{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 3: Black Litterman Mean Risk Optimization\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  25 of 25 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-2.0257%</td>\n",
       "      <td>0.4057%</td>\n",
       "      <td>0.4035%</td>\n",
       "      <td>1.9693%</td>\n",
       "      <td>0.0180%</td>\n",
       "      <td>0.9305%</td>\n",
       "      <td>0.3678%</td>\n",
       "      <td>0.5783%</td>\n",
       "      <td>0.9483%</td>\n",
       "      <td>-1.1953%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5881%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>2.8236%</td>\n",
       "      <td>0.9758%</td>\n",
       "      <td>0.6987%</td>\n",
       "      <td>1.7539%</td>\n",
       "      <td>-0.1730%</td>\n",
       "      <td>0.2409%</td>\n",
       "      <td>1.3735%</td>\n",
       "      <td>-1.0857%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-11.4863%</td>\n",
       "      <td>-1.5879%</td>\n",
       "      <td>0.2412%</td>\n",
       "      <td>-1.7557%</td>\n",
       "      <td>-0.7727%</td>\n",
       "      <td>-1.2473%</td>\n",
       "      <td>-0.1736%</td>\n",
       "      <td>-1.1239%</td>\n",
       "      <td>-3.5867%</td>\n",
       "      <td>-0.9551%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5547%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>0.1592%</td>\n",
       "      <td>-1.5647%</td>\n",
       "      <td>-0.1466%</td>\n",
       "      <td>-1.0155%</td>\n",
       "      <td>-0.7653%</td>\n",
       "      <td>-3.0048%</td>\n",
       "      <td>-0.9034%</td>\n",
       "      <td>-2.9145%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-5.1389%</td>\n",
       "      <td>-4.1922%</td>\n",
       "      <td>-1.6573%</td>\n",
       "      <td>-2.7699%</td>\n",
       "      <td>-1.1047%</td>\n",
       "      <td>-1.9769%</td>\n",
       "      <td>-1.2207%</td>\n",
       "      <td>-0.8855%</td>\n",
       "      <td>-4.6058%</td>\n",
       "      <td>-2.5394%</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2066%</td>\n",
       "      <td>-3.0309%</td>\n",
       "      <td>-1.0411%</td>\n",
       "      <td>-3.1557%</td>\n",
       "      <td>-1.6148%</td>\n",
       "      <td>-0.2700%</td>\n",
       "      <td>-2.2844%</td>\n",
       "      <td>-2.0570%</td>\n",
       "      <td>-0.5492%</td>\n",
       "      <td>-3.0020%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.2736%</td>\n",
       "      <td>-2.2705%</td>\n",
       "      <td>-1.6037%</td>\n",
       "      <td>-2.5425%</td>\n",
       "      <td>0.1099%</td>\n",
       "      <td>-0.2241%</td>\n",
       "      <td>0.5707%</td>\n",
       "      <td>-1.6402%</td>\n",
       "      <td>-1.7642%</td>\n",
       "      <td>-0.1649%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1539%</td>\n",
       "      <td>-1.1366%</td>\n",
       "      <td>-0.7308%</td>\n",
       "      <td>-0.1448%</td>\n",
       "      <td>0.0895%</td>\n",
       "      <td>-3.3839%</td>\n",
       "      <td>-0.1117%</td>\n",
       "      <td>-1.1387%</td>\n",
       "      <td>-0.9719%</td>\n",
       "      <td>-1.1254%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-4.3384%</td>\n",
       "      <td>0.1693%</td>\n",
       "      <td>-1.6851%</td>\n",
       "      <td>-1.0215%</td>\n",
       "      <td>0.0915%</td>\n",
       "      <td>-1.1791%</td>\n",
       "      <td>0.5674%</td>\n",
       "      <td>0.5287%</td>\n",
       "      <td>0.6616%</td>\n",
       "      <td>0.0330%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6436%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9869%</td>\n",
       "      <td>-0.1450%</td>\n",
       "      <td>1.2224%</td>\n",
       "      <td>1.4570%</td>\n",
       "      <td>0.5366%</td>\n",
       "      <td>-0.4607%</td>\n",
       "      <td>0.5800%</td>\n",
       "      <td>-1.9919%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 APA       BA      BAX      BMY    CMCSA      CNP      CPB  \\\n",
       "Date                                                                         \n",
       "2016-01-05  -2.0257%  0.4057%  0.4035%  1.9693%  0.0180%  0.9305%  0.3678%   \n",
       "2016-01-06 -11.4863% -1.5879%  0.2412% -1.7557% -0.7727% -1.2473% -0.1736%   \n",
       "2016-01-07  -5.1389% -4.1922% -1.6573% -2.7699% -1.1047% -1.9769% -1.2207%   \n",
       "2016-01-08   0.2736% -2.2705% -1.6037% -2.5425%  0.1099% -0.2241%  0.5707%   \n",
       "2016-01-11  -4.3384%  0.1693% -1.6851% -1.0215%  0.0915% -1.1791%  0.5674%   \n",
       "\n",
       "                 DE      HPQ      JCI  ...       NI     PCAR      PSA  \\\n",
       "Date                                   ...                              \n",
       "2016-01-05  0.5783%  0.9483% -1.1953%  ...  1.5881%  0.0212%  2.8236%   \n",
       "2016-01-06 -1.1239% -3.5867% -0.9551%  ...  0.5547%  0.0212%  0.1592%   \n",
       "2016-01-07 -0.8855% -4.6058% -2.5394%  ... -2.2066% -3.0309% -1.0411%   \n",
       "2016-01-08 -1.6402% -1.7642% -0.1649%  ... -0.1539% -1.1366% -0.7308%   \n",
       "2016-01-11  0.5287%  0.6616%  0.0330%  ...  1.6436%  0.0000%  0.9869%   \n",
       "\n",
       "                SEE        T      TGT      TMO      TXT       VZ     ZION  \n",
       "Date                                                                       \n",
       "2016-01-05  0.9758%  0.6987%  1.7539% -0.1730%  0.2409%  1.3735% -1.0857%  \n",
       "2016-01-06 -1.5647% -0.1466% -1.0155% -0.7653% -3.0048% -0.9034% -2.9145%  \n",
       "2016-01-07 -3.1557% -1.6148% -0.2700% -2.2844% -2.0570% -0.5492% -3.0020%  \n",
       "2016-01-08 -0.1448%  0.0895% -3.3839% -0.1117% -1.1387% -0.9719% -1.1254%  \n",
       "2016-01-11 -0.1450%  1.2224%  1.4570%  0.5366% -0.4607%  0.5800% -1.9919%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Black Litterman Portfolios\n",
    "\n",
    "### 2.1 Calculating a reference portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1590%</td>\n",
