examples/Tutorial 30.ipynb

{
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  {
   "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 30: Nested Clustered Optimization (NCO)\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>0.4036%</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>-1.6850%</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%  0.4036%  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% -1.6850% -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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "# Plotting Assets Clusters\n",
    "\n",
    "ax = rp.plot_clusters(returns=Y,\n",
    "                      codependence='pearson',\n",
    "                      linkage='ward',\n",
    "                      k=None,\n",
    "                      max_k=10,\n",
    "                      leaf_order=True,\n",
    "                      dendrogram=True,\n",
    "                      #linecolor='tab:purple',\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above suggest that optimal number of clusters are four.\n",
    "\n",
    "## 2. Estimating NCO Portfolio\n",
    "\n",
    "This is the original model proposed by López de Prado (2019). Riskfolio-Lib expand this model to 13 risk measures and for objective functions: \"Minimize Risk\", \"Maximize Utility Function\", \"Maximize Return/Risk Ratio\" and \"Equal Risk Contribution\".\n",
    "\n",
    "### 2.1 Calculating the NCO portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>1.1740%</td>\n",
       "      <td>5.1896%</td>\n",
       "      <td>2.8275%</td>\n",
       "      <td>5.0140%</td>\n",
       "      <td>10.9774%</td>\n",
       "      <td>3.7100%</td>\n",
       "      <td>0.7671%</td>\n",
       "      <td>0.3473%</td>\n",
       "      <td>2.4553%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0075%</td>\n",
       "      <td>0.1999%</td>\n",
       "      <td>12.0295%</td>\n",
       "      <td>2.2941%</td>\n",
       "      <td>4.8193%</td>\n",
       "      <td>3.0216%</td>\n",
       "      <td>1.1121%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.6248%</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% 1.1740% 5.1896% 2.8275% 5.0140% 10.9774% 3.7100% 0.7671%   \n",
       "\n",
       "            HPQ     JCI  ...      NI    PCAR      PSA     SEE       T     TGT  \\\n",
       "weights 0.3473% 2.4553%  ... 7.0075% 0.1999% 12.0295% 2.2941% 4.8193% 3.0216%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 1.1121% 0.0000% 8.6248% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building the portfolio object\n",
    "port = rp.HCPortfolio(returns=Y)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='NCO' # Could be HRP or HERC\n",
    "codependence = 'pearson' # Correlation matrix used to group assets in clusters\n",
    "covariance = 'hist' # Covariance estimation technique\n",
    "obj = \"MinRisk\" # Posible values are \"MinRisk\", \"Utility\", \"Sharpe\" and \"ERC\"\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "rf = 0 # Risk free rate\n",
    "l = 2 # Risk aversion factor, only usefull with \"Utility\" objective\n",
    "linkage = 'ward' # Linkage method used to build clusters\n",
    "max_k = 10 # Max number of clusters used in two difference gap statistic\n",
    "leaf_order = True # Consider optimal order of leafs in dendrogram\n",
    "\n",
    "w = port.optimization(model=model,\n",
    "                      codependence=codependence,\n",
    "                      covariance=covariance,\n",
    "                      obj=obj,\n",
    "                      rm=rm,\n",
    "                      rf=rf,\n",
    "                      l=l,\n",
    "                      linkage=linkage,\n",
    "                      max_k=max_k,\n",
    "                      leaf_order=leaf_order)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = rp.plot_pie(w=w,\n",
    "                 title='NCO MinRisk',\n",
    "                 others=0.05,\n",
    "                 nrow=25,\n",
    "                 cmap=\"tab20\",\n",
    "                 height=8,\n",
    "                 width=10,\n",
    "                 ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting Risk Contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 the risk contribution per asset\n",
    "\n",
    "mu = Y.mean()\n",
    "cov = Y.cov() # Covariance matrix\n",
    "returns = Y # Returns of the assets\n",
    "\n",
    "ax = rp.plot_risk_con(w=w,\n",
    "                      cov=cov,\n",
    "                      returns=returns,\n",
    "                      rm=rm,\n",
    "                      rf=0,\n",
    "                      alpha=0.05,\n",
    "                      color=\"tab:blue\",\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      t_factor=252,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Calculate Optimal NCO Portfolios for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Variance.\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',\n",
    "       'CVaR', 'EVaR', 'WR', 'MDD', 'ADD',\n",
    "       'CDaR', 'EDaR', 'UCI']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model,\n",
    "                          codependence=codependence,\n",
    "                          covariance=covariance,\n",
    "                          obj=obj,\n",
    "                          rm=i,\n",
    "                          rf=rf,\n",
    "                          linkage=linkage,\n",
    "                          max_k=max_k,\n",
    "                          leaf_order=leaf_order)\n",
    "\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col5,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col8,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col11,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col0,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col1,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col2,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col3,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col4,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col6,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col9,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col10,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col12,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col7{\n",
       "            background-color:  #004529;\n",
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       "            background-color:  #9cd687;\n",
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       "            color:  #000000;\n",
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       "            background-color:  #eaf7af;\n",
