Project1_Midterm_2015_2016_2017 .ipynb

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   "source": [
    "# Create Custom Portfolio: 12 months Before \n",
    "## Time Period: November 2015 to October 2016\n",
    "In this section, we chose three stocks from the top six invested in industries, calculate the returns, and compare the results to determine which industry performed best\n",
    "\n",
    "1. Choose 3 stocks with at last 1 year's worth of historic prices and create a DataFrame of the closing prices and dates for each stock.\n",
    "2. Calculate the weighted returns for the portfolio assuming an equal number of shares for each stock\n",
    "3. Join your portfolio returns to the DataFrame that contains all of the portfolio returns\n",
    "4. Re-run the performance and risk analysis with your portfolio to see how it compares to the others\n",
    "5. Include correlation analysis to determine which stocks (if any) are correlated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6085b36",
   "metadata": {},
   "source": [
    "## Choose 3-5 custom stocks with at last 1 year's worth of historic prices and create a DataFrame of the closing prices and dates for each stock."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import yfinance as yf\n",
    "\n",
    "# Initial imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841014a0",
   "metadata": {},
   "source": [
    "## Technology Sector\n",
    "These are the three stocks we chose within the Technology Industry"
   ]
  },
  {
   "cell_type": "code",
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     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n"
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    }
   ],
   "source": [
    "# Define the stock ticker and date range: Apple Inc. \n",
    "AAPL_stock = 'AAPL'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_AAPL = yf.download(AAPL_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Define the stock ticker and date range: Microsoft \n",
    "MSFT_stock = 'MSFT'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_MSFT = yf.download(MSFT_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Google \n",
    "GOOG_stock = 'GOOG'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_GOOG = yf.download(GOOG_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d130962b",
   "metadata": {},
   "source": [
    "## Healthcare Sector \n",
    "These are the three stocks we chose in the Healthcare Industry"
   ]
  },
  {
   "cell_type": "code",
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     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Define the stock ticker and date range: Johnson and Johnson \n",
    "JNJ_stock = 'JNJ'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_JNJ = yf.download(JNJ_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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    }
   ],
   "source": [
    "# Define the stock ticker and date range: Pfizer Inc\n",
    "PFE_stock = 'PFE'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_PFE = yf.download(PFE_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: United Health Group\n",
    "UNH_stock = 'UNH'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_UNH = yf.download(UNH_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b69275",
   "metadata": {},
   "source": [
    "## Financials Sector \n",
    "These are the three stocks we chose in the Healthcare Industry"
   ]
  },
  {
   "cell_type": "code",
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     "output_type": "stream",
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   ],
   "source": [
    "# Define the stock ticker and date range: JPMorgan Chase and Co. \n",
    "JPM_stock = 'JPM'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_JPM = yf.download(JPM_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Bank of America \n",
    "BAC_stock = 'BAC'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_BAC = yf.download(BAC_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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    }
   ],
   "source": [
    "# Define the stock ticker and date range: Visa\n",
    "V_stock = 'V'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_V = yf.download(V_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a42818",
   "metadata": {},
   "source": [
    "## Consumer Discretionary \n",
    "These are the three stocks we chose in the consumer discretionary industry: Amazon, Nike, and Walt Disney Company"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Amazon\n",
    "AMZN_stock = 'AMZN'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_AMZN = yf.download(AMZN_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Nike\n",
    "NKE_stock = 'NKE'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_NKE = yf.download(NKE_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Walt Disney Company \n",
    "DIS_stock = 'DIS'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_DIS = yf.download(DIS_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82e2c50",
   "metadata": {},
   "source": [
    "## Energy Sector \n",
    "These are the three stocks we chose in the energy industry: Exxon Mobile Incorporation, First Solar Inc. , NextEra Energy Resources "
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Exxon Mobile Incorporation\n",
    "XOM_stock = 'XOM'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_XOM = yf.download(XOM_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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    }
   ],
   "source": [
    "# Define the stock ticker and date range: First Solar Inc. \n",
    "FSLR_stock = 'FSLR'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_FSLR = yf.download(FSLR_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: NextEra Energy Resources\n",
    "NEE_stock = 'NEE'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_NEE = yf.download(NEE_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63844053",
   "metadata": {},
   "source": [
    "## Weapons Manufacturing \n",
    "These are the three stocks we chose in the weapons manufacturing instury: Lockheed Martin Corporation, Raytheon Technologies Corporations (RTX), Northrup Grumman Corporation (NOC) "
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Lockheed Martin Corporation\n",
    "LMT_stock = 'LMT'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_LMT = yf.download(LMT_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Raytheon Technologies Corporation\n",
    "RTX_stock = 'RTX'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_RTX = yf.download(RTX_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "# Define the stock ticker and date range: Northrup Grumman Corporation\n",
    "NOC_stock = 'NOC'\n",
    "start_date = '2015-11-01'  # example start date\n",
    "end_date = '2016-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_NOC = yf.download(NOC_stock, start=start_date, end=end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "865c2afa",
   "metadata": {},
   "source": [
    "## Combined Stocks Dataframe"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>189.539993</td>\n",
       "      <td>166.175171</td>\n",
       "      <td>1110800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>30.197500</td>\n",
       "      <td>30.872499</td>\n",
       "      <td>30.174999</td>\n",
       "      <td>30.642500</td>\n",
       "      <td>27.742418</td>\n",
       "      <td>182076000</td>\n",
       "      <td>52.930000</td>\n",
       "      <td>54.389999</td>\n",
       "      <td>52.900002</td>\n",
       "      <td>54.150002</td>\n",
       "      <td>...</td>\n",
       "      <td>62.177471</td>\n",
       "      <td>62.932663</td>\n",
<<<<<<< HEAD
       "      <td>51.426613</td>\n",
=======
       "      <td>51.426620</td>\n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "      <td>4910010</td>\n",
       "      <td>189.110001</td>\n",
       "      <td>189.320007</td>\n",
       "      <td>186.520004</td>\n",
       "      <td>186.639999</td>\n",
<<<<<<< HEAD
       "      <td>163.632690</td>\n",
=======
       "      <td>163.632675</td>\n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "      <td>941400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>30.782499</td>\n",
       "      <td>30.955000</td>\n",
       "      <td>30.405001</td>\n",
       "      <td>30.500000</td>\n",
<<<<<<< HEAD
       "      <td>27.613400</td>\n",
=======
       "      <td>27.613403</td>\n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "      <td>179544400</td>\n",
       "      <td>54.180000</td>\n",
       "      <td>54.880001</td>\n",
       "      <td>54.060001</td>\n",
       "      <td>54.400002</td>\n",
       "      <td>...</td>\n",
       "      <td>62.932663</td>\n",
       "      <td>63.140339</td>\n",
       "      <td>51.596325</td>\n",
       "      <td>4951483</td>\n",
       "      <td>187.289993</td>\n",
       "      <td>188.449997</td>\n",
       "      <td>185.410004</td>\n",
       "      <td>186.169998</td>\n",
<<<<<<< HEAD
       "      <td>163.220596</td>\n",
=======
       "      <td>163.220566</td>\n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "      <td>1088700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>30.462500</td>\n",
       "      <td>30.672501</td>\n",
       "      <td>30.045000</td>\n",
       "      <td>30.230000</td>\n",
       "      <td>27.486111</td>\n",
       "      <td>158210800</td>\n",
       "      <td>54.490002</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.380001</td>\n",
       "      <td>...</td>\n",
       "      <td>62.756451</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>51.838028</td>\n",
       "      <td>4500684</td>\n",
       "      <td>186.419998</td>\n",
       "      <td>187.500000</td>\n",
       "      <td>185.470001</td>\n",
       "      <td>186.419998</td>\n",
       "      <td>163.439789</td>\n",
       "      <td>703300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>30.277500</td>\n",
       "      <td>30.452499</td>\n",
       "      <td>30.155001</td>\n",
       "      <td>30.264999</td>\n",
       "      <td>27.517933</td>\n",
       "      <td>132169200</td>\n",
       "      <td>54.090000</td>\n",
       "      <td>54.980000</td>\n",
       "      <td>53.959999</td>\n",
       "      <td>54.919998</td>\n",
       "      <td>...</td>\n",
       "      <td>62.485840</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>51.838028</td>\n",
       "      <td>4346074</td>\n",
       "      <td>186.020004</td>\n",
       "      <td>186.410004</td>\n",
       "      <td>183.119995</td>\n",
       "      <td>184.009995</td>\n",
       "      <td>161.326920</td>\n",
       "      <td>1421500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume  \\\n",
       "Date                                                                           \n",
<<<<<<< HEAD
       "2015-11-02  30.200001  30.340000  29.902500  30.295000  27.427799  128813200   \n",
       "2015-11-03  30.197500  30.872499  30.174999  30.642500  27.742416  182076000   \n",
       "2015-11-04  30.782499  30.955000  30.405001  30.500000  27.613400  179544400   \n",
       "2015-11-05  30.462500  30.672501  30.045000  30.230000  27.486111  158210800   \n",
       "2015-11-06  30.277500  30.452499  30.155001  30.264999  27.517931  132169200   \n",
=======
       "2015-11-02  30.200001  30.340000  29.902500  30.295000  27.427805  128813200   \n",
       "2015-11-03  30.197500  30.872499  30.174999  30.642500  27.742418  182076000   \n",
       "2015-11-04  30.782499  30.955000  30.405001  30.500000  27.613403  179544400   \n",
       "2015-11-05  30.462500  30.672501  30.045000  30.230000  27.486107  158210800   \n",
       "2015-11-06  30.277500  30.452499  30.155001  30.264999  27.517933  132169200   \n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "\n",
       "                 Open       High        Low      Close  ...        Low  \\\n",
       "Date                                                    ...              \n",
       "2015-11-02  52.849998  53.360001  52.619999  53.240002  ...  61.894272   \n",
       "2015-11-03  52.930000  54.389999  52.900002  54.150002  ...  62.177471   \n",
       "2015-11-04  54.180000  54.880001  54.060001  54.400002  ...  62.932663   \n",
       "2015-11-05  54.490002  54.700001  54.000000  54.380001  ...  62.756451   \n",
       "2015-11-06  54.090000  54.980000  53.959999  54.919998  ...  62.485840   \n",
       "\n",
       "                Close  Adj Close   Volume        Open        High         Low  \\\n",
       "Date                                                                            \n",
<<<<<<< HEAD
       "2015-11-02  62.548775  51.112911  4874258  188.479996  189.830002  188.009995   \n",
       "2015-11-03  62.932663  51.426613  4910010  189.110001  189.320007  186.520004   \n",
       "2015-11-04  63.140339  51.596329  4951483  187.289993  188.449997  185.410004   \n",
=======
       "2015-11-02  62.548775  51.112915  4874258  188.479996  189.830002  188.009995   \n",
       "2015-11-03  62.932663  51.426620  4910010  189.110001  189.320007  186.520004   \n",
       "2015-11-04  63.140339  51.596325  4951483  187.289993  188.449997  185.410004   \n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "2015-11-05  63.436123  51.838028  4500684  186.419998  187.500000  185.470001   \n",
       "2015-11-06  63.436123  51.838028  4346074  186.020004  186.410004  183.119995   \n",
       "\n",
       "                 Close   Adj Close   Volume  \n",
       "Date                                         \n",
       "2015-11-02  189.539993  166.175171  1110800  \n",
<<<<<<< HEAD
       "2015-11-03  186.639999  163.632690   941400  \n",
       "2015-11-04  186.169998  163.220596  1088700  \n",
       "2015-11-05  186.419998  163.439819   703300  \n",
       "2015-11-06  184.009995  161.326889  1421500  \n",
=======
       "2015-11-03  186.639999  163.632675   941400  \n",
       "2015-11-04  186.169998  163.220566  1088700  \n",
       "2015-11-05  186.419998  163.439789   703300  \n",
       "2015-11-06  184.009995  161.326920  1421500  \n",
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 40,
=======
     "execution_count": 83,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine all stocks into a single Dataframe \n",
    "custom_portfolio_df = pd.concat([data_AAPL,data_MSFT,data_GOOG,data_JNJ,data_PFE,data_UNH,data_JPM,data_BAC,data_V,data_AMZN,data_NKE,data_DIS,data_XOM,data_FSLR,data_NEE,data_LMT,data_RTX,data_NOC], axis=\"columns\", join=\"inner\")\n",
    "custom_portfolio_df.head()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 41,
=======
   "execution_count": 84,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "5ad1cadd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>PFE</th>\n",
       "      <th>UNH</th>\n",
       "      <th>JPM</th>\n",
       "      <th>BAC</th>\n",
