Black_Litterman_Main.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio Optimization using Markowitz & Black-Litterman Model "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mathematical symbols used in this notebook\n",
    "\n",
    "| Python symbol | Math Symbol | Comment\n",
    "| -- | -- | --\n",
    "| rf | $r$ | risk free rate\n",
    "| lambdaa | $\\lambda$ | risk aversion coefficient\n",
    "| C | $C$ | Assets covariance matrix\n",
    "| V | $V$ | Assets variances (diagonal in covariance matrix)\n",
    "| W | $W$ | Assets weights\n",
    "| R | $R$ | Assets returns\n",
    "| mean | $\\mu$ | Portfolio historical return\n",
    "| var | $\\sigma$ | Portfolio historical variance\n",
    "| Pi | $\\Pi$ | Portfolio equilibrium excess returns\n",
    "| tau | $\\tau$ | Scaling factor for Black-litterman"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib_inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "pylab.rcParams['figure.figsize'] = (16, 9)\n",
    "import scipy.optimize\n",
    "from pandas_datareader import data as web\n",
    "import yfinance as yf\n",
    "from pytickersymbols import PyTickerSymbols\n",
    "from collections import defaultdict\n",
    "from IPython.core.display import HTML\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Historical Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data = PyTickerSymbols()\n",
    "stocks_list = stock_data.get_dow_jones_nyc_yahoo_tickers()\n",
    "stocks_list.remove('DOW')\n",
    "len(stocks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = yf.Tickers(stocks_list)\n",
    "market_cap = {}\n",
    "for str in stocks_list:\n",
    "    market_cap[str] = tickers.tickers[str].info['marketCap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function loads historical stock prices of Dow Jones companies and returns them together\n",
    "# with their market capitalizations\n",
    "def load_data():\n",
    "    symbols = stocks_list\n",
    "    n = len(symbols)\n",
    "    prices_out, caps_out = [], []\n",
    "    for s in symbols:\n",
    "        file_name = 'dow-jones/{}.csv'.format(s)\n",
    "        dataframe = pd.read_csv(file_name, index_col = None, parse_dates = ['date'])\n",
    "        prices = list(dataframe['close'])[-2000:] # trailing window 2000 days\n",
    "        prices_out.append(prices)\n",
    "        caps_out.append(market_cap[s])\n",
    "    return symbols, prices_out, caps_out\n",
    "\n",
    "names, prices, caps = load_data()\n",
    "prices = np.array(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01139749  0.12995906  0.26771352  0.14330435  0.19238213  0.12186442\n",
      "  0.10622025  0.06863905  0.00242946  0.13351046  0.18575797  0.02421168\n",
      "  0.01385409  0.09013986  0.16080835  0.17237675  0.16905813  0.10793506\n",
      "  0.27721477  0.08804824  0.10664208  0.25998061 -0.00486174  0.19883916\n",
      "  0.08740008 -0.04320811  0.02958325  0.0985969   0.13577698  0.17975741]\n"
     ]
    }
   ],
   "source": [
    "# Function takes historical stock prices together with market capitalizations and\n",
    "# calculates weights, historical returns and historical covariances\n",
    "def daily_asset_estimator(prices):\n",
    "    prices = np.matrix(prices)\n",
    "    r, c = prices.shape\n",
    "    historical_returns = np.empty([r, c-1])\n",
    "    for ri in range(r):\n",
    "        for ci in range(c-1):\n",
    "            prior_price, after_price = prices[ri, ci], prices[ri, ci+1]\n",
    "            historical_returns[ri, ci] = (after_price/prior_price)-1\n",
    "    expected_returns = np.array([])\n",
    "    for ri in range(r):\n",
    "        expected_returns = np.append(expected_returns, np.mean(historical_returns[ri]))\n",
    "    covariance = np.cov(historical_returns)\n",
    "    expected_returns = (1 + expected_returns) ** 252 - 1\n",
    "    print(expected_returns)\n",
    "    covariance = covariance * 252\n",
    "    return expected_returns, covariance\n",
    "\n",
    "W = np.array(caps)/sum(caps)\n",
    "R, C = daily_asset_estimator(prices)\n",
    "rf = 0.0353"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CVX</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>KO</th>\n",
       "      <th>GS-PK</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>VZ</th>\n",
       "      <th>V</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WBA</th>\n",
       "      <th>DIS</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>HON</th>\n",
       "      <th>CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Return</th>\n",
       "      <td>-0.011397</td>\n",
       "      <td>0.129959</td>\n",
       "      <td>0.267714</td>\n",
       "      <td>0.143304</td>\n",
       "      <td>0.192382</td>\n",
       "      <td>0.121864</td>\n",
       "      <td>0.106220</td>\n",
       "      <td>0.068639</td>\n",
       "      <td>0.002429</td>\n",
       "      <td>0.133510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106642</td>\n",
       "      <td>0.259981</td>\n",
       "      <td>-0.004862</td>\n",
       "      <td>0.198839</td>\n",
       "      <td>0.08740</td>\n",
       "      <td>-0.043208</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.098597</td>\n",
       "      <td>0.135777</td>\n",
       "      <td>0.179757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight (based on market cap)</th>\n",
       "      <td>0.005046</td>\n",
       "      <td>0.010018</td>\n",
       "      <td>0.247467</td>\n",
       "      <td>0.010953</td>\n",
       "      <td>0.009808</td>\n",
       "      <td>0.026981</td>\n",
       "      <td>0.017344</td>\n",
       "      <td>0.025134</td>\n",
       "      <td>0.005769</td>\n",
       "      <td>0.009721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.041461</td>\n",
       "      <td>0.014341</td>\n",
       "      <td>0.043910</td>\n",
       "      <td>0.03743</td>\n",
       "      <td>0.002471</td>\n",
       "      <td>0.016691</td>\n",
       "      <td>0.011340</td>\n",
       "      <td>0.011909</td>\n",
       "      <td>0.018181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   MMM       AXP      AAPL        BA  \\\n",
       "Return                       -0.011397  0.129959  0.267714  0.143304   \n",
       "Weight (based on market cap)  0.005046  0.010018  0.247467  0.010953   \n",
       "\n",
       "                                   CAT       CVX      CSCO        KO  \\\n",
       "Return                        0.192382  0.121864  0.106220  0.068639   \n",
       "Weight (based on market cap)  0.009808  0.026981  0.017344  0.025134   \n",
       "\n",
       "                                 GS-PK        GS  ...       TRV       UNH  \\\n",
       "Return                        0.002429  0.133510  ...  0.106642  0.259981   \n",
       "Weight (based on market cap)  0.005769  0.009721  ...  0.003835  0.041461   \n",
       "\n",
       "                                    VZ         V      WMT       WBA       DIS  \\\n",
       "Return                       -0.004862  0.198839  0.08740 -0.043208  0.029583   \n",
       "Weight (based on market cap)  0.014341  0.043910  0.03743  0.002471  0.016691   \n",
       "\n",
       "                                  AMGN       HON       CRM  \n",
       "Return                        0.098597  0.135777  0.179757  \n",
       "Weight (based on market cap)  0.011340  0.011909  0.018181  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.DataFrame({'Return': R, 'Weight (based on market cap)': W}, index = names).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CVX</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>KO</th>\n",
       "      <th>GS-PK</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>VZ</th>\n",
       "      <th>V</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WBA</th>\n",
       "      <th>DIS</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>HON</th>\n",
       "      <th>CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.055676</td>\n",
       "      <td>0.039020</td>\n",
       "      <td>0.031454</td>\n",
       "      <td>0.042619</td>\n",
       "      <td>0.043129</td>\n",
       "      <td>0.034246</td>\n",
       "      <td>0.033934</td>\n",
       "      <td>0.022534</td>\n",
       "      <td>0.006730</td>\n",
       "      <td>0.039561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028235</td>\n",
       "      <td>0.027265</td>\n",
       "      <td>0.017047</td>\n",
       "      <td>0.031692</td>\n",
       "      <td>0.015694</td>\n",
       "      <td>0.032906</td>\n",
       "      <td>0.030632</td>\n",
       "      <td>0.025396</td>\n",
       "      <td>0.038317</td>\n",
       "      <td>0.028581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AXP</th>\n",
       "      <td>0.039020</td>\n",
       "      <td>0.103630</td>\n",
       "      <td>0.042249</td>\n",
       "      <td>0.084360</td>\n",
       "      <td>0.054507</td>\n",
       "      <td>0.058980</td>\n",
       "      <td>0.041853</td>\n",
       "      <td>0.030378</td>\n",
       "      <td>0.015698</td>\n",
       "      <td>0.067624</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046075</td>\n",
       "      <td>0.042366</td>\n",
       "      <td>0.019516</td>\n",
       "      <td>0.055494</td>\n",
       "      <td>0.015800</td>\n",
       "      <td>0.039406</td>\n",
       "      <td>0.054407</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>0.052520</td>\n",
       "      <td>0.044761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.031454</td>\n",
       "      <td>0.042249</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>0.053437</td>\n",
       "      <td>0.037102</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>0.044688</td>\n",
       "      <td>0.021921</td>\n",
       "      <td>0.010677</td>\n",
       "      <td>0.044733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026244</td>\n",
       "      <td>0.036326</td>\n",
       "      <td>0.015510</td>\n",
       "      <td>0.046569</td>\n",
       "      <td>0.022358</td>\n",
       "      <td>0.031281</td>\n",
       "      <td>0.038504</td>\n",
       "      <td>0.032058</td>\n",
       "      <td>0.036203</td>\n",
       "      <td>0.058264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.042619</td>\n",
       "      <td>0.084360</td>\n",
       "      <td>0.053437</td>\n",
       "      <td>0.180151</td>\n",
       "      <td>0.065614</td>\n",
       "      <td>0.069045</td>\n",
       "      <td>0.046005</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>0.019903</td>\n",
       "      <td>0.072058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049670</td>\n",
       "      <td>0.043013</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>0.056293</td>\n",
       "      <td>0.017538</td>\n",
       "      <td>0.045312</td>\n",
       "      <td>0.062272</td>\n",
       "      <td>0.026050</td>\n",
       "      <td>0.063554</td>\n",
       "      <td>0.053303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAT</th>\n",
       "      <td>0.043129</td>\n",
       "      <td>0.054507</td>\n",
       "      <td>0.037102</td>\n",
       "      <td>0.065614</td>\n",
       "      <td>0.092222</td>\n",
       "      <td>0.056162</td>\n",
       "      <td>0.039267</td>\n",
       "      <td>0.022905</td>\n",
       "      <td>0.007880</td>\n",
       "      <td>0.056290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035548</td>\n",
       "      <td>0.030836</td>\n",
       "      <td>0.018608</td>\n",
       "      <td>0.039205</td>\n",
       "      <td>0.016820</td>\n",
       "      <td>0.037299</td>\n",
       "      <td>0.039834</td>\n",
       "      <td>0.028903</td>\n",
       "      <td>0.047004</td>\n",
       "      <td>0.035667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.034246</td>\n",
       "      <td>0.058980</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>0.069045</td>\n",
       "      <td>0.056162</td>\n",
       "      <td>0.098577</td>\n",
       "      <td>0.036036</td>\n",
       "      <td>0.026002</td>\n",
       "      <td>0.015513</td>\n",
       "      <td>0.054596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041814</td>\n",
       "      <td>0.036705</td>\n",
       "      <td>0.020457</td>\n",
       "      <td>0.040715</td>\n",
       "      <td>0.014860</td>\n",
       "      <td>0.033232</td>\n",
       "      <td>0.040509</td>\n",
       "      <td>0.024951</td>\n",
       "      <td>0.045060</td>\n",
       "      <td>0.033270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>0.033934</td>\n",
       "      <td>0.041853</td>\n",
       "      <td>0.044688</td>\n",
       "      <td>0.046005</td>\n",
       "      <td>0.039267</td>\n",
       "      <td>0.036036</td>\n",
       "      <td>0.069537</td>\n",
       "      <td>0.022452</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.041331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028602</td>\n",
       "      <td>0.031708</td>\n",
       "      <td>0.018935</td>\n",
       "      <td>0.039709</td>\n",
       "      <td>0.022037</td>\n",
       "      <td>0.034211</td>\n",
       "      <td>0.037192</td>\n",
       "      <td>0.030038</td>\n",
       "      <td>0.036890</td>\n",
       "      <td>0.045105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>0.022534</td>\n",
       "      <td>0.030378</td>\n",
       "      <td>0.021921</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>0.022905</td>\n",
       "      <td>0.026002</td>\n",
       "      <td>0.022452</td>\n",
       "      <td>0.035649</td>\n",
       "      <td>0.007123</td>\n",
       "      <td>0.024511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024923</td>\n",
       "      <td>0.021903</td>\n",
       "      <td>0.017557</td>\n",
       "      <td>0.025159</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.021925</td>\n",
       "      <td>0.024005</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.026100</td>\n",
       "      <td>0.020407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS-PK</th>\n",
       "      <td>0.006730</td>\n",
       "      <td>0.015698</td>\n",
       "      <td>0.010677</td>\n",
       "      <td>0.019903</td>\n",
       "      <td>0.007880</td>\n",
       "      <td>0.015513</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.007123</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.011795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010404</td>\n",
       "      <td>0.011178</td>\n",
       "      <td>0.004427</td>\n",
       "      <td>0.012031</td>\n",
       "      <td>0.002312</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.009782</td>\n",
       "      <td>0.006056</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>0.011626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS</th>\n",
       "      <td>0.039561</td>\n",
       "      <td>0.067624</td>\n",
       "      <td>0.044733</td>\n",
       "      <td>0.072058</td>\n",
       "      <td>0.056290</td>\n",
       "      <td>0.054596</td>\n",
       "      <td>0.041331</td>\n",
       "      <td>0.024511</td>\n",
       "      <td>0.011795</td>\n",
       "      <td>0.088572</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043195</td>\n",
       "      <td>0.038549</td>\n",
       "      <td>0.019257</td>\n",
       "      <td>0.046429</td>\n",
       "      <td>0.017675</td>\n",
       "      <td>0.039183</td>\n",
       "      <td>0.048252</td>\n",
       "      <td>0.030403</td>\n",
       "      <td>0.047291</td>\n",
       "      <td>0.044724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>0.030487</td>\n",
       "      <td>0.040950</td>\n",
       "      <td>0.040236</td>\n",
       "      <td>0.048672</td>\n",
       "      <td>0.033414</td>\n",
       "      <td>0.033954</td>\n",
       "      <td>0.035249</td>\n",
       "      <td>0.022767</td>\n",
       "      <td>0.011038</td>\n",
       "      <td>0.041197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031437</td>\n",
       "      <td>0.033632</td>\n",
       "      <td>0.017707</td>\n",
       "      <td>0.038047</td>\n",
       "      <td>0.023156</td>\n",
       "      <td>0.029259</td>\n",
       "      <td>0.035274</td>\n",
       "      <td>0.027542</td>\n",
       "      <td>0.034334</td>\n",
       "      <td>0.043483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.032632</td>\n",
       "      <td>0.041886</td>\n",
       "      <td>0.032292</td>\n",
       "      <td>0.048302</td>\n",
       "      <td>0.039022</td>\n",
       "      <td>0.038639</td>\n",
       "      <td>0.036650</td>\n",
       "      <td>0.023493</td>\n",
       "      <td>0.008320</td>\n",
       "      <td>0.039154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029785</td>\n",
       "      <td>0.028566</td>\n",
       "      <td>0.019709</td>\n",
       "      <td>0.034885</td>\n",
       "      <td>0.017175</td>\n",
       "      <td>0.031284</td>\n",
       "      <td>0.030760</td>\n",
       "      <td>0.026189</td>\n",
       "      <td>0.033909</td>\n",
       "      <td>0.031082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTC</th>\n",
       "      <td>0.035459</td>\n",
       "      <td>0.047840</td>\n",
       "      <td>0.055091</td>\n",
       "      <td>0.059292</td>\n",
       "      <td>0.045177</td>\n",
       "      <td>0.042102</td>\n",
       "      <td>0.049138</td>\n",
       "      <td>0.024001</td>\n",
       "      <td>0.009475</td>\n",
       "      <td>0.048596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033408</td>\n",
       "      <td>0.035774</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>0.044445</td>\n",
       "      <td>0.022878</td>\n",
       "      <td>0.036897</td>\n",
       "      <td>0.040118</td>\n",
       "      <td>0.034535</td>\n",
       "      <td>0.040001</td>\n",
       "      <td>0.051544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNJ</th>\n",
       "      <td>0.022027</td>\n",
       "      <td>0.023395</td>\n",
       "      <td>0.021918</td>\n",
       "      <td>0.024061</td>\n",
       "      <td>0.020981</td>\n",
       "      <td>0.022214</td>\n",
       "      <td>0.024511</td>\n",
       "      <td>0.018518</td>\n",
       "      <td>0.005263</td>\n",
       "      <td>0.021836</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019169</td>\n",
       "      <td>0.024901</td>\n",
       "      <td>0.016105</td>\n",
       "      <td>0.022298</td>\n",
       "      <td>0.016132</td>\n",
       "      <td>0.023381</td>\n",
       "      <td>0.017500</td>\n",
       "      <td>0.025862</td>\n",
       "      <td>0.021624</td>\n",
       "      <td>0.019400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>0.037999</td>\n",
       "      <td>0.069155</td>\n",
       "      <td>0.038736</td>\n",
       "      <td>0.071486</td>\n",
       "      <td>0.054239</td>\n",
       "      <td>0.055332</td>\n",
       "      <td>0.040286</td>\n",
       "      <td>0.026480</td>\n",
       "      <td>0.011889</td>\n",
       "      <td>0.072159</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045776</td>\n",
       "      <td>0.037276</td>\n",
       "      <td>0.020788</td>\n",
       "      <td>0.045087</td>\n",
       "      <td>0.016862</td>\n",
       "      <td>0.038589</td>\n",
       "      <td>0.047197</td>\n",
       "      <td>0.029719</td>\n",
       "      <td>0.047364</td>\n",
       "      <td>0.039163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCD</th>\n",
       "      <td>0.023132</td>\n",
       "      <td>0.034876</td>\n",
       "      <td>0.027480</td>\n",
       "      <td>0.040302</td>\n",
       "      <td>0.024003</td>\n",
       "      <td>0.030125</td>\n",
       "      <td>0.026249</td>\n",
       "      <td>0.021226</td>\n",
       "      <td>0.010406</td>\n",
       "      <td>0.030789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028544</td>\n",
       "      <td>0.025707</td>\n",
       "      <td>0.014606</td>\n",
       "      <td>0.031189</td>\n",
       "      <td>0.014840</td>\n",
       "      <td>0.019029</td>\n",
       "      <td>0.027360</td>\n",
       "      <td>0.019549</td>\n",
       "      <td>0.028633</td>\n",
       "      <td>0.029109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKE</th>\n",
       "      <td>0.031869</td>\n",
       "      <td>0.046872</td>\n",
       "      <td>0.042996</td>\n",
       "      <td>0.055530</td>\n",
       "      <td>0.037945</td>\n",
       "      <td>0.034353</td>\n",
       "      <td>0.036604</td>\n",
       "      <td>0.023265</td>\n",
       "      <td>0.012234</td>\n",
       "      <td>0.042931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028563</td>\n",
       "      <td>0.031789</td>\n",
       "      <td>0.016445</td>\n",
       "      <td>0.042197</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.029734</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>0.023400</td>\n",
       "      <td>0.038798</td>\n",
       "      <td>0.047211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRK</th>\n",
       "      <td>0.018631</td>\n",
       "      <td>0.023404</td>\n",
       "      <td>0.021665</td>\n",
       "      <td>0.023187</td>\n",
       "      <td>0.022313</td>\n",
       "      <td>0.025804</td>\n",
       "      <td>0.023983</td>\n",
       "      <td>0.018078</td>\n",
       "      <td>0.006975</td>\n",
       "      <td>0.024188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022743</td>\n",
       "      <td>0.026421</td>\n",
       "      <td>0.017250</td>\n",
       "      <td>0.023713</td>\n",
       "      <td>0.014916</td>\n",
       "      <td>0.021673</td>\n",
       "      <td>0.016782</td>\n",
       "      <td>0.030151</td>\n",
       "      <td>0.021995</td>\n",
       "      <td>0.022690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.029964</td>\n",
       "      <td>0.043470</td>\n",
       "      <td>0.059883</td>\n",
       "      <td>0.049089</td>\n",
       "      <td>0.036885</td>\n",
       "      <td>0.035234</td>\n",
       "      <td>0.045954</td>\n",
       "      <td>0.023537</td>\n",
       "      <td>0.010861</td>\n",
       "      <td>0.043663</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028805</td>\n",
       "      <td>0.037504</td>\n",
       "      <td>0.017745</td>\n",
       "      <td>0.049633</td>\n",
       "      <td>0.022932</td>\n",
       "      <td>0.031710</td>\n",
       "      <td>0.038897</td>\n",
       "      <td>0.033085</td>\n",
       "      <td>0.036835</td>\n",
       "      <td>0.065486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>0.019852</td>\n",
       "      <td>0.021851</td>\n",
       "      <td>0.023790</td>\n",
       "      <td>0.022446</td>\n",
       "      <td>0.018098</td>\n",
       "      <td>0.017782</td>\n",
       "      <td>0.024414</td>\n",
       "      <td>0.023023</td>\n",
       "      <td>0.004746</td>\n",
       "      <td>0.020910</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020125</td>\n",
       "      <td>0.021984</td>\n",
       "      <td>0.018162</td>\n",
       "      <td>0.022408</td>\n",
       "      <td>0.020112</td>\n",
       "      <td>0.022526</td>\n",
       "      <td>0.019269</td>\n",
       "      <td>0.021251</td>\n",
       "      <td>0.019877</td>\n",
       "      <td>0.019763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRV</th>\n",
       "      <td>0.028235</td>\n",
       "      <td>0.046075</td>\n",
       "      <td>0.026244</td>\n",
       "      <td>0.049670</td>\n",
       "      <td>0.035548</td>\n",
       "      <td>0.041814</td>\n",
       "      <td>0.028602</td>\n",
       "      <td>0.024923</td>\n",
       "      <td>0.010404</td>\n",
       "      <td>0.043195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060088</td>\n",
       "      <td>0.030931</td>\n",
       "      <td>0.019819</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.015974</td>\n",
       "      <td>0.027821</td>\n",
       "      <td>0.030218</td>\n",
       "      <td>0.023478</td>\n",
       "      <td>0.034226</td>\n",
       "      <td>0.026686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNH</th>\n",
       "      <td>0.027265</td>\n",
       "      <td>0.042366</td>\n",
       "      <td>0.036326</td>\n",
       "      <td>0.043013</td>\n",
       "      <td>0.030836</td>\n",
       "      <td>0.036705</td>\n",
       "      <td>0.031708</td>\n",
       "      <td>0.021903</td>\n",
       "      <td>0.011178</td>\n",
       "      <td>0.038549</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030931</td>\n",
       "      <td>0.070173</td>\n",
       "      <td>0.018935</td>\n",
       "      <td>0.035641</td>\n",
       "      <td>0.019165</td>\n",
       "      <td>0.030590</td>\n",
       "      <td>0.027815</td>\n",
       "      <td>0.033326</td>\n",
       "      <td>0.032597</td>\n",
       "      <td>0.033829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>0.017047</td>\n",
       "      <td>0.019516</td>\n",
       "      <td>0.015510</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>0.018608</td>\n",
       "      <td>0.020457</td>\n",
       "      <td>0.018935</td>\n",
       "      <td>0.017557</td>\n",
       "      <td>0.004427</td>\n",
       "      <td>0.019257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019819</td>\n",
       "      <td>0.018935</td>\n",
       "      <td>0.035544</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.014968</td>\n",
       "      <td>0.021232</td>\n",
       "      <td>0.016974</td>\n",
       "      <td>0.016923</td>\n",
       "      <td>0.017322</td>\n",
       "      <td>0.013802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0.031692</td>\n",
       "      <td>0.055494</td>\n",
       "      <td>0.046569</td>\n",
       "      <td>0.056293</td>\n",
       "      <td>0.039205</td>\n",
       "      <td>0.040715</td>\n",
       "      <td>0.039709</td>\n",
       "      <td>0.025159</td>\n",
       "      <td>0.012031</td>\n",
       "      <td>0.046429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.035641</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.068257</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.029527</td>\n",
       "      <td>0.042607</td>\n",
       "      <td>0.029492</td>\n",
       "      <td>0.040102</td>\n",
       "      <td>0.053669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>0.015694</td>\n",
       "      <td>0.015800</td>\n",
       "      <td>0.022358</td>\n",
       "      <td>0.017538</td>\n",
       "      <td>0.016820</td>\n",
       "      <td>0.014860</td>\n",
       "      <td>0.022037</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.002312</td>\n",
       "      <td>0.017675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015974</td>\n",
       "      <td>0.019165</td>\n",
       "      <td>0.014968</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.048830</td>\n",