       "      <td>11.5018%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.4807%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8194%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.8263%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.1804%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.2741%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.0000% 6.1590% 11.5018% 0.0000% 0.0000% 8.4807% 0.0000% 3.8194%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.8263% 0.0000% 0.0000% 0.0000% 0.0000% 7.1804%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.0000% 0.0000% 4.2741% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "# Building the portfolio object\n",
    "port = rp.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimal portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "port.alpha = 0.05\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = rp.plot_pie(w=w, title='Sharpe Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                 height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Disabled</th>\n",
       "      <th>Type</th>\n",
       "      <th>Set</th>\n",
       "      <th>Position</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type Relative</th>\n",
       "      <th>Relative Set</th>\n",
       "      <th>Relative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Energy</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>8.0000%</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Financials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>10.0000%</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Materials</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>9.0000%</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disabled     Type       Set          Position Sign   Weight Type Relative  \\\n",
       "0     False  Classes  Industry            Energy   >=  8.0000%       Classes   \n",
       "1     False  Classes  Industry  Consumer Staples   >= 10.0000%       Classes   \n",
       "2     False  Classes  Industry         Materials   >=  9.0000%       Classes   \n",
       "\n",
       "  Relative Set     Relative  \n",
       "0     Industry   Financials  \n",
       "1     Industry    Utilities  \n",
       "2     Industry  Industrials  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asset_classes = {'Assets': ['JCI','TGT','CMCSA','CPB','MO','APA','MMC','JPM',\n",
    "                            'ZION','PSA','BAX','BMY','LUV','PCAR','TXT','TMO',\n",
    "                            'DE','MSFT','HPQ','SEE','VZ','CNP','NI','T','BA'], \n",
    "                 'Industry': ['Consumer Discretionary','Consumer Discretionary',\n",
    "                              'Consumer Discretionary', 'Consumer Staples',\n",
    "                              'Consumer Staples','Energy','Financials',\n",
    "                              'Financials','Financials','Financials',\n",
    "                              'Health Care','Health Care','Industrials','Industrials',\n",
    "                              'Industrials','Health care','Industrials',\n",
    "                              'Information Technology','Information Technology',\n",
    "                              'Materials','Telecommunications Services','Utilities',\n",
    "                              'Utilities','Telecommunications Services','Financials']}\n",
    "\n",
    "asset_classes = pd.DataFrame(asset_classes)\n",
    "asset_classes = asset_classes.sort_values(by=['Assets'])\n",
    "\n",
    "views = {'Disabled': [False, False, False],\n",
    "         'Type': ['Classes', 'Classes', 'Classes'],\n",
    "         'Set': ['Industry', 'Industry', 'Industry'],\n",
    "         'Position': ['Energy', 'Consumer Staples', 'Materials'],\n",
    "         'Sign': ['>=', '>=', '>='],\n",
    "         'Weight': [0.08, 0.1, 0.09], # Annual terms \n",
    "         'Type Relative': ['Classes', 'Classes', 'Classes'],\n",
    "         'Relative Set': ['Industry', 'Industry', 'Industry'],\n",
    "         'Relative': ['Financials', 'Utilities', 'Industrials']}\n",
    "\n",
    "views = pd.DataFrame(views)\n",
    "\n",
    "display(views)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2\n",
       "0  100.0000%   0.0000%   0.0000%\n",
       "1  -20.0000%   0.0000%   0.0000%\n",
       "2    0.0000%   0.0000%   0.0000%\n",
       "3    0.0000%   0.0000%   0.0000%\n",
       "4    0.0000%   0.0000%   0.0000%\n",
       "5    0.0000% -50.0000%   0.0000%\n",
       "6    0.0000%  50.0000%   0.0000%\n",
       "7    0.0000%   0.0000% -25.0000%\n",
       "8    0.0000%   0.0000%   0.0000%\n",
       "9    0.0000%   0.0000%   0.0000%\n",
       "10 -20.0000%   0.0000%   0.0000%\n",
       "11   0.0000%   0.0000% -25.0000%\n",
       "12 -20.0000%   0.0000%   0.0000%\n",
       "13   0.0000%  50.0000%   0.0000%\n",
       "14   0.0000%   0.0000%   0.0000%\n",
       "15   0.0000% -50.0000%   0.0000%\n",
       "16   0.0000%   0.0000% -25.0000%\n",
       "17 -20.0000%   0.0000%   0.0000%\n",
       "18   0.0000%   0.0000% 100.0000%\n",
       "19   0.0000%   0.0000%   0.0000%\n",
       "20   0.0000%   0.0000%   0.0000%\n",
       "21   0.0000%   0.0000%   0.0000%\n",
       "22   0.0000%   0.0000% -25.0000%\n",
       "23   0.0000%   0.0000%   0.0000%\n",
       "24 -20.0000%   0.0000%   0.0000%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0  8.0000%\n",
       "1 10.0000%\n",
       "2  9.0000%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P, Q = rp.assets_views(views, asset_classes)\n",
    "\n",
    "display(pd.DataFrame(P.T))\n",
    "display(pd.DataFrame(Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.5339%</td>\n",
       "      <td>5.0289%</td>\n",
       "      <td>11.1757%</td>\n",
       "      <td>0.0030%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.3385%</td>\n",
       "      <td>5.8492%</td>\n",
       "      <td>0.6323%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8567%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.1659%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.8847%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.5135%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.5339% 5.0289% 11.1757% 0.0030% 0.0000% 2.3385% 5.8492% 0.6323%   \n",
       "\n",
