FourAsstPort.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>GOOGL</th>\n",
       "      <th>FB</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>AMZN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>549.489990</td>\n",
       "      <td>81.669998</td>\n",
       "      <td>59.017143</td>\n",
       "      <td>370.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02</th>\n",
       "      <td>541.309998</td>\n",
       "      <td>81.559998</td>\n",
       "      <td>59.154285</td>\n",
       "      <td>372.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-06</th>\n",
       "      <td>543.950012</td>\n",
       "      <td>82.440002</td>\n",
       "      <td>60.330002</td>\n",
       "      <td>377.040009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-07</th>\n",
       "      <td>544.859985</td>\n",
       "      <td>82.320000</td>\n",
       "      <td>60.494286</td>\n",
       "      <td>374.410004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GOOGL         FB       NFLX        AMZN\n",
       "Date                                                    \n",
       "2015-04-01  549.489990  81.669998  59.017143  370.260010\n",
       "2015-04-02  541.309998  81.559998  59.154285  372.250000\n",
       "2015-04-06  543.950012  82.440002  60.330002  377.040009\n",
       "2015-04-07  544.859985  82.320000  60.494286  374.410004"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "assets = pd.read_csv('/home/riverstone/FANG_Port.csv',header='infer',index_col='Date')\n",
    "assets.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4f45bdc88>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4f45bdc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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8RpTFRMbobGylZX7BYk9qHlVxKQH5f33iWI7JD0wXBycJFkL0Ut3bbwcdbtpk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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4c80b8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in assets.columns.values:\n",
    "    plt.plot(assets.index,assets[c], lw=3, alpha=0.8, label=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe4c5943f60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4c80b8dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.legend(loc='upper left',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'daily returns')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fZuqLoXZvTFJJFu2jkaP0RZJf6L43HkjyR/Nd43wZ4Wfk+UnuSHJP93Ny/kLU2bck1yfZ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\nQSFJajIoJElNBoUkqen/A+q4T0X8JL8qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcec7a1b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('price in $')\n",
    "plt.legend(loc='upper right', fontsize=12)\n",
    "plt.xlabel('daily returns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log_returns = np.log((assets/assets.shift(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>GOOGL</th>\n",
       "      <th>FB</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>AMZN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02</th>\n",
       "      <td>-0.014998</td>\n",
       "      <td>-0.001348</td>\n",
       "      <td>0.002321</td>\n",
       "      <td>0.005360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-06</th>\n",
       "      <td>0.004865</td>\n",
       "      <td>0.010732</td>\n",
       "      <td>0.019680</td>\n",
       "      <td>0.012786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-07</th>\n",
       "      <td>0.001672</td>\n",
       "      <td>-0.001457</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>-0.007000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-08</th>\n",
       "      <td>0.007278</td>\n",
       "      <td>-0.000486</td>\n",
       "      <td>0.041379</td>\n",
       "      <td>0.017973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-09</th>\n",
       "      <td>-0.001495</td>\n",
       "      <td>-0.001338</td>\n",
       "      <td>-0.004200</td>\n",
       "      <td>0.006120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-10</th>\n",
       "      <td>0.000948</td>\n",
       "      <td>-0.001583</td>\n",
       "      <td>0.033714</td>\n",
       "      <td>-0.002323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-13</th>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.011754</td>\n",
       "      <td>0.043289</td>\n",
       "      <td>-0.000758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-14</th>\n",
       "      <td>-0.016281</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.008454</td>\n",
       "      <td>0.007166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>0.002331</td>\n",
       "      <td>-0.009746</td>\n",
       "      <td>-0.006812</td>\n",
       "      <td>-0.004320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-16</th>\n",
       "      <td>0.004573</td>\n",
       "      <td>-0.004848</td>\n",
       "      <td>0.167308</td>\n",
       "      <td>0.006732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>-0.020033</td>\n",
       "      <td>-0.018763</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>-0.027523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td>0.021890</td>\n",
       "      <td>0.028195</td>\n",
       "      <td>-0.007305</td>\n",
       "      <td>0.036471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-21</th>\n",
       "      <td>-0.002961</td>\n",
       "      <td>0.006358</td>\n",
       "      <td>-0.012325</td>\n",
       "      <td>0.004278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-22</th>\n",
       "      <td>0.011464</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>-0.004937</td>\n",
       "      <td>-0.003534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-23</th>\n",
       "      <td>0.014965</td>\n",
       "      <td>-0.026582</td>\n",
       "      <td>0.002471</td>\n",
       "      <td>0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-24</th>\n",
       "      <td>0.028646</td>\n",
       "      <td>-0.010736</td>\n",
       "      <td>-0.001181</td>\n",