       "      <td>0.024588</td>\n",
       "      <td>0.016559</td>\n",
       "      <td>0.018921</td>\n",
       "      <td>0.015479</td>\n",
       "      <td>0.019844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>0.032906</td>\n",
       "      <td>0.039406</td>\n",
       "      <td>0.031281</td>\n",
       "      <td>0.045312</td>\n",
       "      <td>0.037299</td>\n",
       "      <td>0.033232</td>\n",
       "      <td>0.034211</td>\n",
       "      <td>0.021925</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.039183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027821</td>\n",
       "      <td>0.030590</td>\n",
       "      <td>0.021232</td>\n",
       "      <td>0.029527</td>\n",
       "      <td>0.024588</td>\n",
       "      <td>0.088883</td>\n",
       "      <td>0.031695</td>\n",
       "      <td>0.030475</td>\n",
       "      <td>0.033111</td>\n",
       "      <td>0.027850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>0.030632</td>\n",
       "      <td>0.054407</td>\n",
       "      <td>0.038504</td>\n",
       "      <td>0.062272</td>\n",
       "      <td>0.039834</td>\n",
       "      <td>0.040509</td>\n",
       "      <td>0.037192</td>\n",
       "      <td>0.024005</td>\n",
       "      <td>0.009782</td>\n",
       "      <td>0.048252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030218</td>\n",
       "      <td>0.027815</td>\n",
       "      <td>0.016974</td>\n",
       "      <td>0.042607</td>\n",
       "      <td>0.016559</td>\n",
       "      <td>0.031695</td>\n",
       "      <td>0.080305</td>\n",
       "      <td>0.023223</td>\n",
       "      <td>0.040163</td>\n",
       "      <td>0.042087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMGN</th>\n",
       "      <td>0.025396</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>0.032058</td>\n",
       "      <td>0.026050</td>\n",
       "      <td>0.028903</td>\n",
       "      <td>0.024951</td>\n",
       "      <td>0.030038</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.006056</td>\n",
       "      <td>0.030403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023478</td>\n",
       "      <td>0.033326</td>\n",
       "      <td>0.016923</td>\n",
       "      <td>0.029492</td>\n",
       "      <td>0.018921</td>\n",
       "      <td>0.030475</td>\n",
       "      <td>0.023223</td>\n",
       "      <td>0.063006</td>\n",
       "      <td>0.026478</td>\n",
       "      <td>0.030146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HON</th>\n",
       "      <td>0.038317</td>\n",
       "      <td>0.052520</td>\n",
       "      <td>0.036203</td>\n",
       "      <td>0.063554</td>\n",
       "      <td>0.047004</td>\n",
       "      <td>0.045060</td>\n",
       "      <td>0.036890</td>\n",
       "      <td>0.026100</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>0.047291</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034226</td>\n",
       "      <td>0.032597</td>\n",
       "      <td>0.017322</td>\n",
       "      <td>0.040102</td>\n",
       "      <td>0.015479</td>\n",
       "      <td>0.033111</td>\n",
       "      <td>0.040163</td>\n",
       "      <td>0.026478</td>\n",
       "      <td>0.056693</td>\n",
       "      <td>0.034603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRM</th>\n",
       "      <td>0.028581</td>\n",
       "      <td>0.044761</td>\n",
       "      <td>0.058264</td>\n",
       "      <td>0.053303</td>\n",
       "      <td>0.035667</td>\n",
       "      <td>0.033270</td>\n",
       "      <td>0.045105</td>\n",
       "      <td>0.020407</td>\n",
       "      <td>0.011626</td>\n",
       "      <td>0.044724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026686</td>\n",
       "      <td>0.033829</td>\n",
       "      <td>0.013802</td>\n",
       "      <td>0.053669</td>\n",
       "      <td>0.019844</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>0.042087</td>\n",
       "      <td>0.030146</td>\n",
       "      <td>0.034603</td>\n",
       "      <td>0.125700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            MMM       AXP      AAPL        BA       CAT       CVX      CSCO  \\\n",
       "MMM    0.055676  0.039020  0.031454  0.042619  0.043129  0.034246  0.033934   \n",
       "AXP    0.039020  0.103630  0.042249  0.084360  0.054507  0.058980  0.041853   \n",
       "AAPL   0.031454  0.042249  0.089736  0.053437  0.037102  0.033323  0.044688   \n",
       "BA     0.042619  0.084360  0.053437  0.180151  0.065614  0.069045  0.046005   \n",
       "CAT    0.043129  0.054507  0.037102  0.065614  0.092222  0.056162  0.039267   \n",
       "CVX    0.034246  0.058980  0.033323  0.069045  0.056162  0.098577  0.036036   \n",
       "CSCO   0.033934  0.041853  0.044688  0.046005  0.039267  0.036036  0.069537   \n",
       "KO     0.022534  0.030378  0.021921  0.034529  0.022905  0.026002  0.022452   \n",
       "GS-PK  0.006730  0.015698  0.010677  0.019903  0.007880  0.015513  0.007820   \n",
       "GS     0.039561  0.067624  0.044733  0.072058  0.056290  0.054596  0.041331   \n",
       "HD     0.030487  0.040950  0.040236  0.048672  0.033414  0.033954  0.035249   \n",
       "IBM    0.032632  0.041886  0.032292  0.048302  0.039022  0.038639  0.036650   \n",
       "INTC   0.035459  0.047840  0.055091  0.059292  0.045177  0.042102  0.049138   \n",
       "JNJ    0.022027  0.023395  0.021918  0.024061  0.020981  0.022214  0.024511   \n",
       "JPM    0.037999  0.069155  0.038736  0.071486  0.054239  0.055332  0.040286   \n",
       "MCD    0.023132  0.034876  0.027480  0.040302  0.024003  0.030125  0.026249   \n",
       "NKE    0.031869  0.046872  0.042996  0.055530  0.037945  0.034353  0.036604   \n",
       "MRK    0.018631  0.023404  0.021665  0.023187  0.022313  0.025804  0.023983   \n",
       "MSFT   0.029964  0.043470  0.059883  0.049089  0.036885  0.035234  0.045954   \n",
       "PG     0.019852  0.021851  0.023790  0.022446  0.018098  0.017782  0.024414   \n",
       "TRV    0.028235  0.046075  0.026244  0.049670  0.035548  0.041814  0.028602   \n",
       "UNH    0.027265  0.042366  0.036326  0.043013  0.030836  0.036705  0.031708   \n",
       "VZ     0.017047  0.019516  0.015510  0.020921  0.018608  0.020457  0.018935   \n",
       "V      0.031692  0.055494  0.046569  0.056293  0.039205  0.040715  0.039709   \n",
       "WMT    0.015694  0.015800  0.022358  0.017538  0.016820  0.014860  0.022037   \n",
       "WBA    0.032906  0.039406  0.031281  0.045312  0.037299  0.033232  0.034211   \n",
       "DIS    0.030632  0.054407  0.038504  0.062272  0.039834  0.040509  0.037192   \n",
       "AMGN   0.025396  0.028300  0.032058  0.026050  0.028903  0.024951  0.030038   \n",
       "HON    0.038317  0.052520  0.036203  0.063554  0.047004  0.045060  0.036890   \n",
       "CRM    0.028581  0.044761  0.058264  0.053303  0.035667  0.033270  0.045105   \n",
       "\n",
       "             KO     GS-PK        GS  ...       TRV       UNH        VZ  \\\n",
       "MMM    0.022534  0.006730  0.039561  ...  0.028235  0.027265  0.017047   \n",
       "AXP    0.030378  0.015698  0.067624  ...  0.046075  0.042366  0.019516   \n",
       "AAPL   0.021921  0.010677  0.044733  ...  0.026244  0.036326  0.015510   \n",
       "BA     0.034529  0.019903  0.072058  ...  0.049670  0.043013  0.020921   \n",
       "CAT    0.022905  0.007880  0.056290  ...  0.035548  0.030836  0.018608   \n",
       "CVX    0.026002  0.015513  0.054596  ...  0.041814  0.036705  0.020457   \n",
       "CSCO   0.022452  0.007820  0.041331  ...  0.028602  0.031708  0.018935   \n",
       "KO     0.035649  0.007123  0.024511  ...  0.024923  0.021903  0.017557   \n",
       "GS-PK  0.007123  0.015124  0.011795  ...  0.010404  0.011178  0.004427   \n",
       "GS     0.024511  0.011795  0.088572  ...  0.043195  0.038549  0.019257   \n",
       "HD     0.022767  0.011038  0.041197  ...  0.031437  0.033632  0.017707   \n",
       "IBM    0.023493  0.008320  0.039154  ...  0.029785  0.028566  0.019709   \n",
       "INTC   0.024001  0.009475  0.048596  ...  0.033408  0.035774  0.019806   \n",
       "JNJ    0.018518  0.005263  0.021836  ...  0.019169  0.024901  0.016105   \n",
       "JPM    0.026480  0.011889  0.072159  ...  0.045776  0.037276  0.020788   \n",
       "MCD    0.021226  0.010406  0.030789  ...  0.028544  0.025707  0.014606   \n",
       "NKE    0.023265  0.012234  0.042931  ...  0.028563  0.031789  0.016445   \n",
       "MRK    0.018078  0.006975  0.024188  ...  0.022743  0.026421  0.017250   \n",
       "MSFT   0.023537  0.010861  0.043663  ...  0.028805  0.037504  0.017745   \n",
       "PG     0.023023  0.004746  0.020910  ...  0.020125  0.021984  0.018162   \n",
       "TRV    0.024923  0.010404  0.043195  ...  0.060088  0.030931  0.019819   \n",
       "UNH    0.021903  0.011178  0.038549  ...  0.030931  0.070173  0.018935   \n",
       "VZ     0.017557  0.004427  0.019257  ...  0.019819  0.018935  0.035544   \n",
       "V      0.025159  0.012031  0.046429  ...  0.033079  0.035641  0.016949   \n",
       "WMT    0.015389  0.002312  0.017675  ...  0.015974  0.019165  0.014968   \n",
       "WBA    0.021925  0.004425  0.039183  ...  0.027821  0.030590  0.021232   \n",
       "DIS    0.024005  0.009782  0.048252  ...  0.030218  0.027815  0.016974   \n",
       "AMGN   0.018360  0.006056  0.030403  ...  0.023478  0.033326  0.016923   \n",
       "HON    0.026100  0.011543  0.047291  ...  0.034226  0.032597  0.017322   \n",
       "CRM    0.020407  0.011626  0.044724  ...  0.026686  0.033829  0.013802   \n",
       "\n",
       "              V       WMT       WBA       DIS      AMGN       HON       CRM  \n",
       "MMM    0.031692  0.015694  0.032906  0.030632  0.025396  0.038317  0.028581  \n",
       "AXP    0.055494  0.015800  0.039406  0.054407  0.028300  0.052520  0.044761  \n",
       "AAPL   0.046569  0.022358  0.031281  0.038504  0.032058  0.036203  0.058264  \n",
       "BA     0.056293  0.017538  0.045312  0.062272  0.026050  0.063554  0.053303  \n",
       "CAT    0.039205  0.016820  0.037299  0.039834  0.028903  0.047004  0.035667  \n",
       "CVX    0.040715  0.014860  0.033232  0.040509  0.024951  0.045060  0.033270  \n",
       "CSCO   0.039709  0.022037  0.034211  0.037192  0.030038  0.036890  0.045105  \n",
       "KO     0.025159  0.015389  0.021925  0.024005  0.018360  0.026100  0.020407  \n",
       "GS-PK  0.012031  0.002312  0.004425  0.009782  0.006056  0.011543  0.011626  \n",
       "GS     0.046429  0.017675  0.039183  0.048252  0.030403  0.047291  0.044724  \n",
       "HD     0.038047  0.023156  0.029259  0.035274  0.027542  0.034334  0.043483  \n",
       "IBM    0.034885  0.017175  0.031284  0.030760  0.026189  0.033909  0.031082  \n",
       "INTC   0.044445  0.022878  0.036897  0.040118  0.034535  0.040001  0.051544  \n",
       "JNJ    0.022298  0.016132  0.023381  0.017500  0.025862  0.021624  0.019400  \n",
       "JPM    0.045087  0.016862  0.038589  0.047197  0.029719  0.047364  0.039163  \n",
       "MCD    0.031189  0.014840  0.019029  0.027360  0.019549  0.028633  0.029109  \n",
       "NKE    0.042197  0.019001  0.029734  0.041113  0.023400  0.038798  0.047211  \n",
       "MRK    0.023713  0.014916  0.021673  0.016782  0.030151  0.021995  0.022690  \n",
       "MSFT   0.049633  0.022932  0.031710  0.038897  0.033085  0.036835  0.065486  \n",
       "PG     0.022408  0.020112  0.022526  0.019269  0.021251  0.019877  0.019763  \n",
       "TRV    0.033079  0.015974  0.027821  0.030218  0.023478  0.034226  0.026686  \n",
       "UNH    0.035641  0.019165  0.030590  0.027815  0.033326  0.032597  0.033829  \n",
       "VZ     0.016949  0.014968  0.021232  0.016974  0.016923  0.017322  0.013802  \n",
       "V      0.068257  0.017143  0.029527  0.042607  0.029492  0.040102  0.053669  \n",
       "WMT    0.017143  0.048830  0.024588  0.016559  0.018921  0.015479  0.019844  \n",
       "WBA    0.029527  0.024588  0.088883  0.031695  0.030475  0.033111  0.027850  \n",
       "DIS    0.042607  0.016559  0.031695  0.080305  0.023223  0.040163  0.042087  \n",
       "AMGN   0.029492  0.018921  0.030475  0.023223  0.063006  0.026478  0.030146  \n",
       "HON    0.040102  0.015479  0.033111  0.040163  0.026478  0.056693  0.034603  \n",
       "CRM    0.053669  0.019844  0.027850  0.042087  0.030146  0.034603  0.125700  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.DataFrame(C, columns = names, index = names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #plotting covariance\n",
    "# df = pd.DataFrame(C, columns = names, index = names)\n",
    "# colormap = sns.color_palette(\"coolwarm\")\n",
    "# ax = sns.heatmap(df, cmap = colormap, annot = True, linewidths = 0.5, linecolor = 'white', cbar = True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.23595793 0.32191603 0.29955897 0.42444232 0.3036807  0.31396997\n",
      " 0.26369833 0.18880858 0.1229788  0.29761011 0.2514801  0.24827327\n",
      " 0.32848747 0.18596122 0.28528387 0.21474116 0.29783138 0.22024845\n",
      " 0.28207494 0.19409466 0.24512931 0.26490156 0.18853155 0.26126043\n",
      " 0.22097575 0.29813305 0.28338172 0.25100968 0.23810308 0.35454174]\n"
     ]
    },
    {
     "data": {
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#T_1a194_row7_col10, #T_1a194_row7_col11, #T_1a194_row7_col12, #T_1a194_row7_col13, #T_1a194_row7_col14, #T_1a194_row7_col15, #T_1a194_row7_col16, #T_1a194_row7_col17, #T_1a194_row7_col18, #T_1a194_row7_col19, #T_1a194_row7_col20, #T_1a194_row7_col21, #T_1a194_row7_col22, #T_1a194_row7_col23, #T_1a194_row7_col24, #T_1a194_row7_col25, #T_1a194_row7_col26, #T_1a194_row7_col27, #T_1a194_row7_col28, #T_1a194_row7_col29, #T_1a194_row8_col8, #T_1a194_row8_col9, #T_1a194_row8_col10, #T_1a194_row8_col11, #T_1a194_row8_col12, #T_1a194_row8_col13, #T_1a194_row8_col14, #T_1a194_row8_col15, #T_1a194_row8_col16, #T_1a194_row8_col17, #T_1a194_row8_col18, #T_1a194_row8_col19, #T_1a194_row8_col20, #T_1a194_row8_col21, #T_1a194_row8_col22, #T_1a194_row8_col23, #T_1a194_row8_col24, #T_1a194_row8_col25, #T_1a194_row8_col26, #T_1a194_row8_col27, #T_1a194_row8_col28, #T_1a194_row8_col29, #T_1a194_row9_col9, #T_1a194_row9_col10, #T_1a194_row9_col11, #T_1a194_row9_col12, #T_1a194_row9_col13, #T_1a194_row9_col14, #T_1a194_row9_col15, #T_1a194_row9_col16, #T_1a194_row9_col17, #T_1a194_row9_col18, #T_1a194_row9_col19, #T_1a194_row9_col20, #T_1a194_row9_col21, #T_1a194_row9_col22, #T_1a194_row9_col23, #T_1a194_row9_col24, #T_1a194_row9_col25, #T_1a194_row9_col26, #T_1a194_row9_col27, #T_1a194_row9_col28, #T_1a194_row9_col29, #T_1a194_row10_col10, #T_1a194_row10_col11, #T_1a194_row10_col12, #T_1a194_row10_col13, #T_1a194_row10_col14, #T_1a194_row10_col15, #T_1a194_row10_col16, #T_1a194_row10_col17, #T_1a194_row10_col18, #T_1a194_row10_col19, #T_1a194_row10_col20, #T_1a194_row10_col21, #T_1a194_row10_col22, #T_1a194_row10_col23, #T_1a194_row10_col24, #T_1a194_row10_col25, #T_1a194_row10_col26, #T_1a194_row10_col27, #T_1a194_row10_col28, #T_1a194_row10_col29, #T_1a194_row11_col11, #T_1a194_row11_col12, #T_1a194_row11_col13, #T_1a194_row11_col14, #T_1a194_row11_col15, #T_1a194_row11_col16, #T_1a194_row11_col17, #T_1a194_row11_col18, #T_1a194_row11_col19, #T_1a194_row11_col20, #T_1a194_row11_col21, #T_1a194_row11_col22, #T_1a194_row11_col23, #T_1a194_row11_col24, #T_1a194_row11_col25, #T_1a194_row11_col26, #T_1a194_row11_col27, #T_1a194_row11_col28, #T_1a194_row11_col29, #T_1a194_row12_col12, #T_1a194_row12_col13, #T_1a194_row12_col14, #T_1a194_row12_col15, #T_1a194_row12_col16, #T_1a194_row12_col17, #T_1a194_row12_col18, #T_1a194_row12_col19, #T_1a194_row12_col20, #T_1a194_row12_col21, #T_1a194_row12_col22, #T_1a194_row12_col23, #T_1a194_row12_col24, #T_1a194_row12_col25, #T_1a194_row12_col26, #T_1a194_row12_col27, #T_1a194_row12_col28, #T_1a194_row12_col29, #T_1a194_row13_col13, #T_1a194_row13_col14, #T_1a194_row13_col15, #T_1a194_row13_col16, #T_1a194_row13_col17, #T_1a194_row13_col18, #T_1a194_row13_col19, #T_1a194_row13_col20, #T_1a194_row13_col21, #T_1a194_row13_col22, #T_1a194_row13_col23, #T_1a194_row13_col24, #T_1a194_row13_col25, #T_1a194_row13_col26, #T_1a194_row13_col27, #T_1a194_row13_col28, #T_1a194_row13_col29, #T_1a194_row14_col14, #T_1a194_row14_col15, #T_1a194_row14_col16, #T_1a194_row14_col17, #T_1a194_row14_col18, #T_1a194_row14_col19, #T_1a194_row14_col20, #T_1a194_row14_col21, #T_1a194_row14_col22, #T_1a194_row14_col23, #T_1a194_row14_col24, #T_1a194_row14_col25, #T_1a194_row14_col26, #T_1a194_row14_col27, #T_1a194_row14_col28, #T_1a194_row14_col29, #T_1a194_row15_col15, #T_1a194_row15_col16, #T_1a194_row15_col17, #T_1a194_row15_col18, #T_1a194_row15_col19, #T_1a194_row15_col20, #T_1a194_row15_col21, #T_1a194_row15_col22, #T_1a194_row15_col23, #T_1a194_row15_col24, #T_1a194_row15_col25, #T_1a194_row15_col26, #T_1a194_row15_col27, #T_1a194_row15_col28, #T_1a194_row15_col29, #T_1a194_row16_col16, #T_1a194_row16_col17, #T_1a194_row16_col18, #T_1a194_row16_col19, #T_1a194_row16_col20, #T_1a194_row16_col21, #T_1a194_row16_col22, #T_1a194_row16_col23, #T_1a194_row16_col24, #T_1a194_row16_col25, #T_1a194_row16_col26, #T_1a194_row16_col27, #T_1a194_row16_col28, #T_1a194_row16_col29, #T_1a194_row17_col17, #T_1a194_row17_col18, #T_1a194_row17_col19, #T_1a194_row17_col20, #T_1a194_row17_col21, #T_1a194_row17_col22, #T_1a194_row17_col23, #T_1a194_row17_col24, #T_1a194_row17_col25, #T_1a194_row17_col26, #T_1a194_row17_col27, #T_1a194_row17_col28, #T_1a194_row17_col29, #T_1a194_row18_col18, #T_1a194_row18_col19, #T_1a194_row18_col20, #T_1a194_row18_col21, #T_1a194_row18_col22, #T_1a194_row18_col23, #T_1a194_row18_col24, #T_1a194_row18_col25, #T_1a194_row18_col26, #T_1a194_row18_col27, #T_1a194_row18_col28, #T_1a194_row18_col29, #T_1a194_row19_col19, #T_1a194_row19_col20, #T_1a194_row19_col21, #T_1a194_row19_col22, #T_1a194_row19_col23, #T_1a194_row19_col24, #T_1a194_row19_col25, #T_1a194_row19_col26, #T_1a194_row19_col27, #T_1a194_row19_col28, #T_1a194_row19_col29, #T_1a194_row20_col20, #T_1a194_row20_col21, #T_1a194_row20_col22, #T_1a194_row20_col23, #T_1a194_row20_col24, #T_1a194_row20_col25, #T_1a194_row20_col26, #T_1a194_row20_col27, #T_1a194_row20_col28, #T_1a194_row20_col29, #T_1a194_row21_col21, #T_1a194_row21_col22, #T_1a194_row21_col23, #T_1a194_row21_col24, #T_1a194_row21_col25, #T_1a194_row21_col26, #T_1a194_row21_col27, #T_1a194_row21_col28, #T_1a194_row21_col29, #T_1a194_row22_col22, #T_1a194_row22_col23, #T_1a194_row22_col24, #T_1a194_row22_col25, #T_1a194_row22_col26, #T_1a194_row22_col27, #T_1a194_row22_col28, #T_1a194_row22_col29, #T_1a194_row23_col23, #T_1a194_row23_col24, #T_1a194_row23_col25, #T_1a194_row23_col26, #T_1a194_row23_col27, #T_1a194_row23_col28, #T_1a194_row23_col29, #T_1a194_row24_col24, #T_1a194_row24_col25, #T_1a194_row24_col26, #T_1a194_row24_col27, #T_1a194_row24_col28, #T_1a194_row24_col29, #T_1a194_row25_col25, #T_1a194_row25_col26, #T_1a194_row25_col27, #T_1a194_row25_col28, #T_1a194_row25_col29, #T_1a194_row26_col26, #T_1a194_row26_col27, #T_1a194_row26_col28, #T_1a194_row26_col29, #T_1a194_row27_col27, #T_1a194_row27_col28, #T_1a194_row27_col29, #T_1a194_row28_col28, #T_1a194_row28_col29, #T_1a194_row29_col29 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "  background-color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row1_col0, #T_1a194_row4_col3, #T_1a194_row10_col0, #T_1a194_row20_col10, #T_1a194_row23_col0, #T_1a194_row23_col7, #T_1a194_row23_col21, #T_1a194_row28_col12 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row2_col0, #T_1a194_row2_col1, #T_1a194_row7_col5, #T_1a194_row10_col4, #T_1a194_row15_col3, #T_1a194_row15_col11, #T_1a194_row15_col13, #T_1a194_row16_col3, #T_1a194_row18_col7, #T_1a194_row20_col6, #T_1a194_row21_col5, #T_1a194_row21_col7, #T_1a194_row23_col19, #T_1a194_row25_col9, #T_1a194_row28_col27, #T_1a194_row29_col12 {\n",
       "  background-color: #f6a586;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row3_col0, #T_1a194_row12_col3, #T_1a194_row13_col10, #T_1a194_row14_col13, #T_1a194_row15_col2, #T_1a194_row20_col19, #T_1a194_row21_col19, #T_1a194_row22_col20, #T_1a194_row25_col11, #T_1a194_row27_col0, #T_1a194_row27_col2, #T_1a194_row29_col9 {\n",
       "  background-color: #f7a889;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row3_col1, #T_1a194_row9_col4, #T_1a194_row14_col5, #T_1a194_row18_col6 {\n",
       "  background-color: #ec7f63;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row3_col2, #T_1a194_row16_col4, #T_1a194_row20_col12, #T_1a194_row20_col13, #T_1a194_row20_col17, #T_1a194_row20_col18, #T_1a194_row22_col11, #T_1a194_row22_col17, #T_1a194_row24_col10, #T_1a194_row25_col13, #T_1a194_row27_col11, #T_1a194_row27_col12, #T_1a194_row27_col14, #T_1a194_row28_col17, #T_1a194_row29_col26 {\n",
       "  background-color: #f7a98b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row4_col0, #T_1a194_row23_col14, #T_1a194_row28_col5 {\n",
       "  background-color: #ed8366;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row4_col1, #T_1a194_row11_col0, #T_1a194_row11_col6, #T_1a194_row12_col2, #T_1a194_row17_col13, #T_1a194_row23_col15, #T_1a194_row28_col15 {\n",
       "  background-color: #f18d6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row4_col2, #T_1a194_row6_col3, #T_1a194_row12_col5, #T_1a194_row16_col7, #T_1a194_row16_col11, #T_1a194_row16_col12, #T_1a194_row17_col6, #T_1a194_row17_col11, #T_1a194_row18_col3, #T_1a194_row19_col2, #T_1a194_row21_col12, #T_1a194_row23_col17, #T_1a194_row25_col1, #T_1a194_row25_col4, #T_1a194_row27_col9, #T_1a194_row27_col25, #T_1a194_row29_col28 {\n",
       "  background-color: #f7aa8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row5_col0, #T_1a194_row13_col11, #T_1a194_row15_col6, #T_1a194_row16_col6, #T_1a194_row19_col10, #T_1a194_row25_col0, #T_1a194_row26_col4, #T_1a194_row27_col18, #T_1a194_row28_col25 {\n",
       "  background-color: #f5a081;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row5_col1, #T_1a194_row9_col5, #T_1a194_row20_col1, #T_1a194_row23_col10, #T_1a194_row26_col14, #T_1a194_row28_col7, #T_1a194_row29_col23 {\n",
       "  background-color: #ef886b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row5_col2, #T_1a194_row10_col8, #T_1a194_row17_col0, #T_1a194_row20_col2, #T_1a194_row24_col22, #T_1a194_row25_col3, #T_1a194_row25_col5, #T_1a194_row27_col22, #T_1a194_row29_col3, #T_1a194_row29_col11 {\n",
       "  background-color: #f7b497;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row5_col3, #T_1a194_row11_col4, #T_1a194_row18_col9, #T_1a194_row18_col16, #T_1a194_row23_col12, #T_1a194_row23_col20, #T_1a194_row26_col3, #T_1a194_row28_col21 {\n",
       "  background-color: #f39577;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row5_col4, #T_1a194_row14_col3, #T_1a194_row18_col10, #T_1a194_row20_col9, #T_1a194_row28_col6, #T_1a194_row28_col20 {\n",
       "  background-color: #ee8669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row6_col0, #T_1a194_row20_col5, #T_1a194_row20_col15, #T_1a194_row23_col16, #T_1a194_row27_col17 {\n",
       "  background-color: #f29072;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row6_col1, #T_1a194_row7_col1, #T_1a194_row11_col5, #T_1a194_row11_col10, #T_1a194_row12_col9, #T_1a194_row12_col11, #T_1a194_row13_col6, #T_1a194_row21_col1, #T_1a194_row21_col14, #T_1a194_row22_col7, #T_1a194_row22_col19, #T_1a194_row23_col4, #T_1a194_row23_col5, #T_1a194_row26_col6, #T_1a194_row26_col10 {\n",
       "  background-color: #f49a7b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row6_col2, #T_1a194_row9_col0, #T_1a194_row12_col6, #T_1a194_row14_col0, #T_1a194_row14_col11, #T_1a194_row19_col13 {\n",
       "  background-color: #f08b6e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row6_col4, #T_1a194_row14_col7, #T_1a194_row14_col12, #T_1a194_row16_col1, #T_1a194_row18_col14, #T_1a194_row18_col15, #T_1a194_row20_col0, #T_1a194_row20_col11, #T_1a194_row21_col9, #T_1a194_row26_col16, #T_1a194_row26_col18, #T_1a194_row28_col13, #T_1a194_row29_col10 {\n",
       "  background-color: #f59c7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row6_col5, #T_1a194_row7_col3, #T_1a194_row9_col7, #T_1a194_row10_col5, #T_1a194_row11_col2, #T_1a194_row17_col7, #T_1a194_row18_col4, #T_1a194_row19_col0, #T_1a194_row19_col17, #T_1a194_row21_col0, #T_1a194_row21_col11, #T_1a194_row25_col6, #T_1a194_row26_col11, #T_1a194_row26_col12, #T_1a194_row26_col20, #T_1a194_row27_col10, #T_1a194_row27_col19, #T_1a194_row28_col19 {\n",
       "  background-color: #f7a688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row7_col0, #T_1a194_row9_col2, #T_1a194_row10_col1, #T_1a194_row11_col7, #T_1a194_row12_col10, #T_1a194_row13_col0, #T_1a194_row15_col1, #T_1a194_row15_col14, #T_1a194_row21_col10, #T_1a194_row21_col13, #T_1a194_row21_col18, #T_1a194_row23_col3, #T_1a194_row27_col21, #T_1a194_row28_col2 {\n",
       "  background-color: #f4987a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row7_col2, #T_1a194_row12_col7, #T_1a194_row19_col12, #T_1a194_row21_col4, #T_1a194_row22_col0, #T_1a194_row22_col14, #T_1a194_row25_col7, #T_1a194_row25_col10, #T_1a194_row25_col19, #T_1a194_row25_col21, #T_1a194_row27_col7, #T_1a194_row28_col22, #T_1a194_row29_col14 {\n",
       "  background-color: #f7af91;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row7_col4, #T_1a194_row8_col5, #T_1a194_row17_col14, #T_1a194_row17_col15, #T_1a194_row21_col16 {\n",
       "  background-color: #f7ac8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row7_col6, #T_1a194_row12_col1, #T_1a194_row12_col4, #T_1a194_row15_col5, #T_1a194_row16_col15, #T_1a194_row18_col0, #T_1a194_row18_col13, #T_1a194_row19_col15, #T_1a194_row21_col15, #T_1a194_row21_col17, #T_1a194_row26_col7, #T_1a194_row26_col15, #T_1a194_row27_col23, #T_1a194_row29_col16 {\n",
       "  background-color: #f6a385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col0, #T_1a194_row13_col8 {\n",
       "  background-color: #f3c7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col1, #T_1a194_row13_col1, #T_1a194_row13_col2, #T_1a194_row13_col9, #T_1a194_row15_col8, #T_1a194_row18_col5, #T_1a194_row18_col17, #T_1a194_row20_col16, #T_1a194_row24_col13, #T_1a194_row28_col8, #T_1a194_row29_col1 {\n",
       "  background-color: #f7ad90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col2, #T_1a194_row19_col5, #T_1a194_row22_col16, #T_1a194_row24_col20, #T_1a194_row28_col24, #T_1a194_row29_col13, #T_1a194_row29_col17 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col3, #T_1a194_row13_col5, #T_1a194_row13_col12, #T_1a194_row19_col14, #T_1a194_row21_col3, #T_1a194_row22_col6, #T_1a194_row22_col21, #T_1a194_row24_col6, #T_1a194_row25_col12, #T_1a194_row25_col18, #T_1a194_row25_col20, #T_1a194_row25_col22, #T_1a194_row25_col23, #T_1a194_row26_col25, #T_1a194_row27_col4, #T_1a194_row27_col20, #T_1a194_row29_col15 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col4, #T_1a194_row24_col5 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col6, #T_1a194_row12_col8 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row8_col7, #T_1a194_row13_col3, #T_1a194_row17_col12, #T_1a194_row19_col4, #T_1a194_row24_col17, #T_1a194_row29_col7, #T_1a194_row29_col20 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row9_col1, #T_1a194_row18_col2 {\n",