       "            HPQ     JCI  ...      NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 4.8567% 0.0000% 0.0000% 8.1659% 0.0000% 6.8847%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.0000% 0.0000% 4.5135% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate Black Litterman inputs:\n",
    "\n",
    "port.blacklitterman_stats(P, Q/252, rf=rf, w=w, delta=None, eq=True)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='BL'# Black Litterman\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = False # Use historical scenarios for risk measures that depend on scenarios\n",
    "\n",
    "w_bl = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w_bl.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = rp.plot_pie(w=w_bl, title='Sharpe Black Litterman', others=0.05, nrow=25,\n",
    "                 cmap = \"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.2635%</td>\n",
       "      <td>4.3886%</td>\n",
       "      <td>2.1705%</td>\n",
       "      <td>6.9871%</td>\n",
       "      <td>3.2390%</td>\n",
       "      <td>0.0788%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.8376%</td>\n",
       "      <td>...</td>\n",
       "      <td>11.4509%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>14.9183%</td>\n",
       "      <td>0.1627%</td>\n",
       "      <td>6.4196%</td>\n",
       "      <td>4.0904%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.3446%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.8898%</td>\n",
       "      <td>7.9755%</td>\n",
       "      <td>2.9166%</td>\n",
       "      <td>1.6685%</td>\n",
       "      <td>5.5405%</td>\n",
       "      <td>4.1246%</td>\n",
       "      <td>0.5333%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.7285%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.3217%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.7679%</td>\n",
       "      <td>3.0750%</td>\n",
       "      <td>4.0054%</td>\n",
       "      <td>5.1909%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.0264%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.6335%</td>\n",
       "      <td>8.7146%</td>\n",
       "      <td>2.2991%</td>\n",
       "      <td>1.2959%</td>\n",
       "      <td>4.9345%</td>\n",
       "      <td>4.5208%</td>\n",
       "      <td>0.6107%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.2609%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4027%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.6736%</td>\n",
       "      <td>4.2328%</td>\n",
       "      <td>3.1397%</td>\n",
       "      <td>5.5898%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.4978%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0887%</td>\n",
       "      <td>3.1881%</td>\n",
       "      <td>9.2750%</td>\n",
       "      <td>1.8154%</td>\n",
       "      <td>0.9588%</td>\n",
       "      <td>4.4141%</td>\n",
       "      <td>4.8317%</td>\n",
       "      <td>0.6478%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.8553%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6759%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.9907%</td>\n",
       "      <td>5.1235%</td>\n",
       "      <td>2.4056%</td>\n",
       "      <td>5.8917%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1126%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2027%</td>\n",
       "      <td>3.6395%</td>\n",
       "      <td>9.7407%</td>\n",
       "      <td>1.4013%</td>\n",
       "      <td>0.6314%</td>\n",
       "      <td>3.9448%</td>\n",
       "      <td>5.0942%</td>\n",
       "      <td>0.6646%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4768%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0409%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.5084%</td>\n",
       "      <td>5.8672%</td>\n",
       "      <td>1.7321%</td>\n",
       "      <td>6.1419%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.8075%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      APA      BA     BAX     BMY   CMCSA     CNP     CPB      DE     HPQ  \\\n",
       "0 0.0000% 0.0000% 5.2635% 4.3886% 2.1705% 6.9871% 3.2390% 0.0788% 0.0000%   \n",
       "1 0.0000% 1.8898% 7.9755% 2.9166% 1.6685% 5.5405% 4.1246% 0.5333% 0.0000%   \n",
       "2 0.0000% 2.6335% 8.7146% 2.2991% 1.2959% 4.9345% 4.5208% 0.6107% 0.0000%   \n",
       "3 0.0887% 3.1881% 9.2750% 1.8154% 0.9588% 4.4141% 4.8317% 0.6478% 0.0000%   \n",
       "4 0.2027% 3.6395% 9.7407% 1.4013% 0.6314% 3.9448% 5.0942% 0.6646% 0.0000%   \n",
       "\n",
       "      JCI  ...       NI    PCAR      PSA     SEE       T     TGT     TMO  \\\n",
       "0 2.8376%  ... 11.4509% 0.0000% 14.9183% 0.1627% 6.4196% 4.0904% 0.0000%   \n",
       "1 1.7285%  ...  9.3217% 0.0000%  9.7679% 3.0750% 4.0054% 5.1909% 0.0000%   \n",
       "2 1.2609%  ...  8.4027% 0.0000%  7.6736% 4.2328% 3.1397% 5.5898% 0.0000%   \n",
       "3 0.8553%  ...  7.6759% 0.0000%  5.9907% 5.1235% 2.4056% 5.8917% 0.0000%   \n",
       "4 0.4768%  ...  7.0409% 0.0000%  4.5084% 5.8672% 1.7321% 6.1419% 0.0000%   \n",
       "\n",
       "      TXT      VZ    ZION  \n",
       "0 0.0000% 8.3446% 0.0000%  \n",
       "1 0.0000% 7.0264% 0.0000%  \n",
       "2 0.0000% 6.4978% 0.0000%  \n",
       "3 0.0000% 6.1126% 0.0000%  \n",
       "4 0.0000% 5.8075% 0.0000%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "mu = port.mu_bl # Expected returns of Black Litterman model\n",
    "cov = port.cov_bl # Covariance matrix of Black Litterman model\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "ax = rp.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                      rf=rf, alpha=0.05, cmap='viridis', w=w_bl, label=label,\n",
    "                      marker='*', s=16, c='r', height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = rp.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Black Litterman Mean Risk Portfolios\n",
    "\n",
    "When we use risk measures different than Standard Deviation, Riskfolio-Lib only considers the vector of expected returns, and use historical returns to calculate risk measures.\n",
    "\n",
    "### 3.4 Calculate Black Litterman Portfolios for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'WR', 'MDD', 'ADD', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "port.alpha = 0.05\n",
