Portfolio Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating an Efficient Portfolio\n",
    "\n",
    "With this project, I wanted to see if I could take 5 random stocks, and then create an efficient portfolio, maximizing the returns based off an investors risk appetite.\n",
    "\n",
    "Some of the things I wanted to do in this project were:\n",
    "- Get a constantly updated list of the stocks inside the S&P500\n",
    "    - [Updated S&P500](#Get-Updated-S&P500)\n",
    "- Get 5 random stocks from the S&P500\n",
    "    - [Get Stocks](#Pick-5-Random-Stocks)\n",
    "- Visually see some of the statistical analysis\n",
    "    - [Visual Representation](#Visual-Representation)\n",
    "- Run some statistical analysis on those stocks\n",
    "    - [Statistical Analysis](#Statistical-Analysis)\n",
    "- Model an efficient frontier and output an efficient portfolio\n",
    "    - [Efficient Portfolio](#Mean-Variance-Optimization)\n",
    "- Take a users inputs, and output the optimal portfolio under his constraints.\n",
    "    - [User Input Portfolio](#User-Input-Portfolio)\n",
    "\n",
    "\n",
    "Though there are other API's I've used in the past (specifically IEX which has more detailed information), I opted to go with the Yahoo api since it's free and gives us the basic information necessary to run some analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules\n",
    "\n",
    "Import the modules we'll be using throughout our project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas_datareader.data as web\n",
    "import math\n",
    "import requests\n",
    "from datetime import datetime\n",
    "import cvxopt as opt\n",
    "from cvxopt import blas, solvers\n",
    "import random\n",
    "\n",
    "now = datetime.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section will:\n",
    "- Get updated S&P500 list\n",
    "- Pick 5 random stocks\n",
    "- Run statistical analysis on those 5 stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Updated S&P500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M Company</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>St. Paul, Minnesota</td>\n",
       "      <td>1976-08-09</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie Inc.</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABMD</td>\n",
       "      <td>Abiomed</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Danvers, Massachusetts</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>815094</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>IT Consulting &amp; Other Services</td>\n",
       "      <td>Dublin, Ireland</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>1467373</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>YUM</td>\n",
       "      <td>Yum! Brands Inc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>Louisville, Kentucky</td>\n",
       "      <td>1997-10-06</td>\n",
       "      <td>1041061</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ZBRA</td>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Electronic Equipment &amp; Instruments</td>\n",
       "      <td>Lincolnshire, Illinois</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>877212</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>ZBH</td>\n",
       "      <td>Zimmer Biomet</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Warsaw, Indiana</td>\n",
       "      <td>2001-08-07</td>\n",
       "      <td>1136869</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>ZION</td>\n",
       "      <td>Zions Bancorp</td>\n",
       "      <td>reports</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Regional Banks</td>\n",
       "      <td>Salt Lake City, Utah</td>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>109380</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>ZTS</td>\n",
       "      <td>Zoetis</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>Parsippany, New Jersey</td>\n",
       "      <td>2013-06-21</td>\n",
       "      <td>1555280</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 9 columns</p>\n",
       "</div>"
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       "    Symbol             Security SEC filings             GICS Sector  \\\n",
       "0      MMM           3M Company     reports             Industrials   \n",
       "1      ABT  Abbott Laboratories     reports             Health Care   \n",
       "2     ABBV          AbbVie Inc.     reports             Health Care   \n",
       "3     ABMD              Abiomed     reports             Health Care   \n",
       "4      ACN            Accenture     reports  Information Technology   \n",
       "..     ...                  ...         ...                     ...   \n",
       "500    YUM      Yum! Brands Inc     reports  Consumer Discretionary   \n",
       "501   ZBRA   Zebra Technologies     reports  Information Technology   \n",
       "502    ZBH        Zimmer Biomet     reports             Health Care   \n",
       "503   ZION        Zions Bancorp     reports              Financials   \n",
       "504    ZTS               Zoetis     reports             Health Care   \n",
       "\n",
       "                      GICS Sub-Industry    Headquarters Location  \\\n",
       "0              Industrial Conglomerates      St. Paul, Minnesota   \n",
       "1                 Health Care Equipment  North Chicago, Illinois   \n",
       "2                       Pharmaceuticals  North Chicago, Illinois   \n",
       "3                 Health Care Equipment   Danvers, Massachusetts   \n",
       "4        IT Consulting & Other Services          Dublin, Ireland   \n",
       "..                                  ...                      ...   \n",
       "500                         Restaurants     Louisville, Kentucky   \n",
       "501  Electronic Equipment & Instruments   Lincolnshire, Illinois   \n",
       "502               Health Care Equipment          Warsaw, Indiana   \n",
       "503                      Regional Banks     Salt Lake City, Utah   \n",
       "504                     Pharmaceuticals   Parsippany, New Jersey   \n",
       "\n",
       "    Date first added      CIK      Founded  \n",
       "0         1976-08-09    66740         1902  \n",
       "1         1964-03-31     1800         1888  \n",
       "2         2012-12-31  1551152  2013 (1888)  \n",
       "3         2018-05-31   815094         1981  \n",
       "4         2011-07-06  1467373         1989  \n",
       "..               ...      ...          ...  \n",
       "500       1997-10-06  1041061         1997  \n",
       "501       2019-12-23   877212         1969  \n",
       "502       2001-08-07  1136869         1927  \n",
       "503       2001-06-22   109380         1873  \n",
       "504       2013-06-21  1555280         1952  \n",
       "\n",
       "[505 rows x 9 columns]"
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     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "#stocks in sp500 always changing, so run this code to get updated sp500 list:\n",
    "\n",
    "table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
    "df = table[0]\n",
    "\n",
    "symbols = sorted(df.Symbol)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick 5 Random Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pick 5 random stocks from sp500 to run some analysis:\n",
    "\n",
    "#random_stocks = random.sample(symbols, 5)\n",
    "random_stocks = ['BLL', 'WDC', 'IVZ', 'PPL', 'TWTR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Symbol</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLL</td>\n",
       "      <td>Ball Corp</td>\n",
       "      <td>reports</td>\n",
       "      <td>Materials</td>\n",
       "      <td>Metal &amp; Glass Containers</td>\n",
       "      <td>Broomfield, Colorado</td>\n",
       "      <td>1984-10-31</td>\n",
       "      <td>9389</td>\n",
       "      <td>1880</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WDC</td>\n",
       "      <td>Western Digital</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Technology Hardware, Storage &amp; Peripherals</td>\n",
       "      <td>San Jose, California</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>106040</td>\n",
       "      <td>1970</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IVZ</td>\n",
       "      <td>Invesco Ltd.</td>\n",
       "      <td>reports</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Asset Management &amp; Custody Banks</td>\n",
       "      <td>Atlanta, Georgia</td>\n",
       "      <td>2008-08-21</td>\n",
       "      <td>914208</td>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PPL</td>\n",
       "      <td>PPL Corp.</td>\n",
       "      <td>reports</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>Electric Utilities</td>\n",
       "      <td>Allentown, Pennsylvania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>922224</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TWTR</td>\n",
       "      <td>Twitter, Inc.</td>\n",
       "      <td>reports</td>\n",
       "      <td>Communication Services</td>\n",
       "      <td>Interactive Media &amp; Services</td>\n",
       "      <td>San Francisco, California</td>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>1418091</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol         Security SEC filings             GICS Sector  \\\n",
       "0    BLL        Ball Corp     reports               Materials   \n",
       "1    WDC  Western Digital     reports  Information Technology   \n",
       "2    IVZ     Invesco Ltd.     reports              Financials   \n",
       "3    PPL        PPL Corp.     reports               Utilities   \n",
       "4   TWTR    Twitter, Inc.     reports  Communication Services   \n",
       "\n",
       "                            GICS Sub-Industry      Headquarters Location  \\\n",
       "0                    Metal & Glass Containers       Broomfield, Colorado   \n",
       "1  Technology Hardware, Storage & Peripherals       San Jose, California   \n",
       "2            Asset Management & Custody Banks           Atlanta, Georgia   \n",
       "3                          Electric Utilities    Allentown, Pennsylvania   \n",
       "4                Interactive Media & Services  San Francisco, California   \n",
       "\n",
       "  Date first added      CIK Founded  \n",
       "0       1984-10-31     9389    1880  \n",
       "1       2009-07-01   106040    1970  \n",
       "2       2008-08-21   914208    1935  \n",
       "3              NaN   922224    1920  \n",
       "4       2018-06-07  1418091    2006  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since I dont know anything about those stocks, get some info on them: \n",
    "\n",
    "stock_information = pd.DataFrame(columns = df.columns)\n",
    "\n",
    "for i in random_stocks:\n",
    "    stock_information = stock_information.append(df[df.Symbol == i], ignore_index = True)\n",
    "\n",
    "stock_information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick Glance\n",
    "\n",
    "Of these 5 random stocks, I have heard of `TWTR` (Twitter - social media) and `WDC` (Western Digital - since I bought a hard drive from them on Black Friday). Other than those 2, I have no prior knowledge of `BLL`, `IVZ`, or `PPL`. Looking at their GICS information, it seems like there shouldnt be any correlation between these stocks. The companies range from social media, to asset management, to metal and glass containers, so I don't think there should be much correlation between these stocks. It's possible that there is some correlation between them, but as of now I'm not seeing it just based off their sectors and quick information.\n",
    "\n",
    "**Note** - I'm looking to see information PRE-pandemic...the pre- and post- pandemic analysis are completely different as they are completely different trading environments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>Attributes</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Adj Close</th>\n",
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       "      <th>Symbols</th>\n",
       "      <th>BLL</th>\n",
       "      <th>WDC</th>\n",
       "      <th>IVZ</th>\n",
       "      <th>PPL</th>\n",
       "      <th>TWTR</th>\n",
       "      <th>BLL</th>\n",
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       "      <th>IVZ</th>\n",
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       "      <th>2017-01-03</th>\n",
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       "      <td>24.925303</td>\n",
       "      <td>27.635124</td>\n",
       "      <td>16.440001</td>\n",
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       "      <td>69.430000</td>\n",
       "      <td>30.930000</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>16.440001</td>\n",
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       "      <td>37.855000</td>\n",
       "      <td>68.949997</td>\n",
       "      <td>30.980000</td>\n",
       "      <td>34.049999</td>\n",
       "      <td>16.309999</td>\n",
       "      <td>1830600.0</td>\n",
       "      <td>4479000.0</td>\n",
       "      <td>3483500.0</td>\n",
       "      <td>3367000.0</td>\n",
       "      <td>11147900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>37.065277</td>\n",
       "      <td>63.201378</td>\n",
       "      <td>25.602228</td>\n",
       "      <td>27.691963</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>38.450001</td>\n",
       "      <td>70.540001</td>\n",
       "      <td>31.770000</td>\n",
       "      <td>34.099998</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>...</td>\n",
       "      <td>37.904999</td>\n",
       "      <td>69.580002</td>\n",
       "      <td>31.040001</td>\n",
       "      <td>34.049999</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>2219800.0</td>\n",
       "      <td>3653200.0</td>\n",
       "      <td>3470400.0</td>\n",
       "      <td>3187400.0</td>\n",
       "      <td>15861300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>37.045986</td>\n",
       "      <td>63.210342</td>\n",
       "      <td>25.400763</td>\n",
       "      <td>27.854383</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>38.430000</td>\n",
       "      <td>70.550003</td>\n",
       "      <td>31.520000</td>\n",
       "      <td>34.299999</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.349998</td>\n",
       "      <td>71.059998</td>\n",
       "      <td>31.559999</td>\n",
       "      <td>34.169998</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>1965800.0</td>\n",
       "      <td>3638300.0</td>\n",
       "      <td>2797500.0</td>\n",
       "      <td>3497000.0</td>\n",
       "      <td>17074400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>37.209869</td>\n",
       "      <td>63.058014</td>\n",
       "      <td>25.408823</td>\n",
       "      <td>28.065523</td>\n",
       "      <td>17.170000</td>\n",
       "      <td>38.599998</td>\n",
       "      <td>70.379997</td>\n",
       "      <td>31.530001</td>\n",
       "      <td>34.560001</td>\n",
       "      <td>17.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.465000</td>\n",
       "      <td>70.480003</td>\n",
       "      <td>31.600000</td>\n",
       "      <td>34.169998</td>\n",
       "      <td>17.240000</td>\n",
       "      <td>1001800.0</td>\n",
       "      <td>3274300.0</td>\n",
       "      <td>3522500.0</td>\n",
       "      <td>3542700.0</td>\n",
       "      <td>14665100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>36.621845</td>\n",
       "      <td>64.384033</td>\n",
       "      <td>24.860834</td>\n",
       "      <td>27.789417</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>37.990002</td>\n",
       "      <td>71.860001</td>\n",
       "      <td>30.850000</td>\n",
       "      <td>34.220001</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.535000</td>\n",
       "      <td>70.900002</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>34.540001</td>\n",
       "      <td>17.219999</td>\n",
       "      <td>1329800.0</td>\n",
       "      <td>3909600.0</td>\n",
       "      <td>3525000.0</td>\n",
       "      <td>5474700.0</td>\n",