       "  background-color: #e36b54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row9_col3, #T_1a194_row23_col6, #T_1a194_row26_col9, #T_1a194_row26_col23, #T_1a194_row28_col10, #T_1a194_row28_col11 {\n",
       "  background-color: #f08a6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row9_col6, #T_1a194_row11_col1, #T_1a194_row11_col9, #T_1a194_row13_col7, #T_1a194_row15_col7 {\n",
       "  background-color: #f39475;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row9_col8, #T_1a194_row22_col1, #T_1a194_row22_col4, #T_1a194_row24_col21, #T_1a194_row27_col26 {\n",
       "  background-color: #f7b99e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row10_col2, #T_1a194_row10_col6, #T_1a194_row14_col6, #T_1a194_row14_col10, #T_1a194_row20_col7, #T_1a194_row23_col11 {\n",
       "  background-color: #f29274;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row10_col3, #T_1a194_row11_col3, #T_1a194_row12_col0, #T_1a194_row14_col2, #T_1a194_row15_col0, #T_1a194_row16_col0, #T_1a194_row19_col11, #T_1a194_row21_col2, #T_1a194_row21_col6, #T_1a194_row22_col13, #T_1a194_row23_col13, #T_1a194_row25_col14, #T_1a194_row26_col0, #T_1a194_row26_col2, #T_1a194_row26_col5, #T_1a194_row27_col6 {\n",
       "  background-color: #f6a283;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row10_col7, #T_1a194_row15_col9, #T_1a194_row16_col2, #T_1a194_row16_col9, #T_1a194_row16_col14, #T_1a194_row18_col1, #T_1a194_row18_col11, #T_1a194_row19_col6, #T_1a194_row20_col3, #T_1a194_row20_col4, #T_1a194_row29_col6 {\n",
       "  background-color: #f59d7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row10_col9, #T_1a194_row15_col10, #T_1a194_row16_col10, #T_1a194_row27_col13, #T_1a194_row28_col16, #T_1a194_row28_col18, #T_1a194_row29_col2 {\n",
       "  background-color: #f18f71;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row11_col8, #T_1a194_row19_col3, #T_1a194_row24_col9, #T_1a194_row24_col14, #T_1a194_row26_col17, #T_1a194_row29_col8 {\n",
       "  background-color: #f5c1a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row13_col4, #T_1a194_row15_col4, #T_1a194_row15_col12, #T_1a194_row16_col5, #T_1a194_row17_col5, #T_1a194_row17_col9, #T_1a194_row17_col10, #T_1a194_row19_col16, #T_1a194_row22_col10, #T_1a194_row23_col8, #T_1a194_row24_col7, #T_1a194_row24_col18, #T_1a194_row25_col24, #T_1a194_row26_col21 {\n",
       "  background-color: #f7b194;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row14_col1 {\n",
       "  background-color: #dd5f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row14_col4, #T_1a194_row19_col7, #T_1a194_row28_col3 {\n",
       "  background-color: #eb7d62;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row14_col8, #T_1a194_row21_col8, #T_1a194_row22_col9, #T_1a194_row24_col2, #T_1a194_row27_col24, #T_1a194_row29_col0, #T_1a194_row29_col27 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row14_col9 {\n",
       "  background-color: #cf453c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row16_col8, #T_1a194_row17_col1, #T_1a194_row17_col2, #T_1a194_row17_col4, #T_1a194_row22_col18, #T_1a194_row25_col16, #T_1a194_row25_col17, #T_1a194_row26_col13, #T_1a194_row29_col4 {\n",
       "  background-color: #f7b89c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row16_col13, #T_1a194_row19_col1, #T_1a194_row20_col8, #T_1a194_row22_col5, #T_1a194_row23_col22, #T_1a194_row25_col2, #T_1a194_row26_col19, #T_1a194_row27_col1 {\n",
       "  background-color: #f7b599;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row17_col3, #T_1a194_row27_col3 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row17_col8, #T_1a194_row24_col4, #T_1a194_row29_col24 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row17_col16, #T_1a194_row24_col0, #T_1a194_row24_col23, #T_1a194_row25_col15, #T_1a194_row29_col5 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row18_col8, #T_1a194_row22_col12, #T_1a194_row24_col11, #T_1a194_row24_col12, #T_1a194_row24_col15, #T_1a194_row26_col22, #T_1a194_row27_col5, #T_1a194_row27_col16 {\n",
       "  background-color: #f7ba9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row18_col12 {\n",
       "  background-color: #ec8165;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row19_col8, #T_1a194_row27_col8 {\n",
       "  background-color: #f2cbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row19_col9, #T_1a194_row22_col15, #T_1a194_row27_col15, #T_1a194_row29_col21 {\n",
       "  background-color: #f7b396;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row19_col18, #T_1a194_row21_col20, #T_1a194_row24_col19 {\n",
       "  background-color: #f59f80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row20_col14, #T_1a194_row28_col4, #T_1a194_row29_col18 {\n",
       "  background-color: #e9785d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row22_col2, #T_1a194_row26_col8 {\n",
       "  background-color: #f5c0a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row22_col3, #T_1a194_row26_col24, #T_1a194_row29_col25 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row22_col8 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row23_col1 {\n",
       "  background-color: #e8765c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row23_col2, #T_1a194_row23_col9, #T_1a194_row26_col1, #T_1a194_row28_col26 {\n",
       "  background-color: #ee8468;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row23_col18 {\n",
       "  background-color: #e67259;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row24_col1 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row24_col3 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row24_col8 {\n",
       "  background-color: #e7d7ce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row24_col16, #T_1a194_row29_col19 {\n",
       "  background-color: #f6bfa6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row25_col8 {\n",
       "  background-color: #ebd3c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a194_row28_col0, #T_1a194_row28_col1 {\n",
       "  background-color: #e57058;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row28_col9 {\n",
       "  background-color: #e7745b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row28_col14 {\n",
       "  background-color: #e36c55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row28_col23 {\n",
       "  background-color: #e97a5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a194_row29_col22 {\n",
       "  background-color: #f2cab5;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1a194\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1a194_level0_col0\" class=\"col_heading level0 col0\" >MMM</th>\n",
       "      <th id=\"T_1a194_level0_col1\" class=\"col_heading level0 col1\" >AXP</th>\n",
       "      <th id=\"T_1a194_level0_col2\" class=\"col_heading level0 col2\" >AAPL</th>\n",
       "      <th id=\"T_1a194_level0_col3\" class=\"col_heading level0 col3\" >BA</th>\n",
       "      <th id=\"T_1a194_level0_col4\" class=\"col_heading level0 col4\" >CAT</th>\n",
       "      <th id=\"T_1a194_level0_col5\" class=\"col_heading level0 col5\" >CVX</th>\n",
       "      <th id=\"T_1a194_level0_col6\" class=\"col_heading level0 col6\" >CSCO</th>\n",
       "      <th id=\"T_1a194_level0_col7\" class=\"col_heading level0 col7\" >KO</th>\n",
       "      <th id=\"T_1a194_level0_col8\" class=\"col_heading level0 col8\" >GS-PK</th>\n",
       "      <th id=\"T_1a194_level0_col9\" class=\"col_heading level0 col9\" >GS</th>\n",
       "      <th id=\"T_1a194_level0_col10\" class=\"col_heading level0 col10\" >HD</th>\n",
       "      <th id=\"T_1a194_level0_col11\" class=\"col_heading level0 col11\" >IBM</th>\n",
       "      <th id=\"T_1a194_level0_col12\" class=\"col_heading level0 col12\" >INTC</th>\n",
       "      <th id=\"T_1a194_level0_col13\" class=\"col_heading level0 col13\" >JNJ</th>\n",
       "      <th id=\"T_1a194_level0_col14\" class=\"col_heading level0 col14\" >JPM</th>\n",
       "      <th id=\"T_1a194_level0_col15\" class=\"col_heading level0 col15\" >MCD</th>\n",
       "      <th id=\"T_1a194_level0_col16\" class=\"col_heading level0 col16\" >NKE</th>\n",
       "      <th id=\"T_1a194_level0_col17\" class=\"col_heading level0 col17\" >MRK</th>\n",
       "      <th id=\"T_1a194_level0_col18\" class=\"col_heading level0 col18\" >MSFT</th>\n",
       "      <th id=\"T_1a194_level0_col19\" class=\"col_heading level0 col19\" >PG</th>\n",
       "      <th id=\"T_1a194_level0_col20\" class=\"col_heading level0 col20\" >TRV</th>\n",
       "      <th id=\"T_1a194_level0_col21\" class=\"col_heading level0 col21\" >UNH</th>\n",
       "      <th id=\"T_1a194_level0_col22\" class=\"col_heading level0 col22\" >VZ</th>\n",
       "      <th id=\"T_1a194_level0_col23\" class=\"col_heading level0 col23\" >V</th>\n",
       "      <th id=\"T_1a194_level0_col24\" class=\"col_heading level0 col24\" >WMT</th>\n",
       "      <th id=\"T_1a194_level0_col25\" class=\"col_heading level0 col25\" >WBA</th>\n",
       "      <th id=\"T_1a194_level0_col26\" class=\"col_heading level0 col26\" >DIS</th>\n",
       "      <th id=\"T_1a194_level0_col27\" class=\"col_heading level0 col27\" >AMGN</th>\n",
       "      <th id=\"T_1a194_level0_col28\" class=\"col_heading level0 col28\" >HON</th>\n",
       "      <th id=\"T_1a194_level0_col29\" class=\"col_heading level0 col29\" >CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row0\" class=\"row_heading level0 row0\" >MMM</th>\n",
       "      <td id=\"T_1a194_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col7\" class=\"data row0 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col8\" class=\"data row0 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col10\" class=\"data row0 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col11\" class=\"data row0 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col12\" class=\"data row0 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col13\" class=\"data row0 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col15\" class=\"data row0 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col16\" class=\"data row0 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col17\" class=\"data row0 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col19\" class=\"data row0 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col20\" class=\"data row0 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col21\" class=\"data row0 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col22\" class=\"data row0 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col23\" class=\"data row0 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col24\" class=\"data row0 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col25\" class=\"data row0 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col27\" class=\"data row0 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col28\" class=\"data row0 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row0_col29\" class=\"data row0 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row1\" class=\"row_heading level0 row1\" >AXP</th>\n",
       "      <td id=\"T_1a194_row1_col0\" class=\"data row1 col0\" >0.51</td>\n",
       "      <td id=\"T_1a194_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col7\" class=\"data row1 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col10\" class=\"data row1 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col12\" class=\"data row1 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col13\" class=\"data row1 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col15\" class=\"data row1 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col17\" class=\"data row1 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col19\" class=\"data row1 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col20\" class=\"data row1 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col21\" class=\"data row1 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col22\" class=\"data row1 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col23\" class=\"data row1 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col24\" class=\"data row1 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col25\" class=\"data row1 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col27\" class=\"data row1 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col28\" class=\"data row1 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row1_col29\" class=\"data row1 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row2\" class=\"row_heading level0 row2\" >AAPL</th>\n",
       "      <td id=\"T_1a194_row2_col0\" class=\"data row2 col0\" >0.45</td>\n",
       "      <td id=\"T_1a194_row2_col1\" class=\"data row2 col1\" >0.44</td>\n",
       "      <td id=\"T_1a194_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col7\" class=\"data row2 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col8\" class=\"data row2 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col10\" class=\"data row2 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col11\" class=\"data row2 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col12\" class=\"data row2 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col13\" class=\"data row2 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col15\" class=\"data row2 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col17\" class=\"data row2 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col19\" class=\"data row2 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col20\" class=\"data row2 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col21\" class=\"data row2 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col22\" class=\"data row2 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col23\" class=\"data row2 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col24\" class=\"data row2 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col25\" class=\"data row2 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col27\" class=\"data row2 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col28\" class=\"data row2 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row2_col29\" class=\"data row2 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row3\" class=\"row_heading level0 row3\" >BA</th>\n",
       "      <td id=\"T_1a194_row3_col0\" class=\"data row3 col0\" >0.43</td>\n",
       "      <td id=\"T_1a194_row3_col1\" class=\"data row3 col1\" >0.62</td>\n",
       "      <td id=\"T_1a194_row3_col2\" class=\"data row3 col2\" >0.42</td>\n",
       "      <td id=\"T_1a194_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col7\" class=\"data row3 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col8\" class=\"data row3 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col9\" class=\"data row3 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col10\" class=\"data row3 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col11\" class=\"data row3 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col12\" class=\"data row3 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col13\" class=\"data row3 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col15\" class=\"data row3 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col16\" class=\"data row3 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col17\" class=\"data row3 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col19\" class=\"data row3 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col20\" class=\"data row3 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col21\" class=\"data row3 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col22\" class=\"data row3 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col23\" class=\"data row3 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col24\" class=\"data row3 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col25\" class=\"data row3 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col27\" class=\"data row3 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col28\" class=\"data row3 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row3_col29\" class=\"data row3 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row4\" class=\"row_heading level0 row4\" >CAT</th>\n",
       "      <td id=\"T_1a194_row4_col0\" class=\"data row4 col0\" >0.60</td>\n",
       "      <td id=\"T_1a194_row4_col1\" class=\"data row4 col1\" >0.56</td>\n",
       "      <td id=\"T_1a194_row4_col2\" class=\"data row4 col2\" >0.41</td>\n",
       "      <td id=\"T_1a194_row4_col3\" class=\"data row4 col3\" >0.51</td>\n",
       "      <td id=\"T_1a194_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col7\" class=\"data row4 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col8\" class=\"data row4 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col10\" class=\"data row4 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col11\" class=\"data row4 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col12\" class=\"data row4 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col13\" class=\"data row4 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col15\" class=\"data row4 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col16\" class=\"data row4 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col17\" class=\"data row4 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col19\" class=\"data row4 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col20\" class=\"data row4 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col21\" class=\"data row4 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col22\" class=\"data row4 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col23\" class=\"data row4 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col24\" class=\"data row4 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col25\" class=\"data row4 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col27\" class=\"data row4 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col28\" class=\"data row4 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row4_col29\" class=\"data row4 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row5\" class=\"row_heading level0 row5\" >CVX</th>\n",
       "      <td id=\"T_1a194_row5_col0\" class=\"data row5 col0\" >0.46</td>\n",
       "      <td id=\"T_1a194_row5_col1\" class=\"data row5 col1\" >0.58</td>\n",
       "      <td id=\"T_1a194_row5_col2\" class=\"data row5 col2\" >0.35</td>\n",
       "      <td id=\"T_1a194_row5_col3\" class=\"data row5 col3\" >0.52</td>\n",
       "      <td id=\"T_1a194_row5_col4\" class=\"data row5 col4\" >0.59</td>\n",
       "      <td id=\"T_1a194_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col7\" class=\"data row5 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col8\" class=\"data row5 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col10\" class=\"data row5 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col11\" class=\"data row5 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col12\" class=\"data row5 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col13\" class=\"data row5 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col15\" class=\"data row5 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col17\" class=\"data row5 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col19\" class=\"data row5 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col20\" class=\"data row5 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col21\" class=\"data row5 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col22\" class=\"data row5 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col23\" class=\"data row5 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col24\" class=\"data row5 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col25\" class=\"data row5 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col27\" class=\"data row5 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col28\" class=\"data row5 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row5_col29\" class=\"data row5 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row6\" class=\"row_heading level0 row6\" >CSCO</th>\n",
       "      <td id=\"T_1a194_row6_col0\" class=\"data row6 col0\" >0.55</td>\n",
       "      <td id=\"T_1a194_row6_col1\" class=\"data row6 col1\" >0.49</td>\n",
       "      <td id=\"T_1a194_row6_col2\" class=\"data row6 col2\" >0.57</td>\n",
       "      <td id=\"T_1a194_row6_col3\" class=\"data row6 col3\" >0.41</td>\n",
       "      <td id=\"T_1a194_row6_col4\" class=\"data row6 col4\" >0.49</td>\n",
       "      <td id=\"T_1a194_row6_col5\" class=\"data row6 col5\" >0.44</td>\n",
       "      <td id=\"T_1a194_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col7\" class=\"data row6 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col8\" class=\"data row6 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col10\" class=\"data row6 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col11\" class=\"data row6 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col12\" class=\"data row6 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col13\" class=\"data row6 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col15\" class=\"data row6 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col17\" class=\"data row6 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col19\" class=\"data row6 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col20\" class=\"data row6 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col21\" class=\"data row6 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col22\" class=\"data row6 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col23\" class=\"data row6 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col24\" class=\"data row6 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col25\" class=\"data row6 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col27\" class=\"data row6 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col28\" class=\"data row6 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row6_col29\" class=\"data row6 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row7\" class=\"row_heading level0 row7\" >KO</th>\n",
       "      <td id=\"T_1a194_row7_col0\" class=\"data row7 col0\" >0.51</td>\n",
       "      <td id=\"T_1a194_row7_col1\" class=\"data row7 col1\" >0.50</td>\n",
       "      <td id=\"T_1a194_row7_col2\" class=\"data row7 col2\" >0.39</td>\n",
       "      <td id=\"T_1a194_row7_col3\" class=\"data row7 col3\" >0.43</td>\n",
       "      <td id=\"T_1a194_row7_col4\" class=\"data row7 col4\" >0.40</td>\n",
       "      <td id=\"T_1a194_row7_col5\" class=\"data row7 col5\" >0.44</td>\n",
       "      <td id=\"T_1a194_row7_col6\" class=\"data row7 col6\" >0.45</td>\n",
       "      <td id=\"T_1a194_row7_col7\" class=\"data row7 col7\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col8\" class=\"data row7 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col10\" class=\"data row7 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col11\" class=\"data row7 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col12\" class=\"data row7 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col13\" class=\"data row7 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col15\" class=\"data row7 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col17\" class=\"data row7 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col19\" class=\"data row7 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col20\" class=\"data row7 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col21\" class=\"data row7 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col22\" class=\"data row7 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col23\" class=\"data row7 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col24\" class=\"data row7 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col25\" class=\"data row7 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col27\" class=\"data row7 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col28\" class=\"data row7 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row7_col29\" class=\"data row7 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row8\" class=\"row_heading level0 row8\" >GS-PK</th>\n",
       "      <td id=\"T_1a194_row8_col0\" class=\"data row8 col0\" >0.23</td>\n",
       "      <td id=\"T_1a194_row8_col1\" class=\"data row8 col1\" >0.40</td>\n",
       "      <td id=\"T_1a194_row8_col2\" class=\"data row8 col2\" >0.29</td>\n",
       "      <td id=\"T_1a194_row8_col3\" class=\"data row8 col3\" >0.38</td>\n",
       "      <td id=\"T_1a194_row8_col4\" class=\"data row8 col4\" >0.21</td>\n",
       "      <td id=\"T_1a194_row8_col5\" class=\"data row8 col5\" >0.40</td>\n",
       "      <td id=\"T_1a194_row8_col6\" class=\"data row8 col6\" >0.24</td>\n",
       "      <td id=\"T_1a194_row8_col7\" class=\"data row8 col7\" >0.31</td>\n",
       "      <td id=\"T_1a194_row8_col8\" class=\"data row8 col8\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col10\" class=\"data row8 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col11\" class=\"data row8 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col12\" class=\"data row8 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col13\" class=\"data row8 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col15\" class=\"data row8 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col17\" class=\"data row8 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col19\" class=\"data row8 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col20\" class=\"data row8 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col21\" class=\"data row8 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col22\" class=\"data row8 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col23\" class=\"data row8 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col24\" class=\"data row8 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col25\" class=\"data row8 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col27\" class=\"data row8 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col28\" class=\"data row8 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row8_col29\" class=\"data row8 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row9\" class=\"row_heading level0 row9\" >GS</th>\n",
       "      <td id=\"T_1a194_row9_col0\" class=\"data row9 col0\" >0.56</td>\n",
       "      <td id=\"T_1a194_row9_col1\" class=\"data row9 col1\" >0.71</td>\n",
       "      <td id=\"T_1a194_row9_col2\" class=\"data row9 col2\" >0.50</td>\n",