    "\n",
    "for i in rms:\n",
    "    if i == 'MV':\n",
    "        hist = False\n",
    "    else:\n",
    "        hist = True\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_835cc_row0_col0 {\n",
       "  background-color: #fdfedd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row0_col1, #T_835cc_row0_col2, #T_835cc_row0_col3, #T_835cc_row0_col4, #T_835cc_row0_col5, #T_835cc_row0_col6, #T_835cc_row0_col7, #T_835cc_row0_col8, #T_835cc_row0_col9, #T_835cc_row0_col10, #T_835cc_row0_col11, #T_835cc_row0_col12, #T_835cc_row1_col5, #T_835cc_row1_col6, #T_835cc_row1_col7, #T_835cc_row1_col8, #T_835cc_row1_col12, #T_835cc_row2_col7, #T_835cc_row2_col8, #T_835cc_row2_col10, #T_835cc_row2_col12, #T_835cc_row3_col0, #T_835cc_row3_col2, #T_835cc_row3_col4, #T_835cc_row3_col5, #T_835cc_row3_col8, #T_835cc_row3_col9, #T_835cc_row3_col10, #T_835cc_row3_col11, #T_835cc_row4_col0, #T_835cc_row4_col1, #T_835cc_row4_col2, #T_835cc_row4_col3, #T_835cc_row4_col4, #T_835cc_row4_col5, #T_835cc_row4_col6, #T_835cc_row4_col7, #T_835cc_row4_col8, #T_835cc_row4_col9, #T_835cc_row4_col10, #T_835cc_row4_col11, #T_835cc_row4_col12, #T_835cc_row5_col7, #T_835cc_row6_col8, #T_835cc_row6_col10, #T_835cc_row6_col11, #T_835cc_row6_col12, #T_835cc_row7_col2, #T_835cc_row7_col4, #T_835cc_row7_col5, #T_835cc_row7_col6, #T_835cc_row7_col7, #T_835cc_row7_col9, #T_835cc_row7_col10, #T_835cc_row7_col11, #T_835cc_row7_col12, #T_835cc_row8_col0, #T_835cc_row8_col1, #T_835cc_row8_col2, #T_835cc_row8_col3, #T_835cc_row8_col4, #T_835cc_row8_col5, #T_835cc_row8_col6, #T_835cc_row8_col7, #T_835cc_row8_col8, #T_835cc_row8_col9, #T_835cc_row8_col10, #T_835cc_row8_col11, #T_835cc_row8_col12, #T_835cc_row9_col0, #T_835cc_row9_col1, #T_835cc_row9_col2, #T_835cc_row9_col3, #T_835cc_row9_col4, #T_835cc_row9_col5, #T_835cc_row9_col6, #T_835cc_row9_col7, #T_835cc_row9_col8, #T_835cc_row9_col10, #T_835cc_row9_col11, #T_835cc_row9_col12, #T_835cc_row10_col5, #T_835cc_row10_col6, #T_835cc_row10_col7, #T_835cc_row10_col8, #T_835cc_row10_col9, #T_835cc_row10_col10, #T_835cc_row10_col11, #T_835cc_row10_col12, #T_835cc_row11_col0, #T_835cc_row11_col1, #T_835cc_row11_col2, #T_835cc_row11_col3, #T_835cc_row11_col4, #T_835cc_row11_col5, #T_835cc_row11_col6, #T_835cc_row11_col7, #T_835cc_row11_col8, #T_835cc_row11_col9, #T_835cc_row11_col10, #T_835cc_row11_col11, #T_835cc_row11_col12, #T_835cc_row12_col8, #T_835cc_row12_col12, #T_835cc_row13_col6, #T_835cc_row13_col7, #T_835cc_row13_col8, #T_835cc_row13_col9, #T_835cc_row13_col10, #T_835cc_row13_col11, #T_835cc_row13_col12, #T_835cc_row15_col6, #T_835cc_row15_col7, #T_835cc_row15_col8, #T_835cc_row15_col12, #T_835cc_row16_col0, #T_835cc_row16_col1, #T_835cc_row16_col2, #T_835cc_row16_col3, #T_835cc_row16_col4, #T_835cc_row16_col5, #T_835cc_row16_col6, #T_835cc_row16_col7, #T_835cc_row16_col8, #T_835cc_row16_col9, #T_835cc_row16_col10, #T_835cc_row16_col11, #T_835cc_row16_col12, #T_835cc_row17_col0, #T_835cc_row17_col1, #T_835cc_row17_col3, #T_835cc_row17_col6, #T_835cc_row17_col7, #T_835cc_row17_col8, #T_835cc_row17_col9, #T_835cc_row17_col10, #T_835cc_row18_col7, #T_835cc_row18_col9, #T_835cc_row18_col10, #T_835cc_row18_col11, #T_835cc_row19_col0, #T_835cc_row19_col2, #T_835cc_row19_col4, #T_835cc_row19_col5, #T_835cc_row19_col6, #T_835cc_row19_col7, #T_835cc_row19_col8, #T_835cc_row19_col9, #T_835cc_row19_col10, #T_835cc_row19_col12, #T_835cc_row20_col8, #T_835cc_row20_col10, #T_835cc_row20_col12, #T_835cc_row21_col0, #T_835cc_row21_col1, #T_835cc_row21_col2, #T_835cc_row21_col3, #T_835cc_row21_col4, #T_835cc_row21_col5, #T_835cc_row21_col6, #T_835cc_row21_col7, #T_835cc_row21_col8, #T_835cc_row21_col10, #T_835cc_row21_col11, #T_835cc_row21_col12, #T_835cc_row22_col0, #T_835cc_row22_col1, #T_835cc_row22_col2, #T_835cc_row22_col3, #T_835cc_row22_col4, #T_835cc_row22_col5, #T_835cc_row22_col6, #T_835cc_row22_col7, #T_835cc_row22_col8, #T_835cc_row22_col9, #T_835cc_row22_col10, #T_835cc_row22_col11, #T_835cc_row22_col12, #T_835cc_row23_col6, #T_835cc_row23_col7, #T_835cc_row23_col8, #T_835cc_row24_col0, #T_835cc_row24_col2, #T_835cc_row24_col4, #T_835cc_row24_col5, #T_835cc_row24_col6, #T_835cc_row24_col7, #T_835cc_row24_col8, #T_835cc_row24_col10, #T_835cc_row24_col11, #T_835cc_row24_col12 {\n",
       "  background-color: #ffffe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col0 {\n",
       "  background-color: #dbf1a4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col1 {\n",
       "  background-color: #cbea9c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col2, #T_835cc_row23_col5 {\n",
       "  background-color: #f4fbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col3, #T_835cc_row23_col12 {\n",
       "  background-color: #e8f6ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col4, #T_835cc_row23_col4 {\n",
       "  background-color: #f7fcb9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col9, #T_835cc_row12_col10, #T_835cc_row18_col4 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col10, #T_835cc_row6_col9 {\n",
       "  background-color: #fbfed0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row1_col11, #T_835cc_row5_col1 {\n",
       "  background-color: #eef9b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col0 {\n",
       "  background-color: #64bc6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row2_col1 {\n",
       "  background-color: #a2d88a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col2, #T_835cc_row2_col4 {\n",
       "  background-color: #7fc97c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col3 {\n",
       "  background-color: #a1d889;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col5 {\n",
       "  background-color: #7ac77a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col6 {\n",
       "  background-color: #b9e294;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col9, #T_835cc_row5_col5 {\n",
       "  background-color: #f9fdc4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row2_col11 {\n",
       "  background-color: #f8fcbe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row3_col1 {\n",