       "      <th>V</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>NKE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>XOM</th>\n",
       "      <th>FSLR</th>\n",
       "      <th>NEE</th>\n",
       "      <th>LMT</th>\n",
       "      <th>RTX</th>\n",
       "      <th>NOC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>30.295000</td>\n",
       "      <td>53.240002</td>\n",
       "      <td>36.055500</td>\n",
       "      <td>102.169998</td>\n",
       "      <td>33.263756</td>\n",
       "      <td>118.690002</td>\n",
       "      <td>65.540001</td>\n",
       "      <td>17.059999</td>\n",
       "      <td>75.220001</td>\n",
       "      <td>31.417500</td>\n",
       "      <td>65.595001</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>85.279999</td>\n",
       "      <td>57.779999</td>\n",
       "      <td>25.557501</td>\n",
       "      <td>220.589996</td>\n",
       "      <td>62.548775</td>\n",
       "      <td>189.539993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>30.642500</td>\n",
       "      <td>54.150002</td>\n",
       "      <td>36.108002</td>\n",
       "      <td>101.959999</td>\n",
       "      <td>33.178368</td>\n",
       "      <td>117.660004</td>\n",
       "      <td>65.779999</td>\n",
       "      <td>17.180000</td>\n",
       "      <td>77.900002</td>\n",
       "      <td>31.265499</td>\n",
       "      <td>65.699997</td>\n",
       "      <td>115.540001</td>\n",
       "      <td>86.849998</td>\n",
       "      <td>58.349998</td>\n",
       "      <td>25.577499</td>\n",
       "      <td>218.899994</td>\n",
       "      <td>62.932663</td>\n",
       "      <td>186.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>30.500000</td>\n",
       "      <td>54.400002</td>\n",
       "      <td>36.405499</td>\n",
       "      <td>101.940002</td>\n",
       "      <td>32.390892</td>\n",
       "      <td>114.639999</td>\n",
       "      <td>65.849998</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>78.349998</td>\n",
       "      <td>32.047501</td>\n",
       "      <td>65.180000</td>\n",
       "      <td>113.250000</td>\n",
       "      <td>85.980003</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>25.805000</td>\n",
       "      <td>218.830002</td>\n",
       "      <td>63.140339</td>\n",
       "      <td>186.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>30.230000</td>\n",
       "      <td>54.380001</td>\n",
       "      <td>36.562500</td>\n",
       "      <td>102.330002</td>\n",
       "      <td>32.400379</td>\n",
       "      <td>116.230003</td>\n",
       "      <td>66.440002</td>\n",
       "      <td>17.309999</td>\n",
       "      <td>79.260002</td>\n",
       "      <td>32.782501</td>\n",
       "      <td>65.930000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>84.809998</td>\n",
       "      <td>58.330002</td>\n",
       "      <td>25.525000</td>\n",
       "      <td>218.600006</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>186.419998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>30.264999</td>\n",
       "      <td>54.919998</td>\n",
       "      <td>36.688000</td>\n",
       "      <td>101.919998</td>\n",
       "      <td>32.191650</td>\n",
       "      <td>114.809998</td>\n",
       "      <td>68.459999</td>\n",
       "      <td>17.950001</td>\n",
       "      <td>78.750000</td>\n",
       "      <td>32.968498</td>\n",
       "      <td>65.889999</td>\n",
       "      <td>115.669998</td>\n",
       "      <td>84.470001</td>\n",
       "      <td>59.669998</td>\n",
       "      <td>24.709999</td>\n",
       "      <td>216.630005</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>184.009995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>29.412500</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>40.655499</td>\n",
       "      <td>113.610001</td>\n",
       "      <td>30.483871</td>\n",
       "      <td>144.690002</td>\n",
       "      <td>68.870003</td>\n",
       "      <td>16.770000</td>\n",
       "      <td>83.169998</td>\n",
       "      <td>41.904499</td>\n",
       "      <td>51.869999</td>\n",
       "      <td>93.370003</td>\n",
       "      <td>86.910004</td>\n",
       "      <td>40.869999</td>\n",
       "      <td>31.094999</td>\n",
       "      <td>232.160004</td>\n",
       "      <td>62.630585</td>\n",
       "      <td>216.460007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>29.562500</td>\n",
       "      <td>60.990002</td>\n",
       "      <td>40.383499</td>\n",
       "      <td>113.959999</td>\n",
       "      <td>30.626186</td>\n",
       "      <td>143.779999</td>\n",
       "      <td>68.800003</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>82.029999</td>\n",
       "      <td>41.758999</td>\n",
       "      <td>51.049999</td>\n",
       "      <td>92.750000</td>\n",
       "      <td>86.720001</td>\n",
       "      <td>40.680000</td>\n",
       "      <td>31.379999</td>\n",
       "      <td>249.259995</td>\n",
       "      <td>63.788548</td>\n",
       "      <td>219.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>28.897499</td>\n",
       "      <td>60.630001</td>\n",
       "      <td>39.953499</td>\n",
       "      <td>114.559998</td>\n",
       "      <td>30.740038</td>\n",
       "      <td>142.229996</td>\n",
       "      <td>69.129997</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>81.750000</td>\n",
       "      <td>41.129501</td>\n",
       "      <td>51.970001</td>\n",
       "      <td>93.489998</td>\n",
       "      <td>87.089996</td>\n",
       "      <td>41.110001</td>\n",
       "      <td>31.549999</td>\n",
       "      <td>250.089996</td>\n",
       "      <td>63.568283</td>\n",
       "      <td>228.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>28.620001</td>\n",
       "      <td>60.099998</td>\n",
       "      <td>39.767502</td>\n",
       "      <td>115.699997</td>\n",
       "      <td>30.815939</td>\n",
       "      <td>141.940002</td>\n",
       "      <td>69.230003</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>81.919998</td>\n",
       "      <td>40.917999</td>\n",
       "      <td>51.889999</td>\n",
       "      <td>94.019997</td>\n",
       "      <td>86.919998</td>\n",
       "      <td>41.060001</td>\n",
       "      <td>31.280001</td>\n",
       "      <td>248.529999</td>\n",
       "      <td>62.976715</td>\n",
       "      <td>225.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>28.430000</td>\n",
       "      <td>59.869999</td>\n",
       "      <td>39.768501</td>\n",
       "      <td>115.330002</td>\n",
       "      <td>30.294119</td>\n",
       "      <td>140.350006</td>\n",
       "      <td>69.110001</td>\n",
       "      <td>16.680000</td>\n",
       "      <td>82.220001</td>\n",
       "      <td>38.816002</td>\n",
       "      <td>52.020000</td>\n",
       "      <td>93.849998</td>\n",
       "      <td>84.779999</td>\n",
       "      <td>40.650002</td>\n",
       "      <td>31.432501</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>64.090622</td>\n",
       "      <td>228.380005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL       MSFT       GOOG         JNJ        PFE  \\\n",
       "Date                                                                 \n",
       "2015-11-02  30.295000  53.240002  36.055500  102.169998  33.263756   \n",
       "2015-11-03  30.642500  54.150002  36.108002  101.959999  33.178368   \n",
       "2015-11-04  30.500000  54.400002  36.405499  101.940002  32.390892   \n",
       "2015-11-05  30.230000  54.380001  36.562500  102.330002  32.400379   \n",
       "2015-11-06  30.264999  54.919998  36.688000  101.919998  32.191650   \n",
       "...               ...        ...        ...         ...        ...   \n",
       "2016-10-24  29.412500  61.000000  40.655499  113.610001  30.483871   \n",
       "2016-10-25  29.562500  60.990002  40.383499  113.959999  30.626186   \n",
       "2016-10-26  28.897499  60.630001  39.953499  114.559998  30.740038   \n",
       "2016-10-27  28.620001  60.099998  39.767502  115.699997  30.815939   \n",
       "2016-10-28  28.430000  59.869999  39.768501  115.330002  30.294119   \n",
       "\n",
       "                   UNH        JPM        BAC          V       AMZN        NKE  \\\n",
       "Date                                                                            \n",
       "2015-11-02  118.690002  65.540001  17.059999  75.220001  31.417500  65.595001   \n",
       "2015-11-03  117.660004  65.779999  17.180000  77.900002  31.265499  65.699997   \n",
       "2015-11-04  114.639999  65.849998  17.010000  78.349998  32.047501  65.180000   \n",
       "2015-11-05  116.230003  66.440002  17.309999  79.260002  32.782501  65.930000   \n",
       "2015-11-06  114.809998  68.459999  17.950001  78.750000  32.968498  65.889999   \n",
       "...                ...        ...        ...        ...        ...        ...   \n",
       "2016-10-24  144.690002  68.870003  16.770000  83.169998  41.904499  51.869999   \n",
       "2016-10-25  143.779999  68.800003  16.719999  82.029999  41.758999  51.049999   \n",
       "2016-10-26  142.229996  69.129997  16.870001  81.750000  41.129501  51.970001   \n",
       "2016-10-27  141.940002  69.230003  16.910000  81.919998  40.917999  51.889999   \n",
       "2016-10-28  140.350006  69.110001  16.680000  82.220001  38.816002  52.020000   \n",
       "\n",
       "                   DIS        XOM       FSLR        NEE         LMT  \\\n",
       "Date                                                                  \n",
       "2015-11-02  115.040001  85.279999  57.779999  25.557501  220.589996   \n",
       "2015-11-03  115.540001  86.849998  58.349998  25.577499  218.899994   \n",
       "2015-11-04  113.250000  85.980003  58.660000  25.805000  218.830002   \n",
       "2015-11-05  113.000000  84.809998  58.330002  25.525000  218.600006   \n",
       "2015-11-06  115.669998  84.470001  59.669998  24.709999  216.630005   \n",
       "...                ...        ...        ...        ...         ...   \n",
       "2016-10-24   93.370003  86.910004  40.869999  31.094999  232.160004   \n",
       "2016-10-25   92.750000  86.720001  40.680000  31.379999  249.259995   \n",
       "2016-10-26   93.489998  87.089996  41.110001  31.549999  250.089996   \n",
       "2016-10-27   94.019997  86.919998  41.060001  31.280001  248.529999   \n",
       "2016-10-28   93.849998  84.779999  40.650002  31.432501  248.000000   \n",
       "\n",
       "                  RTX         NOC  \n",
       "Date                               \n",
       "2015-11-02  62.548775  189.539993  \n",
       "2015-11-03  62.932663  186.639999  \n",
       "2015-11-04  63.140339  186.169998  \n",
       "2015-11-05  63.436123  186.419998  \n",
       "2015-11-06  63.436123  184.009995  \n",
       "...               ...         ...  \n",
       "2016-10-24  62.630585  216.460007  \n",
       "2016-10-25  63.788548  219.529999  \n",
       "2016-10-26  63.568283  228.050003  \n",
       "2016-10-27  62.976715  225.350006  \n",
       "2016-10-28  64.090622  228.380005  \n",
       "\n",
       "[251 rows x 18 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 41,
=======
     "execution_count": 84,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_df=custom_portfolio_df['Close']\n",
    "small_df.columns=['AAPL','MSFT', 'GOOG', 'JNJ','PFE','UNH','JPM','BAC','V','AMZN','NKE','DIS','XOM','FSLR','NEE','LMT','RTX','NOC']\n",
    "small_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc284ec",
   "metadata": {},
   "source": [
    "### Technology Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 42,
=======
   "execution_count": 85,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "113cdc10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>30.295000</td>\n",
       "      <td>53.240002</td>\n",
       "      <td>36.055500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>30.642500</td>\n",
       "      <td>54.150002</td>\n",
       "      <td>36.108002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>30.500000</td>\n",
       "      <td>54.400002</td>\n",
       "      <td>36.405499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>30.230000</td>\n",
       "      <td>54.380001</td>\n",
       "      <td>36.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>30.264999</td>\n",
       "      <td>54.919998</td>\n",
       "      <td>36.688000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>29.412500</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>40.655499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>29.562500</td>\n",
       "      <td>60.990002</td>\n",
       "      <td>40.383499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>28.897499</td>\n",
       "      <td>60.630001</td>\n",
       "      <td>39.953499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>28.620001</td>\n",
       "      <td>60.099998</td>\n",
       "      <td>39.767502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>28.430000</td>\n",
       "      <td>59.869999</td>\n",
       "      <td>39.768501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL       MSFT       GOOG\n",
       "Date                                       \n",
       "2015-11-02  30.295000  53.240002  36.055500\n",
       "2015-11-03  30.642500  54.150002  36.108002\n",
       "2015-11-04  30.500000  54.400002  36.405499\n",
       "2015-11-05  30.230000  54.380001  36.562500\n",
       "2015-11-06  30.264999  54.919998  36.688000\n",
       "...               ...        ...        ...\n",
       "2016-10-24  29.412500  61.000000  40.655499\n",
       "2016-10-25  29.562500  60.990002  40.383499\n",
       "2016-10-26  28.897499  60.630001  39.953499\n",
       "2016-10-27  28.620001  60.099998  39.767502\n",
       "2016-10-28  28.430000  59.869999  39.768501\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 42,
=======
     "execution_count": 85,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tech_df=small_df[['AAPL','MSFT','GOOG']]\n",
    "tech_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207a644b",
   "metadata": {},
   "source": [
    "### Healthcare Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 43,
=======
   "execution_count": 86,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "4d62722b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JNJ</th>\n",
       "      <th>PFE</th>\n",
       "      <th>UNH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>102.169998</td>\n",
       "      <td>33.263756</td>\n",
       "      <td>118.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>101.959999</td>\n",
       "      <td>33.178368</td>\n",
       "      <td>117.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>101.940002</td>\n",
       "      <td>32.390892</td>\n",
       "      <td>114.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>102.330002</td>\n",
       "      <td>32.400379</td>\n",