       "      <td id=\"T_1a194_row9_col3\" class=\"data row9 col3\" >0.57</td>\n",
       "      <td id=\"T_1a194_row9_col4\" class=\"data row9 col4\" >0.62</td>\n",
       "      <td id=\"T_1a194_row9_col5\" class=\"data row9 col5\" >0.58</td>\n",
       "      <td id=\"T_1a194_row9_col6\" class=\"data row9 col6\" >0.53</td>\n",
       "      <td id=\"T_1a194_row9_col7\" class=\"data row9 col7\" >0.44</td>\n",
       "      <td id=\"T_1a194_row9_col8\" class=\"data row9 col8\" >0.32</td>\n",
       "      <td id=\"T_1a194_row9_col9\" class=\"data row9 col9\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col10\" class=\"data row9 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col11\" class=\"data row9 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col12\" class=\"data row9 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col13\" class=\"data row9 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col15\" class=\"data row9 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col17\" class=\"data row9 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col19\" class=\"data row9 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col20\" class=\"data row9 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col21\" class=\"data row9 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col22\" class=\"data row9 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col23\" class=\"data row9 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col24\" class=\"data row9 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col25\" class=\"data row9 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col28\" class=\"data row9 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row9_col29\" class=\"data row9 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row10\" class=\"row_heading level0 row10\" >HD</th>\n",
       "      <td id=\"T_1a194_row10_col0\" class=\"data row10 col0\" >0.51</td>\n",
       "      <td id=\"T_1a194_row10_col1\" class=\"data row10 col1\" >0.51</td>\n",
       "      <td id=\"T_1a194_row10_col2\" class=\"data row10 col2\" >0.53</td>\n",
       "      <td id=\"T_1a194_row10_col3\" class=\"data row10 col3\" >0.46</td>\n",
       "      <td id=\"T_1a194_row10_col4\" class=\"data row10 col4\" >0.44</td>\n",
       "      <td id=\"T_1a194_row10_col5\" class=\"data row10 col5\" >0.43</td>\n",
       "      <td id=\"T_1a194_row10_col6\" class=\"data row10 col6\" >0.53</td>\n",
       "      <td id=\"T_1a194_row10_col7\" class=\"data row10 col7\" >0.48</td>\n",
       "      <td id=\"T_1a194_row10_col8\" class=\"data row10 col8\" >0.36</td>\n",
       "      <td id=\"T_1a194_row10_col9\" class=\"data row10 col9\" >0.55</td>\n",
       "      <td id=\"T_1a194_row10_col10\" class=\"data row10 col10\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col11\" class=\"data row10 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col12\" class=\"data row10 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col13\" class=\"data row10 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col15\" class=\"data row10 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col16\" class=\"data row10 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col17\" class=\"data row10 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col18\" class=\"data row10 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col19\" class=\"data row10 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col20\" class=\"data row10 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col21\" class=\"data row10 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col22\" class=\"data row10 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col23\" class=\"data row10 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col24\" class=\"data row10 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col25\" class=\"data row10 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col26\" class=\"data row10 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col27\" class=\"data row10 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col28\" class=\"data row10 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row10_col29\" class=\"data row10 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row11\" class=\"row_heading level0 row11\" >IBM</th>\n",
       "      <td id=\"T_1a194_row11_col0\" class=\"data row11 col0\" >0.56</td>\n",
       "      <td id=\"T_1a194_row11_col1\" class=\"data row11 col1\" >0.52</td>\n",
       "      <td id=\"T_1a194_row11_col2\" class=\"data row11 col2\" >0.43</td>\n",
       "      <td id=\"T_1a194_row11_col3\" class=\"data row11 col3\" >0.46</td>\n",
       "      <td id=\"T_1a194_row11_col4\" class=\"data row11 col4\" >0.52</td>\n",
       "      <td id=\"T_1a194_row11_col5\" class=\"data row11 col5\" >0.50</td>\n",
       "      <td id=\"T_1a194_row11_col6\" class=\"data row11 col6\" >0.56</td>\n",
       "      <td id=\"T_1a194_row11_col7\" class=\"data row11 col7\" >0.50</td>\n",
       "      <td id=\"T_1a194_row11_col8\" class=\"data row11 col8\" >0.27</td>\n",
       "      <td id=\"T_1a194_row11_col9\" class=\"data row11 col9\" >0.53</td>\n",
       "      <td id=\"T_1a194_row11_col10\" class=\"data row11 col10\" >0.49</td>\n",
       "      <td id=\"T_1a194_row11_col11\" class=\"data row11 col11\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col12\" class=\"data row11 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col13\" class=\"data row11 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col15\" class=\"data row11 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col16\" class=\"data row11 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col17\" class=\"data row11 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col18\" class=\"data row11 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col19\" class=\"data row11 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col20\" class=\"data row11 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col21\" class=\"data row11 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col22\" class=\"data row11 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col23\" class=\"data row11 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col24\" class=\"data row11 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col25\" class=\"data row11 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col26\" class=\"data row11 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col27\" class=\"data row11 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col28\" class=\"data row11 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row11_col29\" class=\"data row11 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row12\" class=\"row_heading level0 row12\" >INTC</th>\n",
       "      <td id=\"T_1a194_row12_col0\" class=\"data row12 col0\" >0.46</td>\n",
       "      <td id=\"T_1a194_row12_col1\" class=\"data row12 col1\" >0.45</td>\n",
       "      <td id=\"T_1a194_row12_col2\" class=\"data row12 col2\" >0.56</td>\n",
       "      <td id=\"T_1a194_row12_col3\" class=\"data row12 col3\" >0.43</td>\n",
       "      <td id=\"T_1a194_row12_col4\" class=\"data row12 col4\" >0.45</td>\n",
       "      <td id=\"T_1a194_row12_col5\" class=\"data row12 col5\" >0.41</td>\n",
       "      <td id=\"T_1a194_row12_col6\" class=\"data row12 col6\" >0.57</td>\n",
       "      <td id=\"T_1a194_row12_col7\" class=\"data row12 col7\" >0.39</td>\n",
       "      <td id=\"T_1a194_row12_col8\" class=\"data row12 col8\" >0.23</td>\n",
       "      <td id=\"T_1a194_row12_col9\" class=\"data row12 col9\" >0.50</td>\n",
       "      <td id=\"T_1a194_row12_col10\" class=\"data row12 col10\" >0.50</td>\n",
       "      <td id=\"T_1a194_row12_col11\" class=\"data row12 col11\" >0.49</td>\n",
       "      <td id=\"T_1a194_row12_col12\" class=\"data row12 col12\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col13\" class=\"data row12 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col14\" class=\"data row12 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col15\" class=\"data row12 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col16\" class=\"data row12 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col17\" class=\"data row12 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col18\" class=\"data row12 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col19\" class=\"data row12 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col20\" class=\"data row12 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col21\" class=\"data row12 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col22\" class=\"data row12 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col23\" class=\"data row12 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col24\" class=\"data row12 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col25\" class=\"data row12 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col26\" class=\"data row12 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col27\" class=\"data row12 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col28\" class=\"data row12 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row12_col29\" class=\"data row12 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row13\" class=\"row_heading level0 row13\" >JNJ</th>\n",
       "      <td id=\"T_1a194_row13_col0\" class=\"data row13 col0\" >0.50</td>\n",
       "      <td id=\"T_1a194_row13_col1\" class=\"data row13 col1\" >0.39</td>\n",
       "      <td id=\"T_1a194_row13_col2\" class=\"data row13 col2\" >0.39</td>\n",
       "      <td id=\"T_1a194_row13_col3\" class=\"data row13 col3\" >0.30</td>\n",
       "      <td id=\"T_1a194_row13_col4\" class=\"data row13 col4\" >0.37</td>\n",
       "      <td id=\"T_1a194_row13_col5\" class=\"data row13 col5\" >0.38</td>\n",
       "      <td id=\"T_1a194_row13_col6\" class=\"data row13 col6\" >0.50</td>\n",
       "      <td id=\"T_1a194_row13_col7\" class=\"data row13 col7\" >0.53</td>\n",
       "      <td id=\"T_1a194_row13_col8\" class=\"data row13 col8\" >0.23</td>\n",
       "      <td id=\"T_1a194_row13_col9\" class=\"data row13 col9\" >0.39</td>\n",
       "      <td id=\"T_1a194_row13_col10\" class=\"data row13 col10\" >0.43</td>\n",
       "      <td id=\"T_1a194_row13_col11\" class=\"data row13 col11\" >0.47</td>\n",
       "      <td id=\"T_1a194_row13_col12\" class=\"data row13 col12\" >0.38</td>\n",
       "      <td id=\"T_1a194_row13_col13\" class=\"data row13 col13\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col14\" class=\"data row13 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col15\" class=\"data row13 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col16\" class=\"data row13 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col17\" class=\"data row13 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col18\" class=\"data row13 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col19\" class=\"data row13 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col20\" class=\"data row13 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col21\" class=\"data row13 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col22\" class=\"data row13 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col23\" class=\"data row13 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col24\" class=\"data row13 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col25\" class=\"data row13 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col26\" class=\"data row13 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col27\" class=\"data row13 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col28\" class=\"data row13 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row13_col29\" class=\"data row13 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row14\" class=\"row_heading level0 row14\" >JPM</th>\n",
       "      <td id=\"T_1a194_row14_col0\" class=\"data row14 col0\" >0.56</td>\n",
       "      <td id=\"T_1a194_row14_col1\" class=\"data row14 col1\" >0.75</td>\n",
       "      <td id=\"T_1a194_row14_col2\" class=\"data row14 col2\" >0.45</td>\n",
       "      <td id=\"T_1a194_row14_col3\" class=\"data row14 col3\" >0.59</td>\n",
       "      <td id=\"T_1a194_row14_col4\" class=\"data row14 col4\" >0.63</td>\n",
       "      <td id=\"T_1a194_row14_col5\" class=\"data row14 col5\" >0.62</td>\n",
       "      <td id=\"T_1a194_row14_col6\" class=\"data row14 col6\" >0.54</td>\n",
       "      <td id=\"T_1a194_row14_col7\" class=\"data row14 col7\" >0.49</td>\n",
       "      <td id=\"T_1a194_row14_col8\" class=\"data row14 col8\" >0.34</td>\n",
       "      <td id=\"T_1a194_row14_col9\" class=\"data row14 col9\" >0.85</td>\n",
       "      <td id=\"T_1a194_row14_col10\" class=\"data row14 col10\" >0.53</td>\n",
       "      <td id=\"T_1a194_row14_col11\" class=\"data row14 col11\" >0.56</td>\n",
       "      <td id=\"T_1a194_row14_col12\" class=\"data row14 col12\" >0.49</td>\n",
       "      <td id=\"T_1a194_row14_col13\" class=\"data row14 col13\" >0.43</td>\n",
       "      <td id=\"T_1a194_row14_col14\" class=\"data row14 col14\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col15\" class=\"data row14 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col16\" class=\"data row14 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col17\" class=\"data row14 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col18\" class=\"data row14 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col19\" class=\"data row14 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col20\" class=\"data row14 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col21\" class=\"data row14 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col22\" class=\"data row14 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col23\" class=\"data row14 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col24\" class=\"data row14 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col25\" class=\"data row14 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col26\" class=\"data row14 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col27\" class=\"data row14 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col28\" class=\"data row14 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row14_col29\" class=\"data row14 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row15\" class=\"row_heading level0 row15\" >MCD</th>\n",
       "      <td id=\"T_1a194_row15_col0\" class=\"data row15 col0\" >0.46</td>\n",
       "      <td id=\"T_1a194_row15_col1\" class=\"data row15 col1\" >0.50</td>\n",
       "      <td id=\"T_1a194_row15_col2\" class=\"data row15 col2\" >0.43</td>\n",
       "      <td id=\"T_1a194_row15_col3\" class=\"data row15 col3\" >0.44</td>\n",
       "      <td id=\"T_1a194_row15_col4\" class=\"data row15 col4\" >0.37</td>\n",
       "      <td id=\"T_1a194_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
       "      <td id=\"T_1a194_row15_col6\" class=\"data row15 col6\" >0.46</td>\n",
       "      <td id=\"T_1a194_row15_col7\" class=\"data row15 col7\" >0.52</td>\n",
       "      <td id=\"T_1a194_row15_col8\" class=\"data row15 col8\" >0.39</td>\n",
       "      <td id=\"T_1a194_row15_col9\" class=\"data row15 col9\" >0.48</td>\n",
       "      <td id=\"T_1a194_row15_col10\" class=\"data row15 col10\" >0.55</td>\n",
       "      <td id=\"T_1a194_row15_col11\" class=\"data row15 col11\" >0.44</td>\n",
       "      <td id=\"T_1a194_row15_col12\" class=\"data row15 col12\" >0.37</td>\n",
       "      <td id=\"T_1a194_row15_col13\" class=\"data row15 col13\" >0.44</td>\n",
       "      <td id=\"T_1a194_row15_col14\" class=\"data row15 col14\" >0.50</td>\n",
       "      <td id=\"T_1a194_row15_col15\" class=\"data row15 col15\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col16\" class=\"data row15 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col17\" class=\"data row15 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col18\" class=\"data row15 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col19\" class=\"data row15 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col20\" class=\"data row15 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col21\" class=\"data row15 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col22\" class=\"data row15 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col23\" class=\"data row15 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col24\" class=\"data row15 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col25\" class=\"data row15 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col26\" class=\"data row15 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col27\" class=\"data row15 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col28\" class=\"data row15 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row15_col29\" class=\"data row15 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row16\" class=\"row_heading level0 row16\" >NKE</th>\n",
       "      <td id=\"T_1a194_row16_col0\" class=\"data row16 col0\" >0.45</td>\n",
       "      <td id=\"T_1a194_row16_col1\" class=\"data row16 col1\" >0.49</td>\n",
       "      <td id=\"T_1a194_row16_col2\" class=\"data row16 col2\" >0.48</td>\n",
       "      <td id=\"T_1a194_row16_col3\" class=\"data row16 col3\" >0.44</td>\n",
       "      <td id=\"T_1a194_row16_col4\" class=\"data row16 col4\" >0.42</td>\n",
       "      <td id=\"T_1a194_row16_col5\" class=\"data row16 col5\" >0.37</td>\n",
       "      <td id=\"T_1a194_row16_col6\" class=\"data row16 col6\" >0.47</td>\n",
       "      <td id=\"T_1a194_row16_col7\" class=\"data row16 col7\" >0.41</td>\n",
       "      <td id=\"T_1a194_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
       "      <td id=\"T_1a194_row16_col9\" class=\"data row16 col9\" >0.48</td>\n",
       "      <td id=\"T_1a194_row16_col10\" class=\"data row16 col10\" >0.55</td>\n",
       "      <td id=\"T_1a194_row16_col11\" class=\"data row16 col11\" >0.41</td>\n",
       "      <td id=\"T_1a194_row16_col12\" class=\"data row16 col12\" >0.41</td>\n",
       "      <td id=\"T_1a194_row16_col13\" class=\"data row16 col13\" >0.35</td>\n",
       "      <td id=\"T_1a194_row16_col14\" class=\"data row16 col14\" >0.48</td>\n",
       "      <td id=\"T_1a194_row16_col15\" class=\"data row16 col15\" >0.45</td>\n",
       "      <td id=\"T_1a194_row16_col16\" class=\"data row16 col16\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col17\" class=\"data row16 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col18\" class=\"data row16 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col19\" class=\"data row16 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col20\" class=\"data row16 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col21\" class=\"data row16 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col22\" class=\"data row16 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col23\" class=\"data row16 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col24\" class=\"data row16 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col25\" class=\"data row16 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col26\" class=\"data row16 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col27\" class=\"data row16 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col28\" class=\"data row16 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row16_col29\" class=\"data row16 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row17\" class=\"row_heading level0 row17\" >MRK</th>\n",
       "      <td id=\"T_1a194_row17_col0\" class=\"data row17 col0\" >0.36</td>\n",
       "      <td id=\"T_1a194_row17_col1\" class=\"data row17 col1\" >0.33</td>\n",
       "      <td id=\"T_1a194_row17_col2\" class=\"data row17 col2\" >0.33</td>\n",
       "      <td id=\"T_1a194_row17_col3\" class=\"data row17 col3\" >0.25</td>\n",
       "      <td id=\"T_1a194_row17_col4\" class=\"data row17 col4\" >0.33</td>\n",
       "      <td id=\"T_1a194_row17_col5\" class=\"data row17 col5\" >0.37</td>\n",
       "      <td id=\"T_1a194_row17_col6\" class=\"data row17 col6\" >0.41</td>\n",
       "      <td id=\"T_1a194_row17_col7\" class=\"data row17 col7\" >0.43</td>\n",
       "      <td id=\"T_1a194_row17_col8\" class=\"data row17 col8\" >0.26</td>\n",
       "      <td id=\"T_1a194_row17_col9\" class=\"data row17 col9\" >0.37</td>\n",
       "      <td id=\"T_1a194_row17_col10\" class=\"data row17 col10\" >0.37</td>\n",
       "      <td id=\"T_1a194_row17_col11\" class=\"data row17 col11\" >0.41</td>\n",
       "      <td id=\"T_1a194_row17_col12\" class=\"data row17 col12\" >0.31</td>\n",
       "      <td id=\"T_1a194_row17_col13\" class=\"data row17 col13\" >0.56</td>\n",
       "      <td id=\"T_1a194_row17_col14\" class=\"data row17 col14\" >0.40</td>\n",
       "      <td id=\"T_1a194_row17_col15\" class=\"data row17 col15\" >0.40</td>\n",
       "      <td id=\"T_1a194_row17_col16\" class=\"data row17 col16\" >0.30</td>\n",
       "      <td id=\"T_1a194_row17_col17\" class=\"data row17 col17\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col18\" class=\"data row17 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col19\" class=\"data row17 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col20\" class=\"data row17 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col21\" class=\"data row17 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col22\" class=\"data row17 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col23\" class=\"data row17 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col24\" class=\"data row17 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col25\" class=\"data row17 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col26\" class=\"data row17 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col27\" class=\"data row17 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col28\" class=\"data row17 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row17_col29\" class=\"data row17 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row18\" class=\"row_heading level0 row18\" >MSFT</th>\n",
       "      <td id=\"T_1a194_row18_col0\" class=\"data row18 col0\" >0.45</td>\n",
       "      <td id=\"T_1a194_row18_col1\" class=\"data row18 col1\" >0.48</td>\n",
       "      <td id=\"T_1a194_row18_col2\" class=\"data row18 col2\" >0.71</td>\n",
       "      <td id=\"T_1a194_row18_col3\" class=\"data row18 col3\" >0.41</td>\n",
       "      <td id=\"T_1a194_row18_col4\" class=\"data row18 col4\" >0.43</td>\n",
       "      <td id=\"T_1a194_row18_col5\" class=\"data row18 col5\" >0.40</td>\n",
       "      <td id=\"T_1a194_row18_col6\" class=\"data row18 col6\" >0.62</td>\n",
       "      <td id=\"T_1a194_row18_col7\" class=\"data row18 col7\" >0.44</td>\n",
       "      <td id=\"T_1a194_row18_col8\" class=\"data row18 col8\" >0.31</td>\n",
       "      <td id=\"T_1a194_row18_col9\" class=\"data row18 col9\" >0.52</td>\n",
       "      <td id=\"T_1a194_row18_col10\" class=\"data row18 col10\" >0.59</td>\n",
       "      <td id=\"T_1a194_row18_col11\" class=\"data row18 col11\" >0.48</td>\n",
       "      <td id=\"T_1a194_row18_col12\" class=\"data row18 col12\" >0.62</td>\n",
       "      <td id=\"T_1a194_row18_col13\" class=\"data row18 col13\" >0.45</td>\n",
       "      <td id=\"T_1a194_row18_col14\" class=\"data row18 col14\" >0.49</td>\n",
       "      <td id=\"T_1a194_row18_col15\" class=\"data row18 col15\" >0.49</td>\n",
       "      <td id=\"T_1a194_row18_col16\" class=\"data row18 col16\" >0.52</td>\n",
       "      <td id=\"T_1a194_row18_col17\" class=\"data row18 col17\" >0.40</td>\n",
       "      <td id=\"T_1a194_row18_col18\" class=\"data row18 col18\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col19\" class=\"data row18 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col20\" class=\"data row18 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col21\" class=\"data row18 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col22\" class=\"data row18 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col23\" class=\"data row18 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col24\" class=\"data row18 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col25\" class=\"data row18 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col26\" class=\"data row18 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col27\" class=\"data row18 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col28\" class=\"data row18 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row18_col29\" class=\"data row18 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row19\" class=\"row_heading level0 row19\" >PG</th>\n",
       "      <td id=\"T_1a194_row19_col0\" class=\"data row19 col0\" >0.43</td>\n",
       "      <td id=\"T_1a194_row19_col1\" class=\"data row19 col1\" >0.35</td>\n",
       "      <td id=\"T_1a194_row19_col2\" class=\"data row19 col2\" >0.41</td>\n",
       "      <td id=\"T_1a194_row19_col3\" class=\"data row19 col3\" >0.27</td>\n",
       "      <td id=\"T_1a194_row19_col4\" class=\"data row19 col4\" >0.31</td>\n",
       "      <td id=\"T_1a194_row19_col5\" class=\"data row19 col5\" >0.29</td>\n",
       "      <td id=\"T_1a194_row19_col6\" class=\"data row19 col6\" >0.48</td>\n",
       "      <td id=\"T_1a194_row19_col7\" class=\"data row19 col7\" >0.63</td>\n",
       "      <td id=\"T_1a194_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
       "      <td id=\"T_1a194_row19_col9\" class=\"data row19 col9\" >0.36</td>\n",
       "      <td id=\"T_1a194_row19_col10\" class=\"data row19 col10\" >0.47</td>\n",
       "      <td id=\"T_1a194_row19_col11\" class=\"data row19 col11\" >0.46</td>\n",
       "      <td id=\"T_1a194_row19_col12\" class=\"data row19 col12\" >0.38</td>\n",
       "      <td id=\"T_1a194_row19_col13\" class=\"data row19 col13\" >0.57</td>\n",
       "      <td id=\"T_1a194_row19_col14\" class=\"data row19 col14\" >0.38</td>\n",
       "      <td id=\"T_1a194_row19_col15\" class=\"data row19 col15\" >0.45</td>\n",
       "      <td id=\"T_1a194_row19_col16\" class=\"data row19 col16\" >0.37</td>\n",
       "      <td id=\"T_1a194_row19_col17\" class=\"data row19 col17\" >0.43</td>\n",
       "      <td id=\"T_1a194_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
       "      <td id=\"T_1a194_row19_col19\" class=\"data row19 col19\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col20\" class=\"data row19 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col21\" class=\"data row19 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col22\" class=\"data row19 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col23\" class=\"data row19 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col24\" class=\"data row19 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col25\" class=\"data row19 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col26\" class=\"data row19 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col27\" class=\"data row19 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col28\" class=\"data row19 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row19_col29\" class=\"data row19 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row20\" class=\"row_heading level0 row20\" >TRV</th>\n",