       "  background-color: #f8fdc1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row3_col3 {\n",
       "  background-color: #f6fcb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row3_col6, #T_835cc_row18_col5 {\n",
       "  background-color: #e4f4ab;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row3_col7 {\n",
       "  background-color: #d9f0a3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row3_col12 {\n",
       "  background-color: #fdfeda;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col0, #T_835cc_row6_col1, #T_835cc_row7_col8 {\n",
       "  background-color: #f8fcbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col2, #T_835cc_row18_col12 {\n",
       "  background-color: #f9fdc7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col3 {\n",
       "  background-color: #edf8b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col4 {\n",
       "  background-color: #fafdcc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col6 {\n",
       "  background-color: #e3f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col8, #T_835cc_row12_col0, #T_835cc_row12_col5, #T_835cc_row12_col7, #T_835cc_row14_col1, #T_835cc_row14_col2, #T_835cc_row14_col3, #T_835cc_row14_col4, #T_835cc_row14_col6, #T_835cc_row14_col9, #T_835cc_row14_col10, #T_835cc_row14_col11, #T_835cc_row14_col12 {\n",
       "  background-color: #004529;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row5_col9 {\n",
       "  background-color: #c9e99c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col10, #T_835cc_row12_col3 {\n",
       "  background-color: #2d914b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row5_col11, #T_835cc_row12_col11 {\n",
       "  background-color: #a9db8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row5_col12 {\n",
       "  background-color: #58b669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row6_col0 {\n",
       "  background-color: #ccea9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row6_col2, #T_835cc_row13_col0 {\n",
       "  background-color: #c7e89a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row6_col3 {\n",
       "  background-color: #eaf7af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row6_col4 {\n",
       "  background-color: #bee596;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row6_col5 {\n",
       "  background-color: #92d183;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row6_col6 {\n",
       "  background-color: #208242;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row6_col7 {\n",
       "  background-color: #90d083;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row7_col0, #T_835cc_row9_col9 {\n",
       "  background-color: #fdfedb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row7_col1, #T_835cc_row15_col10, #T_835cc_row18_col8 {\n",
       "  background-color: #fafdcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row7_col3 {\n",
       "  background-color: #feffe2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row10_col0 {\n",
       "  background-color: #c3e698;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row10_col1 {\n",
       "  background-color: #5fba6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row10_col2 {\n",
       "  background-color: #b5e092;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row10_col3 {\n",
       "  background-color: #62bb6e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row10_col4 {\n",
       "  background-color: #bae394;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row12_col1 {\n",
       "  background-color: #31974f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row12_col2 {\n",
       "  background-color: #005a31;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row12_col4 {\n",
       "  background-color: #005931;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row12_col6 {\n",
       "  background-color: #11753d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row12_col9 {\n",
       "  background-color: #9dd688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row13_col1 {\n",
       "  background-color: #e1f3a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row13_col2, #T_835cc_row13_col4, #T_835cc_row23_col10 {\n",
       "  background-color: #ebf7b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row13_col3 {\n",
       "  background-color: #eff9b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row13_col5 {\n",
       "  background-color: #f7fcba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row14_col0 {\n",
       "  background-color: #0e743c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row14_col5 {\n",
       "  background-color: #0a703a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row14_col7 {\n",
       "  background-color: #61bb6d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row14_col8 {\n",
       "  background-color: #298c48;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row15_col0 {\n",
       "  background-color: #ddf1a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col1 {\n",
       "  background-color: #aedd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col2 {\n",
       "  background-color: #d2eda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col3 {\n",
       "  background-color: #cfec9e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col4, #T_835cc_row23_col3 {\n",
       "  background-color: #d0ec9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col5, #T_835cc_row24_col3 {\n",
       "  background-color: #fcfed3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col9 {\n",
       "  background-color: #f9fdc2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row15_col11 {\n",
       "  background-color: #fafdc9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row17_col2 {\n",
       "  background-color: #fcfed4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row17_col4, #T_835cc_row19_col3 {\n",
       "  background-color: #fbfed2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row17_col5 {\n",
       "  background-color: #b8e293;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row17_col11, #T_835cc_row19_col11, #T_835cc_row21_col9 {\n",
       "  background-color: #ffffe4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row17_col12 {\n",
