqHQ2Sknjbe9A4Nvbqrb4Ky+T6m8W6bQukYNluTXBjB6l1DiFWcD7E4DXugFLbpOdjLQGUnbKlouxU6RVEBGRk+DytzPyDZHm5p/MAZY9Csx5B8hJlvYjLNJpXVxcpE/p4Z+NN1UvypUG6rmnpRq566SWbczvqMqKga9uku0DB9wIHP5R9qVu30eCTf9waTbO3opE5EQYVDqrzuOlonTDK7IFY2VTdM5Utj7dpstWd0mbgc7jgNxUYMvbwN7PJdeyus4TgFt+sM047U1Fhezm0mFIzWBca9ku8dRm4KpFQN+rgWkvyXaK2z+Qpua3LWM7HCJyOgwqndm4x4CTm6SgYPCtcow5la1PlwnSs3D3YumRuPdz2dKx1+VAxADZSzqgA3B8PbDun8Cx1TJj6ew2vwmsfhYIiAJGPwgMuFkqqtf+UwLIS5+WgBIAPHxlJ6NBtwJlRdI7lIjIyXDvb2eXcxpYMBq4kAW4eQFPpHLJrjX6/BrZss/VAxhwEzDqwbqpDmXFwNtDZZ/4ezbWbTnlTHLPyJ7q4f0kAE/ZLltwxk6R4HzgXNkJh50SiKiVa8ze34wenF1ApPSvBKSvIQPK1mnCk/Lx4H7gsjeM5866eQKTngUyDgH7vmz5MdqTVc9IjvGcd2U/6lt+AoK7SkDZeTww6z8MKImIauHyN0khx7SX2Sy5NYsYIB8N6X2l9DBd84J83dwNuptLwgr5mPYS4ObRuOsmbZbl7bF/rQq+O4+Tj4wj8uardsETERFxppIMht8jOWHk3JQCprwA5KUCW9+x9WiaZs/nwJfXAzsXNf5nqCiXjgj+HYDRf657ebsefPNFRGQCg0oiqil6pDTI3/RfID+z+e+v4CxQVmKd29r8tuwYFTMOiJ0KrH9F2v4Yk3ZQPqrnle/8EEg/AEx9wXFnaYmIbIRBJRHVNenvQGmhVD83VzFfeSmw4d/A672AheOAzISm35bWwOp/ACufBHrNAW78Gpj5mly2/LG65x9bDSwcDywYBbzRF/j1ESDuZ1n27zRGboOIiBqFQSUR1RUSC4x8QNoPfX+PVIbXpjWQfsj4ZQ05vVuCujXPS8uj/Az5fv83jb+tsmLgp/uBTa8Dg28Drv5Qio7adgTG/w2IXwYc+bXq/FNbga9uBkJ7yO5SYf2AvV8AX98MFOcBM15lEQ4RUROwUIeIjJv0d2kvtPYF2XXn+s9ke09AgsJVzwAnNwLtegGXvwNEDmr4NotyZEl66/+ANu2B678AesyUFj7f3gEsvUt6p059ESg8J0vX508BJYVAn6sA3+Cat5dzGvhmLnB6FzD2Udlru3pAOPxeqWRf9qgsiWcdBz6/FvCPAOYuBdq0k1zi0gvACUMbpXY9rfQLJCJyLuxTSUT1279E8hTbRgOzXgd2fQwc/A7wCZaZwb2fy0zjqAeB8Y/LLGFtRbnAtgWyk09RjgRyk58DvAKqzikvkwB203+Mj8PdBxh6BzDyTxIMntwELJknAeGcd4Fes41f79RW4MOpQN9rgMS10o/19t9kJpOIiOrVmD6VDCqJqGEn/wC+vkm2dXT3AUbcJ8Gdlz9w4bzkMu75TJaUL5kPePrJrjLu3sCZvRJMXsgGus+QwDO8v+n7Or4eSPoDCOgItI2Sj9ILwB9vSKsfVw+g2zTJgQzqDFz/ORDavf7x/3g/sOdTwCdEAsqQWOv+foiIWikGlURkfWePyQzloFsA//C6lx9dDfz8JyD3dN3Luk2TYDJioGVjOJcIbHxdlrS7TZPG/V7+DV+vMAtY9TQw7B4grK9lYyAiciIMKonINspLgYJMmVksLZTPXgENzyQ2VnGe5HuyoIaIqFk1JqhkoQ4RWY+ruxTBNDdPv+a/DyIiahS2FCIiIiIiizGoJCIiIiKLMagkIiIiIosxqCQiIiIiizGoJCIiIiKLNTmoVEp1VEqtVUrFKaUOKaUeNHLOeKVUjlJqr+HjGcuGS0RERET2yJKWQmUAHtFa71ZK+QHYpZRapbU+XOu8jVrrWRbcDxERERHZuSbPVGqtU7XWuw1f5wGIAxBprYERERERkeOwSk6lUqoTgIEAthm5eIRSap9SarlSqrc17o+IiIiI7IvFO+oopdoA+A7AQ1rr3FoX7wYQrbXOV0rNAPADgFgTtzMfwHwAiIqKsnRYRERERNSCLJqpVEq5QwLKz7XWS2tfrrXO1VrnG75eBsBdKRVi7La01gu11kO01kNCQ0MtGRYRERERtTBLqr8VgEUA4rTWr5s4J8xwHpRSlxju71xT75OIiIiI7JMly9+jAMwFcEAptddw7AkAUQCgtV4A4GoA9yqlygBcAHC91lpbcJ9EREREZIeaHFRqrTcBUA2c8zaAt5t6H0RERETkGLijDhERERFZTNnjarRS6gKAQ7Yeh5UEAMix9SCsKArAKVsPwkr42NgvPjb2i4+N/eJjY78c+bGJ1lqbVUFtr0Flprk/gL1TSi3UWs+39TishY+N/eJjY7/42NgvPjb2i4+N47HX5e/zth6AFf1s6wFYGR8b+8XHxn7xsbFffGzsFx8bB2OvQaWjThHXobVubX9IfGzsFx8b+8XHxn7xsbFffGwcjL0GlQttPQAyiY+N/eJjY7/42NgvPjb2i4+Ng7HLnEoiIiIiciz2OlNJRERERA6kRYJKpVRHpdRapVScUuqQUupBw/EgpdQqpdRRw+dAw3GllHpTKXVMKbVfKTXIcHyCUmpvtY8ipdSclvgZWitrPTaGy15WSh00fFxnq5+ptWjCY9NDKbVFKVWslPpLrdv6UCmVoZQ6aIufpbWx1mOjlPJSSm1XSu0z3M4/bPUztRZWft6cVEodMPy/2WmLn6c1seLzpnutWCBXKfWQrX4uqtIiy99KqXAA4Vrr3UopPwC7AMwBMA9Altb6JaXU4wACtdaPKaVmAHgAwAwAwwD8V2s9rNZtBgE4BqCD1rqw2X+IVspaj41SaiaAhwBMB+AJYD2AS7XWuS3/U7UOTXhs2gGINpyTrbX+d7XbGgsgH8AnWus+Lf2ztDbWemyUUgqAr9Y6XynlDmATgAe11ltt8GO1ClZ+3pwEMERrfbalf47WyJqPTbXbdAVwGsAwrXVSS/0sZFyLzFRqrVO11rsNX+cBiAMQCeByAIsNpy2G/OHAcPwTLbYCaGv4Y6zuagDLGVBaxoqPTS8A67XWZVrrAgD7AExrwR+l1WnsY6O1ztBa7wBQauS2NgDIaolxOwNrPTaG51G+4Vt3wwcT3S1gzecNWVczPTYTASQyoLQPLZ5TqZTqBGAggG0A2mutUwH5YwPQznBaJIDkaldLMRyr7noAXzbnWJ2NhY/NPgDTlVI+SqkQABMAdGyZkbd+Zj42ZAOWPjZKKVel1F4AGQBWaa23Nd9onYsVnjcawEql1C6lVKtvXN2SrPiaxljAjri15J0ppdoA+A7AQ1rrXFn5MX6qkWMX370bZsb6Alhh9UE6KUsfG631SqXUUOD/27ubEKvKMIDj/wczs6kM0mhhNQhKX0yziAhq4SKFIqJFExSUtIkC20Sb2giR0EoopqiFEEQKgg61sxYFkQXJMBaR0EKIMGaEvjRzMTNPi/MO3AKLO++5x+bO/7e5c97zwfvyzD089/04h2PAGeALYH4glV1l+oiNOtZGbDJzARiPiGuBqYi4IzOd+1qppe/NvZl5ugzDfhwRJ0uvvyq0dU+LiMuBh4GXWqyeKnTWU1nmCx0G3s/MI6V4dmlYu3zOlfIf+Xsv12bgdM/2Y8BUZjpc0YK2YpOZezNzPDN30CSf33dR/2HWZ2zUobZjk5m/Ap/itJFqbcUmM5fubXPAFHD3YGq8erT8vXkAmM7M2fZrquXoavV3APuB7zJzX8+uD4Fd5e9dwAc95U9F4x7gt6Wu8eJx7O5uRVuxKUN415VrjgFjwEedNGJILSM26khbsYmITaWHkohYD9wPnGy/xqtHi7EZKYtJiIgRYCdgD3KFAdzTzAX+Z7pa/X0f8BnwDbBYil+mmUtxCLgJ+AGYyMyfyz/eJM0v9vPA05l5vFxrFPgcuDEzF1GVtmITEVcA0+X834FnM3Omu5YMn2XE5gbgOHBNOf4ccFsZXjoIbAc2ArPAnszc32FzhkpbsQFGaRYmrKH5kX8oM1/priXDp8XYbKTpnYRmqtiBzNzbVTuGUcv3tCtp5vdvycyheZ3jSucbdSRJklTNN+pIkiSpmkmlJEmSqplUSpIkqZpJpSRJkqqZVEqSJKmaSaUk9SEiFiJiJiK+jYgTEfFCRPzrvTQiRiPiia7qKEmXgkmlJPXnz/LmqNuBHcCDwJ7/OGcUMKmUNNR8TqUk9SEizmXmVT3bW4CvaB6WfTPwHjBSdu/OzGMR8SVwK3CK5mHnbwCv0TyQfh3wZma+01kjJGkATColqQ//TCpL2S/ALcBZYDEzL0TEVuBgZt4VEduBFzPzoXL8M8D1mflqRKyjeUvYRGae6rQxktSiyy51BSRpCET5XAtMRsQ4sABsu8jxO4GxiHi0bG8AttL0ZErSimRSKUkVyvD3AjBHM7dyFriTZs76hYudBjyfmUc7qaQkdcCFOpK0TBGxCXgbmMxmLtEG4KfMXASeBNaUQ88CV/ecehR4LiLWlutsi4gRJGkFs6dSkvqzPiJmaIa652kW5uwr+94CDkfEBPAJ8Ecp/xqYj4gTwLvA6zQrwqcjIoAzwCNdNUCSBsGFOpIkSarm8LckSZKqmVRKkiSpmkmlJEmSqplUSpIkqZpJpSRJkqqZVEqSJKmaSaUkSZKqmVRKkiSp2l/mVF3180POZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pestats.plot(figsize=FIGSIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>2.566807</td>\n",
       "      <td>0.420473</td>\n",
       "      <td>0.800664</td>\n",
       "      <td>-1.161772</td>\n",
       "      <td>1.177298</td>\n",
       "      <td>0.473694</td>\n",
       "      <td>-1.443171</td>\n",
       "      <td>-0.504237</td>\n",
       "      <td>-0.347238</td>\n",
       "      <td>1.150122</td>\n",
       "      <td>...</td>\n",
       "      <td>1.402655</td>\n",
       "      <td>0.157392</td>\n",
       "      <td>-1.051061</td>\n",
       "      <td>1.004546</td>\n",
       "      <td>-1.869390</td>\n",
       "      <td>-0.143852</td>\n",
       "      <td>0.050891</td>\n",
       "      <td>-0.473107</td>\n",
       "      <td>-0.314658</td>\n",
       "      <td>-1.334128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>2.587353</td>\n",
       "      <td>0.408479</td>\n",
       "      <td>0.772778</td>\n",
       "      <td>-1.081498</td>\n",
       "      <td>1.148585</td>\n",
       "      <td>0.449944</td>\n",
       "      <td>-1.639364</td>\n",
       "      <td>-0.518097</td>\n",
       "      <td>-0.474868</td>\n",
       "      <td>0.985655</td>\n",
       "      <td>...</td>\n",
       "      <td>1.054949</td>\n",
       "      <td>0.929172</td>\n",
       "      <td>-1.107684</td>\n",
       "      <td>0.993174</td>\n",
       "      <td>-1.812080</td>\n",
       "      <td>-0.071411</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>-0.488523</td>\n",
       "      <td>-0.197649</td>\n",
       "      <td>-1.342877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>2.604480</td>\n",
       "      <td>0.291522</td>\n",
       "      <td>0.738498</td>\n",
       "      <td>-0.933307</td>\n",
       "      <td>1.262957</td>\n",
       "      <td>0.476726</td>\n",
       "      <td>-1.564870</td>\n",
       "      <td>-0.657203</td>\n",
       "      <td>-0.405297</td>\n",
       "      <td>0.942817</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605305</td>\n",
       "      <td>1.189960</td>\n",
       "      <td>-1.129419</td>\n",
       "      <td>0.971671</td>\n",
       "      <td>-1.805081</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>-0.037697</td>\n",
       "      <td>-0.449461</td>\n",
       "      <td>-0.185137</td>\n",
       "      <td>-1.268311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-30</th>\n",
       "      <td>2.715588</td>\n",
       "      <td>0.362362</td>\n",
       "      <td>0.695748</td>\n",
       "      <td>-0.868985</td>\n",
       "      <td>0.896127</td>\n",
       "      <td>0.318753</td>\n",
       "      <td>-1.568423</td>\n",
       "      <td>-0.468045</td>\n",
       "      <td>-0.322924</td>\n",
       "      <td>1.236660</td>\n",
       "      <td>...</td>\n",
       "      <td>0.677093</td>\n",
       "      <td>0.996549</td>\n",
       "      <td>-1.155834</td>\n",
       "      <td>0.805961</td>\n",
       "      <td>-1.832177</td>\n",
       "      <td>-0.162460</td>\n",
       "      <td>-0.120244</td>\n",
       "      <td>-0.602223</td>\n",
       "      <td>-0.349816</td>\n",
       "      <td>-1.283734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-31</th>\n",
       "      <td>3.382426</td>\n",
       "      <td>0.416504</td>\n",
       "      <td>0.762395</td>\n",
       "      <td>-0.689479</td>\n",
       "      <td>0.656822</td>\n",
       "      <td>0.179618</td>\n",
       "      <td>-1.399011</td>\n",
       "      <td>-0.468626</td>\n",
       "      <td>-0.293618</td>\n",
       "      <td>1.053446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532945</td>\n",
       "      <td>0.654707</td>\n",
       "      <td>-1.047989</td>\n",
       "      <td>0.508090</td>\n",
       "      <td>-1.709666</td>\n",
       "      <td>-0.181960</td>\n",
       "      <td>-0.087982</td>\n",
       "      <td>-0.290220</td>\n",
       "      <td>-0.457897</td>\n",
       "      <td>-1.144203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       AXP        BA       CAT      CSCO       DIS  \\\n",
       "Date                                                                     \n",
       "2007-01-31  2.566807  0.420473  0.800664 -1.161772  1.177298  0.473694   \n",
       "2007-02-28  2.587353  0.408479  0.772778 -1.081498  1.148585  0.449944   \n",
       "2007-03-30  2.604480  0.291522  0.738498 -0.933307  1.262957  0.476726   \n",
       "2007-04-30  2.715588  0.362362  0.695748 -0.868985  0.896127  0.318753   \n",
       "2007-05-31  3.382426  0.416504  0.762395 -0.689479  0.656822  0.179618   \n",
       "\n",
       "                  GS        HD       IBM      INTC  ...      MSFT       NKE  \\\n",
       "Date                                                ...                       \n",
       "2007-01-31 -1.443171 -0.504237 -0.347238  1.150122  ...  1.402655  0.157392   \n",
       "2007-02-28 -1.639364 -0.518097 -0.474868  0.985655  ...  1.054949  0.929172   \n",
       "2007-03-30 -1.564870 -0.657203 -0.405297  0.942817  ...  0.605305  1.189960   \n",
       "2007-04-30 -1.568423 -0.468045 -0.322924  1.236660  ...  0.677093  0.996549   \n",
       "2007-05-31 -1.399011 -0.468626 -0.293618  1.053446  ...  0.532945  0.654707   \n",
       "\n",
       "                 PFE        PG       TRV       UNH       UTX        VZ  \\\n",
       "Date                                                                     \n",
       "2007-01-31 -1.051061  1.004546 -1.869390 -0.143852  0.050891 -0.473107   \n",
       "2007-02-28 -1.107684  0.993174 -1.812080 -0.071411  0.000310 -0.488523   \n",
       "2007-03-30 -1.129419  0.971671 -1.805081  0.029282 -0.037697 -0.449461   \n",
       "2007-04-30 -1.155834  0.805961 -1.832177 -0.162460 -0.120244 -0.602223   \n",
       "2007-05-31 -1.047989  0.508090 -1.709666 -0.181960 -0.087982 -0.290220   \n",
       "\n",
       "                 WMT       XOM  \n",
       "Date                            \n",
       "2007-01-31 -0.314658 -1.334128  \n",
       "2007-02-28 -0.197649 -1.342877  \n",
       "2007-03-30 -0.185137 -1.268311  \n",
       "2007-04-30 -0.349816 -1.283734  \n",
       "2007-05-31 -0.457897 -1.144203  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pezscore = pedf.subtract(pestats['mean'],axis='rows').divide(pestats['std'],axis='rows')\n",
    "pezscore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>3.867503</td>\n",
       "      <td>2.992677</td>\n",
       "      <td>1.541382</td>\n",
       "      <td>3.039207</td>\n",
       "      <td>1.491222</td>\n",
       "      <td>1.248566</td>\n",
       "      <td>2.147197</td>\n",
       "      <td>2.393517</td>\n",
       "      <td>0.390293</td>\n",
       "      <td>1.487701</td>\n",
       "      <td>...</td>\n",
       "      <td>2.025279</td>\n",
       "      <td>2.750551</td>\n",
       "      <td>1.725851</td>\n",
       "      <td>1.908680</td>\n",
       "      <td>1.151405</td>\n",
       "      <td>1.088685</td>\n",
       "      <td>0.835491</td>\n",
       "      <td>1.670791</td>\n",
       "      <td>1.323102</td>\n",
       "      <td>3.558878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>-1.316667</td>\n",
       "      <td>-1.326248</td>\n",
       "      <td>-1.565684</td>\n",
       "      <td>-1.604811</td>\n",
       "      <td>-1.073875</td>\n",
       "      <td>-0.719054</td>\n",
       "      <td>-2.032113</td>\n",
       "      <td>-0.814053</td>\n",
       "      <td>-1.921435</td>\n",
       "      <td>-1.867904</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.362131</td>\n",
       "      <td>-0.289964</td>\n",
       "      <td>-1.445815</td>\n",
       "      <td>-0.261343</td>\n",
       "      <td>-1.892566</td>\n",
       "      <td>-1.467931</td>\n",
       "      <td>-0.561093</td>\n",
       "      <td>-1.461241</td>\n",
       "      <td>-0.860428</td>\n",
       "      <td>-1.475769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AAPL       AXP        BA       CAT      CSCO       DIS        GS  \\\n",
       "Max  3.867503  2.992677  1.541382  3.039207  1.491222  1.248566  2.147197   \n",
       "Min -1.316667 -1.326248 -1.565684 -1.604811 -1.073875 -0.719054 -2.032113   \n",
       "\n",
       "           HD       IBM      INTC  ...      MSFT       NKE       PFE  \\\n",
       "Max  2.393517  0.390293  1.487701  ...  2.025279  2.750551  1.725851   \n",
       "Min -0.814053 -1.921435 -1.867904  ... -1.362131 -0.289964 -1.445815   \n",
       "\n",
       "           PG       TRV       UNH       UTX        VZ       WMT       XOM  \n",
       "Max  1.908680  1.151405  1.088685  0.835491  1.670791  1.323102  3.558878  \n",
       "Min -0.261343 -1.892566 -1.467931 -0.561093 -1.461241 -0.860428 -1.475769  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxmin = pd.DataFrame([pezscore.max(),pezscore.min()],index=['Max','Min'])\n",