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       "            background-color:  #f7fcb9;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col4,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col3{\n",
       "            background-color:  #f9fdc4;\n",
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       "            color:  #000000;\n",
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       "            background-color:  #e6f5ac;\n",
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       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col2,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col1,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col1,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col3{\n",
       "            background-color:  #edf8b2;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col3,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col5{\n",
       "            background-color:  #d5eea1;\n",
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       "            background-color:  #f3fab6;\n",
       "            color:  #000000;\n",
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       "            background-color:  #12763d;\n",
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       "            background-color:  #3fa85b;\n",
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       "            background-color:  #66bd70;\n",
       "            color:  #000000;\n",
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       "            background-color:  #5fba6c;\n",
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       "            background-color:  #c8e99b;\n",
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       "            background-color:  #f5fbb8;\n",
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       "            background-color:  #00502d;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #006737;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #83cb7d;\n",
       "            color:  #000000;\n",
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       "            background-color:  #a7db8c;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b2df90;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b3e091;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fdfeda;\n",
       "            color:  #000000;\n",
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       "            background-color:  #feffde;\n",
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       "            background-color:  #005931;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #3ba358;\n",
       "            color:  #000000;\n",
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       "            background-color:  #005e33;\n",
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       "            background-color:  #7ec97b;\n",
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       "            background-color:  #1f8142;\n",
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       "            background-color:  #006636;\n",
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       "            background-color:  #005a31;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col9,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col2{\n",
       "            background-color:  #ddf1a6;\n",
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       "            background-color:  #0b713b;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #0a703a;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ebf7b0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #d3eda0;\n",
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       "            background-color:  #e4f4ab;\n",
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       "            background-color:  #f8fcc0;\n",
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       "            background-color:  #ddf2a6;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col2,#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col10{\n",
       "            background-color:  #daf0a4;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col5{\n",
       "            background-color:  #bde496;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col0{\n",
       "            background-color:  #fafdc8;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col9{\n",
       "            background-color:  #ceeb9e;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col0{\n",
       "            background-color:  #379e54;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col1{\n",
       "            background-color:  #88cd7f;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col2{\n",
       "            background-color:  #40aa5c;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col3{\n",
       "            background-color:  #a6da8b;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col4{\n",
       "            background-color:  #4fb264;\n",
       "            color:  #000000;\n",
       "        }#T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col5{\n",
       "            background-color:  #096f3a;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >EDaR</th>        <th class=\"col_heading level0 col12\" >UCI</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col0\" class=\"data row1 col0\" >1.17%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col1\" class=\"data row1 col1\" >1.61%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col2\" class=\"data row1 col2\" >0.67%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col3\" class=\"data row1 col3\" >1.60%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col4\" class=\"data row1 col4\" >0.65%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col5\" class=\"data row1 col5\" >0.14%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col8\" class=\"data row1 col8\" >4.29%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col9\" class=\"data row1 col9\" >2.27%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col10\" class=\"data row1 col10\" >0.86%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col11\" class=\"data row1 col11\" >2.67%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col0\" class=\"data row2 col0\" >5.19%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col1\" class=\"data row2 col1\" >4.84%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col2\" class=\"data row2 col2\" >4.74%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col3\" class=\"data row2 col3\" >6.10%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col4\" class=\"data row2 col4\" >5.20%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col5\" class=\"data row2 col5\" >4.79%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col6\" class=\"data row2 col6\" >8.31%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col9\" class=\"data row2 col9\" >10.91%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col10\" class=\"data row2 col10\" >6.