       "  background-color: #feffdf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row18_col0, #T_835cc_row20_col2 {\n",
       "  background-color: #a4d98a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row18_col1, #T_835cc_row18_col3 {\n",
       "  background-color: #f3fab6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row18_col2 {\n",
       "  background-color: #e7f6ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row18_col6 {\n",
       "  background-color: #b2df90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row19_col1 {\n",
       "  background-color: #f7fcbc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row20_col0 {\n",
       "  background-color: #bce395;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row20_col1 {\n",
       "  background-color: #ddf2a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row20_col3 {\n",
       "  background-color: #d6efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row20_col4, #T_835cc_row20_col5 {\n",
       "  background-color: #9cd687;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row20_col6 {\n",
       "  background-color: #15793e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row20_col7 {\n",
       "  background-color: #5db96b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_835cc_row20_col9 {\n",
       "  background-color: #feffde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row20_col11 {\n",
       "  background-color: #feffe1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row23_col0, #T_835cc_row23_col11 {\n",
       "  background-color: #e0f3a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row23_col1 {\n",
       "  background-color: #def2a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row23_col2 {\n",
       "  background-color: #f1fab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row23_col9 {\n",
       "  background-color: #dff3a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row24_col1 {\n",
       "  background-color: #fbfdce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_835cc_row24_col9 {\n",
       "  background-color: #fdfed9;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_835cc_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >MV</th>\n",
       "      <th class=\"col_heading level0 col1\" >MAD</th>\n",
       "      <th class=\"col_heading level0 col2\" >MSV</th>\n",
       "      <th class=\"col_heading level0 col3\" >FLPM</th>\n",
       "      <th class=\"col_heading level0 col4\" >SLPM</th>\n",
       "      <th class=\"col_heading level0 col5\" >CVaR</th>\n",
       "      <th class=\"col_heading level0 col6\" >EVaR</th>\n",
       "      <th class=\"col_heading level0 col7\" >WR</th>\n",
       "      <th class=\"col_heading level0 col8\" >MDD</th>\n",
       "      <th class=\"col_heading level0 col9\" >ADD</th>\n",
       "      <th class=\"col_heading level0 col10\" >CDaR</th>\n",
       "      <th class=\"col_heading level0 col11\" >UCI</th>\n",
       "      <th class=\"col_heading level0 col12\" >EDaR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_835cc_row0_col0\" class=\"data row0 col0\" >0.53%</td>\n",
       "      <td id=\"T_835cc_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_835cc_row1_col0\" class=\"data row1 col0\" >5.03%</td>\n",
       "      <td id=\"T_835cc_row1_col1\" class=\"data row1 col1\" >6.11%</td>\n",
       "      <td id=\"T_835cc_row1_col2\" class=\"data row1 col2\" >3.10%</td>\n",
       "      <td id=\"T_835cc_row1_col3\" class=\"data row1 col3\" >4.17%</td>\n",
       "      <td id=\"T_835cc_row1_col4\" class=\"data row1 col4\" >2.81%</td>\n",
       "      <td id=\"T_835cc_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row1_col9\" class=\"data row1 col9\" >7.94%</td>\n",
       "      <td id=\"T_835cc_row1_col10\" class=\"data row1 col10\" >2.64%</td>\n",
       "      <td id=\"T_835cc_row1_col11\" class=\"data row1 col11\" >6.80%</td>\n",
       "      <td id=\"T_835cc_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_835cc_row2_col0\" class=\"data row2 col0\" >11.18%</td>\n",
       "      <td id=\"T_835cc_row2_col1\" class=\"data row2 col1\" >8.35%</td>\n",
       "      <td id=\"T_835cc_row2_col2\" class=\"data row2 col2\" >10.66%</td>\n",
       "      <td id=\"T_835cc_row2_col3\" class=\"data row2 col3\" >8.86%</td>\n",
       "      <td id=\"T_835cc_row2_col4\" class=\"data row2 col4\" >10.66%</td>\n",
       "      <td id=\"T_835cc_row2_col5\" class=\"data row2 col5\" >11.94%</td>\n",
       "      <td id=\"T_835cc_row2_col6\" class=\"data row2 col6\" >7.64%</td>\n",
       "      <td id=\"T_835cc_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row2_col9\" class=\"data row2 col9\" >3.89%</td>\n",
       "      <td id=\"T_835cc_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row2_col11\" class=\"data row2 col11\" >4.66%</td>\n",
       "      <td id=\"T_835cc_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_835cc_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col1\" class=\"data row3 col1\" >2.18%</td>\n",
       "      <td id=\"T_835cc_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col3\" class=\"data row3 col3\" >2.87%</td>\n",
       "      <td id=\"T_835cc_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col6\" class=\"data row3 col6\" >4.59%</td>\n",
       "      <td id=\"T_835cc_row3_col7\" class=\"data row3 col7\" >8.94%</td>\n",
       "      <td id=\"T_835cc_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row3_col12\" class=\"data row3 col12\" >1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_835cc_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col3\" class=\"data row4 col3\" >0.06%</td>\n",
       "      <td id=\"T_835cc_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_835cc_row5_col0\" class=\"data row5 col0\" >2.34%</td>\n",
       "      <td id=\"T_835cc_row5_col1\" class=\"data row5 col1\" >3.36%</td>\n",
       "      <td id=\"T_835cc_row5_col2\" class=\"data row5 col2\" >1.91%</td>\n",
       "      <td id=\"T_835cc_row5_col3\" class=\"data row5 col3\" >3.72%</td>\n",
       "      <td id=\"T_835cc_row5_col4\" class=\"data row5 col4\" >1.58%</td>\n",