       "      <td>11918100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Attributes  Adj Close                                                  Close  \\\n",
       "Symbols           BLL        WDC        IVZ        PPL       TWTR        BLL   \n",
       "Date                                                                           \n",
       "2017-01-03  36.510979  62.206829  24.925303  27.635124  16.440001  37.875000   \n",
       "2017-01-04  37.065277  63.201378  25.602228  27.691963  16.860001  38.450001   \n",
       "2017-01-05  37.045986  63.210342  25.400763  27.854383  17.090000  38.430000   \n",
       "2017-01-06  37.209869  63.058014  25.408823  28.065523  17.170000  38.599998   \n",
       "2017-01-09  36.621845  64.384033  24.860834  27.789417  17.500000  37.990002   \n",
       "\n",
       "Attributes                                              ...       Open  \\\n",
       "Symbols           WDC        IVZ        PPL       TWTR  ...        BLL   \n",
       "Date                                                    ...              \n",
       "2017-01-03  69.430000  30.930000  34.029999  16.440001  ...  37.855000   \n",
       "2017-01-04  70.540001  31.770000  34.099998  16.860001  ...  37.904999   \n",
       "2017-01-05  70.550003  31.520000  34.299999  17.090000  ...  38.349998   \n",
       "2017-01-06  70.379997  31.530001  34.560001  17.170000  ...  38.465000   \n",
       "2017-01-09  71.860001  30.850000  34.220001  17.500000  ...  38.535000   \n",
       "\n",
       "Attributes                                                 Volume             \\\n",
       "Symbols           WDC        IVZ        PPL       TWTR        BLL        WDC   \n",
       "Date                                                                           \n",
       "2017-01-03  68.949997  30.980000  34.049999  16.309999  1830600.0  4479000.0   \n",
       "2017-01-04  69.580002  31.040001  34.049999  16.500000  2219800.0  3653200.0   \n",
       "2017-01-05  71.059998  31.559999  34.169998  16.910000  1965800.0  3638300.0   \n",
       "2017-01-06  70.480003  31.600000  34.169998  17.240000  1001800.0  3274300.0   \n",
       "2017-01-09  70.900002  31.250000  34.540001  17.219999  1329800.0  3909600.0   \n",
       "\n",
       "Attributes                                    \n",
       "Symbols           IVZ        PPL        TWTR  \n",
       "Date                                          \n",
       "2017-01-03  3483500.0  3367000.0  11147900.0  \n",
       "2017-01-04  3470400.0  3187400.0  15861300.0  \n",
       "2017-01-05  2797500.0  3497000.0  17074400.0  \n",
       "2017-01-06  3522500.0  3542700.0  14665100.0  \n",
       "2017-01-09  3525000.0  5474700.0  11918100.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now want to see if i can pull those random symbols from yahoo api using datareader\n",
    "#starting from 2017 to 2019 (pre-pandemic 2019)\n",
    "\n",
    "start = datetime(2017, 1, 1)\n",
    "end = datetime(2019, 1, 2)\n",
    "\n",
    "stocks = web.DataReader(random_stocks, start=start, end=end, data_source = 'yahoo')\n",
    "\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Have a dataframe now of the 5 stocks we picked with some prices. let's see exactly what were looking at:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('Adj Close',  'BLL'),\n",
       "            ('Adj Close',  'WDC'),\n",
       "            ('Adj Close',  'IVZ'),\n",
       "            ('Adj Close',  'PPL'),\n",
       "            ('Adj Close', 'TWTR'),\n",
       "            (    'Close',  'BLL'),\n",
       "            (    'Close',  'WDC'),\n",
       "            (    'Close',  'IVZ'),\n",
       "            (    'Close',  'PPL'),\n",
       "            (    'Close', 'TWTR'),\n",
       "            (     'High',  'BLL'),\n",
       "            (     'High',  'WDC'),\n",
       "            (     'High',  'IVZ'),\n",
       "            (     'High',  'PPL'),\n",
       "            (     'High', 'TWTR'),\n",
       "            (      'Low',  'BLL'),\n",
       "            (      'Low',  'WDC'),\n",
       "            (      'Low',  'IVZ'),\n",
       "            (      'Low',  'PPL'),\n",
       "            (      'Low', 'TWTR'),\n",
       "            (     'Open',  'BLL'),\n",
       "            (     'Open',  'WDC'),\n",
       "            (     'Open',  'IVZ'),\n",
       "            (     'Open',  'PPL'),\n",
       "            (     'Open', 'TWTR'),\n",
       "            (   'Volume',  'BLL'),\n",
       "            (   'Volume',  'WDC'),\n",
       "            (   'Volume',  'IVZ'),\n",
       "            (   'Volume',  'PPL'),\n",
       "            (   'Volume', 'TWTR')],\n",
       "           names=['Attributes', 'Symbols'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- So we have Open, Close, High, Low, Volume, and Adj Close (this is the closing price accounting for dividends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Symbols</th>\n",
       "      <th>BLL</th>\n",
       "      <th>WDC</th>\n",
       "      <th>IVZ</th>\n",
       "      <th>PPL</th>\n",
       "      <th>TWTR</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>36.510979</td>\n",
       "      <td>62.206829</td>\n",
       "      <td>24.925303</td>\n",
       "      <td>27.635124</td>\n",
       "      <td>16.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>37.065277</td>\n",
       "      <td>63.201378</td>\n",
       "      <td>25.602228</td>\n",
       "      <td>27.691963</td>\n",
       "      <td>16.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>37.045986</td>\n",
       "      <td>63.210342</td>\n",
       "      <td>25.400763</td>\n",
       "      <td>27.854383</td>\n",
       "      <td>17.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>37.209869</td>\n",
       "      <td>63.058014</td>\n",
       "      <td>25.408823</td>\n",
       "      <td>28.065523</td>\n",
       "      <td>17.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>36.621845</td>\n",
       "      <td>64.384033</td>\n",
       "      <td>24.860834</td>\n",
       "      <td>27.789417</td>\n",
       "      <td>17.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols           BLL        WDC        IVZ        PPL       TWTR\n",
       "Date                                                             \n",
       "2017-01-03  36.510979  62.206829  24.925303  27.635124  16.440001\n",
       "2017-01-04  37.065277  63.201378  25.602228  27.691963  16.860001\n",
       "2017-01-05  37.045986  63.210342  25.400763  27.854383  17.090000\n",
       "2017-01-06  37.209869  63.058014  25.408823  28.065523  17.170000\n",
       "2017-01-09  36.621845  64.384033  24.860834  27.789417  17.500000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at the adjusted closing prices since thats the closing price of the stocks per day.\n",
    "adj_close = stocks[\"Adj Close\"]\n",
    "\n",
    "adj_close.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Clean dataframe of day-by-day closing prices of our 5 stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Symbols</th>\n",
       "      <th>BLL</th>\n",
       "      <th>WDC</th>\n",
       "      <th>IVZ</th>\n",
       "      <th>PPL</th>\n",
       "      <th>TWTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>503.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.325435</td>\n",
       "      <td>71.177453</td>\n",
       "      <td>25.231004</td>\n",
       "      <td>28.134408</td>\n",
       "      <td>25.332942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.232766</td>\n",
       "      <td>13.130418</td>\n",
       "      <td>4.168596</td>\n",
       "      <td>3.097631</td>\n",
       "      <td>8.628170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.476700</td>\n",
       "      <td>32.824841</td>\n",
       "      <td>13.717840</td>\n",
       "      <td>22.070314</td>\n",
       "      <td>14.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.990002</td>\n",
       "      <td>66.776264</td>\n",
       "      <td>22.806392</td>\n",
       "      <td>25.438858</td>\n",
       "      <td>17.555000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.843060</td>\n",
       "      <td>75.374367</td>\n",
       "      <td>26.125170</td>\n",
       "      <td>27.653137</td>\n",
       "      <td>24.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.735876</td>\n",
       "      <td>80.052589</td>\n",
       "      <td>28.397279</td>\n",
       "      <td>31.244924</td>\n",
       "      <td>32.300001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.510494</td>\n",
       "      <td>97.646362</td>\n",
       "      <td>32.033100</td>\n",
       "      <td>33.360001</td>\n",
       "      <td>46.759998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols         BLL         WDC         IVZ         PPL        TWTR\n",
       "count    503.000000  503.000000  503.000000  503.000000  503.000000\n",
       "mean      39.325435   71.177453   25.231004   28.134408   25.332942\n",
       "std        3.232766   13.130418    4.168596    3.097631    8.628170\n",
       "min       34.476700   32.824841   13.717840   22.070314   14.290000\n",
       "25%       36.990002   66.776264   22.806392   25.438858   17.555000\n",
       "50%       38.843060   75.374367   26.125170   27.653137   24.230000\n",
       "75%       40.735876   80.052589   28.397279   31.244924   32.300001\n",
       "max       49.510494   97.646362   32.033100   33.360001   46.759998"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# want to see a quick summary of key stats to have some understanding going forward \n",
    "adj_close.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- So we see some good information here about each of the stocks. Looking at `TWTR` specifically (since its the most known of the stocks):\n",
    "    - the average price of `TWTR` from 2017/01 - 2019/01 was <font color=blue>25.33</font>\n",
    "    - the standard deviation was <font color=blue>8.62</font> (*that is a pretty big STD for a tech stock...TWTR must've been volatile*)\n",
    "    - had a pretty big high...<font color=blue>46.76</font> is almost double its starting price from 2017/01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sweet, got the 5 random stocks, now want to see price over time\n",
    "\n",
    "plt.figure(figsize = (12,10))\n",
    "plt.plot(adj_close)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.title(\"Stock Price Over Time\")\n",
    "plt.legend(adj_close.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick Glance\n",
    "\n",
    "1) *Volatility Abundant*:\n",
    "- There is some serious volatility in these stocks.\n",
    "    - `WDC` has a 50 buck range and has a huge sell off from January 2018 -> January 2019\n",
    "    - `TWTR` meanwhile almost doubles in value from January 2017 -> July 2018.\n",
    "        - There's also a HUGE drop off sometime after July...?\n",
    "            - Possibly a dividend? Horrible earnings? Bad news?\n",
    "\n",
    "2) *Correlation*:\n",
    "- There doesn't seem to be any correlation between these stocks. It's possible that `BLL` and `IVZ` have a negative correlation? Seems they are moving in opposite directions, but the others seem to be quite sporadic. Visually - nothing conclusive here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some statistics about each of these 5 random stocks (which we'll use to calculate our efficient portfolio):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simple_return = adj_close.pct_change()\n",
    "\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "ax1 = fig.add_subplot(3, 2, 1)\n",
    "ax2 = fig.add_subplot(3, 2, 2)\n",
    "ax3 = fig.add_subplot(3, 2, 3)\n",
    "ax4 = fig.add_subplot(3, 2, 4)\n",
    "ax5 = fig.add_subplot(3, 2, 5)\n",
    "\n",
    "ax1.plot(simple_return['BLL'])\n",
    "ax1.set_title(\"BLL\")\n",
    "\n",
    "ax2.plot(simple_return['WDC'])\n",
    "ax2.set_title(\"WDC\")\n",
    "\n",
    "ax3.plot(simple_return['IVZ'])\n",
    "ax3.set_title(\"IVZ\")\n",
    "\n",
    "ax4.plot(simple_return['PPL'])\n",
    "ax4.set_title(\"PPL\")\n",
    "\n",
    "ax5.plot(simple_return['TWTR'])\n",
    "ax5.set_title(\"TWTR\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Daily Moves Analysis\n",
    "\n",
    "- `BLL`:\n",
    "    - seems pretty consistent, minus a few big up moves\n",
    "- `WDC`:\n",
    "    - Much more volatile than the other stocks. Moves are in the +/-0.05 range\n",
    "    - Huge spike down in November 2018\n",
    "    - Daily moves are consistent on the downside. Multiple days with losses >-0.5\n",
    "- `IVZ`:\n",
    "    - Pretty whippy but consistent \n",
    "- `PPL`:\n",
    "    - Extremely uninspiring in 2017. It has no big up/down days and consitently stay in a 0.02 range\n",
    "    - 2018 shows volatility - especially with a few big daily downside moves\n",
    "- `TWTR`:\n",
    "    - EXTREMELY volatile. Has multiple days where daily moves are >0.10\n",
    "    - Has 2 days that it moves about 0.20 (November 2017 and August 2018)\n",
    "\n",
    "\n",
    "- `Overall`:\n",
    "    - `TWTR`, followed by `WDC`, are showing huge volatility. Daily moves are in the .05 and higher range\n",
    "    - The other stocks show relatively calm moves compared to the two above\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see what the average daily return is on each stock\n",
    "\n",
    "simple_returns_daily_return = simple_return.mean()\n",
    "\n",
    "plt.bar(random_stocks, simple_returns_daily_return)\n",
    "plt.xlabel(\"Stocks\")\n",
    "plt.ylabel(\"Average Daily Return\")\n",
    "plt.title(\"Average Daily Return of Stocks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see the variance of each stock \n",
    "\n",
    "simple_return_var = simple_return.var()\n",
    "\n",
    "plt.bar(random_stocks, simple_return_var)\n",
    "plt.xlabel(\"Stocks\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.title(\"Variance of Daily Stock Returns\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see the standard deviation of each stock\n",
    "\n",
    "simple_return_std = simple_return.std()\n",
    "\n",
    "plt.bar(random_stocks, simple_return_std)\n",
    "plt.xlabel(\"Stocks\")\n",
    "plt.ylabel(\"Std Dev\")\n",
    "plt.title(\"Std Dev of Stocks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Symbols</th>\n",
       "      <th>BLL</th>\n",
       "      <th>WDC</th>\n",
       "      <th>IVZ</th>\n",
       "      <th>PPL</th>\n",
       "      <th>TWTR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</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>BLL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245366</td>\n",
       "      <td>0.328791</td>\n",
       "      <td>0.189251</td>\n",
       "      <td>0.147777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDC</th>\n",
       "      <td>0.245366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325156</td>\n",
       "      <td>0.024846</td>\n",
       "      <td>0.250495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVZ</th>\n",
       "      <td>0.328791</td>\n",
       "      <td>0.325156</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081179</td>\n",
       "      <td>0.131610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPL</th>\n",
       "      <td>0.189251</td>\n",
       "      <td>0.024846</td>\n",
       "      <td>0.081179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR</th>\n",
       "      <td>0.147777</td>\n",
       "      <td>0.250495</td>\n",
       "      <td>0.131610</td>\n",
       "      <td>-0.027687</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols       BLL       WDC       IVZ       PPL      TWTR\n",
       "Symbols                                                  \n",
       "BLL      1.000000  0.245366  0.328791  0.189251  0.147777\n",
       "WDC      0.245366  1.000000  0.325156  0.024846  0.250495\n",
       "IVZ      0.328791  0.325156  1.000000  0.081179  0.131610\n",
       "PPL      0.189251  0.024846  0.081179  1.000000 -0.027687\n",
       "TWTR     0.147777  0.250495  0.131610 -0.027687  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see the correlation of the stocks\n",