       "      <td>116.230003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>101.919998</td>\n",
       "      <td>32.191650</td>\n",
       "      <td>114.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>113.610001</td>\n",
       "      <td>30.483871</td>\n",
       "      <td>144.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>113.959999</td>\n",
       "      <td>30.626186</td>\n",
       "      <td>143.779999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>114.559998</td>\n",
       "      <td>30.740038</td>\n",
       "      <td>142.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>115.699997</td>\n",
       "      <td>30.815939</td>\n",
       "      <td>141.940002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>115.330002</td>\n",
       "      <td>30.294119</td>\n",
       "      <td>140.350006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   JNJ        PFE         UNH\n",
       "Date                                         \n",
       "2015-11-02  102.169998  33.263756  118.690002\n",
       "2015-11-03  101.959999  33.178368  117.660004\n",
       "2015-11-04  101.940002  32.390892  114.639999\n",
       "2015-11-05  102.330002  32.400379  116.230003\n",
       "2015-11-06  101.919998  32.191650  114.809998\n",
       "...                ...        ...         ...\n",
       "2016-10-24  113.610001  30.483871  144.690002\n",
       "2016-10-25  113.959999  30.626186  143.779999\n",
       "2016-10-26  114.559998  30.740038  142.229996\n",
       "2016-10-27  115.699997  30.815939  141.940002\n",
       "2016-10-28  115.330002  30.294119  140.350006\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 43,
=======
     "execution_count": 86,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_df=small_df[['JNJ','PFE','UNH']]\n",
    "health_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47efb6b4",
   "metadata": {},
   "source": [
    "### Financials Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 44,
=======
   "execution_count": 87,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "b573adae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>65.540001</td>\n",
       "      <td>17.059999</td>\n",
       "      <td>75.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>65.779999</td>\n",
       "      <td>17.180000</td>\n",
       "      <td>77.900002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>65.849998</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>78.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>66.440002</td>\n",
       "      <td>17.309999</td>\n",
       "      <td>79.260002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>68.459999</td>\n",
       "      <td>17.950001</td>\n",
       "      <td>78.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>68.870003</td>\n",
       "      <td>16.770000</td>\n",
       "      <td>83.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>68.800003</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>82.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>69.129997</td>\n",
       "      <td>16.870001</td>\n",
       "      <td>81.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>69.230003</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>81.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>69.110001</td>\n",
       "      <td>16.680000</td>\n",
       "      <td>82.220001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  JPM        BAC          V\n",
       "Date                                       \n",
       "2015-11-02  65.540001  17.059999  75.220001\n",
       "2015-11-03  65.779999  17.180000  77.900002\n",
       "2015-11-04  65.849998  17.010000  78.349998\n",
       "2015-11-05  66.440002  17.309999  79.260002\n",
       "2015-11-06  68.459999  17.950001  78.750000\n",
       "...               ...        ...        ...\n",
       "2016-10-24  68.870003  16.770000  83.169998\n",
       "2016-10-25  68.800003  16.719999  82.029999\n",
       "2016-10-26  69.129997  16.870001  81.750000\n",
       "2016-10-27  69.230003  16.910000  81.919998\n",
       "2016-10-28  69.110001  16.680000  82.220001\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 44,
=======
     "execution_count": 87,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance_df=small_df[['JPM','BAC','V']]\n",
    "finance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976f8d9d",
   "metadata": {},
   "source": [
    "### Consumer Discretionary Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 45,
=======
   "execution_count": 88,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "fad14ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMZN</th>\n",
       "      <th>NKE</th>\n",
       "      <th>DIS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>31.417500</td>\n",
       "      <td>65.595001</td>\n",
       "      <td>115.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>31.265499</td>\n",
       "      <td>65.699997</td>\n",
       "      <td>115.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>32.047501</td>\n",
       "      <td>65.180000</td>\n",
       "      <td>113.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>32.782501</td>\n",
       "      <td>65.930000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>32.968498</td>\n",
       "      <td>65.889999</td>\n",
       "      <td>115.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>41.904499</td>\n",
       "      <td>51.869999</td>\n",
       "      <td>93.370003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>41.758999</td>\n",
       "      <td>51.049999</td>\n",
       "      <td>92.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>41.129501</td>\n",
       "      <td>51.970001</td>\n",
       "      <td>93.489998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>40.917999</td>\n",
       "      <td>51.889999</td>\n",
       "      <td>94.019997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>38.816002</td>\n",
       "      <td>52.020000</td>\n",
       "      <td>93.849998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AMZN        NKE         DIS\n",
       "Date                                        \n",
       "2015-11-02  31.417500  65.595001  115.040001\n",
       "2015-11-03  31.265499  65.699997  115.540001\n",
       "2015-11-04  32.047501  65.180000  113.250000\n",
       "2015-11-05  32.782501  65.930000  113.000000\n",
       "2015-11-06  32.968498  65.889999  115.669998\n",
       "...               ...        ...         ...\n",
       "2016-10-24  41.904499  51.869999   93.370003\n",
       "2016-10-25  41.758999  51.049999   92.750000\n",
       "2016-10-26  41.129501  51.970001   93.489998\n",
       "2016-10-27  40.917999  51.889999   94.019997\n",
       "2016-10-28  38.816002  52.020000   93.849998\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 45,
=======
     "execution_count": 88,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_df=small_df[['AMZN','NKE','DIS']]\n",
    "consumer_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c98df3c",
   "metadata": {},
   "source": [
    "### Energy Dataframe "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 46,
=======
   "execution_count": 89,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "8a2ccb20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>XOM</th>\n",
       "      <th>FSLR</th>\n",
       "      <th>NEE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>85.279999</td>\n",
       "      <td>57.779999</td>\n",
       "      <td>25.557501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>86.849998</td>\n",
       "      <td>58.349998</td>\n",
       "      <td>25.577499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>85.980003</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>25.805000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>84.809998</td>\n",
       "      <td>58.330002</td>\n",
       "      <td>25.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>84.470001</td>\n",
       "      <td>59.669998</td>\n",
       "      <td>24.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>86.910004</td>\n",
       "      <td>40.869999</td>\n",
       "      <td>31.094999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>86.720001</td>\n",
       "      <td>40.680000</td>\n",
       "      <td>31.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>87.089996</td>\n",
       "      <td>41.110001</td>\n",
       "      <td>31.549999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>86.919998</td>\n",
       "      <td>41.060001</td>\n",
       "      <td>31.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>84.779999</td>\n",
       "      <td>40.650002</td>\n",
       "      <td>31.432501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  XOM       FSLR        NEE\n",
       "Date                                       \n",
       "2015-11-02  85.279999  57.779999  25.557501\n",
       "2015-11-03  86.849998  58.349998  25.577499\n",
       "2015-11-04  85.980003  58.660000  25.805000\n",
       "2015-11-05  84.809998  58.330002  25.525000\n",
       "2015-11-06  84.470001  59.669998  24.709999\n",
       "...               ...        ...        ...\n",
       "2016-10-24  86.910004  40.869999  31.094999\n",
       "2016-10-25  86.720001  40.680000  31.379999\n",
       "2016-10-26  87.089996  41.110001  31.549999\n",
       "2016-10-27  86.919998  41.060001  31.280001\n",
       "2016-10-28  84.779999  40.650002  31.432501\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 46,
=======
     "execution_count": 89,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_df=small_df[['XOM','FSLR','NEE']]\n",
    "energy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47726a63",
   "metadata": {},
   "source": [
    "### Weapons Manufacturing Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 47,
=======
   "execution_count": 90,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "46d57326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>LMT</th>\n",
       "      <th>RTX</th>\n",
       "      <th>NOC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>220.589996</td>\n",
       "      <td>62.548775</td>\n",
       "      <td>189.539993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>218.899994</td>\n",
       "      <td>62.932663</td>\n",
       "      <td>186.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>218.830002</td>\n",
       "      <td>63.140339</td>\n",
       "      <td>186.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>218.600006</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>186.419998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>216.630005</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>184.009995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>232.160004</td>\n",
       "      <td>62.630585</td>\n",
       "      <td>216.460007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>249.259995</td>\n",
       "      <td>63.788548</td>\n",
       "      <td>219.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-26</th>\n",
       "      <td>250.089996</td>\n",
       "      <td>63.568283</td>\n",
       "      <td>228.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-27</th>\n",
       "      <td>248.529999</td>\n",
       "      <td>62.976715</td>\n",
       "      <td>225.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-28</th>\n",
       "      <td>248.000000</td>\n",
       "      <td>64.090622</td>\n",
       "      <td>228.380005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   LMT        RTX         NOC\n",
       "Date                                         \n",
       "2015-11-02  220.589996  62.548775  189.539993\n",
       "2015-11-03  218.899994  62.932663  186.639999\n",
       "2015-11-04  218.830002  63.140339  186.169998\n",
       "2015-11-05  218.600006  63.436123  186.419998\n",
       "2015-11-06  216.630005  63.436123  184.009995\n",
       "...                ...        ...         ...\n",
       "2016-10-24  232.160004  62.630585  216.460007\n",
       "2016-10-25  249.259995  63.788548  219.529999\n",
       "2016-10-26  250.089996  63.568283  228.050003\n",
       "2016-10-27  248.529999  62.976715  225.350006\n",
       "2016-10-28  248.000000  64.090622  228.380005\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 47,
=======
     "execution_count": 90,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weapons_df=small_df[['LMT','RTX','NOC']]\n",
    "weapons_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e9f3a5",
   "metadata": {},
   "source": [
    "# Calculate: Daily Returns and Weighted Returns"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 48,
=======
   "execution_count": 91,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "13e5d5c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                AAPL      MSFT      GOOG       JNJ       PFE       UNH  \\\n",
      "Date                                                                     \n",
      "2015-11-03  0.011471  0.017092  0.001456 -0.002055 -0.002567 -0.008678   \n",
      "2015-11-04 -0.004650  0.004617  0.008239 -0.000196 -0.023735 -0.025667   \n",
      "2015-11-05 -0.008852 -0.000368  0.004313  0.003826  0.000293  0.013870   \n",
      "2015-11-06  0.001158  0.009930  0.003432 -0.004007 -0.006442 -0.012217   \n",
      "2015-11-09 -0.004048 -0.013838 -0.012088 -0.010597 -0.008252 -0.004790   \n",
      "\n",
      "                 JPM       BAC         V      AMZN       NKE       DIS  \\\n",
      "Date                                                                     \n",
      "2015-11-03  0.003662  0.007034  0.035629 -0.004838  0.001601  0.004346   \n",
      "2015-11-04  0.001064 -0.009895  0.005777  0.025012 -0.007915 -0.019820   \n",
      "2015-11-05  0.008960  0.017637  0.011615  0.022935  0.011507 -0.002208   \n",
      "2015-11-06  0.030403  0.036973 -0.006435  0.005674 -0.000607  0.023628   \n",
      "2015-11-09 -0.015630 -0.015042 -0.009016 -0.005884 -0.009865  0.006484   \n",
      "\n",
      "                 XOM      FSLR       NEE       LMT       RTX       NOC  \n",
      "Date                                                                    \n",
      "2015-11-03  0.018410  0.009865  0.000782 -0.007661  0.006137 -0.015300  \n",
      "2015-11-04 -0.010017  0.005313  0.008895 -0.000320  0.003300 -0.002518  \n",
      "2015-11-05 -0.013608 -0.005626 -0.010851 -0.001051  0.004685  0.001343  \n",
      "2015-11-06 -0.004009  0.022973 -0.031930 -0.009012  0.000000 -0.012928  \n",
      "2015-11-09 -0.029833 -0.021451  0.000708 -0.008909 -0.011309 -0.012771  \n",
      "                Tech    Health   Finance  Consumer    Energy   Weapons\n",
      "Date                                                                  \n",
      "2015-11-03  0.010006 -0.004433  0.015442  0.000370  0.009686 -0.005608\n",
      "2015-11-04  0.002735 -0.016533 -0.001018 -0.000908  0.001397  0.000154\n",