    "maxmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a0d793f28>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d5/OAfmEhCQAATZG6evoASSdLepO7P5WnSQAAAGg3qXMaz5W0haRrzewuMzs/Q5sAAADQZpJ6Gt19x1wNAQAAQPviijAAAACIIjQCAAAgitAIAACAKEIjAAAAogiNAAAAiCI0AgAAICppy52cnnvuOS1btkyrVq1qdVNKMXz4cHV1dWnYsGGtbgoAAEC/tU1oXLZsmbbYYguNGTNGZtbq5mTl7lqxYoWWLVumsWPHtro5AAAA/dY2w9OrVq3SNttsM+gCoySZmbbZZptB24sKAAAGv7YJjZIGZWCsGMx/NwAAMPi1VWhsNTPTEUccseb26tWrNXLkSE2ePLmFrQIAAGi9tpnTWGvMrAVZ6y2dPSn6nBEjRmjx4sV6+umntemmm+raa6/VqFGjsrYDAACgE9HTWOPAAw/UggUhsM6dO1fTpk1b89htt92mCRMmaM8999SECRN03333SZLOPvtsTZ8+XZK0aNEi7bbbbnrqqaea33gAAICSEBprTJ06VfPmzdOqVau0cOFC7b333mse23nnnXXjjTfqzjvv1Omnn65TTz1VknTcccfpgQce0Pz583X00Ufrggsu0GabbdaqvwIAAEB2bTs83Srjx4/X0qVLNXfuXB100EG9Hlu5cqWOPPJI3X///TIzPffcc5KkIUOG6KKLLtL48eN1zDHHaOLEia1oOgAAQGnoaaxjypQpOvHEE3sNTUvSJz/5Se2zzz5avHixrrjiil5b6Nx///3afPPN9dhjjzW7uQAAAKUjNNYxffp0fepTn9K4ceN63b9y5co1C2MuuuiiXvfPnDlTN954o1asWKEf/ehHzWwuAABA6QiNdXR1dWnmzJnr3H/SSSfplFNO0cSJE/X888+vuf/444/Xhz/8Ye2000668MILNWvWLD3xxBPNbDIAAECpzN2bftDu7m7v6enpdd+SJUu0yy67NL0tzbQh/B2BbD6zVT+eu7K8dgDAIGdmt7t7d+x59DQCAAAgitAIAACAKEIjAAAAogiNAAAAiCI0AgAAIIrQCAAAgChCYxUz0xFHHLHm9urVqzVy5EhNnjxZknT55Zdr9uzZrWoeAABAy7Tvtaf7s0fbetWL7+M2YsQILV68WE8//bQ23XRTXXvttWuuACOFywtOmTIlb7sAAAA6QPuGxhY58MADtWDBAh122GGaO3eupk2bpptuuklSuHRgT0+Pzj33XB111FHacsst1dPToz/96U8644wzdNhhh7W49cAgwobdANBWGJ6uMXXqVM2bN0+rVq3SwoULtffee/f53Mcff1w333yzrrzySs2aNauJrQQAAGguQmON8ePHa+nSpZo7d64OOuighs899NBDNWTIEO26667685//3KQWAgAANB/D03VMmTJFJ554om644QatWLGiz+dtsskma35uxTW8AQAAmoXQWMf06dO11VZbady4cbrhhhta3RwAAICWY3i6jq6uLs2cObPVzQAAAGgb1oph1e7ubu/p6el135IlS7TLLrs0vS3NtCH8HQEAQGcxs9vdvTv2PHoaAQAAEEVoBAAAQBShEQAAAFFtFRoH87Y1g/nvBgAABr+2CY3Dhw/XihUrBmW4cnetWLFCw4cPb3VTAAAABqRt9mns6urSsmXLtHz58lY3pRTDhw9XV1dXq5sBAAAwIG0TGocNG6axY8e2uhkAAACoo22GpwEAANC+CI0AAACIIjQCAAAgqiWXETSz5ZIe6ccf2VbSX0pqTpm1O70+bR+c9Wn74KxP2wdn/U5ue9n1aXu++i9z95GxJ7UkNPaXmfWszzUR2612p9en7YOzPm0fnPVp++Cs38ltL7s+bW9+fYanAQAAEEVoBAAAQFSnhMY5HVq70+vT9sFZn7YPzvq0fXDW7+S2l12ftje5fkfMaQQAAEBrdUpPIwAAAFqI0AgAAICotrn2dIyZ7e3ut7a6HQAkM3t7g4efkfSQuy9pVnvQPGa2sbs/2+p2oH/M7ERJP3D3R1vdFnSujgmNkn4oaXRKATPbU9LLJd3dzA80M3uppKnufmZCjYvc/ah8rerzOMMl7SjJJT3o7qtKOs42kt4o6f/c/fbEWptJes7dnytuv1LSQZIecfcfJzdWa9r7bkk7F3ctkTTX3VfkqF8WM3uvwtzli2vu/4Ckf7n79wdY+uAGjw2VtIuZ/cbd/3sgxc3sq40eH2jdOsd5iaSPSHqVwmv+Hklfd/c/Z6rfqa+bT7r7/9S5fytJP5X05oTan3f3UxOaF6t/krufUfz8Tnf/Yc5jm9m+7n598fNYd3+46rG35zrnlGCUpN+Y2cOS5kr6obuXubl0VmZ2gqRz3P35mvu3kXSGu78/ofYr3f2+Ph6b6O6/Hmjtosax7n5uSo0GtRt9gVfu12PHLIQxs0fd/aUJf/5Tkt4r6XZJe0v6grt/M1f76hxvW0nvlDRN4c06391PTKh3h7vvlat9deoPlfR5SdMVrtYzRFKXpO9IOq0SyBLqXylplrsvNrPtJd0hqUchxM9x93MSat8o6f3ufr+Z7SjpNkmXSNpV0m3ufkpi23eRdL2kqyXdKckk7SnpLZL2dfd7E+t/rNHj7n52Qu07Jb3R3f9Rc/8Wkm5w91cPtHbkuEMkLXL3Vw3wzz8rabGkSyU9pvBvvoa7fzdDGydK+r6kixTOCyZpL0lHSnpPhg+K0l43ZvYPhZC75q7itklyd99yoLWL+tdI+p27n1Z133YKf5fL3P30hNpln8vW1K89Vo5jl1nfzA6R1OXu5xW3b5VUuUrHSe7+o4Smy8xM4cv6VEmHSPq9QoCcX3uOGEDt0s5jRf05kl4r6SOV96aZfVjSxyV9JfEz5AVJFxe1/1nzWNbXTG5F2+8q/pN6nyvd3afnPF4n9TSmptvDJe3h7k8V30x+LilraCw+iN+m0LOwk6T5knZw964M5Tcrekqt3oPufkdi/TMlbSFpbOXkYWZbSjqr+G9mYv2x7r64+PloSde6+/uKf7NfSxrwG17S1u5+f/HzkQo9OR81s40VwkBSaJT0P5Jmuvul1Xea2Tsk/T9J70isf5bCG/4qhaHdur/jAdqo3oeBu//DzIYNtKiZva/Bw+7uF5vZ/gOtL2l7hS9dh0taLekHCmHlbwk1a31J0qHufmfVfT81s/mSLlD4cpmizNfNdZK2k/RjSfPc/f8SatUzRdKPzOxsd/+Ymb1C4fV5prtfkFh7IzPbWn2fy/6aWN/6+Lne7Xarf5JCoKvYRNJrJI1Q+AKfFBo99BL9StKvzOxYSftLmi3pfEmbpdRW+PyoOEbhPZSNu88wswmSzjWzuxV67++XNMHdH08sf7ekZZLuMLP3ufstVY/lPB+X4R0K58nxCqMAc939gbIO1lah0cyuUP1waJK2SSy/yt2fkiR3X1H0hOT2hEIv1yck3ezubmZvy1R7lMKHXL0XsEvaN7H+ZEk7eVXXs7v/3cw+JOlepYfG6p7K/VQE9iK8vJBYu/o1s69CAJa7P5uhtiSNc/fD1jmo+2Vm9vkM9fdS+KCYpBBy50q6rvp3kWCYmY1w939V31mE9Y0T6r6mzn2mMGw9StLFKSfyYvj2fEnnm9kohR77u83s5Nqh9gRb1gTGyrHvKv59UpX2unH3Q4uh4rdL+mYxreQHCgEyNXTJ3VcV5655ZjZP0uslHefu81NrK3zYV3p21zm0pB0S63sfP9e73W71N66Zc3hz8V5YYWYjEmuvYWbjFM45h0taISl5uoC7f7aq/qHVtzNaLOl3kg5QeP2ckCEwSmF602lmdrWk/zWz70n6nLu/oDyvmfFm9vc69yePDBTvyfnF6+MQSV8qOsZOc/dfDbRuX9oqNCr0uAzksfXxcjO7vPjZam7L3ack1pfCG2+qpG9I+r6Z/SBDzYoH3D01GDbi9UKKuz9vZjneNI+a2UcVvs3tpdDTKzPbVNKAe7wKC83sLEl/VJiPeU1R+0WJdSv+NcDH1ou7V4YWZhXfpKdJ+loRkC5v/KejLlToMfqQuy+VJDMbI+m84rGBtvmjlZ+LIa/3SDpZ0i0KvWhZmNleCv8eb1Ho6Uqa/7puedu6tvfSzF6sPDtLlP26WSnpO2b2XYUP/69JGi4paRhQ6jXUeJtC79dNksZW7k8carzH3fdMbGIjuxcf0CZp06oPa1P490m1Q/HZYVU/V+qPTay9dfUNdz+26uZIJSh6i6cqvJ+elzRP0lvd/aGUun3IPu/Nwvzs0xV6MF8uaXdJ55nZHySd6O5PpJSXJHe/0cy6JX1d0k1m9p7EZlcsKvk1L0mrJK2U9HeF9R85XuvraKvQ6O6/svIWqxxSczs1hK7D3b8s6ctmtoPCG/Mnkv7dzE5WmDPyh9zHzOieolv+e9V3Fm/UpDl7hfcrvOH3l3S4uz9Z3P86hWGXFB9Q6Akdo3ASfKq4fxfl+T2/pI/5OqbEE3mvYmYjFea8jVMI1yknQUmSu59lZv9UGI7aXOFk/i9Js939G4ntHSrpKEknSLpV0mHex2TyAdT+rELv9xKFD7dT3H11jtpVvizpGgurSivTO14t6YtKmy5RUerrpuoLxn9IulnS29z9ptS6heqe1q/Wua9tuftGfT1mZptkOET1Z0nt+SX1fHOrmX3Aa+bbm9kxCgE+xdUKoxiHu/uixFqt8E5J+7j7I8Xt283s9ZI+qPBlNaWHunqE7UlJ7zazIxW+LKUO25fKzPZROA+8VtIvFOZ39pR2vDwjYHlYiYtVzGzz2gmuVY+93N0fzHGcOrXHKcxxfJe7vzyhzlvd/Zo69yevzC7qjFKYH/W0wr+/KwxBbqrwYfTHlPplikweP9mrVk8OsP6nGz2eOgxjZkcr9BQNV5izdGnit+bq2mtWcxah0VInvBe1PqIQ1K9TCKCPRP5If+u/IOkhhdejtPakXhnOGZ/pOJMVetKqV0+f6e5XZKhd2uvGzJZKelIhUF+vMO+zunbqHOdGx15nukM///wMdy/tEmrW98rvLSVd7u5vTqw/uoQ5pJXaL1HobHhGvb/IbKIw/zbLqv6aY26k8BlySWKdRVr7Pt1RUmVeXZb3rDVYmW5mI919eULtFyS9wd1/U3P/DgoLkD440NpFnVPdPcdUpnq1X5C0UOGLo6uml9cz7TSx5nhtFhrvlvQar1qs4u715k4NpPaDCr0Vl1bdN1xh/uHh7v6KHMepc9xtJa3IND+tuma2ldk1tfdV+AA1hd7e6zLV7Wu+qqS06QFm9muFk96jxe27FOZNjpD0HXffb6C1m6F40y+SVPkgqn3Tp/zblLJqr2jzE5KWq84q3gwfEC9r9HiOkGpmXe6+rI/HDs4RHMtiZjeo7/eT55jKUnyR3F7SwmJ+8EskHSfpKHf/94S61auPv1Y91SEHa7zy+8cZvuRVt/8yd09dCFfvGJXzsBTOw9dnqLmlwvZSoyRdLulaScdKOlHSXe5eOxrX3/qlvmfLOpcVtY9T+OK+vcLc4LnFtKFc9UvbQqzoEW1UO3mniWptNTytchervFVh1dUHJH1I4Q15lsK3uixzDczsdQor0f6qsHLyYknbShpSDP3+PKF2mSuzZWavkbStu1+l0HNRuf9gSY954l6KKmE6QJVSJ4+X+YYv7JP451thrEqYt1Th7o+Y2aEKPRaL3P3qEg5znZn9Z2WuZ4WZTZd0mqSk0GhmZyhscn5+zf3HS9rO3U8eaO3U3rKY4kP0NIXeok3M7CsKcyW/p9DzlVS+6ueJibXqKXPlt9S7/amLdnoXDh0ZH1Txupd0YcZpGRdL+puk30r6L4WtajaWdEiOgJR7tKGZPGzXc04RfKcqzBUerjCcP9fX7s4xUB9Ugy3EUlSHwsoUpJSRgJh262l8UtKNlZsKc3Uqt7MsVjGzj0v6gqQ/SfpPd787tWZV7R6FxTBbSZoj6UB3v8XMdlZ44Q04nJrZ01p3ZfZD7p7lpFX0XBxV5wN0R4V9FLMtwinm7illOKGm3gPuvmMfjz2YMi2gqFH9Te6zknoNO+b+JpeTmT2ltcNEvR5SQo+grbtPYLVnJD2osHpvQD3VZvZ1hS92v1HoNb6i3pBjCjM7SNJXJB1U+VAws1MUvpgd2FcvZD/q3yNpNw8rMKvvH6LQe7dbQu1SN/QubNGWAAAK3UlEQVQt2v4Gd/+rmY1WeA290XtvRTLQ2n3uc5iLhe2k5ins2pBz5Xep7bewePI5hbl0B0pa6u7HZaq9yN3HFT9vJOkvkkbnmK5S1HxY9fcOlcK5JvU8XMq5rMHx9pT0bUnjvcE82fWstY1K3ELMwi4npyiMrknSPyV90d2/nqN+r2O1WWh8U6PHPWH5uIVJ+x9XWJBxhsIVQ7aQ9GHPN3n/Lnffo/h5ibvvUvXYnYmh8XiFb0AjFDYk/oHCXoe5QuOaE0qdx37v7rtnOManJX1U4U0+ROHN8zVP2Ci4qHuJwkbV9SaPv9ndp6XUr6mZ9Hvso+YrFL5s/E2hN+ebCl+YHlTYtHzAk5qLKR8H9fV4Gb0DxQfSbpIuGWgwMrPFknb3sHp/M0k3eQkbkZvZfgqrMQ9V6H15jaTJOU7mZna397G5eaPH1rN2qRv61oYhM1ucEnJralc+/E1h0WPuuW+VxUfDtHbld3XnQ+om088rLCYzhTnflYV3ydun1AS7oQoXJ8gSSuv8TnMH3tpt8YZIepfC8PcdqcP4zTiXFV82DlD4rN1PYU/Lue7+k9TaVceobCH2MYU590lbiJnZJyRNkHSsFyvhi7mYX5F0q7t/LrHJvbTV8HRfodCKxR4Kv8CBurP486/2sFXFHAuT4H9qZvM98aohheoehadrHktK5973yuyTJP3E01dmb9rgsRxDvMdLeoPCnNWHi/t2kPQNMzu++PsN1PGSfmJm71adyeMJdesp41vWdxSG/bZUWIV8nMJUhP9Q2BonZZPpZ5s9bOThMl+/N7OvJZR5tqgjD3OcS9lg192vM7OjJN2golfT81068ykze0Xt0FbxJaH2/NBfZW/o21UzLeMl1bcTp2TsEn9KklJXfqf2OkWs2c/W3VdnftnvYb23H6psR5TlKkLFlKBKT/oRCp00d0ma5O73pNQuPFPWuczM3qLwuTpJYURvnqQZuYd5rZwtxI5Q+IK95rzl7g+Z2bsUrviTNTS2VU9jNct/Gb5X15uXZ2ZvkDTN3T8y4MaurdXoG+hwd0+5AseOkv7Nqy5tZmbjFbYGeVOG7vPzFTZ5/YRXvSgsbH2yvbvPSKx/p6S3eM21Touh6mty9N5ZCZPH6xwj+3BaTQ91r6H26scGWPtc773XW0copmPcr7U9aJUeqWxDUVVD7KbwBeM5hf3rcl2K70CFvRM/p7UfDt0Kw0jHufvPUuoXx6hs6Hu4wgUQsmzoa02cXG8Zr0PfDDXzDhdK+rZnmndY9Rki9f4cydGLmX2UpKb+MIXL0B6vsJL3C55xVxILW4d9quouVxhiv9mrrv89wNq/VBjBu8wzbI5fp37tFmI/z/iauc/dX9nHY/e6+871HhuotupptBIXe1SfjMxsj+IY75L0sKTLUusXxyjzG+g5qtm1390XWtgDsuHWHuvpBEnfkvSAhdXHkrSHwu77/5Wh/rDawCiFeY2WcDm7mlrXq2oRTy418/c2q/m2nhwu1LuHuvaqAalXtPmdNbjkn9fsy9lGdlaJC20kyd1L3XfQ3a+ysJjn4wrTMqRwubJ3eL598krZ0DdnKKxlDa5Db2ZJ16Ev6n+qwcPu6XNjv6u18w4PUviimnrFLEmlf4aU3UP0sMKUo3MUdoLY3czWTGvyxHm2Wnup22pjJJ1mZp9x93kDLezuZS9G/KTCFmK7F/99vuhFzvEleJmZ7ec188eLTpQcV8vppa16Gq3ExR5mtpPW7oa/QmFO4Inu3nCbgHbRaE5Ro/mIAzjODurdW5flagGNeujKmgzfKSJzvHZw9wFPD+hjiHjN5f7cva2+OFZYyQttOp2tu6HvvJS5r3XqN7wSkadtA7VmPqeZnSppZ6+6Dn2GOY0n1Ll7hMJ89m3cffPE+qXNOyyTmS1Tg6sFZZjrWblIQ+1imKJ82jzbBsd9saRftPPvoJiedbPCvPXnah9PGXY3s1cpTFG5Wb33WJ6osDI+22Jfqc16GlXuZfjuVfhmeHBl7k/xi+wUjXoQGs1HXC8Wthp4sgiJDxUfSjPN7BFJ57r7s4mHqFzaa51Dq6TLHXWQ0uZ4eZMu95dbo17A6oU2xf/bUpnBS2FT9cqGvptIel91j3LinEMprDh+VGHLkVulfFuEqNzr0Mvdv1T5uQiiMyUdrTAs+KW+/lw/lDnvsEwbSdpceX+X1RZr7XQPFT8vV4bh40Y8rPBv91/CKIWFKTsrvG9/I+nXkn6bYTj87wrnwXdr7R7LN0o6RiE8ZtVWobHBYo8cl+F7h0Ig/aWZ/VzhBNLuL7Rqv7P6l5d6v/JMpr1UYWrAymL4/ocKWxPtrnAdzqQh6pKHXTpa2QtVrMTL/bVCpoU2zVBm8Jqucocbt1OYrD9N4cNogcJimxy9FmVeh15FrRcrrE59j8Jw8l6eaXsT9f4CnH1BSYke98SdKiLq9eC+TBmGjxsphmFz/W5LUVmPYWYbK8xrnqDwHv6mmT3p7rsmlP+VpPMlnV2ZJ2lm/6Yw3eyVyhwc22p4up6qxR4v88R9nop6IxRW1E6TtK/CCWW+17lEXzspXgTzJT2r3pPqN1a4zN+fEusvrAwLmdlZkl5w95OKlXB35Vh4gPoaDMXmmPxe6uX+0LeiR7QSvMYrb/BqGgvXa56mMKfsdHdPCusWrixzusLVN86rnHuL0Y1Xu3vShQDM7ExJb1fYK/c87+PysRuashfCNDhuluFj632ZwooXK2yW/T53vzelfjOY2VYKXyYnFv9/kcLFC45OqLm1wkVFJiic68cpfGE6Q9I3vGaf2FRtGxqL3q5pCqsClyqsasras1C8mN+pcBnBbJtXl6k4sVaG5LKtEK6Zp3OHwiUXry5uLyQ0diYr+XJ/WD8lBK8yh74rx9hEYQuSaQoLDi5XWCncttehl9a85p9RWJRR7zXfrj2BpTKzF5exMng9j50cWG3dyxS6wiV6S7v6SS5mNkdh6PgfCqMOt0i6JWPvt8xspqQvK4To13nixQn60lbD030sVjEv6ZJZxRvoguK/juDuv5T0yxJKX29mlyqsttpaxSrkYnVj6nxGtM7YVjdgQ1YneH1VUuoqUqncoW+Z2XcVvpxeJemz7r44Y+1SA6+757z87KDRwsCYZfi4w0dJRivMPb5f0h8VpmY8maOwmb1I0hcV9vM9QGFF/1VmNjNXp1Kv47VTT2PxDfEmhatgVBarZLtUHvpmYcPhwxX2CLu00ptgZm+UNM7dz2tl+4BOUxO85mUOXqUOfRfn4koPTtbeOjNbrgaB1zPsM4nmGwzDx2UqFuu8SmEYeYLCueGvCothBrxtnpk9pLDu4JyqOY17FPc94hmviCa1X2h8m0JP4wSFydHzJH3L3ektKVmxd9qp7r6w5v5uSZ9294Nb0zKkKHO+JBorM3jVHCfr0HfZBstcT/TWycPHzWRmXQpzGicobPi9jbu/KKVeX0PR9RbPpmqr0FjRqYtVOlmz9oEEkEenzjms1mmBFxgIM/tvhZA4UWHLpl9L+m3x/0W5F6uUqS1DY7VOXKzSiazm8nXr+xiA5itz6LsZBkPgBdaXmZ2tYm9Gd89+lZZmavvQiOYws7mSru9jH8i3uvvhrWkZgFrNGvouQ6cHXmBDRmiEpPL3gQQAqbMDL7ChIzSil7L2gQQAAJ2N0AgAAIAoNkEFAABAFKERAAAAUYRGAAAARBEaAQAAEEVoBAAAQNT/B/WeU0tPnCrlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxmin.T.plot.bar(stacked=True,figsize=FIGSIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"border:3px;border-style:solid;border-color:black;background-color:powderblue;line-height:125%;\">\n",
    "    <font size=\"5\">\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;Annualized Return calculation:<br>\n",
    "</h2>\n",
    "<h2 style=\"border:3px;border-style:solid;border-color:black;background-color:lightgreen;line-height:125%;\">\n",
    "    <font size=\"5\">\n",
    "    <br>\n",
    "    \\begin{align} \n",
    "    Return & = (FinalValue\\: /\\: StartingValue) - 1.0 \\\\ \\\\\n",
    "    Annualized Return & = (FinalValue\\: /\\: StartingValue)^{(365\\:/\\:Days)}  - 1.0 \\\\\n",
    "    \\end{align}\n",
    "    </font>\n",
    "    <br>\n",
    "</h2>\n",
    "<h2 style=\"border:3px;border-style:solid;border-color:black;background-color:powderblue;line-height:125%;\">\n",
    "    \\begin{align} \n",
    "    \\\\\n",
    "    StartingValue & = Starting\\:  Price \\\\\n",
    "    FinalValue & = Final\\:  Price\\:  +\\:  Dividends\\:  Earned \\\\\n",
    "    Days & = Number\\:  of\\:  Days\\:  between\\:  StartingValue\\:  and\\:  FinalValue \\\\\n",
    "    \\\\\n",
    "    \\end{align}\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Date</td>\n",
       "      <td>AAPL UW Equity</td>\n",
       "      <td>AXP UN Equity</td>\n",
       "      <td>BA UN Equity</td>\n",
       "      <td>CAT UN Equity</td>\n",
       "      <td>CSCO UW Equity</td>\n",
       "      <td>CVX UN Equity</td>\n",
       "      <td>DIS UN Equity</td>\n",
       "      <td>DWDP UN Equity</td>\n",
       "      <td>GS UN Equity</td>\n",
       "      <td>...</td>\n",
       "      <td>PG UN Equity</td>\n",
       "      <td>TRV UN Equity</td>\n",
       "      <td>UNH UN Equity</td>\n",
       "      <td>UTX UN Equity</td>\n",
       "      <td>V UN Equity</td>\n",
       "      <td>VZ UN Equity</td>\n",
       "      <td>WBA UW Equity</td>\n",
       "      <td>WMT UN Equity</td>\n",
       "      <td>XOM UN Equity</td>\n",
       "      <td>INDU Index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/31/1989</td>\n",
       "      <td>1.3482</td>\n",
       "      <td>7.8274</td>\n",
       "      <td>14.083</td>\n",
       "      <td>7.7344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5625</td>\n",
       "      <td>6.1669</td>\n",
       "      <td>14.5105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7578</td>\n",
       "      <td>12.063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.8065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2031</td>\n",
       "      <td>11.5938</td>\n",
       "      <td>2342.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/28/1989</td>\n",
       "      <td>1.2946</td>\n",
       "      <td>7.5398</td>\n",
       "      <td>13.75</td>\n",
       "      <td>7.3438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.125</td>\n",
       "      <td>6.0847</td>\n",
       "      <td>13.9316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5391</td>\n",
       "      <td>11.875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.9844</td>\n",
       "      <td>10.9688</td>\n",
       "      <td>2258.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/31/1989</td>\n",
       "      <td>1.2723</td>\n",
       "      <td>8.0829</td>\n",
       "      <td>15.167</td>\n",
       "      <td>7.1406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.1875</td>\n",
       "      <td>6.4547</td>\n",
       "      <td>13.7075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7813</td>\n",
       "      <td>12.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0469</td>\n",
       "      <td>10.9688</td>\n",
       "      <td>2293.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/28/1989</td>\n",
       "      <td>1.3929</td>\n",
       "      <td>8.3385</td>\n",
       "      <td>16.833</td>\n",
       "      <td>7.5625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.5938</td>\n",
       "      <td>6.9994</td>\n",
       "      <td>14.1557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9844</td>\n",
       "      <td>12.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.6832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4844</td>\n",
       "      <td>10.8125</td>\n",
       "      <td>2418.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0               1              2             3              4   \\\n",
       "0       Date  AAPL UW Equity  AXP UN Equity  BA UN Equity  CAT UN Equity   \n",
       "1  1/31/1989          1.3482         7.8274        14.083         7.7344   \n",
       "2  2/28/1989          1.2946         7.5398         13.75         7.3438   \n",
       "3  3/31/1989          1.2723         8.0829        15.167         7.1406   \n",
       "4  4/28/1989          1.3929         8.3385        16.833         7.5625   \n",
       "\n",
       "               5              6              7               8             9   \\\n",
       "0  CSCO UW Equity  CVX UN Equity  DIS UN Equity  DWDP UN Equity  GS UN Equity   \n",
       "1             NaN        12.5625         6.1669         14.5105           NaN   \n",
       "2             NaN         12.125         6.0847         13.9316           NaN   \n",
       "3             NaN        13.1875         6.4547         13.7075           NaN   \n",
       "4             NaN        13.5938         6.9994         14.1557           NaN   \n",
