{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Putting it all together - Building a value-weighted index\n",
    "> A Summary of lecture \"Manipulating Time Series Data in Python\", via datacamp\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Time_Series_Analysis]\n",
    "- image: images/market_cap.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select index components & import data\n",
    "- Market value-weighted index\n",
    "    - Composite performance of various stocks\n",
    "    - Components weighted by market capitalization\n",
    "        - ```Share Price``` * ```Number of Shares``` => ```Market Value```\n",
    "    - Larger components get higher percentage weightings\n",
    "    - Key market indexes are value-weighted\n",
    "        - S&P500, NASDAQ, Wilshire 5000, Hang Seng, ...\n",
    "- Build a cap-weighted Index\n",
    "    - Apply new skills to construct value-weighted index\n",
    "        - Select components from exchange listing data\n",
    "        - Get component number of shares and stock prices\n",
    "        - Calculate component weights\n",
    "        - Calculate index\n",
    "        - Evaluate performance of components and index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore and clean company listing information\n",
    "To get started with the construction of a market-value based index, you'll work with the combined listing info for the three largest US stock exchanges, the NYSE, the NASDAQ and the AMEX.\n",
    "\n",
    "In this and the next exercise, you will calculate market-cap weights for these stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "listings_nyse = pd.read_excel('./dataset/listings.xlsx', sheet_name='nyse', na_values='n/a')\n",
    "listings_amex = pd.read_excel('./dataset/listings.xlsx', sheet_name='amex', na_values='n/a')\n",
    "listings_nasdaq = pd.read_excel('./dataset/listings.xlsx', sheet_name='nasdaq', na_values='n/a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "listings_nyse['Exchange'] = 'nyse'\n",
    "listings_amex['Exchange'] = 'amex'\n",
    "listings_nasdaq['Exchange'] = 'nasdaq'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "listings = pd.concat([listings_amex, listings_nasdaq, listings_nyse], axis=0)\n",
    "listings.reset_index(inplace=True)\n",
    "listings.drop(['index'], axis=1, inplace=True)\n",
    "listings['Market Capitalization'] /= 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6674 entries, 0 to 6673\n",
      "Data columns (total 8 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Stock Symbol           6674 non-null   object \n",
      " 1   Company Name           6674 non-null   object \n",
      " 2   Last Sale              6590 non-null   float64\n",
      " 3   Market Capitalization  6674 non-null   float64\n",
      " 4   IPO Year               2852 non-null   float64\n",
      " 5   Sector                 5182 non-null   object \n",
      " 6   Industry               5182 non-null   object \n",
      " 7   Exchange               6674 non-null   object \n",
      "dtypes: float64(3), object(5)\n",
      "memory usage: 417.2+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2349 entries, ACU to ZTO\n",
      "Data columns (total 7 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Company Name           2349 non-null   object \n",
      " 1   Last Sale              2349 non-null   float64\n",
      " 2   Market Capitalization  2349 non-null   float64\n",
      " 3   IPO Year               2349 non-null   float64\n",
      " 4   Sector                 2349 non-null   object \n",
      " 5   Industry               2349 non-null   object \n",
      " 6   Exchange               2349 non-null   object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 146.8+ KB\n",
      "None\n",
      "Sector\n",
      "Health Care              445\n",
      "Consumer Services        402\n",
      "Technology               386\n",
      "Finance                  351\n",
      "Energy                   144\n",
      "Capital Goods            143\n",
      "Public Utilities         104\n",
      "Basic Industries         104\n",
      "Consumer Non-Durables     89\n",
      "Miscellaneous             68\n",
      "Transportation            58\n",
      "Consumer Durables         55\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Inspect listings\n",
    "print(listings.info())\n",