       "      <td id=\"T_1a194_row20_col0\" class=\"data row20 col0\" >0.49</td>\n",
       "      <td id=\"T_1a194_row20_col1\" class=\"data row20 col1\" >0.58</td>\n",
       "      <td id=\"T_1a194_row20_col2\" class=\"data row20 col2\" >0.36</td>\n",
       "      <td id=\"T_1a194_row20_col3\" class=\"data row20 col3\" >0.48</td>\n",
       "      <td id=\"T_1a194_row20_col4\" class=\"data row20 col4\" >0.48</td>\n",
       "      <td id=\"T_1a194_row20_col5\" class=\"data row20 col5\" >0.54</td>\n",
       "      <td id=\"T_1a194_row20_col6\" class=\"data row20 col6\" >0.44</td>\n",
       "      <td id=\"T_1a194_row20_col7\" class=\"data row20 col7\" >0.54</td>\n",
       "      <td id=\"T_1a194_row20_col8\" class=\"data row20 col8\" >0.35</td>\n",
       "      <td id=\"T_1a194_row20_col9\" class=\"data row20 col9\" >0.59</td>\n",
       "      <td id=\"T_1a194_row20_col10\" class=\"data row20 col10\" >0.51</td>\n",
       "      <td id=\"T_1a194_row20_col11\" class=\"data row20 col11\" >0.49</td>\n",
       "      <td id=\"T_1a194_row20_col12\" class=\"data row20 col12\" >0.41</td>\n",
       "      <td id=\"T_1a194_row20_col13\" class=\"data row20 col13\" >0.42</td>\n",
       "      <td id=\"T_1a194_row20_col14\" class=\"data row20 col14\" >0.65</td>\n",
       "      <td id=\"T_1a194_row20_col15\" class=\"data row20 col15\" >0.54</td>\n",
       "      <td id=\"T_1a194_row20_col16\" class=\"data row20 col16\" >0.39</td>\n",
       "      <td id=\"T_1a194_row20_col17\" class=\"data row20 col17\" >0.42</td>\n",
       "      <td id=\"T_1a194_row20_col18\" class=\"data row20 col18\" >0.42</td>\n",
       "      <td id=\"T_1a194_row20_col19\" class=\"data row20 col19\" >0.42</td>\n",
       "      <td id=\"T_1a194_row20_col20\" class=\"data row20 col20\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col21\" class=\"data row20 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col22\" class=\"data row20 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col23\" class=\"data row20 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col24\" class=\"data row20 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col25\" class=\"data row20 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col26\" class=\"data row20 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col27\" class=\"data row20 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col28\" class=\"data row20 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row20_col29\" class=\"data row20 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row21\" class=\"row_heading level0 row21\" >UNH</th>\n",
       "      <td id=\"T_1a194_row21_col0\" class=\"data row21 col0\" >0.44</td>\n",
       "      <td id=\"T_1a194_row21_col1\" class=\"data row21 col1\" >0.50</td>\n",
       "      <td id=\"T_1a194_row21_col2\" class=\"data row21 col2\" >0.46</td>\n",
       "      <td id=\"T_1a194_row21_col3\" class=\"data row21 col3\" >0.38</td>\n",
       "      <td id=\"T_1a194_row21_col4\" class=\"data row21 col4\" >0.38</td>\n",
       "      <td id=\"T_1a194_row21_col5\" class=\"data row21 col5\" >0.44</td>\n",
       "      <td id=\"T_1a194_row21_col6\" class=\"data row21 col6\" >0.45</td>\n",
       "      <td id=\"T_1a194_row21_col7\" class=\"data row21 col7\" >0.44</td>\n",
       "      <td id=\"T_1a194_row21_col8\" class=\"data row21 col8\" >0.34</td>\n",
       "      <td id=\"T_1a194_row21_col9\" class=\"data row21 col9\" >0.49</td>\n",
       "      <td id=\"T_1a194_row21_col10\" class=\"data row21 col10\" >0.50</td>\n",
       "      <td id=\"T_1a194_row21_col11\" class=\"data row21 col11\" >0.43</td>\n",
       "      <td id=\"T_1a194_row21_col12\" class=\"data row21 col12\" >0.41</td>\n",
       "      <td id=\"T_1a194_row21_col13\" class=\"data row21 col13\" >0.51</td>\n",
       "      <td id=\"T_1a194_row21_col14\" class=\"data row21 col14\" >0.49</td>\n",
       "      <td id=\"T_1a194_row21_col15\" class=\"data row21 col15\" >0.45</td>\n",
       "      <td id=\"T_1a194_row21_col16\" class=\"data row21 col16\" >0.40</td>\n",
       "      <td id=\"T_1a194_row21_col17\" class=\"data row21 col17\" >0.45</td>\n",
       "      <td id=\"T_1a194_row21_col18\" class=\"data row21 col18\" >0.50</td>\n",
       "      <td id=\"T_1a194_row21_col19\" class=\"data row21 col19\" >0.43</td>\n",
       "      <td id=\"T_1a194_row21_col20\" class=\"data row21 col20\" >0.48</td>\n",
       "      <td id=\"T_1a194_row21_col21\" class=\"data row21 col21\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col22\" class=\"data row21 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col23\" class=\"data row21 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col24\" class=\"data row21 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col25\" class=\"data row21 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col26\" class=\"data row21 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col27\" class=\"data row21 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col28\" class=\"data row21 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row21_col29\" class=\"data row21 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row22\" class=\"row_heading level0 row22\" >VZ</th>\n",
       "      <td id=\"T_1a194_row22_col0\" class=\"data row22 col0\" >0.38</td>\n",
       "      <td id=\"T_1a194_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
       "      <td id=\"T_1a194_row22_col2\" class=\"data row22 col2\" >0.27</td>\n",
       "      <td id=\"T_1a194_row22_col3\" class=\"data row22 col3\" >0.26</td>\n",
       "      <td id=\"T_1a194_row22_col4\" class=\"data row22 col4\" >0.33</td>\n",
       "      <td id=\"T_1a194_row22_col5\" class=\"data row22 col5\" >0.35</td>\n",
       "      <td id=\"T_1a194_row22_col6\" class=\"data row22 col6\" >0.38</td>\n",
       "      <td id=\"T_1a194_row22_col7\" class=\"data row22 col7\" >0.49</td>\n",
       "      <td id=\"T_1a194_row22_col8\" class=\"data row22 col8\" >0.19</td>\n",
       "      <td id=\"T_1a194_row22_col9\" class=\"data row22 col9\" >0.34</td>\n",
       "      <td id=\"T_1a194_row22_col10\" class=\"data row22 col10\" >0.37</td>\n",
       "      <td id=\"T_1a194_row22_col11\" class=\"data row22 col11\" >0.42</td>\n",
       "      <td id=\"T_1a194_row22_col12\" class=\"data row22 col12\" >0.32</td>\n",
       "      <td id=\"T_1a194_row22_col13\" class=\"data row22 col13\" >0.46</td>\n",
       "      <td id=\"T_1a194_row22_col14\" class=\"data row22 col14\" >0.39</td>\n",
       "      <td id=\"T_1a194_row22_col15\" class=\"data row22 col15\" >0.36</td>\n",
       "      <td id=\"T_1a194_row22_col16\" class=\"data row22 col16\" >0.29</td>\n",
       "      <td id=\"T_1a194_row22_col17\" class=\"data row22 col17\" >0.42</td>\n",
       "      <td id=\"T_1a194_row22_col18\" class=\"data row22 col18\" >0.33</td>\n",
       "      <td id=\"T_1a194_row22_col19\" class=\"data row22 col19\" >0.50</td>\n",
       "      <td id=\"T_1a194_row22_col20\" class=\"data row22 col20\" >0.43</td>\n",
       "      <td id=\"T_1a194_row22_col21\" class=\"data row22 col21\" >0.38</td>\n",
       "      <td id=\"T_1a194_row22_col22\" class=\"data row22 col22\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col23\" class=\"data row22 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col24\" class=\"data row22 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col25\" class=\"data row22 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col26\" class=\"data row22 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col27\" class=\"data row22 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col28\" class=\"data row22 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row22_col29\" class=\"data row22 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row23\" class=\"row_heading level0 row23\" >V</th>\n",
       "      <td id=\"T_1a194_row23_col0\" class=\"data row23 col0\" >0.51</td>\n",
       "      <td id=\"T_1a194_row23_col1\" class=\"data row23 col1\" >0.66</td>\n",
       "      <td id=\"T_1a194_row23_col2\" class=\"data row23 col2\" >0.60</td>\n",
       "      <td id=\"T_1a194_row23_col3\" class=\"data row23 col3\" >0.51</td>\n",
       "      <td id=\"T_1a194_row23_col4\" class=\"data row23 col4\" >0.49</td>\n",
       "      <td id=\"T_1a194_row23_col5\" class=\"data row23 col5\" >0.50</td>\n",
       "      <td id=\"T_1a194_row23_col6\" class=\"data row23 col6\" >0.58</td>\n",
       "      <td id=\"T_1a194_row23_col7\" class=\"data row23 col7\" >0.51</td>\n",
       "      <td id=\"T_1a194_row23_col8\" class=\"data row23 col8\" >0.37</td>\n",
       "      <td id=\"T_1a194_row23_col9\" class=\"data row23 col9\" >0.60</td>\n",
       "      <td id=\"T_1a194_row23_col10\" class=\"data row23 col10\" >0.58</td>\n",
       "      <td id=\"T_1a194_row23_col11\" class=\"data row23 col11\" >0.54</td>\n",
       "      <td id=\"T_1a194_row23_col12\" class=\"data row23 col12\" >0.52</td>\n",
       "      <td id=\"T_1a194_row23_col13\" class=\"data row23 col13\" >0.46</td>\n",
       "      <td id=\"T_1a194_row23_col14\" class=\"data row23 col14\" >0.60</td>\n",
       "      <td id=\"T_1a194_row23_col15\" class=\"data row23 col15\" >0.56</td>\n",
       "      <td id=\"T_1a194_row23_col16\" class=\"data row23 col16\" >0.54</td>\n",
       "      <td id=\"T_1a194_row23_col17\" class=\"data row23 col17\" >0.41</td>\n",
       "      <td id=\"T_1a194_row23_col18\" class=\"data row23 col18\" >0.67</td>\n",
       "      <td id=\"T_1a194_row23_col19\" class=\"data row23 col19\" >0.44</td>\n",
       "      <td id=\"T_1a194_row23_col20\" class=\"data row23 col20\" >0.52</td>\n",
       "      <td id=\"T_1a194_row23_col21\" class=\"data row23 col21\" >0.51</td>\n",
       "      <td id=\"T_1a194_row23_col22\" class=\"data row23 col22\" >0.34</td>\n",
       "      <td id=\"T_1a194_row23_col23\" class=\"data row23 col23\" >nan</td>\n",
       "      <td id=\"T_1a194_row23_col24\" class=\"data row23 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row23_col25\" class=\"data row23 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row23_col26\" class=\"data row23 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row23_col27\" class=\"data row23 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row23_col28\" class=\"data row23 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row23_col29\" class=\"data row23 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row24\" class=\"row_heading level0 row24\" >WMT</th>\n",
       "      <td id=\"T_1a194_row24_col0\" class=\"data row24 col0\" >0.30</td>\n",
       "      <td id=\"T_1a194_row24_col1\" class=\"data row24 col1\" >0.22</td>\n",
       "      <td id=\"T_1a194_row24_col2\" class=\"data row24 col2\" >0.34</td>\n",
       "      <td id=\"T_1a194_row24_col3\" class=\"data row24 col3\" >0.19</td>\n",
       "      <td id=\"T_1a194_row24_col4\" class=\"data row24 col4\" >0.25</td>\n",
       "      <td id=\"T_1a194_row24_col5\" class=\"data row24 col5\" >0.21</td>\n",
       "      <td id=\"T_1a194_row24_col6\" class=\"data row24 col6\" >0.38</td>\n",
       "      <td id=\"T_1a194_row24_col7\" class=\"data row24 col7\" >0.37</td>\n",
       "      <td id=\"T_1a194_row24_col8\" class=\"data row24 col8\" >0.09</td>\n",
       "      <td id=\"T_1a194_row24_col9\" class=\"data row24 col9\" >0.27</td>\n",
       "      <td id=\"T_1a194_row24_col10\" class=\"data row24 col10\" >0.42</td>\n",
       "      <td id=\"T_1a194_row24_col11\" class=\"data row24 col11\" >0.31</td>\n",
       "      <td id=\"T_1a194_row24_col12\" class=\"data row24 col12\" >0.32</td>\n",
       "      <td id=\"T_1a194_row24_col13\" class=\"data row24 col13\" >0.39</td>\n",
       "      <td id=\"T_1a194_row24_col14\" class=\"data row24 col14\" >0.27</td>\n",
       "      <td id=\"T_1a194_row24_col15\" class=\"data row24 col15\" >0.31</td>\n",
       "      <td id=\"T_1a194_row24_col16\" class=\"data row24 col16\" >0.29</td>\n",
       "      <td id=\"T_1a194_row24_col17\" class=\"data row24 col17\" >0.31</td>\n",
       "      <td id=\"T_1a194_row24_col18\" class=\"data row24 col18\" >0.37</td>\n",
       "      <td id=\"T_1a194_row24_col19\" class=\"data row24 col19\" >0.47</td>\n",
       "      <td id=\"T_1a194_row24_col20\" class=\"data row24 col20\" >0.29</td>\n",
       "      <td id=\"T_1a194_row24_col21\" class=\"data row24 col21\" >0.33</td>\n",
       "      <td id=\"T_1a194_row24_col22\" class=\"data row24 col22\" >0.36</td>\n",
       "      <td id=\"T_1a194_row24_col23\" class=\"data row24 col23\" >0.30</td>\n",
       "      <td id=\"T_1a194_row24_col24\" class=\"data row24 col24\" >nan</td>\n",
       "      <td id=\"T_1a194_row24_col25\" class=\"data row24 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row24_col26\" class=\"data row24 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row24_col27\" class=\"data row24 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row24_col28\" class=\"data row24 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row24_col29\" class=\"data row24 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row25\" class=\"row_heading level0 row25\" >WBA</th>\n",
       "      <td id=\"T_1a194_row25_col0\" class=\"data row25 col0\" >0.47</td>\n",
       "      <td id=\"T_1a194_row25_col1\" class=\"data row25 col1\" >0.41</td>\n",
       "      <td id=\"T_1a194_row25_col2\" class=\"data row25 col2\" >0.35</td>\n",
       "      <td id=\"T_1a194_row25_col3\" class=\"data row25 col3\" >0.36</td>\n",
       "      <td id=\"T_1a194_row25_col4\" class=\"data row25 col4\" >0.41</td>\n",
       "      <td id=\"T_1a194_row25_col5\" class=\"data row25 col5\" >0.36</td>\n",
       "      <td id=\"T_1a194_row25_col6\" class=\"data row25 col6\" >0.44</td>\n",
       "      <td id=\"T_1a194_row25_col7\" class=\"data row25 col7\" >0.39</td>\n",
       "      <td id=\"T_1a194_row25_col8\" class=\"data row25 col8\" >0.12</td>\n",
       "      <td id=\"T_1a194_row25_col9\" class=\"data row25 col9\" >0.44</td>\n",
       "      <td id=\"T_1a194_row25_col10\" class=\"data row25 col10\" >0.39</td>\n",
       "      <td id=\"T_1a194_row25_col11\" class=\"data row25 col11\" >0.42</td>\n",
       "      <td id=\"T_1a194_row25_col12\" class=\"data row25 col12\" >0.38</td>\n",
       "      <td id=\"T_1a194_row25_col13\" class=\"data row25 col13\" >0.42</td>\n",
       "      <td id=\"T_1a194_row25_col14\" class=\"data row25 col14\" >0.45</td>\n",
       "      <td id=\"T_1a194_row25_col15\" class=\"data row25 col15\" >0.30</td>\n",
       "      <td id=\"T_1a194_row25_col16\" class=\"data row25 col16\" >0.33</td>\n",
       "      <td id=\"T_1a194_row25_col17\" class=\"data row25 col17\" >0.33</td>\n",
       "      <td id=\"T_1a194_row25_col18\" class=\"data row25 col18\" >0.38</td>\n",
       "      <td id=\"T_1a194_row25_col19\" class=\"data row25 col19\" >0.39</td>\n",
       "      <td id=\"T_1a194_row25_col20\" class=\"data row25 col20\" >0.38</td>\n",
       "      <td id=\"T_1a194_row25_col21\" class=\"data row25 col21\" >0.39</td>\n",
       "      <td id=\"T_1a194_row25_col22\" class=\"data row25 col22\" >0.38</td>\n",
       "      <td id=\"T_1a194_row25_col23\" class=\"data row25 col23\" >0.38</td>\n",
       "      <td id=\"T_1a194_row25_col24\" class=\"data row25 col24\" >0.37</td>\n",
       "      <td id=\"T_1a194_row25_col25\" class=\"data row25 col25\" >nan</td>\n",
       "      <td id=\"T_1a194_row25_col26\" class=\"data row25 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row25_col27\" class=\"data row25 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row25_col28\" class=\"data row25 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row25_col29\" class=\"data row25 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row26\" class=\"row_heading level0 row26\" >DIS</th>\n",
       "      <td id=\"T_1a194_row26_col0\" class=\"data row26 col0\" >0.46</td>\n",
       "      <td id=\"T_1a194_row26_col1\" class=\"data row26 col1\" >0.60</td>\n",
       "      <td id=\"T_1a194_row26_col2\" class=\"data row26 col2\" >0.45</td>\n",
       "      <td id=\"T_1a194_row26_col3\" class=\"data row26 col3\" >0.52</td>\n",
       "      <td id=\"T_1a194_row26_col4\" class=\"data row26 col4\" >0.46</td>\n",
       "      <td id=\"T_1a194_row26_col5\" class=\"data row26 col5\" >0.46</td>\n",
       "      <td id=\"T_1a194_row26_col6\" class=\"data row26 col6\" >0.50</td>\n",
       "      <td id=\"T_1a194_row26_col7\" class=\"data row26 col7\" >0.45</td>\n",
       "      <td id=\"T_1a194_row26_col8\" class=\"data row26 col8\" >0.28</td>\n",
       "      <td id=\"T_1a194_row26_col9\" class=\"data row26 col9\" >0.57</td>\n",
       "      <td id=\"T_1a194_row26_col10\" class=\"data row26 col10\" >0.49</td>\n",
       "      <td id=\"T_1a194_row26_col11\" class=\"data row26 col11\" >0.44</td>\n",
       "      <td id=\"T_1a194_row26_col12\" class=\"data row26 col12\" >0.43</td>\n",
       "      <td id=\"T_1a194_row26_col13\" class=\"data row26 col13\" >0.33</td>\n",
       "      <td id=\"T_1a194_row26_col14\" class=\"data row26 col14\" >0.58</td>\n",
       "      <td id=\"T_1a194_row26_col15\" class=\"data row26 col15\" >0.45</td>\n",
       "      <td id=\"T_1a194_row26_col16\" class=\"data row26 col16\" >0.49</td>\n",
       "      <td id=\"T_1a194_row26_col17\" class=\"data row26 col17\" >0.27</td>\n",
       "      <td id=\"T_1a194_row26_col18\" class=\"data row26 col18\" >0.49</td>\n",
       "      <td id=\"T_1a194_row26_col19\" class=\"data row26 col19\" >0.35</td>\n",
       "      <td id=\"T_1a194_row26_col20\" class=\"data row26 col20\" >0.44</td>\n",
       "      <td id=\"T_1a194_row26_col21\" class=\"data row26 col21\" >0.37</td>\n",
       "      <td id=\"T_1a194_row26_col22\" class=\"data row26 col22\" >0.32</td>\n",
       "      <td id=\"T_1a194_row26_col23\" class=\"data row26 col23\" >0.58</td>\n",
       "      <td id=\"T_1a194_row26_col24\" class=\"data row26 col24\" >0.26</td>\n",
       "      <td id=\"T_1a194_row26_col25\" class=\"data row26 col25\" >0.38</td>\n",
       "      <td id=\"T_1a194_row26_col26\" class=\"data row26 col26\" >nan</td>\n",
       "      <td id=\"T_1a194_row26_col27\" class=\"data row26 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row26_col28\" class=\"data row26 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row26_col29\" class=\"data row26 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row27\" class=\"row_heading level0 row27\" >AMGN</th>\n",
       "      <td id=\"T_1a194_row27_col0\" class=\"data row27 col0\" >0.43</td>\n",
       "      <td id=\"T_1a194_row27_col1\" class=\"data row27 col1\" >0.35</td>\n",
       "      <td id=\"T_1a194_row27_col2\" class=\"data row27 col2\" >0.43</td>\n",
       "      <td id=\"T_1a194_row27_col3\" class=\"data row27 col3\" >0.24</td>\n",
       "      <td id=\"T_1a194_row27_col4\" class=\"data row27 col4\" >0.38</td>\n",
       "      <td id=\"T_1a194_row27_col5\" class=\"data row27 col5\" >0.32</td>\n",
       "      <td id=\"T_1a194_row27_col6\" class=\"data row27 col6\" >0.45</td>\n",
       "      <td id=\"T_1a194_row27_col7\" class=\"data row27 col7\" >0.39</td>\n",
       "      <td id=\"T_1a194_row27_col8\" class=\"data row27 col8\" >0.20</td>\n",
       "      <td id=\"T_1a194_row27_col9\" class=\"data row27 col9\" >0.41</td>\n",
       "      <td id=\"T_1a194_row27_col10\" class=\"data row27 col10\" >0.44</td>\n",
       "      <td id=\"T_1a194_row27_col11\" class=\"data row27 col11\" >0.42</td>\n",
       "      <td id=\"T_1a194_row27_col12\" class=\"data row27 col12\" >0.42</td>\n",
       "      <td id=\"T_1a194_row27_col13\" class=\"data row27 col13\" >0.55</td>\n",
       "      <td id=\"T_1a194_row27_col14\" class=\"data row27 col14\" >0.42</td>\n",
       "      <td id=\"T_1a194_row27_col15\" class=\"data row27 col15\" >0.36</td>\n",
       "      <td id=\"T_1a194_row27_col16\" class=\"data row27 col16\" >0.31</td>\n",
       "      <td id=\"T_1a194_row27_col17\" class=\"data row27 col17\" >0.55</td>\n",
       "      <td id=\"T_1a194_row27_col18\" class=\"data row27 col18\" >0.47</td>\n",
       "      <td id=\"T_1a194_row27_col19\" class=\"data row27 col19\" >0.44</td>\n",
       "      <td id=\"T_1a194_row27_col20\" class=\"data row27 col20\" >0.38</td>\n",
       "      <td id=\"T_1a194_row27_col21\" class=\"data row27 col21\" >0.50</td>\n",
       "      <td id=\"T_1a194_row27_col22\" class=\"data row27 col22\" >0.36</td>\n",
       "      <td id=\"T_1a194_row27_col23\" class=\"data row27 col23\" >0.45</td>\n",
       "      <td id=\"T_1a194_row27_col24\" class=\"data row27 col24\" >0.34</td>\n",
       "      <td id=\"T_1a194_row27_col25\" class=\"data row27 col25\" >0.41</td>\n",
       "      <td id=\"T_1a194_row27_col26\" class=\"data row27 col26\" >0.33</td>\n",
       "      <td id=\"T_1a194_row27_col27\" class=\"data row27 col27\" >nan</td>\n",
       "      <td id=\"T_1a194_row27_col28\" class=\"data row27 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row27_col29\" class=\"data row27 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row28\" class=\"row_heading level0 row28\" >HON</th>\n",
       "      <td id=\"T_1a194_row28_col0\" class=\"data row28 col0\" >0.68</td>\n",
       "      <td id=\"T_1a194_row28_col1\" class=\"data row28 col1\" >0.69</td>\n",
       "      <td id=\"T_1a194_row28_col2\" class=\"data row28 col2\" >0.51</td>\n",
       "      <td id=\"T_1a194_row28_col3\" class=\"data row28 col3\" >0.63</td>\n",
       "      <td id=\"T_1a194_row28_col4\" class=\"data row28 col4\" >0.65</td>\n",
       "      <td id=\"T_1a194_row28_col5\" class=\"data row28 col5\" >0.60</td>\n",
       "      <td id=\"T_1a194_row28_col6\" class=\"data row28 col6\" >0.59</td>\n",
       "      <td id=\"T_1a194_row28_col7\" class=\"data row28 col7\" >0.58</td>\n",
       "      <td id=\"T_1a194_row28_col8\" class=\"data row28 col8\" >0.39</td>\n",
       "      <td id=\"T_1a194_row28_col9\" class=\"data row28 col9\" >0.67</td>\n",
       "      <td id=\"T_1a194_row28_col10\" class=\"data row28 col10\" >0.57</td>\n",
       "      <td id=\"T_1a194_row28_col11\" class=\"data row28 col11\" >0.57</td>\n",
       "      <td id=\"T_1a194_row28_col12\" class=\"data row28 col12\" >0.51</td>\n",
       "      <td id=\"T_1a194_row28_col13\" class=\"data row28 col13\" >0.49</td>\n",
       "      <td id=\"T_1a194_row28_col14\" class=\"data row28 col14\" >0.70</td>\n",
       "      <td id=\"T_1a194_row28_col15\" class=\"data row28 col15\" >0.56</td>\n",
       "      <td id=\"T_1a194_row28_col16\" class=\"data row28 col16\" >0.55</td>\n",
       "      <td id=\"T_1a194_row28_col17\" class=\"data row28 col17\" >0.42</td>\n",
       "      <td id=\"T_1a194_row28_col18\" class=\"data row28 col18\" >0.55</td>\n",
       "      <td id=\"T_1a194_row28_col19\" class=\"data row28 col19\" >0.43</td>\n",
       "      <td id=\"T_1a194_row28_col20\" class=\"data row28 col20\" >0.59</td>\n",
       "      <td id=\"T_1a194_row28_col21\" class=\"data row28 col21\" >0.52</td>\n",
       "      <td id=\"T_1a194_row28_col22\" class=\"data row28 col22\" >0.39</td>\n",
       "      <td id=\"T_1a194_row28_col23\" class=\"data row28 col23\" >0.64</td>\n",
       "      <td id=\"T_1a194_row28_col24\" class=\"data row28 col24\" >0.29</td>\n",
       "      <td id=\"T_1a194_row28_col25\" class=\"data row28 col25\" >0.47</td>\n",
       "      <td id=\"T_1a194_row28_col26\" class=\"data row28 col26\" >0.60</td>\n",
       "      <td id=\"T_1a194_row28_col27\" class=\"data row28 col27\" >0.44</td>\n",
       "      <td id=\"T_1a194_row28_col28\" class=\"data row28 col28\" >nan</td>\n",
       "      <td id=\"T_1a194_row28_col29\" class=\"data row28 col29\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a194_level0_row29\" class=\"row_heading level0 row29\" >CRM</th>\n",
       "      <td id=\"T_1a194_row29_col0\" class=\"data row29 col0\" >0.34</td>\n",
       "      <td id=\"T_1a194_row29_col1\" class=\"data row29 col1\" >0.39</td>\n",
       "      <td id=\"T_1a194_row29_col2\" class=\"data row29 col2\" >0.55</td>\n",
       "      <td id=\"T_1a194_row29_col3\" class=\"data row29 col3\" >0.35</td>\n",
       "      <td id=\"T_1a194_row29_col4\" class=\"data row29 col4\" >0.33</td>\n",
       "      <td id=\"T_1a194_row29_col5\" class=\"data row29 col5\" >0.30</td>\n",
       "      <td id=\"T_1a194_row29_col6\" class=\"data row29 col6\" >0.48</td>\n",
       "      <td id=\"T_1a194_row29_col7\" class=\"data row29 col7\" >0.30</td>\n",
       "      <td id=\"T_1a194_row29_col8\" class=\"data row29 col8\" >0.27</td>\n",
       "      <td id=\"T_1a194_row29_col9\" class=\"data row29 col9\" >0.42</td>\n",
       "      <td id=\"T_1a194_row29_col10\" class=\"data row29 col10\" >0.49</td>\n",
       "      <td id=\"T_1a194_row29_col11\" class=\"data row29 col11\" >0.35</td>\n",
       "      <td id=\"T_1a194_row29_col12\" class=\"data row29 col12\" >0.44</td>\n",
       "      <td id=\"T_1a194_row29_col13\" class=\"data row29 col13\" >0.29</td>\n",
       "      <td id=\"T_1a194_row29_col14\" class=\"data row29 col14\" >0.39</td>\n",
       "      <td id=\"T_1a194_row29_col15\" class=\"data row29 col15\" >0.38</td>\n",
       "      <td id=\"T_1a194_row29_col16\" class=\"data row29 col16\" >0.45</td>\n",
       "      <td id=\"T_1a194_row29_col17\" class=\"data row29 col17\" >0.29</td>\n",
       "      <td id=\"T_1a194_row29_col18\" class=\"data row29 col18\" >0.65</td>\n",
       "      <td id=\"T_1a194_row29_col19\" class=\"data row29 col19\" >0.29</td>\n",
       "      <td id=\"T_1a194_row29_col20\" class=\"data row29 col20\" >0.31</td>\n",
       "      <td id=\"T_1a194_row29_col21\" class=\"data row29 col21\" >0.36</td>\n",
       "      <td id=\"T_1a194_row29_col22\" class=\"data row29 col22\" >0.21</td>\n",
       "      <td id=\"T_1a194_row29_col23\" class=\"data row29 col23\" >0.58</td>\n",
       "      <td id=\"T_1a194_row29_col24\" class=\"data row29 col24\" >0.25</td>\n",
       "      <td id=\"T_1a194_row29_col25\" class=\"data row29 col25\" >0.26</td>\n",
       "      <td id=\"T_1a194_row29_col26\" class=\"data row29 col26\" >0.42</td>\n",
       "      <td id=\"T_1a194_row29_col27\" class=\"data row29 col27\" >0.34</td>\n",
       "      <td id=\"T_1a194_row29_col28\" class=\"data row29 col28\" >0.41</td>\n",
       "      <td id=\"T_1a194_row29_col29\" class=\"data row29 col29\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb520d43b80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plotting correlation\n",