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col11\" class=\"data row2 col11\" >5.04%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row2_col12\" class=\"data row2 col12\" >8.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col0\" class=\"data row3 col0\" >2.83%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col1\" class=\"data row3 col1\" >3.26%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col2\" class=\"data row3 col2\" >2.53%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col3\" class=\"data row3 col3\" >2.80%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col4\" class=\"data row3 col4\" >2.38%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col5\" class=\"data row3 col5\" >1.81%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col6\" class=\"data row3 col6\" >6.46%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col9\" class=\"data row3 col9\" >0.61%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col11\" class=\"data row3 col11\" >0.56%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row3_col12\" class=\"data row3 col12\" >0.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col0\" class=\"data row4 col0\" >5.01%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col1\" class=\"data row4 col1\" >5.22%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col2\" class=\"data row4 col2\" >4.56%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col3\" class=\"data row4 col3\" >5.30%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col4\" class=\"data row4 col4\" >4.75%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col5\" class=\"data row4 col5\" >2.94%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col6\" class=\"data row4 col6\" >0.36%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col8\" class=\"data row4 col8\" >0.73%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col9\" class=\"data row4 col9\" >3.39%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col11\" class=\"data row4 col11\" >0.01%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col0\" class=\"data row5 col0\" >10.98%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col1\" class=\"data row5 col1\" >9.44%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col2\" class=\"data row5 col2\" >11.69%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col3\" class=\"data row5 col3\" >10.02%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col4\" class=\"data row5 col4\" >11.94%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col5\" class=\"data row5 col5\" >15.62%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col6\" class=\"data row5 col6\" >13.28%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col7\" class=\"data row5 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col8\" class=\"data row5 col8\" >29.27%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col9\" class=\"data row5 col9\" >9.67%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col10\" class=\"data row5 col10\" >18.59%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col11\" class=\"data row5 col11\" >22.03%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row5_col12\" class=\"data row5 col12\" >17.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col0\" class=\"data row6 col0\" >3.71%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col1\" class=\"data row6 col1\" >3.20%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col2\" class=\"data row6 col2\" >5.47%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col3\" class=\"data row6 col3\" >2.40%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col4\" class=\"data row6 col4\" >5.17%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col5\" class=\"data row6 col5\" >6.55%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col6\" class=\"data row6 col6\" >6.21%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col7\" class=\"data row6 col7\" >2.83%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col10\" class=\"data row6 col10\" >0.03%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col11\" class=\"data row6 col11\" >0.02%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col0\" class=\"data row7 col0\" >0.77%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col1\" class=\"data row7 col1\" >1.02%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col2\" class=\"data row7 col2\" >0.59%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col3\" class=\"data row7 col3\" >1.04%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col4\" class=\"data row7 col4\" >0.61%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col5\" class=\"data row7 col5\" >0.10%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col8\" class=\"data row7 col8\" >12.76%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col9\" class=\"data row7 col9\" >0.65%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col10\" class=\"data row7 col10\" >1.16%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col11\" class=\"data row7 col11\" >6.34%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col0\" class=\"data row8 col0\" >0.35%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col1\" class=\"data row8 col1\" >0.58%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col3\" class=\"data row8 col3\" >0.68%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col0\" class=\"data row9 col0\" >2.46%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col1\" class=\"data row9 col1\" >2.77%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col2\" class=\"data row9 col2\" >1.89%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col3\" class=\"data row9 col3\" >2.29%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col4\" class=\"data row9 col4\" >1.55%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col5\" class=\"data row9 col5\" >0.28%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col9\" class=\"data row9 col9\" >1.51%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col10\" class=\"data row9 col10\" >0.23%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col11\" class=\"data row9 col11\" >0.24%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col0\" class=\"data