    "\n",
    "correlation = simple_return.corr()\n",
    "correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PPL TWTR\n",
      "BLL IVZ\n"
     ]
    }
   ],
   "source": [
    "#Minimum value in the corr matrix:\n",
    "a, b = correlation.stack().idxmin()\n",
    "print(a, b)\n",
    "\n",
    "#Maximum value in the corr matrix:\n",
    "c, d = correlation.where(correlation < 1.00).stack().idxmax()\n",
    "print(c, d)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "These are pretty weak correlations across the board. The most correlated stocks are `BLL` and `IVZ` with a correlation of 0.329. The least correlated stocks are `PPL` and `TWTR` with a correlation of -0.028. What we saw visually before was true, we didnt visually see much a correlation between the stocks, and the stats confirmed it. \n",
    "\n",
    "We also see 3 stocks produce an average *negative* daily return in a time when the market had a pretty big upswing...showing us that these 3 stocks were losers in the overall market rally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean-Variance Optimization\n",
    "\n",
    "Can we return a set of portfolio options with volatility and annualized returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5000 random sample portfolios to create optimal portfolio\n",
    "\n",
    "def return_portfolios(expected_returns, cov_matrix):\n",
    "    port_returns = []\n",
    "    port_volatility = []\n",
    "    stock_weights = []\n",
    "    \n",
    "    selected = (expected_returns.axes)[0]\n",
    "    \n",
    "    num_assets = len(selected) \n",
    "    num_portfolios = 5000\n",
    "    \n",
    "    for single_portfolio in range(num_portfolios):\n",
    "        weights = np.random.random(num_assets)\n",
    "        weights /= np.sum(weights)\n",
    "        returns = np.dot(weights, expected_returns)\n",
    "        volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n",
    "        port_returns.append(returns)\n",
    "        port_volatility.append(volatility)\n",
    "        stock_weights.append(weights)\n",
    "    \n",
    "    portfolio = {'Returns': port_returns,\n",
    "                 'Volatility': port_volatility}\n",
    "    \n",
    "    for counter,symbol in enumerate(selected):\n",
    "        portfolio[symbol +' Weight'] = [Weight[counter] for Weight in stock_weights]\n",
    "    \n",
    "    df = pd.DataFrame(portfolio)\n",
    "    \n",
    "    column_order = ['Returns', 'Volatility'] + [stock+' Weight' for stock in selected]\n",
    "    \n",
    "    df = df[column_order]\n",
    "   \n",
    "    return df\n",
    "  \n",
    "def optimal_portfolio(returns):\n",
    "    n = returns.shape[1]\n",
    "    returns = np.transpose(returns.values)\n",
    "\n",
    "    N = 100\n",
    "    mus = [10**(5.0 * t/N - 1.0) for t in range(N)]\n",
    "\n",
    "    # Convert to cvxopt matrices\n",
    "    S = opt.matrix(np.cov(returns))\n",
    "    pbar = opt.matrix(np.mean(returns, axis=1))\n",
    "\n",
    "    # Create constraint matrices\n",
    "    G = -opt.matrix(np.eye(n))   # negative n x n identity matrix\n",
    "    h = opt.matrix(0.0, (n ,1))\n",
    "    A = opt.matrix(1.0, (1, n))\n",
    "    b = opt.matrix(1.0)\n",
    "\n",
    "    # Calculate efficient frontier weights using quadratic programming\n",
    "    portfolios = [solvers.qp(mu*S, -pbar, G, h, A, b)['x'] for mu in mus]\n",
    "    \n",
    "    ## CALCULATE RISKS AND RETURNS FOR FRONTIER\n",
    "    returns = [blas.dot(pbar, x) for x in portfolios]\n",
    "    risks = [np.sqrt(blas.dot(x, S*x)) for x in portfolios]\n",
    "    ## CALCULATE THE 2ND DEGREE POLYNOMIAL OF THE FRONTIER CURVE\n",
    "    m1 = np.polyfit(returns, risks, 2)\n",
    "    x1 = np.sqrt(m1[2] / m1[0])\n",
    "    # CALCULATE THE OPTIMAL PORTFOLIO\n",
    "    wt = solvers.qp(opt.matrix(x1 * S), -pbar, G, h, A, b)['x']\n",
    "    return np.asarray(wt), returns, risks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Symbols</th>\n",
       "      <th>BLL</th>\n",
       "      <th>WDC</th>\n",
       "      <th>IVZ</th>\n",
       "      <th>PPL</th>\n",
       "      <th>TWTR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</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>BLL</th>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDC</th>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVZ</th>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPL</th>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>-0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR</th>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.001004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols       BLL       WDC       IVZ       PPL      TWTR\n",
       "Symbols                                                  \n",
       "BLL      0.000149  0.000067  0.000062  0.000026  0.000057\n",
       "WDC      0.000067  0.000498  0.000112  0.000006  0.000177\n",
       "IVZ      0.000062  0.000112  0.000239  0.000014  0.000065\n",
       "PPL      0.000026  0.000006  0.000014  0.000124 -0.000010\n",
       "TWTR     0.000057  0.000177  0.000065 -0.000010  0.001004"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the covariance of the portfolio\n",
    "\n",
    "simple_return.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_options = return_portfolios(simple_return.mean(), simple_return.cov())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.7349e-05 -1.0016e+00  1e+00  2e-16  3e+00\n",
      " 1: -3.9064e-05 -1.1603e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.8013e-04 -1.6992e-03  2e-03  1e-16  4e-03\n",
      " 3: -1.4233e-03 -2.2050e-03  8e-04  1e-15  1e-03\n",
      " 4: -1.5626e-03 -1.5790e-03  2e-05  2e-16  2e-05\n",
      " 5: -1.5726e-03 -1.5728e-03  2e-07  2e-16  2e-07\n",
      " 6: -1.5727e-03 -1.5727e-03  2e-09  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.6572e-05 -1.0016e+00  1e+00  3e-16  3e+00\n",
      " 1: -3.8285e-05 -1.1601e-02  1e-02  7e-17  3e-02\n",
      " 2: -1.7915e-04 -1.6963e-03  2e-03  6e-17  4e-03\n",
      " 3: -1.4209e-03 -2.1984e-03  8e-04  8e-16  1e-03\n",
      " 4: -1.5566e-03 -1.5729e-03  2e-05  1e-16  2e-05\n",
      " 5: -1.5665e-03 -1.5666e-03  2e-07  2e-16  2e-07\n",
      " 6: -1.5666e-03 -1.5666e-03  2e-09  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.5700e-05 -1.0016e+00  1e+00  1e-16  3e+00\n",
      " 1: -3.7411e-05 -1.1598e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.7806e-04 -1.6931e-03  2e-03  2e-16  4e-03\n",
      " 3: -1.4183e-03 -2.1910e-03  8e-04  1e-15  9e-04\n",
      " 4: -1.5500e-03 -1.5660e-03  2e-05  1e-16  2e-05\n",
      " 5: -1.5596e-03 -1.5598e-03  2e-07  2e-16  2e-07\n",
      " 6: -1.5597e-03 -1.5597e-03  2e-09  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.4722e-05 -1.0016e+00  1e+00  1e-16  3e+00\n",
      " 1: -3.6430e-05 -1.1595e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.7682e-04 -1.6894e-03  2e-03  6e-17  4e-03\n",
      " 3: -1.4153e-03 -2.1827e-03  8e-04  3e-16  9e-04\n",
      " 4: -1.5425e-03 -1.5583e-03  2e-05  4e-18  2e-05\n",
      " 5: -1.5519e-03 -1.5521e-03  2e-07  2e-16  2e-07\n",
      " 6: -1.5520e-03 -1.5520e-03  2e-09  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.3624e-05 -1.0016e+00  1e+00  8e-17  3e+00\n",
      " 1: -3.5330e-05 -1.1592e-02  1e-02  9e-17  3e-02\n",
      " 2: -1.7544e-04 -1.6854e-03  2e-03  1e-16  4e-03\n",
      " 3: -1.4121e-03 -2.1734e-03  8e-04  6e-16  9e-04\n",
      " 4: -1.5342e-03 -1.5496e-03  2e-05  2e-16  2e-05\n",
      " 5: -1.5432e-03 -1.5434e-03  2e-07  1e-16  2e-07\n",
      " 6: -1.5433e-03 -1.5433e-03  2e-09  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.2392e-05 -1.0016e+00  1e+00  2e-16  3e+00\n",
      " 1: -3.4095e-05 -1.1588e-02  1e-02  9e-17  3e-02\n",
      " 2: -1.7389e-04 -1.6808e-03  2e-03  6e-17  4e-03\n",
      " 3: -1.4085e-03 -2.1631e-03  8e-04  8e-16  9e-04\n",
      " 4: -1.5248e-03 -1.5399e-03  2e-05  1e-16  2e-05\n",
      " 5: -1.5335e-03 -1.5337e-03  2e-07  2e-16  2e-07\n",
      " 6: -1.5336e-03 -1.5336e-03  2e-09  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.1010e-05 -1.0016e+00  1e+00  1e-16  3e+00\n",
      " 1: -3.2710e-05 -1.1584e-02  1e-02  9e-17  3e-02\n",
      " 2: -1.7216e-04 -1.6757e-03  2e-03  1e-16  4e-03\n",
      " 3: -1.4046e-03 -2.1516e-03  7e-04  3e-16  8e-04\n",
      " 4: -1.5143e-03 -1.5290e-03  1e-05  2e-16  2e-05\n",
      " 5: -1.5227e-03 -1.5228e-03  1e-07  2e-16  2e-07\n",
      " 6: -1.5227e-03 -1.5227e-03  1e-09  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.9460e-05 -1.0016e+00  1e+00  3e-16  3e+00\n",
      " 1: -3.1156e-05 -1.1579e-02  1e-02  9e-17  3e-02\n",
      " 2: -1.7021e-04 -1.6699e-03  1e-03  6e-17  4e-03\n",
      " 3: -1.4003e-03 -2.1387e-03  7e-04  4e-16  8e-04\n",
      " 4: -1.5026e-03 -1.5168e-03  1e-05  1e-16  1e-05\n",
      " 5: -1.5104e-03 -1.5106e-03  1e-07  2e-16  1e-07\n",
      " 6: -1.5105e-03 -1.5105e-03  1e-09  2e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.7720e-05 -1.0016e+00  1e+00  8e-17  3e+00\n",
      " 1: -2.9412e-05 -1.1573e-02  1e-02  2e-16  3e-02\n",
      " 2: -1.6803e-04 -1.6635e-03  1e-03  1e-16  4e-03\n",
      " 3: -1.3956e-03 -2.1244e-03  7e-04  4e-16  7e-04\n",
      " 4: -1.4894e-03 -1.5031e-03  1e-05  1e-16  1e-05\n",
      " 5: -1.4967e-03 -1.4969e-03  1e-07  2e-16  1e-07\n",
      " 6: -1.4968e-03 -1.4968e-03  1e-09  1e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.5768e-05 -1.0016e+00  1e+00  2e-16  3e+00\n",
      " 1: -2.7456e-05 -1.1567e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.6558e-04 -1.6563e-03  1e-03  7e-17  4e-03\n",
      " 3: -1.3906e-03 -2.1086e-03  7e-04  4e-16  7e-04\n",
      " 4: -1.4747e-03 -1.4877e-03  1e-05  2e-16  1e-05\n",
      " 5: -1.4813e-03 -1.4815e-03  1e-07  2e-16  1e-07\n",
      " 6: -1.4814e-03 -1.4814e-03  1e-09  1e-21  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.3578e-05 -1.0016e+00  1e+00  8e-17  3e+00\n",
      " 1: -2.5260e-05 -1.1561e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.6285e-04 -1.6482e-03  1e-03  6e-17  4e-03\n",
      " 3: -1.3850e-03 -2.0910e-03  7e-04  3e-16  6e-04\n",
      " 4: -1.4582e-03 -1.4704e-03  1e-05  2e-16  1e-05\n",
      " 5: -1.4641e-03 -1.4642e-03  1e-07  1e-16  1e-07\n",
      " 6: -1.4641e-03 -1.4641e-03  1e-09  4e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.1121e-05 -1.0016e+00  1e+00  2e-16  3e+00\n",
      " 1: -2.2798e-05 -1.1553e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.5978e-04 -1.6392e-03  1e-03  1e-16  4e-03\n",
      " 3: -1.3791e-03 -2.0714e-03  7e-04  3e-16  5e-04\n",
      " 4: -1.4398e-03 -1.4510e-03  1e-05  2e-16  8e-06\n",
      " 5: -1.4447e-03 -1.4448e-03  1e-07  1e-16  8e-08\n",
      " 6: -1.4448e-03 -1.4448e-03  1e-09  1e-16  8e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.8364e-05 -1.0015e+00  1e+00  3e-17  3e+00\n",
      " 1: -2.0034e-05 -1.1545e-02  1e-02  8e-17  3e-02\n",
      " 2: -1.5634e-04 -1.6290e-03  1e-03  8e-17  4e-03\n",
      " 3: -1.3728e-03 -2.0498e-03  7e-04  1e-15  4e-04\n",
      " 4: -1.4191e-03 -1.4293e-03  1e-05  3e-18  6e-06\n",
      " 5: -1.4230e-03 -1.4231e-03  1e-07  1e-16  6e-08\n",
      " 6: -1.4230e-03 -1.4230e-03  1e-09  1e-21  6e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.5271e-05 -1.0015e+00  1e+00  1e-16  3e+00\n",
      " 1: -1.6934e-05 -1.1535e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.5249e-04 -1.6177e-03  1e-03  7e-17  4e-03\n",
      " 3: -1.3662e-03 -2.0260e-03  7e-04  3e-16  3e-04\n",
      " 4: -1.3960e-03 -1.4049e-03  9e-06  1e-16  4e-06\n",
      " 5: -1.3986e-03 -1.3987e-03  9e-08  2e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.1800e-05 -1.0015e+00  1e+00  6e-17  3e+00\n",
      " 1: -1.3456e-05 -1.1524e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.4818e-04 -1.6049e-03  1e-03  7e-17  4e-03\n",
      " 3: -1.3525e-03 -1.9966e-03  6e-04  3e-16  2e-04\n",
      " 4: -1.3695e-03 -1.3775e-03  8e-06  2e-16  3e-06\n",
      " 5: -1.3713e-03 -1.3713e-03  8e-08  1e-16  3e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -7.9068e-06 -1.0015e+00  1e+00  3e-17  3e+00\n",
      " 1: -9.5539e-06 -1.1512e-02  1e-02  9e-17  3e-02\n",
      " 2: -1.4336e-04 -1.5907e-03  1e-03  2e-16  4e-03\n",
      " 3: -1.3229e-03 -1.9568e-03  6e-04  2e-16  1e-04\n",
      " 4: -1.3382e-03 -1.3467e-03  9e-06  2e-16  2e-06\n",
      " 5: -1.3406e-03 -1.3406e-03  9e-08  1e-16  2e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.5384e-06 -1.0015e+00  1e+00  2e-16  3e+00\n",
      " 1: -5.1760e-06 -1.1499e-02  1e-02  9e-17  3e-02\n",
      " 2: -1.3797e-04 -1.5747e-03  1e-03  6e-17  4e-03\n",
      " 3: -1.2828e-03 -1.9086e-03  6e-04  6e-16  8e-05\n",
      " 4: -1.3023e-03 -1.3122e-03  1e-05  1e-16  1e-06\n",
      " 5: -1.3061e-03 -1.3062e-03  1e-07  1e-16  1e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3627e-06 -1.0015e+00  1e+00  0e+00  3e+00\n",
      " 1: -2.6436e-07 -1.1484e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.3193e-04 -1.5569e-03  1e-03  1e-16  4e-03\n",
      " 3: -1.2389e-03 -1.8546e-03  6e-04  4e-16  2e-05\n",
      " 4: -1.2620e-03 -1.2734e-03  1e-05  1e-18  3e-07\n",
      " 5: -1.2674e-03 -1.2675e-03  1e-07  1e-16  3e-09\n",
      " 6: -1.2675e-03 -1.2675e-03  1e-09  1e-16  3e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.8613e-06 -1.0015e+00  1e+00  2e-16  3e+00\n",
      " 1:  5.2459e-06 -1.1467e-02  1e-02  6e-17  3e-02\n",
      " 2: -1.2518e-04 -1.5369e-03  1e-03  6e-17  4e-03\n",
      " 3: -1.1804e-03 -1.7891e-03  6e-04  1e-16  7e-19\n",
      " 4: -1.2155e-03 -1.2298e-03  1e-05  1e-16  7e-19\n",
      " 5: -1.2240e-03 -1.2242e-03  2e-07  1e-16  9e-19\n",
      " 6: -1.2241e-03 -1.2241e-03  2e-09  3e-16  8e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3030e-05 -1.0014e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.1428e-05 -1.1447e-02  1e-02  6e-17  3e-02\n",
      " 2: -1.1764e-04 -1.5145e-03  1e-03  8e-17  4e-03\n",
      " 3: -1.1147e-03 -1.7153e-03  6e-04  3e-16  1e-18\n",
      " 4: -1.1629e-03 -1.1811e-03  2e-05  2e-16  6e-19\n",
      " 5: -1.1753e-03 -1.1755e-03  3e-07  3e-16  4e-19\n",
      " 6: -1.1755e-03 -1.1755e-03  3e-09  1e-16  3e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9951e-05 -1.0014e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.8363e-05 -1.1426e-02  1e-02  1e-16  3e-02\n",
      " 2: -1.0922e-04 -1.4895e-03  1e-03  9e-17  4e-03\n",
      " 3: -1.0465e-03 -1.6352e-03  6e-04  1e-16  1e-18\n",
      " 4: -1.1049e-03 -1.1267e-03  2e-05  1e-16  4e-19\n",
      " 5: -1.1204e-03 -1.1209e-03  5e-07  2e-16  3e-19\n",
      " 6: -1.1208e-03 -1.1209e-03  5e-09  2e-16  3e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7716e-05 -1.0014e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.6143e-05 -1.1402e-02  1e-02  1e-16  3e-02\n",
      " 2: -9.9816e-05 -1.4616e-03  1e-03  1e-16  4e-03\n",
      " 3: -9.7666e-04 -1.5487e-03  6e-04  4e-16  1e-18\n",
      " 4: -1.0419e-03 -1.0664e-03  2e-05  2e-16  9e-19\n",
      " 5: -1.0584e-03 -1.0597e-03  1e-06  1e-16  7e-19\n",
      " 6: -1.0596e-03 -1.0596e-03  2e-08  1e-16  9e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.6427e-05 -1.0014e+00  1e+00  0e+00  3e+00\n",
      " 1:  3.4871e-05 -1.1375e-02  1e-02  7e-17  3e-02\n",
      " 2: -8.9323e-05 -1.4305e-03  1e-03  8e-17  4e-03\n",
      " 3: -9.0575e-04 -1.4560e-03  6e-04  1e-16  9e-19\n",
      " 4: -9.7535e-04 -1.0004e-03  3e-05  2e-18  5e-19\n",
      " 5: -9.8998e-04 -9.9166e-04  2e-06  1e-16  2e-19\n",
      " 6: -9.9084e-04 -9.9101e-04  2e-07  2e-18  6e-19\n",
      " 7: -9.9089e-04 -9.9091e-04  2e-08  2e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.6199e-05 -1.0013e+00  1e+00  6e-17  3e+00\n",
      " 1:  4.4661e-05 -1.1345e-02  1e-02  1e-16  3e-02\n",
      " 2: -7.7623e-05 -1.3957e-03  1e-03  7e-17  3e-03\n",
      " 3: -8.3468e-04 -1.3574e-03  5e-04  1e-16  7e-19\n",
      " 4: -9.0795e-04 -9.2996e-04  2e-05  2e-16  1e-18\n",
      " 5: -9.2171e-04 -9.2201e-04  3e-07  1e-16  7e-19\n",
      " 6: -9.2193e-04 -9.2194e-04  3e-09  2e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.7161e-05 -1.0013e+00  1e+00  2e-16  3e+00\n",
      " 1:  5.5644e-05 -1.1311e-02  1e-02  6e-17  3e-02\n",
      " 2: -6.4587e-05 -1.3569e-03  1e-03  1e-16  3e-03\n",