    "def correlation_from_covariance(covariance):\n",
    "    v = np.sqrt(np.diag(covariance))\n",
    "    print(v)\n",
    "    outer_v = np.outer(v, v)\n",
    "    correlation = covariance / outer_v\n",
    "    return correlation\n",
    "\n",
    "correlation = pd.DataFrame(correlation_from_covariance(C), columns = names, index = names)\n",
    "mask = np.zeros_like(correlation, dtype = bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "correlation[mask] = np.nan\n",
    "(correlation.style.background_gradient(cmap = 'coolwarm', axis = None, vmin = -1, vmax = 1).highlight_null(color = '#f1f1f1').format(precision = 2))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions for Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Mean\n",
    "def port_mean(R, W):\n",
    "    return np.sum(R * W)\n",
    "\n",
    "# Calculate Variance\n",
    "def port_var(W, C):\n",
    "    return np.dot(np.dot(W, C), W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimize weights\n",
    "def optimize_weights(R, C, rf):\n",
    "    # Utility function to solve weights which we have to optimize\n",
    "    def util_function(W, R, C, rf):\n",
    "        mean = port_mean(R, W)\n",
    "        var = port_var(W, C)\n",
    "        util = (mean - rf) / np.sqrt(var) # Sharpe ratio\n",
    "        return 1/util # To Maximize the sharpe ratio\n",
    "\n",
    "    n = len(R)\n",
    "    W = np.ones(n)/n # initialize weights with equal values\n",
    "    bound = [(0., 1.) for i in range(n)]  # defining bounds for weights\n",
    "    constraint = [{'type': 'eq', 'fun': lambda W: np.sum(W) - 1.}] # constraint for weights\n",
    "\n",
    "    optimized_weights = scipy.optimize.minimize(util_function, W, (R, C, rf), method = 'SLSQP', constraints = constraint, bounds = bound)\n",
    "    if not optimized_weights.success: \n",
    "        raise BaseException(optimized_weights.message)\n",
    "    return optimized_weights.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For different returns we will find optimal weights and mean, variance which will help in plotting graph\n",
    "def optimize_frontier(R, C, rf):\n",
    "    # utility function for a particular return \n",
    "    def util_function(W, R, C, r):\n",
    "        mean = port_mean(R, W)\n",
    "        var = port_var(W, C)\n",
    "        penalty = 100 * abs(mean - r)\n",
    "        return var + penalty\n",
    "\n",
    "    frontier_means = []\n",
    "    frontier_vars = []\n",
    "    frontier_weights = []\n",
    "    n = len(R)\n",
    "\n",
    "    for r in np.linspace(min(R), max(R), num = 30):\n",
    "        W = np.ones(n)/n\n",
    "        bound = [(0, 1) for i in range(n)]  # Defining bounds for weights\n",
    "        constraint = [{'type': 'eq', 'fun': lambda W: np.sum(W) - 1}] # constraint for weights\n",
    "\n",
    "        optimize_weights = scipy.optimize.minimize(util_function, W, (R, C, r), method = 'SLSQP', bounds = bound, constraints = constraint)\n",
    "        \n",
    "        if not optimize_weights.success: \n",
    "            raise BaseException(optimize_weights.message)\n",
    "        frontier_weights.append(optimize_weights.x)\n",
    "        frontier_means.append(r)\n",
    "        frontier_vars.append(port_var(optimize_weights.x, C))\n",
    "\n",
    "    return np.array(frontier_means), np.array(frontier_vars), np.array(frontier_weights)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Result:\n",
    "    def __init__(self, W, tangent_mean, tangent_var, frontier_mean, frontier_var, frontier_weights):\n",
    "        self.W = W\n",
    "        self.tangent_mean = tangent_mean\n",
    "        self.tangent_var = tangent_var\n",
    "        self.frontier_mean = frontier_mean\n",
    "        self.frontier_var = frontier_var\n",
    "        self.frontier_weights = frontier_weights"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Efficient Frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get the Efficient Frontier\n",
    "def get_frontier(R, C, rf):\n",
    "    W = optimize_weights(R, C, rf)\n",
    "    tangent_mean = port_mean(R, W)\n",
    "    tangent_var = port_var(W, C)\n",
    "    frontier_means, frontier_vars, frontier_weights = optimize_frontier(R, C, rf)\n",
    "    return Result(W, tangent_mean, tangent_var, frontier_means, frontier_vars, frontier_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display Assets\n",
    "def display_assets(names, R, C, color = 'black'):\n",
    "    plt.scatter([C[i, i] ** .5 for i in range(n)], R, marker = 'X', color = color), plt.grid(True)  # draw assets\n",
    "    for i in range(n): \n",
    "        plt.text(C[i, i] ** .5, R[i], '%s' % names[i], verticalalignment = 'center', color = color) # draw labels\n",
    "\n",
    "# Display Efficient Frontier\n",
    "def display_frontier(result: Result, label = None, color = 'black'):\n",
    "    plt.text(result.tangent_var ** .5, result.tangent_mean, 'tangent', verticalalignment = 'center', color = color)\n",
    "    plt.scatter(result.tangent_var ** .5, result.tangent_mean, marker = 'o', color = color), plt.grid(True)\n",
    "    plt.plot(list(result.frontier_var ** .5), list(result.frontier_mean), label = label, color = color), plt.grid(True) # draw efficient frontier\n",
    "\n",
    "result_1 = get_frontier(R, C, rf)\n",
    "n = len(R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_assets(names, R, C, color = 'red')\n",
    "display_frontier(result_1, color = 'blue')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markowitz Portfolio Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To load the SPY data\n",
    "def load_SPY_data():\n",
    "    dataframe = pd.read_csv('dow-jones/SPY.csv', index_col = None, parse_dates = ['date'])\n",
    "    spy_prices = list(dataframe['close'])[-2000:] # trailing window 2000 days\n",
    "    return spy_prices\n",
    "\n",
    "spy_prices = load_SPY_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10098743222175921\n",
      "0.03502158651449702\n",
      "0.1871405528326157\n"
     ]
    }
   ],
   "source": [
    "# Calculate SPY market returns\n",
    "def SPY_market_return(spy_prices):\n",
    "    # Convert the input stock prices into a numpy matrix\n",
    "    spy_prices = np.matrix(spy_prices)\n",
    "    r, c = spy_prices.shape\n",
    "\n",
    "    # Create an empty numpy array to store historical returns\n",
    "    historical_returns = np.empty([r, c-1])\n",
    "    for ri in range(r):\n",
    "        for ci in range(c-1):\n",
    "            # Get the current price and the price from the previous day\n",
    "            prior_price, after_price = spy_prices[ri, ci], spy_prices[ri, ci+1]\n",
    "            historical_returns[ri, ci] = (after_price/prior_price)-1\n",
    "\n",
    "    # Calculate the market variance of the historical returns\n",
    "    market_variance = np.cov(historical_returns)\n",
    "\n",
    "    # Calculate the annualized market return\n",
    "    market_return = (1 + np.mean(historical_returns[0])) ** 252 - 1\n",
    "    print(market_return)\n",
    "\n",
    "    # Calculate the annualized market variance\n",
    "    market_variance = market_variance * 252\n",
    "    print(market_variance)\n",
    "    \n",
    "    # Calculate the volatility of the portfolio\n",
    "    volatility = np.sqrt(market_variance)\n",
    "    print(volatility)\n",
    "\n",
    "    # Return the annualized market return and covariance\n",
    "    return market_return, market_variance\n",
    "\n",
    "market_return, market_variance = SPY_market_return(spy_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Covariance matrix\n",
    "def calculate_cov_matrix(stock_prices, spy_prices):\n",
    "    # Convert prices to numpy matrices\n",
    "    stock_prices = np.matrix(stock_prices)\n",
    "    spy_prices = np.matrix(spy_prices)\n",
    "    \n",
    "    # Get number of rows and columns in stock_prices\n",
    "    rows, cols = stock_prices.shape\n",
    "    \n",
    "    # Create empty matrices to store returns\n",
    "    stock_returns = np.empty([rows, cols - 1])\n",
    "    spy_returns = np.empty([rows, cols - 1])\n",
    "    \n",
    "    # Calculate returns\n",
    "    for r in range(rows):\n",
    "        for c in range(cols - 1):\n",
    "            # Calculate stock returns\n",
    "            p0, p1 = stock_prices[r, c], stock_prices[r, c + 1]\n",
    "            stock_returns[r, c] = (p1 / p0) - 1\n",
    "            \n",
    "            # Calculate spy returns\n",
    "            if r == 0:\n",
    "                p2, p3 = spy_prices[r, c], spy_prices[r, c + 1]\n",
    "                spy_returns[r, c] = (p3 / p2) - 1\n",
    "    \n",
    "    # Calculate covariance matrix\n",
    "    cov_matrix = []\n",
    "    for r in range(rows):\n",
    "        covar = np.cov(stock_returns[r], spy_returns)[0, 1]\n",
    "        cov_matrix.append(covar)\n",
    "    \n",
    "    # Annualize covariance matrix and return as numpy array\n",
    "    return np.asarray(cov_matrix) * 252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83085545 1.22628579 1.22143333 1.4284734  1.05664751 1.05179016\n",
      " 1.01807791 0.63472756 0.27432637 1.20388293 0.99600448 0.87271306\n",
      " 1.20313542 0.58568924 1.14144191 0.71982371 1.05078952 0.61393043\n",
      " 1.22498879 0.59707424 0.82881168 0.91861691 0.48116264 1.10618162\n",
      " 0.53319981 0.84522246 1.01425628 0.78566514 0.98950626 1.24637569]\n"
     ]
    }
   ],
   "source": [
    "# Covariance of each stock return with market return and calculating Beta\n",
    "cov_matrix = calculate_cov_matrix(prices, spy_prices)\n",
    "beta = cov_matrix/market_variance\n",
    "print(beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.08987676 0.11585156 0.11553282 0.12913275 0.10470846 0.10438939\n",
      " 0.10217492 0.07699362 0.05331979 0.11437998 0.10072498 0.09262628\n",
      " 0.11433088 0.07377242 0.11027839 0.08258337 0.10432367 0.07562751\n",
      " 0.11576637 0.07452027 0.08974251 0.09564159 0.06690634 0.10796223\n",
      " 0.07032453 0.09082049 0.10192389 0.08690833 0.10029813 0.11717122]\n"
     ]
    }
   ],
   "source": [
    "# Caluclating the expected return of each asset using the markowitz formula\n",
    "def markowitz_portfolio_return(result: Result, C, rf, market_return, market_variance):\n",
    "    return rf + np.dot(beta, (market_return - rf))\n",
    "\n",
    "expected_asset_return = markowitz_portfolio_return(result_1, C, rf, market_return, market_variance)\n",
    "print(expected_asset_return)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Black-Litterman Reverse Optimization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Black-Litterman Mathematical formula:\n",
    "$$E(R) = [(\\tau \\Sigma)^{-1} + (P^T\\Omega^{-1} P)^{-1}][(\\tau \\Sigma)^{-1}\\pi + (P^T\\Omega^{-1}Q)]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14786434 0.19777713 0.23923658 0.22380406 0.17279789 0.1727062\n",
      " 0.18399147 0.12259652 0.07359235 0.19518012 0.17486766 0.15543719\n",
      " 0.21020682 0.120538   0.18555761 0.13804577 0.18079728 0.12340404\n",
      " 0.22995692 0.12371728 0.14482544 0.16792073 0.10178208 0.19520393\n",
      " 0.11579411 0.15092917 0.17200013 0.14780096 0.16850353 0.21871446]\n"
     ]
    }
   ],
   "source": [
    "# Calculate portfolio historical return and variance\n",
    "mean = port_mean(R, W)\n",
    "var = port_var(W, C)\n",
    "\n",
    "# Calculate risk aversion\n",
    "lambdaa = (mean - rf) / var\n",
    "\n",
    "# Calculate equilibrium excess returns\n",
    "Pi = np.dot(np.dot(lambdaa, C), W)\n",
    "print(Pi + rf)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equillibrium Excess return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hD3/IwQcfnC222CI9evTIoEGDcsQRR+TZZ5+twioAAAAAgNWp+g7I0aNHZ/HixVm4cGF++ctfZuTIkTnllFNy0EEHZcWKFSuNf+6557Lffvtls802y+zZszN//vxMnz49AwYMyEsvvVSFFQAAAAAAq1Nb7QKKxWL69++fJNl6663zwQ9+MB/+8Iez3377ZcaMGfnsZz/bbvydd96ZlpaWXHbZZamtfaP87bbbLiNHjuz02gEAAACANav6DshV2XffffO+970v119//UrX+vfvnxUrVuSGG25IqVRap/na2trS2trargEAAAAAldclA8gkGTZsWBYuXLhS/4c//OGceeaZ+fSnP51+/frlwAMPzPnnn5+//e1vq51r8uTJaWhoKLfGxsYKVg4AAAAAvKnLBpClUimFQmGV17797W9nyZIlufTSS7PLLrvk0ksvzbBhw/LQQw+tcnxzc3NaWlrKbdGiRZUsHQAAAAD4/3XZAHL+/PnZbrvtVnu9b9++Ofzww3PBBRdk/vz5GTBgQC644IJVji0Wi6mvr2/XAAAAAIDK65IB5G9/+9s89NBDGTdu3DqN7969e7bffnunYAMAAABAF1P1U7Db2tqyZMmSvPbaa/nb3/6Wm2++OZMnT85BBx2UY489dqXxs2bNytVXX50jjzwyO+64Y0qlUn7xi1/kpptuyvTp06uwAgAAAABgdaoeQN58883ZaqutUltbm/e85z153/velwsvvDDHHXdcampW3qC58847p2fPnjn99NOzaNGiFIvF7LDDDrnssstyzDHHVGEFAAAAAMDqFEqlUqnaRXS21tbWN07DPvXa1BR7VrscAADgHW7hlDHVLgEAOtWb+VpLS8taz1vpkt+ABAAAAADeHar+CnY1PXz2KCdiAwAAAEAF2QEJAAAAAFSMABIAAAAAqBgBJAAAAABQMRv1NyCHT5rtFGwAACrK6cgAwMbODkgAAAAAoGIEkAAAAABAxQggAQAAAICKEUACAAAAABXT4QHkpZdemt69e2fFihXlvmXLlmWTTTbJPvvs027snDlzUigU8qc//SmDBg1KoVBIoVBIt27dMmDAgBx//PFZunTpKp8zbNiwFIvFLFmypKOXAAAAAAB0kA4PIEeOHJlly5blvvvuK/fNnTs3/fv3z+9///u8/PLL5f5bb701AwcOzPbbb58kOeecc7J48eL85S9/yX//93/n9ttvzxe+8IWVnnHHHXdk+fLl+eQnP5nLL7+8o5cAAAAAAHSQDg8ghw4dmq222ipz5swp982ZMyeHHHJItttuu9x9993t+keOHFn+3bt37/Tv3z9bb711Ro4cmeOOOy7333//Ss+YOnVqPv3pT+eYY47JtGnTOnoJAAAAAEAHqcg3IEeOHJlbb721/PvWW2/NPvvskxEjRpT7ly9fnt///vftAsh/9de//jW/+MUvsscee7Trf/HFFzNz5syMHz8+BxxwQFpaWjJ37tw11tPW1pbW1tZ2DQAAAACovIoFkHfeeWdWrFiRF198MQ888EBGjBiRvffeu7wz8q677kpbW1u7APKMM85IXV1dNt1002yzzTYpFAr53ve+127uq6++OjvssEN22WWXdOvWLUceeWSmTp26xnomT56choaGcmtsbOzwNQMAAAAAK6tIALnPPvvkpZdeyr333pu5c+dmxx13zOabb54RI0aUvwM5Z86cDB48OAMHDizf9+Uvfznz5s3Lgw8+mFtuuSVJMmbMmLz22mvlMdOmTcv48ePLv8ePH5+ZM2fmxRdfXG09zc3NaWlpKbdFixZVYNUAAAAAwFvVVmLSIUOGZJtttsmtt96apUuXZsSIEUmSAQMGpLGxMb/73e9y6623Zt999213X79+/TJkyJAkyQ477JAf/OAH2XPPPXPrrbdm//33zyOPPJK7774799xzT84444zyfa+99lquvvrqnHDCCausp1gsplgsVmKpAAAAAMAaVGQHZPLGa9hz5szJnDlzss8++5T799577/zyl7/MPffcs9rvP76pW7duSd74XmTyxuEze++9d/7whz9k3rx55Xbaaaet9TVsAAAAAKDzVWQHZPJGADlx4sS8+uqr5R2QSTJixIh8/vOfzyuvvLJSAPniiy9myZIlKZVKWbRoUb7yla9k8803z1577ZVXX301/+f//J+cc845GT58eLv7PvvZz+Z73/te/vjHP2aXXXap1JIAAAAAgPVU0R2Qy5cvz5AhQ7LllluW+0eMGJEXX3wxQ4cOzVZbbdXunm984xvZaqutMmDAgBx00EHp1atXfvWrX6Vv37658cYb8/zzz+ewww5b6Vk77bRTdtppJ7sgAQAAAKCLqdgOyEGDBqVUKq3Uv+22266yf+HChWucb9y4ce0Oo3mrRx55ZL1rBAAAAAAqq2I7IAEAAAAAKrYD8p3g4bNHpb6+vtplAAAAAMC7lh2QAAAAAEDFCCABAAAAgIoRQAIAAAAAFbNRfwNy+KTZqSn2rHYZAPCOs3DKmGqXAAAAvEPYAQkAAAAAVIwAEgAAAACoGAEkAAAAAFAxAkgAAAAAoGI6NIC89NJL07t376xYsaLct2zZsmyyySbZZ5992o2dM2dOCoVC/vSnP2XQoEEpFAq5+uqrV5pzl112SaFQyIwZM8r3rKnNmTOnI5cEAAAAALwNHRpAjhw5MsuWLct9991X7ps7d2769++f3//+93n55ZfL/bfeemsGDhyY7bffPknS2NiY6dOnt5vv7rvvzpIlS9KrV68kyV577ZXFixeX26c+9amMHj26Xd9ee+3VkUsCAAAAAN6GDg0ghw4dmq222qrdLsQ5c+bkkEMOyXbbbZe77767Xf/IkSPLv48++ujcdtttWbRoUblv2rRpOfroo1NbW5sk6d69e/r3719um266aYrFYru+7t27d+SSAAAAAIC3ocO/ATly5Mjceuut5d+33npr9tlnn4wYMaLcv3z58vz+979vF0BuueWWGTVqVC6//PIkyT//+c9cc801aWpqets1tbW1pbW1tV0DAAAAACqvIgHknXfemRUrVuTFF1/MAw88kBEjRmTvvfcu74y866670tbW1i6ATJKmpqbMmDEjpVIpP/3pT7P99tvn/e9//9uuafLkyWloaCi3xsbGtz0nAAAAALB2HR5A7rPPPnnppZdy7733Zu7cudlxxx2z+eabZ8SIEeXvQM6ZMyeDBw/OwIED2907ZsyYLFu2LLfffnumTZvWIbsfk6S5uTktLS3l9q+veQMAAAAAlVPb0RMOGTIk22yzTW699dYsXbo0I0aMSJIMGDAgjY2N+d3vfpdbb701++6778rF1NbmmGOOyaRJk/L73/8+N9xwQ4fUVCwWUywWO2QuAAAAAGDddfgOyOSN17DnzJmTOXPmZJ999in377333vnlL3+Ze+65Z6XXr9/U1NSU2267LYccckje8573VKI8AAAAAKCTdPgOyOSNAHLixIl59dVXyzsgk2TEiBH5/Oc/n1deeWW1AeROO+2Uv//97+nZs2clSgMAAAAAOlHFAsjly5dn2LBh2XLLLcv9I0aMyIsvvpihQ4dmq622Wu39ffv2rURZAAAAAEAnq0gAOWjQoJRKpZX6t91221X2L1y4cI3zvfDCC6vsnzFjxgZUBwAAAAB0lop8AxIAAAAAIKnQDsh3iofPHpX6+vpqlwEAAAAA71p2QAIAAAAAFSOABAAAAAAqRgAJAAAAAFSMABIAAAAAqJiN+hCa4ZNmp6bYs9plAAAA8C60cMqYapcA0CXYAQkAAAAAVIwAEgAAAACoGAEkAAAAAFAx76gAcuzYsRk9evQqr82dOzeFQiEPPvhgJ1cFAAAAAKzOOyqAPP744/PrX/86Tz/99ErXpk+fnn/7t3/Le9/73ipUBgAAAACsyjsqgDzooIOy+eabZ8aMGe36ly1blpkzZ+b444+vTmEAAAAAwCq9owLI2traHHvssZkxY0ZKpVK5f+bMmXnttddy1FFHrfK+tra2tLa2tmsAAAAAQOW9owLIJGlqasqf/vSn3HbbbeW+6dOnZ9y4cWloaFjlPZMnT05DQ0O5NTY2dla5AAAAALBRe8cFkMOGDctee+2VadOmJUmeeOKJzJ07d42vXzc3N6elpaXcFi1a1FnlAgAAAMBG7R0XQCZvHEZz3XXX5cUXX8z06dOz/fbbZ8SIEasdXywWU19f364BAAAAAJX3jgwgP/WpT6WmpiZXXnllrrjiijQ1NaVQKFS7LAAAAADgLWqrXcCGqKuryxFHHJHm5ua0trZmwoQJ1S4JAAAAAFiFd+QOyOSN17CXLl2aUaNGZcCAAdUuBwAAAABYhXfkDsgk2XPPPVMqlapdBgAAAACwBu/YHZAAAAAAQNcngAQAAAAAKuYd+wp2R3j47FGpr6+vdhkAAAAA8K5lByQAAAAAUDECSAAAAACgYgSQAAAAAEDFbNTfgBw+aXZqij2rXQYAnWzhlDHVLgEAAGCjYQckAAAAAFAxAkgAAAAAoGIEkAAAAABAxXSZAHLs2LEZPXr0Kq/NnTs3hUIhm222WQqFwmrbbbfd1slVAwAAAABr0mUOoTn++OMzbty4PP3009lmm23aXZs+fXr+7d/+LbNnz84rr7zS7torr7ySMWPGpEePHtljjz06s2QAAAAAYC26TAB50EEHZfPNN8+MGTPy9a9/vdy/bNmyzJw5M+eff34222yzle474YQT8ve//z333ntvevTo0ZklAwAAAABr0WVewa6trc2xxx6bGTNmpFQqlftnzpyZ1157LUcdddRK9/zoRz/KFVdckeuuu26lXZP/qq2tLa2tre0aAAAAAFB5XSaATJKmpqb86U9/avctx+nTp2fcuHFpaGhoN/b222/Pqaeemosvvjh77bXXGuedPHlyGhoayq2xsbEi9QMAAAAA7XWpAHLYsGHZa6+9Mm3atCTJE088kblz5+b4449vN+4vf/lLPvnJT+bf//3f89nPfnat8zY3N6elpaXcFi1aVJH6AQAAAID2ulQAmbxxGM11112XF198MdOnT8/222+fESNGlK8vX748hx12WHbZZZf84Ac/WKc5i8Vi6uvr2zUAAAAAoPK6XAD5qU99KjU1NbnyyitzxRVXpKmpKYVCoXz9s5/9bP7xj39k5syZqa3tMmfoAAAAAACr0OUSvLq6uhxxxBFpbm5Oa2trJkyYUL52/vnnZ+bMmfnFL36RFStWZMmSJe3ubWhoyKabbtrJFQMAAAAAq9PldkAmb7yGvXTp0owaNSoDBgwo9//oRz/Kq6++mtGjR2errbZaqV1zzTVVrBoAAAAAeKsutwMySfbcc8+USqWV+v/85z9XoRoAAAAAYEN1yR2QAAAAAMC7gwASAAAAAKiYLvkKdmd5+OxRqa+vr3YZAAAAAPCuZQckAAAAAFAxAkgAAAAAoGIEkAAAAABAxWzU34AcPml2aoo9q10GAABQYQunjKl2CQCw0bIDEgAAAACoGAEkAAAAAFAxAkgAAAAAoGIEkAAAAABAxVQ0gNxnn31y6qmnrtQ/Y8aM9OnTJ0ly1llnpVAo5HOf+1y7MfPmzUuhUMjChQuTJAsXLkyhUMi8efPW+TkAAAAAQHV1iR2QPXr0yNSpU/P4449XuxQAAAAAoAN1iQBy6NChGTlyZL72ta9VZP62tra0tra2awAAAABA5XWJADJJpkyZkuuuuy733Xdfh889efLkNDQ0lFtjY2OHPwMAAAAAWFmXCSA/+MEP5lOf+lTOOOOMNY7ba6+9UldX167NnTt3jfc0NzenpaWl3BYtWtSRpQMAAAAAq1Fb7QL+1be+9a3stNNO+dWvfpUttthilWOuueaa7LTTTu36jj766DXOWywWUywWO6xOAAAAAGDdVDSArK+vT0tLy0r9L7zwQhoaGlbq33777XPCCSfkq1/9aqZOnbrKORsbGzNkyJB2fZtuumnHFAwAAAAAdKiKvoI9dOjQ3H///Sv133///dlxxx1Xec83vvGNPPbYY7n66qsrWRoAAAAA0AkqGkCeeOKJeeyxx/KFL3whDz74YBYsWJDvfe97ueqqq3L66aev8p4tt9wyp512Wi688MJKlgYAAAAAdIKKBpCDBw/O7bffnkcffTT7779/9thjj1x77bWZOXNmRo8evdr7vvSlL6Wurq6SpQEAAAAAnaBQKpVK1S6is7W2tqahoSGNp16bmmLPapcDAABU2MIpY6pdAgC8q7yZr7W0tKS+vn6NYyu6AxIAAAAA2LhV9BTsru7hs0etNaEFAAAAADacHZAAAAAAQMUIIAEAAACAihFAAgAAAAAVs1F/A3L4pNlOwQYAeBdy4jEAQNdhByQAAAAAUDECSAAAAACgYgSQAAAAAEDFCCABAAAAgIrptACyUCissZ111llZuHBhu77NNtssI0aMyNy5c5Mk1113Xbp165a//vWvq3zGDjvskNNOO62zlgQAAAAArEWnBZCLFy8utx/84Aepr69v1/elL32pPPY3v/lNFi9enNtvvz0DBgzIQQcdlL/97W85+OCD07dv31x++eUrzX/77bfniSeeyPHHH99ZSwIAAAAA1qLTAsj+/fuXW0NDQwqFQru+urq68ti+ffumf//+GT58eM4888y0trbm97//fTbZZJMcc8wxmTFjxkrzT5s2LXvssUd22WWXzloSAAAAALAWXfobkMuXL88VV1yRJOnevXuS5Pjjj8/jjz+e22+/vTxu2bJl+elPf7ra3Y9tbW1pbW1t1wAAAACAyqutdgGrstdee6Wmpib//Oc/UyqVsttuu2W//fZLkuy888758Ic/nGnTpmXvvfdOklx77bUplUo58sgjVznf5MmTc/bZZ3da/QAAAADAG7rkDshrrrkmDzzwQK677roMGTIkM2bMyCabbFK+3tTUlJ/+9Kd58cUXk7zx+vXhhx+e3r17r3K+5ubmtLS0lNuiRYs6ZR0AAAAAsLHrkjsgGxsbs8MOO2SHHXbIihUrcthhh+Xhhx9OsVhMkhx55JH54he/mGuvvTZ777137rzzzkyePHm18xWLxfK9AAAAAEDn6ZI7IP/VJz/5ydTW1uZHP/pRua937945/PDDM23atEyfPj077rhjPvaxj1WxSgAAAABgVbp8AFkoFPKFL3whU6ZMyT//+c9y//HHH5/f/e53ufTSS9PU1FTFCgEAAACA1enyAWSSHHfccXn11Vdz0UUXlfs++tGPZujQoWltbc2xxx5bxeoAAAAAgNUplEqlUrWL6Gytra1paGhI46nXpqbYs9rlAADQwRZOGVPtEgAA3tXezNdaWlpSX1+/xrHviB2QAAAAAMA7U5c8BbuzPHz2qLUmtAAAAADAhrMDEgAAAACoGAEkAAAAAFAxAkgAAAAAoGI26m9ADp802ynYALARcTIyAAB0PjsgAQAAAICKEUACAAAAABUjgAQAAAAAKkYACQAAAABUTJcKICdMmJBCoZBCoZDu3btnyJAhOeecc7JixYokSalUyn/9139lzz33TH19ferq6rLLLrvklFNOyRNPPFHl6gEAAACAt+pSAWSSjB49OosXL87jjz+e008/PWeddVbOP//8lEqlfPrTn84XvvCFfOITn8ivfvWrPPLII5k6dWp69OiRb33rW9UuHQAAAAB4i9pqF/BWxWIx/fv3T5KceOKJueGGG3LjjTdmu+22y9VXX52f//znOfjgg8vjBw4cmA9/+MMplUrVKhkAAAAAWI0uF0C+1aabbprnn38+V111VYYOHdoufPxXhUJhtXO0tbWlra2t/Lu1tbXD6wQAAAAAVtblXsF+U6lUym9+85vMnj07++67bx577LEMHTq03ZhTTz01dXV1qauryzbbbLPauSZPnpyGhoZya2xsrHT5AAAAAEC6YAA5a9as1NXVpUePHjnwwANzxBFH5Kyzzlrl2K997WuZN29evvGNb2TZsmWrnbO5uTktLS3ltmjRogpVDwAAAAD8qy73CvbIkSNzySWXpHv37hkwYEBqa98ocYcddsiCBQvajd18882z+eabZ4sttljjnMViMcVisWI1AwAAAACr1uV2QPbq1StDhgzJwIEDy+Fjkhx11FFZsGBBfv7zn1exOgAAAABgfXS5HZCrc+SRR+b666/PkUcemebm5owaNSpbbrllnnrqqVxzzTXp1q1btUsEAAAAAN6iy+2AXJ1CoZBrrrkmP/jBD3LTTTdlv/32y9ChQ9PU1JTGxsbccccd1S4RAAAAAHiLQqlUKlW7iM7W2tr6xmnYp16bmmLPapcDAHSShVPGVLsEAAB4V3gzX2tpaUl9ff0ax75jdkACAAAAAO8875hvQFbCw2ePWmtCCwAAAABsODsgAQAAAICKEUACAAAAABUjgAQAAAAAKmaj/gbk8EmznYINAAAAUCULp4ypdgl0AjsgAQAAAICKEUACAAAAABUjgAQAAAAAKkYACQAAAABUTIcEkBMmTEihUMjnPve5la5NnDgxhUIhEyZMSJI899xzOfHEEzNw4MAUi8X0798/o0aNyp133lm+Z9CgQSkUCu3aNttsk7POOmul/rc2AAAAAKDr6LBTsBsbG3P11Vfn+9//fjbddNMkycsvv5wrr7wyAwcOLI8bN25cXnnllVx++eUZPHhw/va3v+WWW27J888/326+c845JyeccEL5d7du3bLpppu2Czk/9KEP5d///d/bjQMAAAAAuo4OCyA/+MEP5k9/+lOuv/76HH300UmS66+/PgMHDsx2222XJHnhhRcyd+7czJkzJyNGjEiSbLvtttl9991Xmq93797p37//Sv11dXXlv7t167bacQAAAABA9XXoNyCbmpoyffr08u9p06blM5/5TPl3XV1d6urq8rOf/SxtbW0d+eg1amtrS2tra7sGAAAAAFRehwaQ48ePzx133JGnnnoqTz31VO68886MHz++fL22tjYzZszI5Zdfnj59+uQjH/lIzjzzzDz44IMrzXXGGWeUA8u6urpceOGFG1zX5MmT09DQUG6NjY0bPBcAAAAAsO46NIDcfPPNM2bMmMyYMSPTp0/PmDFj0q9fv3Zjxo0bl2eeeSY33nhjRo8enTlz5uSDH/xgZsyY0W7cl7/85cybN6/cjj322A2uq7m5OS0tLeW2aNGiDZ4LAAAAAFh3HfYNyDc1NTXl85//fJLk4osvXuWYHj165IADDsgBBxyQ/+f/+X/y2c9+NpMmTSqflJ0k/fr1y5AhQzqkpmKxmGKx2CFzAQAAAADrrkN3QCbJ6NGj88orr+TVV1/NqFGj1umenXfeOS+99FJHlwIAAAAAVFmH74Ds1q1b5s+fX/77Xz3//PM5/PDD09TUlPe+973p3bt37rvvvnznO9/JIYcc0tGlAAAAAABV1uEBZJLU19evsr+uri577LFHvv/97+dPf/pTXn311TQ2NuaEE07ImWeeWYlSAAAAAIAqKpRKpVK1i+hsra2tb5yGfeq1qSn2rHY5AAAAABulhVPGVLsENtCb+VpLS8tqNyO+qcO/AQkAAAAA8KaKvIL9TvHw2aPWmtACAAAAABvODkgAAAAAoGIEkAAAAABAxQggAQAAAICK2ai/ATl80mynYAPARsIJiwAAUB12QAIAAAAAFSOABAAAAAAqRgAJAAAAAFSMABIAAAAAqJiKBpATJkxIoVDI5z73uZWuTZw4MYVCIRMmTGg3tlAoZJNNNsl2222Xr3zlK3n55Zfb3VcoFPKzn/2s/PvVV1/NUUcdla233joPP/xwJZcDAAAAAKyniu+AbGxszNVXX53ly5eX+15++eVceeWVGThwYLuxo0ePzuLFi/Pkk0/m+9//fv7zP/8zkyZNWu3c//znP3PwwQfn3nvvzR133JHhw4dXbB0AAAAAwPqreAD5wQ9+MI2Njbn++uvLfddff30GDhyYD3zgA+3GFovF9O/fP42NjTn00EOz//7759e//vUq533hhRdywAEH5Jlnnskdd9yR7bbbrqLrAAAAAADWX6d8A7KpqSnTp08v/542bVo+85nPrPGehx9+OL/73e/SvXv3la4tWbIkI0aMSJLcdttt6d+//xrnamtrS2tra7sGAAAAAFRepwSQ48ePzx133JGnnnoqTz31VO68886MHz9+pXGzZs1KXV1devTokV133TXPPvtsvvzlL6807pRTTskrr7ySX//61+nTp89anz958uQ0NDSUW2NjY0csCwAAAABYi04JIDfffPOMGTMmM2bMyPTp0zNmzJj069dvpXEjR47MvHnz8vvf/z7HHXdcPvOZz2TcuHErjTvooIPy2GOP5T//8z/X6fnNzc1paWkpt0WLFr3tNQEAAAAAa1fbWQ9qamrK5z//+STJxRdfvMoxvXr1ypAhQ5K88Zr2+973vkydOjXHH398u3HHHHNMDj744DQ1NaVUKuW0005b47OLxWKKxWIHrAIAAAAAWB+dFkCOHj06r7zySgqFQkaNGrXW8TU1NTnzzDNz2mmn5dOf/nQ23XTTdtePO+641NTU5DOf+Uxef/31fOlLX6pU6QAAAADABuqUV7CTpFu3bpk/f34eeeSRdOvWbZ3uOfzww9OtW7fV7pg85phjcvnll+erX/1qzj///I4sFwAAAADoAJ22AzJJ6uvr12t8bW1tPv/5z+c73/lOTjzxxPTq1WulMUcffXRqampyzDHH5PXXX88ZZ5zRUeUCAAAAAG9ToVQqlapdRGdrbW194zTsU69NTbFntcsBADrBwiljql0CAAC8a7yZr7W0tKx102GnvYINAAAAAGx8OvUV7K7m4bNHrfdr4QAAAADAurMDEgAAAACoGAEkAAAAAFAxAkgAAAAAoGIEkAAAAABAxWzUh9AMnzQ7NcWe1S4DAACgy1g4ZUy1SwDgXcYOSAAAAACgYgSQAAAAAEDFCCABAAAAgIrptABywoQJKRQKmTJlSrv+n/3sZykUCkmSOXPmpFAo5IUXXihff+aZZ7Lrrrtm7733TktLS3nMqtqSJUs6azkAAAAAwDro1B2QPXr0yHnnnZelS5eu0/g//elP+ehHP5ptt902s2fPTkNDQ/naggULsnjx4nZtiy22qFTpAAAAAMAG6NQAcv/990///v0zefLktY598MEH89GPfjR77rlnfvazn2XTTTdtd32LLbZI//7927WaGm+UAwAAAEBX0qmJXbdu3XLuuefmP/7jP/L000+vdtzvfve7jBgxIuPGjctPfvKT1NbWvq3ntrW1pbW1tV0DAAAAACqv07cMHnbYYXn/+9+fSZMmrXHM2LFjc9FFF5W/D/lW22yzTerq6sptl112We18kydPTkNDQ7k1Nja+7XUAAAAAAGv39rYWbqDzzjsv++67b770pS+t8vohhxySG264IXPnzs3HPvaxVY6ZO3duevfuXf69ySabrPZ5zc3NOe2008q/W1tbhZAAAAAA0Amq8tHEvffeO6NGjUpzc/Mqr//nf/5njjzyyBx44IG5/fbbVzlmu+22y5AhQ8pt2223Xe3zisVi6uvr2zUAAAAAoPKqsgMySaZMmZL3v//9GTp06ErXCoVCfvzjH6empiaf+MQn8j//8z8ZMWJEFaoEAAAAAN6OqgWQu+66a44++uhceOGFq7xeKBRy6aWXplu3buUQcp999ilff/bZZ/Pyyy+3u6dv375rfBUbAAAAAOhcVQsgk+Scc87JNddcs9rrhUIhF198cWpqajJmzJjMmjWrfCjNqnZO3nXXXfnwhz9csXoBAAAAgPVTKJVKpWoX0dlaW1vfOA371GtTU+xZ7XIAAAC6jIVTxlS7BADeAd7M11paWtZ63kpVDqEBAAAAADYOAkgAAAAAoGKq+g3Ianv47FFr3SIKAAAAAGw4OyABAAAAgIoRQAIAAAAAFSOABAAAAAAqZqP+BuTwSbNTU+xZ7TIAAN5xFk4ZU+0SAAB4h7ADEgAAAACoGAEkAAAAAFAxAkgAAAAAoGIqFkBOmDAhhUIhn/vc51a6NnHixBQKhUyYMKHct2TJkpx88skZPHhwisViGhsbM3bs2Nxyyy3lMYMGDUqhUEihUMimm26aQYMG5VOf+lR++9vfVmoZAAAAAMDbUNEdkI2Njbn66quzfPnyct/LL7+cK6+8MgMHDiz3LVy4MLvttlt++9vf5vzzz89DDz2Um2++OSNHjszEiRPbzXnOOedk8eLFWbBgQa644or06dMn+++/f7797W9XcikAAAAAwAao6CnYH/zgB/OnP/0p119/fY4++ugkyfXXX5+BAwdmu+22K4876aSTUigUcs8996RXr17l/l122SVNTU3t5uzdu3f69++fJBk4cGD23nvvbLXVVvnGN76RT37ykxk6dGgllwQAAAAArIeKfwOyqakp06dPL/+eNm1aPvOZz5R//+Mf/8jNN9+ciRMntgsf39SnT5+1PuOUU05JqVTKz3/+81Veb2trS2tra7sGAAAAAFRexQPI8ePH54477shTTz2Vp556KnfeeWfGjx9fvv7EE0+kVCpl2LBhG/yMzTbbLFtssUUWLly4yuuTJ09OQ0NDuTU2Nm7wswAAAACAdVfRV7CTZPPNN8+YMWMyY8aMlEqljBkzJv369StfL5VKHfKcUqmUQqGwymvNzc057bTTyr9bW1uFkAAAAADQCSoeQCZvvIb9+c9/Pkly8cUXt7u2ww47pFAo5NFHH93g+Z9//vk899xz7b4r+a+KxWKKxeIGzw8AAAAAbJiKv4KdJKNHj84rr7ySV199NaNGjWp3bbPNNsuoUaNy8cUX56WXXlrp3hdeeGGt8//whz9MTU1NDj300A6qGAAAAADoCJ2yA7Jbt26ZP39++e+3uvjii/ORj3wku+++e84555y8973vzYoVK/LrX/86l1xySfneJHnxxRezZMmSvPrqq/nzn/+cn/zkJ7nssssyefLkDBkypDOWAwAAAACso04JIJOkvr5+tdcGDx6c+++/P9/+9rdz+umnZ/Hixdl8882z22675ZJLLmk39hvf+Ea+8Y1vpHv37unfv38+/OEP55ZbbsnIkSMrvQQAAAAAYD0VSh11Csw7SGtr6xunYZ96bWqKPatdDgDAO87CKWOqXQIAAFX0Zr7W0tKyxo2HSSd9AxIAAAAA2DgJIAEAAACAium0b0B2RQ+fPWqtW0QBAAAAgA1nByQAAAAAUDECSAAAAACgYgSQAAAAAEDFbNTfgBw+aXZqij2rXQYAAMBGaeGUMdUuAYBOYAckAAAAAFAxAkgAAAAAoGIEkAAAAABAxQggAQAAAICKqVoAOWHChBx66KHlvwuFQgqFQrp3754hQ4bknHPOyYoVK5Ikc+bMSaFQyHve8568/PLL7ea59957y/cCAAAAAF1Ll9kBOXr06CxevDiPP/54Tj/99Jx11lk5//zz243p3bt3brjhhnZ9U6dOzcCBAzuzVAAAAABgHXWZALJYLKZ///7Zdtttc+KJJ2b//ffPjTfe2G7Mcccdl2nTppV/L1++PFdffXWOO+64Nc7d1taW1tbWdg0AAAAAqLwuE0C+1aabbppXXnmlXd8xxxyTuXPn5i9/+UuS5LrrrsugQYPywQ9+cI1zTZ48OQ0NDeXW2NhYsboBAAAAgP+rywWQpVIpv/nNbzJ79uzsu+++7a5tscUWOfDAAzNjxowkybRp09LU1LTWOZubm9PS0lJuixYtqkTpAAAAAMBbdJkActasWamrq0uPHj1y4IEH5ogjjshZZ5210rimpqbMmDEjTz75ZO66664cffTRa527WCymvr6+XQMAAAAAKq/LBJAjR47MvHnz8vjjj2f58uW5/PLL06tXr5XGHXjggVm+fHmOP/74jB07Nn379q1CtQAAAADAuqitdgFv6tWrV4YMGbLWcbW1tTn22GPzne98J7/85S87oTIAAAAAYEN1mR2Q6+Ob3/xmnnvuuYwaNarapQAAAAAAa1C1HZCvv/56ams37PHdu3dPv379OrgiAAAAAKCjVS2AfPbZZ8uvXL95qvXq7LPPPimVSqu9fuihh67xOgAAAABQHZ3+CvbSpUsza9aszJkzJ/vvv39nPx4AAAAA6ESdvgOyqakp9957b04//fQccsghnf34dh4+e1Tq6+urWgMAAAAAvJt1egB5ww03dPYjAQAAAIAqeUeegg0AAAAAvDMIIAEAAACAiqnaKdhdwfBJs1NT7FntMgCAtVg4ZUy1SwAAADaQHZAAAAAAQMUIIAEAAACAihFAAgAAAAAVI4AEAAAAACqmywWQEyZMyKGHHlr+u1AoZMqUKe3G/OxnP0uhUCj/njNnTgqFQl544YVOrBQAAAAAWJsuF0C+VY8ePXLeeedl6dKl1S4FAAAAAFhPXT6A3H///dO/f/9Mnjx5g+doa2tLa2truwYAAAAAVF6XDyC7deuWc889N//xH/+Rp59+eoPmmDx5choaGsqtsbGxg6sEAAAAAFalyweQSXLYYYfl/e9/fyZNmrRB9zc3N6elpaXcFi1a1MEVAgAAAACrUlvtAtbVeeedl3333Tdf+tKX1vveYrGYYrFYgaoAAAAAgDV5R+yATJK99947o0aNSnNzc7VLAQAAAADW0TtmB2SSTJkyJe9///szdOjQapcCAAAAAKyDd8wOyCTZddddc/TRR+fCCy+sdikAAAAAwDrocgHk66+/ntra1W/MPOecc/L666+vdE+SNd4HAAAAAHS+LpfYPfvssxkyZEiSZMaMGStdHzRoUNra2la6p66uLnV1dZ1RIgAAAACwjrrMDsilS5dm1qxZmTNnTvbff/91uqetrS2PPPJILrroouy3334VrhAAAAAAWF9dZgdkU1NT7r333px++uk55JBD1umeX/7ylznmmGOy1157bdB3IR8+e1Tq6+vX+z4AAAAAYN0USqVSqdpFdLbW1tY0NDSkpaVFAAkAAAAA62l98rUu8wo2AAAAAPDuI4AEAAAAACqmy3wDshqGT5qdmmLPapcBAAAArIeFU8ZUuwRgPdgBCQAAAABUjAASAAAAAKgYASQAAAAAUDECSAAAAACgYioWQE6YMCGHHnpo+e9CoZApU6a0G/Ozn/0shUKh3ZjVtUGDBiVJWltb87WvfS3Dhg1Ljx490r9//+y///65/vrrUyqVKrUcAAAAAGADdNoOyB49euS8887L0qVLV3n9hz/8YRYvXlxuSTJ9+vTy73vvvTcvvPBC9tprr1xxxRVpbm