    "\n",
    "# Move 'stock symbol' into the index\n",
    "listings.set_index('Stock Symbol', inplace=True)\n",
    "\n",
    "# Drop rows with missing 'sector' data\n",
    "listings.dropna(subset=['Sector'], inplace=True)\n",
    "\n",
    "# Select companies with IPO Year befor 2019\n",
    "listings = listings[listings['IPO Year'] < 2019]\n",
    "\n",
    "# Inspect the new listings data\n",
    "print(listings.info())\n",
    "\n",
    "# Show the number of companies per sector\n",
    "print(listings.groupby('Sector').size().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select and inspect index components\n",
    "Now that you have imported and cleaned the listings data, you can proceed to select the index components as the largest company for each sector by market capitalization.\n",
    "\n",
    "You'll also have the opportunity to take a closer look at the components, their last market value, and last price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sector                 Stock Symbol\n",
      "Technology             AAPL            740024.467000\n",
      "Consumer Services      AMZN            422138.530626\n",
      "Miscellaneous          BABA            275525.000000\n",
      "Health Care            AMGN            118927.210535\n",
      "Transportation         UPS              90180.886756\n",
      "Finance                GS               88840.590477\n",
      "Consumer Non-Durables  ABEV             88240.198455\n",
      "Basic Industries       RIO              70431.476895\n",
      "Public Utilities       TEF              54609.806092\n",
      "Capital Goods          GM               50086.335099\n",
      "Energy                 PAA              22223.001416\n",
      "Consumer Durables      CPRT             13620.922869\n",
      "Name: Market Capitalization, dtype: float64\n",
      "Index(['RIO', 'GM', 'CPRT', 'ABEV', 'AMZN', 'PAA', 'GS', 'AMGN', 'BABA', 'TEF',\n",
      "       'AAPL', 'UPS'],\n",
      "      dtype='object', name='Stock Symbol')\n",
      "                                    Company Name  Market Capitalization  \\\n",
      "Stock Symbol                                                              \n",
      "AAPL                                  Apple Inc.          740024.467000   \n",
      "AMZN                            Amazon.com, Inc.          422138.530626   \n",
      "BABA               Alibaba Group Holding Limited          275525.000000   \n",
      "AMGN                                  Amgen Inc.          118927.210535   \n",
      "UPS                  United Parcel Service, Inc.           90180.886756   \n",
      "GS               Goldman Sachs Group, Inc. (The)           88840.590477   \n",
      "ABEV                                  Ambev S.A.           88240.198455   \n",
      "RIO                                Rio Tinto Plc           70431.476895   \n",
      "TEF                                Telefonica SA           54609.806092   \n",
      "GM                        General Motors Company           50086.335099   \n",
      "PAA           Plains All American Pipeline, L.P.           22223.001416   \n",
      "CPRT                                Copart, Inc.           13620.922869   \n",
      "\n",
      "              Last Sale  \n",
      "Stock Symbol             \n",
      "AAPL             141.05  \n",
      "AMZN             884.67  \n",
      "BABA             110.21  \n",
      "AMGN             161.61  \n",
      "UPS              103.74  \n",
      "GS               223.32  \n",
      "ABEV               5.62  \n",
      "RIO               38.94  \n",
      "TEF               10.84  \n",
      "GM                33.39  \n",
      "PAA               30.72  \n",
      "CPRT              29.65  \n"
     ]
    }
   ],
   "source": [
    "# Select largest company for each sector\n",
    "components = listings.groupby('Sector')['Market Capitalization'].nlargest(1)\n",
    "\n",
    "# Print components, sorted by market cap\n",
    "print(components.sort_values(ascending=False))\n",
    "\n",
    "# Select stock symbols and print the result\n",
    "tickers = components.index.get_level_values('Stock Symbol')\n",
    "print(tickers)\n",
    "\n",
    "# Print company name, market cap, and last price for each components\n",
    "info_cols = ['Company Name', 'Market Capitalization', 'Last Sale']\n",