      "2015-11-05 -0.001636  0.005996  0.012737  0.010745 -0.010028  0.001659\n",
      "2015-11-06  0.004840 -0.007555  0.020314  0.009565 -0.004322 -0.007313\n",
      "2015-11-09 -0.009991 -0.007880 -0.013229 -0.003088 -0.016859 -0.010997\n",
      "...              ...       ...       ...       ...       ...       ...\n",
      "2016-10-24  0.016218 -0.001578  0.007168  0.009636 -0.007966  0.006170\n",
      "2016-10-25 -0.000585  0.000487 -0.005902 -0.008640  0.000777  0.035443\n",
      "2016-10-26 -0.013015 -0.000599  0.003451  0.003642  0.006751  0.012896\n",
      "2016-10-27 -0.007667  0.003460  0.001966 -0.000338 -0.003909 -0.009128\n",
      "2016-10-28 -0.003480 -0.010444 -0.003891 -0.016891 -0.009910  0.009667\n",
      "\n",
      "[250 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate daily returns, drop nas, and display the data \n",
    "# Tech Daily Returns \n",
    "tech_daily = tech_df.pct_change().dropna()\n",
    "tech_daily.head()\n",
    "\n",
    "# Healthcare Daily Returns \n",
    "health_daily = health_df.pct_change().dropna()\n",
    "health_daily.head()\n",
    "\n",
    "# Financials Daily Returns \n",
    "finance_daily = finance_df.pct_change().dropna()\n",
    "finance_daily.head()\n",
    "\n",
    "# Consumer Discretionary Daily Returns \n",
    "consumer_daily = consumer_df.pct_change().dropna()\n",
    "consumer_daily.head()\n",
    "\n",
    "# Energy Daily Returns \n",
    "energy_daily = energy_df.pct_change().dropna()\n",
    "energy_daily.head()\n",
    "\n",
    "# Weapons Manufacturing Daily Returns \n",
    "weapons_daily = weapons_df.pct_change().dropna()\n",
    "weapons_daily.head()\n",
    "\n",
    "combined_daily_returns = pd.concat([\n",
    "    tech_daily.head(),\n",
    "    health_daily.head(),\n",
    "    finance_daily.head(),\n",
    "    consumer_daily.head(),\n",
    "    energy_daily.head(),\n",
    "    weapons_daily.head()\n",
    "], axis=1)\n",
    "\n",
    "# Rename the columns for clarity\n",
    "combined_daily_returns.columns = ['AAPL','MSFT', 'GOOG', 'JNJ','PFE','UNH','JPM','BAC','V','AMZN','NKE','DIS','XOM','FSLR','NEE','LMT','RTX','NOC']\n",
    "\n",
    "# Display the combined DataFrame\n",
    "print(combined_daily_returns.head())\n",
    "\n",
    "# Set weights\n",
    "weights = [1/3, 1/3, 1/3]\n",
    "\n",
    "# Define a dictionary containing the daily returns for each sector\n",
    "daily_returns_dict = {\n",
    "    \"Tech\": tech_daily,\n",
    "    \"Health\": health_daily,\n",
    "    \"Finance\": finance_daily,\n",
    "    \"Consumer\": consumer_daily,\n",
    "    \"Energy\": energy_daily,\n",
    "    \"Weapons\": weapons_daily\n",
    "}\n",
    "\n",
    "# Create an empty dictionary to store the returns for each sector\n",
    "sector_returns = {}\n",
    "\n",
    "# Calculate the returns for each sector and store them in the dictionary\n",
    "for sector, combined_daily_returns in daily_returns_dict.items():\n",
    "    sector_returns[sector] = (weights * combined_daily_returns).sum(axis=1)\n",
    "\n",
    "# Create a DataFrame from the sector_returns dictionary\n",
    "returns_df = pd.DataFrame(sector_returns)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(returns_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca746b4",
   "metadata": {},
   "source": [
    "# Risk Analysis Performance \n",
    "1. Cumulative Returns\n",
    "2. Annualized Standard Deviation \n",
    "3. Annualized Sharpe Ratios (Reward to Risk)\n",
    "4. Covariance and Variance \n",
    "5. BETA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed664118",
   "metadata": {},
   "source": [
    "## Cumulative Returns"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 49,
=======
   "execution_count": 92,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "9a28a027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Tech    Health   Finance  Consumer    Energy   Weapons\n",
      "Date                                                                  \n",
      "2015-11-03  1.010006  0.995567  1.015442  1.000370  1.009686  0.994392\n",
      "2015-11-04  1.012769  0.979107  1.014408  0.999462  1.011096  0.994545\n",
      "2015-11-05  1.011112  0.984978  1.027328  1.010200  1.000957  0.996195\n",
      "2015-11-06  1.016006  0.977536  1.048197  1.019863  0.996631  0.988909\n",
      "2015-11-09  1.005855  0.969833  1.034331  1.016713  0.979829  0.978035\n",
      "...              ...       ...       ...       ...       ...       ...\n",
      "2016-10-24  1.086780  1.081015  1.051959  0.961533  0.979997  1.067928\n",
      "2016-10-25  1.086144  1.081541  1.045751  0.953225  0.980758  1.105778\n",
      "2016-10-26  1.072008  1.080893  1.049361  0.956697  0.987380  1.120037\n",
      "2016-10-27  1.063789  1.084633  1.051423  0.956374  0.983521  1.109814\n",
      "2016-10-28  1.060087  1.073305  1.047332  0.940220  0.973774  1.120542\n",
      "\n",
      "[250 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_returns = (1 + returns_df).cumprod()\n",
    "print(cumulative_returns) \n",
    "\n",
    "# Export and save Cumulative Returns dataframe into a csv file \n",
    "cumulative_returns.to_csv('cumulative_returns_data3.csv', index=False)\n",
    "\n",
    "cumulative_returns.plot(figsize=(10, 6))  # Adjust figsize as needed\n",
    "plt.title('Cumulative Returns of Each Industry')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cumulative Returns')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be59e2e",
   "metadata": {},
   "source": [
    "## Annualized Standard Deviation "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 50,
=======
   "execution_count": 93,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "def340b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tech        0.011833\n",
      "Health      0.008553\n",
      "Finance     0.014817\n",
      "Consumer    0.011392\n",
      "Energy      0.012652\n",
      "Weapons     0.008464\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/Utilities/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3643: FutureWarning: The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Calculate the daily standard deviation \n",
    "daily_std_dev = np.std(returns_df)\n",
    "print(daily_std_dev)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 51,
=======
   "execution_count": 94,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "ec4468c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the annualized standard deviation (252 trading days)\n",
    "annualized_std = daily_std_dev * np.sqrt(252)\n",
    "\n",
    "# Plot the Annualized standard deviation\n",
    "annualized_std.plot.bar(figsize=(10, 6))  # Adjust figsize as needed\n",
    "plt.title('Annualized Standard Deviation of Each Industry')\n",
    "plt.xlabel('Industry')\n",
    "plt.ylabel('Annualized Standard Deviation')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75cba56",
   "metadata": {},
   "source": [
    "## Sharpe Ratios"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 52,
=======
   "execution_count": 95,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "f06123ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Annualized Sharpe Ratios \n",
    "sharpe_ratios = (returns_df.mean() * 252) / (returns_df.std() * np.sqrt(252))\n",
    "sharpe_ratios \n",
    "\n",
    "# Save the Sharpe Ratios dataset into a csv file \n",
    "sharpe_ratios.to_csv('sharperatios2015_2016.csv', index=False)\n",
    "\n",
    "# Visualize the sharpe ratios as a bar plot\n",
    "sharpe_ratios.plot(kind='bar', color='skyblue', figsize=(10, 6))\n",
    "\n",
    "# Customizing the plot\n",
    "plt.title('Sharpe Ratios of Each Industry')\n",
    "plt.xlabel('Industry')\n",
    "plt.ylabel('Sharpe Ratio')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)  # Adding grid lines\n",
    "\n",
    "# Adding data labels\n",
    "for i, val in enumerate(sharpe_ratios.values):\n",
    "    plt.text(i, val, round(val, 2), ha='center', va='bottom')\n",
    "\n",
    "# Adding horizontal line at Sharpe ratio of 0 for reference\n",
    "plt.axhline(y=0, color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101dc808",
   "metadata": {},
   "source": [
    "# Create Custom Portfolio: 12 months After \n",
    "## Time Period: November 2016 to October 2017\n",
    "In this section, we chose three stocks from the top six invested in industries, calculate the returns, and compare the results to determine which industry performed best\n",
    "\n",
    "1. Choose 3 stocks with at last 1 year's worth of historic prices and create a DataFrame of the closing prices and dates for each stock.\n",
    "2. Calculate the weighted returns for the portfolio assuming an equal number of shares for each stock\n",
    "3. Join your portfolio returns to the DataFrame that contains all of the portfolio returns\n",
    "4. Re-run the performance and risk analysis with your portfolio to see how it compares to the others\n",
    "5. Include correlation analysis to determine which stocks (if any) are correlated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd4a065",
   "metadata": {},
   "source": [
    "## Choose 3-5 custom stocks with at last 1 year's worth of historic prices and create a DataFrame of the closing prices and dates for each stock."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd08ed1",
   "metadata": {},
   "source": [
    "## Technology Sector\n",
    "These are the three stocks we chose within the Technology Industry"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "c1cb2135",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 96,
   "id": "c1cb2135",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  28.365000  28.442499  27.632500  27.872499  25.774117\n",
      "2016-11-02  27.850000  28.087500  27.807501  27.897499  25.797230\n",
      "2016-11-03  27.745001  27.865000  27.387501  27.457500  25.520714\n",
      "2016-11-04  27.132500  27.562500  27.027500  27.209999  25.290676\n",
      "2016-11-07  27.520000  27.627501  27.365000  27.602501  25.655487\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  39.072498  39.355000  39.049999  39.275002  36.958801\n",
      "2017-10-25  39.227501  39.387501  38.817501  39.102501  36.796482\n",
      "2017-10-26  39.307499  39.457500  39.195000  39.352501  37.031731\n",
      "2017-10-27  39.822498  40.900002  39.674999  40.762501  38.358582\n",
      "2017-10-30  40.972500  42.017502  40.930000  41.680000  39.221973\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Apple Inc. \n",
    "AAPL_stock = 'AAPL'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_AAPL = yf.download(AAPL_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_AAPL))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_AAPL.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "339fc1d9",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 98,
   "id": "339fc1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  59.970001  60.020000  59.250000  59.799999  54.234051\n",
      "2016-11-02  59.820000  59.930000  59.299999  59.430000  53.898502\n",
      "2016-11-03  59.529999  59.639999  59.110001  59.209999  53.698982\n",
      "2016-11-04  58.650002  59.279999  58.520000  58.709999  53.245514\n",
      "2016-11-07  59.779999  60.520000  59.779999  60.419998  54.796360\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  78.900002  79.199997  78.459999  78.860001  73.243103\n",
      "2017-10-25  78.580002  79.099998  78.010002  78.629997  73.029480\n",
      "2017-10-26  79.199997  79.419998  78.750000  78.760002  73.150223\n",
      "2017-10-27  84.370003  86.199997  83.610001  83.809998  77.840530\n",
      "2017-10-30  83.699997  84.330002  83.110001  83.889999  77.914841\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Microsoft \n",
    "MSFT_stock = 'MSFT'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_MSFT = yf.download(MSFT_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_MSFT))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_MSFT.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "030ba095",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 99,
   "id": "030ba095",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  39.144501  39.474499  38.777000  39.180500  39.180500\n",
      "2016-11-02  38.910000  39.082500  38.172501  38.435001  38.435001\n",
      "2016-11-03  38.362499  38.497501  37.951500  38.106499  38.106499\n",
      "2016-11-04  37.533001  38.518002  37.528049  38.101002  38.101002\n",
      "2016-11-07  38.724998  39.259499  38.627499  39.125999  39.125999\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  48.500000  48.611500  48.049999  48.527000  48.527000\n",
      "2017-10-25  48.418499  48.804501  48.026001  48.666500  48.666500\n",
      "2017-10-26  49.000000  49.380001  48.610001  48.627998  48.627998\n",
      "2017-10-27  50.459499  52.419498  50.410000  50.963501  50.963501\n",
      "2017-10-30  50.700001  51.248501  50.375000  50.855499  50.855499\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Google \n",
    "GOOG_stock = 'GOOG'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_GOOG = yf.download(GOOG_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_GOOG))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_GOOG.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0cffac",
   "metadata": {},
   "source": [
    "## Healthcare Sector \n",
    "These are the three stocks we chose in the Healthcare Industry"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "fe9106ad",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 100,
   "id": "fe9106ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                  Open        High         Low       Close   Adj Close\n",
      "Date                                                                  \n",
      "2016-11-01  114.760002  115.940002  114.629997  115.339996   94.939972\n",
      "2016-11-02  115.519997  115.760002  114.830002  114.860001   94.544868\n",
      "2016-11-03  114.879997  115.440002  114.750000  115.029999   94.684807\n",
      "2016-11-04  115.040001  115.940002  115.040001  115.110001   94.750664\n",
      "2016-11-07  115.889999  116.720001  115.839996  116.660004   96.026527\n",
      "...                ...         ...         ...         ...         ...\n",
      "2017-10-24  143.380005  143.380005  140.929993  141.639999  119.711136\n",
      "2017-10-25  141.160004  143.229996  140.960007  142.360001  120.319633\n",
      "2017-10-26  142.509995  143.750000  141.380005  141.809998  119.854805\n",
      "2017-10-27  141.000000  142.699997  140.199997  141.779999  119.829453\n",
      "2017-10-30  141.080002  141.210007  139.320007  140.000000  118.325005\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Johnson and Johnson \n",
    "JNJ_stock = 'JNJ'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_JNJ = yf.download(JNJ_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_JNJ))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_JNJ.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "fc30c9fc",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 101,