      " 3: -7.6443e-04 -1.2538e-03  5e-04  6e-16  1e-18\n",
      " 4: -8.3994e-04 -8.7534e-04  4e-05  3e-16  4e-19\n",
      " 5: -8.5775e-04 -8.5825e-04  5e-07  1e-16  1e-19\n",
      " 6: -8.5798e-04 -8.5799e-04  5e-09  1e-16  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.9460e-05 -1.0013e+00  1e+00  0e+00  3e+00\n",
      " 1:  6.7963e-05 -1.1273e-02  1e-02  9e-17  3e-02\n",
      " 2: -5.0075e-05 -1.3136e-03  1e-03  1e-16  3e-03\n",
      " 3: -6.9602e-04 -1.1459e-03  4e-04  2e-16  9e-19\n",
      " 4: -7.7439e-04 -8.2995e-04  6e-05  6e-17  4e-19\n",
      " 5: -7.9782e-04 -7.9867e-04  8e-07  1e-16  2e-19\n",
      " 6: -7.9820e-04 -7.9820e-04  8e-09  2e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.3255e-05 -1.0012e+00  1e+00  8e-17  3e+00\n",
      " 1:  8.1782e-05 -1.1230e-02  1e-02  2e-16  3e-02\n",
      " 2: -3.3935e-05 -1.2654e-03  1e-03  7e-17  3e-03\n",
      " 3: -6.3045e-04 -1.0355e-03  4e-04  2e-16  7e-19\n",
      " 4: -7.1841e-04 -7.7113e-04  5e-05  1e-16  5e-19\n",
      " 5: -7.4147e-04 -7.4214e-04  7e-07  1e-16  3e-19\n",
      " 6: -7.4178e-04 -7.4178e-04  7e-09  1e-16  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.8731e-05 -1.0012e+00  1e+00  1e-16  3e+00\n",
      " 1:  9.7282e-05 -1.1183e-02  1e-02  8e-17  3e-02\n",
      " 2: -1.6005e-05 -1.2118e-03  1e-03  1e-16  3e-03\n",
      " 3: -5.6869e-04 -9.2416e-04  4e-04  2e-16  3e-19\n",
      " 4: -6.7470e-04 -7.0200e-04  3e-05  2e-16  7e-19\n",
      " 5: -6.8783e-04 -6.8812e-04  3e-07  1e-18  5e-19\n",
      " 6: -6.8798e-04 -6.8798e-04  3e-09  4e-20  9e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1609e-04 -1.0011e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.1467e-04 -1.1129e-02  1e-02  8e-17  3e-02\n",
      " 2:  3.8913e-06 -1.1522e-03  1e-03  1e-16  3e-03\n",
      " 3: -5.1154e-04 -8.1441e-04  3e-04  1e-16  6e-19\n",
      " 4: -6.2876e-04 -6.5028e-04  2e-05  6e-17  4e-19\n",
      " 5: -6.3598e-04 -6.3623e-04  2e-07  1e-16  5e-19\n",
      " 6: -6.3608e-04 -6.3608e-04  2e-09  2e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3556e-04 -1.0011e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.3416e-04 -1.1069e-02  1e-02  2e-16  3e-02\n",
      " 2:  2.5938e-05 -1.0861e-03  1e-03  1e-16  3e-03\n",
      " 3: -4.5958e-04 -7.0900e-04  2e-04  2e-16  7e-19\n",
      " 4: -5.7784e-04 -5.9929e-04  2e-05  8e-17  7e-19\n",
      " 5: -5.8530e-04 -5.8555e-04  3e-07  6e-17  3e-19\n",
      " 6: -5.8540e-04 -5.8540e-04  3e-09  1e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5740e-04 -1.0010e+00  1e+00  0e+00  3e+00\n",
      " 1:  1.5603e-04 -1.1001e-02  1e-02  9e-17  3e-02\n",
      " 2:  5.0332e-05 -1.0128e-03  1e-03  9e-17  3e-03\n",
      " 3: -4.0259e-04 -6.1983e-04  2e-04  1e-16  8e-05\n",
      " 4: -5.2646e-04 -5.4385e-04  2e-05  1e-16  7e-19\n",
      " 5: -5.3514e-04 -5.3535e-04  2e-07  1e-16  5e-19\n",
      " 6: -5.3526e-04 -5.3527e-04  2e-09  1e-16  2e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8190e-04 -1.0009e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.8055e-04 -1.0926e-02  1e-02  8e-17  3e-02\n",
      " 2:  7.7275e-05 -9.3165e-04  1e-03  8e-17  3e-03\n",
      " 3: -3.4167e-04 -5.5097e-04  2e-04  6e-17  2e-04\n",
      " 4: -4.7592e-04 -4.9163e-04  2e-05  6e-17  2e-19\n",
      " 5: -4.8484e-04 -4.8508e-04  2e-07  1e-16  2e-19\n",
      " 6: -4.8501e-04 -4.8501e-04  2e-09  1e-16  3e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0937e-04 -1.0008e+00  1e+00  6e-17  3e+00\n",
      " 1:  2.0805e-04 -1.0841e-02  1e-02  1e-16  3e-02\n",
      " 2:  1.0698e-04 -8.4200e-04  9e-04  1e-16  3e-03\n",
      " 3: -2.9483e-04 -4.8628e-04  2e-04  2e-16  3e-04\n",
      " 4: -4.2531e-04 -4.3870e-04  1e-05  6e-17  6e-19\n",
      " 5: -4.3370e-04 -4.3401e-04  3e-07  1e-16  2e-19\n",
      " 6: -4.3396e-04 -4.3396e-04  3e-09  6e-17  6e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4018e-04 -1.0007e+00  1e+00  3e-17  3e+00\n",
      " 1:  2.3888e-04 -1.0746e-02  1e-02  2e-16  3e-02\n",
      " 2:  1.3966e-04 -7.4316e-04  9e-04  6e-17  2e-03\n",
      " 3: -2.7908e-04 -4.2370e-04  1e-04  2e-16  1e-04\n",
      " 4: -3.7535e-04 -3.8383e-04  8e-06  1e-16  8e-19\n",
      " 5: -3.8110e-04 -3.8148e-04  4e-07  1e-16  7e-19\n",
      " 6: -3.8145e-04 -3.8145e-04  4e-09  1e-16  9e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7473e-04 -1.0006e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.7345e-04 -1.0639e-02  1e-02  8e-17  3e-02\n",
      " 2:  1.7552e-04 -6.3444e-04  8e-04  8e-17  2e-03\n",
      " 3: -2.6657e-04 -4.0119e-04  1e-04  2e-16  4e-19\n",
      " 4: -3.2181e-04 -3.2934e-04  8e-06  2e-16  3e-19\n",
      " 5: -3.2627e-04 -3.2688e-04  6e-07  2e-16  5e-19\n",
      " 6: -3.2674e-04 -3.2678e-04  4e-08  1e-16  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1348e-04 -1.0005e+00  1e+00  0e+00  3e+00\n",
      " 1:  3.1221e-04 -1.0520e-02  1e-02  1e-16  3e-02\n",
      " 2:  2.1476e-04 -5.1518e-04  7e-04  9e-17  2e-03\n",
      " 3: -2.2607e-04 -3.9986e-04  2e-04  2e-16  1e-18\n",
      " 4: -2.6667e-04 -2.7436e-04  8e-06  1e-16  5e-19\n",
      " 5: -2.6978e-04 -2.7030e-04  5e-07  2e-16  4e-19\n",
      " 6: -2.6987e-04 -2.6989e-04  2e-08  6e-17  3e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.5692e-04 -1.0004e+00  1e+00  6e-17  3e+00\n",
      " 1:  3.5565e-04 -1.0386e-02  1e-02  1e-16  3e-02\n",
      " 2:  2.5845e-04 -4.7675e-04  7e-04  4e-17  2e-03\n",
      " 3: -1.6887e-04 -3.6028e-04  2e-04  2e-16  3e-19\n",
      " 4: -2.1073e-04 -2.1728e-04  7e-06  1e-16  6e-19\n",
      " 5: -2.1328e-04 -2.1342e-04  1e-07  3e-16  2e-19\n",
      " 6: -2.1331e-04 -2.1331e-04  1e-09  6e-17  2e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.0563e-04 -1.0004e+00  1e+00  2e-16  3e+00\n",
      " 1:  4.0435e-04 -1.0378e-02  1e-02  1e-16  3e-02\n",
      " 2:  3.0730e-04 -4.6180e-04  8e-04  9e-17  2e-03\n",
      " 3: -1.0761e-04 -3.0764e-04  2e-04  1e-16  5e-19\n",
      " 4: -1.5412e-04 -1.5957e-04  5e-06  3e-17  4e-19\n",
      " 5: -1.5682e-04 -1.5688e-04  6e-08  6e-17  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.6024e-04 -1.0004e+00  1e+00  3e-17  3e+00\n",
      " 1:  4.5892e-04 -1.0370e-02  1e-02  7e-17  3e-02\n",
      " 2:  3.6040e-04 -4.4385e-04  8e-04  7e-17  2e-03\n",
      " 3: -4.4864e-05 -2.5003e-04  2e-04  1e-16  4e-19\n",
      " 4: -9.6606e-05 -1.0144e-04  5e-06  1e-16  6e-19\n",
      " 5: -9.9684e-05 -9.9732e-05  5e-08  6e-17  7e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.2146e-04 -1.0004e+00  1e+00  2e-16  3e+00\n",
      " 1:  5.2007e-04 -1.0361e-02  1e-02  1e-16  3e-02\n",
      " 2:  4.1806e-04 -4.2241e-04  8e-04  6e-17  2e-03\n",
      " 3:  1.9365e-05 -1.8830e-04  2e-04  1e-16  5e-19\n",
      " 4: -3.7053e-05 -4.3452e-05  6e-06  1e-16  4e-19\n",
      " 5: -4.1122e-05 -4.1187e-05  6e-08  6e-17  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.9007e-04 -1.0004e+00  1e+00  3e-17  3e+00\n",
      " 1:  5.8859e-04 -1.0351e-02  1e-02  1e-16  3e-02\n",
      " 2:  4.8061e-04 -3.9690e-04  9e-04  9e-17  2e-03\n",
      " 3:  8.5379e-05 -1.2293e-04  2e-04  2e-16  1e-18\n",
      " 4:  2.4519e-05  1.7608e-05  7e-06  8e-17  5e-19\n",
      " 5:  1.9637e-05  1.9566e-05  7e-08  1e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.6696e-04 -1.0003e+00  1e+00  3e-17  3e+00\n",
      " 1:  6.6534e-04 -1.0340e-02  1e-02  1e-16  3e-02\n",
      " 2:  5.4841e-04 -3.6670e-04  9e-04  1e-16  2e-03\n",
      " 3:  1.5372e-04 -5.4168e-05  2e-04  6e-17  9e-19\n",
      " 4:  8.9166e-05  8.1868e-05  7e-06  1e-16  5e-19\n",
      " 5:  8.3401e-05  8.3324e-05  8e-08  1e-16  7e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.5312e-04 -1.0003e+00  1e+00  2e-16  3e+00\n",
      " 1:  7.5129e-04 -1.0326e-02  1e-02  1e-16  3e-02\n",
      " 2:  6.2188e-04 -3.3110e-04  1e-03  1e-16  3e-03\n",
      " 3:  2.2514e-04  1.8096e-05  2e-04  1e-16  9e-19\n",
      " 4:  1.5812e-04  1.4932e-04  9e-06  3e-17  3e-19\n",
      " 5:  1.5103e-04  1.5093e-04  1e-07  1e-16  3e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.4966e-04 -1.0003e+00  1e+00  8e-17  3e+00\n",
      " 1:  8.4754e-04 -1.0316e-02  1e-02  1e-16  3e-02\n",
      " 2:  7.0152e-04 -2.8942e-04  1e-03  7e-17  3e-03\n",
      " 3:  3.0052e-04  9.4439e-05  2e-04  2e-16  1e-18\n",
      " 4:  2.3192e-04  2.2154e-04  1e-05  2e-16  4e-19\n",
      " 5:  2.2341e-04  2.2328e-04  1e-07  1e-16  3e-19\n",
      " 6:  2.2329e-04  2.2329e-04  1e-09  1e-16  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.5780e-04 -1.0004e+00  1e+00  1e-16  3e+00\n",
      " 1:  9.5529e-04 -1.0367e-02  1e-02  9e-17  3e-02\n",
      " 2:  7.8872e-04 -2.5680e-04  1e-03  2e-16  3e-03\n",
      " 3:  3.8145e-04  1.7584e-04  2e-04  9e-17  8e-19\n",
      " 4:  3.1154e-04  2.9948e-04  1e-05  6e-17  4e-19\n",
      " 5:  3.0152e-04  3.0133e-04  2e-07  1e-16  3e-19\n",
      " 6:  3.0135e-04  3.0135e-04  2e-09  2e-16  6e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0789e-03 -1.0004e+00  1e+00  0e+00  3e+00\n",
      " 1:  1.0759e-03 -1.0424e-02  1e-02  5e-17  3e-02\n",
      " 2:  8.8419e-04 -2.6084e-04  1e-03  7e-17  3e-03\n",
      " 3:  4.6995e-04  2.6405e-04  2e-04  6e-17  5e-19\n",
      " 4:  3.9796e-04  3.8426e-04  1e-05  1e-16  6e-19\n",
      " 5:  3.8641e-04  3.8613e-04  3e-07  1e-16  2e-19\n",
      " 6:  3.8615e-04  3.8615e-04  3e-09  6e-17  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2145e-03 -1.0005e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.2108e-03 -1.0488e-02  1e-02  8e-17  3e-02\n",
      " 2:  9.8824e-04 -2.6170e-04  1e-03  1e-16  3e-03\n",
      " 3:  5.6543e-04  3.5753e-04  2e-04  8e-17  9e-19\n",
      " 4:  4.9227e-04  4.7636e-04  2e-05  8e-17  5e-19\n",
      " 5:  4.7925e-04  4.7880e-04  5e-07  1e-16  6e-19\n",
      " 6:  4.7882e-04  4.7881e-04  5e-09  2e-16  7e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3663e-03 -1.0006e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.3617e-03 -1.0559e-02  1e-02  8e-17  3e-02\n",
      " 2:  1.1018e-03 -2.5859e-04  1e-03  1e-16  3e-03\n",
      " 3:  6.6924e-04  4.4104e-04  2e-04  1e-16  2e-18\n",
      " 4:  5.9575e-04  5.7694e-04  2e-05  8e-17  6e-19\n",
      " 5:  5.8151e-04  5.8052e-04  1e-06  2e-16  4e-19\n",
      " 6:  5.8059e-04  5.8058e-04  1e-08  1e-16  4e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5362e-03 -1.0006e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.5304e-03 -1.0637e-02  1e-02  8e-17  3e-02\n",
      " 2:  1.2259e-03 -2.5059e-04  1e-03  8e-17  4e-03\n",
      " 3:  7.8176e-04  5.3418e-04  2e-04  4e-17  1e-18\n",
      " 4:  7.0880e-04  6.8742e-04  2e-05  1e-16  3e-19\n",
      " 5:  6.9444e-04  6.9263e-04  2e-06  1e-16  6e-19\n",
      " 6:  6.9282e-04  6.9279e-04  3e-08  1e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.7263e-03 -1.0007e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.7190e-03 -1.0724e-02  1e-02  9e-17  3e-02\n",
      " 2:  1.3617e-03 -2.3659e-04  2e-03  6e-17  4e-03\n",
      " 3:  9.0426e-04  6.3825e-04  3e-04  3e-17  8e-19\n",
      " 4:  8.3255e-04  8.0891e-04  2e-05  2e-16  7e-19\n",
      " 5:  8.1923e-04  8.1650e-04  3e-06  2e-16  4e-19\n",
      " 6:  8.1706e-04  8.1694e-04  1e-07  2e-16  3e-19\n",
      " 7:  8.1694e-04  8.1694e-04  1e-09  8e-17  6e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9390e-03 -1.0008e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.9296e-03 -1.0820e-02  1e-02  3e-17  3e-02\n",
      " 2:  1.5107e-03 -2.1523e-04  2e-03  6e-17  4e-03\n",
      " 3:  1.0382e-03  7.5476e-04  3e-04  7e-17  1e-18\n",
      " 4:  9.6833e-04  9.4235e-04  3e-05  1e-16  2e-19\n",
      " 5:  9.5678e-04  9.5341e-04  3e-06  2e-16  9e-19\n",
      " 6:  9.5491e-04  9.5454e-04  4e-07  4e-17  7e-19\n",
      " 7:  9.5469e-04  9.5466e-04  3e-08  1e-16  8e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1767e-03 -1.0009e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.1648e-03 -1.0926e-02  1e-02  6e-17  4e-02\n",
      " 2:  1.6741e-03 -1.8489e-04  2e-03  8e-17  5e-03\n",
      " 3:  1.1851e-03  8.8545e-04  3e-04  2e-16  1e-18\n",
      " 4:  1.1174e-03  1.0897e-03  3e-05  2e-16  9e-19\n",
      " 5:  1.1079e-03  1.1045e-03  3e-06  8e-17  8e-19\n",
      " 6:  1.1065e-03  1.1062e-03  3e-07  2e-16  7e-19\n",
      " 7:  1.1063e-03  1.1063e-03  1e-08  1e-16  8e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4423e-03 -1.0011e+00  1e+00  1e-16  3e+00\n",
      " 1:  2.4273e-03 -1.1044e-02  1e-02  1e-16  4e-02\n",
      " 2:  1.8537e-03 -1.4359e-04  2e-03  1e-16  5e-03\n",
      " 3:  1.3470e-03  1.0323e-03  3e-04  8e-17  1e-18\n",
      " 4:  1.2815e-03  1.2526e-03  3e-05  2e-17  4e-19\n",
      " 5:  1.2737e-03  1.2707e-03  3e-06  6e-17  5e-19\n",
      " 6:  1.2727e-03  1.2723e-03  3e-07  1e-16  6e-19\n",
      " 7:  1.2725e-03  1.2724e-03  4e-08  1e-16  8e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7391e-03 -1.0012e+00  1e+00  1e-16  3e+00\n",
      " 1:  2.7200e-03 -1.1172e-02  1e-02  6e-17  4e-02\n",
      " 2:  2.0511e-03 -8.8923e-05  2e-03  8e-17  5e-03\n",
      " 3:  1.5258e-03  1.1974e-03  3e-04  1e-16  3e-18\n",
      " 4:  1.4624e-03  1.4330e-03  3e-05  1e-16  1e-18\n",
      " 5:  1.4560e-03  1.4535e-03  3e-06  8e-17  1e-18\n",
      " 6:  1.4553e-03  1.4550e-03  3e-07  6e-17  5e-19\n",
      " 7:  1.4552e-03  1.4552e-03  3e-08  1e-16  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.0705e-03 -1.0014e+00  1e+00  1e-16  3e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1:  3.0463e-03 -1.1314e-02  1e-02  1e-16  4e-02\n",
      " 2:  2.2685e-03 -1.7978e-05  2e-03  9e-17  5e-03\n",
      " 3:  1.7238e-03  1.3833e-03  3e-04  1e-16  2e-18\n",
      " 4:  1.6625e-03  1.6331e-03  3e-05  4e-17  1e-18\n",
      " 5:  1.6571e-03  1.6548e-03  2e-06  2e-16  9e-19\n",
      " 6:  1.6566e-03  1.6564e-03  3e-07  1e-16  9e-19\n",
      " 7:  1.6566e-03  1.6566e-03  6e-09  1e-16  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.4403e-03 -1.0015e+00  1e+00  6e-17  3e+00\n",
      " 1:  3.4097e-03 -1.1468e-02  1e-02  8e-17  4e-02\n",
      " 2:  2.5078e-03  7.2778e-05  2e-03  7e-17  5e-03\n",
      " 3:  1.9443e-03  1.5897e-03  4e-04  1e-16  1e-05\n",
      " 4:  1.8843e-03  1.8549e-03  3e-05  1e-16  2e-18\n",
      " 5:  1.8796e-03  1.8773e-03  2e-06  1e-16  1e-18\n",
      " 6:  1.8793e-03  1.8791e-03  2e-07  1e-16  8e-19\n",
      " 7:  1.8793e-03  1.8793e-03  2e-09  1e-16  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.8528e-03 -1.0017e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.8141e-03 -1.1637e-02  2e-02  8e-17  4e-02\n",
      " 2:  2.7713e-03  1.8762e-04  3e-03  8e-17  6e-03\n",
      " 3:  2.1904e-03  1.8162e-03  4e-04  8e-17  5e-05\n",
      " 4:  2.1306e-03  2.1009e-03  3e-05  1e-16  2e-18\n",
      " 5:  2.1265e-03  2.1241e-03  2e-06  1e-16  1e-18\n",
      " 6:  2.1263e-03  2.1262e-03  1e-07  8e-17  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.3125e-03 -1.0019e+00  1e+00  1e-16  3e+00\n",
      " 1:  4.2638e-03 -1.1820e-02  2e-02  1e-16  4e-02\n",
      " 2:  3.0615e-03  3.3171e-04  3e-03  2e-16  6e-03\n",
      " 3:  2.4641e-03  2.0721e-03  4e-04  1e-16  9e-05\n",
      " 4:  2.4045e-03  2.3748e-03  3e-05  2e-16  1e-18\n",
      " 5:  2.4009e-03  2.3987e-03  2e-06  1e-16  2e-18\n",
      " 6:  2.4007e-03  2.4007e-03  6e-08  1e-16  5e-19\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.8246e-03 -1.0021e+00  1e+00  8e-17  3e+00\n",
      " 1:  4.7634e-03 -1.2017e-02  2e-02  9e-17  4e-02\n",
      " 2:  3.3812e-03  5.1127e-04  3e-03  1e-16  6e-03\n",
      " 3:  2.7691e-03  2.3613e-03  4e-04  1e-16  1e-04\n",
      " 4:  2.7097e-03  2.6802e-03  3e-05  8e-17  1e-18\n",
      " 5:  2.7064e-03  2.7045e-03  2e-06  3e-16  1e-18\n",
      " 6:  2.7064e-03  2.7063e-03  4e-08  1e-16  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.3944e-03 -1.0024e+00  1e+00  1e-16  3e+00\n",
      " 1:  5.3178e-03 -1.2230e-02  2e-02  9e-17  5e-02\n",
      " 2:  3.7333e-03  7.3372e-04  3e-03  1e-16  6e-03\n",
      " 3:  3.1090e-03  2.6882e-03  4e-04  8e-17  2e-04\n",
      " 4:  3.0502e-03  3.0210e-03  3e-05  1e-16  2e-18\n",
      " 5:  3.0473e-03  3.0456e-03  2e-06  6e-17  2e-18\n",
      " 6:  3.0472e-03  3.0472e-03  2e-08  1e-16  2e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.0282e-03 -1.0026e+00  1e+00  2e-16  3e+00\n",