       "      <td>0.132178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-27</th>\n",
       "      <td>-0.013231</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.013660</td>\n",
       "      <td>-0.014802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-28</th>\n",
       "      <td>-0.003096</td>\n",
       "      <td>-0.015130</td>\n",
       "      <td>-0.005349</td>\n",
       "      <td>-0.021317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-29</th>\n",
       "      <td>-0.005294</td>\n",
       "      <td>-0.002606</td>\n",
       "      <td>-0.000373</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>-0.022736</td>\n",
       "      <td>-0.021352</td>\n",
       "      <td>-0.011346</td>\n",
       "      <td>-0.017835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0.004346</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.000952</td>\n",
       "      <td>0.002581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-04</th>\n",
       "      <td>0.003044</td>\n",
       "      <td>-0.002281</td>\n",
       "      <td>-0.003831</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-05</th>\n",
       "      <td>-0.017886</td>\n",
       "      <td>-0.015988</td>\n",
       "      <td>0.019011</td>\n",
       "      <td>-0.004383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-06</th>\n",
       "      <td>-0.014767</td>\n",
       "      <td>0.006938</td>\n",
       "      <td>-0.008898</td>\n",
       "      <td>-0.004974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-07</th>\n",
       "      <td>0.012923</td>\n",
       "      <td>0.004216</td>\n",
       "      <td>0.008350</td>\n",
       "      <td>0.018393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-08</th>\n",
       "      <td>0.012668</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.016424</td>\n",
       "      <td>0.015827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-11</th>\n",
       "      <td>-0.005791</td>\n",
       "      <td>-0.006389</td>\n",
       "      <td>0.026364</td>\n",
       "      <td>-0.001939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-12</th>\n",
       "      <td>-0.013002</td>\n",
       "      <td>-0.007075</td>\n",
       "      <td>-0.010753</td>\n",
       "      <td>-0.004237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-13</th>\n",
       "      <td>0.001410</td>\n",
       "      <td>0.012572</td>\n",
       "      <td>-0.006067</td>\n",
       "      <td>-0.009675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>0.017246</td>\n",
       "      <td>0.002448</td>\n",
       "      <td>0.052257</td>\n",
       "      <td>0.007354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>0.003786</td>\n",
       "      <td>-0.014553</td>\n",
       "      <td>-0.006264</td>\n",
       "      <td>-0.008982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>0.007358</td>\n",
       "      <td>-0.007641</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.013480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-21</th>\n",
       "      <td>0.009164</td>\n",
       "      <td>0.010737</td>\n",
       "      <td>0.008900</td>\n",
       "      <td>0.009874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <td>-0.003463</td>\n",
       "      <td>0.006052</td>\n",
       "      <td>-0.010372</td>\n",
       "      <td>0.001631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <td>0.016256</td>\n",
       "      <td>0.023740</td>\n",
       "      <td>0.027622</td>\n",
       "      <td>0.009821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-26</th>\n",
       "      <td>0.013743</td>\n",
       "      <td>0.008908</td>\n",
       "      <td>0.028377</td>\n",
       "      <td>0.014527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-27</th>\n",
       "      <td>-0.023166</td>\n",
       "      <td>-0.018942</td>\n",
       "      <td>-0.012142</td>\n",
       "      <td>-0.006572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>-0.012235</td>\n",
       "      <td>-0.017456</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>-0.029892</td>\n",
       "      <td>-0.013437</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.012642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-02</th>\n",
       "      <td>0.011811</td>\n",
       "      <td>0.003857</td>\n",
       "      <td>0.036051</td>\n",
       "      <td>0.004543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <td>0.009748</td>\n",
       "      <td>0.021176</td>\n",
       "      <td>0.045296</td>\n",
       "      <td>0.015451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-06</th>\n",
       "      <td>0.005593</td>\n",
       "      <td>-0.003443</td>\n",
       "      <td>0.031929</td>\n",
       "      <td>0.009166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-07</th>\n",
       "      <td>0.012762</td>\n",
       "      <td>0.021625</td>\n",
       "      <td>-0.012562</td>\n",
       "      <td>0.004775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>0.012779</td>\n",
       "      <td>-0.007485</td>\n",
       "      <td>-0.013038</td>\n",
       "      <td>0.004430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-09</th>\n",
       "      <td>0.027475</td>\n",
       "      <td>0.015725</td>\n",
       "      <td>0.044545</td>\n",
       "      <td>0.017268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-12</th>\n",
       "      <td>0.004375</td>\n",
       "      <td>-0.002541</td>\n",
       "      <td>-0.031072</td>\n",
       "      <td>0.012275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-13</th>\n",
       "      <td>-0.022570</td>\n",
       "      <td>-0.015711</td>\n",
       "      <td>-0.017013</td>\n",
       "      <td>-0.006408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <td>0.007847</td>\n",
       "      <td>0.012621</td>\n",
       "      <td>0.017791</td>\n",
       "      <td>0.001774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-15</th>\n",
       "      <td>0.001496</td>\n",
       "      <td>-0.001793</td>\n",