   "id": "fc30c9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  29.354839  30.094877  29.174574  29.478178  22.062824\n",
      "2016-11-02  29.402277  29.449715  28.804554  29.060720  21.750387\n",
      "2016-11-03  29.127134  29.127134  28.301708  28.358635  21.224911\n",
      "2016-11-04  28.377609  28.814041  28.368120  28.462997  21.303019\n",
      "2016-11-07  28.681213  28.880455  28.538898  28.823528  21.572857\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  34.582542  34.658443  34.146111  34.411766  26.790739\n",
      "2017-10-25  34.345352  34.487667  34.079697  34.307400  26.709484\n",
      "2017-10-26  34.240986  34.364326  33.776093  33.908916  26.399254\n",
      "2017-10-27  33.709679  33.918407  33.586338  33.776093  26.295847\n",
      "2017-10-30  33.614799  33.652752  33.225807  33.349148  25.963457\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Pfizer Inc\n",
    "PFE_stock = 'PFE'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_PFE = yf.download(PFE_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_PFE))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_PFE.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "729df1d3",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 102,
   "id": "729df1d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                  Open        High         Low       Close   Adj Close\n",
      "Date                                                                  \n",
      "2016-11-01  141.509995  141.509995  138.820007  139.429993  125.269714\n",
      "2016-11-02  139.020004  141.229996  138.929993  138.979996  124.865395\n",
      "2016-11-03  139.179993  140.339996  137.649994  137.830002  123.832199\n",
      "2016-11-04  138.449997  138.889999  137.690002  137.710007  123.724403\n",
      "2016-11-07  139.600006  142.440002  139.550003  141.929993  127.515808\n",
      "...                ...         ...         ...         ...         ...\n",
      "2017-10-24  206.380005  208.229996  205.880005  208.149994  189.951447\n",
      "2017-10-25  208.320007  209.139999  207.070007  207.559998  189.413086\n",
      "2017-10-26  207.919998  211.699997  207.580002  209.149994  190.864075\n",
      "2017-10-27  209.149994  212.770004  208.550003  212.600006  194.012421\n",
      "2017-10-30  212.210007  212.770004  209.380005  209.389999  191.083069\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: United Health Group\n",
    "UNH_stock = 'UNH'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_UNH = yf.download(UNH_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_UNH))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_UNH.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e2f32d7",
   "metadata": {},
   "source": [
    "# Financials Sector \n",
    "These are the three stocks we chose in the Healthcare Industry"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "7790e755",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 103,
   "id": "7790e755",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                  Open        High         Low       Close  Adj Close\n",
      "Date                                                                 \n",
      "2016-11-01   69.480003   69.779999   68.330002   68.970001  56.297249\n",
      "2016-11-02   68.650002   68.849998   68.010002   68.680000  56.060532\n",
      "2016-11-03   68.860001   69.190002   68.220001   68.379997  55.815651\n",
      "2016-11-04   68.489998   68.559998   67.639999   67.760002  55.309582\n",
      "2016-11-07   69.080002   69.970001   69.000000   69.879997  57.040058\n",
      "...                ...         ...         ...         ...        ...\n",
      "2017-10-24   99.879997  101.190002   99.760002  100.919998  84.256294\n",
      "2017-10-25  101.250000  101.449997  100.190002  101.019997  84.339767\n",
      "2017-10-26  101.349998  102.419998  101.320000  101.739998  84.940887\n",
      "2017-10-27  101.430000  102.230003  101.050003  101.769997  84.965942\n",
      "2017-10-30  101.129997  101.930000  100.870003  101.410004  84.665382\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: JPMorgan Chase and Co. \n",
    "JPM_stock = 'JPM'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_JPM = yf.download(JPM_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_JPM))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_JPM.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "530a84fc",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 104,
   "id": "530a84fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  16.559999  16.750000  16.330000  16.610001  14.163930\n",
      "2016-11-02  16.459999  16.559999  16.280001  16.480000  14.053074\n",
      "2016-11-03  16.480000  16.670000  16.450001  16.480000  14.053074\n",
      "2016-11-04  16.530001  16.709999  16.350000  16.549999  14.112765\n",
      "2016-11-07  16.860001  17.040001  16.850000  17.010000  14.505026\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  27.350000  27.840000  27.340000  27.680000  23.963497\n",
      "2017-10-25  27.889999  27.920000  27.340000  27.629999  23.920212\n",
      "2017-10-26  27.690001  27.980000  27.680000  27.740000  24.015444\n",
      "2017-10-27  27.680000  27.959999  27.620001  27.799999  24.067389\n",
      "2017-10-30  27.650000  27.760000  27.459999  27.600000  23.894236\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Bank of America \n",
    "BAC_stock = 'BAC'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_BAC = yf.download(BAC_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_BAC))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_BAC.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "eecbef7e",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 105,
   "id": "eecbef7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                  Open        High         Low       Close   Adj Close\n",
      "Date                                                                  \n",
      "2016-11-01   82.639999   82.690002   81.330002   81.620003   77.502655\n",
      "2016-11-02   80.889999   81.379997   80.519997   80.580002   76.515099\n",
      "2016-11-03   80.750000   81.129997   80.540001   80.650002   76.581573\n",
      "2016-11-04   80.650002   81.239998   80.330002   80.360001   76.306190\n",
      "2016-11-07   81.639999   82.529999   81.449997   82.510002   78.347763\n",
      "...                ...         ...         ...         ...         ...\n",
      "2017-10-24  107.559998  108.639999  107.180000  108.410004  103.704338\n",
      "2017-10-25  109.410004  110.610001  108.000000  109.489998  104.737442\n",
      "2017-10-26  110.379997  110.739998  109.669998  109.800003  105.033989\n",
      "2017-10-27  110.070000  110.129997  108.690002  109.709999  104.947891\n",
      "2017-10-30  109.669998  110.239998  108.919998  110.040001  105.263573\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Visa\n",
    "V_stock = 'V'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_V = yf.download(V_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_V))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_V.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2a3487",
   "metadata": {},
   "source": [
    "## Consumer Discretionary \n",
    "These are the three stocks we chose in the consumer discretionary industry: Amazon, Nike, and Walt Disney Company"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "898cb861",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 106,
   "id": "898cb861",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  39.950001  40.042000  38.835499  39.270500  39.270500\n",
      "2016-11-02  39.196499  39.237499  38.177502  38.278000  38.278000\n",
      "2016-11-03  38.252499  38.849998  38.200001  38.351501  38.351501\n",
      "2016-11-04  38.139500  38.299999  37.661499  37.752499  37.752499\n",
      "2016-11-07  38.582001  39.386501  38.547001  39.246498  39.246498\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  48.450001  48.992500  48.250000  48.794998  48.794998\n",
      "2017-10-25  48.900002  49.222000  48.312000  48.645500  48.645500\n",
      "2017-10-26  49.016499  49.145000  48.427502  48.621498  48.621498\n",
      "2017-10-27  52.907001  55.278999  52.527500  55.047501  55.047501\n",
      "2017-10-30  54.750500  56.139500  54.678001  55.542500  55.542500\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Amazon\n",
    "AMZN_stock = 'AMZN'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_AMZN = yf.download(AMZN_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_AMZN))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_AMZN.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "15ccfc36",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 107,
   "id": "15ccfc36",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  50.500000  50.509998  49.139999  49.619999  45.844654\n",
      "2016-11-02  49.240002  50.270000  49.009998  49.720001  45.937042\n",
      "2016-11-03  49.980000  50.000000  49.310001  49.730000  45.946270\n",
      "2016-11-04  49.799999  50.450001  49.759998  49.959999  46.158779\n",
      "2016-11-07  50.480000  50.930000  50.279999  50.910000  47.036499\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  53.900002  54.000000  53.099998  53.419998  50.028614\n",
      "2017-10-25  53.430000  55.910000  52.669998  54.939999  51.452118\n",
      "2017-10-26  55.259998  57.250000  55.139999  56.810001  53.203411\n",
      "2017-10-27  55.820000  56.360001  55.650002  55.959999  52.407372\n",
      "2017-10-30  55.709999  55.930000  54.840000  55.270000  51.761173\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Nike\n",
    "NKE_stock = 'NKE'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_NKE = yf.download(NKE_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_NKE))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_NKE.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
   "id": "4f38dcf7",
   "metadata": {},
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=======
   "execution_count": 108,
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   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
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      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  92.779999  92.989998  91.660004  92.389999  87.512695\n",
      "2016-11-02  91.910004  92.559998  91.690002  91.910004  87.058029\n",
      "2016-11-03  92.260002  94.129997  92.239998  93.370003  88.440948\n",
      "2016-11-04  93.500000  93.790001  92.360001  92.449997  87.569519\n",
      "2016-11-07  93.769997  94.480003  92.949997  94.430000  89.444992\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  98.519997  98.860001  98.059998  98.290001  94.516663\n",
      "2017-10-25  98.050003  98.220001  97.449997  97.800003  94.045479\n",
      "2017-10-26  97.580002  99.480003  97.250000  98.559998  94.776299\n",
      "2017-10-27  97.900002  98.360001  96.889999  98.309998  94.535889\n",
      "2017-10-30  98.120003  98.330002  97.699997  98.040001  94.276268\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Walt Disney Company \n",
    "DIS_stock = 'DIS'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_DIS = yf.download(DIS_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_DIS))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_DIS.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62c069d",
   "metadata": {},
   "source": [
    "## Energy Sector \n",
    "These are the three stocks we chose in the energy industry: Exxon Mobile Incorporation, First Solar Inc. , NextEra Energy Resources "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "execution_count": 109,
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    {
     "name": "stderr",
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      "[*********************100%%**********************]  1 of 1 completed"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  83.500000  83.919998  83.269997  83.650002  59.387981\n",
      "2016-11-02  83.300003  83.879997  82.940002  83.449997  59.245964\n",
      "2016-11-03  83.680000  83.879997  83.050003  83.660004  59.395073\n",
      "2016-11-04  83.629997  84.330002  82.760002  83.570000  59.331177\n",
      "2016-11-07  84.489998  85.470001  84.400002  85.449997  60.665894\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  83.550003  83.809998  83.309998  83.470001  61.491642\n",
      "2017-10-25  83.449997  83.730003  82.949997  83.169998  61.270622\n",
      "2017-10-26  83.370003  83.650002  83.110001  83.470001  61.491642\n",
      "2017-10-27  83.000000  84.239998  82.010002  83.709999  61.668449\n",
      "2017-10-30  83.709999  83.949997  83.410004  83.540001  61.543190\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Exxon Mobile Incorporation\n",
    "XOM_stock = 'XOM'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_XOM = yf.download(XOM_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_XOM))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_XOM.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6d779524",
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   "execution_count": 111,
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    {
     "name": "stderr",
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      "[*********************100%%**********************]  1 of 1 completed"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  40.570000  41.250000  40.279999  41.049999  41.049999\n",
      "2016-11-02  40.799999  41.230000  40.320000  40.580002  40.580002\n",
      "2016-11-03  38.500000  38.599998  32.900002  34.509998  34.509998\n",
      "2016-11-04  34.380001  34.400002  32.410000  32.480000  32.480000\n",
      "2016-11-07  33.630001  34.099998  33.320000  33.759998  33.759998\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  49.099998  49.689999  48.520000  48.610001  48.610001\n",
      "2017-10-25  48.919998  48.919998  46.779999  47.389999  47.389999\n",
      "2017-10-26  47.380001  48.080002  46.880001  47.919998  47.919998\n",
      "2017-10-27  52.099998  58.970001  51.709999  57.669998  57.669998\n",
      "2017-10-30  57.330002  61.470001  56.549999  60.430000  60.430000\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: First Solar Inc. \n",
    "FSLR_stock = 'FSLR'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_FSLR = yf.download(FSLR_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_FSLR))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_FSLR.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
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   "metadata": {},
   "outputs": [],
=======
   "execution_count": 112,
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   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