       "\n",
       "   ...            22             23             24             25  \\\n",
       "0  ...  PG UN Equity  TRV UN Equity  UNH UN Equity  UTX UN Equity   \n",
       "1  ...        5.7578         12.063            NaN          5.516   \n",
       "2  ...        5.5391         11.875            NaN          5.469   \n",
       "3  ...        5.7813         12.125            NaN          5.688   \n",
       "4  ...        5.9844         12.625            NaN            6.5   \n",
       "\n",
       "            26            27             28             29             30  \\\n",
       "0  V UN Equity  VZ UN Equity  WBA UW Equity  WMT UN Equity  XOM UN Equity   \n",
       "1          NaN       16.8065            NaN         4.2031        11.5938   \n",
       "2          NaN       16.3863            NaN         3.9844        10.9688   \n",
       "3          NaN       17.0866            NaN         4.0469        10.9688   \n",
       "4          NaN       18.6832            NaN         4.4844        10.8125   \n",
       "\n",
       "           31  \n",
       "0  INDU Index  \n",
       "1     2342.32  \n",
       "2     2258.39  \n",
       "3     2293.62  \n",
       "4      2418.8  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pxdf = pd.read_csv(\"data/indu_px_last_1989-2018.csv\",header=None)\n",
    "pxdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
       "      <th>DWDP</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>...</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>INDU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1989-01-31</th>\n",
       "      <td>1.3482</td>\n",
       "      <td>7.8274</td>\n",
       "      <td>14.083</td>\n",
       "      <td>7.7344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5625</td>\n",
       "      <td>6.1669</td>\n",
       "      <td>14.5105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.025</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7578</td>\n",
       "      <td>12.063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.8065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2031</td>\n",
       "      <td>11.5938</td>\n",
       "      <td>2342.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-02-28</th>\n",
       "      <td>1.2946</td>\n",
       "      <td>7.5398</td>\n",
       "      <td>13.750</td>\n",
       "      <td>7.3438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.1250</td>\n",
       "      <td>6.0847</td>\n",
       "      <td>13.9316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.144</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5391</td>\n",
       "      <td>11.875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.9844</td>\n",
       "      <td>10.9688</td>\n",
       "      <td>2258.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-03-31</th>\n",
       "      <td>1.2723</td>\n",
       "      <td>8.0829</td>\n",
       "      <td>15.167</td>\n",
       "      <td>7.1406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.1875</td>\n",
       "      <td>6.4547</td>\n",
       "      <td>13.7075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.193</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7813</td>\n",
       "      <td>12.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0469</td>\n",
       "      <td>10.9688</td>\n",
       "      <td>2293.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL     AXP      BA     CAT  CSCO      CVX     DIS     DWDP  \\\n",
       "Date                                                                         \n",
       "1989-01-31  1.3482  7.8274  14.083  7.7344   NaN  12.5625  6.1669  14.5105   \n",
       "1989-02-28  1.2946  7.5398  13.750  7.3438   NaN  12.1250  6.0847  13.9316   \n",
       "1989-03-31  1.2723  8.0829  15.167  7.1406   NaN  13.1875  6.4547  13.7075   \n",
       "\n",
       "            GS     HD  ...      PG     TRV  UNH    UTX   V       VZ  WBA  \\\n",
       "Date                   ...                                                 \n",
       "1989-01-31 NaN  1.025  ...  5.7578  12.063  NaN  5.516 NaN  16.8065  NaN   \n",
       "1989-02-28 NaN  1.144  ...  5.5391  11.875  NaN  5.469 NaN  16.3863  NaN   \n",
       "1989-03-31 NaN  1.193  ...  5.7813  12.125  NaN  5.688 NaN  17.0866  NaN   \n",
       "\n",
       "               WMT      XOM     INDU  \n",
       "Date                                  \n",
       "1989-01-31  4.2031  11.5938  2342.32  \n",
       "1989-02-28  3.9844  10.9688  2258.39  \n",
       "1989-03-31  4.0469  10.9688  2293.62  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pxdf = pd.read_csv(\"data/indu_px_last_1989-2018.csv\",\n",
    "                   header=0,index_col=0,parse_dates=True)\n",
    "pxdf.columns = [x.split()[0] for x in pxdf.columns]\n",
    "pxdf.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"border:3px;border-style:solid;border-color:black;background-color:powderblue;line-height:125%;text-indent:5%;\">\n",
    "    <br><font size=\"5.6\">&nbsp;&nbsp;&nbsp;&nbsp;Data Frame Shape:</font>\n",
    "    <br><font size=\"5\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&bull;&nbsp;&nbsp;Want prices dataframe (pxdf) to be same shape as P/E dataframe (pedf).</font>\n",
    "    <br>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start_date = 2007-01-31 \n",
      "end_date   = 2018-12-31\n"
     ]
    }
   ],
   "source": [
    "start_date = str(pedf.index[ 0]).split()[0]\n",
    "end_date   = str(pedf.index[-1]).split()[0]\n",
    "print('start_date =',start_date,'\\nend_date   =',end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "BEFORE slicing/filtering, pxdf.shape = (360, 31)\n",
      "\n",
      "AFTER slicing/filtering, pxdf.shape = (144, 26)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nBEFORE slicing/filtering, pxdf.shape =\",pxdf.shape)\n",
    "\n",
    "pxdf = pxdf.loc[start_date:end_date,pedf.columns]\n",
    "\n",
    "print(\"\\nAFTER slicing/filtering, pxdf.shape =\",pxdf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"border:3px;border-style:solid;border-color:black;background-color:powderblue;line-height:125%;\">\n",
    "    <font size=\"4.5\">\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;Have prices; Now need Dividends to Calculate Returns.\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;How Will Dividends be Used?<br>\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#x25cf;&nbsp;Final Value = Final Price + Dividends.\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#x25cf;&nbsp;Calculate various returns (1mo, 3mo, 6mo, 1yr, 2yr)\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#x25cf;&nbsp;Need Dividend amounts<font size=\"5\"> specifically between Start Date and End Date.</font><br>\n",
    "\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>262</th>\n",
       "      <th>263</th>\n",
       "      <th>264</th>\n",
       "      <th>265</th>\n",
       "      <th>266</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dividend History - Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>Dividend History - Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>DVD_HIST</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>DVD_HIST</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>DVD_HIST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL UW Equity</td>\n",
       "      <td>1/29/2019</td>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>2/11/2019</td>\n",
       "      <td>2/14/2019</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AXP UN Equity</td>\n",
       "      <td>...</td>\n",
       "      <td>1/30/2019</td>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>2/11/2019</td>\n",
       "      <td>3/11/2019</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INDU Index</td>\n",
       "      <td>#N/A Field Not Applicable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11/1/2018</td>\n",
       "      <td>11/8/2018</td>\n",
       "      <td>11/12/2018</td>\n",
       "      <td>11/15/2018</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10/31/2018</td>\n",
       "      <td>11/9/2018</td>\n",
       "      <td>11/13/2018</td>\n",
       "      <td>12/10/2018</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7/31/2018</td>\n",
       "      <td>8/10/2018</td>\n",
       "      <td>8/13/2018</td>\n",
       "      <td>8/16/2018</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7/25/2018</td>\n",
       "      <td>8/10/2018</td>\n",
       "      <td>8/13/2018</td>\n",
       "      <td>9/10/2018</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5/1/2018</td>\n",
       "      <td>5/11/2018</td>\n",
       "      <td>5/14/2018</td>\n",
       "      <td>5/17/2018</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4/25/2018</td>\n",
       "      <td>5/11/2018</td>\n",
       "      <td>5/14/2018</td>\n",
       "      <td>6/11/2018</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2/1/2018</td>\n",
       "      <td>2/9/2018</td>\n",
       "      <td>2/12/2018</td>\n",
       "      <td>2/15/2018</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1/31/2018</td>\n",
       "      <td>2/9/2018</td>\n",
       "      <td>2/12/2018</td>\n",
       "      <td>3/9/2018</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11/2/2017</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>11/13/2017</td>\n",
       "      <td>11/16/2017</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10/25/2017</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>11/13/2017</td>\n",
       "      <td>12/11/2017</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0                        1           2           3    \\\n",
       "0             NaN  Dividend History - Cash         NaN         NaN   \n",
       "1             NaN                 DVD_HIST         NaN         NaN   \n",
       "2  AAPL UW Equity                1/29/2019    2/8/2019   2/11/2019   \n",
       "3             NaN                11/1/2018   11/8/2018  11/12/2018   \n",
       "4             NaN                7/31/2018   8/10/2018   8/13/2018   \n",
       "5             NaN                 5/1/2018   5/11/2018   5/14/2018   \n",
       "6             NaN                 2/1/2018    2/9/2018   2/12/2018   \n",
       "7             NaN                11/2/2017  11/10/2017  11/13/2017   \n",
       "\n",
       "          4     5        6             7    8              9    ...  \\\n",
       "0         NaN   NaN      NaN           NaN  NaN            NaN  ...   \n",
       "1         NaN   NaN      NaN           NaN  NaN            NaN  ...   \n",
       "2   2/14/2019  0.73  Quarter  Regular Cash  NaN  AXP UN Equity  ...   \n",
       "3  11/15/2018  0.73  Quarter  Regular Cash  NaN            NaN  ...   \n",
       "4   8/16/2018  0.73  Quarter  Regular Cash  NaN            NaN  ...   \n",
       "5   5/17/2018  0.73  Quarter  Regular Cash  NaN            NaN  ...   \n",
       "6   2/15/2018  0.63  Quarter  Regular Cash  NaN            NaN  ...   \n",
       "7  11/16/2017  0.63  Quarter  Regular Cash  NaN            NaN  ...   \n",
       "\n",
       "                       262         263         264         265   266      267  \\\n",
       "0  Dividend History - Cash         NaN         NaN         NaN   NaN      NaN   \n",
       "1                 DVD_HIST         NaN         NaN         NaN   NaN      NaN   \n",
       "2                1/30/2019    2/8/2019   2/11/2019   3/11/2019  0.82  Quarter   \n",
       "3               10/31/2018   11/9/2018  11/13/2018  12/10/2018  0.82  Quarter   \n",
       "4                7/25/2018   8/10/2018   8/13/2018   9/10/2018  0.82  Quarter   \n",
       "5                4/25/2018   5/11/2018   5/14/2018   6/11/2018  0.82  Quarter   \n",
       "6                1/31/2018    2/9/2018   2/12/2018    3/9/2018  0.77  Quarter   \n",
       "7               10/25/2017  11/10/2017  11/13/2017  12/11/2017  0.77  Quarter   \n",
       "\n",
       "            268  269         270                        271  \n",
       "0           NaN  NaN         NaN    Dividend History - Cash  \n",
       "1           NaN  NaN         NaN                   DVD_HIST  \n",
       "2  Regular Cash  NaN  INDU Index  #N/A Field Not Applicable  \n",
       "3  Regular Cash  NaN         NaN                        NaN  \n",
       "4  Regular Cash  NaN         NaN                        NaN  \n",
       "5  Regular Cash  NaN         NaN                        NaN  \n",
       "6  Regular Cash  NaN         NaN                        NaN  \n",
       "7  Regular Cash  NaN         NaN                        NaN  \n",
       "\n",
       "[8 rows x 272 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dvdf = pd.read_csv(\"data/indu_dvd_hist_cash.csv\",header=None) \n",
    "dvdf.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>261</th>\n",
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       "      <th>269</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL UW Equity</td>\n",
       "      <td>1/29/2019</td>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>2/11/2019</td>\n",
       "      <td>2/14/2019</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AXP UN Equity</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>XOM UN Equity</td>\n",
       "      <td>1/30/2019</td>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>2/11/2019</td>\n",
       "      <td>3/11/2019</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11/1/2018</td>\n",
       "      <td>11/8/2018</td>\n",
       "      <td>11/12/2018</td>\n",
       "      <td>11/15/2018</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/31/2018</td>\n",
       "      <td>11/9/2018</td>\n",
       "      <td>11/13/2018</td>\n",
       "      <td>12/10/2018</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7/31/2018</td>\n",
       "      <td>8/10/2018</td>\n",
       "      <td>8/13/2018</td>\n",
       "      <td>8/16/2018</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7/25/2018</td>\n",
       "      <td>8/10/2018</td>\n",
       "      <td>8/13/2018</td>\n",
       "      <td>9/10/2018</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5/1/2018</td>\n",
       "      <td>5/11/2018</td>\n",
       "      <td>5/14/2018</td>\n",
       "      <td>5/17/2018</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/25/2018</td>\n",
       "      <td>5/11/2018</td>\n",
       "      <td>5/14/2018</td>\n",
       "      <td>6/11/2018</td>\n",
       "      <td>0.82</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2/1/2018</td>\n",