       "      <td>-0.001432</td>\n",
       "      <td>-0.005471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-16</th>\n",
       "      <td>-0.014171</td>\n",
       "      <td>0.006668</td>\n",
       "      <td>-0.008256</td>\n",
       "      <td>-0.006747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-19</th>\n",
       "      <td>-0.030748</td>\n",
       "      <td>-0.070097</td>\n",
       "      <td>-0.015730</td>\n",
       "      <td>-0.017167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20</th>\n",
       "      <td>-0.003889</td>\n",
       "      <td>-0.025889</td>\n",
       "      <td>0.012742</td>\n",
       "      <td>0.026558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>-0.001644</td>\n",
       "      <td>0.007347</td>\n",
       "      <td>-0.003218</td>\n",
       "      <td>-0.002935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>-0.038055</td>\n",
       "      <td>-0.026925</td>\n",
       "      <td>-0.031390</td>\n",
       "      <td>-0.023629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>-0.025582</td>\n",
       "      <td>-0.033925</td>\n",
       "      <td>-0.018959</td>\n",
       "      <td>-0.032471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>0.026474</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>0.062503</td>\n",
       "      <td>0.039528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>-0.045762</td>\n",
       "      <td>-0.050222</td>\n",
       "      <td>-0.063334</td>\n",
       "      <td>-0.038532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-28</th>\n",
       "      <td>-0.001749</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>-0.050893</td>\n",
       "      <td>-0.044830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>0.031300</td>\n",
       "      <td>0.043226</td>\n",
       "      <td>0.032974</td>\n",
       "      <td>0.011060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               GOOGL        FB      NFLX      AMZN\n",
       "Date                                              \n",
       "2015-04-01       NaN       NaN       NaN       NaN\n",
       "2015-04-02 -0.014998 -0.001348  0.002321  0.005360\n",
       "2015-04-06  0.004865  0.010732  0.019680  0.012786\n",
       "2015-04-07  0.001672 -0.001457  0.002719 -0.007000\n",
       "2015-04-08  0.007278 -0.000486  0.041379  0.017973\n",
       "2015-04-09 -0.001495 -0.001338 -0.004200  0.006120\n",
       "2015-04-10  0.000948 -0.001583  0.033714 -0.002323\n",
       "2015-04-13  0.000182  0.011754  0.043289 -0.000758\n",
       "2015-04-14 -0.016281  0.006125  0.008454  0.007166\n",
       "2015-04-15  0.002331 -0.009746 -0.006812 -0.004320\n",
       "2015-04-16  0.004573 -0.004848  0.167308  0.006732\n",
       "2015-04-17 -0.020033 -0.018763  0.016761 -0.027523\n",
       "2015-04-20  0.021890  0.028195 -0.007305  0.036471\n",
       "2015-04-21 -0.002961  0.006358 -0.012325  0.004278\n",
       "2015-04-22  0.011464  0.012006 -0.004937 -0.003534\n",
       "2015-04-23  0.014965 -0.026582  0.002471  0.000487\n",
       "2015-04-24  0.028646 -0.010736 -0.001181  0.132178\n",
       "2015-04-27 -0.013231  0.004650  0.013660 -0.014802\n",
       "2015-04-28 -0.003096 -0.015130 -0.005349 -0.021317\n",
       "2015-04-29 -0.005294 -0.002606 -0.000373  0.000140\n",
       "2015-04-30 -0.022736 -0.021352 -0.011346 -0.017835\n",
       "2015-05-01  0.004346  0.002789  0.000952  0.002581\n",
       "2015-05-04  0.003044 -0.002281 -0.003831  0.000402\n",
       "2015-05-05 -0.017886 -0.015988  0.019011 -0.004383\n",
       "2015-05-06 -0.014767  0.006938 -0.008898 -0.004974\n",
       "2015-05-07  0.012923  0.004216  0.008350  0.018393\n",
       "2015-05-08  0.012668  0.001020  0.016424  0.015827\n",
       "2015-05-11 -0.005791 -0.006389  0.026364 -0.001939\n",
       "2015-05-12 -0.013002 -0.007075 -0.010753 -0.004237\n",
       "2015-05-13  0.001410  0.012572 -0.006067 -0.009675\n",
       "...              ...       ...       ...       ...\n",
       "2018-02-15  0.017246  0.002448  0.052257  0.007354\n",
       "2018-02-16  0.003786 -0.014553 -0.006264 -0.008982\n",
       "2018-02-20  0.007358 -0.007641  0.000108  0.013480\n",
       "2018-02-21  0.009164  0.010737  0.008900  0.009874\n",
       "2018-02-22 -0.003463  0.006052 -0.010372  0.001631\n",
       "2018-02-23  0.016256  0.023740  0.027622  0.009821\n",
       "2018-02-26  0.013743  0.008908  0.028377  0.014527\n",
       "2018-02-27 -0.023166 -0.018942 -0.012142 -0.006572\n",
       "2018-02-28 -0.012235 -0.017456  0.002646  0.000311\n",
       "2018-03-01 -0.029892 -0.013437 -0.003403 -0.012642\n",
       "2018-03-02  0.011811  0.003857  0.036051  0.004543\n",
       "2018-03-05  0.009748  0.021176  0.045296  0.015451\n",
       "2018-03-06  0.005593 -0.003443  0.031929  0.009166\n",
       "2018-03-07  0.012762  0.021625 -0.012562  0.004775\n",
       "2018-03-08  0.012779 -0.007485 -0.013038  0.004430\n",
       "2018-03-09  0.027475  0.015725  0.044545  0.017268\n",
       "2018-03-12  0.004375 -0.002541 -0.031072  0.012275\n",
       "2018-03-13 -0.022570 -0.015711 -0.017013 -0.006408\n",
       "2018-03-14  0.007847  0.012621  0.017791  0.001774\n",
       "2018-03-15  0.001496 -0.001793 -0.001432 -0.005471\n",
       "2018-03-16 -0.014171  0.006668 -0.008256 -0.006747\n",
       "2018-03-19 -0.030748 -0.070097 -0.015730 -0.017167\n",
       "2018-03-20 -0.003889 -0.025889  0.012742  0.026558\n",
       "2018-03-21 -0.001644  0.007347 -0.003218 -0.002935\n",
       "2018-03-22 -0.038055 -0.026925 -0.031390 -0.023629\n",
       "2018-03-23 -0.025582 -0.033925 -0.018959 -0.032471\n",
       "2018-03-26  0.026474  0.004195  0.062503  0.039528\n",
       "2018-03-27 -0.045762 -0.050222 -0.063334 -0.038532\n",
       "2018-03-28 -0.001749  0.005307 -0.050893 -0.044830\n",
       "2018-03-29  0.031300  0.043226  0.032974  0.011060\n",
       "\n",
       "[755 rows x 4 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GOOGL    0.212306\n",
       "FB       0.224318\n",
       "NFLX     0.538202\n",