    "print(listings.loc[tickers,info_cols].sort_values('Market Capitalization', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import index component price information\n",
    "Now you'll use the stock symbols for the companies you selected in the last exercise to calculate returns for each company.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = tickers.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RIO', 'GM', 'CPRT', 'ABEV', 'AMZN', 'PAA', 'GS', 'AMGN', 'BABA', 'TEF', 'AAPL', 'UPS']\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1762 entries, 2010-01-04 to 2016-12-30\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AAPL    1761 non-null   float64\n",
      " 1   AMGN    1761 non-null   float64\n",
      " 2   AMZN    1761 non-null   float64\n",
      " 3   CPRT    1761 non-null   float64\n",
      " 4   EL      1762 non-null   float64\n",
      " 5   GS      1762 non-null   float64\n",
      " 6   ILMN    1761 non-null   float64\n",
      " 7   MA      1762 non-null   float64\n",
      " 8   PAA     1762 non-null   float64\n",
      " 9   RIO     1762 non-null   float64\n",
      " 10  TEF     1762 non-null   float64\n",
      " 11  UPS     1762 non-null   float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 179.0 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print tickers\n",
    "print(tickers)\n",
    "\n",
    "# Import prices and inspect result\n",
    "stock_prices = pd.read_csv('./dataset/stocks_4.csv', parse_dates=['Date'], index_col='Date')\n",
    "print(stock_prices.info())\n",
    "\n",
    "# Calculate the returns\n",
    "price_return = stock_prices.iloc[-1].div(stock_prices.iloc[0]).sub(1).mul(100)\n",
    "\n",
    "# Plot horizontal bar chart of sorted price_return\n",
    "price_return.sort_values().plot(kind='barh', title='Stock Price Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a market-cap weighted index\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate number of shares outstanding\n",
    "The next step towards building a value-weighted index is to calculate the number of shares for each index component.\n",
    "\n",
    "The number of shares will allow you to calculate the total market capitalization for each component given the historical price series in the next exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2349 entries, ACU to ZTO\n",
      "Data columns (total 7 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Company Name           2349 non-null   object \n",
      " 1   Last Sale              2349 non-null   float64\n",
      " 2   Market Capitalization  2349 non-null   float64\n",
      " 3   IPO Year               2349 non-null   float64\n",
      " 4   Sector                 2349 non-null   object \n",
      " 5   Industry               2349 non-null   object \n",
      " 6   Exchange               2349 non-null   object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 226.8+ KB\n",
      "None\n",
      "['RIO', 'GM', 'CPRT', 'ABEV', 'AMZN', 'PAA', 'GS', 'AMGN', 'BABA', 'TEF', 'AAPL', 'UPS']\n",
      "              Market Capitalization  Last Sale\n",
      "Stock Symbol                                  \n",
      "RIO                    70431.476895      38.94\n",
      "GM                     50086.335099      33.39\n",
      "CPRT                   13620.922869      29.65\n",
      "ABEV                   88240.198455       5.62\n",
      "AMZN                  422138.530626     884.67\n",
      "Stock Symbol\n",
      "ABEV    15701.102928\n",
      "AAPL     5246.540000\n",
      "TEF      5037.804990\n",
      "BABA     2500.000000\n",
      "RIO      1808.717948\n",
      "GM       1500.039985\n",
      "UPS       869.297154\n",
      "AMGN      735.890171\n",
      "PAA       723.404994\n",
      "AMZN      477.170618\n",
      "CPRT      459.390316\n",
      "GS        397.817439\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Inspect listings and print tickers\n",
    "print(listings.info())\n",
    "print(tickers)\n",
    "\n",
    "# Select components and relevant columns from listings\n",
    "components = listings[['Market Capitalization', 'Last Sale']].loc[tickers]\n",
    "\n",
    "# Print the first rows of components\n",
    "print(components.head(5))\n",
    "\n",
    "# Calculate the number of shares here\n",
    "no_shares = components['Market Capitalization'].div(components['Last Sale'])\n",
    "\n",