row10 col0\" >2.57%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col1\" class=\"data row10 col1\" >4.41%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col2\" class=\"data row10 col2\" >2.44%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col3\" class=\"data row10 col3\" >4.83%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col4\" class=\"data row10 col4\" >2.27%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col5\" class=\"data row10 col5\" >0.39%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col8\" class=\"data row10 col8\" >3.25%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col9\" class=\"data row10 col9\" >2.63%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col10\" class=\"data row10 col10\" >1.90%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col11\" class=\"data row10 col11\" >5.60%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col0\" class=\"data row11 col0\" >1.28%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col1\" class=\"data row11 col1\" >1.94%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col2\" class=\"data row11 col2\" >1.16%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col3\" class=\"data row11 col3\" >1.44%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col4\" class=\"data row11 col4\" >0.91%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col5\" class=\"data row11 col5\" >0.21%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col0\" class=\"data row12 col0\" >12.98%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col1\" class=\"data row12 col1\" >15.35%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col2\" class=\"data row12 col2\" >14.67%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col3\" class=\"data row12 col3\" >18.25%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col4\" class=\"data row12 col4\" >15.99%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col5\" class=\"data row12 col5\" >12.68%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col6\" class=\"data row12 col6\" >25.46%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col7\" class=\"data row12 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col9\" class=\"data row12 col9\" >23.18%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col10\" class=\"data row12 col10\" >18.69%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col11\" class=\"data row12 col11\" >15.06%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row12_col12\" class=\"data row12 col12\" >25.47%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col0\" class=\"data row13 col0\" >10.42%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col1\" class=\"data row13 col1\" >9.21%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col2\" class=\"data row13 col2\" >9.34%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col3\" class=\"data row13 col3\" >7.98%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col4\" class=\"data row13 col4\" >8.63%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col5\" class=\"data row13 col5\" >9.96%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col6\" class=\"data row13 col6\" >13.56%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col7\" class=\"data row13 col7\" >28.30%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col8\" class=\"data row13 col8\" >16.28%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col9\" class=\"data row13 col9\" >3.34%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col10\" class=\"data row13 col10\" >12.61%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col11\" class=\"data row13 col11\" >14.05%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row13_col12\" class=\"data row13 col12\" >7.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col0\" class=\"data row14 col0\" >1.18%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col1\" class=\"data row14 col1\" >2.07%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col2\" class=\"data row14 col2\" >1.47%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col3\" class=\"data row14 col3\" >2.85%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col4\" class=\"data row14 col4\" >2.07%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col5\" class=\"data row14 col5\" >2.25%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col6\" class=\"data row14 col6\" >0.05%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col7\" class=\"data row14 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col8\" class=\"data row14 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col9\" class=\"data row14 col9\" >22.21%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col10\" class=\"data row14 col10\" >18.57%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col11\" class=\"data row14 col11\" >8.13%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row14_col12\" class=\"data row14 col12\" >22.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col0\" class=\"data row15 col0\" >7.01%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col1\" class=\"data row15 col1\" >7.29%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col2\" class=\"data row15 col2\" >5.73%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col3\" class=\"data row15 col3\" >6.63%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col4\" class=\"data row15 col4\" >5.74%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col5\" class=\"data row15 col5\" >5.80%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col6\" class=\"data row15 col6\" >0.27%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col7\" class=\"data row15 col7\" >21.98%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col9\" class=\"data row15 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col10\" class=\"data row15 col10\" >0.59%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col11\" class=\"data row15 col11\" >0.37%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row15_col12\" class=\"data row15 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col0\" class=\"data row16 col0\" >0.20%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col1\" class=\"data row16 col1\" >0.33%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col2\" class=\"data row16 col2\" >0.39%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col3\" class=\"data row16 col3\" >0.65%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col4\" class=\"data row16 col4\" >0.35%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col0\" class=\"data row17 col0\" >12.03%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col1\" class=\"data