       "AMZN     0.455631\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.mean()*252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>FB</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>AMZN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GOOGL</th>\n",
       "      <td>0.051703</td>\n",
       "      <td>0.037923</td>\n",
       "      <td>0.040084</td>\n",
       "      <td>0.037761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>0.037923</td>\n",
       "      <td>0.063865</td>\n",
       "      <td>0.041359</td>\n",
       "      <td>0.041199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFLX</th>\n",
       "      <td>0.040084</td>\n",
       "      <td>0.041359</td>\n",
       "      <td>0.174764</td>\n",
       "      <td>0.048329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.037761</td>\n",
       "      <td>0.041199</td>\n",
       "      <td>0.048329</td>\n",
       "      <td>0.077447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GOOGL        FB      NFLX      AMZN\n",
       "GOOGL  0.051703  0.037923  0.040084  0.037761\n",
       "FB     0.037923  0.063865  0.041359  0.041199\n",
       "NFLX   0.040084  0.041359  0.174764  0.048329\n",
       "AMZN   0.037761  0.041199  0.048329  0.077447"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.cov()*252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>FB</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>AMZN</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GOOGL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659948</td>\n",
       "      <td>0.421682</td>\n",
       "      <td>0.596727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>0.659948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.391481</td>\n",
       "      <td>0.585806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFLX</th>\n",
       "      <td>0.421682</td>\n",
       "      <td>0.391481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.415408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.596727</td>\n",
       "      <td>0.585806</td>\n",
       "      <td>0.415408</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GOOGL        FB      NFLX      AMZN\n",
       "GOOGL  1.000000  0.659948  0.421682  0.596727\n",
       "FB     0.659948  1.000000  0.391481  0.585806\n",
       "NFLX   0.421682  0.391481  1.000000  0.415408\n",
       "AMZN   0.596727  0.585806  0.415408  1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_assets = len(assets.columns)\n",
    "number_assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights = np.random.random(number_assets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights /= np.sum(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30007786, 0.27718373, 0.21946655, 0.20327186])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[0]+weights[1]+weights[2]+weights[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expected Portfolio Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33661970647923983"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(weights * log_returns.mean())*252"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expected Portfolio Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05136148196311498"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(weights.T,np.dot(log_returns.cov()*252,weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expected Portfolio Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2266307171658665"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.dot(weights.T,np.dot(log_returns.cov()*252,weights)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  0.25084912214852995,\n",
       "  0.25923912009383576,\n",
       "  0.22516041337677342,\n",
       "  0.23377341246393604,\n",
       "  0.2753416533949231,\n",
       "  0.2548903838513215,\n",
       "  0.21471777033059955,\n",
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       "  0.22534613011539487,\n",
       "  0.24407043232412845,\n",
       "  0.2175808878499382])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_returns = []\n",
    "port_volatilities = []\n",
    "\n",
    "for x in range(1000):\n",
    "    weights = np.random.random(number_assets)\n",
    "    weights /= np.sum(weights)\n",
    "    port_returns.append(np.sum(weights*log_returns.mean())*252)\n",
    "    port_volatilities.append(np.sqrt(np.dot(weights.T,np.dot(log_returns.cov()*252, weights))))\n",
    "    \n",
    "    \n",
    "port_returns, port_volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "port_returns = np.array(port_returns)\n",
    "port_volatilities = np.array(port_volatilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0.31820954, 0.40221444, 0.41185807, 0.41468146, 0.38971355,\n",
       "        0.38779547, 0.31277163, 0.41738228, 0.31967675, 0.36791266,\n",
       "        0.29400842, 0.30883224, 0.34614817, 0.34059079, 0.32248602,\n",
       "        0.38793603, 0.37443061, 0.44966417, 0.40678126, 0.29551342,\n",
       "        0.327065  , 0.41563429, 0.37474931, 0.31868614, 0.33876016,\n",
       "        0.45229241, 0.34453464, 0.36926937, 0.37174496, 0.36679649,\n",
       "        0.3654531 , 0.38685964, 0.30785805, 0.38740068, 0.33387136,\n",
       "        0.3151234 , 0.41739135, 0.37695979, 0.34838876, 0.38846507,\n",
       "        0.36385337, 0.25328355, 0.39859639, 0.38633885, 0.38454311,\n",
       "        0.40339716, 0.28762406, 0.34271303, 0.35454534, 0.30062934,\n",
       "        0.36907802, 0.39352547, 0.37134365, 0.32734579, 0.36593759,\n",
       "        0.34396323, 0.30488578, 0.3209764 , 0.31180007, 0.27985829,\n",
       "        0.40548757, 0.40854856, 0.31214024, 0.3252276 , 0.37492109,\n",
       "        0.33077895, 0.35610223, 0.36281759, 0.41288653, 0.35913343,\n",
       "        0.32269562, 0.31341114, 0.32326322, 0.40066029, 0.3878422 ,\n",
       "        0.36375366, 0.35229361, 0.34731214, 0.38338123, 0.38468546,\n",
       "        0.34744313, 0.3508891 , 0.32374682, 0.39922815, 0.36569996,\n",