    "# Print the sorted no_shares\n",
    "print(no_shares.sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create time series of market value\n",
    "You can now use the number of shares to calculate the total market capitalization for each component and trading date from the historical price series.\n",
    "\n",
    "The result will be the key input to construct the value-weighted stock index, which you will complete in the next exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "components['Number of Shares'] = no_shares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock Symbol\n",
      "GS        397.817439\n",
      "CPRT      459.390316\n",
      "AMZN      477.170618\n",
      "PAA       723.404994\n",
      "AMGN      735.890171\n",
      "UPS       869.297154\n",
      "GM       1500.039985\n",
      "RIO      1808.717948\n",
      "BABA     2500.000000\n",
      "TEF      5037.804990\n",
      "AAPL     5246.540000\n",
      "ABEV    15701.102928\n",
      "Name: Number of Shares, dtype: float64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the number of shares\n",
    "no_shares = components['Number of Shares']\n",
    "print(no_shares.sort_values())\n",
    "\n",
    "# Create the series of market cap per ticker\n",
    "market_cap = stock_prices.mul(no_shares)\n",
    "\n",
    "# Select first and last market cap here\n",
    "first_value = market_cap.iloc[0]\n",
    "last_value = market_cap.iloc[-1]\n",
    "\n",
    "# Concatenate and plot first and last market cap here\n",
    "pd.concat([first_value, last_value], axis=1).dropna().plot(kind='barh');\n",
    "plt.savefig('../images/market_cap.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate & plot the composite index\n",
    "By now you have all ingredients that you need to calculate the aggregate stock performance for your group of companies.\n",
    "\n",
    "Use the time series of market capitalization that you created in the last exercise to aggregate the market value for each period, and then normalize this series to convert it to an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "market_cap_series = market_cap[pd.concat([first_value, last_value], axis=1).dropna().index.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>AMGN</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>CPRT</th>\n",
       "      <th>GS</th>\n",
       "      <th>PAA</th>\n",
       "      <th>RIO</th>\n",
       "      <th>TEF</th>\n",
       "      <th>UPS</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>160386.7278</td>\n",
       "      <td>42475.580670</td>\n",
       "      <td>63893.145750</td>\n",
       "      <td>2090.225938</td>\n",
       "      <td>68854.242342</td>\n",
       "      <td>19531.934838</td>\n",
       "      <td>101342.466626</td>\n",
       "      <td>143829.332464</td>\n",
       "      <td>50575.708420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>160701.5202</td>\n",
       "      <td>42107.635585</td>\n",
       "      <td>64270.110538</td>\n",
       "      <td>2090.225938</td>\n",
       "      <td>70071.563705</td>\n",
       "      <td>19748.956336</td>\n",
       "      <td>102916.051241</td>\n",
       "      <td>143728.576365</td>\n",
       "      <td>50662.638135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>158130.7156</td>\n",
       "      <td>41791.202811</td>\n",
       "      <td>63105.814231</td>\n",
       "      <td>2081.038131</td>\n",
       "      <td>69323.666920</td>\n",
       "      <td>19741.722286</td>\n",
       "      <td>106063.220471</td>\n",
       "      <td>142217.234868</td>\n",
       "      <td>50288.840359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>157815.9232</td>\n",
       "      <td>41408.539922</td>\n",
       "      <td>62032.180340</td>\n",
       "      <td>2067.256422</td>\n",
       "      <td>70680.224387</td>\n",
       "      <td>19502.998638</td>\n",
       "      <td>106081.307650</td>\n",
       "      <td>139799.088472</td>\n",
       "      <td>49906.349611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>158865.2312</td>\n",
       "      <td>41776.485008</td>\n",
       "      <td>63711.820915</td>\n",
       "      <td>2076.444228</td>\n",
       "      <td>69343.557792</td>\n",
       "      <td>19568.105088</td>\n",
       "      <td>107256.974316</td>\n",
       "      <td>138892.283574</td>\n",