      " 1:  5.9325e-03 -1.2458e-02  2e-02  1e-16  5e-02\n",
      " 2:  4.1211e-03  1.0079e-03  3e-03  1e-16  5e-03\n",
      " 3:  3.4884e-03  3.0580e-03  4e-04  1e-16  2e-04\n",
      " 4:  3.4304e-03  3.4019e-03  3e-05  6e-17  2e-18\n",
      " 5:  3.4279e-03  3.4264e-03  1e-06  8e-17  2e-18\n",
      " 6:  3.4278e-03  3.4278e-03  2e-08  6e-17  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.7323e-03 -1.0029e+00  1e+00  3e-17  3e+00\n",
      " 1:  6.6133e-03 -1.2700e-02  2e-02  7e-17  5e-02\n",
      " 2:  4.5481e-03  1.3443e-03  3e-03  1e-16  5e-03\n",
      " 3:  3.9120e-03  3.4766e-03  4e-04  1e-16  2e-04\n",
      " 4:  3.8554e-03  3.8279e-03  3e-05  9e-17  2e-18\n",
      " 5:  3.8533e-03  3.8520e-03  1e-06  6e-17  2e-18\n",
      " 6:  3.8533e-03  3.8532e-03  1e-08  1e-16  1e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.5139e-03 -1.0032e+00  1e+00  0e+00  3e+00\n",
      " 1:  7.3664e-03 -1.2955e-02  2e-02  6e-17  5e-02\n",
      " 2:  5.0187e-03  1.7552e-03  3e-03  4e-17  4e-03\n",
      " 3:  4.3853e-03  3.9507e-03  4e-04  1e-16  2e-04\n",
      " 4:  4.3310e-03  4.3028e-03  3e-05  9e-17  1e-06\n",
      " 5:  4.3291e-03  4.3280e-03  1e-06  1e-16  1e-08\n",
      " 6:  4.3291e-03  4.3291e-03  1e-08  7e-17  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.3806e-03 -1.0035e+00  1e+00  8e-17  3e+00\n",
      " 1:  8.1984e-03 -1.3220e-02  2e-02  1e-16  6e-02\n",
      " 2:  5.5377e-03  2.2547e-03  3e-03  1e-16  4e-03\n",
      " 3:  4.9143e-03  4.4881e-03  4e-04  2e-16  1e-04\n",
      " 4:  4.8633e-03  4.8349e-03  3e-05  6e-17  2e-06\n",
      " 5:  4.8617e-03  4.8608e-03  9e-07  2e-16  2e-08\n",
      " 6:  4.8617e-03  4.8617e-03  1e-08  8e-17  2e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.3406e-03 -1.0039e+00  1e+00  4e-16  3e+00\n",
      " 1:  9.1165e-03 -1.3494e-02  2e-02  6e-17  6e-02\n",
      " 2:  6.1110e-03  2.8589e-03  3e-03  1e-16  2e-03\n",
      " 3:  5.5059e-03  5.0979e-03  4e-04  6e-17  9e-05\n",
      " 4:  5.4594e-03  5.4322e-03  3e-05  9e-17  2e-06\n",
      " 5:  5.4581e-03  5.4574e-03  8e-07  1e-16  2e-08\n",
      " 6:  5.4581e-03  5.4581e-03  8e-09  2e-16  2e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0403e-02 -1.0042e+00  1e+00  3e-17  3e+00\n",
      " 1:  1.0128e-02 -1.3773e-02  2e-02  1e-16  6e-02\n",
      " 2:  6.7457e-03  3.5856e-03  3e-03  1e-16  9e-04\n",
      " 3:  6.1678e-03  5.7904e-03  4e-04  8e-17  3e-05\n",
      " 4:  6.1273e-03  6.1028e-03  2e-05  1e-16  6e-07\n",
      " 5:  6.1263e-03  6.1258e-03  6e-07  1e-16  6e-09\n",
      " 6:  6.1263e-03  6.1263e-03  6e-09  5e-17  6e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1577e-02 -1.0046e+00  1e+00  6e-17  3e+00\n",
      " 1:  1.1242e-02 -1.4050e-02  3e-02  5e-17  7e-02\n",
      " 2:  7.4802e-03  4.2022e-03  3e-03  7e-17  1e-17\n",
      " 3:  6.9131e-03  6.5453e-03  4e-04  1e-16  3e-18\n",
      " 4:  6.8758e-03  6.8530e-03  2e-05  8e-17  4e-18\n",
      " 5:  6.8751e-03  6.8747e-03  4e-07  1e-16  4e-18\n",
      " 6:  6.8751e-03  6.8751e-03  4e-09  7e-18  3e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2873e-02 -1.0050e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.2466e-02 -1.4321e-02  3e-02  4e-17  7e-02\n",
      " 2:  8.3344e-03  4.6579e-03  4e-03  7e-17  1e-17\n",
      " 3:  7.7535e-03  7.3598e-03  4e-04  6e-17  5e-18\n",
      " 4:  7.7151e-03  7.6914e-03  2e-05  1e-16  3e-18\n",
      " 5:  7.7144e-03  7.7140e-03  4e-07  1e-16  3e-18\n",
      " 6:  7.7144e-03  7.7144e-03  4e-09  2e-16  3e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4302e-02 -1.0054e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.3809e-02 -1.4575e-02  3e-02  7e-17  8e-02\n",
      " 2:  9.2922e-03  5.2029e-03  4e-03  4e-17  2e-17\n",
      " 3:  8.6954e-03  8.2777e-03  4e-04  1e-16  8e-18\n",
      " 4:  8.6560e-03  8.6318e-03  2e-05  7e-17  5e-18\n",
      " 5:  8.6554e-03  8.6550e-03  4e-07  1e-16  5e-18\n",
      " 6:  8.6554e-03  8.6554e-03  4e-09  8e-17  5e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5876e-02 -1.0058e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.5282e-02 -1.4803e-02  3e-02  7e-17  8e-02\n",
      " 2:  1.0366e-02  5.8558e-03  5e-03  2e-16  2e-17\n",
      " 3:  9.7511e-03  9.3117e-03  4e-04  2e-16  5e-18\n",
      " 4:  9.7111e-03  9.6866e-03  2e-05  2e-16  7e-18\n",
      " 5:  9.7106e-03  9.7102e-03  4e-07  1e-16  7e-18\n",
      " 6:  9.7106e-03  9.7105e-03  4e-09  1e-16  3e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.7607e-02 -1.0062e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.6896e-02 -1.4992e-02  3e-02  1e-16  9e-02\n",
      " 2:  1.1570e-02  6.6378e-03  5e-03  1e-16  9e-18\n",
      " 3:  1.0935e-02  1.0476e-02  5e-04  6e-17  9e-18\n",
      " 4:  1.0894e-02  1.0870e-02  2e-05  6e-17  5e-18\n",
      " 5:  1.0894e-02  1.0894e-02  3e-07  2e-16  6e-18\n",
      " 6:  1.0894e-02  1.0894e-02  3e-09  2e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9509e-02 -1.0065e+00  1e+00  0e+00  3e+00\n",
      " 1:  1.8662e-02 -1.5126e-02  3e-02  2e-16  9e-02\n",
      " 2:  1.2918e-02  7.5720e-03  5e-03  1e-16  1e-17\n",
      " 3:  1.2262e-02  1.1788e-02  5e-04  8e-17  8e-18\n",
      " 4:  1.2221e-02  1.2197e-02  2e-05  1e-16  6e-18\n",
      " 5:  1.2221e-02  1.2221e-02  3e-07  1e-16  1e-17\n",
      " 6:  1.2221e-02  1.2221e-02  3e-09  1e-16  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1597e-02 -1.0069e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.0593e-02 -1.5185e-02  4e-02  1e-16  1e-01\n",
      " 2:  1.4429e-02  8.6839e-03  6e-03  6e-17  8e-18\n",
      " 3:  1.3750e-02  1.3264e-02  5e-04  1e-16  9e-18\n",
      " 4:  1.3710e-02  1.3687e-02  2e-05  1e-16  8e-18\n",
      " 5:  1.3710e-02  1.3709e-02  3e-07  1e-16  6e-18\n",
      " 6:  1.3710e-02  1.3710e-02  3e-09  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3887e-02 -1.0072e+00  1e+00  0e+00  3e+00\n",
      " 1:  2.2704e-02 -1.5148e-02  4e-02  8e-17  1e-01\n",
      " 2:  1.6120e-02  1.0001e-02  6e-03  3e-17  2e-17\n",
      " 3:  1.5419e-02  1.4927e-02  5e-04  6e-17  8e-18\n",
      " 4:  1.5380e-02  1.5357e-02  2e-05  2e-16  1e-17\n",
      " 5:  1.5380e-02  1.5379e-02  3e-07  2e-16  9e-18\n",
      " 6:  1.5380e-02  1.5380e-02  3e-09  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.6396e-02 -1.0074e+00  1e+00  8e-17  3e+00\n",
      " 1:  2.5010e-02 -1.4989e-02  4e-02  8e-17  1e-01\n",
      " 2:  1.8013e-02  1.1552e-02  6e-03  2e-16  3e-17\n",
      " 3:  1.7291e-02  1.6797e-02  5e-04  1e-16  1e-17\n",
      " 4:  1.7253e-02  1.7232e-02  2e-05  6e-17  1e-17\n",
      " 5:  1.7253e-02  1.7253e-02  2e-07  6e-17  2e-17\n",
      " 6:  1.7253e-02  1.7253e-02  2e-09  8e-17  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.9144e-02 -1.0075e+00  1e+00  6e-17  3e+00\n",
      " 1:  2.7529e-02 -1.4677e-02  4e-02  1e-16  1e-01\n",
      " 2:  2.0132e-02  1.3368e-02  7e-03  1e-16  3e-17\n",
      " 3:  1.9390e-02  1.8900e-02  5e-04  2e-16  1e-17\n",
      " 4:  1.9355e-02  1.9335e-02  2e-05  8e-17  7e-18\n",
      " 5:  1.9354e-02  1.9354e-02  2e-07  6e-17  8e-18\n",
      " 6:  1.9354e-02  1.9354e-02  2e-09  8e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.2153e-02 -1.0075e+00  1e+00  8e-17  3e+00\n",
      " 1:  3.0283e-02 -1.4176e-02  4e-02  9e-17  1e-01\n",
      " 2:  2.2504e-02  1.5484e-02  7e-03  1e-16  3e-17\n",
      " 3:  2.1746e-02  2.1265e-02  5e-04  2e-16  1e-17\n",
      " 4:  2.1712e-02  2.1695e-02  2e-05  2e-16  1e-17\n",
      " 5:  2.1712e-02  2.1712e-02  2e-07  1e-16  7e-18\n",
      " 6:  2.1712e-02  2.1712e-02  2e-09  6e-17  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.5446e-02 -1.0074e+00  1e+00  8e-17  3e+00\n",
      " 1:  3.3295e-02 -1.3446e-02  5e-02  7e-17  1e-01\n",
      " 2:  2.5159e-02  1.7934e-02  7e-03  1e-16  3e-17\n",
      " 3:  2.4389e-02  2.3921e-02  5e-04  9e-17  2e-17\n",
      " 4:  2.4357e-02  2.4342e-02  2e-05  6e-17  1e-17\n",
      " 5:  2.4357e-02  2.4357e-02  2e-07  1e-16  8e-18\n",
      " 6:  2.4357e-02  2.4357e-02  2e-09  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.9052e-02 -1.0071e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.6593e-02 -1.2441e-02  5e-02  2e-16  1e-01\n",
      " 2:  2.8130e-02  2.0756e-02  7e-03  1e-16  2e-17\n",
      " 3:  2.7353e-02  2.6904e-02  4e-04  1e-16  3e-17\n",
      " 4:  2.7325e-02  2.7312e-02  1e-05  8e-17  1e-17\n",
      " 5:  2.7325e-02  2.7325e-02  1e-07  1e-16  1e-17\n",
      " 6:  2.7325e-02  2.7325e-02  1e-09  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.3002e-02 -1.0065e+00  1e+00  3e-16  3e+00\n",
      " 1:  4.0208e-02 -1.1109e-02  5e-02  5e-17  1e-01\n",
      " 2:  3.1457e-02  2.3992e-02  7e-03  3e-16  2e-17\n",
      " 3:  3.0681e-02  3.0235e-02  4e-04  2e-16  2e-17\n",
      " 4:  3.0654e-02  3.0643e-02  1e-05  6e-17  1e-17\n",
      " 5:  3.0654e-02  3.0654e-02  1e-07  1e-16  6e-18\n",
      " 6:  3.0654e-02  3.0654e-02  1e-09  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7331e-02 -1.0057e+00  1e+00  6e-17  3e+00\n",
      " 1:  4.4177e-02 -9.3904e-03  5e-02  1e-16  1e-01\n",
      " 2:  3.5183e-02  2.7687e-02  7e-03  2e-16  2e-17\n",
      " 3:  3.4414e-02  3.3969e-02  4e-04  9e-17  2e-17\n",
      " 4:  3.4390e-02  3.4379e-02  1e-05  1e-16  8e-18\n",
      " 5:  3.4390e-02  3.4390e-02  1e-07  1e-16  3e-17\n",
      " 6:  3.4390e-02  3.4390e-02  1e-09  8e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.2081e-02 -1.0045e+00  1e+00  3e-16  3e+00\n",
      " 1:  4.8544e-02 -7.2195e-03  6e-02  1e-16  1e-01\n",
      " 2:  3.9358e-02  3.1890e-02  7e-03  7e-17  4e-17\n",
      " 3:  3.8603e-02  3.8158e-02  4e-04  2e-16  3e-17\n",
      " 4:  3.8581e-02  3.8571e-02  1e-05  1e-16  2e-17\n",
      " 5:  3.8581e-02  3.8581e-02  1e-07  2e-16  2e-17\n",
      " 6:  3.8581e-02  3.8581e-02  1e-09  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.7298e-02 -1.0029e+00  1e+00  7e-17  3e+00\n",
      " 1:  5.3357e-02 -4.5224e-03  6e-02  2e-16  2e-01\n",
      " 2:  4.4036e-02  3.6655e-02  7e-03  2e-16  4e-17\n",
      " 3:  4.3303e-02  4.2865e-02  4e-04  1e-16  3e-17\n",
      " 4:  4.3284e-02  4.3274e-02  1e-05  6e-17  2e-17\n",
      " 5:  4.3284e-02  4.3283e-02  1e-07  7e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3037e-02 -1.0008e+00  1e+00  2e-16  3e+00\n",
      " 1:  5.8675e-02 -1.2164e-03  6e-02  1e-16  2e-01\n",
      " 2:  4.9280e-02  4.2043e-02  7e-03  1e-16  4e-17\n",
      " 3:  4.8577e-02  4.8152e-02  4e-04  1e-16  1e-17\n",
      " 4:  4.8560e-02  4.8551e-02  9e-06  2e-16  3e-17\n",
      " 5:  4.8560e-02  4.8560e-02  9e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.9361e-02 -9.9822e-01  1e+00  1e-16  3e+00\n",
      " 1:  6.4563e-02  2.7900e-03  6e-02  7e-17  2e-01\n",
      " 2:  5.5161e-02  4.8121e-02  7e-03  2e-16  5e-17\n",
      " 3:  5.4494e-02  5.4088e-02  4e-04  2e-16  4e-17\n",
      " 4:  5.4480e-02  5.4472e-02  8e-06  2e-16  1e-17\n",
      " 5:  5.4479e-02  5.4479e-02  8e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.6341e-02 -9.9497e-01  1e+00  8e-17  3e+00\n",
      " 1:  7.1099e-02  7.5982e-03  6e-02  6e-17  2e-01\n",
      " 2:  6.1758e-02  5.4965e-02  7e-03  1e-16  3e-17\n",
      " 3:  6.1134e-02  6.0752e-02  4e-04  2e-16  3e-17\n",
      " 4:  6.1122e-02  6.1114e-02  7e-06  2e-16  4e-17\n",
      " 5:  6.1122e-02  6.1121e-02  7e-08  8e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.4059e-02 -9.9096e-01  1e+00  1e-16  3e+00\n",
      " 1:  7.8370e-02  1.3320e-02  7e-02  7e-17  2e-01\n",
      " 2:  6.9160e-02  6.2660e-02  6e-03  1e-16  6e-17\n",
      " 3:  6.8584e-02  6.8231e-02  4e-04  2e-16  4e-17\n",
      " 4:  6.8574e-02  6.8568e-02  6e-06  8e-17  3e-17\n",
      " 5:  6.8574e-02  6.8574e-02  6e-08  1e-16  6e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.2611e-02 -9.8610e-01  1e+00  8e-17  3e+00\n",
      " 1:  8.6476e-02  2.0080e-02  7e-02  1e-16  2e-01\n",
      " 2:  7.7467e-02  7.1300e-02  6e-03  1e-16  1e-16\n",
      " 3:  7.6943e-02  7.6622e-02  3e-04  1e-16  4e-17\n",
      " 4:  7.6936e-02  7.6930e-02  5e-06  2e-16  4e-17\n",
      " 5:  7.6936e-02  7.6935e-02  5e-08  8e-17  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0210e-01 -9.8026e-01  1e+00  7e-17  3e+00\n",
      " 1:  9.5531e-02  2.8014e-02  7e-02  1e-16  2e-01\n",
      " 2:  8.6804e-02  8.0767e-02  6e-03  1e-16  9e-04\n",
      " 3:  8.6324e-02  8.6028e-02  3e-04  2e-16  1e-05\n",
      " 4:  8.6317e-02  8.6313e-02  4e-06  1e-16  1e-07\n",
      " 5:  8.6317e-02  8.6317e-02  4e-08  1e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1266e-01 -9.7330e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.0566e-01  3.7272e-02  7e-02  1e-16  2e-01\n",
      " 2:  9.7284e-02  9.1397e-02  6e-03  1e-16  2e-03\n",
      " 3:  9.6849e-02  9.6581e-02  3e-04  2e-16  2e-05\n",
      " 4:  9.6844e-02  9.6840e-02  4e-06  2e-16  2e-07\n",
      " 5:  9.6844e-02  9.6844e-02  4e-08  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2442e-01 -9.6507e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.1702e-01  4.8017e-02  7e-02  1e-16  2e-01\n",
      " 2:  1.0904e-01  1.0335e-01  6e-03  8e-17  2e-03\n",
      " 3:  1.0866e-01  1.0842e-01  2e-04  6e-17  3e-05\n",
      " 4:  1.0865e-01  1.0865e-01  3e-06  1e-16  4e-07\n",
      " 5:  1.0865e-01  1.0865e-01  3e-08  2e-16  4e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3754e-01 -9.5539e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.2977e-01  6.0430e-02  7e-02  1e-16  2e-01\n",
      " 2:  1.2225e-01  1.1680e-01  5e-03  1e-16  3e-03\n",
      " 3:  1.2191e-01  1.2170e-01  2e-04  1e-16  4e-05\n",
      " 4:  1.2191e-01  1.2190e-01  2e-06  8e-17  4e-07\n",
      " 5:  1.2191e-01  1.2191e-01  2e-08  6e-17  4e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5219e-01 -9.4407e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.4410e-01  7.4709e-02  7e-02  2e-16  2e-01\n",
      " 2:  1.3706e-01  1.3189e-01  5e-03  1e-16  4e-03\n",
      " 3:  1.3678e-01  1.3660e-01  2e-04  2e-16  5e-05\n",
      " 4:  1.3678e-01  1.3677e-01  2e-06  1e-16  5e-07\n",
      " 5:  1.3678e-01  1.3678e-01  2e-08  1e-16  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6859e-01 -9.3091e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.6020e-01  9.1069e-02  7e-02  2e-16  2e-01\n",
      " 2:  1.5370e-01  1.4885e-01  5e-03  6e-17  4e-03\n",
      " 3:  1.5346e-01  1.5331e-01  2e-04  2e-16  5e-05\n",
      " 4:  1.5346e-01  1.5346e-01  2e-06  2e-16  5e-07\n",
      " 5:  1.5346e-01  1.5346e-01  2e-08  1e-16  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8696e-01 -9.1567e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.7833e-01  1.0975e-01  7e-02  1e-16  2e-01\n",
      " 2:  1.7237e-01  1.6787e-01  5e-03  8e-17  4e-03\n",
      " 3:  1.7218e-01  1.7205e-01  1e-04  2e-16  5e-05\n",
      " 4:  1.7218e-01  1.7218e-01  1e-06  6e-17  5e-07\n",
      " 5:  1.7218e-01  1.7218e-01  1e-08  9e-17  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0754e-01 -8.9808e-01  1e+00  3e-16  3e+00\n",
      " 1:  1.9873e-01  1.3100e-01  7e-02  6e-17  2e-01\n",
      " 2:  1.9334e-01  1.8920e-01  4e-03  6e-17  4e-03\n",
      " 3:  1.9318e-01  1.9308e-01  1e-04  6e-17  5e-05\n",
      " 4:  1.9318e-01  1.9318e-01  1e-06  8e-17  5e-07\n",
      " 5:  1.9318e-01  1.9318e-01  1e-08  8e-17  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3063e-01 -8.7785e-01  1e+00  7e-17  3e+00\n",
      " 1:  2.2169e-01  1.5512e-01  7e-02  1e-16  2e-01\n",
      " 2:  2.1687e-01  2.1311e-01  4e-03  2e-16  4e-03\n",
      " 3:  2.1675e-01  2.1666e-01  8e-05  2e-16  5e-05\n",
      " 4:  2.1675e-01  2.1675e-01  8e-07  1e-16  5e-07\n",
      " 5:  2.1675e-01  2.1675e-01  8e-09  8e-17  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5655e-01 -8.5465e-01  1e+00  1e-16  3e+00\n",
      " 1:  2.4755e-01  1.8242e-01  7e-02  1e-16  2e-01\n",
      " 2:  2.4328e-01  2.3991e-01  3e-03  1e-16  4e-03\n",
      " 3:  2.4319e-01  2.4312e-01  7e-05  1e-16  5e-05\n",
      " 4:  2.4319e-01  2.4319e-01  7e-07  2e-16  5e-07\n",
      " 5:  2.4319e-01  2.4319e-01  7e-09  2e-16  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.8566e-01 -8.2814e-01  1e+00  7e-17  3e+00\n",
      " 1:  2.7665e-01  2.1324e-01  6e-02  8e-17  2e-01\n",
      " 2:  2.7292e-01  2.6994e-01  3e-03  1e-16  4e-03\n",