       "      <td>2/9/2018</td>\n",
       "      <td>2/12/2018</td>\n",
       "      <td>2/15/2018</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/31/2018</td>\n",
       "      <td>2/9/2018</td>\n",
       "      <td>2/12/2018</td>\n",
       "      <td>3/9/2018</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11/2/2017</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>11/13/2017</td>\n",
       "      <td>11/16/2017</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/25/2017</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>11/13/2017</td>\n",
       "      <td>12/11/2017</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8/1/2017</td>\n",
       "      <td>8/10/2017</td>\n",
       "      <td>8/14/2017</td>\n",
       "      <td>8/17/2017</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7/26/2017</td>\n",
       "      <td>8/10/2017</td>\n",
       "      <td>8/14/2017</td>\n",
       "      <td>9/11/2017</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5/2/2017</td>\n",
       "      <td>5/11/2017</td>\n",
       "      <td>5/15/2017</td>\n",
       "      <td>5/18/2017</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/26/2017</td>\n",
       "      <td>5/10/2017</td>\n",
       "      <td>5/12/2017</td>\n",
       "      <td>6/9/2017</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Quarter</td>\n",
       "      <td>Regular Cash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 270 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0          1           2           3           4     5    \\\n",
       "0  AAPL UW Equity  1/29/2019    2/8/2019   2/11/2019   2/14/2019  0.73   \n",
       "1             NaN  11/1/2018   11/8/2018  11/12/2018  11/15/2018  0.73   \n",
       "2             NaN  7/31/2018   8/10/2018   8/13/2018   8/16/2018  0.73   \n",
       "3             NaN   5/1/2018   5/11/2018   5/14/2018   5/17/2018  0.73   \n",
       "4             NaN   2/1/2018    2/9/2018   2/12/2018   2/15/2018  0.63   \n",
       "5             NaN  11/2/2017  11/10/2017  11/13/2017  11/16/2017  0.63   \n",
       "6             NaN   8/1/2017   8/10/2017   8/14/2017   8/17/2017  0.63   \n",
       "7             NaN   5/2/2017   5/11/2017   5/15/2017   5/18/2017  0.63   \n",
       "\n",
       "       6             7    8              9    ... 260            261  \\\n",
       "0  Quarter  Regular Cash  NaN  AXP UN Equity  ... NaN  XOM UN Equity   \n",
       "1  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "2  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "3  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "4  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "5  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "6  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "7  Quarter  Regular Cash  NaN            NaN  ... NaN            NaN   \n",
       "\n",
       "          262         263         264         265   266      267  \\\n",
       "0   1/30/2019    2/8/2019   2/11/2019   3/11/2019  0.82  Quarter   \n",
       "1  10/31/2018   11/9/2018  11/13/2018  12/10/2018  0.82  Quarter   \n",
       "2   7/25/2018   8/10/2018   8/13/2018   9/10/2018  0.82  Quarter   \n",
       "3   4/25/2018   5/11/2018   5/14/2018   6/11/2018  0.82  Quarter   \n",
       "4   1/31/2018    2/9/2018   2/12/2018    3/9/2018  0.77  Quarter   \n",
       "5  10/25/2017  11/10/2017  11/13/2017  12/11/2017  0.77  Quarter   \n",
       "6   7/26/2017   8/10/2017   8/14/2017   9/11/2017  0.77  Quarter   \n",
       "7   4/26/2017   5/10/2017   5/12/2017    6/9/2017  0.77  Quarter   \n",
       "\n",
       "            268 269  \n",
       "0  Regular Cash NaN  \n",
       "1  Regular Cash NaN  \n",
       "2  Regular Cash NaN  \n",
       "3  Regular Cash NaN  \n",
       "4  Regular Cash NaN  \n",
       "5  Regular Cash NaN  \n",
       "6  Regular Cash NaN  \n",
       "7  Regular Cash NaN  \n",
       "\n",
       "[8 rows x 270 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dvdf = pd.read_csv(\"data/indu_dvd_hist_cash.csv\",\n",
    "                   header=None,skiprows=2,\n",
    "                   usecols=range(270))\n",
    "dvdf.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are 9 columns per stock in the dividends dataframe.\n",
    "# Loop through the dividends data frame, 9 columns at a time, stripping out\n",
    "# the data needed:  Place into a dictionary, where the Key is the ticker symbol \n",
    "# and value is the dividend stream (dates and amounts for dividends payments)''')\n",
    "dvs = {}\n",
    "for col in range(0,270,9):\n",
    "    \n",
    "    ticker = dvdf.iloc[0,col].split()[0]\n",
    "    \n",
    "    index  = pd.DatetimeIndex( dvdf.iloc[:,col+2].values[::-1] )\n",
    "    \n",
    "    dvs[ticker] = pd.Series( dvdf.iloc[:,col+5].values[::-1],\n",
    "                             index=index, name='Dividend' )\n",
    "    \n",
    "    dvs[ticker].index.name = 'ExDate'   # 'Ex-Dividend date - after this date the buyer is not entitled to the dividend.'\n",
    "    dvs[ticker].dropna(inplace=True)    # Remove 'N/A's from the dividend stream."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExDate\n",
       "1987-05-11    0.002143\n",
       "1987-08-10    0.002143\n",
       "1987-11-17    0.002857\n",
       "1988-02-12    0.002857\n",
       "1988-05-16    0.002857\n",
       "Name: Dividend, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "ExDate\n",
       "2018-02-09    0.63\n",
       "2018-05-11    0.73\n",
       "2018-08-10    0.73\n",
       "2018-11-08    0.73\n",
       "2019-02-08    0.73\n",
       "Name: Dividend, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DIS', 'DWDP', 'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE', 'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WBA', 'WMT', 'XOM'])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Poke around the dividend dict data;  make sure it looks reasonable:\n",
    "dvs['AAPL'].head()\n",
    "dvs['AAPL'].tail()\n",
    "dvs.keys()\n",
    "len(dvs.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      " TEST: Can we easily sum up the dividends paid \n",
      " for a given stock and a given period of time? \n",
      "==================================================\n",
      "\n",
      "ExDate\n",
      "2007-08-15    0.32\n",
      "2007-11-14    0.32\n",
      "2008-02-13    0.32\n",
      "Name: Dividend, dtype: float64\n",
      "\n",
      " Total dividends paid =  0.96\n"
     ]
    }
   ],
   "source": [
    "print('==================================================')\n",
    "print(' TEST: Can we easily sum up the dividends paid ')\n",
    "print(' for a given stock and a given period of time? ')\n",
    "print('==================================================\\n')\n",
    "\n",
    "dv_paid = dvs['UTX']['06/01/2007':'05/13/2008']   # Dividend stream between two dates for a specific stock.\n",
    "print(dv_paid)\n",
    "\n",
    "print('\\n Total dividends paid = ',dv_paid.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"border:3px;border-style:solid;border-color:black;background-color:powderblue;line-height:125%;\">\n",
    "    <br><font size=\"6\">&nbsp;&nbsp;&nbsp;&nbsp;Returns Calculation:</font>\n",
    "    <br><br><font size=\"5\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;For each stock, given series of prices, and dividends,\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;calculate returns for 1mo, 3mo, 6mo, 1yr, 2yr,\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<i>for each date</i> in the series.</font>\n",
    "    <br>\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&bull;&nbsp;&nbsp;Use Pandas DataFrame.apply() method\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to apply a returns calcuation function to each column (stock) in the dataframe.<br>\n",
    "\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # import numpy as np was done above\n",
    "_NaN_ = np.nan  # using a global variable repeatedly is twice as fast as using np.nan repeatedly\n",
    "\n",
    "def calc_returns(prices,num_months,annual=True,dvds=None):\n",
    "    #   prices is a Series.  \n",
    "    #   prices is one column at a time from the prices dataframe\n",
    "    #   on which apply(calc_returns, axis=0, ... ) was called.\n",
    "    \n",
    "    # debugging print statements to confirm this is being called as expected:\n",
    "    # print(\"type(prices)=\",type(prices))\n",
    "    # print(\"prices.index=\",prices.index)\n",
    "    # print(\"prices[0:2]=\",prices[0:2])\n",
    "    # print(\"type(prices)=\",type(prices))\n",
    "    # print(\"calc_returns called with prices.name=\\\"\",prices.name,\"\\\" num_months=\",num_months,\"  annual=\",annual)\n",
    "    # print(\"calc_returns: The type of variable dvds is \",type(dvds))\n",
    "    \n",
    "    if dvds is not None:\n",
    "        if not isinstance(dvds, dict):\n",
    "            raise TypeError(\"calc_returns() got NON-DICT for Dividends!\")\n",
    "            return None\n",
    "            \n",
    "    if not isinstance(num_months, int):\n",
    "        raise TypeError(\"calc_returns() got NON-INTEGER for num_months!\")\n",
    "        return None\n",
    "    \n",
    "    if num_months < 1 or num_months > len(prices):\n",
    "        raise IndexError(\"calc_returns got num_months OUT OF RANGE( 1,\",len(prices),\") !\")\n",
    "        return None\n",
    "\n",
    "    # construct empty returns series for this stock:\n",
    "    returns = pd.Series(name=prices.name)\n",
    "    \n",
    "    size    = len(prices.index) \n",
    "    limit   = size - num_months\n",
    "    once    = False\n",
    "    for jj in range(0,limit):\n",
    "        d1  = prices.index[jj]\n",
    "        d2  = prices.index[jj+num_months]\n",
    "        #print('d1=',d1,' d2=',d2)\n",
    "        if (annual):\n",
    "            num_days = float((d2 - d1).days)\n",
    "            # debugging num_days:\n",
    "            # if not once:\n",
    "            #     print(\"num_days=\",num_days)\n",
    "            #     once = True\n",
    "            p1  = prices.loc[d1]   # Starting Value\n",
    "            p2  = prices.loc[d2]   # Ending Price\n",
    "            if isinstance(dvds, dict):\n",
    "                ds  = dvds[prices.name][d1:d2].sum()\n",
    "                p2 += ds           # Ending Value\n",
    "                #print(\">>> \", prices.name,\" dividends from \",d1,\" to \",d2,\" sum = \", ds)\n",
    "            ret = ( ((p2/p1)**(365.0/num_days)) - 1.0 ) * 100.0\n",
    "        else:\n",
    "            ret = ((p2/p1) - 1.0) * 100.0\n",
    "\n",
    "        # print(\"d1=\",d1,\"  d2=\",d2,\"  p1=\",prices.loc[d1],\"  p2=\",prices.loc[d2],\"  ret=\",ret)\n",
    "        returns[d1] = ret\n",
    "\n",
    "    for jj in range(limit,size):\n",
    "        d1  = prices.index[jj]\n",
    "        returns[d1] = _NaN_\n",
    "        \n",
    "    # print(\"\\n ===> returns=\",returns)\n",
    "    return returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of 3 month returns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply to axis=0 (vertical axis) means apply the \n",
    "# function once for each column in the dataframe.\n",
    "ret3mo = pxdf.apply(calc_returns, axis=0, args=(3,True,dvs))  # this takes several seconds to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-31</th>\n",
       "      <td>11.349364</td>\n",
       "      <td>69.423911</td>\n",
       "      <td>81.327550</td>\n",
       "      <td>108.787687</td>\n",
       "      <td>-53.111461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-28</th>\n",
       "      <td>-5.908027</td>\n",
       "      <td>98.359555</td>\n",
       "      <td>40.471267</td>\n",
       "      <td>13.379912</td>\n",
       "      <td>-31.690856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>-13.918373</td>\n",
       "      <td>76.253303</td>\n",
       "      <td>2.298226</td>\n",
       "      <td>-15.090694</td>\n",
       "      <td>-31.434430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL        AXP         BA         CAT       CSCO\n",
       "2011-01-31  11.349364  69.423911  81.327550  108.787687 -53.111461\n",
       "2011-02-28  -5.908027  98.359555  40.471267   13.379912 -31.690856\n",
       "2011-03-31 -13.918373  76.253303   2.298226  -15.090694 -31.434430"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ret3mo.head()\n",
    "ret3mo.loc['2011-01-31':'2011-03-31','AAPL':'CSCO']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"border:3px;border-style:solid;border-color:black;background-color:powderblue;line-height:125%;\">\n",
    "    <font size=\"4\">\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;Determine relationship between PE ratio and Returns:<br>\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#x25cf;&nbsp;Calculate various returns (1mo, 3mo, 6mo, 1yr, 2yr) for each stock and each date.\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#x25cf;&nbsp;Determine z-score for each return value.\n",
    "    <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&#x25cf;&nbsp;Compare with z-score for Price/Earnings ratio.<br>\n",
    "    <br>\n",
    "\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(1, '1mo'), (3, '3mo'), (6, '6mo'), (12, '1yr'), (24, '2yr')])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "periods = OrderedDict([ (1, '1mo'), (3, '3mo'),  (6, '6mo'), (12, '1yr'), (24, '2yr') ])\n",
    "periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================================================\n",
      " This cell takes about 30 to 60 seconds to run (depending on your CPU).\n",
      "=======================================================================\n",
      "\n",
      "Calculate  1mo  returns ...\n",
      "                AAPL       AXP        BA       CAT      CSCO       DIS\n",
      "2007-01-31  0.156667 -0.166677 -0.120457  0.862047 -0.204102 -0.245937\n",
      "2007-02-28  3.717217 -0.535604  0.135840  0.839466 -0.689289 -0.232538\n",
      "\n",
      "Calculate  3mo  returns ...\n",
      "                AAPL       AXP        BA       CAT      CSCO       DIS\n",
      "2007-01-31  2.228217 -0.062208 -0.099719  1.663279 -0.654393 -0.809593\n",
      "2007-02-28  4.064394  0.151037  0.258542  0.966432 -0.717112 -0.740157\n",
      "\n",
      "Calculate  6mo  returns ...\n",
      "                AAPL       AXP        BA       CAT      CSCO       DIS\n",
      "2007-01-31  3.645334 -0.384662  0.605810  1.165114  0.096772 -0.735138\n",