       "      <td>52305.609756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>611326.8408</td>\n",
       "      <td>108580.594731</td>\n",
       "      <td>362931.200345</td>\n",
       "      <td>6380.931489</td>\n",
       "      <td>95862.068276</td>\n",
       "      <td>24168.960850</td>\n",
       "      <td>68948.328178</td>\n",
       "      <td>46196.671758</td>\n",
       "      <td>100812.390949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>615209.2804</td>\n",
       "      <td>109176.665770</td>\n",
       "      <td>368089.414725</td>\n",
       "      <td>6431.464424</td>\n",
       "      <td>96096.780565</td>\n",
       "      <td>24226.833249</td>\n",
       "      <td>69364.333306</td>\n",
       "      <td>45995.159559</td>\n",
       "      <td>100951.478494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>612586.0104</td>\n",
       "      <td>108668.901552</td>\n",
       "      <td>368437.749276</td>\n",
       "      <td>6380.931489</td>\n",
       "      <td>95734.766695</td>\n",
       "      <td>23908.535052</td>\n",
       "      <td>70304.866639</td>\n",
       "      <td>45491.379060</td>\n",
       "      <td>100143.032141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>612428.6142</td>\n",
       "      <td>108749.849470</td>\n",
       "      <td>365107.098363</td>\n",
       "      <td>6376.337586</td>\n",
       "      <td>94752.157621</td>\n",
       "      <td>23488.960155</td>\n",
       "      <td>70359.128177</td>\n",
       "      <td>45995.159559</td>\n",
       "      <td>99951.786767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>607654.2628</td>\n",
       "      <td>107594.501902</td>\n",
       "      <td>357815.931320</td>\n",
       "      <td>6362.555877</td>\n",
       "      <td>95257.385769</td>\n",
       "      <td>23358.747256</td>\n",
       "      <td>69563.292280</td>\n",
       "      <td>46347.805908</td>\n",
       "      <td>99656.225735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1762 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AAPL           AMGN           AMZN         CPRT  \\\n",
       "Date                                                                 \n",
       "2010-01-04  160386.7278   42475.580670   63893.145750  2090.225938   \n",
       "2010-01-05  160701.5202   42107.635585   64270.110538  2090.225938   \n",
       "2010-01-06  158130.7156   41791.202811   63105.814231  2081.038131   \n",
       "2010-01-07  157815.9232   41408.539922   62032.180340  2067.256422   \n",
       "2010-01-08  158865.2312   41776.485008   63711.820915  2076.444228   \n",
       "...                 ...            ...            ...          ...   \n",
       "2016-12-23  611326.8408  108580.594731  362931.200345  6380.931489   \n",
       "2016-12-27  615209.2804  109176.665770  368089.414725  6431.464424   \n",
       "2016-12-28  612586.0104  108668.901552  368437.749276  6380.931489   \n",
       "2016-12-29  612428.6142  108749.849470  365107.098363  6376.337586   \n",
       "2016-12-30  607654.2628  107594.501902  357815.931320  6362.555877   \n",
       "\n",
       "                      GS           PAA            RIO            TEF  \\\n",
       "Date                                                                   \n",
       "2010-01-04  68854.242342  19531.934838  101342.466626  143829.332464   \n",
       "2010-01-05  70071.563705  19748.956336  102916.051241  143728.576365   \n",
       "2010-01-06  69323.666920  19741.722286  106063.220471  142217.234868   \n",
       "2010-01-07  70680.224387  19502.998638  106081.307650  139799.088472   \n",
       "2010-01-08  69343.557792  19568.105088  107256.974316  138892.283574   \n",
       "...                  ...           ...            ...            ...   \n",
       "2016-12-23  95862.068276  24168.960850   68948.328178   46196.671758   \n",
       "2016-12-27  96096.780565  24226.833249   69364.333306   45995.159559   \n",
       "2016-12-28  95734.766695  23908.535052   70304.866639   45491.379060   \n",
       "2016-12-29  94752.157621  23488.960155   70359.128177   45995.159559   \n",
       "2016-12-30  95257.385769  23358.747256   69563.292280   46347.805908   \n",
       "\n",
       "                      UPS  \n",
       "Date                       \n",
       "2010-01-04   50575.708420  \n",
       "2010-01-05   50662.638135  \n",