      " 3:  2.7285e-01  2.7280e-01  5e-05  8e-17  4e-05\n",
      " 4:  2.7285e-01  2.7285e-01  5e-07  6e-17  4e-07\n",
      " 5:  2.7285e-01  2.7285e-01  5e-09  8e-17  4e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1835e-01 -7.9789e-01  1e+00  7e-17  3e+00\n",
      " 1:  3.0941e-01  2.4797e-01  6e-02  8e-17  2e-01\n",
      " 2:  3.0619e-01  3.0358e-01  3e-03  1e-16  3e-03\n",
      " 3:  3.0614e-01  3.0610e-01  4e-05  6e-17  4e-05\n",
      " 4:  3.0614e-01  3.0614e-01  4e-07  2e-16  4e-07\n",
      " 5:  3.0614e-01  3.0614e-01  4e-09  6e-17  4e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -4.3659e-05 -1.0016e+00  1e+00  2e-16  3e+00\n",
      " 1: -4.5389e-05 -1.1623e-02  1e-02  8e-17  3e-02\n",
      " 2: -1.8811e-04 -1.7227e-03  2e-03  1e-16  4e-03\n",
      " 3: -1.4438e-03 -2.2599e-03  8e-04  4e-16  1e-03\n",
      " 4: -1.6109e-03 -1.6288e-03  2e-05  3e-16  2e-05\n",
      " 5: -1.6223e-03 -1.6225e-03  2e-07  1e-16  2e-07\n",
      " 6: -1.6224e-03 -1.6224e-03  2e-09  3e-16  2e-09\n",
      "Optimal solution found.\n",
      "(5, 1)\n"
     ]
    }
   ],
   "source": [
    "weights, returns, risks = optimal_portfolio(simple_return[1:])\n",
    "\n",
    "print(weights.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 1)\n",
      "(1, 5)\n",
      "0.0016229076692144016\n"
     ]
    }
   ],
   "source": [
    "print(weights.shape)\n",
    "simp_returns = simple_returns_daily_return.to_numpy().reshape(1,5)\n",
    "print(simp_returns.shape)\n",
    "opt_returns = np.ndarray.item(np.dot(simp_returns, weights))\n",
    "print(opt_returns) #this is the maximum returns available on the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PmbIaAEyzdIkKhQEfERFRhs6fP5LoV/tTDAz8GkAMDQ0zcPHFn0NzcxsmTHgPPB7rj9gpU1YzwKOiYsBHRETkQCmFwcF96OmJ78c7d+63AIDGxvlobb0PLS0fxrhxV7IIMpUtBnxEREQmlIrhzJmd6On5Kbq7f4rz598CAFx00XWYM+fv0dz8YTQ2XlbiURK5w4CPiIgoIRYbRn//NvT0dKCn52lEIl2JThc3oLV1DZqaVqCh4eJSD5MoYwz4iIiopo2OnkFf33+iu/un6Ot7FtHoWXi94zBp0gfR3PxhNDXdyE4XVPEY8BERUc0ZGelCT8/mRGbtL6HUCHy+FkyevArNzW0IBP4QXu+YUg+TKG8Y8BERUU0Ih99KFkE+c+Y3ABTGjJmNYPAuNDd/GBMmXAcRb6mHSVQQDPiIiKgqKaVw7lxnYj/eTzE4+BoAYNy4RZg1ay2am9swduwCZtZSTWDAR0REFaera4Np4eJYbBSnT/+/RJDXkehb68GECf8Dl1zyGJqbb4HfP7vUwycqOgZ8RERUUbq6NqS0JhsePoL9+z+LUOj/Ymjodxgd7YVIAyZNeh9mzXoQTU03o76+pcSjJiotBnxERFRRDh26P6UPLQAoNYwzZ/4fJk/+eKKd2QdQVzeuRCMkKj8M+IiIqCLEe9Y+nVimNXfFFT8o4oiIKgcDPiIiKlvDwycSnS42YWDgVwCiALyJP1M1NLQWe3hEFYMBHxERlZXz54+gu/sn6O7elCyf4vfPRWvrX6GlZSUGB1/HG2/ckbKs6/E0Ys6cL5du0AXU0RnC+q0HcHwgjGkBP9Ysn4u2xcFSD4sqDAM+IiIquaGhN9HdvQk9PZtw9uwuAMDYse/GrFlr0dKyEo2NVyTLp4wffyVEYJqlW206OkO47yevIhyJz2iGBsK47yevAgCDPsoIAz4iIio6pRQGB19LBnlajbzx438fc+asQ3PzSjQ2Xmp5/pQpq6sywDNav/VAMtjThCNRrN96gAEfZYQBHxERFYVSCmfP7kZPzyZ0d29COPwmAMGECf8Dl176VTQ3fxhjxnAfnt7xgXBGtxNZYcBHREQFo1QMZ868gO7uTeju/kkiw9aLiROXYfr0v0BzcxsaGqYmj+d+tVTTAn6ETIK7aQF/CUZDlYwBHxER5VW828Wv0N39E/T0/BQjIycgUo+JE9+HWbMeQnPzCvh8TWnncb9aujXL56a8JgDg93mxZvncEo6KKhEDPiIiylksNoz+/l8m9uQ9jdHRXng8fkya9EG0tKxEU9OHUFd3ke01uF8tnfa8OetJuWLAR0REWYlGh9DXtxXd3ZvQ2/sMotEz8HrHo6npZrS0rMSkScvh9Y51fT3uVzPXtjjIAI9yxoCPiIhcGx09g97eZ9HTswm9vc8iFhtCXd0ktLSsREvLSkyc+EfweBqyujb3qxEVDgM+IiJK0dW1IaXGXWvrX8PjqUdPzyb09f0cSg3D55uCqVP/GM3NKxEIvBcejy/nx+V+NaLCYcBHRERJXV0bcODAhS4Ww8NH8OabfwYAaGiYgWnT7kRLy0pMmPAHEPHm9bG5X42ocBjwERERAGBk5BTefPMLKS3LND7fVFx77ZFkt4tCqab9aiwxQ+WEAR8RUQ0bGelGT89PcOrUkxgYeB5AzPS4SKSr4MFeNWGJGSo3nlIPgIiIimtkpBvHjz+OPXv+CL/5zVS88cadGB5+BzNn/jXq6y82PaehgR0wMmFXYoaoFDjDR0RUA0ZGetDT81N0dz+F/v5tAKLw+y9Fa2s7Jk++DWPHvhsigsbGeSl7+ADA42nEnDlfLt3gKxBLzFC5YcBHRFSlIpFe9PR04NSpJ9Hf/0sAUYwZcwlaW/8/tLTchnHjFqYt006ZshoAUrJ058z5cvJ2coclZqjcMOAjIqoikUhfMsgbGPgllBrFmDFz0Nq6JhHkLXLcizdlymoGeDliiRkqNwz4iIgqXCTSj56eDnR3P4n+/l8kgrzZmD79Xkye/FGMG3clEy6KjCVmqNww4CMiqkDxIO9pXZAXwZgxszB9+j1oabkN48dfxSCvxKqpxAxVPgZ8REQVIhIZQG/v5sSevOegVAQNDTMxffoXE0HeEgZ5RGSKAR8RURkbHT2Nnp7N6O5+En19WxNB3gwEg1/A5Mm3Yfz432eQR0SOGPAREZWYsXftzJkPwOMZowvyRtDQMB3B4J+jpeWjuOiiaxjkEVFGGPAREZWQWe/aN974HAAkgrz/jZaW2xJBHmvlE1F2GPAREZVINHoeBw/ebdG7dkqidy2DPCLKHQM+IqIiisVGMTDwS3R1PYGenp8iGj1jelwkcorBHhHlDQM+IqICUyqGM2deQFfXE+jufgqRyCl4vRehpeVW9PY+i0jkVNo57F1LRPnEgI+IqACUUhgcfAVdXT/EqVM/wvDwUXg8Y9DU9CFMnvxxTJr0QXi9Y9L28AHsXUtE+ceAj4goj4aGDuLUqSdw6tQTGBp6HYAXkya9H7NnP4Lm5ltQV3dRyvG10Lu2ozPEjhNEJcaAj4goR8PDx3Hq1EacOvUEzp59GQAwYcL/xGWXfRMtLR9BfX2z7fnV3Lu2ozOU0lM2NBDGfT95FQAY9BEVEQM+IqIsRCJ96O7+MU6degIDA78CoDBu3JWYM2c9Jk9ehTFjZpR6iGVh/dYDyWBPE45EsX7rAQZ8REXEgI+IyKXR0XOJ1mZPJLte+P2XY9ashzB58sfQ2Di31EMsO8cHwhndTkSFwYCPiAjp3S60fXSx2DD6+rbi1Kkn0NOzGbHYEBoapmP69C9i8uTbMW7cYna9sDEt4EfIJLibFvCXYDREtYsBHxHVPLNuF/v3fxah0L9iaGgvRkcHUFfXhKlTP4XJk2/HhAlLWSPPpTXL56bs4QMAv8+LNcs5G0pUTAz4iKjmHTp0f1q3C6WGcebMrzFlyicwefLtmDjxj+Dx+Eo0wsql7dNjli5RaTHgI6Kadv78EQwPH7G8/13v+l4RR1Od2hYHGeARlRgDPiKqOaOjZ9Dd/WOcPPk9nD79K8vj2O2CiKoFAz4iqgmx2Cj6+59DV9f30dPTgVjsfCLD9m/h9Y7D4cP3s9sFEVUtBnxEVLWUUjh3rhNdXd9HV9cPEYmcQl3dJEyd+llMnfpJjB9/dTLDtr6+paq7XRBRbWPAR0RV5/z5Yzh1agNOnvwehoZ+B5F6NDXdjKlTP4lJkz4Ij6c+7Zxq7nZBRMSAj4iqwujoWfT0/AQnT34PAwPbAShcdNFSXH75t9DScht8vomlHiIRUckw4COiihXfl/eLxL68nyIWC2PMmEswa9ZDmDLlE/D7Lyn1EImIygIDPiIqa2YdMMaO/T2cPPk9nDr1Q4yMnERd3URMnfopTJnySVx00XXsfEFEZMCAj4jKllkHjNdf/yQABREfmppuwpQpn0RT003weBpKO1giojLGgI+IytahQ3+d1gEDUKirm4hrrnkTPl9TScZFRFRpStoMUkQ+ICIHROSgiLSb3C8i8rXE/a+IyJVO54rIR0Vkn4jERGSJ4Xr3JY4/ICLLC/vsiChb587txZtvfhHDw0dN7x8dHWCwR0SUgZLN8ImIF8A3ALwPwDEAL4vIZqXU73SHfRDAZYmvawB8E8A1Due+BuBWAP/X8HhXAPgYgPkApgH4hYhcrpSKgohKLhLpx6lTT+DEiW/j3LndEKmHx9NoMsPHDhhERJkq5Qzf1QAOKqUOKaVGAPwIwC2GY24B8D0VtxNAQEQutjtXKfW6UuqAyePdAuBHSqlhpdRhAAcT1yGiElEqhv7+X+J3v1uNF16Yhjff/DyUGsWll34Nf/AHxzF37uPweBpTzmEHDCKizJVyD18QwDu6748hPovndEzQ5blmj7fT5FppROQOAHcAQGsrZxKI8u38+aM4efI/cPLkd3D+/Nuoqwtg6tTP4uKL/wTjxyd3biQLIbMDBhFRbkoZ8JnVTVAuj3FzbjaPF79RqccBPA4AS5YscbouEbkQjZ5Hb+/TOHHi39Hf/wsAChMn/hFmz/47NDd/GF7vGNPz2AGDiCh3pQz4jgGYoft+OoDjLo+pd3FuNo9HRFkyq5c3ZcpqnD27BydPfhtdXT/A6Gg/GhpaMXPmg5g69dPw+2eVethERDWhlAHfywAuE5HZAEKIJ1R83HDMZgB3iciPEF+yPa2UOiEi3S7ONdoM4Ici8k+IJ21cBuClvD0bohpmVi9v//7PJALAIxBpQEvLhzF16p9g4sQ/hEhJCwQQEdWckgV8SqlREbkLwFYAXgDfVkrtE5E7E/d/C8CzAG5EPMFiCMBn7M4FABH5MICvA2gBsEVE9iillieu/SSA3wEYBfB5ZugS5cehQ/enZdMqFcHIyHFceunXMWXKx+HzTSrR6IiISJTiFjU7S5YsUbt27Sr1MIjKViTSix07mi3uFVx/fayo4ymVjs4Q1m89gOMDYUwL+LFm+Vy0LTbNC6Ms8PUlckdEdiullhhvZ6cNIsqYUgpnzuzE8ePfxKlTT1oeVyv18jo6Q7jvJ68iHIkvGoQGwrjvJ68CAIOSPODrS5Q7bqQhItdGR88iFPomdu1ahM7OP0BPTwcuvvizmD3772q6Xt76rQeSwYgmHIli/VazkqCUKb6+RLnjDB8ROTp7dg+OH/8WTp3agGj0HMaNW4TLL/+/mDz546irGwcAGDOmtWbr5R0fCGd0O2WGry9R7hjwEZGpaDSM7u4ncfz4t3DmzE54PGMwefLHMG3a/8L48b8PkdTSlrVcL29awI+QSfAxLeAvwWiqD19fotxxSZeohnV1bcALL8zC88978MILs9DVtQFDQwdw8OA9eOGFIPbv/zRGRwdw6aVfxXXXHce8ed/BRRddnRbs1bo1y+fC7/Om3Ob3ebFm+dwSjai68PUlyh1n+IhqlFntvNdf/2MAMYj40Nx8K6ZNuxOBwHsZ4DnQEgeYRVoYfH2JcseyLA5YloWq1QsvzMLw8JG0273eAK65Zj/q66eUYFRERJQLq7IsXNIlqkFnz+42DfYAIBo9zWCPiKjKcEmXqEbEYqPo6enAsWNfxZkzOwAIgPQZ/lqpnUdEVEsY8BFVuUikDydO/BtCoW9gePgoxoyZjUsueQxe7zgcPPjFlJZo1Vw7L9NODaXo7MBuEkRUKAz4iKrU4ODrCIW+hpMnv4dYbAiBwDJcdtnX0NT0IYjEMx69Xn9F185zGyBl2qmhFJ0d2E2CiAqJSRsOmLRB5a6ra4MuaJuBlpZVGBzci/7+5yDSgClTVmP69C9i3Lh3l3qoeWUMkIB4qY5Hb12QFiAtXbfNtI5bMODHjvYb0m7P9Ph8KMVjElH1YS9doiqUXlrlKI4dWw+vdwJmzfpbTJv2Z6ivbynxKAvDrt2WMeDLtFNDKTo7sJsEERUSAz6iCnboUHvKHjxNXd0EzJr1QAlGlKqQe9IyCZAy7dRQis4OuT4m9/8RkR2WZSGqQOHwYbzxxl0YHj5mev/w8DtFHlE6bck1NBCGwoU9aR2dobxc3y5YM8q0U0MpOjvk8piFfq2JqPIx4COqIOfOvYLf/W41XnzxMpw48Tg8nrGmx5VDaRW7Jdd8yCRAalscxKO3LkAw4Icgvi/ObK9ftsfnQy6PWejXmogqH5d0icqcUgqnT/83jh79e/T1PQuvdxymT78b06ffjdOnf5Wyhw8on9Iqhd6Tlmm7rbbFwYwCNrfH53MpNdMxarj/j4icMOAjKiPpGbcrcebMTpw58wJ8vhbMmvW3CAb/N3y+SQCAMWPiJVRKWVrFKuDJxz44p2DKGPRpM1rF2rtWLqVUSrHnkIgqC8uyOGBZFioWY8atpq6uGbNnr8XUqZ+B19tYotGZsyuNAsB12ZRMr62dn0lplkKwKqXiFcFXbltYssATKO7rQETlg710icrcoUN/bZpx6/U2Ihj8fNkFe4BzaZRc9sE57Uvr6Azh3if3ut671tEZwtJ12zC7fQuWrtuWl4QGqyXTqFJFTZooxZ5DIqosXNIlKrFYLIKuru9jePio6f3lkHFrxWnvWLZ70pyurc1oRS1WKIznFmrp1WopFbCuCVgoubzWRFT9OMNHVCKx2ChOnvwuXnrpXThw4LMQqTc9rhwybq1kUholn9c2m/2zO9dqtvDeJ/fmNAtnlimsx6QJIioXDPiICqyrawNeeGEWnn/egxdemIWTJ7+Pkyd/gJdfvgL7938adXXj8Xu/9zTmzv02PJ7UZdtSZNxmsvRZyHp1ZtcWxGfnrGbVrB6/UEuv2lKqV8T0fiZNEFG54JIuUQGltz47gv37PwVAYezYd2P+/J+gubkNkggYREqfcZvJ0mempVEyob+2FuA5pZh5RUz3rhVy6VU7zyxpopCFmomIMsEsXQfM0qVcvPDCLAwPH0m7va6uGUuXdkGkvCbZrbJOgwE/drTfUIIRxc1/8L8wOGK9hAvYZ6WaZbHqCYDD627KaYxsbUZE5cAqS5czfEQFZJWIMTraW3bBHlCeBXw7OkO2wZ4AroouA8C9T+41TfSwWnrNJIhj0gQRlTMGfEQFMDT0Jg4dug9Wi5DlmohRjgV8ndqDuZ2Za1scxK4jffjBzvQgfNm8lrTbyqWoMhFRPpTfFANRBRsZ6cabb34BL798Bfr6/gvNzbfC40kNlsql9ZmZQiZhZMtudjHg92V0re37u13fzv60RFRNOMNHlAfRaBjHjn0VR4+uQzQ6iGnTPoeZMx9CQ8NUQ7u04idiZKKQSRjZsku4WLtifkbXymTJuhyXt4mIssWAjygHSkVx8uT38fbbf4Ph4WNoalqBOXPWYezYdyWPmTJlddECvHwkDpTbXrQ1y+emJVwIgNXXtmY8Tqvg0SOC2e1b8t4LWMOEDiIqNQZ8RC4ZZ+omT16Fvr7/wuDgKxg//mq8610bEAi8p2Tj6+gMYc2P9yISje8bDA2EsebHewFU9p6zfM46mgWPAJKJHPp9embHZrO8zb2ARFQOWJbFAcuyEJBeT09TV9eMyy//F7S03JaspVcqi7/0HPqHImm3T2z0ofPB95dgROVJP9vmETHN2tXK0ORjZq5cS90QUXViWRaiHBw6dH9asAcAXm8jJk9eVYIRpTML9uxur1X6JevZ7VtMj8lHL2DjtdzeTkRUCMzSJXIQjYZNiycDwPDwO0UeDeVTIXsBF/MxiIicMOAjsqCUwqlTP8ZLL73L8hinenqZ9KXNlVWJkkxLl9SSYpShKcdSN0RUe7ikS2Ti7Nk9OHjwizh9+tcYO/bdmDz5foRCj6Us6zrV0yv2Zv21K+ZjzVN7EYld2JPm84hj6ZJaziAtRhmaXB6jlt8bIsovJm04YNJGbRkZOYXDh/8GJ078K3y+Jsye/QguvvhPIeLNuJ5eKTbrZxogmPWYtetJm8/HJnv5fG+IqHYwaYPIRiw2glDoX/D22w8jFhvC9Ol3Y+bMB+HzBZLHZFpPr1Cb9e0Cq0yTDOy6SWQyAxUaCENwoZFcNrOZlRYwFnq8ub43RER6DPiopiml0Nf3LA4e/AuEw29g0qQbccklX8HYsfNyvnYh+tLmukxsDFKsOlhoQaldUGMci3GtINPAsZJq1RVjvMzuJaJ8YsBHNWtw8HUcPHgP+vu3wu+fiwULtqCp6ca8XT8fhXuNAdfQyKjprM/azfscZ5vMghT9rJzetIDfMagxm4EychucVNpsVjHGW4j/MBBR7WLARzUhdf9dEH7/fAwM/AJe7zhccsljCAY/D48nv9ms2WzW1wd4E/w+DI6MpnTOsDIQjmAgHEkeZzbbZBakKCAt6NOCUqug5u6Ne5LLuE4CjemvqX4Z2GtR+FhTrrNZxZh9y1enDyIigAEf1QBjl4zh4WMYHj6GQOAPccUVT6C+vqVgj23cU6eVaXGzRKoFcNkwm22yCkYU4okkxjHds3GP5fXtZgf1zp0fRUdnyPI52gV72tiWrttWdvv5sp19y2TfXzEyiImodjDgo6pn1SUjHD5Y0GDP+OG+bF4LNu0O5bREmgljgGcVpFhlDdvt8QPMZweNIjGVEnhm8xzLcT9fNrNv2ez7y0enDyIigIWXqcqNjJyy6ZJxtGCPq324hwbCUIh/uG/YedRy3xfgfjnQbctebR+eVvh5cHgUPm/qyXZBilnBYCM3RZ30zyvbJU/961QO2hYH8eitCxAM+CGIB81O5VLs9v0RERUaZ/ioKikVxfHj/4rDh++zPMapS0YurPbLmdGCIKcZNSAeoLmZIfP7vFg2ryVtidjnEUxs9GFgKJLRkqKb/Xp2CSD6v7u5lply28+X6ewbs26JqJQ4w0dV5+zZ3fjtb/8Ab775vzBu3GLMnv0P8HgaU45x6pKRq0w+xLWAyGkzvkeAhjrnX1mvCB69dQG27+9OCw4jMYV+F8Gepm1xEDvab8BXVy1yNdtnnHw0ziC6mTW0ko/s1GK2ujNiT10iKiXO8FHViEQG8Pbbf4NQ6P/A52vBu961AZMn3w4RwZgx0zLqkpFrUV2rmSyrjFggHlw9/Mw+9A+ZJ2vElHMih74Tg1PSRSb74toWB7HrSB827Dxqu4yrAExs9CWfgzFAzXTWUKN/nbJ9b0pR68+Yde3zSjLrGmDWLREVD2f4qOIppdDVtQEvvTQPodD/QTD4eVxzzQFMmfJxSGLD25Qpq3HddW/j+utjuO66tx2DPeP+u/t+8mpGs0FmM1l+nxerr2213ff10M3z02bJnHgTz9F4PaeZo0z3j23f3+24Z08EKQHrQDiS9tq1LQ5i2bwWx+fpFUl7nXJ5b4q9h8441oFwBFDxgNjtvj8ionzhDB9VtMHB1/Hmm5/HwMB2jB9/Nd797mcxfvyVOV0zH0V1sy2p0bY4iKd2HcWOt/pcPY4AeOtR82LRZpmkRnZLzx2dIdsZRzNmVVaMr11HZwg/2GmfMGPVMzaX96bYe+jMxhqJKTTW16HzwfcX5DGJiKww4KOKkVo8eTrGjl2M/v7/hNc7Fpdf/i1cfPGfQiS7/WF6+QoMsimp0dEZwktv97s+3m4Wz83yqdX5D3S86hiU6WVSQHnt5n221wraBMe5vDfF7lzBJA0iKidc0qWKoBVPjpdYURgefgd9fZsxfvy1uPrqA5g27c9yCvb0m/k9FnVPirG5fv3WAyl7vPScEiLM2CVdWJ3f0RnCBpfBnt/nxVdXLULMoYCy/rWz24eo1QS0CpRzSXywWmYv1B46JmkQUTlhwEcVwap48vDwUdTXT87p2sa9VmYzVfkODKyyRe1mfxSAgD+7/V9ti4NYeVUwud/PK4KVV5nPQK7fesBVfT39NZyCGLevndNxuQRt2dTOy0WxA0wiIjtc0qWyp5QqaPFkq+4PXhHElMp7Syu7bFGnOnWDI6OY4Pfh+EA4mWzgNkN10+5QMpiNKoVNu0NYMnNS2vlulxz111izfC7u2bjHNFCcmOinq7WUEzHf6ze23utqjyOQfbuxYnauYGs0IionohyWYmrdkiVL1K5du0o9jJoVDh/CgQOfw8DANtP7Gxpm4rrr3s7pMWa3bzENVATA4XU3ZX1dq/IhS9dts2xxtmxeS0Z75wTA6mtb8UjbAtvjrB5Te1x9IGJ3rNX5O9pvwAMdr6aVbfH7vFh5VTClpZwZn1ew/iMLATBAIiLKhYjsVkotMd7OGT4qS0pFcezY13H48P0Q8WLq1M/g1KmNKcu6+SqenOtmfrPADoDlLJ7VDFpoIIxNuzMrBKwAbNh51HSmznhtu/v09ejWLJ+LNU/tRSTm7j+D2vN5pG0BlsyclPZauJ1BBaxfs1IFfbnWYyQiKhfcw0dlZ3BwH37726V46617EAgsw+///u8wb963MXfu42homAlA0NAwE3PnPm5bT8+Kcf/csnktWe+1sqoL9/Az+yzLh1gFkl4RV23TjBRgW0uuozPkWPNOX4+ubXEQ48a4/7+g9nysgiOrADemFA6vuymZpFFuvWbzUY+RiKhccIaPykYsNoKjR/8eR478Lbzei1I6ZQDx4snZBHh6ZvvnNu0O4crWCdh5qB9RpWwTGoxBzeDwqGmQYhW4HR8I47FVi9Lq47ntkWvFbt+d2yQM/TUGXNbe0wLjbPYlGgPfcitjko96jERE5YIBH5WMvq6ezzcFInUYGTmGyZM/hksv/Rrq61vy/phWH+K/easvGRRZJTSYBTWZmhbwm27mXzavBU+8+I5tLTun61pxGzDpr+GUPAKk7v1bum6bZXBkVgDabAa12HXynJRbAEpElAvHJV0RuUREGhJ/v15EviAigYKPjKqasa5eJHISIyMhTJ/+F7jiiicKEuwB1h/WxjDLbCnRai+aW8a+uTvab8DhdTdhzfK5KRm0uVzXjJuAyeeRlGuYlRTRP95XVy1KqZdnFxy5LYdSbmVMWEePiKqJmz18mwBEReRSAP8OYDaAHxZ0VFT1zOvqKRwJbTStT5cvmXxYG4OYXGZ2vCKWNd+yCST9Po/rWnJu+tYaD9AHadr4YfN4Vq+rQjzrF0AywLUqrFzsOnlOyi0AJSLKhZsl3ZhSalREPgzgq0qpr4tIZ6EHRtUrFhu1rKvniR1PLusVIkvTTX9ZjTGIcbPMaSWmlOVzyC6QFDy2alFKf1qzhAmt/p7T3GEkqtL2pmVSs87udXX7PlplO2v1+4qdJcs6ekRUTRzr8InIiwC+CuB+ADcrpQ6LyGtKqd8rwvhKjnX48mto6E28/voncPbsS6b394Rb8Je/+k7KbVqdt3zRAgu74M3nEYwbU4eBoYhl2RAgPjHmtivFV25bmAzC9EHE0Mgo+l0mSZhd02xcfp8Xj966wPF56pnVHTSOddm8Fmzf320aADm9rvr30ey6xlp9Pq8ACinlYbTnlUnQlclzICKqdFZ1+NwEfFcAuBPAC0qpJ0RkNoBVSql1hRlqeWHAlx9KKZw48TgOHvwLeDwNmDx5NU6e/HbKsu7waAO+s+8u7DyxLOXcXAsgm+noDFl2hhAAdV5J6WmrBRpAerKFU1Fh/TXMihD7PAIILHvoOl2zoc5j2p82mOGMpDGwNiapWD3+yquCKQGU3WO+ve4m0+u6DZzNxmnHzXPweQVj6+twOhxhAEhEFS/rwstKqd8B+ILu+8MAaiLYo/wYHj6JAwc+i76+ZzFx4vswb9530NAQxIQJ1yazdBsaWvHkmx/HzhNL087P9yb5js4Q7n1yr2WA0VjvxeCIecap2f4zrdhwaCBsG7iEI1HTTFxtBssrgqhS8AjgsuaxbQmYTII9QXofWzd7C8ORaEpnELvHFFyYbTNeN5NQN5MlcDfPIRJVyYC5HIo9ExEVgmPAJyJLAawFMDNxvABQSqk5hR0aVarUcivNiEbDAEZx6aVfQzD4eYjEc4WMdfW6xoSw/ahz+Y5caDM+dhmxxmBPc3wgbLlXzq5lmp7d42r3uQ32nGgBpBOtPVu2PXXd0gpE53rdXBJv3GCtPSKqRm6SNv4dwD0AdgPIviYF1QSt3Iq2VBuJdAMQzJ69DtOn/7ntucXYJJ9LaZVAo8+29Vep6rNNbPThfCSWdSFnq168uSSpWLFb9nWzrGs2E2kn2+fAWntEVG3clGU5rZT6T6XUKaVUr/ZV8JFRRbIqt3L8+P9xdb6+Pp1V+Q4zxnZpVuVcsv0g9/u8UAq2rb+cZp4cS6NkOa6Hbp6fVkIlHIkmS6k42b6/2/R2u1p82dKCeLNyJ6uvbcXERp/t+dosoduyPdk+h2y2Ebj9GSQiKgU3Ad92EVkvIteJyJXaV8FHRhVHKWVZbmV4+Kjp7flg1fP0gY5X0z6A3X6QN/o8afXgzBIjkHi8js6QaXChhVzBgD+jfWpu6Gv7tS0OJh9fW8Z1W8g5NBA2DVDM6uJ94tpW14GkkTY7Z1Vv75G2BWist190kMR43fa2tXoO2vcTG33xpBmdbLYRsO8uEZU7N1m6201uVkqp/NXJKGPM0nVndPQ0Dhz4HLq7nzK9v6FhJq677u2CPLbV3jnjEmEmmaABvw97Hnp/ym2X3PesZRClZav+9Leh5B5AbW+ctlzqZo9fpgQXZs2sSqJ4RRBLjNvu+bsteWKXZeu0b/Btm7IvgUafbXkaq/cv17I9VvsyM2H13ua7pBARkZOssnRFxAtgs1LqsYKNjCre2bO/xb59t+H8+bfR0rIKvb3PpCzrejyNmDPnywV7fLft0jKZYTttMptnF8gYs1W1x/vhzqPY8soJDAxF0Fif3+VR7TG02SSrPXtRpSCId+cYisQsr+U2WcFur6VduZugYXbVGDg61SK0evVz3W+XSYFpK+y7S0TlzjbgU0pFRWQFAAZ8lKTPwq2rm4jR0dOor5+KxYt/hQkTlqbc39DQijlzvpySjZtvE/w+y+XWbJkt/WZa1w4AYrgQyFhl/9rxCHDdnEn4zVt9tgGrtmfPKihVgG2wp3EboFgFSW2Lg3hq11HseKsv5XazZdKHn9mXU29iTTn0trVKDimHsRERAe6ydH8jIv8CYCOAQe1GpdRvCzYqKlvGLNzR0T4AHsyc+QAmTIjX0DOWW8kns+W3LLeU2Vo2ryXttkzasuWL1yP46JJWfHRJKx5+Zp/tLFhUKfg8ktKZIlMeEcxu35I2a+d2ybOjM4SX3u5Pu33lVakBYkdnKKvuIkaZZu0WitnPBvvuElE54R4+B9zDl+qFF2aZJmYUco+eJtcODZkI6vbFGXu7rt28L+8zina0PXgeh/1xAb8PgyOjWXXsMKO9tsbX2Gyvn1NbtYmNPnQ+eGFPZD73Mxr3BZZKPvYCEhHlKpdOG8ucjsmWiHwAwD8D8AL4N2O7NhGRxP03AhgC8GltZtHqXBGZhPhs5CwAbwO4TSnVLyKzALwO4EDi8juVUncW6rlVK6ts20Jm4Wpy7dCQCeO+OO37R29dgD0PvR8PdLyKDS8ehctE2Jy4ybr1+7yQLNuzAYAI0p6LMvypMe71c9O+zDibl+neNruEjXKRj72ARESF4qbTxoNmtyulvpTLAycSQr4B4H0AjgF4WUQ2J1q5aT4I4LLE1zUAvgngGodz2wH8Uim1TkTaE9//VeJ6bymlFuUy7lqllMLRo38HqxCroaG14GMo9gZ4u5p7m3aHHIM9nwdwsW0uL1ZeFcSGndkH3ZkGrqGBcDJr2W1Hj47OUDIgclsQWRAveD0ciabtQeSSKRGRe27q8A3qvqKIB2Gz8vDYVwM4qJQ6pJQaAfAjALcYjrkFwPdU3E4AARG52OHcWwB8N/H37wJoy8NYa1o0Oojf/W4VDh9+AOPH/wE8ntRZlUJn4Wom+O2L8joJBvz46qpFOc0KHR8Iu+7WMVqkYA+IB6ABh6LF+ZZpvb/1Ww8kixNrfYftBPy+ZKkWY7AX8PtclZAhIqI4N0u6X9F/LyL/CGBzHh47COAd3ffHEJ/Fczom6HDuFKXUCQBQSp0Qkcm642aLSCeAMwAeUEr9t9nAROQOAHcAQGtr4WeuypE+01akDkpFMGfOP2DGjL/EqVM/NM3CzcceJqtrdHSGMDgymtNzCg2Ece+Te3HtnInoGxzJKvliWsDveqYx19XeTPYnhiNRNNR5bM+xu8+sPVu+GZfJ9fsDjWPzecR2P+LYhrqqC/a4B5CICslNlq5RI4A5eXhss//gG/91tzrGzblGJwC0KqV6ReQqAB0iMl8pdSbtQko9DuBxIJ604XDdqmPMxFUqApEGNDRMg4iYZuEa93EZ+8y6YXeN9VsP5CUZIaoUdrzVh3pv5qm92hKiXXJCPjXWexEztHOz65F7OhzB6mtb0+oBahTigZ1xP53Wnm3XkT488eI7rmfsMqW1fDOOCYgv2yoVfw7TAn4MjYzaZvFWW327fPz+EBHZcVzSFZFXReSVxNc+xJMevpaHxz4GYIbu++kAjrs8xu7crsSyLxJ/ngIApdSw1gNYKbUbwFsALs/D86g6Zv1wlRrGoUP3W55jtsyp3/Nmxth71Kwum3aNfH/Aj+iCRzehn76N2bJ5LQXpi2s0OBLFyquCaS3IrJakpwX8eKRtgWU/2mDAj84H359c1hbEl0bH+Dy4e+MebNh5NKdgzyuSvKbPEFDrW76Z6R+KYHg0hsdWLcKO9hsw4FCypdrq22Xz+0NElAk3e/g+BODmxNf7AUxTSn09D4/9MoDLRGS2iNQD+BjSl4o3A/hjibsWwOnEcq3duZsBfCrx908BeBoARKQlkewBEZmDeCLIoTw8j6qSbT/cTDsNmPUetZrR0Za4CsUpxBEAX7ltYXJpedPuUMGyg4227+/GjvYbcHjdTckWXYPD6UvbPo8kExgeunl+Wk9ffYJD2+IgdrTfgMdWLcLwaCz5uufynPw+L75y20IcXncT9jz0fqz/yELXgapGH+DYvd/VmKzBTh1EVGhulnQfUUp9Un+DiHzfeFumlFKjInIXgK2Il1b5tlJqn4jcmbj/WwCeRbwky0HEy7J8xu7cxKXXAXhSRD4L4CiAjyZufw+AL4nIKOLJJ3cqpVLbAdQ4pWI4ePAvLO+3y8S1yrpUABY9/BxEgIGhSErfV7f7xaYF/Fg2r8VyqbLQFIB7n9yLuzfucZ2Rmi/6D3zb8ie6CTW71md6mbwHdnwepCVQWJUocSrfoj1fqyLXAb8Pa1fMr7plTnbqIKJCc1N4+bdKqSt139cBeEUpdUWhB1