       "        0.2922637 , 0.32432681, 0.44105664, 0.3028087 , 0.32475863,\n",
       "        0.38895166, 0.31713897, 0.45040105, 0.32163938, 0.34623994,\n",
       "        0.46728707, 0.31652288, 0.34886847, 0.3471408 , 0.35013887,\n",
       "        0.39073719, 0.35588744, 0.34244088, 0.38188129, 0.39760027,\n",
       "        0.39374355, 0.28388788, 0.36252615, 0.32304355, 0.3559929 ,\n",
       "        0.40770394, 0.43883493, 0.37919106, 0.43802515, 0.31719478,\n",
       "        0.26928336, 0.39492062, 0.46409457, 0.37950018, 0.28860808,\n",
       "        0.39619454, 0.36767496, 0.45201062, 0.33431829, 0.35303803,\n",
       "        0.39676074, 0.31009196, 0.33033903, 0.26809042, 0.38837787,\n",
       "        0.29560523, 0.42313476, 0.42373817, 0.37967948, 0.34403826,\n",
       "        0.47394903, 0.45886523, 0.44756874, 0.30598965, 0.29878743,\n",
       "        0.344118  , 0.30015107, 0.36949891, 0.35982688, 0.37687379,\n",
       "        0.29921847, 0.35485052, 0.32626895, 0.34548309, 0.42234029,\n",
       "        0.32688209, 0.25680517, 0.31513305, 0.3929862 , 0.353772  ,\n",
       "        0.36353814, 0.40263158, 0.34103498, 0.31227152, 0.3721012 ,\n",
       "        0.30915756, 0.34601471, 0.40627089, 0.4108635 , 0.35953939,\n",
       "        0.29148548, 0.33225939, 0.41582608, 0.37219741, 0.38159274,\n",
       "        0.36886797, 0.3798901 , 0.36451545, 0.36896073, 0.31835531,\n",
       "        0.36579249, 0.36304191, 0.31151124, 0.37829372, 0.35247604,\n",
       "        0.29061105, 0.34594154, 0.41094659, 0.37209885, 0.45334952,\n",
       "        0.38062128, 0.38518569, 0.3247552 , 0.34933932, 0.43929346,\n",
       "        0.35843449, 0.40965385, 0.40467787, 0.34821571, 0.2780736 ,\n",
       "        0.34882775, 0.37396768, 0.33713103, 0.3407107 , 0.34824677,\n",
       "        0.34211586, 0.39653993, 0.34473519, 0.36143338, 0.38286584,\n",
       "        0.38407473, 0.34055994, 0.36392533, 0.37487485, 0.32810326,\n",
       "        0.3521718 , 0.41011184, 0.33604083, 0.30268469, 0.40292019,\n",
       "        0.37347032, 0.33871621, 0.40972325, 0.30792735, 0.33747125,\n",
       "        0.36112554, 0.36229384, 0.36935375, 0.28600471, 0.40628915,\n",
       "        0.30043332, 0.38303532, 0.39526582, 0.32993918, 0.40524424,\n",
       "        0.33828868, 0.33277385, 0.31572187, 0.29674048, 0.34201324,\n",
       "        0.28328476, 0.31418709, 0.36653436, 0.49954374, 0.2895597 ,\n",
       "        0.4399255 , 0.31548042, 0.43232343, 0.37307399, 0.36375713,\n",
       "        0.45015495, 0.34419242, 0.36089389, 0.35002379, 0.40747912,\n",
       "        0.38704402, 0.39172309, 0.32266359, 0.3460838 , 0.32176617,\n",
       "        0.45817343, 0.38241235, 0.38606709, 0.27635284, 0.39581185,\n",
       "        0.45412237, 0.33877314, 0.2997628 , 0.40203293, 0.35216405,\n",
       "        0.36346201, 0.41747963, 0.44201549, 0.35271162, 0.31140249,\n",
       "        0.36247898, 0.36854791, 0.40585852, 0.32747137, 0.33337505,\n",
       "        0.33469665, 0.29079351, 0.35157542, 0.3504559 , 0.34840204,\n",
       "        0.40610878, 0.39445494, 0.242782  , 0.47767389, 0.39694561,\n",
       "        0.3961903 , 0.34671177, 0.33618436, 0.35573863, 0.34695947,\n",
       "        0.35634216, 0.25809239, 0.33100333, 0.37284066, 0.34642264,\n",
       "        0.33051225, 0.338457  , 0.40581703, 0.34512112, 0.31233324,\n",
       "        0.34662817, 0.44539247, 0.24477451, 0.31853685, 0.35210388,\n",
       "        0.30637048, 0.37456412, 0.45350635, 0.35226783, 0.36029668,\n",
       "        0.33388959, 0.36340797, 0.37122464, 0.3156361 , 0.39592139,\n",
       "        0.28062516, 0.47218757, 0.32619319, 0.33310539, 0.43012488,\n",
       "        0.32720898, 0.29835831, 0.42191677, 0.33686806, 0.36855438,\n",
       "        0.38874383, 0.34604556, 0.40501621, 0.32257464, 0.40012241,\n",
       "        0.32244422, 0.31044715, 0.39710299, 0.39759962, 0.40947648,\n",
       "        0.33416223, 0.40779208, 0.37179768, 0.29817172, 0.40637252,\n",
       "        0.36458076, 0.34148412, 0.41442387, 0.34028359, 0.33880361,\n",
       "        0.39559468, 0.2963743 , 0.22685709, 0.36631239, 0.36278947,\n",
       "        0.30112501, 0.32367519, 0.39626726, 0.34512977, 0.43018989,\n",
       "        0.32654741, 0.38573677, 0.29868147, 0.27665546, 0.38165417,\n",
       "        0.29969247, 0.38850548, 0.34811891, 0.3471401 , 0.38540338,\n",
       "        0.36925233, 0.40871664, 0.36790107, 0.32463512, 0.39443836,\n",
       "        0.35137094, 0.40870448, 0.41369889, 0.34205172, 0.37406154,\n",
       "        0.33522936, 0.35663291, 0.40649988, 0.37882276, 0.35588782,\n",
       "        0.4219168 , 0.39179715, 0.38994217, 0.31825223, 0.33495909,\n",
       "        0.42483821, 0.36998878, 0.37808435, 0.481654  , 0.44893274,\n",
       "        0.30589073, 0.35389491, 0.35188264, 0.33700186, 0.36020542,\n",
       "        0.34889446, 0.42644928, 0.34005678, 0.39622978, 0.45751435,\n",
       "        0.35747796, 0.38142548, 0.36274154, 0.31664265, 0.4425078 ,\n",
       "        0.37907501, 0.36591929, 0.34711772, 0.34647221, 0.36988621,\n",
       "        0.27418741, 0.36419435, 0.33405816, 0.3368965 , 0.3971528 ,\n",
       "        0.42941594, 0.35474898, 0.36721913, 0.39445954, 0.27303008,\n",
       "        0.25359444, 0.36454223, 0.37143321, 0.42264779, 0.38378909,\n",
       "        0.2811205 , 0.35242281, 0.28069982, 0.33866256, 0.43193555,\n",
       "        0.36346457, 0.32182489, 0.34007098, 0.32819341, 0.40404426,\n",
       "        0.41392486, 0.33588222, 0.33416079, 0.37229506, 0.40896622,\n",
       "        0.27604719, 0.37032061, 0.29348943, 0.3472471 , 0.28750408]),\n",
       " array([0.22397513, 0.22127824, 0.23333815, 0.21886818, 0.23179286,\n",
       "        0.22007844, 0.21826511, 0.23038152, 0.22781723, 0.23653179,\n",
       "        0.22533171, 0.22422981, 0.24943573, 0.22717562, 0.22929916,\n",
       "        0.25504944, 0.26454938, 0.25696046, 0.23607673, 0.24612394,\n",