7O/fffn9tvvz1HHHFEvvKVr6SlpaWzlgMAAAAArIPaznrQ/vvvnyeeeCKTJ0/Od77znZWuNzQ0pKGhoV1fnz590r9///Lvk046KQsXLsxjjz2WAQMGlPt33HHHHHXUUenRo0flFgAAAAAArLdO2wHZrVu3nHvuufmP//iPPP300+t9/+uvv56rr746Rx99dLvw8U11dXWprV11ntrW1pbW1tZ2DQAAAACovE49hOawww7L+9///kyaNGm97/373/+epUuXZtiwYet97+TJk8s7LBsaGtLY2LjecwAAAAAA66/TT8E+77zzcvnll2f+/Pnrdd/bOWCmubk5LS0t5bZo0aINngsAAAAAWHedHkDuvffeGTVqVJqbm9frvs033zx9+vTJo48+ut7PLBaLqa+vb9cAAAAAgMrr9AAySaZMmZJf/OIXueuuu9b5npqamhx55JH57//+7zzzzDMrXV+2bFlWrFjRkWUCAAAAAG9TVQLIXXfdNUcffXQuvPDC9brv29/+dhobG7PHHnvkiiuuyCOPPJLHH38806ZNywc+8IEsW7asQhUDAAAAABuiKgFkkpxzzjl5/fXX1+uezTbbLHfffXfGjx+fb33rW/nABz6Qj33sY7nqqqty/vnnp6GhoULVAgAAAAAbolB6O6e7vEO1tra+cRr2qdemptiz2uUAAAAA62HhlDHVLgE2em/may0tLWs9b6VqOyABAAAAgHe/2moXUE0Pnz3KidgAAAAAUEF2QAIAAAAAFSOABAAAAAAqRgAJAAAAAFTMRv0NyOGTZjsFGwCAdwUnwgIAXZUdkAAAAABAxQggAQAAAICKEUACAAAAABUjgAQAAAAAKqbTA8gJEybk0EMPLf9dKBTKrW/fvhk9enQefPDBdve8ef3uu+9u19/W1pa+ffumUChkzpw5nbQCAAAAAGBdVX0H5OjRo7N48eIsXrw4t9xyS2pra3PQQQetNK6xsTHTp09v13fDDTekrq6us0oFAAAAANZT1QPIYrGY/v37p3///nn/+9+fr371q1m0aFGee+65duOOO+64XH311Vm+fHm5b9q0aTnuuOM6u2QAAAAAYB1VPYD8V8uWLctPfvKTDBkyJH379m13bbfddsugQYNy3XXXJUn+8pe/5Pbbb88xxxyz1nnb2trS2trargEAAAAAlVf1AHLWrFmpq6tLXV1devfunRtvvDHXXHNNampWLq2pqSnTpk1LksyYMSOf+MQnsvnmm6/1GZMnT05DQ0O5NTY2dvg6AAAAAICVVT2AHDlyZObNm5d58+blnnvuyahRo3LggQfmqaeeWmns+PHjc9ddd+XJJ5/MjBkz0tTUtE7PaG5uTktLS7ktWrSoo5cBAAAAAKxC1QPIXr16ZciQIRkyZEg+9KEP5bLLLstLL72U//qv/1ppbN++fXPQQQfl+OOPz8svv5wDDzxwnZ5RLBZTX1/frgEAAAAAlVf1APKtCoVCampq2h0286+ampoyZ86cHHvssenWrVsnVwcAAAAArI/aahfQ1taWJUuWJEmWLl2aiy66KMuWLcvYsWNXOX706NF57rnn7GIEAAAAgHeAqgeQN998c7baaqskSe/evTNs2LDMnDkz++yzzyrHFwqF9OvXrxMrBAAAAAA2VKFUKpWqXURna21tfeM07FOvTU2xZ7XLAQCAt23hlDHVLgEA2Ii8ma+1tLSs9U3lLvcNSAAAAADg3aPqr2BX08Nnj/ItSQAAAACoIDsgAQAAAICKEUACAAAAABUjgAQAAAAAKmaj/gbk8EmznYINAAB0CU4yB+Ddyg5IAAAAAKBiBJAAAAAAQMUIIAEAAACAihFAAgAAAAAV0yUCyAkTJuTQQw9dqX/OnDkpFAp54YUXyn8XCoXU1NSkoaEhH/jAB/KVr3wlixcv7vyiAQAAAIC16hIB5PpYsGBBnnnmmdx7770544wz8pvf/CbDhw/PQw89VO3SAAAAAIC3eMcFkFtssUX69++fHXfcMUceeWTuvPPObL755jnxxBOrXRoAAAAA8BbvuADyrTbddNN87nOfy5133plnn312lWPa2trS2trargEAAAAAlVdb7QLeNGvWrNTV1bXre+2119bp3mHDhiVJFi5cmC222GKl65MnT87ZZ5/99osEAAAAANZLl9kBOXLkyMybN69du+yyy9bp3lKplCQpFAqrvN7c3JyWlpZyW7RoUYfVDQAAAACsXpfZAdmrV68MGTKkXd/TTz+9TvfOnz8/STJo0KBVXi8WiykWi2+rPgAAAABg/XWZHZAbavny5fnxj3+cvffeO5tvvnm1ywEAAAAA/kWX2QG5rp599tm8/PLLefHFF/P//r//b77zne/k73//e66//vpqlwYAAAAAvMU7LoAcOnRoCoVC6urqMnjw4Hz84x/Paaedlv79+1e7NAAAAADgLbpEADljxoxV9u+zzz7lA2b+9W8AAAAA4J3hHf8NSAAAAACg6+oSOyCr5eGzR6W+vr7aZQAAAADAu5YdkAAAAABAxQggAQAAAICKEUACAAAAABUjgAQAAAAAKmajPoRm+KTZqSn2rHYZAAAAvAstnDKm2iUAdAl2QAIAAAAAFSOABAAAAAAqRgAJAAAAAFRMlwsglyxZklNOOSVDhgxJjx49suWWW+YjH/lILrnkkvzzn/9MkvzhD3/IwQcfnC222CI9evTIoEGDcsQRR+TZZ5+tcvUAAAAAwL/qUofQPPnkk/nIRz6SPn365Nxzz82uu+6aYrGYhx56KD/+8Y+z9dZbZ88998x+++2Xgw46KLNnz06fPn2ycOHC3HjjjXnppZeqvQQAAAAA4F90qQDypJNOSm1tbe6777706tWr3D948OAccsghKZVK+fnPf56WlpZcdtllqa19o/ztttsuI0eOrFbZAAAAAMBqdJlXsJ9//vn86le/ysSJE9uFj/+qUCikf//+WbFiRW644YaUSqV1mrutrS2tra3tGgAAAABQeV0mgHziiSdSKpUydOjQdv39+vVLXV1d6urqcsYZZ+TDH/5wzjzzzHz6059Ov379cuCBB+b888/P3/72t9XOPXny5DQ0NJRbY2NjpZcDAAAAAKQLBZCrc88992TevHnZZZdd0tbWliT59re/nSVLluTSSy/NLrvskksvvTTDhg3LQw89tMo5mpub09LSUm6LFi3qzCUAAAAAwEarywSQQ4YMSaFQyIIFC9r1Dx48OEOGDMmmm27arr9v3745/PDDc8EFF2T+/PkZMGBALrjgglXOXSwWU19f364BAAAAAJXXZQLIvn375oADDshFF1203qdZd+/ePdtvv71TsAEAAACgi+kyAWSS/OhHP8qKFSvyb//2b7nmmmsyf/78LFiwID/5yU/y6KOPplu3bpk1a1bGjx+fWbNm5bHHHsuCBQtywQUX5KabbsohhxxS7SUAAAAAAP+ittoF/Kvtt98+DzzwQM4999w0Nzfn6aefTrFYzM4775wvfelLOemkk7JkyZL07Nkzp59+ehYtWpRisZgddtghl112WY455phqLwEAAAAA+BeFUqlUqnYRna21tfWN07BPvTY1xZ7VLgcAAIB3oYVTxlS7BICKeTNfa2lpWet5K13qFWwAAAAA4N1FAAkAAAAAVEyX+gZkZ3v47FFr3SIKAAAAAGw4OyABAAAAgIoRQAIAAAAAFSOABAAAAAAqZqP+BuTwSbNTU+xZ7TJgo7FwyphqlwAAAAB0MjsgAQAAAICKEUACAAAAABUjgAQAAAAAKmaDA8glS5bklFNOyZAhQ9KjR49sueWW+chHPpJLLrkk//znP5Mkf/jDH3LwwQdniy22SI8ePTJo0KAcccQRefbZZ1c775w5c1IoFMptyy23zLhx4/Lkk0+WxwwaNCg/+MEPyr9LpVK+9KUvpb6+PnPmzNnQJQEAAAAAHWyDDqF58skn85GPfCR9+vTJueeem1133TXFYjEPPfRQfvzjH2frrbfOnnvumf322y8HHXRQZs+enT59+mThwoW58cYb89JLL631GQsWLEjv3r3z+OOP59///d8zduzYPPjgg+nWrVu7ca+99lpOOOGEzJo1K7feemt22223DVkSAAAAAFABGxRAnnTSSamtrc19992XXr16lfsHDx6cQw45JKVSKT//+c/T0tKSyy67LLW1bzxmu+22y8iRI9fpGVtssUX69OmTrbbaKt/4xjdy9NFH54knnsjQoUPLY9ra2nLUUUflvvvuy9y5c9tdAwAAAACqb71fwX7++efzq1/9KhMnTmwXPv6rQqGQ/v37Z8WKFbnhhhtSKpXeVpGbbrppkuSVV14p9y1btixjxozJI488kjvvvHON4WNbW1taW1vbNQAAAACg8tY7gHziiSdSKpVWCvz69euXurq61NXV5YwzzsiHP/zhnHnmmfn0pz+dfv365cADD8z555+fv/3tb+v1vMWLF+eCCy7I1ltv3e6Z3/zmNzNv3rzMnTs3jY2Na5xj8uTJaWhoKLe1jQcAAAAAOkaHnYJ9zz33ZN68edlll13S1taWJPn2t7+dJUuW5NJLL80uu+ySSy+9NMOGDctDDz2UJNlll13KoeWBBx7Ybr5tttkmvXr1yoABA/LSSy/luuuuS/fu3cvXP/7xj+ell17Kueeeu9bampub09LSUm6LFi3qqGUDAAAAAGuw3t+AHDJkSAqFQhYsWNCuf/DgwUn+7+vSb+rbt28OP/zwHH744Tn33HPzgQ98IBdccEEuv/zy3HTTTXn11VdXed/cuXNTX1+fLbbYIr17916pjv322y8nn3xyDjnkkLz++uv54Q9/uNqai8ViisXi+i4VAAAAAHib1juA7Nu3bw444IBcdNFFOfnkk1f7HchV6d69e7bffvvyKdjbbrvtasdut9126dOnzxrn+/jHP55f/OIXOfjgg1MqlXLhhReucy0AAAAAQOVt0CvYP/rRj7JixYr827/9W6655prMnz8/CxYsyE9+8pM8+uij6datW2bNmpXx48dn1qxZeeyxx7JgwYJccMEFuemmm3LIIYd02AL233//zJo1K1OnTs3nP//5DpsXAAAAAHj71nsHZJJsv/32eeCBB3Luueemubk5Tz/9dIrFYnbeeed86UtfykknnZQlS5akZ8+eOf3007No0aIUi8XssMMOueyyy3LMMcd06CL23Xff/M///E8OOuiglEqlXHTRRSkUCh36DAAAAABg/RVKpVKp2kV0ttbW1jdOwz712tQUe1a7HNhoLJwyptolAAAAAB3gzXytpaUl9fX1axzbYadgAwAAAAC8lQASAAAAAKiYDfoG5LvFw2ePWusWUQAAAABgw9kBCQAAAABUjAASAAAAAKgYASQAAAAAUDEb9Tcgh0+anZpiz2qXAQB0sIVTxlS7BAAA4P9nByQAAAAAUDECSAAAAACgYgSQAAAAAEDFCCABAAAAgIrpUgHkhAkTcuihh7br++lPf5oePXrku9/9bpYvX55JkyZlxx13TLFYTL9+/XL44Yfnj3/8Y3UKBgAAAADWqEsFkG912WWX5eijj84ll1ySz3/+89l///0zbdq0fOtb38pjjz2Wm266KStWrMgee+yRu+++u9rlAgAAAABvUVvtAlbnO9/5TiZNmpSrr746hx12WM4777zcddddeeCBB/K+970vSbLtttvmuuuuyx577JHjjz8+Dz/8cAqFwkpztbW1pa2trfy7tbW109YBAAAAABuzLrkD8owzzsg3v/nNzJo1K4cddliS5Morr8wBBxxQDh/fVFNTky9+8Yt55JFH8oc//GGV802ePDkNDQ3l1tjYWPE1AAAAAABdMID85S9/me985zv5+c9/nv3226/c/9hjj2WnnXZa5T1v9j/22GOrvN7c3JyWlpZyW7RoUccXDgAAAACspMu9gv3e9743f//73zNp0qTsvvvuqaurK18rlUobNGexWEyxWOyoEgEAAACAddTldkBuvfXWmTNnTv76179m9OjRefHFF5MkO+64Y+bPn7/Ke97s33HHHTutTgAAAABg7bpcAJm8cbjMbbfdliVLlpRDyCOPPDK/+c1vVvrO4+uvv57vf//72XnnnVf6PiQAAAAAUF1dMoBMksbGxsyZMyfPPvtsRo0alYkTJ2b33XfP2LFjM3PmzPzlL3/Jvffem3HjxmX+/PmZOnXqKk/ABgAAAACqp8sGkEmyzTbbZM6cOfn73/+eUaNG5Ve/+lWOPfbYnHnmmRkyZEhGjx6dbt265e67786HP/zhapcLAAAAALxFobShJ7u8g7W2tqahoSGNp16bmmLPapcDAHSwhVPGVLsEAAB4V3szX2tpaUl9ff0ax3bpHZAAAAAAwDtbbbULqKaHzx611oQWAAAAANhwdkACAAAAABUjgAQAAAAAKkYACQAAAABUzEb9Dcjhk2Y7BRsAAKACFk4ZU+0SAOgi7IAEAAAAACpGAAkAAAAAVIwAEgAAAACoGAEkAAAAAFAxbzuAXLJkSU4++eQMHjw4xWIxjY2NGTt2bG655ZYkyR/+8IccfPDB2WKLLdKjR48MGjQoRxxxRJ599tl281x33XXZZ5990tDQkLq6urz3ve/NOeeck3/84x/lMcuXL8+kSZOy4447plgspl+/fjn88MPzxz/+8e0uAwAAAACogLcVQC5cuDC77bZbfvvb3+b888/PQw89lJtvvjkjR47MxIkT89xzz2W//fbLZpttltmzZ2f+/PmZPn16BgwYkJdeeqk8z9e+9rUcccQR+dCHPpRf/vKXefjhh/Pd7343f/jDH/J//s//SZK0tbVl//33z7Rp0/Ktb30rjz32WG666aasWLEie+yxR+6+++639y8BAAAAAHS4QqlUKm3ozZ/4xCfy4IMPZsGCBenVq1e7ay+88ELmzJmTww8/PMuXL09tbe0q57jnnnuyxx575Ac/+EFOOeWUla6/8MIL6dOnT84777w0NzfngQceyPve977y9ddffz177LFH/vnPf+bhhx9OoVBYaY62tra0tbWVf7e2tqaxsTGNp16bmmLPDV0+AAAAq7FwyphqlwBABbW2tqahoSEtLS2pr69f49gN3gH5j3/8IzfffHMmTpy4UviYJH369En//v2zYsWK3HDDDVldzvnf//3fqaury0knnbTK63369EmSXHnllTnggAPahY9JUlNTky9+8Yt55JFH8oc//GGVc0yePDkNDQ3l1tjYuB4rBQAAAAA21AYHkE888URKpVKGDRu22jEf/vCHc+aZZ+bTn/50+vXrlwMPPDDnn39+/va3v5XHPP744xk8eHA22WSTNT7vsccey0477bTKa2/2P/bYY6u83tzcnJaWlnJbtGjR2pYHAAAAAHSADQ4g1/XN7W9/+9tZsmRJLr300uyyyy659NJLM2zYsDz00EPrNc/6jv1XxWIx9fX17RoAAAAAUHkbHEDusMMOKRQKefTRR9c6tm/fvjn88MNzwQUXZP78+RkwYEAuuOCCJMmOO+6YJ598Mq+++uoa59hxxx0zf/78VV57s3/HHXdcz1UAAAAAAJW0wQHkZpttllGjRuXiiy9ud6L1m1544YVV3te9e/dsv/325Xs+/elPZ9myZfnRj360yvFvznPkkUfmN7/5zUrfeXz99dfz/e9/PzvvvPNK34cEAAAAAKprgwPIJLn44ovz2muvZffdd891112Xxx9/PPPnz8+FF16YPffcM7Nmzcr48eMza9asPPbYY1mwYEEuuOCC3HTTTTnkkEOSJHvssUe+8pWv5PTTT89XvvKV3HXXXXnqqadyyy235PDDD8/ll1+eJPniF7+Y3XffPWPHjs3MmTPzl7/8Jffee2/GjRuX+fPnZ+rUqas8ARsAAAAAqJ7at3Pz4MGDc//99+fb3/52Tj/99CxevDibb755dtttt1xyySUZOHBgevbsmdNPPz2LFi1KsVjMDjvskMsuuyzHHHNMeZ7zzjsvu+22Wy6++OJceumlef3117P99tvnk5/8ZI477rgkSY8ePfLb3/425557bs4888w89dRT6d27d0aOHJm77747w4cPf3v/EgAAAABAhyuUNvRkl3ew1tbWNDQ0pPHUa1NT7FntcgAAAN51Fk4ZU+0SAKigN/O1lpaWtR74/LZewQYAAAAAWJO39Qr2O93DZ49aa0ILAAAAAGw4OyABAAAAgIoRQAIAAAAAFSOABAAAAAAqZqP+BuTwSbOdgg0AAHQ4J0ADwP9lByQAAAAAUDECSAAAAACgYgSQAAAAAEDFCCABAAAAgIrp1AByyZIlOfnkkzN48OAUi8U0NjZm7NixmT17dvr165cpU6as8r5vfvOb2XLLLfPqq6/mjDPOyKBBg/Liiy+2GzN27Njsvffeef311ztjKQAAAADAOui0AHLhwoXZbbfd8tvf/jbnn39+Hnroodx8880ZOXJkTjnllIwfPz7Tp09f6b5SqZQZM2bk2GOPzSabbJJzzjkndXV1Oe2008pjpk2blltvvTXTp09PTY1NnQAAAADQVdR21oNOOumkFAqF3HPPPenVq1e5f5dddklTU1MWLVqUH/7wh7njjjvy0Y9+tHz9tttuy5NPPpnjjz8+SVIsFnP55Zdnzz33zLhx47Lzzjvni1/8Yr7zne9k++2376zlAAAAAADroFMCyH/84x+5+eab8+1vf7td+PimPn36pE+fPvnQhz6UadOmtQsgp0+fnr322ivDhg0r9+22225pbm7OZz/72Wy//fbZfffdc+KJJ672+W1tbWlrayv/bm1t7aCVAQAAAABr0invKz/xxBMplUrtQsRVOf744zNz5swsW7YsSfLiiy/mpz/9aZqamlYa+/Wvfz01NTX5/e9/n6lTp6ZQKKx23smTJ6ehoaHcGhsb396CAAAAAIB10ikBZKlUWqdxRx11VF577bVce+21SZJrrrkmNTU1OeKII1Ya++tf/zpLlizJ66+/nnvvvXeN8zY3N6elpaXcFi1atP6LAAAAAADWW6e8gr3DDjukUCjk0UcfXeO4+vr6fPKTn8z06dPT1NSU6dOn51Of+lTq6urajVu6dGlOOOGEfP3rX0+pVMpJJ52UESNGpF+/fquct1gsplgsdth6AAAAAIB10yk7IDfbbLOMGjUqF198cV566aWVrr/wwgvlv48//vjccccdmTVrVn73u9+VD5/5VyeffHL69++fM888M1/72tey9dZbZ+LEiZVcAgAAAACwATolgEySiy++OK+99lp23333XHfddXn88cczf/78XHjhhdlzzz3L4/bee+8MGTIkxx57bIYNG5a99tqr3Tw33HBDZs6cmcsvvzy1tbWpra3N5Zdfnp/97Ge57rrrOms5AAAAAMA66LQAcvDgwbn//vszcuTInH766Rk+fHgOOOCA3HLLLbnkkkvK4wqFQpqamrJ06dKVDp/5+9//ns997nOZNGlShg8fXu7fddddM2nSpJx00kn5+9//3llLAgAAAADWolBa1xNi3kVaW1vfOA371GtTU+xZ7XIAAIB3mYVTxlS7BACoqDfztZaWltTX169xbKftgAQAAAAANj6dcgp2V/Xw2aPWmtACAAAAABvODkgAAAAAoGIEkAAAAABAxQggAQAAAICK2ai/ATl80mynYAMAABXhJGwAeIMdkAAAAABAxQggAQAAAICKEUACAAAAABUjgAQAAAAAKqYqAeSSJUty8sknZ/DgwSkWi2lsbMzYsWNzyy23tBs3efLkdOvWLeeff365b9CgQSkUCqttEyZM6OTVAAAAAACr0+mnYC9cuDAf+chH0qdPn5x//vnZdddd8+qrr2b27NmZOHFiHn300fLYadOm5Stf+UqmTZuWL3/5y0mSe++9N6+99lqS5He/+13GjRuXBQsWpL6+Pkmy6aabdvaSAAAAAIDV6PQA8qSTTkqhUMg999yTXr16lft32WWXNDU1lX/fdtttWb58ec4555xcccUV+d3vfpe99torm2++eXnMZpttliTZYost0qdPn05bAwAAAACwbjr1Fex//OMfufnmmzNx4sR24eOb/jVEnDp1ao466qhssskmOeqoozJ16tQNfm5bW1taW1vbNQAAAACg8jo1gHziiSdSKpUybNiwNY5rbW3NT3/604wfPz5JMn78+Fx77bVZtmzZBj138uTJaWhoKLfGxsYNmgcAAAAAWD+dGkCWSqV1GnfVVVdl++23z/ve974kyfvf//5su+22ueaaazbouc3NzWlpaSm3RYsWbdA8AAAAAMD66dQAcocddkihUGh30MyqTJ06NX/84x9TW1tbbo888kimTZu2Qc8tFoupr69v1wAAAACAyuvUQ2g222yzjBo1KhdffHG+8IUvrPQdyBdeeCGLFi3Kfffdlzlz5pQPmUne+H7kPvvsk0cffXStr3ADAAAAAF1Dp5+CffHFF+cjH/lIdt9995xzzjl573vfmxUrVuTXv/51LrnkkowaNSq777579t5775Xu/dCHPpSpU6fm/PPP7+yyAQAAAIAN0KmvYCfJ4MGDc//992fkyJE5/fTTM3z48BxwwAG55ZZb8sMf/jA/+clPMm7cuFXeO27cuFxxxRV59dVXO7lqAAAAAGBDFErrejLMu0hra+sbp2Gfem1qij2rXQ4AAPAutHDKmGqXAAAV82a+1tLSstbzVjp9ByQAAAAAsPHo9G9AdiUPnz3KidgAAAAAUEF2QAIAAAAAFSOABAAAAAAqRgAJAAAAAFTMRv0NyOGTZjsFGwAAANhoLJwyptolsBGyAxIAAAAAqBgBJAAAAABQMQJIAAAAAKBiBJAAAAAAQMV0qQBywoQJKRQK5da3b9+MHj06Dz744Epj//f//t/p1q1bZs6cWYVKAQAAAIB10aUCyCQZPXp0Fi9enMWLF+eWW25JbW1tDjrooHZj/vnPf+bqq6/OV77ylUybNq1KlQIAAAAAa1Nb7QLeqlgspn///kmS/v3756tf/Wo+9rGP5bnnnsvmm2+eJJk5c2Z23nnnfPWrX82AAQOyaNGiNDY2VrNsAAAAAGAVutwOyH+1bNmy/OQnP8mQIUPSt2/fcv/UqVMzfvz4NDQ05MADD8yMGTPWOE9bW1taW1vbNQAAAACg8rpcADlr1qzU1dWlrq4uvXv3zo033phrrrkmNTVvlPr444/n7rvvzhFHHJEkGT9+fKZPn55SqbTaOSdPnpyGhoZys1sSAAAAADpHlwsgR44cmXnz5mXevHm55557MmrUqBx44IF56qmnkiTTpk3LqFGj0q9fvyTJJz7xibS0tOS3v/3taudsbm5OS0tLuS1atKhT1gIAAAAAG7su9w3IXr16ZciQIeXfl112WRoaGvJf//VfOfvss3P55ZdnyZIlqa39v6W/9tprmTZtWvbbb79VzlksFlMsFiteOwAAAADQXpcLIN+qUCikpqYmy5cvz0033ZQXX3wxDzzwQLp161Ye8/DDD+czn/lMXnjhhfTp06d6xQIAAAAA7XS5ALKtrS1LlixJkixdujQXXXRRli1blrFjx+YHP/hBxowZk/e9733t7tl5553zxS9+Mf/93/+diRMnVqNsAAAAAGAVutw3IG+++eZstdVW2WqrrbLHHnvk3nvvzcyZM7PTTjvlf/7nfzJu3LiV7qmpqclhhx2WqVOnVqFiAAAAAGB1CqU1HR/9LtXa2vrGadinXpuaYs9qlwMAAADQKRZOGVPtEniXeDNfa2lpSX19/RrHdrkdkAAAAADAu0eX+wZkZ3r47FFrTWgBAAAAgA1nByQAAAAAUDECSAAAAACgYgSQAAAAAEDFCCABAAAAgIrZqA+hGT5pdmqKPatdBgAAAADvEgunjKl2CV2OHZAAAAAAQMUIIAEAAACAihFAAgAAAAAV0+EB5F133ZVu3bplzJjVv+9+1VVXpVu3bpk4ceJK1+bMmZNCoVBuW265ZcaNG5cnn3yyPGbQoEH5wQ9+0NGlAwAAAAAdrMMDyKlTp+bkk0/O7bffnmeeeWa1Y77yla/kqquuyssvv7zKMQsWLMgzzzyTmTNn5o9//GPGjh2b1157raPLBQAAAAAqqEMDyGXLluWaa67JiSeemDFjxmTGjBkrjfnzn/+c3/3ud/nqV7+aHXfcMddff/0q59piiy2y1VZbZe+99843vvGNPPLII3niiSc6slwAAAAAoMI6NIC89tprM2zYsAwdOjTjx4/PtGnTUiqV2o2ZPn16xowZk4aGhowfPz5Tp05d67ybbrppkuSVV17ZoLra2trS2trargEAAAAAldehAeTUqVMzfvz4JMno0aPT0tKS2267rXz99ddfz4wZM8pjjjzyyNxxxx3585//vNo5Fy9enAsuuCBbb711hg4dukF1TZ48OQ0NDeXW2Ni4QfMAAAAAAOunwwLIBQsW5J577slRRx2VJKmtrc0RRxzRbofjr3/967z00kv5xCc+kSTp169fDjjggEybNm2l+bbZZpv06tUrAwYMyEsvvZTrrrsu3bt336Dampub09LSUm6LFi3aoHkAAAAAgPVT21ETTZ06NStWrMiAAQPKfaVSKcViMRdddFEaGhoyderU/OMf/yi/Up28sSvywQcfzNlnn52amv+bh86dOzf19fXZYost0rt377dVW7FYTLFYfFtzAAAAAADrr0MCyBUrVuSKK67Id7/73Xz84x9vd+3QQw/NVVddlcMPPzw///nPc/XVV2eXXXYpX3/ttdfy0Y9+NL/61a8yevTocv92222XPn36dER5AAAAAECVdEgAOWvWrCxdujTHH398Ghoa2l0bN25cpk6dmpdffjl9+/bNpz71qRQKhXZjPvGJT2Tq1KntAsi1+etf/5p58+a169t2223znve8Z4PXAQAAAAB0rA75BuTUqVOz//77rxQ+Jm8EkPfdd19OO+20HHbYYSuFj2+OufHGG/P3v/99nZ95wQUX5AMf+EC79j//8z9vax0AAAAAQMcqlEqlUrWL6Gytra1vnIZ96rWpKfasdjkAAAAAvEssnDKm2iV0ijfztZaWltTX169xbIedgg0AAAAA8FYddgr2O9HDZ49aa0ILAAAAAGw4OyABAAAAgIoRQAIAAAAAFSOABAAAAAAqRgAJAAAAAFTMRn0IzfBJs1NT7FntMgAAAKCdhVPGVLsEgA5jByQAAAAAUDECSAAAAACgYgSQAAAAAEDFdHoAedddd6Vbt24ZM6b99yxuuummdO/ePffff3+7/u9+97vp169flixZkiSZMGFCCoVCCoVCunfvniFDhuScc87JihUrOm0NAAAAAMC66fQAcurUqTn55JNz++2355lnnin3f+ITn8ixxx6bY489Nm1tbUmSRx55JF//+tdz8cUXp3///uWxo0ePzuLFi/P444/n9NNPz1lnnZXzzz+/s5cCAAAAAKxFpwaQy5YtyzXXXJMTTzwxY8aMyYwZM9pd//73v59ly5Zl0qRJWbFiRY477riMHTs2RxxxRLtxxWIx/fv3z7bbbpsTTzwx+++/f2688cZOXAkAAAAAsC5qO/Nh1157bYYNG5ahQ4dm/PjxOfXUU9Pc3JxCoZAk6d27d6ZNm5ZRo0blz3/+cxYtWpSbb755rfNuuummef7551d7va2trbyrMklaW1vf/mIAAAAAgLXq1B2QU6dOzfjx45O88Rp1S0tLbrvttnZj9t1333zyk5/MtddemwsvvDB9+/Zd7XylUim/+c1vMnv27Oy7776rHTd58uQ0NDSUW2NjY8csCAAAAABYo04LIBcsWJB77rknRx11VJKktrY2RxxxRKZOndpu3F//+tfcfPPN6dmzZ+bOnbvKuWbNmpW6urr06NEjBx54YI444oicddZZq312c3NzWlpaym3RokUdti4AAAAAYPU67RXsqVOnZsWKFRkwYEC5r1QqpVgs5qKLLkpDQ0OS5IQTTshuu+2Wr33taznggAPyyU9+MiNGjGg318iRI3PJJZeke/fuGTBgQGpr17yMYrGYYrHY8YsCAAAAANaoUwLIFStW5Iorrsh3v/vdfPzjH2937dBDD81VV12Vz33uc7nssstyxx135KGHHiofMNPU1JQHH3wwvXr1Kt/Tq1evDBkypDNKBwAAAADehk55BXvWrFlZunRpjj/++AwfPrxdGzduXKZOnZqnnnoqp512Wi644IJsu+22SZLzzjsvhUIhX/3qVzujTAAAAACgg3VKADl16tTsv//+5des/9W4ceNy33335TOf+Uz23HPP/Pu//3v5Ws+ePTNjxoxccsklKx1WAwAAAAB0fYVSqVSqdhGdrbW19Y3TsE+9NjXFntUuBwAAANpZOGVMtUsAWKM387WWlpbU19evcWynnYINAAAAAGx8BJAAAAAAQMV0yinYXdXDZ49a6xZRAAAAAGDD2QEJAAAAAFSMABIAAAAAqBgBJAAAAABQMRv1NyCHT5qdmmLPapcBAEAXtHDKmGqXAADwrmAHJAAAAABQMQJIAAAAAKBiBJAAAAAAQMUIIAEAAACAiumwAHLChAkpFAr53Oc+t9K1iRMnplAoZMKECes9dkPGAwAAAABdQ4fugGxsbMzVV1+d5cuXl/tefvnlXHnllRk4cOAGj92Q8QAAAABA9XVoAPnBD34wjY2Nuf7668t9119/fQYOHJgPfOADGzx2Q8b/q7a2trS2trZrAAAAAEDldfg3IJuamjJ9+vTy72nTpuUzn/nM2x67IePfNHny5DQ0NJRbY2PjuiwFAAAAAHibOjyAHD9+fO6444489dRTeeqpp3LnnXdm/Pjxb3vshox/U3Nzc1paWspt0aJFG7w+AAAAAGDd1Xb0hJtvvnnGjBmTGTNmpFQqZcyYMenXr9/bHrsh499ULBZTLBY3eE0AAAAAwIbp8AAyeeNV6c9//vNJkosvvrjDxm7IeAAAAACgeioSQI4ePTqvvPJKCoVCRo0a1WFjN2Q8AAAAAFA9FQkgu3Xrlvnz55f/7qixGzIeAAAAAKieigSQSVJfX1+RsRsyHgAAAACojkKpVCpVu4jO1tramoaGhjSeem1qij2rXQ4AAF3Qwiljql0CAECX9Wa+1tLSstbNgjWdVBMAAAAAsBGq2CvY7wQPnz3K69wAAAAAUEF2QAIAAAAAFSOABAAAAAAqRgAJAAAAAFSMABIAAAAAqBgBJAAAAABQMQJIAAAAAKBiBJAAAAAAQMUIIAEAAACAihFAAgAAAAAVI4AEAAAAACpGAAkAAAAAVIwAEgAAAACoGAEkAAAAAFAxAkgAAAAAoGIEkAAAAABAxQggAQAAAICKEUACAAAAABUjgAQAAAAAKkYACQAAAABUjAASAAAAAKgYASQAAAAAUDECSAAAAACgYgSQAAAAAEDFCCABAAAAgIoRQAIAAAAAFSOABAAAAAAqRgAJAAAAAFSMABIAAAAAqBgBJAAAAABQMbXVLqAaSqVSkqS1tbXKlQAAAADAO8+budqbOduabJQB5PPPP58kaWxsrHIlAAAAAPDO9eKLL6ahoWGNYzbKAHKzzTZLkvzlL39Z6z8QrEpra2saGxuzaNGi1NfXV7sc3oH8H+Lt8n+It8P/H94u/4d4u/wf4u3yf4i3y/+ht69UKuXFF1/MgAED1jp2owwga2re+PTl/9fe/cdEXf9xAH+dyB2awPFDfmWcgD9oCkwpLmpKi1tCblK6acgSy1AMl61oZKso25Kpy7bmXGuCbjZJW+mW/eKHNMVTgkH+ABkwwln8KNiRDhGQ5/eP5ufbR/AH3H1O7no+tlv0/rzu87l3e+71OV8dnq+vL0NGdvHx8WGGyC7MENmLGSJ7MD9kL2aI7MUMkb2YIbIXM2Sfe/1gH7+EhoiIiIiIiIiIiDTDASQRERERERERERFp5j85gDQYDJKfny8Gg+F+vxRyUcwQ2YsZInsxQ2QP5ofsxQyRvZghshczRPZihpxLh3v5rmwiIiIiIiIiIiKicfhPfgKSiIiIiIiIiIiInIMDSCIiIiIiIiIiItIMB5BERERERERERESkGQ4giYiIiIiIiIiISDMcQBIREREREREREZFmXHYAuXv3bpk5c6Z4eXmJ2WyWqqqqO9YfPnxYoqOjxcvLS2JiYuS7775THQcg7733noSGhsqUKVPEYrFIU1OTqqanp0cyMjLEx8dHjEajrFu3Tq5everwvZH2HJmfwcFBycvLk5iYGHnggQckLCxM1qxZI3/88YfqHDNnzhSdTqd6FBQUaLI/0p6je9DatWtH5CMlJUVVwx7kXhydoVvzc/OxY8cOpYZ9yL2MJUMXLlyQFStWKBn45JNPxnXO/v5+ycnJkYCAAJk2bZqsWLFCOjs7HbktchJH52fbtm3y6KOPire3twQFBcmzzz4rjY2Nqponn3xyRA/Kzs529NbISRydoffff39EPqKjo1U17EHuxdEZGu19jk6nk5ycHKWGfci9jCVDn3/+uSxatEj8/PzEz89PLBbLiHrOhTQGF1RcXAy9Xo/CwkJcuHABWVlZMBqN6OzsHLW+srISHh4e2L59O+rr6/HOO+/A09MT586dU2oKCgrg6+uLI0eO4Ndff8WyZcsQERGBa9euKTUpKSmIi4vD6dOnceLECcyaNQvp6ema75ccy9H5sdlssFgs+PLLL3Hx4kVYrVYkJCQgPj5edR6TyYStW7eivb1deVy9elXz/ZLjadGDMjMzkZKSospHT0+P6jzsQe5Diwz9Ozvt7e0oLCyETqdDS0uLUsM+5D7GmqGqqirk5ubi4MGDCAkJwa5du8Z1zuzsbDz00EMoKytDdXU1HnvsMTz++ONabZM0okV+lixZgqKiIpw/fx51dXV45plnEB4eruoxSUlJyMrKUvWg3t5erbZJGtIiQ/n5+Zg3b54qH3/++aeqhj3IfWiRoa6uLlV+SkpKICI4fvy4UsM+5D7GmqHVq1dj9+7dqK2tRUNDA9auXQtfX19cvnxZqeFcSFsuOYBMSEhATk6O8u83btxAWFgYtm3bNmr9ypUrsXTpUtWa2WzGhg0bAADDw8MICQnBjh07lOM2mw0GgwEHDx4EANTX10NE8Msvvyg133//PXQ6HX7//XeH7Y205+j8jKaqqgoigra2NmXNZDKNeqMk16NFhjIzM5GWlnbba7IHuRdn9KG0tDQ89dRTqjX2Ifcx1gz92+1ycLdz2mw2eHp64vDhw0pNQ0MDRARWq9WO3ZCzaZGfW3V1dUFE8PPPPytrSUlJ2Lx583heMk0wWmQoPz8fcXFxt30ee5B7cUYf2rx5M6KiojA8PKyssQ+5D3syBABDQ0Pw9vbG/v37AXAu5Awu9yvYAwMDUlNTIxaLRVmbNGmSWCwWsVqtoz7HarWq6kVElixZotS3trZKR0eHqsbX11fMZrNSY7VaxWg0yiOPPKLUWCwWmTRpkpw5c8Zh+yNtaZGf0fT29opOpxOj0ahaLygokICAAFmwYIHs2LFDhoaGxr8Zui+0zFBFRYUEBQXJ3LlzZePGjdLd3a06B3uQe3BGH+rs7JRjx47JunXrRhxjH3J948mQI85ZU1Mjg4ODqpro6GgJDw8f93XJ+bTIz2h6e3tFRMTf31+1/sUXX0hgYKDMnz9ftmzZIn19fQ67JjmHlhlqamqSsLAwiYyMlIyMDLl06ZJyjD3IfTijDw0MDMiBAwfkpZdeEp1OpzrGPuT6HJGhvr4+GRwcVO5TnAtpb/L9fgFj9ddff8mNGzckODhYtR4cHCwXL14c9TkdHR2j1nd0dCjHb67dqSYoKEh1fPLkyeLv76/U0MSnRX5u1d/fL3l5eZKeni4+Pj7K+quvvioLFy4Uf39/OXXqlGzZskXa29vl448/tnNX5ExaZSglJUWWL18uERER0tLSIm+//bakpqaK1WoVDw8P9iA34ow+tH//fvH29pbly5er1tmH3MN4MuSIc3Z0dIherx/xP9fulEWaeLTIz62Gh4fltddekyeeeELmz5+vrK9evVpMJpOEhYXJ2bNnJS8vTxobG+Xrr792yHXJObTKkNlsln379sncuXOlvb1dPvjgA1m0aJGcP39evL292YPciDP60JEjR8Rms8natWtV6+xD7sERGcrLy5OwsDBl4Mi5kPZcbgBJNJENDg7KypUrBYDs2bNHdez1119Xfo6NjRW9Xi8bNmyQbdu2icFgcPZLpQnm+eefV36OiYmR2NhYiYqKkoqKCklOTr6Pr4xcUWFhoWRkZIiXl5dqnX2IiJwhJydHzp8/LydPnlStr1+/Xvk5JiZGQkNDJTk5WVpaWiQqKsrZL5MmmNTUVOXn2NhYMZvNYjKZ5NChQ6N+op/oTvbu3SupqakSFhamWmcfIpF/fiOouLhYKioqRrxfJu243K9gBwYGioeHx4hvO+vs7JSQkJBRnxMSEnLH+pv/vFtNV1eX6vjQ0JD09PTc9ro08WiRn5tuDh/b2tqkpKRE9enH0ZjNZhkaGpLffvtt7Buh+0bLDP1bZGSkBAYGSnNzs3IO9iD3oHWGTpw4IY2NjfLyyy/f9bWwD7mm8WTIEecMCQmRgYEBsdlsDrsuOZ8W+fm3TZs2ybfffivHjx+XGTNm3LHWbDaLiCj3OnINWmfoJqPRKHPmzFG9F2IPcg9aZ6itrU1KS0vv+b2QCPuQq7EnQzt37pSCggL56aefJDY2VlnnXEh7LjeA1Ov1Eh8fL2VlZcra8PCwlJWVSWJi4qjPSUxMVNWLiJSUlCj1EREREhISoqr5+++/5cyZM0pNYmKi2Gw2qampUWrKy8tleHhYaVo08WmRH5H/Dx+bmpqktLRUAgIC7vpa6urqZNKkSSM+wk0Tm1YZutXly5elu7tbQkNDlXOwB7kHrTO0d+9eiY+Pl7i4uLu+FvYh1zSeDDninPHx8eLp6amqaWxslEuXLo37uuR8WuRHRASAbNq0Sb755hspLy+XiIiIuz6nrq5ORES515Fr0CpDt7p69aq0tLQo+WAPch9aZ6ioqEiCgoJk6dKld61lH3JN483Q9u3b5cMPP5QffvhB9fc4inAu5BT3+1twxqO4uBgGgwH79u1DfX091q9fD6PRiI6ODgDACy+8gLfeekupr6ysxOTJk7Fz5040NDQgPz8fnp6eOHfunFJTUFAAo9GIo0eP4uzZs0hLSxv169YXLFiAM2fO4OTJk5g9eza/bt0FOTo/AwMDWLZsGWbMmIG6ujq0t7crj+vXrwMATp06hV27dqGurg4tLS04cOAApk+fjjVr1jj/PwDZzdEZunLlCnJzc2G1WtHa2orS0lIsXLgQs2fPRn9/v3Ie9iD3ocV9DAB6e3sxdepU7NmzZ8Q12Yfcy1gzdP36ddTW1qK2thahoaHIzc1FbW0tmpqa7vmcAJCdnY3w8HCUl5ejuroaiYmJSExMdN7GySG0yM/GjRvh6+uLiooK1Xuhvr4+AEBzczO2bt2K6upqtLa24ujRo4iMjMTixYudu3lyCC0y9MYbb6CiogKtra2orKyExWJBYGAgurq6lBr2IPehRYaAf74JOTw8HHl5eSOuyT7kXsaaoYKCAuj1enz11Veq+9SVK1dUNZwLacclB5AA8OmnnyI8PBx6vR4JCQk4ffq0ciwpKQmZmZmq+kOHDmHOnDnQ6/WYN28ejh07pjo+PDyMd999F8HBwTAYDEhOTkZjY6Oqpru7G+np6Zg2bRp8fHzw4osvqsJKrsOR+WltbYWIjPo4fvw4AKCmpgZmsxm+vr7w8vLCww8/jI8++kg1XCLX4sgM9fX14emnn8b06dPh6ekJk8mErKws1R/6AfYgd+Po+xgAfPbZZ5gyZQpsNtuIY+xD7mcsGbrdvSopKemezwkA165dwyuvvAI/Pz9MnToVzz33HNrb27XcJmnE0fm53XuhoqIiAMClS5ewePFi+Pv7w2AwYNasWXjzzTfR29vrpB2Tozk6Q6tWrUJoaCj0ej0efPBBrFq1Cs3Nzaprsge5Fy3uYz/++CNEZMSf5QH2IXc0lgyZTKZRM5Sfn6/UcC6kLR0AaPkJSyIiIiIiIiIiIvrvcrm/A5KIiIiIiIiIiIhcBweQREREREREREREpBkOIImIiIiIiIiIiEgzHEASERERERERERGRZjiAJCIiIiIiIiIiIs1wAElERERERERERESa4QCSiIiIiIiIiIiINMMBJBEREREREREREWmGA0giIiIiIiIiIiLSDAeQREREREREREREpBkOIImIiIiIiIiIiEgz/wOK+rFNveTg/wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Equilibrium excess return calculated during Reverse optimization\n",
    "plt.barh(stocks_list, Pi)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kf/WV9zEAAAAAAIBqjhGaKD+DBknNmknp6dL8+YFuTdktWyYlJ5tRmR6eMFMywWVyshmdWZDDIUVFSSdPSv/+d/5U9bKqVcu8njiRvy87W7rqKmn9eumbbwgzAQAAAABAjUOgifITGirdc4/ZnjrVOxC0k/h4afTo4uuMGSP16iVFRkp165rywAPSxRdLEydKN9xgjl1xhfTcc1JGRunakJZmfobNmnmHlpMnS2vWmJGZLVqU9s4AAAAAAABsj0AT5WvECCk83IwgTE4OdGvKxuGQpk83q7f7EhcnTZtm6q1caQLGjh3NVHBJevJJM0p19myzf/ZsqX176ccfzfGOHfND0Cuu8L528+ZSnTr5z9x8/30TFHv062f2P/VUud82AAAAAACAHdgy0HzxxRfVunVrhYWFqXv37lq5cmWx9d999121b99eYWFhOv/88/XJJ594HbcsS4888oiio6NVq1Yt9e3bV5s3b67IW6i+6tWT7rjDbE+dGti2lJVlmYWACk4zL2jtWvOMUMsyz89s2zZ/erhHo0bSwIHmZ7BhgwkoPT+PTz4xIeiaNeZZmwV99ZX0ww9SVpY53r279/E+fcxzOmfPNqNEAQAAAAAAahjbBZrvvPOOxo0bp0cffVSrV69WXFycEhISlJmZ6bP+smXLNGTIEA0fPlzff/+9kpKSlJSUpJ9++imvzrPPPqtZs2Zp9uzZWrFiherUqaOEhAQdP368sm6rehkzxiyIs3Sp9Pe/m0V07CQlxSxsVJyZM6UvvvDveqGh0lln5a9y3qqVCUHbtjVTygtq08bUDQ8v+nr9+kkffSTNmVPy1HgAAAAAAIBqxnaB5rRp0zRixAjdeuutOvfcczV79mzVrl1br732ms/6M2fOVGJiou677z516NBBkydPVteuXfXCCy9IMqMzZ8yYoYceekj9+/dXp06d9MYbb2j37t1auHBhJd5ZNdKihTRsmNm++26zYvfdd0urVwe2Xf7q2VNKTJScBf54nD79PC5OatBA2rTJrHi+caMUFCR9/LG5948/ln7+2RyfOtWMyuzfv/za2Lev+YxXX5VGjSq/6wIAAAAAAFRxwYFuQGmcOHFCq1at0oQJE/L2OZ1O9e3bV6mnrzj9u9TUVI0bN85rX0JCQl5YuW3bNqWnp6tv3755x+vVq6fu3bsrNTVVgwcP9nndnJwc5XiemSgpKytLkpSbm6vc3Nwy3V9F8LSl0ts0c6acsbFyzp0rx7ZtZqTm3/8uq0sXuYcPl3vQIDM9vSpyOqX33pOGDJE++0y66y7zzMoJE6S//13OqCg5Dx2SLrpIcrlkdegga+xYue+8U0pPV1BYmBzjxkm//WaOt20r9z/+IWvwYKmI/w6OkycVrN//OxVRJ1iS+9QpuT3HL71Ujg8/VFBSktk/c6Z5rmc1ErD+C5QT+jDsjP4Lu6MPw87ov7A7+jDKyt8+47Asy6rgtpSb3bt3q1mzZlq2bJl69OiRt//+++/XF198oRUrVhQ6JzQ0VPPmzdOQIUPy9r300kt6/PHHlZGRoWXLlumSSy7R7t27FR0dnVfnz3/+sxwOh9555x2fbXnsscf0+OOPF9o/f/581a5d+0xus3pxuxX5449qtXixopcvV9DJk5Kkky6Xdl9yibb366cD55xT7YI4AAAAAAAAlM7Ro0d1ww036NChQ4qIiCiynq1GaFYlEyZM8Br5mZWVpRYtWqhfv37F/sArW25urhYvXqzLL79cISEhgWnE1VdLEybIvXev9NZbcr76qoI3blTLzz9Xy88/l9Whg9y33Sb3sGFmMR3gd1Wi/wJngD4MO6P/wu7ow7Az+i/sjj6MsvLMgC6JrQLNyMhIBQUFKSMjw2t/RkaGoqKifJ4TFRVVbH3Pa0ZGhtcIzYyMDHXu3LnItrhcLrlcrkL7Q0JCquQf1irRruhoafx46d57pWXLzArf77wjx4YNCrrvPgU9+KB03XXS7bdLl13m/QxL1GhVov8CZ4A+DDuj/8Lu6MOwM/ov7I4+jNLyt7/YKjEKDQ3VBRdcoCVLluTtc7vdWrJkidcU9IJ69OjhVV+SFi9enFe/TZs2ioqK8qqTlZWlFStWFHlNnCGHQ7rkEmnuXCktTXrpJalLF+nECWnBArPgzdlnS1OmmOMAAAAAAADA72wVaErSuHHjNGfOHM2bN08bNmzQXXfdpSNHjujWW2+VJN10001eiwaNGTNGycnJ+tvf/qaNGzfqscce03fffadRv68M7XA4dM899+iJJ57Qf/7zH/3444+66aabFBMTo6SkpEDcYs1Sr55ZdGf1amnVKunOO6XwcGnrVmniRLNielKS9N//SqdOBbq1AKqaa66REhN9H/vqK/MLlIYNzWtR5YsvKrfNAAAAAIAzYrtAc9CgQZo6daoeeeQRde7cWWvWrFFycrKaNm0qSdqxY4fSCozq69mzp+bPn6+XX35ZcXFxeu+997Rw4UKdd955eXXuv/9+/eUvf9Edd9yhiy66SNnZ2UpOTlZYWFil31+N1rWrWQ09Lc2M3uzZ04SYH35onsPZurX06KPSr78GuqUAAiknR1q6VLIsafhwafFi6V//MvsLmjtXuvBCacsW871SsPz6q3TeeeZ49+6BuQ8AAAAAQJnY6hmaHqNGjcobYXm6lJSUQvsGDhyogQMHFnk9h8OhSZMmadKkSeXVRJyJOnWkW24xZd066dVXpTfekH77TZo0SZo8WerXTxoxwozOCg0NdIsBVJacHDNqOzlZGjNGevZZKSxMGjTIjNRcuFByuaTsbOndd6XnnjMjNE83YoS0d6/07bfmfAAAAACAbdhuhCZqmI4dpWnTpF27pLfflnr3NqOyPv1Uuv56MyX9/vuln38OdEsBVDRPmLlokXk/c6bUrZt09Kh5/+mn5nhOjgkzT52ShgwpfJ2XXjK/JHn/fal588pqPQAAAACgnBBowh5cLmnwYGnJEjN9dMIEKSpKysw0I7DOOUfq1Uv65z+lY8cC3VoAFWHZMjMy0+3O37d2bf62ZZnjqalmuvmAAeY5vQV9+aV0zz3Siy+ax1oAAAAAAGyHQBP2c9ZZ0lNPSTt2mOmlV10lOZ0mqLjxRikmRho9Wvrhh0C3FEB5io83f7aLM2aM1KyZWRBo+HDvYzt2mJHdd9wh3X57hTUTAAAAAFCxCDRhXyEhUv/+0scfmwU+Jk2SWrWSDh6Unn9eioszi3288op0+HCgWwvgTDkc0vTp5s+2L06n9Pjj0uuvm1989OqVf+zYMenaa81jLGbMqIzWAgAAAAAqCIEmqofmzaWHH5a2bjVTTq+/XgoOllauNIt/xMSY15UrzbRUAPZjWdLYsd7TzAtyu6XrrjPPx7ztNhOAetx+u7R/v3m2ZrAt18MDAAAAAPyOQBPVS1CQlJBgQotdu8wKyGefbVY8fuUVM2Kzc2czgvPAgUC3FkBppKRIs2YVX+fzz6Xdu6Vbbsnf99xz5jth9mzp5EkpPd278NxdAAAAALAVAk1UX02aSPfdJ23cKH3xhTRsmBQWZp6tOXq0GbV5443mGKM2gaqvZ08pMdFMLfcoOP3cMyKzXz/z59vjpZek3FxzbnR04fLOO5XTfgAAAABAuSDQRPXncEh//KP05ptm5Nbzz0udOknHj5tV0ePjpfbtzSiuzMxAtxZAUVwusxBYv37m/Zgx0urV+QsFJSSYP9f/+5/3edu2mV9aFFUKjuYEAAAAAFR5BJqoWRo0kEaNktaskVasMM/VrFtX+vln6f77zerI118vffqpeR4fgKrFE2ouXWoWCHI6zSI/S5ea/S5XgBsIAAAAAKhoBJqomRwOqVs36eWXzajNOXPM8zVPnpTef99MTY2NlSZPln77LdCtBVCQy2VGVnummDsc5j1hJgAAAADUCASaQHi4WQF5+XKzevJf/iLVry/9+qv0yCNSq1bS1VdLH35onsMHAIC/brnFhO533ln42MiR5pjnsQelqVuW+gAAAEA1QaAJFNSpk1lFefdu83zNXr3M1PP//ldKSpJatpQmTpS2bg10SwEAVVFOjnkEQsHF5po0kRYskI4dy993/Lg0f775/0pBLVr4X7cs9QEAAIBqgEAT8KVWLWnoUCklRdq0yTxfs0kTKT1dmjJFattWat7cLDZ0yy3SpEkmAF22TEpLY9V0AKiJcnLML79695bGjjW/EFu50iw4d+qU9M47+XU/+MAEjl26eF+ja1cTUn7wQcl1y1IfAAAAqAYINIGSnH229Mwz0s6d0nvvmedrOhzSrl3SV19J8+ZJjz4q3XijdMklUkyMVKeO1LGjdM01ZiXmmTOljz6S1q2Tjh4N9B0BAMqbJ8xctMi8nznThI0bNpj3R45I48ebepL02mvSrbf6vtZtt0lz5+a/L65uWeoDAAAANhcc6AYAthEaKg0YYMr+/dKWLdIvv+SXbdvM644dZurf+vWm+NK0qVl06PTSpo1Zad3J7xoAwFaWLZOSk733rV2bv21Z0r590r//LfXoIX3zjZkqnpJS+FrDhkkTJphnOUvF1y1LfQAAAMDmCDSBsmjY0KyS3q1b4WO5uSbU9BV2/vKLdOCAlJFhSmpq4fNDQ6XWrX2HnbGxUkREhd8eAKCU4uOl0aPNc5iLctZZ0saNplx1lRQZ6bte48bm+OuvmyC0uLplqQ8AAADYHIEmUN5CQsw/Ws86y/fxAwe8A86Cgef27dKJE9LPP5viS6NGRYedLVpIwfyxBoBK53BI06dLX3zhPTLTIy5OmjzZhJ6S9OKLxV/vttukUaP8q1uW+gAAAICNkXwAla1BA1O6di187ORJ82zOokZ37tljpizu2yd9+23h84OCpFatfIedsbHmcx2Oir9HAKhpLMssBOQrzJTM/sWLzS+tHA4pIaH46yUm+l+3LPUBAAAAGyPQBKqS4GATSLZqJV12WeHjhw97B5ynb+fk5L/3pV69osPOVq3MdHcAQOmlpBQ/3VySnn9e+vhj6dJLzS+gihMUlL+gUEl1y1IfAAAAsDECTcBOwsOlTp1MOZ3bLaWlFT26My1NOnRI+v57U07ndErNmxcdeDZuzOhOAChKz55mlOSiReb7WDLTzD0jNp1OqV8/qW9fyeXy75qlfWYyz1gGAABADUGgCVQXTqdZIb1ZMzP653RHj5pndBYVeB49ahYz2rHD98q4deoUHXa2bi3VqlXBNwgAVZjLJS1cKCUlmdXOx4yRpk0z09BnzTJh5sKFhcPMhQvzt19/vfjPKFi3LPUBAACAaoJAE6gpateWzj3XlNNZlpSZWXTY+dtv0pEj0o8/muJLTIzvsDM2VoqKMoErAFRnnlAzNVXq1cuMap8xQ7r2WqlHD/9HZgIAAAAoFoEmAPOP7qZNTenRo/DxnBzp1199B55bt5pne+7ebcrXXxc+PyzMO+Q8fbtu3Yq/RwCoDC6XFB+f/97h8H4PAAAA4IwRaAIomcslnX22KaezLGn//qJHd+7YIR0/bhar8CxYcbomTXyHnS1aSKdOVey9AQAAAAAAWyHQBHBmHA6pUSNTLrqo8PHcXGnnTt9h5y+/mDA0M9OU5cu9Tg2RdE1wsBytW0tnnVV4KntsrFm5HQAAAAAA1BgEmgAqVkhIfvjoy8GD3iFngW1r+3Y5c3OlLVtM8aVhQ9/P7fSM8AwJqbBbAwAAAAAAlY9AE0Bg1a8vdeliymlOHj+upf/8p3q3bq3gHTsKj+7MzDQjPPfvl777rvC1g4Kkli19h52xsSYMdTgq/h4BAAAAAEC5IdAEUHUFBelY48ayevXyPdIyO7vI0Z3ats08u3PbNlM+/7zw+RERRY/ubNWKFYkBAAAAAKiCCDQB2FfdutL555tyOrdbSk8v+tmdu3dLWVnSmjWmnM7hkJo3L3p0Z5MmjO4EAAAAACAACDQBVE9OpxQTY8of/lD4+LFj0vbtvsPOX36Rjhwxixnt3Cl9+WXh82vXLjrsbN3aHAcAAAAAAOWOQBNAzVSrltShgymnsyxpz56iR3fu3CkdPSqtW2eKL1FRvsPO2FgpOtoErgAAAAAAoNQINAHgdA6HmVLepIl08cWFj584If36q++wc+tWM5U9Pd2UZcsKn+9ymVGcvsLONm2k8PAKv0UAAAAAAOyKQBMASis0VGrXzpTTWZZ04EDRozt//VXKyZE2bTLFl8jIosPO5s2lYL66AQAAAAA1F/8qBoDy5HBIDRuacuGFhY+fPGmmrBf17M59+6S9e01ZubLw+cHBZgX208POli3N80KbNvW9IjwAAAAAANUEgSYAVKbgYDPSsk0b38cPHTJBp6+wc/t2M91961ZTfPFMl/csiFRUadxYCgqqsNsEAAAAAKCiEGgCQFVSr57UubMppzt1Stq92/fozt9+k9LSzAjQjAxTvv++6M8JCjILF5UUfDZqZEJSAAAAAACqCAJNALCLoCCpRQtTevUqfNztNlPVd+8uvmRkmHB01y5TihMaalZlLyn4rFeP4BMAAAAAUCkINAGgunA681dn9zXC0+PkSSkzs+Tgc8+e/BXdf/21+M+uVavk0DMmRqpbt1xvGQAAAABQ8xBoAkBNExycHzAW58QJKT295ODzwAHp2LHin+3pER5ecugZHW0CUgAAAAAAfCDQBAD4FhpqVk9v2bL4eseOmed3Fhd67tolZWdLhw9LmzaZUpwGDYoOPJs1M69RUazoDgAAAAA1EIEmAODM1KolxcaaUpzDh0se7bl7t3T8uBn1eeCAtG5d8df0Z0X3Jk1Y0R0AAAAAqhECTQBA5QgPl845x5SiWJZ08GDJoWdampSba54FmpkprVlT9DWdTv9XdHc6y/uuAQAAAADljEATAFB1OBxmunmDBlLHjkXXc7ulfftKDj7T001dz/vihIT4t6J7/fqs6A4AAAAAAUSgCQCwH6dTatzYlLi4ouudOuXfiu6ZmWbE544dphQnLMy/Fd3Dw8v3ngEAAAAAkgg0AQDVWVCQGXUZHS1dcEHR9U6ckDIySg4+9+83z/j85RdTilO3roKjo9XT5VLQggVS8+a+V3SvXbt87xkAAAAAqjkCTQAAQkOlFi1MKc7x4yWv6L57t5SVJWVny7F5sxpL0k8/FX3N+vVLHu0ZHW3aCAAAAAAg0AQAwG9hYVKbNqYUJztbSkvTyR07tOaTT9SlaVMFnT4CdNcu6dgxswjSwYPS+vXFXzMysuTgs2lTKZj/tQMAAACo3vhXDwAA5a1uXaldO1mtW2tXdrbirrxSQSEh3nUsy4zkLBhwFjXiMzdX2rvXlB9+KPpznU4TapYUfEZGsqI7AAAAANsi0AQAIBAcDqlePVM6dCi6nmX5v6L7qVNmSnxamrRqVdHXDA72b0X3Bg1Y0R0AAABAlUOgCQBAVeZwmBGVkZFSp05F1zt1Stqzx78V3U+elHbuNKU4Lpf/K7oTfAIAAACoJASaAABUB0FBUlSUKV27Fl0vN9e/Fd337ZNycqRt20wpTp06/gWfrOgOAAAAoBwQaAIAUJOEhEjNm5tSnOPHzTT2koLPQ4ekI0ekzZtNKU69ev6t6O5yld/9AgAAAKh2CDQBAEBhYWFS69amFOfIEfPMzuJCz127pKNHTfh56JC0YUPx12zUyL8V3U9faAkAAABAjUCgCQAAyq5OHaltW1OKYlnS4cMlj/bcvdtMc9+3z5Qffyz6mg6H1KRJycFn48ZmOj4AAACAaoNAEwAAVCyHQ4qIMKV9+6LrWZZ04EDJoWdamlnYKCPDlO+/L/qanmeLegLOZs18B58NG7KwEQAAAGATBJoAAKBqcDhMsNiwoXTeeUXXc7ulvXvzp7MXFXxmZJjV33ftMqU4oaGFn+UZHW2mtnsWW2ra1IwKZao7AAAAEFAEmgAAwF6cThMsNmkide5cdD3PKM6SRnzu3SudOCFt325KSRo18g45T9/2vEZGMt0dAAAAqAAEmgAAoHoKDjZTzJs1K75eTo7vFd3T000gmp5uSmamGfHpecbnunXFX9fpNM/wLCr4LLjNlHcAAADAbwSaAACgZnO5pFatTCmO222CzIJBZ8HAs+C+PXtMfc9zPksSHFx4hGdRIWhEBOEnAAAAajQCTQAAAH94Rlw2biydf37xdU+eNKFmUYFnwX3795v6/jzrUzIBbEnT3T3bdeuWz70DAAAAVQiBJgAAQHkLDs5fWKgkJ06Y6ewlBZ/p6VJWlpki/+uvppSkTp2SR302bWpKrVpnft8AAABAJSDQBAAACKTQUKl5c1NKcuxYydPdPfuOHpWOHJG2bjWlJPXqeQWezsaN1e7gQTkyMsxzSAuu9B4aeub3DQAAAJQRgSYAAIBd1KoltW5tSkmys/0b9ZmRYUZ9Hjpkys8/S5KCJJ0rSW+9VfjajRqVPOozKspMz2eldwAAAJQzAk0AAIDqqG5dqW1bU4pjWSbIPC3wPLV7t35btUotQkPl9OzPzDTP+/Ss9L5+ffHXdjqlyMiSn/XpWend6Sy/+wcAAEC1RaAJAABQkzkcUv36prRvn7fbnZurNZ98opgrr5QzJOT3nW6ziJE/oz4zM039zExTfvih+HYEB5vp7P4seFSvHiu9AwAA1GAEmgAAAPCPZ8RlZKR03nnF1z15Utq717+V3vftM/V37zalJKGhRQefp4egdesSfgIAAFQzBJoAAAAof8HB+QFjXFzxdU+ckPbsKRx8+gpBDx0y9XfsMKUktWuXPN3ds81K7wAAALZAoAkAAIDACg01K6k3a1Zy3ePHi5/uXnD7yBGz2vsvv5hSkogI/0Z9Nm3KSu8AAAABRKAJAAAA+wgLk1q1MqUk2dmFw8+iQtCcHCkry5TNm0u+dsOG/o36bNzYjFYFAABAueFvVwAAAKie6tY15ayziq9nWSbI9GfUZ0aGed7n/v2mbNhQ/LUdDt8rvfuaBt+oESu9AwAA+IFAEwAAADWbw2FWTq9XTzrnnOLrut3SgQP+jfrcs8fU37PHlB9/LP7aQUHFr/RecLt+fRY7AgAANRaBJgAAAOAvp9OMpGzUSOrYsfi6p075v9L73r2mflqaKSUJDS08wrOoEJSV3gEAQDVDoAkAAABUhKCg/EWEOnUqvm5urpSZWfyoT8/rwYNmpfedO00pSa1a/o36bNrUrApvR7NnS/fdZ0bPep5Zmp2tawYMkC65RPrii/y6KSnSZZdJW7ZIffpIv/5q9jud5mdwxRXS1KlSgwaFP6d9e2nbNnNOVFSF3xYAAPCNQBMAAAAItJCQ0q30nplZ8qjP9HSzMNKxYyaE27at5GuHh/s36rNJE8nlOvP7LqucHGnZMik+3ow+jY8397psmfTHP0qSHF9/rZz69RW2cqX5mYWFmXOXLpVatsx/tuqkSdKIEWaE7M8/S3fcIY0eLb35pvdnfv21+Vlef700b570wAOVdrsAAMAbgSYAAABgJ2FhJpBr2bLkukeO+DfqMz3dhH6HD5viz0rvDRoUv8iRZ7tJk/Jd6T0nR0pKkpKTpTFjpGnTpL//3RwbMUL64QfJ5ZLjiy+U1r272vzyi7R8uQk9pfwRmh6eEFcygfLNN0tvv134c199VbrhBqlXL/O5BJoAAAQMgSYAAABQXdWpI8XGmlIcyzJBpj+jPjMyzBT5AwdM2bix+Gt7VnovbtSn5zUysviV3j1h5qJF5v3MmSagXLvWvP/5Z3N84UI5vvhCe3v3VqvmzRW0dKkJNI8dk1askG67zff1d+2SPvpI6t7de//hw9K775pz27eXDh2SvvpKuvTS4u8dAABUCAJNAAAAoKZzOKSICFPOPrv4upZVeKX3okLQzEzvld5/+qn4awcFSY0bFx14pqWZkZkFecJMj+RkackSOdas0b6//EWWwyG98oo5lppqQtGCIzQfeEB66CEz5fz4cRNmTpvmfc0FC6R27fIXgho82IzYJNAEACAgCDQBAAAA+M/hkBo2NOXcc4uve+qUtG+f7+Dz9BDUs9K751hZ/fnPJnRt104n6tWT1bm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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>MMM</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CVX</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>KO</th>\n",
