VaR Analysis and Efficient Frontier Analysis.ipynb

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   "source": [
    "## Value-at-Risk Analysis\n",
    "\n",
    "### Part A. For this analysis, we would like to use Fama-French “48 Industry Portfolios,” which are available on Ken French’s data library. We need to use the set of monthly returns for value weighted portfolios since January 1990. \n",
    "\n",
    "a)\tCompute the 95% VaR for all industries using the modified Cornish-Fisher approach, sort them based on values, and show the results on a bar plot. \n",
    "b)\tCompute the Sharpe ratio for all industries using annualized return and volatility, and assuming that the annual risk free rate is rf=3%. Sort the Sharpe ratios based on value and show the results on a bar plot. \n",
    "c)\tDo you observe a relation between the results of parts (a) and (b)?\n",
    "(HINT: For example, you can investigate whether industries with the highest values in part (a) also have the highest values in part (b), or other relations like this) \n",
    "d)\tUse the VaR estimates from part (a) and compute the CVaR (i.e., Beyond VaR) for all portfolios. (Note: you need to build your own code for this part) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8e5a79f2-f446-4a0a-8b4c-2b7cdbb6ef31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Import libraries for data manipulation\n",
    "# these two lines of code usually help to get the code running again. its called a magic function.\n",
    "# use this when your code downloads external packages\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "700aaff8-1c04-4dfc-a835-bd44eca02fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "befbcb7d-3339-4dc6-88f1-caa23d30a2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03198328],\n",
       "       [0.06112917],\n",
       "       [0.01385746],\n",
       "       ...,\n",
       "       [0.04406412],\n",
       "       [0.00867178],\n",
       "       [0.02984359]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns_norm = np.random.normal(0.01, 0.04, (10000,1))\n",
    "returns_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10895ba7-8b39-48bb-bc52-6737d1f47f4d",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010469831391801339"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(returns_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b955439f-5ce0-41d7-8897-f66ed8353bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([199001, 199002, 199003, 199004, 199005, 199006, 199007, 199008,\n",
       "            199009, 199010,\n",
       "            ...\n",
       "            202203, 202204, 202205, 202206, 202207, 202208, 202209, 202210,\n",
       "            202211, 202212],\n",
       "           dtype='int64', length=396)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret = pd.read_csv(\"48_Industry_Portfolios_Returns_monthly.csv\", header=0, index_col=0, na_values=-99.99)/100\n",
    "\n",
    "# Check the initial index column of the data. it shows \"192607\" instead of \"1926-07\". We need to change this\n",
    "ret.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebcf9a7f-f62f-4257-b1e2-b6ac32fe24b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1990-01', '1990-02', '1990-03', '1990-04', '1990-05', '1990-06',\n",
       "             '1990-07', '1990-08', '1990-09', '1990-10',\n",
       "             ...\n",
       "             '2022-03', '2022-04', '2022-05', '2022-06', '2022-07', '2022-08',\n",
       "             '2022-09', '2022-10', '2022-11', '2022-12'],\n",
       "            dtype='period[M]', length=396)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret.index = pd.to_datetime(ret.index, format=\"%Y%m\").to_period(\"M\")\n",
    "ret.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f551152b-f0c9-4789-ab7f-9686b09d58ca",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agric</th>\n",
       "      <th>Food</th>\n",
       "      <th>Soda</th>\n",
       "      <th>Beer</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>Toys</th>\n",
       "      <th>Fun</th>\n",
       "      <th>Books</th>\n",
       "      <th>Hshld</th>\n",
       "      <th>Clths</th>\n",
       "      <th>...</th>\n",
       "      <th>Boxes</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Whlsl</th>\n",
       "      <th>Rtail</th>\n",
       "      <th>Meals</th>\n",
       "      <th>Banks</th>\n",
       "      <th>Insur</th>\n",
       "      <th>RlEst</th>\n",
       "      <th>Fin</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01</th>\n",
       "      <td>-0.1145</td>\n",
       "      <td>-0.1013</td>\n",
       "      <td>-0.1063</td>\n",
       "      <td>-0.0903</td>\n",
       "      <td>-0.0865</td>\n",
       "      <td>-0.1138</td>\n",
       "      <td>-0.1074</td>\n",
       "      <td>-0.0926</td>\n",
       "      <td>-0.0771</td>\n",
       "      <td>-0.0916</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0496</td>\n",
       "      <td>-0.0732</td>\n",
       "      <td>-0.0762</td>\n",
       "      <td>-0.0565</td>\n",
       "      <td>-0.1217</td>\n",
       "      <td>-0.0973</td>\n",
       "      <td>-0.1059</td>\n",
       "      <td>-0.0850</td>\n",
       "      <td>-0.0826</td>\n",
       "      <td>-0.0988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02</th>\n",
       "      <td>0.0940</td>\n",
       "      <td>-0.0080</td>\n",
       "      <td>-0.0089</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>-0.0256</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0.0586</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0222</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03</th>\n",
       "      <td>0.0269</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.0989</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>0.0681</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>-0.0086</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0412</td>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>-0.0040</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>-0.0212</td>\n",