       "        0.22475715, 0.22535707, 0.26553396, 0.25798435, 0.23614679,\n",
       "        0.24563772, 0.22673844, 0.2836617 , 0.24622023, 0.24575122,\n",
       "        0.24606249, 0.22403794, 0.23007705, 0.22674856, 0.23509218,\n",
       "        0.21511235, 0.22990642, 0.30696248, 0.24738713, 0.22654477,\n",
       "        0.2559248 , 0.21843959, 0.2555754 , 0.22335825, 0.22573122,\n",
       "        0.241906  , 0.230214  , 0.21469987, 0.23083764, 0.22265454,\n",
       "        0.22279546, 0.23332083, 0.22483897, 0.25814793, 0.23335489,\n",
       "        0.2481033 , 0.25302351, 0.22959945, 0.2406695 , 0.2261777 ,\n",
       "        0.27194754, 0.22222742, 0.2251498 , 0.22582242, 0.23159017,\n",
       "        0.2315553 , 0.26673011, 0.21931525, 0.2161035 , 0.21532529,\n",
       "        0.22742383, 0.24204674, 0.22808829, 0.24374791, 0.2338226 ,\n",
       "        0.2411027 , 0.23051252, 0.24007833, 0.24557682, 0.24906788,\n",
       "        0.24715857, 0.22350897, 0.23628619, 0.2285002 , 0.21692831,\n",
       "        0.22435657, 0.21592931, 0.22980858, 0.24783543, 0.21972504,\n",
       "        0.22169452, 0.24136746, 0.22741415, 0.22224496, 0.29185642,\n",
       "        0.22910796, 0.25901689, 0.22580825, 0.22538048, 0.2186765 ,\n",
       "        0.23022231, 0.23557917, 0.27374041, 0.23085856, 0.23735154,\n",
       "        0.24970028, 0.22335619, 0.23013157, 0.21937174, 0.2187934 ,\n",
       "        0.23590038, 0.26185114, 0.25640868, 0.22837677, 0.23132813,\n",
       "        0.22107429, 0.22314323, 0.22656571, 0.23107656, 0.25699971,\n",
       "        0.2393535 , 0.23326635, 0.22859309, 0.23497728, 0.22832319,\n",
       "        0.21902652, 0.22395668, 0.21623533, 0.25012972, 0.21961518,\n",
       "        0.2545385 , 0.23739407, 0.23569066, 0.22500256, 0.24358221,\n",
       "        0.22154301, 0.22462429, 0.23923602, 0.2391053 , 0.23093015,\n",
       "        0.2693685 , 0.23818981, 0.22766744, 0.23741525, 0.23507353,\n",
       "        0.24847305, 0.22831377, 0.22077593, 0.27577451, 0.23488088,\n",
       "        0.23355447, 0.23777847, 0.28108113, 0.21907101, 0.24442591,\n",
       "        0.23341551, 0.24591462, 0.26358653, 0.23291443, 0.31008307,\n",
       "        0.23644562, 0.24037062, 0.26363629, 0.23363514, 0.30389771,\n",
       "        0.25647598, 0.2519103 , 0.21956425, 0.24757752, 0.25639547,\n",
       "        0.22907369, 0.21669471, 0.22597894, 0.21686257, 0.22911555,\n",
       "        0.25470224, 0.26462691, 0.28010508, 0.32217334, 0.23950248,\n",
       "        0.22870114, 0.22878857, 0.2633755 , 0.22555708, 0.23877632,\n",
       "        0.21545498, 0.24341186, 0.25908305, 0.27249262, 0.24347082,\n",
       "        0.25823846, 0.24543736, 0.22782075, 0.21801029, 0.22016856,\n",
       "        0.25554874, 0.22192688, 0.22596118, 0.23045115, 0.28219036,\n",
       "        0.23892438, 0.24525749, 0.23414564, 0.26417212, 0.22647159,\n",
       "        0.27562616, 0.22588609, 0.21868215, 0.22651624, 0.27846138,\n",
       "        0.22371804, 0.21708391, 0.24806505, 0.24482158, 0.23126112,\n",
       "        0.23141593, 0.23384129, 0.23216678, 0.24551957, 0.22989385,\n",
       "        0.22781413, 0.25177087, 0.25355242, 0.25154892, 0.23896085,\n",
       "        0.22800265, 0.23587037, 0.23906415, 0.24310646, 0.23097579,\n",
       "        0.26584252, 0.24134793, 0.24637036, 0.2299102 , 0.24711408,\n",
       "        0.23264718, 0.26602923, 0.21763752, 0.23127824, 0.22531332,\n",
       "        0.26667245, 0.25855474, 0.24140725, 0.2366708 , 0.22711552,\n",
       "        0.2494546 , 0.25641135, 0.25934925, 0.22409419, 0.2300734 ,\n",
       "        0.25004947, 0.24582293, 0.22290388, 0.21981128, 0.21905448,\n",
       "        0.22636768, 0.25530988, 0.22397955, 0.24309118, 0.22700526,\n",
       "        0.22309169, 0.23882902, 0.24628263, 0.25783587, 0.23333929,\n",
       "        0.22314372, 0.24632236, 0.22090026, 0.24988333, 0.22396715,\n",
       "        0.23790721, 0.21405093, 0.22823388, 0.26677143, 0.21628203,\n",
       "        0.2487233 , 0.26305416, 0.25746094, 0.29046486, 0.23828641,\n",
       "        0.25471872, 0.22890956, 0.23122459, 0.26287654, 0.21940873,\n",
       "        0.21682425, 0.2189276 , 0.23853219, 0.2320646 , 0.2487154 ,\n",
       "        0.25404731, 0.23290156, 0.23725064, 0.22278773, 0.24162034,\n",
       "        0.21342894, 0.22157005, 0.25172219, 0.23064378, 0.22128853,\n",
       "        0.22656006, 0.26483221, 0.22846796, 0.25859272, 0.2454237 ,\n",
       "        0.22912076, 0.22603835, 0.2898097 , 0.22933761, 0.22049515,\n",
       "        0.22249973, 0.2538675 , 0.26288887, 0.22638106, 0.2355905 ,\n",
       "        0.22888058, 0.22272479, 0.22013679, 0.22124538, 0.25062143,\n",
       "        0.28573685, 0.26284653, 0.21443477, 0.23253706, 0.22935375,\n",
       "        0.21604143, 0.22076046, 0.23470304, 0.24823152, 0.2174358 ,\n",
       "        0.22487306, 0.23315166, 0.23948112, 0.27599461, 0.2273045 ,\n",
       "        0.2377458 , 0.27997932, 0.25557964, 0.26568502, 0.23116626,\n",
       "        0.23911846, 0.21999963, 0.22906313, 0.24843811, 0.23230325,\n",
       "        0.23959602, 0.24005193, 0.22034986, 0.23670866, 0.2393182 ,\n",
       "        0.24655641, 0.23469482, 0.23220076, 0.22100365, 0.29813609,\n",
       "        0.21632798, 0.22296462, 0.26199396, 0.22541909, 0.23173752,\n",
       "        0.27292529, 0.24488583, 0.25646505, 0.25091249, 0.21724821,\n",
       "        0.23282104, 0.24982835, 0.24926056, 0.24852398, 0.2375933 ,\n",
       "        0.26758993, 0.2432381 , 0.21391561, 0.23541787, 0.22022099,\n",
       "        0.2249041 , 0.25825248, 0.23330657, 0.24478021, 0.22511322,\n",
       "        0.24138706, 0.26085834, 0.21922717, 0.22190238, 0.25788184,\n",
       "        0.22409154, 0.22015219, 0.25579028, 0.23015304, 0.24672989,\n",
       "        0.24225431, 0.23407241, 0.30262333, 0.25081818, 0.23796511,\n",
       "        0.24240333, 0.22134655, 0.22381314, 0.22443572, 0.23277201,\n",