       "      <th>GS-PK</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>VZ</th>\n",
       "      <th>V</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WBA</th>\n",
       "      <th>DIS</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>HON</th>\n",
       "      <th>CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.005111</td>\n",
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       "      <td>0.247562</td>\n",
       "      <td>0.011143</td>\n",
       "      <td>0.009643</td>\n",
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       "      <td>0.017163</td>\n",
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       "      <td>0.004649</td>\n",
       "      <td>0.009476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.041497</td>\n",
       "      <td>0.014596</td>\n",
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       "      <td>0.037423</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.016689</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             MMM       AXP      AAPL        BA       CAT      CVX      CSCO  \\\n",
       "Weight  0.005111  0.009965  0.247562  0.011143  0.009643  0.02708  0.017163   \n",
       "\n",
       "              KO     GS-PK        GS  ...       TRV       UNH        VZ  \\\n",
       "Weight  0.025182  0.004649  0.009476  ...  0.003773  0.041497  0.014596   \n",
       "\n",
       "               V       WMT       WBA       DIS      AMGN      HON       CRM  \n",
       "Weight  0.043939  0.037423  0.002366  0.016689  0.011277  0.01211  0.018402  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the Black-Litterman Model optimal weights and plotting the efficient frontier\n",
    "result_2 = get_frontier(Pi + rf, C, rf)\n",
    "\n",
    "display_assets(names, R, C, color = 'red')\n",
    "display_frontier(result_1, label = 'Historical returns', color = 'red')\n",
    "display_assets(names, Pi + rf, C, color = 'green')\n",
    "display_frontier(result_2, label = 'Implied returns', color = 'green')\n",
    "plt.xlabel('variance $\\sigma$')\n",
    "plt.ylabel('mean $\\mu$')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "display(pd.DataFrame({'Weight': result_2.W}, index = names).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>MMM</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CVX</th>\n",
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       "      <th>CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.152144</td>\n",
       "      <td>2.611167e-16</td>\n",
       "      <td>0.021393</td>\n",
       "      <td>3.077795e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.638121e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "        MMM  AXP      AAPL            BA       CAT           CVX  CSCO   KO  \\\n",
       "Weight  0.0  0.0  0.152144  2.611167e-16  0.021393  3.077795e-18   0.0  0.0   \n",
       "\n",
       "        GS-PK            GS  ...  TRV      UNH   VZ    V  WMT           WBA  \\\n",
       "Weight    0.0  1.638121e-16  ...  0.0  0.39825  0.0  0.0  0.0  2.178549e-16   \n",
       "\n",
       "                 DIS  AMGN  HON           CRM  \n",
       "Weight  5.745687e-17   0.0  0.0  7.164412e-17  \n",
       "\n",
       "[1 rows x 30 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CVX</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>KO</th>\n",
       "      <th>GS-PK</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>VZ</th>\n",
       "      <th>V</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WBA</th>\n",
       "      <th>DIS</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>HON</th>\n",
       "      <th>CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.005111</td>\n",
       "      <td>0.009965</td>\n",
       "      <td>0.247562</td>\n",
       "      <td>0.011143</td>\n",
       "      <td>0.009643</td>\n",
       "      <td>0.02708</td>\n",
       "      <td>0.017163</td>\n",
       "      <td>0.025182</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>0.009476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.041497</td>\n",
       "      <td>0.014596</td>\n",
       "      <td>0.043939</td>\n",
       "      <td>0.037423</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.016689</td>\n",
       "      <td>0.011277</td>\n",
       "      <td>0.01211</td>\n",
       "      <td>0.018402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MMM       AXP      AAPL        BA       CAT      CVX      CSCO  \\\n",
       "Weight  0.005111  0.009965  0.247562  0.011143  0.009643  0.02708  0.017163   \n",
       "\n",
       "              KO     GS-PK        GS  ...       TRV       UNH        VZ  \\\n",
       "Weight  0.025182  0.004649  0.009476  ...  0.003773  0.041497  0.014596   \n",
       "\n",
       "               V       WMT       WBA       DIS      AMGN      HON       CRM  \n",
       "Weight  0.043939  0.037423  0.002366  0.016689  0.011277  0.01211  0.018402  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Markowitz Model weight distribution\n",
    "display(pd.DataFrame({'Weight': result_1.W}, index = names).T)\n",
    "\n",
    "# Black-Litterman Model weight distribution\n",
    "display(pd.DataFrame({'Weight': result_2.W}, index = names).T)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization based on Equilibrium returns with adjusted views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Views Matrix\n"
     ]
    },
    {
     "data": {
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       "      <th>Views</th>\n",
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       "      <td>0.176</td>\n",
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       "      <td>0.205</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.218</td>\n",
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       "      <th>15</th>\n",
       "      <td>0.106</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.066</td>\n",
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       "      <th>20</th>\n",
       "      <td>0.135</td>\n",
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       "      <th>21</th>\n",
       "      <td>0.044</td>\n",
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       "      <th>22</th>\n",
       "      <td>0.075</td>\n",
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       "      <th>23</th>\n",
       "      <td>0.120</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.125</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.312</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Views\n",
       "0   0.003\n",
       "1   0.018\n",
       "2   0.047\n",
       "3   0.089\n",
       "4   0.249\n",
       "5   0.176\n",
       "6   0.151\n",
       "7   0.128\n",
       "8   0.246\n",
       "9   0.205\n",
       "10  0.347\n",
       "11  0.218\n",
       "12  0.119\n",
       "13  0.145\n",
       "14  0.146\n",
       "15  0.106\n",
       "16  0.125\n",
       "17  0.200\n",
       "18  0.084\n",
       "19  0.066\n",
       "20  0.135\n",
       "21  0.044\n",
       "22  0.075\n",
       "23  0.120\n",
       "24  0.050\n",
       "25  0.078\n",
       "26  0.206\n",
       "27  0.164\n",
       "28  0.125\n",
       "29  0.312\n",
       "30  0.074"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Link Matrix\n"
     ]
    },
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MMM  AXP  AAPL   BA  CAT  CVX  CSCO   KO  GS-PK   GS  ...  TRV  UNH   VZ  \\\n",
       "0   0.0  0.0   1.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  1.0  ...  0.0  0.0  0.0   \n",
       "2   0.0  0.0   1.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4   0.0  1.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5   0.0  0.0   0.0  1.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "6   0.0  0.0   0.0  0.0  1.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "7   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0   0.0  0.0  0.0  0.0   1.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9   0.0  0.0   0.0  0.0  0.0  1.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "10  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "11  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  1.0  ...  0.0  0.0  0.0   \n",
       "12  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "13  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "14  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "15  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "16  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "17  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "18  0.0  0.0   0.0  0.0  0.0  0.0   0.0  1.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "19  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "20  1.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "21  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "22  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "23  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "24  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "25  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  1.0  0.0  0.0   \n",
       "26  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  1.0  0.0   \n",
       "27  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "28  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  1.0   \n",
       "29  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "30  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "      V  WMT  WBA  DIS  AMGN  HON  CRM  \n",
       "0   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "1   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "2   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "3   0.0  0.0  0.0  0.0   1.0  0.0  0.0  \n",
       "4   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "5   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "6   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "7   0.0  0.0  0.0  0.0   0.0  0.0  1.0  \n",
       "8   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "9   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "10  0.0  0.0  0.0  1.0   0.0  0.0  0.0  \n",
       "11  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "12  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "13  0.0  0.0  0.0  0.0   0.0  1.0  0.0  \n",
       "14  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "15  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "16  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "17  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "18  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "19  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "20  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "21  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "22  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "23  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "24  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "25  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "26  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "27  1.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "28  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "29  0.0  0.0  1.0  0.0   0.0  0.0  0.0  \n",
       "30  0.0  1.0  0.0  0.0   0.0  0.0  0.0  \n",
       "\n",
       "[31 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to create the view and link matrix\n",
    "def create_views_and_link_matrix(names, relative_views, absolute_views_1):\n",
    "    r, a, c = len(relative_views), len(absolute_views_1), len(names)\n",
    "    Q = [relative_views[i][3] for i in range(r)]\n",
    "    if(absolute_views_1):\n",
    "        for i in absolute_views_1:\n",
    "            Q.append(i[1])\n",
    "    P = np.zeros([r+a, c])\n",
    "    nameToIndex = dict()\n",
    "    for i, n in enumerate(names):\n",
    "        nameToIndex[n] = i\n",
    "    for i, v in enumerate(relative_views):\n",
    "        name1, name2 = relative_views[i][0], relative_views[i][2]\n",
    "        P[i, nameToIndex[name1]] = +1 if relative_views[i][1] == '>' else -1\n",
    "        P[i, nameToIndex[name2]] = -1 if relative_views[i][1] == '>' else +1\n",
    "    for i, v in enumerate(absolute_views_1):\n",
    "        name  = absolute_views_1[i][0]\n",
    "        P[i+r, nameToIndex[name]] += 1\n",
    "    return np.array(Q), P\n",
    "\n",
    "relative_views = [('AAPL', '>', 'MSFT', 0.003),\n",
    "                  ('GS', '>', 'JPM', 0.018)]\n",
    "\n",
    "absolute_views = [('AAPL', 0.047),\n",
    "                    ('AMGN', 0.089),\n",
    "                    ('AXP', 0.249),\n",
    "                    ('BA', 0.176),\n",
    "                    ('CAT', 0.151),\n",
    "                    ('CRM', 0.128),\n",
    "                    ('CSCO', 0.246),\n",
    "                    ('CVX', 0.205),\n",
    "                    ('DIS', 0.347),\n",
    "                    ('GS', 0.218),\n",
    "                    ('HD', 0.119),\n",
    "                    ('HON', 0.145),\n",
    "                    ('IBM', 0.146),\n",
    "                    ('INTC', 0.106),\n",
    "                    ('JNJ', 0.125),\n",
    "                    ('JPM', 0.20),\n",
    "                    ('KO', 0.084),\n",
    "                    ('MCD', 0.066),\n",
    "                    ('MMM', 0.135),\n",
    "                    ('MRK', 0.044),\n",
    "                    ('MSFT', 0.075),\n",
    "                    ('NKE', 0.12),\n",
    "                    ('PG', 0.05),\n",
    "                    ('TRV', 0.078),\n",
    "                    ('UNH', 0.206),\n",
    "                    ('V', 0.164),\n",
    "                    ('VZ', 0.125),\n",
    "                    ('WBA', 0.312),\n",
    "                    ('WMT', 0.074)]\n",
    "\n",
    "Q, P = create_views_and_link_matrix(names, relative_views, absolute_views)\n",
    "print('Views Matrix')\n",
    "display(pd.DataFrame({'Views':Q}))\n",
    "print('Link Matrix')\n",
    "P = pd.DataFrame(P)\n",
    "P.columns = stocks_list\n",
    "display(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for Black-Litterman Model Equation\n",
    "tau = 0.025  # Scaling Factor\n",
    "\n",
    "# Calculate Omega - uncertainty matrix about views\n",
    "omega = np.dot(np.dot(np.dot(tau, P), C), np.transpose(P))  # 0.25 * P * C * transpose(P)\n",
    "\n",
    "new_omega = np.zeros(omega.shape)\n",
    "np.fill_diagonal(new_omega, np.diag(omega))\n",
    "\n",
    "# Calculate Equilibrium excess returns with views incorporated\n",
    "sub_a = np.linalg.inv(np.dot(tau, C))  # (tau.C)^-1\n",
    "sub_b = np.dot(np.dot(np.transpose(P), np.linalg.inv(new_omega)), P)  # (P^T.omega^-1).P\n",
    "sub_c = np.dot(np.linalg.inv(np.dot(tau, C)), Pi)  # ((Tau.C)^-1).Pi\n",
    "sub_d = np.dot(np.dot(np.transpose(P), np.linalg.inv(new_omega)), Q)   # (P^T.omega^-1).Q\n",
    "\n",
    "# Expected_return = ((tau.C)^-1 + (P^T.omega^-1).P) * ((Tau.C)^-1).Pi + (P^T.omega^-1).Q)\n",
    "Pi_adj = np.dot(np.linalg.inv(sub_a + sub_b), (sub_c + sub_d))  \n",
    "\n",
    "# print(omega)\n",
    "\n",
    "result_3 = get_frontier(Pi_adj + rf, C, rf)\n",
    "\n",
    "final_weights = result_3.W\n",
    "excess_weight = 0\n",
    "for i in range(len(final_weights)):\n",
    "    if final_weights[i] > 0.10:\n",
    "        excess_weight += final_weights[i] - 0.1\n",
    "\n",
    "individual_weights = excess_weight/30\n",
    "\n",
    "for i in range(len(final_weights)):\n",
    "    final_weights[i] += individual_weights\n",
    "\n",
    "final_tan_mean = port_mean(Pi_adj+rf, final_weights)\n",
    "final_tan_var = port_var(final_weights, C)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the Black-Litterman Model optimal weights with adjusted views and plotting the efficient frontier\n",
    "display_assets(names, Pi + rf, C, color = 'green')\n",
    "display_frontier(result_2, label = 'Implied returns', color = 'green')\n",
    "display_assets(names, Pi_adj + rf, C, color = 'blue')\n",
    "display_frontier(result_3, label = 'Implied returns (adjusted views)', color = 'blue')\n",
    "\n",
    "#plt.scatter(np.sqrt(final_tan_var), final_tan_mean, marker='*', color='r' )\n",
    "plt.xlabel('variance $\\sigma$')\n",
    "plt.ylabel('mean $\\mu$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Markowitz Model Optimal Weights</th>\n",
       "      <th>Black Litterman Model Optimal Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.005111</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AXP</th>\n",
       "      <td>0.009965</td>\n",
       "      <td>0.047336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.247562</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.011143</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAT</th>\n",
       "      <td>0.009643</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.027080</td>\n",
       "      <td>0.046814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>0.017163</td>\n",
       "      <td>0.137117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>0.025182</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS-PK</th>\n",
       "      <td>0.004649</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS</th>\n",
       "      <td>0.009476</td>\n",
       "      <td>0.045158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>0.026737</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.010226</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTC</th>\n",
       "      <td>0.011370</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNJ</th>\n",
       "      <td>0.038285</td>\n",
       "      <td>0.045249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>0.036273</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCD</th>\n",
       "      <td>0.019884</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKE</th>\n",
       "      <td>0.017249</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRK</th>\n",
       "      <td>0.027238</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.210511</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>0.033183</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRV</th>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNH</th>\n",
       "      <td>0.041497</td>\n",
       "      <td>0.118370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>0.014596</td>\n",
       "      <td>0.106598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0.043939</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>0.037423</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.242425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>0.016689</td>\n",
       "      <td>0.322117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMGN</th>\n",
       "      <td>0.011277</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HON</th>\n",
       "      <td>0.012110</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRM</th>\n",
       "      <td>0.018402</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Markowitz Model Optimal Weights  Black Litterman Model Optimal Weights\n",
       "MMM                           0.005111                               0.012354\n",
       "AXP                           0.009965                               0.047336\n",
       "AAPL                          0.247562                               0.012354\n",
       "BA                            0.011143                               0.012354\n",
       "CAT                           0.009643                               0.012354\n",
       "CVX                           0.027080                               0.046814\n",
       "CSCO                          0.017163                               0.137117\n",
       "KO                            0.025182                               0.012354\n",
       "GS-PK                         0.004649                               0.012354\n",
       "GS                            0.009476                               0.045158\n",
       "HD                            0.026737                               0.012354\n",
       "IBM                           0.010226                               0.012354\n",
       "INTC                          0.011370                               0.012354\n",
       "JNJ                           0.038285                               0.045249\n",
       "JPM                           0.036273                               0.012354\n",
       "MCD                           0.019884                               0.012354\n",
       "NKE                           0.017249                               0.012354\n",
       "MRK                           0.027238                               0.012354\n",
       "MSFT                          0.210511                               0.012354\n",
       "PG                            0.033183                               0.012354\n",
       "TRV                           0.003773                               0.012354\n",
       "UNH                           0.041497                               0.118370\n",
       "VZ                            0.014596                               0.106598\n",
       "V                             0.043939                               0.012354\n",
       "WMT                           0.037423                               0.012354\n",
       "WBA                           0.002366                               0.242425\n",
       "DIS                           0.016689                               0.322117\n",
       "AMGN                          0.011277                               0.012354\n",
       "HON                           0.012110                               0.012354\n",
       "CRM                           0.018402                               0.012354"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Black-Litterman Model weight distribution\n",
    "# display(pd.DataFrame({'Weight': result_2.W}, index = names).T)\n",
    "\n",
    "# # Black-Litterman Model with adjusted views weight distribution\n",
    "# display(pd.DataFrame({'Weight': result_3.W}, index = names).T)\n",
    "\n",
    "display(pd.DataFrame({'Markowitz Model Optimal Weights': result_2.W, 'Black Litterman Model Optimal Weights': final_weights}, index = names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Plot - Before and After view weights\n",
    "df = pd.DataFrame({'Before Views Weights': result_2.W, 'After Views Weights ': final_weights}, index = stocks_list)\n",
    "ax = df.plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Black-Litterman Weights after Views')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparison b/w Black-Litterman Weights Before Views & Black-Litterman Weights after Views\n",
    "labels = stocks_list\n",
    "BL_weights = result_2.W\n",
    "BL_views_weights = final_weights\n",
    "fig, axs = plt.subplots(1, 2, figsize = (16, 8))\n",
    "# ...\n",
    "axs[0].pie(BL_weights,labels = labels, autopct = '%1.1f%%')\n",
    "axs[0].set_title('Black-Litterman Weights Before Views')\n",
    "# ...\n",
    "axs[1].pie(BL_views_weights,labels = labels, autopct = '%1.1f%%')\n",
    "axs[1].set_title('Black-Litterman Weights after Views')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('base')",
   "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": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e50e539b80a1c06ff80119ceb247a161512d83640b1afb6e95ff03fd015a83cd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}