       "2010-01-06   50288.840359  \n",
       "2010-01-07   49906.349611  \n",
       "2010-01-08   52305.609756  \n",
       "...                   ...  \n",
       "2016-12-23  100812.390949  \n",
       "2016-12-27  100951.478494  \n",
       "2016-12-28  100143.032141  \n",
       "2016-12-29   99951.786767  \n",
       "2016-12-30   99656.225735  \n",
       "\n",
       "[1762 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_cap_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2010-01-04    6.529794e+05\n",
      "2010-01-05    6.562973e+05\n",
      "2010-01-06    6.527435e+05\n",
      "2010-01-07    6.492939e+05\n",
      "2010-01-08    6.537965e+05\n",
      "                  ...     \n",
      "2016-12-23    1.425208e+06\n",
      "2016-12-27    1.435541e+06\n",
      "2016-12-28    1.431656e+06\n",
      "2016-12-29    1.427209e+06\n",
      "2016-12-30    1.413611e+06\n",
      "Length: 1762, dtype: float64\n",
      "Date\n",
      "2010-01-04    100.000000\n",
      "2010-01-05    100.508119\n",
      "2010-01-06     99.963872\n",
      "2010-01-07     99.435588\n",
      "2010-01-08    100.125141\n",
      "                 ...    \n",
      "2016-12-23    218.262332\n",
      "2016-12-27    219.844835\n",
      "2016-12-28    219.249834\n",
      "2016-12-29    218.568789\n",
      "2016-12-30    216.486276\n",
      "Length: 1762, dtype: float64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aggregate and print the market cap per trading day\n",
    "raw_index = market_cap_series.sum(axis=1)\n",
    "print(raw_index)\n",
    "\n",
    "# Normalize the aggregate market cap here\n",
    "index = raw_index.div(raw_index.iloc[0]).mul(100)\n",
    "print(index)\n",
    "\n",
    "# Plot the index here\n",
    "index.plot(title='Market-Cap Weighted Index');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate index performance\n",
    "- Index return:\n",
    "    - Total index return\n",
    "    - Contribution by component\n",
    "- Performance vs Benchmark\n",
    "    - Total period return\n",
    "    - Rolling returns for sub periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the contribution of each stock to the index\n",
    "You have successfully built the value-weighted index. Let's now explore how it performed over the 2010-2016 period.\n",
    "\n",
    "Let's also determine how much each stock has contributed to the index return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116.48627582170698\n",
      "Stock Symbol\n",
      "CPRT    0.006694\n",
      "PAA     0.010921\n",
      "GM      0.024614\n",
      "TEF     0.026837\n",
      "RIO     0.034613\n",
      "ABEV    0.043365\n",
      "GS      0.043660\n",
      "UPS     0.044318\n",
      "AMGN    0.058445\n",
      "BABA    0.135403\n",
      "AMZN    0.207455\n",
      "AAPL    0.363675\n",
      "Name: Market Capitalization, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate and print the index return here\n",
    "index_return = (index.iloc[-1] / index.iloc[0] - 1) * 100\n",
    "print(index_return)\n",
    "\n",
    "# Select the market capitalization\n",
    "market_cap = components['Market Capitalization']\n",
    "\n",
    "# Calculate the total market cap\n",
    "total_market_cap = market_cap.sum()\n",
    "\n",
    "# Calculate the component weights , and print the result\n",
    "weights = market_cap.div(total_market_cap)\n",
    "print(weights.sort_values())\n",
    "\n",
    "# Calculate and plot the distribution by component\n",
    "weights.mul(index_return).sort_values().plot(kind='barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare index performance against benchmark I\n",
    "The next step in analyzing the performance of your index is to compare it against a benchmark.\n",
    "\n",
    "In the video, we used the S&P 500 as benchmark. You can also use the Dow Jones Industrial Average, which contains the 30 largest stocks, and would also be a reasonable benchmark for the largest stocks from all sectors across the three exchanges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "djia = pd.read_csv('./dataset/djia2.csv', parse_dates=['DATE'], index_col='DATE')['DJIA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index    116.486276\n",
      "DJIA      86.722172\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert index series to dataframe here\n",
    "data = index.to_frame('Index')\n",