row17 col1\" >9.96%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col2\" class=\"data row17 col2\" >13.35%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col3\" class=\"data row17 col3\" >8.86%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col4\" class=\"data row17 col4\" >12.24%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col5\" class=\"data row17 col5\" >13.79%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col6\" class=\"data row17 col6\" >20.40%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col7\" class=\"data row17 col7\" >46.88%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col8\" class=\"data row17 col8\" >27.06%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col9\" class=\"data row17 col9\" >5.49%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col10\" class=\"data row17 col10\" >15.60%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col11\" class=\"data row17 col11\" >18.43%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row17_col12\" class=\"data row17 col12\" >10.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col0\" class=\"data row18 col0\" >2.29%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col1\" class=\"data row18 col1\" >2.55%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col2\" class=\"data row18 col2\" >1.68%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col3\" class=\"data row18 col3\" >1.73%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col4\" class=\"data row18 col4\" >1.26%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col5\" class=\"data row18 col5\" >0.28%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col8\" class=\"data row18 col8\" >1.68%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col9\" class=\"data row18 col9\" >0.86%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col10\" class=\"data row18 col10\" >0.53%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col11\" class=\"data row18 col11\" >1.38%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col0\" class=\"data row19 col0\" >4.82%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col1\" class=\"data row19 col1\" >4.09%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col2\" class=\"data row19 col2\" >3.46%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col3\" class=\"data row19 col3\" >3.58%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col4\" class=\"data row19 col4\" >3.28%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col5\" class=\"data row19 col5\" >4.12%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col6\" class=\"data row19 col6\" >0.73%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col8\" class=\"data row19 col8\" >1.63%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col9\" class=\"data row19 col9\" >2.51%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col11\" class=\"data row19 col11\" >0.01%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col0\" class=\"data row20 col0\" >3.02%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col1\" class=\"data row20 col1\" >2.56%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col2\" class=\"data row20 col2\" >3.62%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col3\" class=\"data row20 col3\" >3.04%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col4\" class=\"data row20 col4\" >3.88%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col5\" class=\"data row20 col5\" >5.13%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col6\" class=\"data row20 col6\" >0.49%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col7\" class=\"data row20 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col8\" class=\"data row20 col8\" >1.10%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col9\" class=\"data row20 col9\" >1.02%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col10\" class=\"data row20 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col11\" class=\"data row20 col11\" >0.01%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row20_col12\" class=\"data row20 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col0\" class=\"data row21 col0\" >1.11%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col1\" class=\"data row21 col1\" >0.69%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col2\" class=\"data row21 col2\" >1.37%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col3\" class=\"data row21 col3\" >0.80%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col4\" class=\"data row21 col4\" >1.64%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col6\" class=\"data row21 col6\" >2.87%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col9\" class=\"data row21 col9\" >6.60%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col10\" class=\"data row21 col10\" >4.65%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col11\" class=\"data row21 col11\" >0.03%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row21_col12\" class=\"data row21 col12\" >8.54%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col1\" class=\"data row22 col1\" >0.46%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col0\" class=\"data row23 col0\" >8.62%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col1\" class=\"data row23 col1\" >7.13%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col2\" class=\"data row23 col2\" >9.18%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col3\" class=\"data row23 col3\" >7.13%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col4\" class=\"data row23 col4\" >9.48%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col5\" class=\"data row23 col5\" >13.15%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col6\" class=\"data row23 col6\" >1.53%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col8\" class=\"data row23 col8\" >1.95%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col9\" class=\"data row23 col9\" >3.14%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col10\" class=\"data row23 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col11\" class=\"data row23 col11\" >0.01%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row23_col12\" class=\"data row23 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col9\" class=\"data row24 col9\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "                        <td id=\"T_1b79073c_0ee0_11ec_8cf0_28cfe94f2cb1row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff8eceab340>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "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 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}