       "        0.22376367, 0.26289885, 0.22052769, 0.21442376, 0.21991025,\n",
       "        0.26185344, 0.2456501 , 0.31728143, 0.2189131 , 0.22368103,\n",
       "        0.23082584, 0.25467547, 0.23769625, 0.21789639, 0.23798136,\n",
       "        0.2292584 , 0.22714596, 0.22768799, 0.23237155, 0.2718172 ,\n",
       "        0.24958758, 0.22210408, 0.21617677, 0.23428096, 0.24188572,\n",
       "        0.23551614, 0.21549785, 0.25736764, 0.21417217, 0.23334959,\n",
       "        0.22811062, 0.22181939, 0.22192753, 0.22880478, 0.22548676,\n",
       "        0.21390633, 0.25570087, 0.21949502, 0.23719275, 0.24355698,\n",
       "        0.24518896, 0.23102151, 0.2187838 , 0.22318071, 0.23438031,\n",
       "        0.22481193, 0.25160581, 0.22874281, 0.21649907, 0.29607478,\n",
       "        0.2270156 , 0.22545783, 0.22934272, 0.26108599, 0.25859964,\n",
       "        0.23966239, 0.24114979, 0.21673724, 0.23565655, 0.23104511,\n",
       "        0.22196454, 0.26915719, 0.23301197, 0.2385721 , 0.22202522,\n",
       "        0.22788531, 0.22022329, 0.24339236, 0.22014798, 0.25070406,\n",
       "        0.24718934, 0.25652489, 0.23225668, 0.22047365, 0.26569773,\n",
       "        0.23039626, 0.24131262, 0.22849384, 0.25831062, 0.26339517,\n",
       "        0.22125971, 0.24212721, 0.23519203, 0.24061483, 0.22708976,\n",
       "        0.24587274, 0.23325088, 0.22051636, 0.25114331, 0.21319276,\n",
       "        0.24863215, 0.21659527, 0.31495895, 0.22812863, 0.23673969,\n",
       "        0.24007477, 0.23700511, 0.23448766, 0.22427147, 0.21891921,\n",
       "        0.22746957, 0.22825626, 0.23408697, 0.24569227, 0.22053451,\n",
       "        0.22537445, 0.22047832, 0.27886263, 0.23557922, 0.22908691,\n",
       "        0.25769788, 0.25504226, 0.26082829, 0.23528459, 0.22881367,\n",
       "        0.2730612 , 0.22442593, 0.24019592, 0.22678759, 0.2760022 ,\n",
       "        0.23459717, 0.24039671, 0.23448729, 0.24455678, 0.21657204,\n",
       "        0.24534376, 0.27424257, 0.26734194, 0.25403519, 0.2329464 ,\n",
       "        0.27778776, 0.26185321, 0.23835072, 0.22025919, 0.23788681,\n",
       "        0.22333741, 0.21862863, 0.2424358 , 0.26876514, 0.24240285,\n",
       "        0.2258627 , 0.23007318, 0.22379005, 0.25883775, 0.25441955,\n",
       "        0.23503353, 0.24570659, 0.24845073, 0.23527557, 0.22253758,\n",
       "        0.22678783, 0.22983715, 0.2565022 , 0.25009175, 0.24080799,\n",
       "        0.22013744, 0.25051948, 0.2597971 , 0.25401661, 0.25599899,\n",
       "        0.27287958, 0.2172263 , 0.25046019, 0.23132555, 0.22993896,\n",
       "        0.22246863, 0.21774864, 0.22843057, 0.2325295 , 0.21909003,\n",
       "        0.26006461, 0.23118345, 0.26093007, 0.27008458, 0.22826976,\n",
       "        0.22015501, 0.24622097, 0.26374033, 0.2279526 , 0.23058468,\n",
       "        0.2588738 , 0.22446389, 0.26610811, 0.2505418 , 0.23121759,\n",
       "        0.23478497, 0.23939801, 0.2187341 , 0.26149372, 0.23164241,\n",
       "        0.22055214, 0.29363527, 0.23857516, 0.25569886, 0.25618486,\n",
       "        0.23048064, 0.21604311, 0.24173703, 0.25463795, 0.27979269,\n",
       "        0.2392696 , 0.23299165, 0.22249389, 0.23947559, 0.22631947,\n",
       "        0.23175567, 0.25590372, 0.23516379, 0.22112036, 0.25343395,\n",
       "        0.22517375, 0.22480268, 0.22168916, 0.21842859, 0.21379664,\n",
       "        0.24951098, 0.24185994, 0.21981214, 0.22005551, 0.2481584 ,\n",
       "        0.21941237, 0.23419527, 0.22847625, 0.25696813, 0.23731461,\n",
       "        0.22965107, 0.22016114, 0.23546544, 0.25440146, 0.24904509,\n",
       "        0.24323884, 0.23637518, 0.22444065, 0.24002125, 0.25660602,\n",
       "        0.24254492, 0.25205147, 0.22145223, 0.25905753, 0.23634486,\n",
       "        0.22576776, 0.22633963, 0.26333386, 0.22153127, 0.24171909,\n",
       "        0.24780599, 0.21854859, 0.28149759, 0.22447212, 0.24301849,\n",
       "        0.31502212, 0.22588204, 0.23256792, 0.22901803, 0.24443947,\n",
       "        0.26037115, 0.23109707, 0.23782792, 0.23807321, 0.24938095,\n",
       "        0.25513656, 0.21811827, 0.23370126, 0.22365851, 0.2256404 ,\n",
       "        0.28625172, 0.30485609, 0.24097313, 0.27740692, 0.21725024,\n",
       "        0.22775042, 0.23940286, 0.28121304, 0.25599876, 0.2170344 ,\n",
       "        0.24284543, 0.24064315, 0.30304555, 0.22600501, 0.2247677 ,\n",
       "        0.25297355, 0.23533479, 0.22937528, 0.22133112, 0.25444542,\n",
       "        0.21864245, 0.26518856, 0.28288901, 0.23405231, 0.25105243,\n",
       "        0.31843665, 0.27663768, 0.26590142, 0.22032841, 0.23301375,\n",
       "        0.22487538, 0.21629359, 0.23401904, 0.22781431, 0.25265406,\n",
       "        0.22314319, 0.23551624, 0.24542878, 0.23314255, 0.24875388,\n",
       "        0.225723  , 0.22206656, 0.22625444, 0.2361301 , 0.22527458,\n",
       "        0.23654787, 0.25831871, 0.22580686, 0.21855243, 0.2332762 ,\n",
       "        0.22923502, 0.22833758, 0.25006672, 0.25475053, 0.22966729,\n",
       "        0.21667829, 0.22149321, 0.2596476 , 0.23697385, 0.24486031,\n",
       "        0.23005218, 0.23668965, 0.23781263, 0.24076668, 0.23299392,\n",
       "        0.24107705, 0.24340335, 0.22379208, 0.23449292, 0.2373121 ,\n",
       "        0.22195839, 0.22394935, 0.25170475, 0.23748685, 0.28519692,\n",
       "        0.25316202, 0.2386481 , 0.22552375, 0.23492644, 0.27457075,\n",
       "        0.25206818, 0.25276346, 0.25921637, 0.25127724, 0.2209894 ,\n",
       "        0.23008478, 0.23388229, 0.24084212, 0.2281665 , 0.22730048,\n",
       "        0.23005665, 0.24119513, 0.22695916, 0.25835354, 0.25473386,\n",
       "        0.23967623, 0.22245659, 0.2398883 , 0.2343698 , 0.22577629,\n",
       "        0.24905783, 0.25783416, 0.22565526, 0.21705352, 0.24393815,\n",
       "        0.24112903, 0.22144021, 0.24775214, 0.22356737, 0.23854086,\n",
       "        0.24707698, 0.24254961, 0.24584885, 0.21404875, 0.24025903,\n",