    "\n",
    "# Normalize djia series and add as new column to data\n",
    "djia = djia.div(djia.iloc[0]).mul(100)\n",
    "data['DJIA'] = djia\n",
    "\n",
    "# Print total return for both index and djia\n",
    "print((data.iloc[-1] / data.iloc[0] - 1) * 100)\n",
    "\n",
    "# Plot both series\n",
    "data.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare index performance against benchmark II\n",
    "The next step in analyzing the performance of your index is to compare it against a benchmark.\n",
    "\n",
    "In the video, we have use the S&P 500 as benchmark. You can also use the Dow Jones Industrial Average, which contains the 30 largest stocks, and would also be a reasonable benchmark for the largest stocks from all sectors across the three exchanges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1762 entries, 2010-01-04 to 2016-12-30\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Index   1762 non-null   float64\n",
      " 1   DJIA    1762 non-null   float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 41.3 KB\n",
      "None\n",
      "                 Index        DJIA\n",
      "Date                              \n",
      "2010-01-04  100.000000  100.000000\n",
      "2010-01-05  100.508119   99.887188\n",
      "2010-01-06   99.963872   99.902872\n",
      "2010-01-07   99.435588  100.216365\n",
      "2010-01-08  100.125141  100.323414\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect data\n",
    "print(data.info())\n",
    "print(data.head(5))\n",
    "\n",
    "# Create multi_period_return function here\n",
    "def multi_period_return(r):\n",
    "    return (np.prod(r + 1) - 1) * 100\n",
    "\n",
    "# Calculate rolling_return_360\n",
    "rolling_return_360 = data.pct_change().rolling('360D').apply(multi_period_return)\n",
    "\n",
    "# Plot rolling_return_360 here\n",
    "rolling_return_360.plot(title='Rolling 360D Return');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index correlation & exporting to Excel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize your index constituent correlations\n",
    "To better understand the characteristics of your index constituents, you can calculate the return correlations.\n",
    "\n",
    "Use the daily stock prices or your index companies, and show a heatmap of the daily return correlations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_prices = pd.read_csv('./dataset/stocks_4.csv', parse_dates=['Date'], index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1762 entries, 2010-01-04 to 2016-12-30\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AAPL    1761 non-null   float64\n",
      " 1   AMGN    1761 non-null   float64\n",
      " 2   AMZN    1761 non-null   float64\n",
      " 3   CPRT    1761 non-null   float64\n",
      " 4   EL      1762 non-null   float64\n",
      " 5   GS      1762 non-null   float64\n",
      " 6   ILMN    1761 non-null   float64\n",
      " 7   MA      1762 non-null   float64\n",
      " 8   PAA     1762 non-null   float64\n",
      " 9   RIO     1762 non-null   float64\n",
      " 10  TEF     1762 non-null   float64\n",
      " 11  UPS     1762 non-null   float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 179.0 KB\n",
      "None\n",
      "          AAPL      AMGN      AMZN      CPRT        EL        GS      ILMN  \\\n",
      "AAPL  1.000000  0.286898  0.327611  0.346616  0.306770  0.344981  0.264791   \n",
      "AMGN  0.286898  1.000000  0.323408  0.355892  0.349893  0.390076  0.336927   \n",
      "AMZN  0.327611  0.323408  1.000000  0.298929  0.334031  0.333402  0.242726   \n",
      "CPRT  0.346616  0.355892  0.298929  1.000000  0.371763  0.423160  0.265665   \n",
      "EL    0.306770  0.349893  0.334031  0.371763  1.000000  0.358318  0.214027   \n",
      "GS    0.344981  0.390076  0.333402  0.423160  0.358318  1.000000  0.266063   \n",
      "ILMN  0.264791  0.336927  0.242726  0.265665  0.214027  0.266063  1.000000   \n",
      "MA    0.391421  0.400230  0.428330  0.401352  0.431556  0.466796  0.301392   \n",
      "PAA   0.212960  0.229255  0.182438  0.221273  0.206056  0.271982  0.162796   \n",
      "RIO   0.361684  0.313878  0.326229  0.384944  0.415416  0.527298  0.234445   \n",
      "TEF   0.325309  0.374555  0.331867  0.376767  0.428925  0.498230  0.231173   \n",
      "UPS   0.366039  0.432468  0.378399  0.462716  0.456952  0.506407  0.267801   \n",