cOqr3wsj45w+PxIxYbwsSJH8Dp079O64c7d+7jlh003NRi09jtQzM79tFbFxRt31w50heBdnoNghls9p/dviUvM5XBgD85++ikozNkW7h6YqMPjfV1OD4QxgS/L+0/CtUW6GnMfn/MClwTETmxKrxsuaQrIveJyFkA7xaRMyJyNvF9FxLLpFTZtOSM+BKuQiw2BBEfpk79BObOfRwNDTMBCBoaZtoGe0B8RmflVUFXe9u0vq9uaB94tby0pW3gdxPwasd2dIYApO+T1G4H8jd7lMl74xS8nDs/mlzmHwhHcD5yYV9fNhnfVs+93LQtDiaXvfXL4Az2iChf3MzwPaqUuq9I4yk71TzDl+82aZm2y3Ka6dPPHOWzFVetWHrJJPz26Om0VnSrr23FI20LMpqVtZPJDB8AzGrfkvP1nUqYcMaMiGpVxjN8OveLyCdE5G8SF5ohIlfnfYRUdPluk5bJTI82gxGwKKbs80rKxvw1y+eaJiI01Ln5ES4Nr6cYubzWdrzVZ1oGZcPOo8muF4/eap7V63bkxUig0H6utBm7We1bcPfGPSkJP/ds3IMHOl5NnsOsVyKiVG4+Lb8B4DoAH098fy5xG1Wgrq4NeOGFWXj+eQ/y3SYtkyVCbUZmbIN53tDY+vTCuvrgbmKjDyuvCmK4mO0sMhSLKbhcuS4qBSQDn7bFQdO2albz/j6vIOD35bTsaFU2xsq0gD8lq9uMPpAFmPVKRGTkJuC7Rin1eQDnAUAp1Q+gvqCjooIw7tkzk0ubNLNZOCv3bNxju0x7WrepX/uw12/0Px+J4Wd7T2Q1zmJRyLxHbaYyDZ40+lkzqwQKM5GowtiGumSZmGyWRx+6eT7cTn7qi107LT3rA1mr/3ww65WIapWbgC+SqF+ngHg9OwDlO61ClswKKsd5AQiiEsSPDnwR1z4WyGqTu3HjuV1ihrYUZ3WE9sH8QMeruHvjHtPlObtARSsCXA2sXkatkHI2FOJZuvc+tTfjc/MxS2YXB2vvnX4G0e1j6su62NUiJCKqNW7q8H0NwE8BTBaRLwP4CIC/KeioqCCs9+bFMDDhnby0dtLXX3OTFGDWR9Xv82LZvBa862/+E+FI5v+30BdKXv2vL2DHW5VZbtErgq/cthBAev06ffAStKjh5kQBiMYyn4LMZZZM+5mwm/mMKYXD625Ke0w3z3FCYk+o21qERES1wjFLFwBEZB6AP0T8s/SXAI4qpQYLPLayUOlZuvo6e9Z79mbiL3/1bdMPVC1D0k1WpL6+2sRGHx66eT6ACx+6dj9pwYA/ee1l81qwaXcoq+xRAfAHl0zCvuNnM1qqLEefSGTTAulZqcvmtWD7/m4cHwgj0OjDufOjiGQRvGXKTaar3c/Kooefc3xfrLJy1zy11/E5+ryC9R9ZyMCOiGqWVZau7QyfiAQBXIx4oeX9IjIZwN0APg1gWgHGSXmk7dkzX8aN0/bsHX/aepO7WWP3NT/ei7Wb9+F0OIIJfh/OnI9A/1ncPxTB3Rv3AIh/gD+2apFlwV2vSEpQsHTdtqyCPa8Ibr9mBja+9E5Rgp9C276/O/l3u5lTq5Z0+aLNwGpFnYH4e2QW0Jn9rGgzxQAcgz19izjTgTiIRBXWbz3AgI+IyMAy4BORuwHcj3hbswYR+WcA/wTgewCuKsroKCf2e/ZiaGhoxZw5X8aUKasxwW8+86K1FTO2E4tEVfJ4pw/x0EAYa57aa7nxM6pUyvJxtnvEYkph+/7uqgj2AOu9cvlqieaGtqzsJqBrWxzMqRyKWds0bbYwkyVrZuISEaWzm+G7A8BcpVSfiLQiHvi9Rym1szhDo2xdWMZNL6ocF8P1118Ivzo6QxgcGbW8Xj56xzoFYVpQ0LY46Hq/ltG0xLJwtVAAFn/pOSgVz1rWlnKLWYD69mtmALgwo+cx6SWsf++sXn+nMX911aK0WblsC0MzE5eIKJ1dlu55pVQfACiljgJ4g8Fe+UstvWLOWGdv/dYDiERLPysWGggnS7VkmmGrJTFMsCjkXKn6hyIYCEeSWc0/2JldUexs/WzviWT9OwXr4F8L9LINtu7euAez2rdg8ZeeS2aHZzOTKQAzcYmITNjN8E0Xka/pvp+s/14p9YXCDYuyZb2MG2dWZ69cZsUEF2aCMgk/42VLVHLPIOWP28QXLdBbs3xuTu3a+ociWPPjeKmYTH8utbZx3L9HRJTOLuBbY/h+dyEHQrnr6trgMLM3M7lnTy/Q6Cv4xn8nxtIsbnkAxBSyKt9C+aEvEdO2OIhdR/rwxIvv2G4FEMB0eRi4kHhht7QfNGQqs+wKEZE9y4BPKfXdYg6EcqMt5VppaJiJ6657O+32js4Qzp233r9XaAL3NdbMMMwrDa8IYkqZZulu2h2yDfa0siuz27dYHnN8IIzHVi0yrT+YTTs3IqJaV76d5ykjdku5du3S1m89YJlQESjwfjitE8fQyGhZ9pytFFpvWyc+j7huaWbH7/PiK7ctxGOrFgG40CZPy6i1W87Vzwba7febFvCndW7JtncvERG567RBZc5pKXfu3MfTlnE1dvukBkdGEfD7HPdxeS2W5pxo55R6ObmSeQRYu2K+q9Il6z9q3rUjU4/euiDtOlp5FrvrBg2zgWuWzzUtpuzV1eLT1x80cioGTkREFzjO8InIpGIMhLLjZil3ypTV6OgMYem6bZjdviWlT67dLEskqhCJxtJ6khrlo2wLZUebJV02r8Vxlu/eJ/di15G+lFmzbNjV27Pqn6wt4+oDsrbFQay6ekbasW6WHbSSLVr2sBZwZtr/mYioVrj5t/VFEXlKRG4U4cJbubFbylXw4/uvfRyz2rfgno17TD8czZrM6w2ORPHorQsKvrxL2YnEFO7ZuAc/2HnUMeklqhR+sPNoMpv5sVWLEMywjIoW0FnNDEeVSvt50i/jGum7iWgiMeVYqDmXAs9ERLXITcB3OYDHAXwSwEER+TsRubywwyI37JZyFYDvvHYXnn1rafJ7PX2xXG2Jzsr6rQfwoYUXw+dlvF+Osplf1YL+ZfNaTAO0pZeYT+xHlcKs9i3w2MzkZbLvzipwdCrJku15RES1ynEPn1JKAfg5gJ+LyDIAPwDwv0VkL4B2pdQLBR4jmXBayu0Nt+DXx95rew3tw7FtcdCyzy0QDw42uJhBosoSjkSxfX83Hr11QcpeOK3ciR2zZXxtJs9u352RVYa2UwHnbM8jIqpVbvbwNYnIF0VkF4C/BPDnAJoB3AvghwUeH1l4440vWi7lDo824Mdv/LHjNfQfjmtXzIfPJoWTwV51Oj4QRtviIHa034DD627CmuVzsWl3yHWZHI8gpwxasy0FdkvAuZ5HRFSr3GTpvgDg+wDalFLHdLfvEpFvFWZYZKerawOi0V7T+5QCvrPvLuw8scz2GsYPR+2DOtNG9VTZFOJ9crWZuUzbmcUU8Pa6mxyPs8qo1f/cZZJtm+15RES1SpRDhqWIiHI6qIotWbJE7dq1q9TDSPHf/91sGfD1hFvwl7/6ju35XhF85baFaFscxAMdrya7InhFcPs1M7B9fzeDvhqjFTS+Z+OejGdzv7pqkW2gpWXUuimgzFIrRES5EZHdSqklxtstZ/hE5BkkVvLMknOVUivyOUByx2l2z81SbkypZLD3g51Hk7drWZyXTR6bdaszqkxaEk82XU/ufTLe+9YqeDO7nj5pSH+8WW0/s2sTEVFm7JZ0/7FooyDXDh263/I+5ZmIvT1/BMB+SU7bu/fEi++Y3v/mqcGsx0eVS2tnluksX1SptMDsgY5XHRN9jBm1dqVWGPAREeXGrpfur4o5EHJnePio5X3z530dK/uDth+0Po9gaGQUs9u3cAaPUmjtzHYd6cs4K1sfmHV0hlydb8yoZakVIqLCsczSFZEnE3++KiKvGL+KN0TSa2hoNb29rq4JU6asxvb93ZYftAG/D5B4KzMGe6Tn815oZ/ZI2wI8tmqRZdcMK1pgtn7rAcefL0G8O4ieVUkVllohIsqd3ZLuFxN/fqgYAyF3Wlvvx5tvptbf83gacdll/wzAejZEAIxtqHPsi0u1aWx9XVrbMwDJrhxuaIGZmxk5BWDT7hCWzJyU0lvXLLlDn01eDkkd5TAGIqJM2S3pnkj8ad7KgYqqq2sDDh26P9lZw+udgNHoGQwMt+DJ/Z/E0d9cjDXLQ5ab7j0izLytMX6f13WJlYFwBLPatyBoCGBE4slAbh5LC8zcJn4Y9+c5lVoph6SOchgDEVE23JRluRbA1wG8C0A9AC+AQaXURYUfXumVQ1kWrauGvtCygh/fee2ulG4afp8XK68KYtPukOkHPTNva49XxLQrhh2/z4srWydgx1t9rh9DK/MDxIOiNU/tRSTm/LgC4LCLOn5AvF6gWSAZDPixo/0GV9fIVTmMgYjIjlVZFje9dP8FwO0A3gTgB/CniAeAVCSHDt2f1lVDEMaKS/4j5TZ9qyyz/VcM9qqHTVOUFJkGe0D858htsOf3eVOCPSA+0zVujJua7pntz8s0qaOjM4Sl67ZhdvsWLF23DR2dIdePla8xEBGVCzcBH5RSBwF4lVJRpdR3ANi3caC8ssrMbRrTk3ab1iorVru1smuCi8mzggkG/I7t1AaG3O0VdWqFpg/aPBZJJGZBo7b0GhoIQ+HC0muuQR8TS4ioUrn5b/iQiNQD2CMi/wDgBICxhR0W6TU0tCb37un1nm9Ou00BuOS+Z+H3eTAUiRVhdFRr3CxdutnHF/D7UpaBjXv3AKTslzObrbRK6nBb7DlTbhJLiIjKkZuA75OIzwTeBeAeADMArCzkoOiCeGeNc2m3j0QbLLtqRJXCUETBI6kzQX6fF2N8HvS7nH0hMprY6HN1nFlgpCcAPrTwYgDWiRANdR7T870iiCnlmNRhJtelV/bwJaJK5RjwKaWOiEhL4u8PF35IpDFP1gDOjYzHhtfvwM4T9ivrCvFlN+Osyb1P7UW0lGuCVJE8Ajx083xXxxoDo8Z6LwZHLgRi+rIsVh02rAK3mFKmiR5m1zHKx9Jr2+IgAzwiqjh2vXQFwEOIz+wJAI+IjAL4ulLqS0UaX00zT9YAhqNjHIM9IF5Ow7j81tEZggfpzdeWXjIJL73dj0iUgSClC/h9WLtivmWgY1WbTjt+6bptGBxJnV3TllgznXWzCtqcrsOlVyKqZXYzfHcDWArg95VShwFAROYA+KaI3KOUeqwI46tpmSRrmBGJf9DqP4TXbz1gWi5j56H+rDI6qXoZy63o6QO8QKMP586PJn+uzGrT2WW3Wu33m9jow/lIzPV+Obt9g8b6gkREtcYu4PtjAO9TSiWjC6XUIRH5BIDnADDgK7BMkjXMCJD8ANQ+hK2WvBjskdF4Q2kVfUKEvqaj2Z5QY4LEBL/PtMvLBL/PMhFCWz52u1/O6jpWmcRERLXELuDz6YM9jVKqW0Tc7dymnDQ13Yjjx7+ZcpuCH5vf+rTteV4RNNRJWpZuOBLNqhAvVT8tGUMfvA2EI7hn4x7cvXEPJhpm8dz8BOln9aza8oo4J0K4DdaYUEFEZM0u4BvJ8j7Kg66uDTh58ruGWwXBaZ/GrRO+iF9b9Dj1iuCtR2/ErPYtpvcz2CMzA0MRTAv402br7GbxnOj32lnV5dNuz1ciBBMqiIjM2RVeXigiZ0y+zgJYUKwB1iqzhA1Aobf3WdvztIDOZSMGIgBIFifOp/7B4WShYxYsJiIqLcsZPqWUt5gDoVRWCRvDw0ex/lcHLM8LBvzo6AyxjRqV3FAkhrs37sHDz+zDTe++OK3Hc6ZZs1aZwIU+l4ioGrhreElF1dW1ATAtngL0hJttZ2K0TFyictE/FMGm3SGsvCqI7fu7Mw66OjpDePiZfSnLyqGBcHJ/oVMGrlVhZ8D9/kAiokrHgK/MaMWWzYK94VHr7hoA4Pd5LNtKEZVSOBLF9v3drtqy6dl1z9BmsZ0COKvCzrm2WSMiqiR2e/ioBMz37gHRmAff2XeXZcFln0cwGlMM9qhsZdPWzE33DOBCAJfJ4+baZo2IqJIw4CszVnv3PKJsu2tEYopdMqisZZOgkUlQZnUsE0aIiBjwlR2vd5Lp7W6LLROVq2XzWjI+J5OgzOrYNcvnwu9LzUFjmzUiqjUM+MpIV9cGxGJn026PxLy2e/eIyonXosryD3YexdJ125KlWtwwC9bM2AVwbYuDePTWBQgG/BDEM9mN3Tc6OkNYum4bZrdvyXiMRESVgEkbZeTQofuhVHpN6/OjjbbLuUSFEvD7MDgymrJdwOcVQMG0J7Pf57Xdc5dphqy+e0ZoIJzsFDOx0QelgNPhiKuMX7uCzMziJaJawBm+MmK1f2+c71yRR0IUNxCOACreek2bHVv/kYVY/9GFCCaWULUZPW3mzGqGT2OXYGGmbXEwOdOnFRbvH4pgeDSGx1Ytwo72G3IKzOyyeImIqgVn+MqI1zsJ0Whv2u3cv0elFIkpNNbXofPB96fcbhVk3W3R9k8v0wzZQpZWYRYvEdUCzvCVCav9ewo+bHK5f8/LfmpUIKGBcHJfm9N+t6CLRItMM2QLGZQxi5eIagEDvjJhtX/PV3cRLpn5KVfXMNlSRZQ392zcg1ntW3DPxj0IDYST/Xfv+8mrKUGfU6JFNhmyhQzKmMVLRLWAAV+ZGB4+Ynr76GgflsycFN8o74DxHhWSMvyp0ZZWtZm/ezbuwRifBwF/fN9fwO9L2QNozJB1o5BBmZssXiKiSsc9fGUg3jtXYBayNTS0Yv3WAyyqTGVNm+nT9tn1D0Xg93nx2KpFeQmc9Nm6bnrxdnSGXB+rXZ8BHhFVMwZ8ZeDQofthPj8nmDPnyzj+NDeP0wUigCqz+N8rUvB+tW6DMpZZISJKxyXdMmC1nAsoTJmympvHKUW5BXs+ryTLpRiFBsJFL2jMMitEROkY8JXYheXcdA0NMwGY719ys6ePak++fio8Avhc/uswtr7OMjNXANsEj0JgmRUionQM+ErMaTlX24sUjkTh0X2ac08fGfl9Hqy+tjWrc42BYkwBEIHP4xxCDoQjWDavJe0/JWa7Uosx08YyK0RE6RjwlZhVdw0FhReOX481T+1FKDEzwbIrZGfS2AY80rYAAb/P9P6JjfFsWSO/z4uAye2RqEJ9ncexcwYAbNodwsqrgimZrlY/roWeaWOZFSKidAz4SszrnWR6+9mR8bhn4x7TfqVEZrRAau2K+aYBz0M3z0fng+/HV1ctSitBMjAUMb3m4EgU186Z6PjY4UgU2/d3Y0f7DTi87ibsaL/Bcpk30Ogr6L4+llkhIkrHLN0Ss5o8MS/SQmRNW7LMtISJdm7IYuZt56F+V49vnLlbs3xuSrYsEN97eu78KPoTAWahMmhZZoWIKBUDvhIbHU3vnQsA43zpbdaIrBiXLK0CHquSJSuvCuIHO823F1hl4BoZ98iZBZ6Dw6MYCKfOJua7fAsREaVjwFdy5nN5McXVdnJnYqMPSsVbn63fesB2Ns+qZMnP9p6wvL5XrMuuaKz2yBkDz9ntW0zPZwYtEVFhMeAroXhJFvMPUo/EijsYqkh+nwcDQ5HkT5HTEqlVYGWcddMIgNuvmYFNu0OpS7MewbgxdRgYilguGZt1u7BaOmYGLRFRYTHgK6F4SRZzvedbijgSqlThSPp/DOyWSO326plRALbv78bKq4LYvr/b9Z5Au6VjY/DIDFoiosJjwFdCVh02lAJ+/MYfF3k0VE2sZvKsEilGY8qyg0doIIwf7DyKiY0+PLZqEYD40vA9G/dYBn9WS8dPvPgOokoll4mDGcwOco8fEVH2GPCVkIIXgqjJ7YKdJ5aVYERULayWSI2JFIFGH86dH3XVrq1/KIK7N+5Juc24hKwFalaziNpewKhSyZk9/Xn6MWklidgLl4godwz4SklFTXthCQuyUA4EsF0i1SdSLF23LVkiJVv67hnG2cNszjMbj9kyNWcBiYjcY8BXQkqZ1+FzWQXDlkfYmaMWCYDV17a6DnzylR17fCBsuoybz/P0Y7XaIwhwFpCIyAxrf5SQZdFl505Wjhjs1Z5gwI/HVi3CI20LXJ+Tr+zYaQG/bfBo1Z7N6TzjsRqrPYKF7tNLRFSpShrwicgHROSAiBwUkXaT+0VEvpa4/xURudLpXBGZJCI/F5E3E39O1N13X+L4AyKyvPDP0EEeAjuiXFj1nR1b77U4I522F88qeAwG/PjKbQst+9u6CTqNmbxWQSLr+RERmSvZkq6IeAF8A8D7ABwD8LKIbFZK/U532AcBXJb4ugbANwFc43BuO4BfKqXWJQLBdgB/JSJXAPgYgPkApgH4hYhcrpTKbA0qT7q6NljGe4qRIGUhNBDGmqf2YteRPtclVIxJHBP8PojE99HZtffT7jNm2Rr38OkTM/SPYxyXWebw2Po6nA6b1/ljPT8iosyUcg/f1QAOKqUOAYCI/AjALQD0Ad8tAL6nlFIAdopIQEQuBjDL5txbAFyfOP+7AJ4H8FeJ23+klBoGcFhEDibG8EIBn6OlN974ouV9TNqgbEViKqVFmpu9bVoSh3FfnMKFwE7r5mEVgOmvbxXUWbV7y6b3r1l5GdbzIyKyVsqALwjgHd33xxCfxXM6Juhw7hSl1AkAUEqdEJHJumvtNLlWSUSj5j10ARZdpvxy26vWbF+cNou3o/0GV49lFdTl+7xsgkQiolpWyoDPbN3SOLVldYybc7N5vPiBIncAuAMAWltbHS6bXyy6TIVwfCDsWMak0vbFZRtcEhHVolImbRwDMEP3/XQAx10eY3duV2LZF4k/T2XweAAApdTjSqklSqklLS3Fn21j0WXKNxFgzVN7ERoIQ+HCUm9HZyh5jNX+N+6LIyKqfKUM+F4GcJmIzBaResQTKjYbjtkM4I8T2brXAjidWK61O3czgE8l/v4pAE/rbv+YiDSIyGzEE0FeKtSTIyonMYVk5wqNsYyJVcaufl9cR2cIS9dtw+z2LVi6bltKwEhEROWrZEu6SqlREbkLwFYAXgDfVkrtE5E7E/d/C8CzAG4EcBDAEIDP2J2buPQ6AE+KyGcBHAXw0cQ5+0TkScQTO0YBfL5UGbpE5UK/XOu0L47FjomIKpeofLR1qGJLlixRu3btyvt1n3/evPSKUsBntv4s749HZCaThIyl67aZlkLJ5BpERFRYIrJbKbXEeDtbqxFVITet9fw+L5bNa8HSddtcZbpWWlIHERFdwNZqRCVQ6NLaMYW0/Xg+ryDg90EQn5VbeVUQm3aHbBM59JjUQURUuTjDR1RkY+u9GBwp7PZRrQOGXRmWpeu2Wfaj1e/b03fh8HkFkeiFqUMWOyYiqgwM+IiKLJNgzyuCaIb7bAVIBnd2yRROS7TGJI2BcAQ+j2Biow8DQ9YdN4iIqPww4CMqUwLgK7ctNG0h1lDnwUA4Ynqegn3WrDZrZxVGaku0Zp03IjGFxvo6dD74/kyeChERlRgDvhIytgxRKv5FBMQDL6tSKfds3GN5XtBkT50W5IUGwsn+uGb0S7RM0iAiqh4M+Erk+usVtm2TlIhPKeBPnmNJllpkDML0gZfZ0qwWvJkZHB7F7PYtyeAQQMosoVWwFzQs0U4L+E0fwypJw6l1GxERlQ4DvhL67HM/c2wATNVPAKy+thXb93e7DpbWLJ+bttQLxMuxaEu9oYEw7t64ByLOM8cCpNXSM3sMqyQNFmUmIipvDPhKyGoGhWqLAvBI24Lk99pM2T0b96QEf8YZtJVXBfGzvSeSAZ5V7T032wSmBfymM3QrrwriiRffQVQpeEWw8irzRBCz/X7GjF8iIioddtpwUKhOG0D6rAjVJr/Pg0ljG3B8IIxAow/nzo+m9L31+7zJmnn6nxWfJ74lQF8mJVufuLY1/fpeAQw9eLXZSH2ACgCz27eYzlYLgMPrbsp5fERE5I5Vpw0WXi6htsVBPHrrAtNN9lQ7wpFYsvhx/1AkJcCK3x/FEy++Y5oxm49gL+D3Yfv+7vTrR1XaWBSADTuPJoszd3SGsHTdNseMXyIiKi0GfCXWtjiIHe03MOgjW5nW4jPjMWnv4fd5sXbF/IwybxXiS7jaDLXVtgQWZSYiKh8M+MoES11QIfl9XvzTbYvw1VWLEAz4k+3VHr11AdoWBzOeiTs+EDbdt6fRX5uIiEqPSRtlggkc5MRYusXtHr6JjT48dPP8ZPBlFoSZZeQa26jpTQv4Lf+TYpbxmyuWfCEiyg1n+MpAR2cIA0MjpR4GlTmF+H47bXZu/UcXYv1HFqbM2H3i2taU77+6ahE6H3y/Y3Ck30+avP5HFuIT17bCuBKsLdVazQrme9+efulY4ULJF20fIREROeMMX4l1dIaw5sd7LWdS7LoiUHVYeskkvN0bdjXDOzwaS9bsM5ZtyZVZgee2xUEsmTnJcnbNbZ2+XLDkCxFR7hjwldj6rQdsl+QY7FU+r4hl0sXERh82fO46ANalTfTCkSh+sPNo8vtiFDg2CwQ1Y3yeZDAW8PuwdsX8lGPzsRTLFm9ERLnjkm6J8UOrugmAtx690XJp9KGb5ye/z3YpVJvtKiZtmbV/KJK8bXg0ZnpMrkuxxVo6JiKqZgz4SowfWtVN62CxaXcoZfZOgLSuFWuWz4Xf583qcUIDYSxdt61o+9rsllkzOcYNs9eFJV+IiDLDgK/E+KFVvQTAsnktpoGPArB9f3fye23pMxyJwiuSPD8TVjNoWnHk2e1b8hYUullmzddSrFlCCUu+EBFlhnv4SqxtcRD3PLnHVb9TKm8+DxDRrWoqIK1dmZ4W+Bhb7EWVgt/nzarlnjGZwXjtfO35syojpJ+xdnOMW3b7CImIyBln+MrA6mtaSz0EyoNILP02u6Btgt8HwHrpU5vpy5R+Bs3q2g8/sy+nWT83y6xciiUiKh+c4SsDWiN6ffYlVb/BkVF0dIYslziznenTZtA6OkOWpV76hyLJhItsZv204+wycN0cQ0RExSGKa4m2lixZonbt2lWUx1q6bhu7bdQYrYey2fseTARI+oDJ6efD7/Pi0Vvj/4Ew1shzM5Z8d8ggIqLiEpHdSqklxtu5pFtGcsnSpMp0fCBsu/TZtjiIHe034PC6m7Cj/YZkgGhGn8xg1+fWbixERFSduKRbRvRLYJzpqxyS+DLZwudoWsDveumzozOEoZFR0+tMbPSlnJNN8OY2mYJ9bYmIKg9n+MpM2+Igls1rybgkB5WOQnbBns8jGBoZxez2LVi/9QDWLJ+Lx1YtAgDcs3FPSjKFWaFjvf6hSEpJlkwzYd0mU+RSTLkQ5WGIiMgd7uFzUMw9fED8Q/GejXvYUq1KeUUQUwoT/D4MjoymtdXzCBDT3aTtyXM766vtwzOWYzGj9WkOZjBLZ7XP1Gn/n9l4tOfG2UEiovyx2sPHJd0ys37rAQZ7VSyqFN5edxOWrtuGgXD6bF3M8OZrdfXcLtFqx5ktEy+b14Lt+7tL0tfWrusGAz4iosJjwFdmuHE+f7QZrHIzu31LRuNym6ELpC7lFqJYcbbFlPPVdYOIiLLDPXxlhr1180cBaPSV3494pkGoNhvnlMFttQ8vn3vnsi2mbPVzzZ93IqLiKL9PwxrH0iz5NWTW/qKC6MuzGPvJfuLaVsf+srkkWZjJtq8tu24QEZUWkzYcFDtpA7hQ9sJs6cy4qZ/KQ9Dlkmum18y15Em2SRaFwHIuRESFx6SNCqJ9CJplWTLYy5+ll0zCjrf6cr6OFpjdvXFP7oPSXTMfAVk57Z0rxJ5CIiJyh0u6ZSqbTgnk3tJLJmHD567DxEZfztfS96LNl3wFZNw7R0REAAO+ssXsxcJ6uzf++t707ovzcr1sgnOfRxDwmwec+QrIuHeOiIgABnxlizMwhRUaCKOjM4RNu0vT7SEY8GP9Rxdi7Yr5eQ3IjBm5ALJKsiAiourCpA0HpUjaAMw7E1D+CKxryuWLWYKNWXeJfCUzuO1mweQJIqLqZZW0wYDPQakCPiD1g9kjgijfq4qhBVoAihZcWWXkTmz0obG+DscHwgg0+nDu/CgiukiULc6IiKoHs3Qr3Pgxdaa9V6n8eEVSAqhiBVJW+z77hyLoH4ok/27EFmdERNWPAV+ZMi7PDYQj8EiJB1VDfB5JmQXLxFduW2gbPBVqSTWXJWomCRERVTcGfGXKrCxLudbg83oE0XIdXJYiMZXXItf6Ytr6Hr/6ki65Bn1rls/Net8nk4SIiKobs3TLVCXNuFRbsKeJKcDnzXxa1di6TN/eDEjvpRuORHHvk3tz7nVr1vbMquyLHsu0EBFVP87wlalCZ5CSM68I1n9koWWbOyvanjgArs/VEnJynfEzdrMwy9z1eQVj6+twOhxhli4RUY1glq6DcirL4vMKoJD13jLK3NvrbgKQXZkcv8+bdVmdfPa6ZRkWIqLawSzdCqN9IBs/qAHgnif3gHF67sbWezE4Yh2QGduuSdpirDWvSE41FPO5pM8etkRExICvjFl9UK/dvA8D4fTyGpQZu2APiJcwueS+ZzOuf+hmZk9L3PBa1FdkEgUREeUTA74KdJrBXtFkGuwJgJVXBfGzvScsg/KgblnVqjvGsnktWLpuW8mXYbkcTERUHRjwVSAmdJQvBWDjS+8gZnKfzyNY/9HUGn1mS/fL5rVg0+5QMgjUJ3IYj7UKwPIRqBmD0XyWkCEiouJi0oaDUrZWs9LRGcLdG/eUehiUoYmNPnQ++H7H4+xapJ2PxFz1ynXTUzfbceQzoYSIiPKLSRtVpG1xkPv4KtBAoq2Z0+ybXYs0I2MJGKu+y9m0T7MaRyXViCQiojgWXq5Qa1fMh9/nLfUwypJXBOXYhW5awJ9ShFnhwjKpvthypgkb2jW0a1rtO8w0ULMaBxNKiIgqDwO+CqXvqkCpYkrh8LqbStp72NihQ+tmYdYyTz9LB8RbpBnP93nFsmuG2xIwmQZqa5bPTftPBbtyEBFVJi7pVjCtbMuc+7aUbZ/dUtACm1K9Jp+4thVLZk5KdtnQAjK7rhuhgXAyKzfQ6EM0ahi8Aj608OKUZA7AfXHnbAI1q1qQTNggIqo8DPiqAIO9VEMjo5jdvqUkjz223psS7AlS26Zp9feMJHE/YL5XLxJT2L6/G4/euiAtALMKJL0iiCmVU6DGos1ERNWBAV8VCLJMSwqzgKlYBkeiKRmyxuDOKthzE7MfHwinBGBa8odZIJlNVi4REVUv7uGrAmZ7rag0PIIM++16XDds0+/B0yd/APFgT9v1Fwz4CxbsdXSGsHTdNsxu34Kl67alJJsQEVH54gxfFWhbHMSuI33YsPNoBt1eKd98XkHEuPfOwciocjVDa9yDZ5b8oVDYGnksxExEVLk4w1cltu/vZrBXJBMbfcnsaK/E59WCAT/G1mf+/6eoUo7JFGYzdlYlVgq5tO8mw5iIiMoTZ/iqRC0Uw53Y6ENjfV1GQY3b/XF6Pq9gbH0dBsIR071xD90833RGK5tEEa8I2hYH8fAz+0z3HlrN2Fm11xPEZ+IKMeOWSSFm9uAlIiovnOGrErVQDPfc+dGMgr2A35dxsOcRYGx9HU6HIwgG/Fh9bSuCAT8EznvjsnkPbr9mBgDgoZvTC2nblVJZs3yuaXFpBRRsxs1tIWY3xaWJiKi4GPBViVpI3IhkWH9m7Yr5lh03An5f2uvl8wq8IhgIR5KByoadRxFyOUtlFYTpaUvAXhF84tpWPNK2AEBqIW03wWXb4qBlMFuo2V63hZi59EtEVH64pFsljEVyzfqp1pJGnwf3PrnXsgzK2hXzAaQWFR4cHk3rT6yd7yZBoW1xEHdv3GM7rrcevdHyvkxr3lklexRqttdtIWb24CUiKj8M+KqEcc/UsnktaV0ZaslQJGZ5n8KF4EUfrDjtwdNmqYwBjv6199oE2hMbzVujZWvN8rkpWbNA4VufuQlKrfYX1sK2AyKicsUl3Spgtmdq0+4QVl4VLNteu42+0v7ozTKpI+cmIDHOUhlfe6tgzyPxfXr5lOkycLGUogcv6wMSEdnjDF8VsNoztX1/N3a034Cl67aVXSeO8Kj1DFyxGJdpzWbMjAKGWTqz1x6IB3jalsOA34e1K8wze3PNZi3H1mfF7sHL+oBERM4Y8FUBpz1TToGMzwPYrIAWRLlsL9Qv0+oDFasAuX8ogsVfei5ZmsXqtVcKeHvdTbaPXc2BSjEDUbskkUp/HYmI8oUBXxVw2jPlFMgUO9gDYLvXrdj0QZs+UFn08HNpSRxAPOhb89RePPzMPstMWTfLw5kGKk6zgbVa+45JIkREzriHrwq42TPVtjiIHe03lMWePr/Pi9uvmVE2ZWQ8IqZ7vk6bBHuaSEyZFkoG3O9Xy7SQsV1tu1qufee2PiARUS1jwFcFjJv3Jzb60FDnwT0b96RtYC/1Xj6vCB69dQEeaVuAR29dUNKxaKJKpQVPS9dty6pVnVckOUvnFGxlEqg41bar5dp3pUgSISKqNFzSrRLaUqTdvjAgu1Zjbvg8AggQidpfPapUyjJjuSzt6oMjp8QNO9pz0V73XUf6sH1/t+kyayZlVZxmA93OFlbjsm+xk0SIiCoRA74qYzfTMzQyWpBgL5j4gAWAv3hyD5waYsxu34IJfh8GR0bLItjThAbCllm32QhHotiw86hl8eZMAhWnfZpuat8xSYSIqHYx4KsyVjM9Tku52cz8+X3etLpvazfvM0100FOA4zGl4BWx3eivvUaBRLDqNJsJpL+mxqQMt4GK02ygm9lCZrMSEdWukuzhE5FJIvJzEXkz8edEi+M+ICIHROSgiLS7OV9E7kscf0BElutufz5x257E1+TCPsvSsNoXpvVwtZJpsCcAVl6VHqzYJTqUu6hSthv9FeKzmXseej/Wf2RhSsHjgN99F41sskediiy7KcLMbFYiotpVqhm+dgC/VEqtSwRy7QD+Sn+AiHgBfAPA+wAcA/CyiGxWSv3O6nwRuQLAxwDMBzANwC9E5HKllDatsVoptasYT7BUrGZ68t1iTQHYvr877XarpcVyYrVvUFuattvDpwVHxpk543IpYD1rmm32qNNsoNP9bHlGRFS7SpWlewuA7yb+/l0AbSbHXA3goFLqkFJqBMCPEufZnX8LgB8ppYaVUocBHExcp2ZYzfQUohyL2cyQWcZkMQUDftvn6vMIGurSZzu15c+2xUFc2TrB8vxpAb9pGy+z1331ta2uskeL1RaM2axERLWrVDN8U5RSJwBAKXXCYnk1COAd3ffHAFzjcH4QwE7DOfopj++ISBTAJgCPKFVGGQN5ZDXTs+apvYg4ZVRkwGxmSHtcN3v58k0fvFjN0kViyvQ1aKiL/9+nozOE37zVZ3p9AbBsXott4oPxdV8yc5JjseRiJVIwm5WIqHYVLOATkV8AmGpy1/1uL2Fym1O0YnfOaqVUSETGIx7wfRLA90wvInIHgDsAoLW11d1oy5ybQMwrglhiH1v/4DCGHFpwWM0MaYHP4i89Z1mcOBM+j2DV1TPws70nkmOf2OjDTe++2LLkCQDcvXGP68cYCEdw309exRifx/KHTAF44sV30paD7RIfnJZZi51IwWxWIqLaVLCATyn1R1b3iUiXiFycmJ27GMApk8OOAZih+346gOOJv1udb3mOUiqU+POsiPwQ8aVe04BPKfU4gMcBYMmSJVUzC6h92D/Q8Sp+sPNo2v23XzMDj7TFiyF3dIZw71N7EbWYEZzY6LMNHDo6Q7bBns8jrmYbA34f1q6I963VxmZHX2cu0xp/4UjUca+j1fWyTXwoZCJFNdbcIyKi7JRqD99mAJ9K/P1TAJ42OeZlAJeJyGwRqUc8GWOzw/mbAXxMRBpEZDaAywC8JCJ1ItIMACLiA/AhAK/l+TlVDLNkC+PtbYuDGN9g/v8BAfDQzfNTbtP2oc1q34I5922xnV0TAKuunuG4r3DpJZOw56H3x//uYo+bsb1YMWv8ZZv4UKi2YLXcao2IiNKVKuBbB+B9IvIm4lm46wBARKaJyLMAoJQaBXAXgK0AXgfwpFJqn935ifufBPA7AP8F4POJDN0GAFtF5BUAewCEAPxrEZ5nWXIzq9TRGbJc+lVA2j60NU/tTWaAOk3cKQBbXjlh29u33ivY8LnrTAOXezbuwSyT4M+qaLJWksahMg2A+IxipkknxsSHTJIwCpVIUcut1oiIKF1JkjaUUr0A/tDk9uMAbtR9/yyAZ92en7jvywC+bLhtEMBVuY26ejiV59CCLCvGIG3t5n0ZJ4P0D0Ww6OHnLINKraixWeBi1bnCKpCNKhUvkeIwRL/Pi7Ur5icf9/hAGB6LZWEtdjQulZolYdyzcQ/u3rgnWfZFHywXKpHCqjROuZfMISKiwmCnjRrk1JXBrr2Y2exTttm4A+GIY606p71s+gQHuxqAVrGe9vjGYEwfwJllN9d5Bes/sjAtMMskQNUUIpHCav+iUwFuIiKqTqVa0qUScurKYBdkGbs35EohPbVaH1S62cumjddsedQpvJkW8OPtdTdhR/sNllm248ak/78oElWmy6NuA9RCs9q/WE69i4mIqHg4w1ej7GaVJvh9prN2Ab95Zu7ERl9O5Ve0GbbjA2FM8PsQicZwd2IZtNHncczo1YJCs+VRpyVMN9mwAxbPLTQQxtJ121JmBvP1mLkKWoyjEAW4iYio/HGGj9JYrfpZ3f7QzfPh82a/VBgM+LGj/QY8tmoRBkdGMThyYUl0KBJDVKlkr1q72UAgHvTtaL8BhxOzdk4BjpsZRLtjjNmvbjqNFKOVmdVspxakMluXiKi2MOCjNFYzWla3ty0OYv1HFtoGV2PrzYMgn1dS9g5qyRp6MQWMbajD2+tuwmOrFlkuRZuxC8DcZsM6BXH6Zdq2xUGsvCpouVeuWK3M9Mv2QGpfX5ZoISKqPVzSpTROWbyAeVHfHe03pGWpAvEgx+f1AEhPBBmNKdyzcQ/Wbz1guxSq3ZdpgoN+mTekK8ZsljHr9hpmtGXajs4QNu0OpeyVs0oMKTTttVq6blvauAvZzYOIiMoPZ/gojVNtOLuivlYJIaetavopJK/htCj8QEdqqRi39e60Zd63192Er9y2MLlfcP3WA65nubRrWM1iasGwVZautmxdigDLas9gaCDsqlYgERFVPs7wURqn2nBO/V/NZuGcZvAA50bJT7z4Dh5pW4COzhAefmZfSqKIVckTPbMaeU7nGDmVtMmkVVqxWp85lavJ5nUgIqLKwhk+MmVMftAHAtn0f3WTzOAkqlQyaDPLCnYqeWIVqN775N6MZvrsStq4bZVWzNZnbl57duEgIqpunOGjjLnZ42dmjM+TDLisCi7b8YrYFoUG7INOu04cmcxw2e0jdJoB1DjNkmrsZgHdzhAaZ2ytXvdilIshIqLSYMBHGXMb1GjMEjnqvAIoZNSS7fZrZmDDzqO2x9gFnXZLm8ZgK9vlVret0tz2M7ZaggaQ0fK0Pkg1S+IAilMuhoiISoMBH2Us0/6vZrNZkajCxEYfzoRHTbs/+H0ejIwqRJWCVwS3XzMDj7QtwPb93ZZBm1PJE7NAVU+faZtJP1wjN5nEbmZJ7WYBtb+b3ef02JkG7EREVPkY8FFWMimPYjWbNTAUwWOrFpkGH1b19ayCtoDfh7Ur5jsGYgBw75N7TYNMp0xbIH8JDm6Crmz2SrpZls00YCciosrHgI8Kzm42K9PgI9dgRTsum0xbTT5q2Ll5Hk6zgLksy2Zaz5CIiCobAz4qOKfZrGyKKRcy2CpWP1yn5+H0unFZloiI3GLARwXnZjarWDXp9GPKJNPWyM1MWq7Pyc3rxmVZIiJyQ5TJXia6YMmSJWrXrl2lHkZVs2rH5tQnt9Bj0opFG0vIuBlbOT4nIiKqfiKyWym1xHg7Cy9TyTllo5aCvh3bY6sWWRZatlKOz4mIiGoXl3Sp5LLJRs2HbAsXa0GbXdBXqudERERkhjN8VHJu25HlUyatzbJpgxZo9GV0OxERUSEx4KOSM+v1WuiM00yWXLNZnrXaGssts0REVApc0qWSK0Uh4EyWXLNZnj0djmR0OxERUSEx4KOyUOxCwG5am2VzbC7nEBERFQqXdKkmZbKMnM2ScymWqYmIiKxwho9qkttlZC2TNxyJwiuCqFIIulhyZr9aIiIqJwz4qCa5KcliLJ4cVSo5S+cmcLNbpi52ZxEiIqptXNKlmuO2zEqhiidnU+aFiIgoFwz4qOa4DeQKVTyZXTiIiKjYGPBRzXEbyBWqIDS7cBARUbEx4KOa4zaQK1SmbSk6ixARUW1jwEc1x20g17Y4iEdvXYBgwA8BEAz48eitC3JOrmDJFiIiKjZm6VLNyaRkSiEKQrNkCxERFZsoNve0tWTJErVr165SD4PKBMupEBFROROR3UqpJcbbOcNH5JKxLp9WTgUAgz4iIipr3MNH5BLLqRARUaViwEfkEsupEBFRpWLAR+QSy6kQEVGlYsBH5BLLqRARUaVi0gaRSyynQkRElYoBH1EGClGXj4iIqNAY8BFVIdYLJCIiPQZ8RFWG9QKJiMiISRtEVYb1AomIyIgBH1GVYb1AIiIyYsBHVGVYL5CIiIwY8BFVGdYLJCIiIyZtEFUZ1gskIiIjBnxEVYj1AomISI9LukRERERVjgEfERERUZVjwEdERERU5RjwEREREVU5BnxEREREVY4BHxEREVGVY8BHREREVOUY8BERERFVOQZ8RERERFWOAR8RERFRlWPAR0RERFTlGPARERERVTkGfERERERVjgEfERERUZVjwEdERERU5UQpVeoxlDUR6QZwpNTjKIFmAD2lHgRlhO9ZZeH7VVn4flWeWn3PZiqlWow3MuAjUyKySym1pNTjIPf4nlUWvl+Vhe9X5eF7lopLukRERERVjgEfERERUZVjwEdWHi/1AChjfM8qC9+vysL3q/LwPdPhHj4iIiKiKscZPiIiIqIqx4CvBojIB0TkgIgcFJF2k/tFRL6WuP8VEblSd9+3ReSUiLxmOGeSiPxcRN5M/DmxGM+lVhToPVsrIiER2ZP4urEYz6UWZPt+icgMEdkuIq+LyD4R+aLuHP6OFVCB3jP+jhVIDu/XGBF5SUT2Jt6vh3Xn1NbvmFKKX1X8BcAL4C0AcwDUA9gL4ArDMTcC+E8AAuBaAC/q7nsPgCsBvGY45x8AtCf+3g7g70v9XKvlq4Dv2VoAf1nq51dtX7m8XwAuBnBl4u/jAbyhncvfsYp8z/g7Vn7vlwAYl/i7D8CLAK5NfF9Tv2Oc4at+VwM4qJQ6pJQaAfAjALcYjrkFwPdU3E4AARG5GACUUr8G0Gdy3VsAfDfx9+8CaCvE4GtUod4zKoys3y+l1Aml1G8BQCl1FsDrAIK6c/g7VhiFes+oMHJ5v5RS6lziGF/iS+nOqZnfMQZ81S8I4B3d98eQ/o+Tm2OMpiilTgBA4s/JOY6TLijUewYAdyWWO75d9csXxZOX90tEZgFYjPgMBMDfsUIq1HsG8HesEHJ6v0TEKyJ7AJwC8HOlVE3+jjHgq35icpsxNdvNMVQ8hXrPvgngEgCLAJwA8JWMR0Zmcn6/RGQcgE0A7lZKncnj2Mhcod4z/o4VRk7vl1IqqpRaBGA6gKtF5PfyO7zKwICv+h0DMEP3/XQAx7M4xqhLW0JM/Hkqx3HSBQV5z5RSXYl/+GIA/hXxZRLKXU7vl4j4EA8cNiilfqI7hr9jhVOQ94y/YwWTl38TlVIDAJ4H8IHETTX1O8aAr/q9DOAyEZktIvUAPgZgs+GYzQD+OJHldC2A09o0t43NAD6V+PunADydz0HXuIK8Z9o/bAkfBvCa1bGUkazfLxERAP8O4HWl1D+ZnMPfscIoyHvG37GCyeX9ahGRAACIiB/AHwHYrzundn7HSp01wq/CfyGevfQG4llO9yduuxPAnYm/C4BvJO5/FcAS3blPIL40EUH8f1CfTdzeBOCXAN5M/Dmp1M+zmr4K9J59P3HsK4j/Q3dxqZ9ntXxl+34B+B+ILzu9AmBP4uvGxH38Hau894y/Y+X3fr0bQGfiPXkNwIO6a9bU7xg7bRARERFVOS7pEhEREVU5BnxEREREVY4BHxEREVGVY8BHREREVOUY8BERERFVOQZ8REQ2ROR5EVluuO1uEfk/Fse/LSLNDtf8a8P3v0n8OUtEXkv8fYmIfC3x9+tF5A9yeR5EVNsY8BER2XsC8UKveh9L3J6tlIBPKZUWzCmldimlvpD49noADPiIKGsM+IiI7P0YwIdEpAGIz8IBmAZguoi8KiKvicjfm50oIh0isltE9onIHYnb1gHwi8geEdmQuO2cybnXi8jPEo93J4B7Euf8TxE5nGjvBRG5KDGr6Mv/UyeiasGAj4jIhlKqF8BLuNB/82MAtgL4ewA3AFgE4PdFpM3k9D9RSl0FYAmAL4hIk1KqHUBYKbVIKbXaxeO/DeBbAB5LnPPfiPcDvUk3nk1KqUh2z5CIagEDPiIiZ/pl3Y8h3rLueaVUt1JqFMAGAO8xOe8LIrIXwE7EG7tflqfx/BuAzyT+/hkA38nTdYmoSjHgIyJy1gHgD0XkSgB+AHudThCR6xFv1H6dUmoh4v08x+RjMEqpHQBmich7AXiVUq/l47pEVL0Y8BEROVBKnUN8GfXbiM/2vQjgvSLSLCJeALcD+JXhtAkA+pVSQyIyD8C1uvsiGe65OwtgvOG27yXGwtk9InLEgI+IyJ0nACwE8COl1AkA9wHYjvhs32+VUk8bjv8vAHUi8gqAv0V8WVfzOIBXtKQNF54B8GEtaSNx2wYAE5FbtjAR1QhRSpV6DERElCER+QiAW5RSnyz1WIio/NWVegBERJQZEfk6gA8CuLHUYyGiysAZPiIiIqIqxz18RERERFWOAR8RERFRlWPAR0RERFTlGPARERERVTkGfERERERVjgEfERERUZX7/wEQgeWiG/r9UQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(10,8))\n",
    "plt.scatter(data = portfolio_options, x = 'Volatility', y = \"Returns\")\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Daily Returns')\n",
    "plt.title('Efficient Frontier')\n",
    "plt.plot(risks, returns, 'y-o')\n",
    "plt.show()\n",
    "plt.clf()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Analysis\n",
    "\n",
    "Can see that a lot of the portfolios actually have a negative daily return (due to `WDC`, `IVZ`, and `PPL` having a bad sell off during this time), so say we want to just pick some portfolios that have a positive return, and a medium volatility level. Lets randomly pick a range for the daily returns (positive of course since no one wants to lose money), and a positive range for vol thats not greater than 0.3 (since no one wants a super volatile portfolio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets pick vol range of 0.15 -> 0.21\n",
    "#lets pick return range of 0.0007 -> 0.0012\n",
    "\n",
    "returns_range = np.array([0.0007, 0.0012])\n",
    "volatility_range = np.array([0.15, 0.21])\n",
    "\n",
    "lower_vol_portfolio = portfolio_options[(portfolio_options.Returns > returns_range[0]) & (portfolio_options.Volatility < volatility_range[0])]\n",
    "higher_returns_portfolio = portfolio_options[(portfolio_options.Returns > returns_range[1]) & (portfolio_options.Volatility < volatility_range[1])]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "So what we've done here is picked two portfolios: one portfolio that is taking lower volatility (and in turn giving you lower returns), and one portfolio that is taking higher volatility (and giving you higher returns).\n",
    "\n",
    "So lets just put the portfolios next to each other (look below):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>BLL Weight</th>\n",
       "      <th>WDC Weight</th>\n",
       "      <th>IVZ Weight</th>\n",
       "      <th>PPL Weight</th>\n",
       "      <th>TWTR Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3539</th>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.013861</td>\n",
       "      <td>0.461171</td>\n",
       "      <td>0.016887</td>\n",
       "      <td>0.01804</td>\n",
       "      <td>0.142447</td>\n",
       "      <td>0.361455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Returns  Volatility  BLL Weight  WDC Weight  IVZ Weight  PPL Weight  \\\n",
       "3539  0.000742    0.013861    0.461171    0.016887     0.01804    0.142447   \n",
       "\n",
       "      TWTR Weight  \n",
       "3539     0.361455  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_vol_portfolio[lower_vol_portfolio.Volatility == lower_vol_portfolio.Volatility.min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>BLL Weight</th>\n",
       "      <th>WDC Weight</th>\n",
       "      <th>IVZ Weight</th>\n",
       "      <th>PPL Weight</th>\n",
       "      <th>TWTR Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.026514</td>\n",
       "      <td>0.034809</td>\n",
       "      <td>0.033479</td>\n",
       "      <td>0.050463</td>\n",
       "      <td>0.056635</td>\n",
       "      <td>0.824614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Returns  Volatility  BLL Weight  WDC Weight  IVZ Weight  PPL Weight  \\\n",
       "839  0.001273    0.026514    0.034809    0.033479    0.050463    0.056635   \n",
       "\n",
       "     TWTR Weight  \n",
       "839     0.824614  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_returns_portfolio[higher_returns_portfolio.Returns == higher_returns_portfolio.Returns.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we've seen that the efficient portfolio with the lowest volatilty and highest return has the weights of:\n",
    "\n",
    "- `BLL`: 0.461\n",
    "- `WDC`: 0.017\n",
    "- `IVZ`: 0.018\n",
    "- `PPL`: 0.142\n",
    "- `TWTR`: 0.361\n",
    "\n",
    "And the efficient portfolio with the highest return for the highest volatility has the weights of:\n",
    "\n",
    "- `BLL`: 0.035\n",
    "- `WDC`: 0.033\n",
    "- `IVZ`: 0.050\n",
    "- `PPL`: 0.056\n",
    "- `TWTR`: 0.825\n",
    "\n",
    "This makes sense since:\n",
    "- __Portfolio 1__:\n",
    "    - `BLL` had the most consistent and least volatile daily returns while `TWTR` and `WDC` were easily the highest. In a portfolio targeting low volatility, we should be more heavily weighting the stock that produced consistency rather than returns (which is shown by a 46% weight to BLL versus the other 4).\n",
    "- __Portfolio 2__:\n",
    "    - `TWTR` had the biggest returns (accompanied with the largest volatility). This portfolio is focused on producing the highest returns with a much higher volatility so we expect to be heavily weighted to `TWTR` and not much to the other stocks (as shown in this portfolio with a 82.5% weight to `TWTR`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Input Portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So can now make a function where the user can input their portfolio money, input the risk they want, and we return the optimized portfolio:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the size of your portfolio: 100000\n",
      "Enter your maximum risk amount as a number from 0.00 to 1.00: .22\n",
      "Enter the 5 stocks you are thinking about investing in (ALL CAPS - SEPERATED BY A COMMA): BLL, WDC, IVZ, PPL, TWTR\n"
     ]
    }
   ],
   "source": [
    "portfolio_size = int(input(\"Enter the size of your portfolio: \"))\n",
    "vol_size = input(\"Enter your maximum risk amount as a number from 0.00 to 1.00: \")\n",
    "stock_picks = input(\"Enter the 5 stocks you are thinking about investing in (ALL CAPS - SEPERATED BY A COMMA): \")\n",
    "\n",
    "try:\n",
    "    val =  float(portfolio_size)\n",
    "except ValueError:\n",
    "    print(\"That's not a number! \\n Please try again and enter numbers only:\")\n",
    "    portfolio_size = input(\"Enter the value of your portfolio: \")\n",
    "    val = float(portfolio_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inputed_portfolio(stock_picks, vol_size, portfolio_size):\n",
    "    portfolio_amount = float(portfolio_size)\n",
    "    vol = float(vol_size)\n",
    "    start = datetime(2017, 1, 1)\n",
    "    end = datetime(2019, 1, 2)\n",
    "    stock_tickers = stock_picks.split(\", \")\n",
    "    stock1, stock2, stock3, stock4, stock5 = [i for i in stock_tickers]\n",
    "    \n",
    "    \n",
    "    stockss = web.DataReader(stock_tickers, start=start, end=end, data_source = 'yahoo')\n",
    "    adjusted_close = stockss[\"Adj Close\"]\n",
    "    stocks_change = adjusted_close.pct_change()\n",
    "    stocks_daily_return = stocks_change.mean()\n",
    "    stocks_daily_cov = stocks_change.cov()\n",
    "    stock_port = return_portfolios(stocks_daily_return, stocks_daily_cov)\n",
    "    #weights, returns, risks = optimal_portfolio(stocks_change[1:])\n",
    "\n",
    "    returns_range = np.array([0.0000, 0.07])\n",
    "    volatility_range = np.array([(vol-0.005), (vol)])\n",
    "\n",
    "    lower_vol_portfolio = stock_port[(stock_port.Returns > returns_range[0]) & (stock_port.Volatility < volatility_range[0])]\n",
    "    higher_returns_portfolio = stock_port[(stock_port.Volatility <= volatility_range[1])]\n",
    "\n",
    "    \n",
    "    x = lower_vol_portfolio[lower_vol_portfolio.Volatility == lower_vol_portfolio.Volatility.min()]\n",
    "    y = higher_returns_portfolio[higher_returns_portfolio.Returns == higher_returns_portfolio.Returns.max()]\n",
    "    \n",
    "        \n",
    "    \n",
    "    elist = []\n",
    "    for stock in stock_tickers:\n",
    "        elist.append(stock + \" Weight\")\n",
    "    elist2 = []\n",
    "    for stock in elist:\n",
    "        elist2.append(y[stock].values)\n",
    "    total = []\n",
    "    for weight in elist2:\n",
    "        total.append(weight*portfolio_amount)\n",
    "    final_prices = []\n",
    "    for stock in adjusted_close:\n",
    "        final_prices.append(adjusted_close[stock][-1])\n",
    "    \n",
    "    final_port = []\n",
    "    for i in range(5):\n",
    "        final_port.append(total[i]/final_prices[i])\n",
    "    final_port = np.round(final_port, 2)\n",
    "    \n",
    "    print(\"The portfolio below shows the returns, volatility, and weights of each stock in order to optimize the portfolio based off daily returns.\")\n",
    "    \n",
    "    print(\"\"\"The amount of shares to buy of the stocks as of the last closing price is:\n",
    "          {x1}: {y1},\n",
    "          {x2}: {y2},\n",
    "          {x3}: {y3},\n",
    "          {x4}: {y4},\n",
    "          {x5}: {y5}\n",
    "          \"\"\".format(x1=stock1, y1=final_port[0], x2=stock2, y2=final_port[1], x3=stock3, y3=final_port[2], x4=stock4, y4=final_port[3], x5=stock5, y5=final_port[4]))\n",
    "    return y\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The portfolio below shows the returns, volatility, and weights of each stock in order to optimize the portfolio based off daily returns.\n",
      "The amount of shares to buy of the stocks as of the last closing price is:\n",
      "          BLL: [207.24],\n",
      "          WDC: [29.05],\n",
      "          IVZ: [598.99],\n",
      "          PPL: [40.65],\n",
      "          TWTR: [2777.42]\n",
      "          \n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\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>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>BLL Weight</th>\n",
       "      <th>WDC Weight</th>\n",
       "      <th>IVZ Weight</th>\n",
       "      <th>PPL Weight</th>\n",
       "      <th>TWTR Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3142</th>\n",
       "      <td>0.001247</td>\n",
       "      <td>0.02584</td>\n",
       "      <td>0.090944</td>\n",
       "      <td>0.010549</td>\n",
       "      <td>0.088078</td>\n",
       "      <td>0.010255</td>\n",
       "      <td>0.800174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Returns  Volatility  BLL Weight  WDC Weight  IVZ Weight  PPL Weight  \\\n",
       "3142  0.001247     0.02584    0.090944    0.010549    0.088078    0.010255   \n",
       "\n",
       "      TWTR Weight  \n",
       "3142     0.800174  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now call the function above and should get the optimal portfolio, \n",
    "# the weights of each stock, and the amount of shares to buy of each based off the portfolio size!\n",
    "\n",
    "inputed_portfolio(stock_picks, vol_size, portfolio_size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Took 5 random stock from the S&P500 and ran analysis to see the simple return, the average daily return, and the correlation and variances between the stocks.\n",
    "- Then created a function where a user can input his portfolio value, his risk appetite (measured as volatility), and the list of 5 tickers he wants to invest in.\n",
    "    - The function then calculates the portfolio that maximizes returns for his risk appetite.\n",
    "    - The function then outputs the weights for each stock in the optimized portfolio, and the amount of shares needed to buy based off the closing price of the last trading day in our date range\n",
    "- Important to note that we did this analysis on the market __PRE-pandemic__. The market post pandemic is completely different. \n",
    "    - If want to see with pandemic included, simply change the end date in the function to __now__ and we will have a completely up to date portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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