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      "[*********************100%%**********************]  1 of 1 completed"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  32.047501  32.070000  31.362499  31.472500  26.428873\n",
      "2016-11-02  30.825001  31.070000  30.547501  30.920000  25.964914\n",
      "2016-11-03  30.924999  31.097500  30.732500  30.959999  25.998505\n",
      "2016-11-04  31.107500  31.174999  30.787500  30.795000  25.859949\n",
      "2016-11-07  30.950001  31.167500  30.700001  31.165001  26.170656\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  38.529999  38.599998  38.297501  38.555000  33.324142\n",
      "2017-10-25  38.485001  38.547501  37.840000  38.465000  33.246357\n",
      "2017-10-26  38.700001  39.000000  38.340000  38.347500  33.144791\n",
      "2017-10-27  38.347500  39.200001  38.345001  38.959999  33.674194\n",
      "2017-10-30  38.827499  39.047501  38.722500  38.840000  33.570480\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: NextEra Energy Resources\n",
    "NEE_stock = 'NEE'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_NEE = yf.download(NEE_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_NEE))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_NEE.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc220fc",
   "metadata": {},
   "source": [
    "## Weapons Manufacturing \n",
    "These are the three stocks we chose in the weapons manufacturing instury: Lockheed Martin Corporation, Raytheon Technologies Corporations (RTX), Northrup Grumman Corporation (NOC)  "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "1464dad3",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 113,
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    {
     "name": "stderr",
     "output_type": "stream",
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      "[*********************100%%**********************]  1 of 1 completed"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                  Open        High         Low       Close   Adj Close\n",
      "Date                                                                  \n",
      "2016-11-01  247.190002  247.190002  244.229996  244.979996  202.374100\n",
      "2016-11-02  244.949997  247.649994  244.199997  245.110001  202.481491\n",
      "2016-11-03  242.550003  243.399994  237.110001  238.699997  197.186325\n",
      "2016-11-04  238.850006  239.699997  236.210007  236.279999  195.187134\n",
      "2016-11-07  239.830002  239.919998  237.520004  239.270004  197.657181\n",
      "...                ...         ...         ...         ...         ...\n",
      "2017-10-24  314.690002  317.000000  310.609985  313.149994  265.527283\n",
      "2017-10-25  312.859985  315.959991  311.309998  312.429993  264.916840\n",
      "2017-10-26  313.440002  314.420013  303.309998  307.890015  261.067261\n",
      "2017-10-27  307.950012  310.380005  307.750000  308.000000  261.160492\n",
      "2017-10-30  308.500000  310.420013  307.820007  308.630005  261.694733\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Lockheed Martin Corporation\n",
    "LMT_stock = 'LMT'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_LMT = yf.download(LMT_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_LMT))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_LMT.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
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   "metadata": {},
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   "execution_count": 114,
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                 Open       High        Low      Close  Adj Close\n",
      "Date                                                             \n",
      "2016-11-01  64.449341  64.770294  63.750786  64.197609  53.871941\n",
      "2016-11-02  64.096916  64.543739  63.662682  63.901825  53.623718\n",
      "2016-11-03  64.065453  64.134674  63.385777  63.593456  53.364960\n",
      "2016-11-04  63.713028  64.046570  63.398365  63.769665  53.512810\n",
      "2016-11-07  64.801765  65.034615  64.512276  64.852104  54.421154\n",
      "...               ...        ...        ...        ...        ...\n",
      "2017-10-24  75.487724  77.583389  74.858398  75.355568  64.744514\n",
      "2017-10-25  75.336693  75.701698  74.808052  74.883575  64.338982\n",
      "2017-10-26  75.173065  75.563248  74.889870  75.475143  64.847260\n",
      "2017-10-27  75.280052  75.487724  74.864693  74.933922  64.382248\n",
      "2017-10-30  75.147896  75.852737  74.795471  75.412209  64.793175\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Raytheon Technologies Corporation\n",
    "RTX_stock = 'RTX'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_RTX = yf.download(RTX_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_RTX))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_RTX.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                  Open        High         Low       Close   Adj Close\n",
      "Date                                                                  \n",
      "2016-11-01  229.500000  230.419998  227.339996  228.419998  203.677399\n",
      "2016-11-02  228.139999  230.699997  227.470001  228.669998  203.900299\n",
      "2016-11-03  229.000000  229.449997  224.720001  225.539993  201.109344\n",
      "2016-11-04  225.679993  227.139999  224.910004  225.229996  200.833008\n",
      "2016-11-07  227.850006  230.169998  227.479996  230.059998  205.139771\n",
      "...                ...         ...         ...         ...         ...\n",
      "2017-10-24  293.200012  294.549988  292.239990  294.029999  266.101074\n",
      "2017-10-25  300.630005  306.609985  299.739990  304.750000  275.802826\n",
      "2017-10-26  306.000000  306.000000  295.559998  297.989990  269.684937\n",
      "2017-10-27  298.000000  299.019989  296.790009  297.660004  269.386261\n",
      "2017-10-30  296.869995  298.290009  295.200012  296.369995  268.218811\n",
      "\n",
      "[251 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Define the stock ticker and date range: Northrup Grumman Corporation\n",
    "NOC_stock = 'NOC'\n",
    "start_date = '2016-11-01'  # example start date\n",
    "end_date = '2017-10-31'    # example end date\n",
    "\n",
    "# Download the stock data\n",
    "data_NOC = yf.download(NOC_stock, start=start_date, end=end_date)\n",
    "\n",
    "# Print the type of the data\n",
    "print(type(data_NOC))\n",
    "\n",
    "# Print the first few columns of the data\n",
    "print(data_NOC.iloc[:, :5])  # This prints the first three columns of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6045156b",
   "metadata": {},
   "source": [
    "## Combined Stocks Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "ab75fa83",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 117,
   "id": "ab75fa83",
   "metadata": {},
   "outputs": [
    {
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       "      <td>27.897499</td>\n",
       "      <td>25.797230</td>\n",
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       "      <td>59.930000</td>\n",
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       "      <td>230.699997</td>\n",
       "      <td>227.470001</td>\n",
       "      <td>228.669998</td>\n",
       "      <td>203.900299</td>\n",
       "      <td>962800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>27.745001</td>\n",
       "      <td>27.865000</td>\n",
       "      <td>27.387501</td>\n",
       "      <td>27.457500</td>\n",
       "      <td>25.520714</td>\n",
       "      <td>107730400</td>\n",
       "      <td>59.529999</td>\n",
       "      <td>59.639999</td>\n",
       "      <td>59.110001</td>\n",
       "      <td>59.209999</td>\n",
       "      <td>...</td>\n",
       "      <td>63.385777</td>\n",
       "      <td>63.593456</td>\n",
       "      <td>53.364960</td>\n",
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       "      <td>1711000</td>\n",
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       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>27.132500</td>\n",
       "      <td>27.562500</td>\n",
       "      <td>27.027500</td>\n",
       "      <td>27.209999</td>\n",
       "      <td>25.290676</td>\n",
       "      <td>123348000</td>\n",
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       "      <td>58.520000</td>\n",
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       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
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       "      <td>1093200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume  \\\n",
       "Date                                                                           \n",
       "2016-11-01  28.365000  28.442499  27.632500  27.872499  25.774117  175303200   \n",
       "2016-11-02  27.850000  28.087500  27.807501  27.897499  25.797230  113326800   \n",
       "2016-11-03  27.745001  27.865000  27.387501  27.457500  25.520714  107730400   \n",
       "2016-11-04  27.132500  27.562500  27.027500  27.209999  25.290676  123348000   \n",
       "2016-11-07  27.520000  27.627501  27.365000  27.602501  25.655487  130240000   \n",
       "\n",
       "                 Open       High        Low      Close  ...        Low  \\\n",
       "Date                                                    ...              \n",
       "2016-11-01  59.970001  60.020000  59.250000  59.799999  ...  63.750786   \n",
       "2016-11-02  59.820000  59.930000  59.299999  59.430000  ...  63.662682   \n",
       "2016-11-03  59.529999  59.639999  59.110001  59.209999  ...  63.385777   \n",
       "2016-11-04  58.650002  59.279999  58.520000  58.709999  ...  63.398365   \n",
       "2016-11-07  59.779999  60.520000  59.779999  60.419998  ...  64.512276   \n",
       "\n",
       "                Close  Adj Close   Volume        Open        High         Low  \\\n",
       "Date                                                                            \n",
       "2016-11-01  64.197609  53.871941  6426711  229.500000  230.419998  227.339996   \n",
       "2016-11-02  63.901825  53.623718  6539530  228.139999  230.699997  227.470001   \n",
       "2016-11-03  63.593456  53.364960  3883834  229.000000  229.449997  224.720001   \n",
       "2016-11-04  63.769665  53.512810  4143794  225.679993  227.139999  224.910004   \n",
       "2016-11-07  64.852104  54.421154  6715432  227.850006  230.169998  227.479996   \n",
       "\n",
       "                 Close   Adj Close   Volume  \n",
       "Date                                         \n",
       "2016-11-01  228.419998  203.677399  1179600  \n",
       "2016-11-02  228.669998  203.900299   962800  \n",
       "2016-11-03  225.539993  201.109344  1711000  \n",
       "2016-11-04  225.229996  200.833008  1176100  \n",
       "2016-11-07  230.059998  205.139771  1093200  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Combine all stocks into a single Dataframe \n",
    "custom_portfolio_df = pd.concat([data_AAPL,data_MSFT,data_GOOG,data_JNJ,data_PFE,data_UNH,data_JPM,data_BAC,data_V,data_AMZN,data_NKE,data_DIS,data_XOM,data_FSLR,data_NEE,data_LMT,data_RTX,data_NOC], axis=\"columns\", join=\"inner\")\n",
    "custom_portfolio_df.head()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "d7cf0814",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 118,
   "id": "d7cf0814",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AAPL</th>\n",
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       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>27.872499</td>\n",
       "      <td>59.799999</td>\n",
       "      <td>39.180500</td>\n",
       "      <td>115.339996</td>\n",
       "      <td>29.478178</td>\n",
       "      <td>139.429993</td>\n",
       "      <td>68.970001</td>\n",
       "      <td>16.610001</td>\n",
       "      <td>81.620003</td>\n",
       "      <td>39.270500</td>\n",
       "      <td>49.619999</td>\n",
       "      <td>92.389999</td>\n",
       "      <td>83.650002</td>\n",
       "      <td>41.049999</td>\n",
       "      <td>31.472500</td>\n",
       "      <td>244.979996</td>\n",
       "      <td>64.197609</td>\n",
       "      <td>228.419998</td>\n",
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       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
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       "      <td>138.979996</td>\n",
       "      <td>68.680000</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>80.580002</td>\n",
       "      <td>38.278000</td>\n",
       "      <td>49.720001</td>\n",
       "      <td>91.910004</td>\n",
       "      <td>83.449997</td>\n",
       "      <td>40.580002</td>\n",
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       "      <td>245.110001</td>\n",
       "      <td>63.901825</td>\n",
       "      <td>228.669998</td>\n",
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       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>27.457500</td>\n",
       "      <td>59.209999</td>\n",
       "      <td>38.106499</td>\n",
       "      <td>115.029999</td>\n",
       "      <td>28.358635</td>\n",
       "      <td>137.830002</td>\n",
       "      <td>68.379997</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>80.650002</td>\n",
       "      <td>38.351501</td>\n",
       "      <td>49.730000</td>\n",
       "      <td>93.370003</td>\n",
       "      <td>83.660004</td>\n",
       "      <td>34.509998</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>238.699997</td>\n",
       "      <td>63.593456</td>\n",
       "      <td>225.539993</td>\n",
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       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>27.209999</td>\n",
       "      <td>58.709999</td>\n",
       "      <td>38.101002</td>\n",
       "      <td>115.110001</td>\n",
       "      <td>28.462997</td>\n",
       "      <td>137.710007</td>\n",
       "      <td>67.760002</td>\n",
       "      <td>16.549999</td>\n",
       "      <td>80.360001</td>\n",
       "      <td>37.752499</td>\n",
       "      <td>49.959999</td>\n",
       "      <td>92.449997</td>\n",
       "      <td>83.570000</td>\n",
       "      <td>32.480000</td>\n",
       "      <td>30.795000</td>\n",
       "      <td>236.279999</td>\n",
       "      <td>63.769665</td>\n",
       "      <td>225.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>27.602501</td>\n",
       "      <td>60.419998</td>\n",
       "      <td>39.125999</td>\n",
       "      <td>116.660004</td>\n",
       "      <td>28.823528</td>\n",
       "      <td>141.929993</td>\n",
       "      <td>69.879997</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>82.510002</td>\n",
       "      <td>39.246498</td>\n",
       "      <td>50.910000</td>\n",
       "      <td>94.430000</td>\n",
       "      <td>85.449997</td>\n",
       "      <td>33.759998</td>\n",