      "\n",
      "            MA       PAA       RIO       TEF       UPS  \n",
      "AAPL  0.391421  0.212960  0.361684  0.325309  0.366039  \n",
      "AMGN  0.400230  0.229255  0.313878  0.374555  0.432468  \n",
      "AMZN  0.428330  0.182438  0.326229  0.331867  0.378399  \n",
      "CPRT  0.401352  0.221273  0.384944  0.376767  0.462716  \n",
      "EL    0.431556  0.206056  0.415416  0.428925  0.456952  \n",
      "GS    0.466796  0.271982  0.527298  0.498230  0.506407  \n",
      "ILMN  0.301392  0.162796  0.234445  0.231173  0.267801  \n",
      "MA    1.000000  0.243761  0.437778  0.448438  0.486512  \n",
      "PAA   0.243761  1.000000  0.337448  0.253598  0.217523  \n",
      "RIO   0.437778  0.337448  1.000000  0.559264  0.509809  \n",
      "TEF   0.448438  0.253598  0.559264  1.000000  0.516242  \n",
      "UPS   0.486512  0.217523  0.509809  0.516242  1.000000  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Daily Return Correlations')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect stock_prices here\n",
    "print(stock_prices.info())\n",
    "\n",
    "# Calculate the dail returns\n",
    "returns = stock_prices.pct_change()\n",
    "\n",
    "# Calculate and print the pairwise correlations\n",
    "correlations = returns.corr()\n",
    "print(correlations)\n",
    "\n",
    "# Plot a heatmap of daily return correlations\n",
    "sns.heatmap(correlations, annot=True);\n",
    "plt.title('Daily Return Correlations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save your analysis to multiple excel worksheets\n",
    "Now that you have completed your analysis, you may want to save all results into a single Excel workbook.\n",
    "\n",
    "Let's practice exporting various ```DataFrame``` to multiple Excel worksheets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = index.to_frame('Index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1762 entries, 2010-01-04 to 2016-12-30\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Index   1762 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 27.5 KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1762 entries, 2010-01-04 to 2016-12-30\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AAPL    1761 non-null   float64\n",
      " 1   AMGN    1761 non-null   float64\n",
      " 2   AMZN    1761 non-null   float64\n",
      " 3   CPRT    1761 non-null   float64\n",
      " 4   EL      1762 non-null   float64\n",
      " 5   GS      1762 non-null   float64\n",
      " 6   ILMN    1761 non-null   float64\n",
      " 7   MA      1762 non-null   float64\n",
      " 8   PAA     1762 non-null   float64\n",
      " 9   RIO     1762 non-null   float64\n",
      " 10  TEF     1762 non-null   float64\n",
      " 11  UPS     1762 non-null   float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 179.0 KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1762 entries, 2010-01-04 to 2016-12-30\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AAPL    1761 non-null   float64\n",
      " 1   AMGN    1761 non-null   float64\n",
      " 2   AMZN    1761 non-null   float64\n",
      " 3   CPRT    1761 non-null   float64\n",
      " 4   EL      1762 non-null   float64\n",
      " 5   GS      1762 non-null   float64\n",
      " 6   ILMN    1761 non-null   float64\n",
      " 7   MA      1762 non-null   float64\n",
      " 8   PAA     1762 non-null   float64\n",
      " 9   RIO     1762 non-null   float64\n",
      " 10  TEF     1762 non-null   float64\n",
      " 11  UPS     1762 non-null   float64\n",
      " 12  Index   1762 non-null   float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 272.7 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Inspect index and stock_prices\n",
    "print(index.info())\n",
    "print(stock_prices.info())\n",
    "\n",
    "# Join index to stock_prices, and inspect the result\n",
    "data = stock_prices.join(index)\n",
    "print(data.info())\n",
    "\n",
    "# Create index & stock price returns\n",
    "returns = data.pct_change()\n",
    "\n",
    "# Export data and data as returns to excel\n",
    "with pd.ExcelWriter('data.xls') as writer:\n",
    "    data.to_excel(writer, sheet_name='data')\n",
    "    returns.to_excel(writer, sheet_name='returns')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
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