       "        0.21657575, 0.2426742 , 0.23827021, 0.21987975, 0.29239   ,\n",
       "        0.23164585, 0.22800755, 0.22116126, 0.2168134 , 0.23484169,\n",
       "        0.21679498, 0.22853858, 0.24221345, 0.3166136 , 0.21519279,\n",
       "        0.32290174, 0.21877125, 0.27079745, 0.2371273 , 0.24111735,\n",
       "        0.33124495, 0.22954507, 0.25978564, 0.24005767, 0.25046923,\n",
       "        0.25619917, 0.24334393, 0.22379984, 0.22790006, 0.21856184,\n",
       "        0.30615864, 0.2698361 , 0.25481675, 0.21556514, 0.27852778,\n",
       "        0.28064154, 0.22354844, 0.2163665 , 0.24683899, 0.22659733,\n",
       "        0.24210415, 0.26831439, 0.27890851, 0.23068933, 0.2190923 ,\n",
       "        0.24220137, 0.23597509, 0.24949828, 0.21905356, 0.22797818,\n",
       "        0.22311788, 0.22711025, 0.24054484, 0.22586988, 0.24281231,\n",
       "        0.25838674, 0.2359746 , 0.21788074, 0.2959175 , 0.24068737,\n",
       "        0.27863201, 0.22491465, 0.22635501, 0.22853941, 0.24210421,\n",
       "        0.22930615, 0.22377193, 0.22951808, 0.25064805, 0.22474522,\n",
       "        0.22076386, 0.23194876, 0.25351637, 0.22402162, 0.21926511,\n",
       "        0.22592859, 0.31859831, 0.21500235, 0.22011801, 0.22774723,\n",
       "        0.21812095, 0.27436101, 0.27324253, 0.25616467, 0.23958329,\n",
       "        0.23330356, 0.26116734, 0.24234558, 0.23139065, 0.23718708,\n",
       "        0.21706534, 0.33957188, 0.22960092, 0.23391026, 0.28659473,\n",
       "        0.22446075, 0.21780138, 0.25857775, 0.22215698, 0.2304466 ,\n",
       "        0.24425191, 0.235639  , 0.24872786, 0.22167982, 0.25576333,\n",
       "        0.23763283, 0.22207751, 0.24150128, 0.24530951, 0.24799289,\n",
       "        0.22232425, 0.25093798, 0.23389129, 0.22290181, 0.26164114,\n",
       "        0.23525309, 0.22262062, 0.25689498, 0.24827864, 0.2214763 ,\n",
       "        0.26349371, 0.21669227, 0.21645062, 0.22796948, 0.23704724,\n",
       "        0.21968775, 0.22854723, 0.25915401, 0.22948068, 0.26496088,\n",
       "        0.22129178, 0.2458731 , 0.22320941, 0.2215468 , 0.23951627,\n",
       "        0.23013354, 0.24974251, 0.22487813, 0.23095596, 0.24617207,\n",
       "        0.23310441, 0.2812591 , 0.23382501, 0.22065772, 0.24350461,\n",
       "        0.23338901, 0.271157  , 0.25231089, 0.22618353, 0.27180827,\n",
       "        0.22122821, 0.22880899, 0.27323236, 0.24253862, 0.22719726,\n",
       "        0.25494696, 0.25252257, 0.24746503, 0.21858224, 0.23456888,\n",
       "        0.26159644, 0.24803365, 0.2335765 , 0.29849813, 0.2570895 ,\n",
       "        0.22486535, 0.25125977, 0.24601455, 0.2224826 , 0.23111264,\n",
       "        0.23104269, 0.26228758, 0.22863397, 0.24226726, 0.27204486,\n",
       "        0.22672348, 0.24322755, 0.24695409, 0.22060362, 0.27470918,\n",
       "        0.24056444, 0.26530006, 0.23172978, 0.2552622 , 0.23136104,\n",
       "        0.2176035 , 0.24355955, 0.23949941, 0.24583328, 0.26846946,\n",
       "        0.2694567 , 0.22573367, 0.23808234, 0.26789445, 0.21383822,\n",
       "        0.21417582, 0.26354955, 0.24618309, 0.26922336, 0.25723912,\n",
       "        0.22137174, 0.23455535, 0.23307701, 0.2215478 , 0.26106745,\n",
       "        0.22998816, 0.21848375, 0.23726175, 0.21955213, 0.25084912,\n",
       "        0.25923912, 0.22516041, 0.23377341, 0.27534165, 0.25489038,\n",
       "        0.21471777, 0.25076519, 0.22534613, 0.24407043, 0.21758089]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_returns, port_volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portfolios = pd.DataFrame({'Return':port_returns,'Volatility':port_volatilities})"
   ]
  },
  {
   "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>Return</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.284958</td>\n",
       "      <td>0.223975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.323866</td>\n",
       "      <td>0.221278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.376294</td>\n",
       "      <td>0.233338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.300949</td>\n",
       "      <td>0.218868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.380132</td>\n",
       "      <td>0.231793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Return  Volatility\n",
       "0  0.284958    0.223975\n",
       "1  0.323866    0.221278\n",
       "2  0.376294    0.233338\n",
       "3  0.300949    0.218868\n",
       "4  0.380132    0.231793"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Return</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.276047</td>\n",
       "      <td>0.214718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.370321</td>\n",
       "      <td>0.250765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.293489</td>\n",
       "      <td>0.225346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.347247</td>\n",
       "      <td>0.244070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.287504</td>\n",
       "      <td>0.217581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Return  Volatility\n",
       "995  0.276047    0.214718\n",
       "996  0.370321    0.250765\n",
       "997  0.293489    0.225346\n",
       "998  0.347247    0.244070\n",
       "999  0.287504    0.217581"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolios.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Expected Return')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A+Ba0J9+tQojns7lWJr9pP9ILj2Rj8ioEHl34KVSXlzoODgeAYFgzlq06fbht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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcec7de6b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolios.plot(x='Volatility', y='Return', kind = 'scatter', figsize=(10,6));\n",
    "plt.xlabel('Expected Volatility')\n",
    "plt.ylabel('Expected Return')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0acdc42710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(['AMZN'],100,align='mid')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}