       "      <td>31.165001</td>\n",
       "      <td>239.270004</td>\n",
       "      <td>64.852104</td>\n",
       "      <td>230.059998</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>39.275002</td>\n",
       "      <td>78.860001</td>\n",
       "      <td>48.527000</td>\n",
       "      <td>141.639999</td>\n",
       "      <td>34.411766</td>\n",
       "      <td>208.149994</td>\n",
       "      <td>100.919998</td>\n",
       "      <td>27.680000</td>\n",
       "      <td>108.410004</td>\n",
       "      <td>48.794998</td>\n",
       "      <td>53.419998</td>\n",
       "      <td>98.290001</td>\n",
       "      <td>83.470001</td>\n",
       "      <td>48.610001</td>\n",
       "      <td>38.555000</td>\n",
       "      <td>313.149994</td>\n",
       "      <td>75.355568</td>\n",
       "      <td>294.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>39.102501</td>\n",
       "      <td>78.629997</td>\n",
       "      <td>48.666500</td>\n",
       "      <td>142.360001</td>\n",
       "      <td>34.307400</td>\n",
       "      <td>207.559998</td>\n",
       "      <td>101.019997</td>\n",
       "      <td>27.629999</td>\n",
       "      <td>109.489998</td>\n",
       "      <td>48.645500</td>\n",
       "      <td>54.939999</td>\n",
       "      <td>97.800003</td>\n",
       "      <td>83.169998</td>\n",
       "      <td>47.389999</td>\n",
       "      <td>38.465000</td>\n",
       "      <td>312.429993</td>\n",
       "      <td>74.883575</td>\n",
       "      <td>304.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>39.352501</td>\n",
       "      <td>78.760002</td>\n",
       "      <td>48.627998</td>\n",
       "      <td>141.809998</td>\n",
       "      <td>33.908916</td>\n",
       "      <td>209.149994</td>\n",
       "      <td>101.739998</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>109.800003</td>\n",
       "      <td>48.621498</td>\n",
       "      <td>56.810001</td>\n",
       "      <td>98.559998</td>\n",
       "      <td>83.470001</td>\n",
       "      <td>47.919998</td>\n",
       "      <td>38.347500</td>\n",
       "      <td>307.890015</td>\n",
       "      <td>75.475143</td>\n",
       "      <td>297.989990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>40.762501</td>\n",
       "      <td>83.809998</td>\n",
       "      <td>50.963501</td>\n",
       "      <td>141.779999</td>\n",
       "      <td>33.776093</td>\n",
       "      <td>212.600006</td>\n",
       "      <td>101.769997</td>\n",
       "      <td>27.799999</td>\n",
       "      <td>109.709999</td>\n",
       "      <td>55.047501</td>\n",
       "      <td>55.959999</td>\n",
       "      <td>98.309998</td>\n",
       "      <td>83.709999</td>\n",
       "      <td>57.669998</td>\n",
       "      <td>38.959999</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>74.933922</td>\n",
       "      <td>297.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>41.680000</td>\n",
       "      <td>83.889999</td>\n",
       "      <td>50.855499</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>33.349148</td>\n",
       "      <td>209.389999</td>\n",
       "      <td>101.410004</td>\n",
       "      <td>27.600000</td>\n",
       "      <td>110.040001</td>\n",
       "      <td>55.542500</td>\n",
       "      <td>55.270000</td>\n",
       "      <td>98.040001</td>\n",
       "      <td>83.540001</td>\n",
       "      <td>60.430000</td>\n",
       "      <td>38.840000</td>\n",
       "      <td>308.630005</td>\n",
       "      <td>75.412209</td>\n",
       "      <td>296.369995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL       MSFT       GOOG         JNJ        PFE  \\\n",
       "Date                                                                 \n",
       "2016-11-01  27.872499  59.799999  39.180500  115.339996  29.478178   \n",
       "2016-11-02  27.897499  59.430000  38.435001  114.860001  29.060720   \n",
       "2016-11-03  27.457500  59.209999  38.106499  115.029999  28.358635   \n",
       "2016-11-04  27.209999  58.709999  38.101002  115.110001  28.462997   \n",
       "2016-11-07  27.602501  60.419998  39.125999  116.660004  28.823528   \n",
       "...               ...        ...        ...         ...        ...   \n",
       "2017-10-24  39.275002  78.860001  48.527000  141.639999  34.411766   \n",
       "2017-10-25  39.102501  78.629997  48.666500  142.360001  34.307400   \n",
       "2017-10-26  39.352501  78.760002  48.627998  141.809998  33.908916   \n",
       "2017-10-27  40.762501  83.809998  50.963501  141.779999  33.776093   \n",
       "2017-10-30  41.680000  83.889999  50.855499  140.000000  33.349148   \n",
       "\n",
       "                   UNH         JPM        BAC           V       AMZN  \\\n",
       "Date                                                                   \n",
       "2016-11-01  139.429993   68.970001  16.610001   81.620003  39.270500   \n",
       "2016-11-02  138.979996   68.680000  16.480000   80.580002  38.278000   \n",
       "2016-11-03  137.830002   68.379997  16.480000   80.650002  38.351501   \n",
       "2016-11-04  137.710007   67.760002  16.549999   80.360001  37.752499   \n",
       "2016-11-07  141.929993   69.879997  17.010000   82.510002  39.246498   \n",
       "...                ...         ...        ...         ...        ...   \n",
       "2017-10-24  208.149994  100.919998  27.680000  108.410004  48.794998   \n",
       "2017-10-25  207.559998  101.019997  27.629999  109.489998  48.645500   \n",
       "2017-10-26  209.149994  101.739998  27.740000  109.800003  48.621498   \n",
       "2017-10-27  212.600006  101.769997  27.799999  109.709999  55.047501   \n",
       "2017-10-30  209.389999  101.410004  27.600000  110.040001  55.542500   \n",
       "\n",
       "                  NKE        DIS        XOM       FSLR        NEE         LMT  \\\n",
       "Date                                                                            \n",
       "2016-11-01  49.619999  92.389999  83.650002  41.049999  31.472500  244.979996   \n",
       "2016-11-02  49.720001  91.910004  83.449997  40.580002  30.920000  245.110001   \n",
       "2016-11-03  49.730000  93.370003  83.660004  34.509998  30.959999  238.699997   \n",
       "2016-11-04  49.959999  92.449997  83.570000  32.480000  30.795000  236.279999   \n",
       "2016-11-07  50.910000  94.430000  85.449997  33.759998  31.165001  239.270004   \n",
       "...               ...        ...        ...        ...        ...         ...   \n",
       "2017-10-24  53.419998  98.290001  83.470001  48.610001  38.555000  313.149994   \n",
       "2017-10-25  54.939999  97.800003  83.169998  47.389999  38.465000  312.429993   \n",
       "2017-10-26  56.810001  98.559998  83.470001  47.919998  38.347500  307.890015   \n",
       "2017-10-27  55.959999  98.309998  83.709999  57.669998  38.959999  308.000000   \n",
       "2017-10-30  55.270000  98.040001  83.540001  60.430000  38.840000  308.630005   \n",
       "\n",
       "                  RTX         NOC  \n",
       "Date                               \n",
       "2016-11-01  64.197609  228.419998  \n",
       "2016-11-02  63.901825  228.669998  \n",
       "2016-11-03  63.593456  225.539993  \n",
       "2016-11-04  63.769665  225.229996  \n",
       "2016-11-07  64.852104  230.059998  \n",
       "...               ...         ...  \n",
       "2017-10-24  75.355568  294.029999  \n",
       "2017-10-25  74.883575  304.750000  \n",
       "2017-10-26  75.475143  297.989990  \n",
       "2017-10-27  74.933922  297.660004  \n",
       "2017-10-30  75.412209  296.369995  \n",
       "\n",
       "[251 rows x 18 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "small_df=custom_portfolio_df['Close']\n",
    "small_df.columns=['AAPL','MSFT', 'GOOG', 'JNJ','PFE','UNH','JPM','BAC','V','AMZN','NKE','DIS','XOM','FSLR','NEE','LMT','RTX','NOC']\n",
    "small_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0fc1c0d",
   "metadata": {},
   "source": [
    "### Technology Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "830368e1",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 119,
   "id": "830368e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>27.872499</td>\n",
       "      <td>59.799999</td>\n",
       "      <td>39.180500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>27.897499</td>\n",
       "      <td>59.430000</td>\n",
       "      <td>38.435001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>27.457500</td>\n",
       "      <td>59.209999</td>\n",
       "      <td>38.106499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>27.209999</td>\n",
       "      <td>58.709999</td>\n",
       "      <td>38.101002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>27.602501</td>\n",
       "      <td>60.419998</td>\n",
       "      <td>39.125999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>39.275002</td>\n",
       "      <td>78.860001</td>\n",
       "      <td>48.527000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>39.102501</td>\n",
       "      <td>78.629997</td>\n",
       "      <td>48.666500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>39.352501</td>\n",
       "      <td>78.760002</td>\n",
       "      <td>48.627998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>40.762501</td>\n",
       "      <td>83.809998</td>\n",
       "      <td>50.963501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>41.680000</td>\n",
       "      <td>83.889999</td>\n",
       "      <td>50.855499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL       MSFT       GOOG\n",
       "Date                                       \n",
       "2016-11-01  27.872499  59.799999  39.180500\n",
       "2016-11-02  27.897499  59.430000  38.435001\n",
       "2016-11-03  27.457500  59.209999  38.106499\n",
       "2016-11-04  27.209999  58.709999  38.101002\n",
       "2016-11-07  27.602501  60.419998  39.125999\n",
       "...               ...        ...        ...\n",
       "2017-10-24  39.275002  78.860001  48.527000\n",
       "2017-10-25  39.102501  78.629997  48.666500\n",
       "2017-10-26  39.352501  78.760002  48.627998\n",
       "2017-10-27  40.762501  83.809998  50.963501\n",
       "2017-10-30  41.680000  83.889999  50.855499\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "tech_df=small_df[['AAPL','MSFT','GOOG']]\n",
    "tech_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f6cff9",
   "metadata": {},
   "source": [
    "### Healthcare Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "9acf36f1",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 120,
   "id": "9acf36f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JNJ</th>\n",
       "      <th>PFE</th>\n",
       "      <th>UNH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>115.339996</td>\n",
       "      <td>29.478178</td>\n",
       "      <td>139.429993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>114.860001</td>\n",
       "      <td>29.060720</td>\n",
       "      <td>138.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>115.029999</td>\n",
       "      <td>28.358635</td>\n",
       "      <td>137.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>115.110001</td>\n",
       "      <td>28.462997</td>\n",
       "      <td>137.710007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>116.660004</td>\n",
       "      <td>28.823528</td>\n",
       "      <td>141.929993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>141.639999</td>\n",
       "      <td>34.411766</td>\n",
       "      <td>208.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>142.360001</td>\n",
       "      <td>34.307400</td>\n",
       "      <td>207.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>141.809998</td>\n",
       "      <td>33.908916</td>\n",
       "      <td>209.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>141.779999</td>\n",
       "      <td>33.776093</td>\n",
       "      <td>212.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>33.349148</td>\n",
       "      <td>209.389999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   JNJ        PFE         UNH\n",
       "Date                                         \n",
       "2016-11-01  115.339996  29.478178  139.429993\n",
       "2016-11-02  114.860001  29.060720  138.979996\n",
       "2016-11-03  115.029999  28.358635  137.830002\n",
       "2016-11-04  115.110001  28.462997  137.710007\n",
       "2016-11-07  116.660004  28.823528  141.929993\n",
       "...                ...        ...         ...\n",
       "2017-10-24  141.639999  34.411766  208.149994\n",
       "2017-10-25  142.360001  34.307400  207.559998\n",
       "2017-10-26  141.809998  33.908916  209.149994\n",
       "2017-10-27  141.779999  33.776093  212.600006\n",
       "2017-10-30  140.000000  33.349148  209.389999\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "health_df=small_df[['JNJ','PFE','UNH']]\n",
    "health_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9df2883",
   "metadata": {},
   "source": [
    "### Financials Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "6e6a96b0",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 121,
   "id": "6e6a96b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>68.970001</td>\n",
       "      <td>16.610001</td>\n",
       "      <td>81.620003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>68.680000</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>80.580002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>68.379997</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>80.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>67.760002</td>\n",
       "      <td>16.549999</td>\n",
       "      <td>80.360001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>69.879997</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>82.510002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>100.919998</td>\n",
       "      <td>27.680000</td>\n",
       "      <td>108.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>101.019997</td>\n",
       "      <td>27.629999</td>\n",
       "      <td>109.489998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>101.739998</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>109.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>101.769997</td>\n",
       "      <td>27.799999</td>\n",
       "      <td>109.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>101.410004</td>\n",
       "      <td>27.600000</td>\n",
       "      <td>110.040001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   JPM        BAC           V\n",
       "Date                                         \n",
       "2016-11-01   68.970001  16.610001   81.620003\n",
       "2016-11-02   68.680000  16.480000   80.580002\n",
       "2016-11-03   68.379997  16.480000   80.650002\n",
       "2016-11-04   67.760002  16.549999   80.360001\n",
       "2016-11-07   69.879997  17.010000   82.510002\n",
       "...                ...        ...         ...\n",