      "2007-02-28  3.700257 -0.455268 -0.007805  0.412598  0.688987 -0.677496\n",
      "\n",
      "Calculate  1yr  returns ...\n",
      "                AAPL       AXP        BA       CAT      CSCO       DIS\n",
      "2007-01-31  3.214935 -1.213446 -0.682488  0.469152 -0.823736 -1.123709\n",
      "2007-02-28  2.522577 -1.622328 -0.414856  0.611431 -0.562904 -0.392932\n",
      "\n",
      "Calculate  2yr  returns ...\n",
      "                AAPL       AXP        BA       CAT      CSCO       DIS\n",
      "2007-01-31  1.191195 -2.114531 -1.026838 -0.916784 -0.751052 -0.506773\n",
      "2007-02-28  1.313733 -2.238875 -1.299617 -1.130246 -0.508759 -0.709198\n",
      "\n",
      "elapsed time= 00:24\n"
     ]
    }
   ],
   "source": [
    "print(\"=======================================================================\")\n",
    "print(\" This cell takes about 30 to 60 seconds to run (depending on your CPU).\")\n",
    "print(\"=======================================================================\")\n",
    "import time\n",
    "stime = time.perf_counter()\n",
    "\n",
    "returns = {}\n",
    "rstats  = {}\n",
    "rzscore = {}\n",
    "for num_months in periods.keys():\n",
    "    print(\"\\nCalculate \",periods[num_months],\" returns ...\")\n",
    "    p = periods[num_months]\n",
    "    returns[p] = pxdf.apply(calc_returns,axis=0,args=(num_months,True,dvs))\n",
    "    rstats[p]  = pd.DataFrame()\n",
    "    rstats[p]['mean'] = returns[p].mean(axis=1)\n",
    "    rstats[p]['std']  = returns[p].std(axis=1)\n",
    "    rzscore[p] = returns[p].subtract(rstats[p]['mean'],axis='rows').divide(rstats[p]['std'],axis='rows')\n",
    "    print(rzscore[p].iloc[0:2,0:6])\n",
    "    #print(returns[periods[num_months]].head(36).tail(2))\n",
    "    \n",
    "print('\\nelapsed time=',time.strftime('%M:%S',time.gmtime(time.perf_counter()-stime)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Plot the 1-year Return z-score, and the P/E z-score, for one example stock.\n",
    "# Also plot a line at zero, _since zero z-score represents the market average_, this will help us to see clearly where the Return z-score and the P/E z-score indicate that this stock's Return or P/E is above or below the market average\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a094e3240>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rtz = rzscore['1yr']['AAPL']\n",
    "pez = pezscore['AAPL']\n",
    "mi1 = pd.Series([0]*len(pez),index=pez.index,name='Zero') \n",
    "pd.concat([rtz,pez,mi1],axis=1,keys=['ReturnZ','PEz','Zero']).plot(figsize=FIGSIZE,linewidth=3.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# \"A Picture is Worth a Thousand Words\" (above)<br><br>&nbsp;&nbsp;*but*<br><br> \"There is Strength In Numbers!\" \n",
    "## ... so let's calculate the correlations for the full data set and see what we get:\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcorrs(beg,end):\n",
    "    ix = [] # data to form the multi-index\n",
    "    co = [] # correlation data\n",
    "    for col in pezscore.columns:\n",
    "        for key in rzscore.keys():\n",
    "            corr = pezscore[col].iloc[beg:end].corr(rzscore[key][col].iloc[beg:end])\n",
    "            ix.append((col,key)) # use tupples\n",
    "            co.append(corr)\n",
    "            #print(\" \",col,\" \",key,\" correlation: \", corr)\n",
    "    return pd.Series(co, \n",
    "                     index=pd.MultiIndex.from_tuples(ix, names=['Ticker','Period']),\n",
    "                     name='Corr'+str(end-beg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## calcorrs() returns a Multi-Index Series:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrs = calcorrs(0,144)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Ticker  Period\n",
       "AAPL    1mo       0.130581\n",
       "        3mo       0.113218\n",
       "        6mo       0.059862\n",
       "        1yr       0.035392\n",
       "        2yr       0.371123\n",
       "AXP     1mo       0.000252\n",
       "        3mo      -0.043654\n",
       "        6mo      -0.041084\n",
       "        1yr      -0.138371\n",
       "        2yr      -0.175158\n",
       "BA      1mo      -0.074695\n",
       "        3mo      -0.076470\n",
       "        6mo      -0.175980\n",
       "        1yr      -0.261332\n",
       "        2yr      -0.254709\n",
       "CAT     1mo       0.167137\n",
       "Name: Corr144, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(corrs)\n",
    "corrs.head(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## A Series (instead of a DataFrame) allows us to \"describe\" _all_ of the correlations, for _all_ stocks and _all_ return periods, all together:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "count    130.000000\n",
       "mean      -0.097865\n",
       "std        0.221582\n",
       "min       -0.756744\n",
       "25%       -0.240861\n",
       "50%       -0.090167\n",
       "75%        0.035328\n",
       "max        0.433013\n",
       "Name: Corr144, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corrs)\n",
    "corrs.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Above: The mean correlation for all stocks (correlated over the entire all 144 months for which we have data) *is negative, but only slightly.*\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker  Period\n",
       "AAPL    2yr       0.371123\n",
       "CAT     3mo       0.342741\n",
       "        6mo       0.433013\n",
       "        1yr       0.359322\n",
       "IBM     6mo       0.306893\n",
       "        1yr       0.393992\n",
       "        2yr       0.256328\n",
       "Name: Corr144, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs[ corrs > 0.25 ]  # Which correlations are significantly positive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker  Period\n",
       "CSCO    1yr      -0.661339\n",
       "        2yr      -0.756744\n",
       "KO      2yr      -0.591505\n",
       "MCD     2yr      -0.534977\n",
       "UTX     2yr      -0.612207\n",
       "Name: Corr144, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs[ corrs < -0.5 ]  # Which correlations are significantly negative?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Above: we see that those stocks that show significant negative correlation (between P/E and Return) do so with the *longer* period Returns.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Side note:\n",
    "## If we want, we can use Series.unstack() to view a Multi-Index Series as a DataFrame:  \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker  Period\n",
       "AAPL    1mo       0.130581\n",
       "        3mo       0.113218\n",
       "        6mo       0.059862\n",
       "        1yr       0.035392\n",
       "        2yr       0.371123\n",
       "AXP     1mo       0.000252\n",
       "        3mo      -0.043654\n",
       "        6mo      -0.041084\n",
       "        1yr      -0.138371\n",
       "        2yr      -0.175158\n",
       "BA      1mo      -0.074695\n",
       "        3mo      -0.076470\n",
       "Name: Corr144, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Period</th>\n",
       "      <th>1mo</th>\n",
       "      <th>1yr</th>\n",
       "      <th>2yr</th>\n",
       "      <th>3mo</th>\n",
       "      <th>6mo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.130581</td>\n",
       "      <td>0.035392</td>\n",
       "      <td>0.371123</td>\n",
       "      <td>0.113218</td>\n",
       "      <td>0.059862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AXP</th>\n",
       "      <td>0.000252</td>\n",
       "      <td>-0.138371</td>\n",
       "      <td>-0.175158</td>\n",
       "      <td>-0.043654</td>\n",
       "      <td>-0.041084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>-0.074695</td>\n",
       "      <td>-0.261332</td>\n",
       "      <td>-0.254709</td>\n",
       "      <td>-0.076470</td>\n",
       "      <td>-0.175980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAT</th>\n",
       "      <td>0.167137</td>\n",
       "      <td>0.359322</td>\n",
       "      <td>-0.027792</td>\n",
       "      <td>0.342741</td>\n",
       "      <td>0.433013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>-0.184393</td>\n",
       "      <td>-0.661339</td>\n",
       "      <td>-0.756744</td>\n",
       "      <td>-0.332008</td>\n",
       "      <td>-0.450473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Period       1mo       1yr       2yr       3mo       6mo\n",
       "Ticker                                                  \n",
       "AAPL    0.130581  0.035392  0.371123  0.113218  0.059862\n",
       "AXP     0.000252 -0.138371 -0.175158 -0.043654 -0.041084\n",
       "BA     -0.074695 -0.261332 -0.254709 -0.076470 -0.175980\n",
       "CAT     0.167137  0.359322 -0.027792  0.342741  0.433013\n",
       "CSCO   -0.184393 -0.661339 -0.756744 -0.332008 -0.450473"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs.unstack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;TEST PLAN\n",
    "\n",
    "## 1. Buy all stocks\n",
    "\n",
    "## 2. Buy stocks with Low-P/E<br>&nbsp;&nbsp;&nbsp;&nbsp;<font size=4px>(original hypothesis)</font>\n",
    "\n",
    "## 3. Buy stocks with Low-P/E *that also* <br>&nbsp;&nbsp;&nbsp;&nbsp;*show a history of negative correlation between P/E and Return* <br>&nbsp;&nbsp;&nbsp;&nbsp;<font size=4px>(new hypothesis)</font>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### We saw that most of the negative correlations between P/E and Return were for the 2-Year Returns,<br>therefore we implement the test plan with 3 specific Trading Strategies:\n",
    "<ol><b>\n",
    "    <li>Buy All stocks and hold for 2 years.</li>\n",
    "    <li>Buy LowPE stocks and hold for 2 years.</li>\n",
    "    <li>Buy LowPE stocks with historical negative correlation between P/E and Return, and hold for 2 years.</li>\n",
    "    </b>\n",
    "</ol>\n",
    "  \n",
    "### The algorithm for testing these 3 trading strategies:\n",
    "#### Let's first review what data we have, then we will describe the algorithm.\n",
    "  - DataFrame of P/E ratios for 144 months (rows) across 26 stocks (columns)\n",
    "  - DataFrame of P/E z-scores for 144 months across 26 stocks.  A stock's P/E z-score on a given date is the number of standard deviations the stock's P/E on that date is *above or below the **average P/E on that date** for all 26 stocks.*\n",
    "  - Dictionary of DataFrames of Returns for 1mo, 3mo, 6mo, 1yr and 2yr Returns.  The dictionary key is the return period, and the value is the DataFrame of Returns:  Each of these 5 DataFrames contains 144 months of Returns across 26 stocks.  The Return value attributed to each date (row) is the Return for *purchasing the stock on that date,* and holding the stock for the period of time (1,3,6,12, or 24 months) specified by the dictionary key.\n",
    "  - Dictionary of DataFrames of \"Return z-scores\" for 1mo, 3mo, 6mo, 1yr and 2yr Returns.  Each of these 5 DataFrames contains 144 months of Return z-scores across the 26 stocks.  A stock's \"Return z-score\" on a given date is the number of standard deviations the stock's Return on that date is *above or below the average Return on that date for all 26 stocks.*\n",
    "\n",
    "---  \n",
    "\n",
    "### NOTE: Our new hypothesis is:\n",
    "### \"Choosing stocks that have BOTH LowPE and a history of negative correlation between P/E z-score and Returns z-score, should give even better returns than choosing stocks with just lowPE alone.\"\n",
    "### As noted above, when formulating this new hypothesis, we noticed that the more negative correlations tended to be with the longer returns, THEREFORE we will at this point concern ourselves ONLY WITH 2 YEAR RETURNS.\n",
    "\n",
    "- Therefore we can reduce our *relevant* data set as follows:\n",
    "  - We are concerned only with 2 year returns\n",
    "  - We measure correlations only between z-scores of P/E and z-scores of Returns (*not* directly between P/E and Returns)\n",
    "  - We do not need explicit P/E values, *only* the P/E z-scores.\n",
    "  - However we do need explicit Return values to measure the performance of each trading strategy.\n",
    "\n",
    "---\n",
    "### Thus our *relevant* data set reduces to:\n",
    "  - **Dataframe of P/E z-Scores ( *pezscore* ) for 144 months by 26 stocks.**\n",
    "  - **Dataframe of 2-year Returns z-scores ( *rzscore['2yr']* ) for 144 months by 26 stocks.**\n",
    "  - **Dataframe of 2-year Returns ( *returns['2yr']* ) for 144 months by 26 stocks.**\n",
    "\n",
    "---\n",
    "### The basic algorithm to test these 3 trading strategies is this:\n",
    "1. We are going to test the each of the 3 trading strategies on various dates, thus various market conditions.\n",
    "1. We will test 36 dates, once per month, from January 2014 through December 2016.<br><br>\n",
    "\n",
    "1. The algorithm for the first 2 trading strategies is simple:<br><br>\n",
    "  1. For the first trading strategy, \"Buy all 26 Stocks\", on any one of the 36 dates mentioned above, the Return for that strategy on the given date, is the average Return of all 26 stocks on that date.  Thus, if `returns` is the Returns dataframe, then the strategy's Return is \n",
    "  \n",
    "      `returns[<date>].mean()`<br><br>\n",
    "  \n",
    "  1. For the second trading strategy, \"Buy low P/E Stocks\", on any one of the 36 dates mentioned above, the Return for that strategy on the given date, is the average Return on that date of *only those stocks with a Low P/E.*  Thus, if `returns` is the Returns dataframe, and `pezscore` is the P/E z-score dataframe, then the strategy's Return is\n",
    "  \n",
    "      `returns[<date>][ pezscore[<date>] < LIMIT ].mean()`\n",
    "      \n",
    "     where `LIMIT` is an arbitrary z-score that we define as being a Low P/E.  In the code below, and the results presented in the video, we have chosen `-0.65` (that is, 0.65 standard deviations below the mean).<br><br>\n",
    "  \n",
    "1. The third trading strategy is trickier<br><br> \n",
    "  1. Getting Strategy Returns for the third strategy (\"Buy stocks with *both* low P/E and Negative Correlation between P/E and Return\") is partially similar to the second strategy in that, before taking the `mean()` Return, we want to filter down from 26 stocks.<br><br>First, as with the second strategy, we include only those stocks with a Low PE (`pezscore[<date>] < LIMIT`),<br> **then** we filter that list down further to *include only those stocks that **also** show a history of a negative correlation between the P/E z-score and the Returns z-score*.<br><br>\n",