       "      <td id=\"T_835cc_row5_col5\" class=\"data row5 col5\" >2.27%</td>\n",
       "      <td id=\"T_835cc_row5_col6\" class=\"data row5 col6\" >4.61%</td>\n",
       "      <td id=\"T_835cc_row5_col7\" class=\"data row5 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row5_col8\" class=\"data row5 col8\" >52.20%</td>\n",
       "      <td id=\"T_835cc_row5_col9\" class=\"data row5 col9\" >11.84%</td>\n",
       "      <td id=\"T_835cc_row5_col10\" class=\"data row5 col10\" >31.88%</td>\n",
       "      <td id=\"T_835cc_row5_col11\" class=\"data row5 col11\" >16.25%</td>\n",
       "      <td id=\"T_835cc_row5_col12\" class=\"data row5 col12\" >30.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_835cc_row6_col0\" class=\"data row6 col0\" >5.85%</td>\n",
       "      <td id=\"T_835cc_row6_col1\" class=\"data row6 col1\" >2.42%</td>\n",
       "      <td id=\"T_835cc_row6_col2\" class=\"data row6 col2\" >6.71%</td>\n",
       "      <td id=\"T_835cc_row6_col3\" class=\"data row6 col3\" >3.94%</td>\n",
       "      <td id=\"T_835cc_row6_col4\" class=\"data row6 col4\" >7.18%</td>\n",
       "      <td id=\"T_835cc_row6_col5\" class=\"data row6 col5\" >10.58%</td>\n",
       "      <td id=\"T_835cc_row6_col6\" class=\"data row6 col6\" >16.92%</td>\n",
       "      <td id=\"T_835cc_row6_col7\" class=\"data row6 col7\" >15.82%</td>\n",
       "      <td id=\"T_835cc_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row6_col9\" class=\"data row6 col9\" >2.40%</td>\n",
       "      <td id=\"T_835cc_row6_col10\" class=\"data row6 col10\" >0.17%</td>\n",
       "      <td id=\"T_835cc_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_835cc_row7_col0\" class=\"data row7 col0\" >0.63%</td>\n",
       "      <td id=\"T_835cc_row7_col1\" class=\"data row7 col1\" >1.61%</td>\n",
       "      <td id=\"T_835cc_row7_col2\" class=\"data row7 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row7_col3\" class=\"data row7 col3\" >0.19%</td>\n",
       "      <td id=\"T_835cc_row7_col4\" class=\"data row7 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row7_col5\" class=\"data row7 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row7_col8\" class=\"data row7 col8\" >5.92%</td>\n",
       "      <td id=\"T_835cc_row7_col9\" class=\"data row7 col9\" >0.06%</td>\n",
       "      <td id=\"T_835cc_row7_col10\" class=\"data row7 col10\" >0.05%</td>\n",
       "      <td id=\"T_835cc_row7_col11\" class=\"data row7 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_835cc_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col1\" class=\"data row8 col1\" >0.07%</td>\n",
       "      <td id=\"T_835cc_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_835cc_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col9\" class=\"data row9 col9\" >1.18%</td>\n",
       "      <td id=\"T_835cc_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_835cc_row10_col0\" class=\"data row10 col0\" >6.41%</td>\n",
       "      <td id=\"T_835cc_row10_col1\" class=\"data row10 col1\" >11.68%</td>\n",
       "      <td id=\"T_835cc_row10_col2\" class=\"data row10 col2\" >7.79%</td>\n",
       "      <td id=\"T_835cc_row10_col3\" class=\"data row10 col3\" >12.03%</td>\n",
       "      <td id=\"T_835cc_row10_col4\" class=\"data row10 col4\" >7.43%</td>\n",
       "      <td id=\"T_835cc_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_835cc_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_835cc_row12_col0\" class=\"data row12 col0\" >20.51%</td>\n",
       "      <td id=\"T_835cc_row12_col1\" class=\"data row12 col1\" >14.44%</td>\n",
       "      <td id=\"T_835cc_row12_col2\" class=\"data row12 col2\" >20.37%</td>\n",
       "      <td id=\"T_835cc_row12_col3\" class=\"data row12 col3\" >15.55%</td>\n",
       "      <td id=\"T_835cc_row12_col4\" class=\"data row12 col4\" >20.52%</td>\n",
       "      <td id=\"T_835cc_row12_col5\" class=\"data row12 col5\" >24.15%</td>\n",
       "      <td id=\"T_835cc_row12_col6\" class=\"data row12 col6\" >18.16%</td>\n",
       "      <td id=\"T_835cc_row12_col7\" class=\"data row12 col7\" >35.57%</td>\n",
       "      <td id=\"T_835cc_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row12_col9\" class=\"data row12 col9\" >16.64%</td>\n",
       "      <td id=\"T_835cc_row12_col10\" class=\"data row12 col10\" >8.81%</td>\n",
       "      <td id=\"T_835cc_row12_col11\" class=\"data row12 col11\" >16.23%</td>\n",
       "      <td id=\"T_835cc_row12_col12\" class=\"data row12 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_835cc_row13_col0\" class=\"data row13 col0\" >6.20%</td>\n",
       "      <td id=\"T_835cc_row13_col1\" class=\"data row13 col1\" >4.50%</td>\n",
       "      <td id=\"T_835cc_row13_col2\" class=\"data row13 col2\" >3.96%</td>\n",
       "      <td id=\"T_835cc_row13_col3\" class=\"data row13 col3\" >3.46%</td>\n",
       "      <td id=\"T_835cc_row13_col4\" class=\"data row13 col4\" >3.96%</td>\n",
       "      <td id=\"T_835cc_row13_col5\" class=\"data row13 col5\" >3.02%</td>\n",
       "      <td id=\"T_835cc_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_835cc_row14_col0\" class=\"data row14 col0\" >16.89%</td>\n",
       "      <td id=\"T_835cc_row14_col1\" class=\"data row14 col1\" >20.92%</td>\n",
       "      <td id=\"T_835cc_row14_col2\" class=\"data row14 col2\" >22.07%</td>\n",
       "      <td id=\"T_835cc_row14_col3\" class=\"data row14 col3\" >21.89%</td>\n",
       "      <td id=\"T_835cc_row14_col4\" class=\"data row14 col4\" >22.10%</td>\n",
       "      <td id=\"T_835cc_row14_col5\" class=\"data row14 col5\" >20.19%</td>\n",
       "      <td id=\"T_835cc_row14_col6\" class=\"data row14 col6\" >22.28%</td>\n",
       "      <td id=\"T_835cc_row14_col7\" class=\"data row14 col7\" >19.67%</td>\n",
       "      <td id=\"T_835cc_row14_col8\" class=\"data row14 col8\" >37.83%</td>\n",
       "      <td id=\"T_835cc_row14_col9\" class=\"data row14 col9\" >40.30%</td>\n",
       "      <td id=\"T_835cc_row14_col10\" class=\"data row14 col10\" >45.05%</td>\n",
       "      <td id=\"T_835cc_row14_col11\" class=\"data row14 col11\" >42.22%</td>\n",