       "2017-10-24  100.919998  27.680000  108.410004\n",
       "2017-10-25  101.019997  27.629999  109.489998\n",
       "2017-10-26  101.739998  27.740000  109.800003\n",
       "2017-10-27  101.769997  27.799999  109.709999\n",
       "2017-10-30  101.410004  27.600000  110.040001\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "finance_df=small_df[['JPM','BAC','V']]\n",
    "finance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3bd9c7",
   "metadata": {},
   "source": [
    "### Consumer Discretionary Dataframe"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "aa5e2b55",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 122,
   "id": "aa5e2b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>AMZN</th>\n",
       "      <th>NKE</th>\n",
       "      <th>DIS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>39.270500</td>\n",
       "      <td>49.619999</td>\n",
       "      <td>92.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>38.278000</td>\n",
       "      <td>49.720001</td>\n",
       "      <td>91.910004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>38.351501</td>\n",
       "      <td>49.730000</td>\n",
       "      <td>93.370003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>37.752499</td>\n",
       "      <td>49.959999</td>\n",
       "      <td>92.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>39.246498</td>\n",
       "      <td>50.910000</td>\n",
       "      <td>94.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>48.794998</td>\n",
       "      <td>53.419998</td>\n",
       "      <td>98.290001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>48.645500</td>\n",
       "      <td>54.939999</td>\n",
       "      <td>97.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>48.621498</td>\n",
       "      <td>56.810001</td>\n",
       "      <td>98.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>55.047501</td>\n",
       "      <td>55.959999</td>\n",
       "      <td>98.309998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>55.542500</td>\n",
       "      <td>55.270000</td>\n",
       "      <td>98.040001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AMZN        NKE        DIS\n",
       "Date                                       \n",
       "2016-11-01  39.270500  49.619999  92.389999\n",
       "2016-11-02  38.278000  49.720001  91.910004\n",
       "2016-11-03  38.351501  49.730000  93.370003\n",
       "2016-11-04  37.752499  49.959999  92.449997\n",
       "2016-11-07  39.246498  50.910000  94.430000\n",
       "...               ...        ...        ...\n",
       "2017-10-24  48.794998  53.419998  98.290001\n",
       "2017-10-25  48.645500  54.939999  97.800003\n",
       "2017-10-26  48.621498  56.810001  98.559998\n",
       "2017-10-27  55.047501  55.959999  98.309998\n",
       "2017-10-30  55.542500  55.270000  98.040001\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "consumer_df=small_df[['AMZN','NKE','DIS']]\n",
    "consumer_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af32c207",
   "metadata": {},
   "source": [
    "### Energy Dataframe "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "b040c8e8",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 123,
   "id": "b040c8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>XOM</th>\n",
       "      <th>FSLR</th>\n",
       "      <th>NEE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>83.650002</td>\n",
       "      <td>41.049999</td>\n",
       "      <td>31.472500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>83.449997</td>\n",
       "      <td>40.580002</td>\n",
       "      <td>30.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>83.660004</td>\n",
       "      <td>34.509998</td>\n",
       "      <td>30.959999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>83.570000</td>\n",
       "      <td>32.480000</td>\n",
       "      <td>30.795000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>85.449997</td>\n",
       "      <td>33.759998</td>\n",
       "      <td>31.165001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>83.470001</td>\n",
       "      <td>48.610001</td>\n",
       "      <td>38.555000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>83.169998</td>\n",
       "      <td>47.389999</td>\n",
       "      <td>38.465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>83.470001</td>\n",
       "      <td>47.919998</td>\n",
       "      <td>38.347500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>83.709999</td>\n",
       "      <td>57.669998</td>\n",
       "      <td>38.959999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>83.540001</td>\n",
       "      <td>60.430000</td>\n",
       "      <td>38.840000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  XOM       FSLR        NEE\n",
       "Date                                       \n",
       "2016-11-01  83.650002  41.049999  31.472500\n",
       "2016-11-02  83.449997  40.580002  30.920000\n",
       "2016-11-03  83.660004  34.509998  30.959999\n",
       "2016-11-04  83.570000  32.480000  30.795000\n",
       "2016-11-07  85.449997  33.759998  31.165001\n",
       "...               ...        ...        ...\n",
       "2017-10-24  83.470001  48.610001  38.555000\n",
       "2017-10-25  83.169998  47.389999  38.465000\n",
       "2017-10-26  83.470001  47.919998  38.347500\n",
       "2017-10-27  83.709999  57.669998  38.959999\n",
       "2017-10-30  83.540001  60.430000  38.840000\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "energy_df=small_df[['XOM','FSLR','NEE']]\n",
    "energy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03a2e73",
   "metadata": {},
   "source": [
    "### Weapons Manufacturing Dataframe "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "38ee2014",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 124,
   "id": "38ee2014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>LMT</th>\n",
       "      <th>RTX</th>\n",
       "      <th>NOC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>244.979996</td>\n",
       "      <td>64.197609</td>\n",
       "      <td>228.419998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>245.110001</td>\n",
       "      <td>63.901825</td>\n",
       "      <td>228.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>238.699997</td>\n",
       "      <td>63.593456</td>\n",
       "      <td>225.539993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>236.279999</td>\n",
       "      <td>63.769665</td>\n",
       "      <td>225.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>239.270004</td>\n",
       "      <td>64.852104</td>\n",
       "      <td>230.059998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-24</th>\n",
       "      <td>313.149994</td>\n",
       "      <td>75.355568</td>\n",
       "      <td>294.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>312.429993</td>\n",
       "      <td>74.883575</td>\n",
       "      <td>304.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>307.890015</td>\n",
       "      <td>75.475143</td>\n",
       "      <td>297.989990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>308.000000</td>\n",
       "      <td>74.933922</td>\n",
       "      <td>297.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>308.630005</td>\n",
       "      <td>75.412209</td>\n",
       "      <td>296.369995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   LMT        RTX         NOC\n",
       "Date                                         \n",
       "2016-11-01  244.979996  64.197609  228.419998\n",
       "2016-11-02  245.110001  63.901825  228.669998\n",
       "2016-11-03  238.699997  63.593456  225.539993\n",
       "2016-11-04  236.279999  63.769665  225.229996\n",
       "2016-11-07  239.270004  64.852104  230.059998\n",
       "...                ...        ...         ...\n",
       "2017-10-24  313.149994  75.355568  294.029999\n",
       "2017-10-25  312.429993  74.883575  304.750000\n",
       "2017-10-26  307.890015  75.475143  297.989990\n",
       "2017-10-27  308.000000  74.933922  297.660004\n",
       "2017-10-30  308.630005  75.412209  296.369995\n",
       "\n",
       "[251 rows x 3 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "weapons_df=small_df[['LMT','RTX','NOC']]\n",
    "weapons_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adddb6d9",
   "metadata": {},
   "source": [
    "# Calculate: Daily Returns and Weighted Returns"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
=======
   "execution_count": 125,
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "id": "696e6920",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate daily returns, drop nas, and display the data \n",
    "# Tech Daily Returns \n",
    "tech_daily = tech_df.pct_change().dropna()\n",
    "tech_daily.head()\n",
    "\n",
    "# Healthcare Daily Returns \n",
    "health_daily = health_df.pct_change().dropna()\n",
    "health_daily.head()\n",
    "\n",
    "# Financials Daily Returns \n",
    "finance_daily = finance_df.pct_change().dropna()\n",
    "finance_daily.head()\n",
    "\n",
    "# Consumer Discretionary Daily Returns \n",
    "consumer_daily = consumer_df.pct_change().dropna()\n",
    "consumer_daily.head()\n",
    "\n",
    "# Energy Daily Returns \n",
    "energy_daily = energy_df.pct_change().dropna()\n",
    "energy_daily.head()\n",
    "\n",
    "# Weapons Manufacturing Daily Returns \n",
    "weapons_daily = weapons_df.pct_change().dropna()\n",
    "weapons_daily.head()\n",
    "\n",
    "combined_daily_returns = pd.concat([\n",
    "    tech_daily.head(),\n",
    "    health_daily.head(),\n",
    "    finance_daily.head(),\n",
    "    consumer_daily.head(),\n",
    "    energy_daily.head(),\n",
    "    weapons_daily.head()\n",
    "], axis=1)\n",
    "\n",
    "# Rename the columns for clarity\n",
    "combined_daily_returns.columns = ['AAPL','MSFT', 'GOOG', 'JNJ','PFE','UNH','JPM','BAC','V','AMZN','NKE','DIS','XOM','FSLR','NEE','LMT','RTX','NOC']\n",
    "\n",
    "# Display the combined DataFrame\n",
    "print(combined_daily_returns.head())\n",
    "\n",
    "# Set weights\n",
    "weights = [1/3, 1/3, 1/3]\n",
    "\n",
    "# Define a dictionary containing the daily returns for each sector\n",
    "daily_returns_dict = {\n",
    "    \"Tech\": tech_daily,\n",
    "    \"Health\": health_daily,\n",
    "    \"Finance\": finance_daily,\n",
    "    \"Consumer\": consumer_daily,\n",
    "    \"Energy\": energy_daily,\n",
    "    \"Weapons\": weapons_daily\n",
    "}\n",
    "\n",
    "# Create an empty dictionary to store the returns for each sector\n",
    "sector_returns = {}\n",
    "\n",
    "# Calculate the returns for each sector and store them in the dictionary\n",
    "for sector, combined_daily_returns in daily_returns_dict.items():\n",
    "    sector_returns[sector] = (weights * combined_daily_returns).sum(axis=1)\n",
    "\n",
    "# Create a DataFrame from the sector_returns dictionary\n",
    "returns_df = pd.DataFrame(sector_returns)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(returns_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7aae5ac",
   "metadata": {},
   "source": [
    "# Risk Analysis Performance \n",
    "1. Cumulative Returns\n",
    "2. Annualized Standard Deviation \n",
    "3. Annualized Sharpe Ratios (Reward to Risk)\n",
    "4. Covariance and Variance \n",
    "5. BETA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d08f26",
   "metadata": {},
   "source": [
    "## Cumulative Returns"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "980bb279",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 126,
   "id": "980bb279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "cumulative_returns = (1 + returns_df).cumprod()\n",
    "\n",
    "# Export and save Cumulative Returns dataframe into a csv file \n",
    "cumulative_returns.to_csv('cumulative_returns_data3.csv', index=False)\n",
    "\n",
    "cumulative_returns.plot(figsize=(10, 6))  # Adjust figsize as needed\n",
    "plt.title('Cumulative Returns of Each Industry')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cumulative Returns')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18352997",
   "metadata": {},
   "source": [
    "## Annualized Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "4d90ed70",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 127,
   "id": "4d90ed70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tech        0.008693\n",
      "Health      0.006294\n",
      "Finance     0.009410\n",
      "Consumer    0.007817\n",
      "Energy      0.011859\n",
      "Weapons     0.006902\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/Utilities/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3643: FutureWarning: The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n"
     ]
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Calculate the daily standard deviation \n",
    "daily_std_dev = np.std(returns_df)\n",
    "print(daily_std_dev)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "71d4da57",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 128,
   "id": "71d4da57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Calculate the annualized standard deviation (252 trading days)\n",
    "annualized_std = daily_std_dev * np.sqrt(252)\n",
    "\n",
    "# Plot the Annualized standard deviation\n",
    "annualized_std.plot.bar(figsize=(10, 6))  # Adjust figsize as needed\n",
    "plt.title('Annualized Standard Deviation of Each Industry')\n",
    "plt.xlabel('Industry')\n",
    "plt.ylabel('Annualized Standard Deviation')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77fb2009",
   "metadata": {},
   "source": [
    "# Sharpe Ratios "
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "id": "d75dcbe1",
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 130,
   "id": "d75dcbe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
>>>>>>> ea7a963a2c7f22a9d6396b5e0fa4bb5a99e02dee
   "source": [
    "# Annualized Sharpe Ratios \n",
    "sharpe_ratios = (returns_df.mean() * 252) / (returns_df.std() * np.sqrt(252))\n",
    "sharpe_ratios \n",
    "\n",
    "# Visualize the sharpe ratios as a bar plot\n",
    "sharpe_ratios.plot(kind='bar', color='skyblue', figsize=(10, 6))\n",
    "\n",
    "# Customizing the plot\n",
    "plt.title('Sharpe Ratios of Each Industry (Nov 2016 to Oct 2017)')\n",
    "plt.xlabel('Industry')\n",
    "plt.ylabel('Sharpe Ratio')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)  # Adding grid lines\n",
    "\n",
    "# Adding data labels\n",
    "for i, val in enumerate(sharpe_ratios.values):\n",
    "    plt.text(i, val, round(val, 2), ha='center', va='bottom')\n",
    "\n",
    "# Adding horizontal line at Sharpe ratio of 0 for reference\n",
    "plt.axhline(y=0, color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a23de6b2-d69e-43ca-9ea7-edd55a047f4c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}