    "  1. This latter filtering (negative correlation between the PE z-score and Returns z-score) is the tricky part: Since we are concerned with 2-year Returns, and since the Return on a given date represents purchasing the stock on that date and holding for two more years, then the time period we choose to measure the correlation must END no later than two years and one day *before* the date on which we are filtering and making a trade decision.  (We will actually do two years and one month, because we have monthly data).  *To put this in terms of a practical example*:<br><br>\n",
    "    1. Let's say we are deciding which stocks to buy on **31-Jan-2014** (the first of our 36 dates).\n",
    "    1. We want stocks have a negative correlation between P/E z-score, `pezscore`, and Returns z-score, `rzscore`.<br><br>\n",
    "    1. We need a period of time over which to measure the correlation.  Let's say we choose a one-year correlation period, say the year leading up to the date on which we are trying to make a trade decision.  Thus the correlation would be:\n",
    "    \n",
    "        `correlation = pezscore['2013-01-31':'2014-01-31'].corr( rzscore['2013-01-31':'2014-01-31'] )`\n",
    "        \n",
    "        **The problem with this** is that *the Returns in `rzscore` are 2-year Returns, calculated as if the stock was purchased on the date specified, held for 2-years, and then sold.*  Thus to calculate Return on 2013-01-31, we need the price on 2013-01-31 (buy date) **and** the price on **2015-01-31** (the 2-year sell date).  Thus effectively we would be using a price in the future (2015-01-31) of the date on which we are trying to make our trade decision (2014-01-31).<br><br>\n",
    "    1. Thus, if we want to calculate a correlation (between pezscore and rzscore) to make a trade decision on 2014-01-31, then we must use a time period that does not include any prices from 2014-01-31 or later, thus:\n",
    "    \n",
    "        `correlation = pezscore['2010-12-31':'2011-12-31'].corr( rzscore['2010-12-31':'2011-12-31'] )`\n",
    "        \n",
    "        The reason we back up an entire month (to December) is because we have monthly data; thus the only way to not include 2014-01-31 is to use returns that stop using prices at 2013-12-31 (and for two-year returns that would be the return for 2011-12-31).\n",
    "<br><br>\n",
    "  1.  Now that we know that the correlation period must end at least 2 years before the trade date, we need to decide:\n",
    "    1. how long of a correlation period to use, and\n",
    "    1. whether the correlation period should be fixed for all 36 test trade dates, or should slide along always being two years behind each trade date.<br><br>\n",
    "  1. Regarding the length of the correlation period, intuition might say that if a stock has a negative correlation for a longer period of time, then it is more likely to also show a negative correlation in the future (when we are making our trade).  *However, the whole point of using Pandas is not to rely on intuition, but rather to run the numbers and see what happens!*  Therefore we will run the test with various correlation periods (1yr, 2yr, 3yr, etc) and see if some correlation periods give better results than others.<br><br>\n",
    "  1. Regarding whether to choose:\n",
    "    1. a fixed period, or fixed end date, for all of the correlation calculations, or \n",
    "    1. sliding correlation periods so that for each trade date the correlation period ends slightly greater than 2 years before the the trade date ...\n",
    "    \n",
    "    Consider the following:  Markets definitely change over time.  A rule or trend that holds for one time period may not hold for later time periods.  Therefore, even though we must use corrlation periods *at least* two years before the trade date, there may be an advantage to using time periods that are as close to the trade date as possible.  Therefore we will use sliding correlation periods that always end just one data point (one month) more that two years before the trade date.]\n",
    "<br><br>\n",
    "1. So here is how we are going to implement the above tests:<br><br>\n",
    "  1. Slice out the 36 dates we want from the Returns dataframe, so we have a smaller 36x26 dataframe (instead of 144x26).\n",
    "  1. Use `DataFrame.apply(axis=1)` method to apply a function to each row (date) in the Returns dataframe.\n",
    "  1. `.apply(axis=1)` will repeatedly call our function, once for each row (date), passing into our function a series that corresponds to the row: the 26 stocks tickers will be the index for the series, the series name will be the date corresponding to that row, and the series values are the 26 stocks' Returns for that date.  \n",
    "  1. In every case the function called by `apply()` will be taking the `.mean()` of the Returns for the set of stocks that we buy, which will be our overall Return for investing in that set of stocks (for two years beginning on that date).  *The difference between the three trading strategies is merely the set of stocks for which we take the `.mean()`\n",
    "    1. The first strategy is simple:  The function needs no arguments.  It simply takes the `.mean()` for the entire row.\n",
    "    1. The second strategy requires us to pass the P/E z-score dataframe as an argument.  We use that to filter on the stocks that have Low P/E and then take the `.mean()`\n",
    "    1. The third strategy requires as arguments both the P/E z-scores dataframe <i>and</i> the Returns z-scores dataframe:  \n",
    "      - We use the P/E z-scores to filter on the Low P/E stocks, and\n",
    "      - We use <i>both</i> z-score dataframes (pezscore and rzscore) to calculate the appropriate correlations, and use the correlations to filter on the stocks that have a negative correlation between P/E z-score and Returns z-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "Here are the relavant dataframes:\n",
      "---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>2.566807</td>\n",
       "      <td>0.420473</td>\n",
       "      <td>0.800664</td>\n",
       "      <td>-1.161772</td>\n",
       "      <td>1.177298</td>\n",
       "      <td>0.473694</td>\n",
       "      <td>-1.443171</td>\n",
       "      <td>-0.504237</td>\n",
       "      <td>-0.347238</td>\n",
       "      <td>1.150122</td>\n",
       "      <td>...</td>\n",
       "      <td>1.402655</td>\n",
       "      <td>0.157392</td>\n",
       "      <td>-1.051061</td>\n",
       "      <td>1.004546</td>\n",
       "      <td>-1.869390</td>\n",
       "      <td>-0.143852</td>\n",
       "      <td>0.050891</td>\n",
       "      <td>-0.473107</td>\n",
       "      <td>-0.314658</td>\n",
       "      <td>-1.334128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>2.587353</td>\n",
       "      <td>0.408479</td>\n",
       "      <td>0.772778</td>\n",
       "      <td>-1.081498</td>\n",
       "      <td>1.148585</td>\n",
       "      <td>0.449944</td>\n",
       "      <td>-1.639364</td>\n",
       "      <td>-0.518097</td>\n",
       "      <td>-0.474868</td>\n",
       "      <td>0.985655</td>\n",
       "      <td>...</td>\n",
       "      <td>1.054949</td>\n",
       "      <td>0.929172</td>\n",
       "      <td>-1.107684</td>\n",
       "      <td>0.993174</td>\n",
       "      <td>-1.812080</td>\n",
       "      <td>-0.071411</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>-0.488523</td>\n",
       "      <td>-0.197649</td>\n",
       "      <td>-1.342877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>2.604480</td>\n",
       "      <td>0.291522</td>\n",
       "      <td>0.738498</td>\n",
       "      <td>-0.933307</td>\n",
       "      <td>1.262957</td>\n",
       "      <td>0.476726</td>\n",
       "      <td>-1.564870</td>\n",
       "      <td>-0.657203</td>\n",
       "      <td>-0.405297</td>\n",
       "      <td>0.942817</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605305</td>\n",
       "      <td>1.189960</td>\n",
       "      <td>-1.129419</td>\n",
       "      <td>0.971671</td>\n",
       "      <td>-1.805081</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>-0.037697</td>\n",
       "      <td>-0.449461</td>\n",
       "      <td>-0.185137</td>\n",
       "      <td>-1.268311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       AXP        BA       CAT      CSCO       DIS  \\\n",
       "Date                                                                     \n",
       "2007-01-31  2.566807  0.420473  0.800664 -1.161772  1.177298  0.473694   \n",
       "2007-02-28  2.587353  0.408479  0.772778 -1.081498  1.148585  0.449944   \n",
       "2007-03-30  2.604480  0.291522  0.738498 -0.933307  1.262957  0.476726   \n",
       "\n",
       "                  GS        HD       IBM      INTC  ...      MSFT       NKE  \\\n",
       "Date                                                ...                       \n",
       "2007-01-31 -1.443171 -0.504237 -0.347238  1.150122  ...  1.402655  0.157392   \n",
       "2007-02-28 -1.639364 -0.518097 -0.474868  0.985655  ...  1.054949  0.929172   \n",
       "2007-03-30 -1.564870 -0.657203 -0.405297  0.942817  ...  0.605305  1.189960   \n",
       "\n",
       "                 PFE        PG       TRV       UNH       UTX        VZ  \\\n",
       "Date                                                                     \n",
       "2007-01-31 -1.051061  1.004546 -1.869390 -0.143852  0.050891 -0.473107   \n",
       "2007-02-28 -1.107684  0.993174 -1.812080 -0.071411  0.000310 -0.488523   \n",
       "2007-03-30 -1.129419  0.971671 -1.805081  0.029282 -0.037697 -0.449461   \n",
       "\n",
       "                 WMT       XOM  \n",
       "Date                            \n",
       "2007-01-31 -0.314658 -1.334128  \n",
       "2007-02-28 -0.197649 -1.342877  \n",
       "2007-03-30 -0.185137 -1.268311  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>1.191195</td>\n",
       "      <td>-2.114531</td>\n",
       "      <td>-1.026838</td>\n",
       "      <td>-0.916784</td>\n",
       "      <td>-0.751052</td>\n",
       "      <td>-0.506773</td>\n",
       "      <td>-1.628058</td>\n",
       "      <td>-0.705773</td>\n",
       "      <td>0.864658</td>\n",
       "      <td>-0.299420</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.660925</td>\n",
       "      <td>0.836333</td>\n",
       "      <td>-0.362300</td>\n",
       "      <td>0.598943</td>\n",
       "      <td>0.288326</td>\n",
       "      <td>-0.844820</td>\n",
       "      <td>0.035391</td>\n",
       "      <td>0.556831</td>\n",
       "      <td>1.104908</td>\n",
       "      <td>1.260426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>1.313733</td>\n",
       "      <td>-2.238875</td>\n",
       "      <td>-1.299617</td>\n",
       "      <td>-1.130246</td>\n",
       "      <td>-0.508759</td>\n",
       "      <td>-0.709198</td>\n",
       "      <td>-0.945623</td>\n",
       "      <td>-0.459578</td>\n",
       "      <td>1.225769</td>\n",
       "      <td>0.037325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.321709</td>\n",
       "      <td>0.543867</td>\n",
       "      <td>-0.377243</td>\n",
       "      <td>0.463607</td>\n",
       "      <td>0.285349</td>\n",
       "      <td>-1.393770</td>\n",
       "      <td>-0.086832</td>\n",
       "      <td>0.674459</td>\n",
       "      <td>1.319458</td>\n",
       "      <td>1.098286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>1.375058</td>\n",
       "      <td>-2.326286</td>\n",
       "      <td>-1.353768</td>\n",
       "      <td>-1.216716</td>\n",
       "      <td>-0.333272</td>\n",
       "      <td>-0.774659</td>\n",
       "      <td>-0.902939</td>\n",
       "      <td>-0.196000</td>\n",
       "      <td>1.164891</td>\n",
       "      <td>0.383428</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.191255</td>\n",
       "      <td>0.656849</td>\n",
       "      <td>-0.416769</td>\n",
       "      <td>0.207057</td>\n",
       "      <td>0.347602</td>\n",
       "      <td>-1.555243</td>\n",
       "      <td>-0.149430</td>\n",
       "      <td>0.591483</td>\n",
       "      <td>1.433083</td>\n",
       "      <td>0.748189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       AXP        BA       CAT      CSCO       DIS  \\\n",
       "2007-01-31  1.191195 -2.114531 -1.026838 -0.916784 -0.751052 -0.506773   \n",
       "2007-02-28  1.313733 -2.238875 -1.299617 -1.130246 -0.508759 -0.709198   \n",
       "2007-03-30  1.375058 -2.326286 -1.353768 -1.216716 -0.333272 -0.774659   \n",
       "\n",
       "                  GS        HD       IBM      INTC  ...      MSFT       NKE  \\\n",
       "2007-01-31 -1.628058 -0.705773  0.864658 -0.299420  ... -0.660925  0.836333   \n",
       "2007-02-28 -0.945623 -0.459578  1.225769  0.037325  ... -0.321709  0.543867   \n",
       "2007-03-30 -0.902939 -0.196000  1.164891  0.383428  ... -0.191255  0.656849   \n",
       "\n",
       "                 PFE        PG       TRV       UNH       UTX        VZ  \\\n",
       "2007-01-31 -0.362300  0.598943  0.288326 -0.844820  0.035391  0.556831   \n",
       "2007-02-28 -0.377243  0.463607  0.285349 -1.393770 -0.086832  0.674459   \n",
       "2007-03-30 -0.416769  0.207057  0.347602 -1.555243 -0.149430  0.591483   \n",
       "\n",
       "                 WMT       XOM  \n",
       "2007-01-31  1.104908  1.260426  \n",
       "2007-02-28  1.319458  1.098286  \n",
       "2007-03-30  1.433083  0.748189  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
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       "      <th>INTC</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>2.534211</td>\n",
       "      <td>-44.273327</td>\n",
       "      <td>-28.872100</td>\n",
       "      <td>-27.313798</td>\n",
       "      <td>-24.967100</td>\n",
       "      <td>-21.508235</td>\n",
       "      <td>-37.385098</td>\n",
       "      <td>-24.325971</td>\n",
       "      <td>-2.089402</td>\n",
       "      <td>-18.572200</td>\n",
       "      <td>...</td>\n",
       "      <td>-23.690945</td>\n",
       "      <td>-2.490470</td>\n",
       "      <td>-19.462552</td>\n",
       "      <td>-5.851810</td>\n",
       "      <td>-10.249992</td>\n",
       "      <td>-26.294823</td>\n",
       "      <td>-13.831432</td>\n",
       "      <td>-6.448086</td>\n",
       "      <td>1.312419</td>\n",
       "      <td>3.514490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>2.740211</td>\n",