       "      <td id=\"T_835cc_row14_col12\" class=\"data row14 col12\" >52.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_835cc_row15_col0\" class=\"data row15 col0\" >4.86%</td>\n",
       "      <td id=\"T_835cc_row15_col1\" class=\"data row15 col1\" >7.82%</td>\n",
       "      <td id=\"T_835cc_row15_col2\" class=\"data row15 col2\" >6.03%</td>\n",
       "      <td id=\"T_835cc_row15_col3\" class=\"data row15 col3\" >6.10%</td>\n",
       "      <td id=\"T_835cc_row15_col4\" class=\"data row15 col4\" >6.13%</td>\n",
       "      <td id=\"T_835cc_row15_col5\" class=\"data row15 col5\" >1.24%</td>\n",
       "      <td id=\"T_835cc_row15_col6\" class=\"data row15 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row15_col9\" class=\"data row15 col9\" >4.06%</td>\n",
       "      <td id=\"T_835cc_row15_col10\" class=\"data row15 col10\" >3.45%</td>\n",
       "      <td id=\"T_835cc_row15_col11\" class=\"data row15 col11\" >3.38%</td>\n",
       "      <td id=\"T_835cc_row15_col12\" class=\"data row15 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_835cc_row16_col0\" class=\"data row16 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row16_col12\" class=\"data row16 col12\" >0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_835cc_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col2\" class=\"data row17 col2\" >1.10%</td>\n",
       "      <td id=\"T_835cc_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col4\" class=\"data row17 col4\" >1.26%</td>\n",
       "      <td id=\"T_835cc_row17_col5\" class=\"data row17 col5\" >8.31%</td>\n",
       "      <td id=\"T_835cc_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row17_col11\" class=\"data row17 col11\" >0.33%</td>\n",
       "      <td id=\"T_835cc_row17_col12\" class=\"data row17 col12\" >0.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_835cc_row18_col0\" class=\"data row18 col0\" >8.17%</td>\n",
       "      <td id=\"T_835cc_row18_col1\" class=\"data row18 col1\" >3.02%</td>\n",
       "      <td id=\"T_835cc_row18_col2\" class=\"data row18 col2\" >4.23%</td>\n",
       "      <td id=\"T_835cc_row18_col3\" class=\"data row18 col3\" >3.11%</td>\n",
       "      <td id=\"T_835cc_row18_col4\" class=\"data row18 col4\" >4.36%</td>\n",
       "      <td id=\"T_835cc_row18_col5\" class=\"data row18 col5\" >4.94%</td>\n",
       "      <td id=\"T_835cc_row18_col6\" class=\"data row18 col6\" >8.03%</td>\n",
       "      <td id=\"T_835cc_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row18_col8\" class=\"data row18 col8\" >4.04%</td>\n",
       "      <td id=\"T_835cc_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row18_col12\" class=\"data row18 col12\" >4.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_835cc_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col1\" class=\"data row19 col1\" >2.51%</td>\n",
       "      <td id=\"T_835cc_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col3\" class=\"data row19 col3\" >1.23%</td>\n",
       "      <td id=\"T_835cc_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row19_col11\" class=\"data row19 col11\" >0.30%</td>\n",
       "      <td id=\"T_835cc_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_835cc_row20_col0\" class=\"data row20 col0\" >6.88%</td>\n",
       "      <td id=\"T_835cc_row20_col1\" class=\"data row20 col1\" >4.85%</td>\n",
       "      <td id=\"T_835cc_row20_col2\" class=\"data row20 col2\" >8.78%</td>\n",
       "      <td id=\"T_835cc_row20_col3\" class=\"data row20 col3\" >5.67%</td>\n",
       "      <td id=\"T_835cc_row20_col4\" class=\"data row20 col4\" >9.17%</td>\n",
       "      <td id=\"T_835cc_row20_col5\" class=\"data row20 col5\" >10.06%</td>\n",
       "      <td id=\"T_835cc_row20_col6\" class=\"data row20 col6\" >17.77%</td>\n",
       "      <td id=\"T_835cc_row20_col7\" class=\"data row20 col7\" >20.00%</td>\n",
       "      <td id=\"T_835cc_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row20_col9\" class=\"data row20 col9\" >0.89%</td>\n",
       "      <td id=\"T_835cc_row20_col10\" class=\"data row20 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row20_col11\" class=\"data row20 col11\" >0.58%</td>\n",
       "      <td id=\"T_835cc_row20_col12\" class=\"data row20 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_835cc_row21_col0\" class=\"data row21 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col9\" class=\"data row21 col9\" >0.19%</td>\n",
       "      <td id=\"T_835cc_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_835cc_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_835cc_row23_col0\" class=\"data row23 col0\" >4.51%</td>\n",
       "      <td id=\"T_835cc_row23_col1\" class=\"data row23 col1\" >4.77%</td>\n",
       "      <td id=\"T_835cc_row23_col2\" class=\"data row23 col2\" >3.28%</td>\n",
       "      <td id=\"T_835cc_row23_col3\" class=\"data row23 col3\" >6.02%</td>\n",
       "      <td id=\"T_835cc_row23_col4\" class=\"data row23 col4\" >2.84%</td>\n",
       "      <td id=\"T_835cc_row23_col5\" class=\"data row23 col5\" >3.31%</td>\n",
       "      <td id=\"T_835cc_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row23_col9\" class=\"data row23 col9\" >9.12%</td>\n",
       "      <td id=\"T_835cc_row23_col10\" class=\"data row23 col10\" >7.94%</td>\n",
       "      <td id=\"T_835cc_row23_col11\" class=\"data row23 col11\" >9.24%</td>\n",
       "      <td id=\"T_835cc_row23_col12\" class=\"data row23 col12\" >9.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_835cc_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_835cc_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col1\" class=\"data row24 col1\" >1.39%</td>\n",
       "      <td id=\"T_835cc_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col3\" class=\"data row24 col3\" >1.13%</td>\n",
       "      <td id=\"T_835cc_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col9\" class=\"data row24 col9\" >1.50%</td>\n",
       "      <td id=\"T_835cc_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "      <td id=\"T_835cc_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff798683eb0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}