       "      <td>-51.440660</td>\n",
       "      <td>-37.116024</td>\n",
       "      <td>-34.532939</td>\n",
       "      <td>-25.054633</td>\n",
       "      <td>-28.111538</td>\n",
       "      <td>-31.717256</td>\n",
       "      <td>-24.304571</td>\n",
       "      <td>1.398669</td>\n",
       "      <td>-16.726302</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.201940</td>\n",
       "      <td>-9.001019</td>\n",
       "      <td>-23.048883</td>\n",
       "      <td>-10.225076</td>\n",
       "      <td>-12.943684</td>\n",
       "      <td>-38.551956</td>\n",
       "      <td>-18.619821</td>\n",
       "      <td>-7.009360</td>\n",
       "      <td>2.827520</td>\n",
       "      <td>-0.545573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-30</th>\n",
       "      <td>6.349848</td>\n",
       "      <td>-48.369905</td>\n",
       "      <td>-33.992436</td>\n",
       "      <td>-31.966290</td>\n",
       "      <td>-18.905675</td>\n",
       "      <td>-25.431027</td>\n",
       "      <td>-27.327490</td>\n",
       "      <td>-16.876278</td>\n",
       "      <td>3.242790</td>\n",
       "      <td>-8.310156</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.806135</td>\n",
       "      <td>-4.267969</td>\n",
       "      <td>-20.140067</td>\n",
       "      <td>-10.917579</td>\n",
       "      <td>-8.839800</td>\n",
       "      <td>-36.970997</td>\n",
       "      <td>-16.187793</td>\n",
       "      <td>-5.234328</td>\n",
       "      <td>7.207678</td>\n",
       "      <td>-2.917621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL        AXP         BA        CAT       CSCO        DIS  \\\n",
       "2007-01-31  2.534211 -44.273327 -28.872100 -27.313798 -24.967100 -21.508235   \n",
       "2007-02-28  2.740211 -51.440660 -37.116024 -34.532939 -25.054633 -28.111538   \n",
       "2007-03-30  6.349848 -48.369905 -33.992436 -31.966290 -18.905675 -25.431027   \n",
       "\n",
       "                   GS         HD       IBM       INTC  ...       MSFT  \\\n",
       "2007-01-31 -37.385098 -24.325971 -2.089402 -18.572200  ... -23.690945   \n",
       "2007-02-28 -31.717256 -24.304571  1.398669 -16.726302  ... -22.201940   \n",
       "2007-03-30 -27.327490 -16.876278  3.242790  -8.310156  ... -16.806135   \n",
       "\n",
       "                 NKE        PFE         PG        TRV        UNH        UTX  \\\n",
       "2007-01-31 -2.490470 -19.462552  -5.851810 -10.249992 -26.294823 -13.831432   \n",
       "2007-02-28 -9.001019 -23.048883 -10.225076 -12.943684 -38.551956 -18.619821   \n",
       "2007-03-30 -4.267969 -20.140067 -10.917579  -8.839800 -36.970997 -16.187793   \n",
       "\n",
       "                  VZ       WMT       XOM  \n",
       "2007-01-31 -6.448086  1.312419  3.514490  \n",
       "2007-02-28 -7.009360  2.827520 -0.545573  \n",
       "2007-03-30 -5.234328  7.207678 -2.917621  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('---------------------------------')\n",
    "print('Here are the relavant dataframes:')\n",
    "print('---------------------------------')\n",
    "pez  = pezscore\n",
    "rzs  = rzscore['2yr']\n",
    "rets = returns['2yr']\n",
    "\n",
    "pez.head(3)\n",
    "rzs.head(3)\n",
    "rets.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slice rets, to include just our test dates:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(36, 26)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>19.024056</td>\n",
       "      <td>-19.14999</td>\n",
       "      <td>0.570761</td>\n",
       "      <td>-14.975168</td>\n",
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       "      <td>16.425426</td>\n",
       "      <td>0.68199</td>\n",
       "      <td>30.155744</td>\n",
       "      <td>-12.931965</td>\n",
       "      <td>15.798559</td>\n",
       "      <td>...</td>\n",
       "      <td>23.35706</td>\n",
       "      <td>31.706794</td>\n",
       "      <td>3.644771</td>\n",
       "      <td>6.525605</td>\n",
       "      <td>17.211664</td>\n",
       "      <td>28.095757</td>\n",
       "      <td>-9.902165</td>\n",
       "      <td>6.414945</td>\n",
       "      <td>-3.026397</td>\n",
       "      <td>-4.867147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL       AXP        BA        CAT     CSCO        DIS  \\\n",
       "2014-01-31  19.024056 -19.14999  0.570761 -14.975168  7.67084  16.425426   \n",
       "\n",
       "                 GS         HD        IBM       INTC  ...      MSFT  \\\n",
       "2014-01-31  0.68199  30.155744 -12.931965  15.798559  ...  23.35706   \n",
       "\n",
       "                  NKE       PFE        PG        TRV        UNH       UTX  \\\n",
       "2014-01-31  31.706794  3.644771  6.525605  17.211664  28.095757 -9.902165   \n",
       "\n",
       "                  VZ       WMT       XOM  \n",
       "2014-01-31  6.414945 -3.026397 -4.867147  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>...</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UTX</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>18.611588</td>\n",
       "      <td>15.036131</td>\n",
       "      <td>46.619352</td>\n",
       "      <td>19.927184</td>\n",
       "      <td>22.992245</td>\n",
       "      <td>4.116718</td>\n",
       "      <td>-14.95721</td>\n",
       "      <td>15.681407</td>\n",
       "      <td>-12.934272</td>\n",
       "      <td>16.453154</td>\n",
       "      <td>...</td>\n",
       "      <td>29.86892</td>\n",
       "      <td>22.002736</td>\n",
       "      <td>19.352174</td>\n",
       "      <td>7.674002</td>\n",
       "      <td>1.292731</td>\n",
       "      <td>26.297727</td>\n",
       "      <td>1.094025</td>\n",
       "      <td>6.815337</td>\n",
       "      <td>18.600387</td>\n",
       "      <td>-9.149096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL        AXP         BA        CAT       CSCO       DIS  \\\n",
       "2016-12-30  18.611588  15.036131  46.619352  19.927184  22.992245  4.116718   \n",
       "\n",
       "                  GS         HD        IBM       INTC  ...      MSFT  \\\n",
       "2016-12-30 -14.95721  15.681407 -12.934272  16.453154  ...  29.86892   \n",
       "\n",
       "                  NKE        PFE        PG       TRV        UNH       UTX  \\\n",
       "2016-12-30  22.002736  19.352174  7.674002  1.292731  26.297727  1.094025   \n",
       "\n",
       "                  VZ        WMT       XOM  \n",
       "2016-12-30  6.815337  18.600387 -9.149096  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('slice rets, to include just our test dates:')\n",
    "rets = rets.loc['2014-01-31':'2016-12-31']\n",
    "rets.shape\n",
    "rets.head(1)\n",
    "rets.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.relativedelta import relativedelta as rdelta\n",
    "\n",
    "def trading_strategy(returns,strategy,pez=None,rzs=None,corrlen=None):\n",
    "    valid_strategies = ['buyall','lowpe','lpe_negcorr']\n",
    "    if strategy not in valid_strategies:\n",
    "        raise ValueError('strategy must be one of '+str(valid_strategies))\n",
    "    \n",
    "    if strategy == 'buyall':\n",
    "        return returns.mean()\n",
    "    \n",
    "    date  = returns.name\n",
    "    lowpe = pez.loc[date] < -0.65  # Low P/E arbitrarily defined as z-score < -0.65\n",
    "\n",
    "    if strategy == 'lowpe':\n",
    "        return returns[lowpe].mean()\n",
    "\n",
    "    if strategy != 'lpe_negcorr':\n",
    "        raise ValueError('Bad value for \"strategy\", must be one of '+str(valid_strategies))\n",
    "        \n",
    "    # Determine correlation date range\n",
    "    allowed_corrlen = [12,24,36,48,60]\n",
    "    if corrlen not in allowed_corrlen:\n",
    "        raise ValueError('corrlen must be one of '+str(allowed_corrlen))\n",
    "\n",
    "    # In the calculation of the correlation date range, below, the\n",
    "    # extra 3 days is to ensure we include, in our slice, those cases\n",
    "    # where the day of the month (24 months ago, or corrlen months ago)\n",
    "    # may be earlier than the day of the month for 'date' ... this can happen\n",
    "    # because, with our monthly data, the day of the month is *not* necessarily\n",
    "    # the last day of the month, but rather the last *trading day* of the\n",
    "    # month (which could be 2 or 3 days earlier if the month ends with a\n",
    "    # weekend and/or holiday).\n",
    "    \n",
    "    # end date of correlation range must be 24 months before 'date' (the date \n",
    "    # of the trade decision) so that date of the 'final price' (in the Returns \n",
    "    # used in the correlation) is before 'date' ... and since we are using\n",
    "    # 2-year returns, then the end date of the correlation range must be \n",
    "    # at least 24 months before 'date'.\n",
    "    \n",
    "    edate = date  - rdelta(months=24,days=3)         # end date for correlation.\n",
    "    sdate = edate - rdelta(months=(corrlen),days=3)  # start date for correlation.\n",
    "    \n",
    "    tpez = pez[sdate:edate]  # slice P/E z-scores for correlation\n",
    "    trzs = rzs[sdate:edate]  # slice Return z-scores for correlation\n",
    "    \n",
    "    # sanity check the slices:\n",
    "    if tpez.index[0] != trzs.index[0] or tpez.index[-1] != trzs.index[-1]:\n",
    "        sp0 = '  tpez.index[0]=' + str(tpez.index[0])\n",
    "        se0 = '  trzs.index[0]=' + str(trzs.index[0])\n",
    "        sp1 = ' tpez.index[-1]=' + str(tpez.index[-1])\n",
    "        se1 = ' trzs.index[-1]=' + str(trzs.index[-1])\n",
    "        raise RuntimeError('Slice Index Mismatch:' + sp0 + se0 + sp1 + se1)\n",
    "\n",
    "    corr = pd.Series(name='Correlations')\n",
    "    for stock in tpez.columns:\n",
    "        corr[stock] = tpez[stock].corr(trzs[stock])\n",
    "    \n",
    "    # This would work also: negcor_list = returns.index[ corr < -0.5 ].values\n",
    "    negcor_list = tpez.columns[ corr < -0.5 ].values\n",
    "    lowpe_list  = tpez.columns[ lowpe ].values\n",
    "    #print('negcor_list=',negcor_list)  # for testing/debugging\n",
    "    #print('lowpe_list=',lowpe_list)    # for testing/debugging\n",
    "    \n",
    "    # set has a method to give intersection of two sets (or a set and a list) ...\n",
    "    lpe_negcor = set(negcor_list).intersection(lowpe_list)\n",
    "    # note: for 60 month correlations we find that, for some months, lpe_negcor \n",
    "    #       is an empty set, in which case returns[lpe_negcor].mean() will be NaN.\n",
    "    #       that just means we should not invest anything those months.\n",
    "    \n",
    "    return returns[lpe_negcor].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.266845836012536"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buy All:\n",
    "rbuyall = rets.apply(trading_strategy,args=('buyall',),axis=1)\n",
    "rbuyall.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.221917046140948"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buy Low PE stocks:\n",
    "rlowpe = rets.apply(trading_strategy,args=('lowpe',pez),axis=1)\n",
    "rlowpe.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.664133356008648"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buy stocks with Negative Correlation and Low PE -- using 2-year (24 month) correlation period:\n",
    "rlpenc = rets.apply(trading_strategy,args=('lpe_negcorr',pez,rzs,24),axis=1)\n",
    "rlpenc.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "data": {
      "text/plain": [
       "12    13.390160\n",
       "24    15.664133\n",
       "36    17.081782\n",
       "48    12.145604\n",
       "60    11.431540\n",
       "Name: LPE_NegCorrMeans, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buy stocks with Negative Correlation and Low PE -- \n",
    "# test for multiple correlation periods (1yr, 2yr, 3yr, etc.)\n",
    "corrlengths = [12,24,36,48,60]\n",
    "data  = []\n",
    "for corrlen in corrlengths:\n",
    "    print('.',end='')\n",
    "    data.append(rets.apply(trading_strategy,args=('lpe_negcorr',pez,rzs,corrlen),axis=1))\n",
    "\n",
    "rlpenc = pd.Series(data,index=corrlengths,name='LPE_NegCorrs')\n",
    "\n",
    "# For each correlation period, take a look at the mean() Return for all 36 dates:\n",
    "means = [ rlpenc[corrlen].mean() for corrlen in rlpenc.index ]\n",
    "rlpenc_means = pd.Series(means,index=corrlengths,name='LPE_NegCorrMeans')\n",
    "rlpenc_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Example Date-by-Date Comparison of Trading Strategies <font size='4pt'>(for 6 of the 36 trade dates)</font>\n",
    "\n",
    "### Note: for this example, for the \"NegCorrLPE\" strategy, use a correlation period of 3 years (36 months)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_results = pd.concat([rbuyall,rlowpe,rlpenc.loc[36]],axis=1,keys=['BuyAll','LowPE','NegCorrLPE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>BuyAll</th>\n",
       "      <th>LowPE</th>\n",
       "      <th>NegCorrLPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>9.132910</td>\n",
       "      <td>2.771242</td>\n",
       "      <td>1.220396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-29</th>\n",
       "      <td>6.964242</td>\n",
       "      <td>4.612356</td>\n",
       "      <td>6.050321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>6.688258</td>\n",
       "      <td>1.921982</td>\n",
       "      <td>1.092266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>4.929743</td>\n",
       "      <td>1.933167</td>\n",
       "      <td>4.764726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-28</th>\n",
       "      <td>5.175219</td>\n",
       "      <td>7.619982</td>\n",
       "      <td>10.527229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>7.239969</td>\n",
       "      <td>9.986415</td>\n",
       "      <td>11.966802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              BuyAll     LowPE  NegCorrLPE\n",
       "2014-07-31  9.132910  2.771242    1.220396\n",
       "2014-08-29  6.964242  4.612356    6.050321\n",
       "2014-09-30  6.688258  1.921982    1.092266\n",
       "2014-10-31  4.929743  1.933167    4.764726\n",
       "2014-11-28  5.175219  7.619982   10.527229\n",
       "2014-12-31  7.239969  9.986415   11.966802"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_results['2014-07-30':'2014-12-31']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Above, we see <br>&nbsp;&nbsp;&nbsp;- for some trade dates we beat the market (i.e. NegCorrLPE beat BuyAll) <br>&nbsp;&nbsp;&nbsp;- but for other trade dates we didn't beat the market.\n",
    "## How did we do overall - what is the mean across all 36 trade dates?\n",
    "## At the same time let's compare the 5 different correlation periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>BuyAll</th>\n",
       "      <th>LowPE</th>\n",
       "      <th>NegCorrLPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Correlation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1yr</th>\n",
       "      <td>13.266846</td>\n",
       "      <td>14.221917</td>\n",
       "      <td>13.390160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2yr</th>\n",
       "      <td>13.266846</td>\n",
       "      <td>14.221917</td>\n",
       "      <td>15.664133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3yr</th>\n",
       "      <td>13.266846</td>\n",
       "      <td>14.221917</td>\n",
       "      <td>17.081782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4yr</th>\n",
       "      <td>13.266846</td>\n",
       "      <td>14.221917</td>\n",
       "      <td>12.145604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5yr</th>\n",
       "      <td>13.266846</td>\n",
       "      <td>14.221917</td>\n",
       "      <td>11.431540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BuyAll      LowPE  NegCorrLPE\n",
       "Correlation                                  \n",
       "1yr          13.266846  14.221917   13.390160\n",
       "2yr          13.266846  14.221917   15.664133\n",
       "3yr          13.266846  14.221917   17.081782\n",
       "4yr          13.266846  14.221917   12.145604\n",
       "5yr          13.266846  14.221917   11.431540"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = pd.Index(['1yr','2yr','3yr','4yr','5yr'],name='Correlation')\n",
    "# BuyAll and LowPE are the same regardless of Correlation period:\n",
    "data = { 'BuyAll': [rbuyall.mean()]*5, 'LowPE': [rlowpe.mean()]*5, 'NegCorrLPE': rlpenc_means.values }\n",
    "mean_results = pd.DataFrame(data,index=ix)\n",
    "mean_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note: Above we see that, on average, the NegCorrLPE strategy beats the market provided we use a correlation period of either 2 years or 3 years.\n",
    "---\n",
    "## What if we treat the 36 trade dates as 36 independent scenarios?<br>Then ...\n",
    "#### (Note: for the Negative Correlation Low P/E strategy we will use a correlation period of 3 years, since that is that case that did best above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Neg Corr Low PE</th>\n",
       "      <th>Low PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>At least 1% BETTER than Market</th>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Within 1% of Market</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>At least 1% WORSE than Market</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Neg Corr Low PE  Low PE\n",
       "At least 1% BETTER than Market               21      18\n",
       "Within 1% of Market                           5       1\n",
       "At least 1% WORSE than Market                10      17"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Yes, there is a typo in the video in the slide corresponding to the above dataframe:\n",
      " In the slide in the video, the middle row, 'Neg Corr Low PE' column says '4' (whereas it should be '5' as it is here).\n"
     ]
    }
   ],
   "source": [
    "rows = ['At least 1% BETTER than Market','Within 1% of Market','At least 1% WORSE than Market']\n",
    "cols = ['Neg Corr Low PE','Low PE']\n",
    "\n",
    "# create a dataframe with rows and columns, but all entries are initially NaN:\n",
    "scenarios = pd.DataFrame(index=rows,columns=cols)  \n",
    "\n",
    "# now set the various values in the dataframe:\n",
    "scenarios.loc['At least 1% BETTER than Market','Neg Corr Low PE'] = rbuyall[ rlpenc.loc[36] > rbuyall+1.0 ].count()\n",
    "scenarios.loc['At least 1% BETTER than Market','Low PE']          = rbuyall[ rlowpe         > rbuyall+1.0 ].count()\n",
    "\n",
    "scenarios.loc['At least 1% WORSE than Market','Neg Corr Low PE']  = rbuyall[ rlpenc.loc[36] < rbuyall-1.0 ].count()\n",
    "scenarios.loc['At least 1% WORSE than Market','Low PE']           = rbuyall[ rlowpe         < rbuyall-1.0 ].count()\n",
    "\n",
    "# Note: .sum() on each scenario column works here ONLY because the\n",
    "# middle row is still NaN at this point, and so does not contribute to the sum() \n",
    "# Find the 'Within 1%' case by subtracting the number of scenarios in the other cases from 36 total scenarios:\n",
    "scenarios.loc['Within 1% of Market','Neg Corr Low PE']  = 36 - scenarios['Neg Corr Low PE'].sum()\n",
    "scenarios.loc['Within 1% of Market','Low PE']           = 36 - scenarios['Low PE'].sum()\n",
    "\n",
    "scenarios\n",
    "print(\"\\nYes, there is a typo in the video in the slide corresponding to the above dataframe:\\n\",\n",
    "      \"In the slide in the video, the middle row, 'Neg Corr Low PE' column says '4' (whereas it should be '5' as it is here).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- font-style:italic; -->\n",
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:powderblue;line-height:150%;\">\n",
    "    <p>&nbsp;</p>\n",
    "    <h1 style=\"text-indent:3%;\">What Next ??</h1>\n",
    "    <h3 style=\"text-indent:3%;margin-left:1.5em;padding: 0 3em 1em 0;line-height:120%;\">The ultimate test of any Trading Strategy Analysis is to use the strategy to actually trade, and see how the strategy performs with real money in real time.  That said, one typically may want do additional analysis and testing (compared with what was done above) in order to increase confidence before putting actual money at risk.  The choice is yours.  In this section I discuss some additional analysis and testing one may want to do.  But first, I repeat what was stated above: </h3>\n",
    "    <p style=\"text-indent:3%;margin-left:1.5em;padding: 0 3em 1em 0;line-height:110%;\">This content, and any presentation thereof, is for informational purposes only.  Any such information, or its associated materials (including but not limited to code, slides, documentation, videos, social media posts, etc.) should not in any way be construed as investment, financial, or other advice. Nothing contained in this presentation or its materials constitutes a solicitation, recommendation, endorsement of, or offer to buy or sell any securities or other financial instruments in this or in in any other jurisdiction.</p>\n",
    "    <p style=\"line-height:70%;\">&nbsp;</p>\n",
    "<div style=\"font-size:120%;text-indent:3%;margin-left:1.5em;padding: 0 3em 1em 0;line-height:125%;\">\n",
    "    <p>Before discussing specific examples of analysis (that we may want to do to increase confidence concerning the positive results that we determined above) I will mention what is typically the last and final test before putting actual money at risk, and that is \"<b><i>Paper Trading</i></b>\".</p>\n",
    "    <p>In <i>Paper Trading</i> we do the same analysis, that our research showed us performs well in the past, only now we do it using data as close to present day as possible, and we make a trade decision exactly as we would if we were actually trading today.  Only instead of actually trading we simply write the trade information down <i>on paper</i> (or in a spreadsheet, etc) as we would normally do to keep track of a real investment.  Then, when our strategy tells us to sell, we again record the information \"on paper\" and we calculate our return on investment to see whether or not our strategy worked.</p>\n",
    "    <p>Our research above showed good results (beating the market by almost 4%) when buying Low P/E stocks that showed \"significantly\" negative correlation between P/E and <i>2-Year Returns</i> for a correlation period of 3 years.  Since we are dealing with 2-year investments, paper-trading this strategy (or actual trading this strategy) would take at least two years to see results.  And if we wanted to paper trade for a several months to gain additional confidence, that would only add to the two years.  (I'm not sure if I would have the patience for that; perhaps I would, depending on how much money I planned to risk.  That said, if we were dealing with shorter, say 3 month, returns, then it might be a good idea to paper trade for 6 months or so, to be more confident that the strategy is working, before putting real money at risk.)</p>\n",
    "    <p>Before paper trading however, there are a number of tests we could run to provide additional confidence in our strategy.  Even if one were to choose to dispense with the paper trading (but perhaps risk a smaller amount of money at first), additional testing would be prudent.</p>\n",
    "    <p>The first and most glaring issue, with the testing we did above, is this: For each of the 36 months that we tested, investing in all 26 stocks for 2 years produced a positive return on investment.  This means that our conclusion (that a Low-P/E-NegCorrelation strategy beats the market) <i>may only apply in a rising market.</i>  What hapens in a falling market?  It's always a good idea to test a strategy in both rising and falling markets.  Ideally we want to see that our strategy, in a falling market, <i>at least</i> does not fall as much as the overall market.</p>\n",
    "        <p>For example, if the overall market falls 10%, but our strategy falls only 6%, that's good (especially if the same strategy also can earn 17% when the market only earns 13%, as we saw in our analysis).  In the data for which we calculated 2-year returns (Jan 2007 through Dec 2016), the 13 months from Jan 2007 through Jan 2008 all show negative market returns.  This can be see with the following code:</p>\n",
    "<pre><br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;r = returns['2yr'].mean(axis=1)\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;r[ r &lt; -0.1 ]\n",
    "<br></pre>\n",
    "    <p>The above code is actually run in the cell below this one.  For now, I will leave it as an exercise for the reader to run the analysis for the for the 13 months from Jan 2007 through Jan 2008.  Note that running the analysis for Jan 2007 will require data going back 5 years to Jan 2002 (for 3 years of correlations with 2-year returns).  There is enough data in the original dataframe that we read from the file, however we front-truncated the dataframe, discarding everything prior to Jan 2007, so you would have to go back to that point in the notebook and keep an additional 60 months of data.  I will consider any pull requests if someone wants to submit the work here.  I would prefer that you submit it as a separate notebook (rather than additions to this one) but either way I'd love to take a look and see the result.</p>\n",
    "    <p>So that's the most important additional test that I would like to see: checking whether the Low-P/E-NegCorr strategy also works in a down market.  Other tests that may increase confidence include:\n",
    "    <ul>\n",
    "        <li>tweaking the definitions of Low-P/E (z_score &lt; -0.65) and Negative Correlation (correlation &lt; -0.5)</li>\n",
    "        <li>using an index with a larger number of stocks (for example the S&amp;P 500)</li>\n",
    "        <li>using weekly and/or daily pricing data (instead of monthly).  (note that earnings are still typically reported only quarterly, so fluctuations in Price/Earnings would be largely due to changes in Price, and might be most inaccurate shortly before a new release of earnings data).</li>\n",
    "    </ul>\n",
    "    <p>Finally, I'd like to comment on a question that I got after giving this presentation at PyLondinium.  It was a very good question, and I don't feel that in the moment I gave a complete enough answer.  The question was this:\n",
    "    <p><i>\"You started with a hypothesis, and then you refined it on the basis of how it matched the data.  And so you might continue refining it.  But then, would you be in danger of simply making your hypothesis fit the data that you happen to have, and not actually fit the mechanics or what's really underlying, what's making those numbers come out as they are?  You're just making your data fit the pattern so you think that you've got a perfect match.\"</i></p>\n",
    "    <p>So the inadequate answer that I gave was <i>\"Yes and No ... Yes, in the sense that you're fitting it, but if it works, it works.\"</i></p>\n",
    "    <p>What I left out was that the <i>\"if it works, it works\"</i> part <i>applies to the actual trading (or paper trading) that you do afterwards.</i></p>\n",
    "        <p>In other words, yes we refine and tweak the hypothesis to get it to perform better with the historical data; refining it through multiple periods of historical data, and particularly in both up and down markets.  But the ultimate test is taking the refined hypothesis, the refined strategy, and applying it to the present day.  And <i>if it works</i> in the present day, beating the market today, <i>then it works!</i> and it doesn't matter if we were \"fitting our hypothesis to the data\" for the historical data.  Ultimately we are going then to take that hypothesis and use it as a trading stategy today.  At that point we are done refining the hypothesis (unless we find it doesn't work with current data, in which case we want to try to figure out why).\n",
    "</div>\n",
    "    <h4>&nbsp;</h4>\n",
    "</div>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007-01-31   -14.332559\n",
       "2007-02-28   -17.295546\n",
       "2007-03-30   -13.978664\n",
       "2007-04-30   -12.822247\n",
       "2007-05-31   -12.721188\n",
       "2007-06-29   -12.108473\n",
       "2007-07-31    -7.886746\n",
       "2007-08-31    -7.024410\n",
       "2007-09-28    -7.460291\n",
       "2007-10-31    -9.373244\n",
       "2007-11-30    -5.152775\n",
       "2007-12-31    -4.288389\n",
       "2008-01-31    -2.493432\n",
       "2008-05-30    -1.963287\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = returns['2yr'].mean(axis=1)\n",
    "r[ r < -0.1 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:2px;border-style:solid;border-color:black;background-color:lightgreen;font-style:italic;line-height:150%;\">\n",
    "\n",
    "<br>&nbsp;&nbsp;&nbsp;Copyright 2019 Daniel Goldfarb\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License.\n",
    "<br>&nbsp;\n",
    "</div>"
   ]
  }
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