       "      <td>0.0446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Agric   Food    Soda    Beer    Smoke   Toys    Fun     Books  \\\n",
       "1990-01 -0.1145 -0.1013 -0.1063 -0.0903 -0.0865 -0.1138 -0.1074 -0.0926   \n",
       "1990-02  0.0940 -0.0080 -0.0089  0.0051 -0.0256  0.0063  0.0179  0.0028   \n",
       "1990-03  0.0269  0.0529  0.0989  0.0621  0.0681  0.0675  0.0109 -0.0086   \n",
       "\n",
       "          Hshld   Clths  ...   Boxes   Trans   Whlsl   Rtail   Meals   Banks  \\\n",
       "1990-01 -0.0771 -0.0916  ... -0.0496 -0.0732 -0.0762 -0.0565 -0.1217 -0.0973   \n",
       "1990-02  0.0017  0.0268  ...  0.0440  0.0586  0.0215  0.0222 -0.0008  0.0394   \n",
       "1990-03  0.0417  0.0820  ...  0.0412  0.0333  0.0307  0.0603 -0.0040 -0.0297   \n",
       "\n",
       "          Insur   RlEst   Fin     Other  \n",
       "1990-01 -0.1059 -0.0850 -0.0826 -0.0988  \n",
       "1990-02  0.0139  0.0083  0.0090  0.0210  \n",
       "1990-03  0.0011  0.0244 -0.0212  0.0446  \n",
       "\n",
       "[3 rows x 48 columns]"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ret.head(3)"
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  {
   "cell_type": "markdown",
   "id": "1bb3147e-8e59-42cb-950f-98c9bff5daa2",
   "metadata": {},
   "source": [
    "## a) Compute the 95% VaR for all industries using the modified Cornish-Fisher approach, sort them based on values, and show the results on a bar plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c03d8649-3b98-47d3-bc5e-c7099d54846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Agric', 'Food ', 'Soda ', 'Beer ', 'Smoke', 'Toys ', 'Fun  ', 'Books',\n",
       "       'Hshld', 'Clths', 'Hlth ', 'MedEq', 'Drugs', 'Chems', 'Rubbr', 'Txtls',\n",
       "       'BldMt', 'Cnstr', 'Steel', 'FabPr', 'Mach ', 'ElcEq', 'Autos', 'Aero ',\n",
       "       'Ships', 'Guns ', 'Gold ', 'Mines', 'Coal ', 'Oil  ', 'Util ', 'Telcm',\n",
       "       'PerSv', 'BusSv', 'Comps', 'Chips', 'LabEq', 'Paper', 'Boxes', 'Trans',\n",
       "       'Whlsl', 'Rtail', 'Meals', 'Banks', 'Insur', 'RlEst', 'Fin  ', 'Other'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the columns in the data\n",
    "ret.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "776216f9-b076-43ef-b335-d532f41b83bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Agric', 'Food', 'Soda', 'Beer', 'Smoke', 'Toys', 'Fun', 'Books',\n",
       "       'Hshld', 'Clths', 'Hlth', 'MedEq', 'Drugs', 'Chems', 'Rubbr', 'Txtls',\n",
       "       'BldMt', 'Cnstr', 'Steel', 'FabPr', 'Mach', 'ElcEq', 'Autos', 'Aero',\n",
       "       'Ships', 'Guns', 'Gold', 'Mines', 'Coal', 'Oil', 'Util', 'Telcm',\n",
       "       'PerSv', 'BusSv', 'Comps', 'Chips', 'LabEq', 'Paper', 'Boxes', 'Trans',\n",
       "       'Whlsl', 'Rtail', 'Meals', 'Banks', 'Insur', 'RlEst', 'Fin', 'Other'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove space from column names\n",
    "ret.columns = ret.columns.str.strip()\n",
    "ret.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bad90369-2a44-42ea-91cf-512439083f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agric    0.009993\n",
       "Food     0.008604\n",
       "Soda     0.011255\n",
       "Beer     0.010469\n",
       "Smoke    0.011493\n",
       "Toys     0.005969\n",
       "Fun      0.010791\n",
       "Books    0.006491\n",
       "Hshld    0.008850\n",
       "Clths    0.010362\n",
       "Hlth     0.007976\n",
       "MedEq    0.011053\n",
       "Drugs    0.010398\n",
       "Chems    0.009198\n",
       "Rubbr    0.010171\n",
       "Txtls    0.006795\n",
       "BldMt    0.009814\n",
       "Cnstr    0.010084\n",
       "Steel    0.008489\n",
       "FabPr    0.007631\n",
       "Mach     0.010673\n",
       "ElcEq    0.011174\n",
       "Autos    0.009919\n",
       "Aero     0.011661\n",
       "Ships    0.011779\n",
       "Guns     0.012634\n",
       "Gold     0.005985\n",
       "Mines    0.011282\n",
       "Coal     0.011260\n",
       "Oil      0.009773\n",
       "Util     0.008223\n",
       "Telcm    0.006288\n",
       "PerSv    0.006754\n",
       "BusSv    0.011554\n",
       "Comps    0.011017\n",
       "Chips    0.013430\n",
       "LabEq    0.011771\n",
       "Paper    0.007192\n",
       "Boxes    0.009006\n",
       "Trans    0.008922\n",
       "Whlsl    0.008520\n",
       "Rtail    0.010185\n",
       "Meals    0.010177\n",
       "Banks    0.009611\n",
       "Insur    0.010393\n",
       "RlEst    0.005990\n",
       "Fin      0.011760\n",
       "Other    0.004196\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean return by industry\n",
    "ret.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce94540b-5256-473a-b44e-c91f9d77faa6",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Agric</th>\n",
       "      <th>Food</th>\n",
       "      <th>Soda</th>\n",
       "      <th>Beer</th>\n",
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       "      <th>Hshld</th>\n",
       "      <th>Clths</th>\n",
       "      <th>...</th>\n",
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       "      <th>Insur</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agric</th>\n",
       "      <td>0.050516</td>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.012377</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>0.014338</td>\n",
       "      <td>0.019074</td>\n",
       "      <td>0.028056</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.009855</td>\n",
       "      <td>0.017932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021933</td>\n",
       "      <td>0.020639</td>\n",
       "      <td>0.020955</td>\n",
       "      <td>0.014472</td>\n",
       "      <td>0.016201</td>\n",
       "      <td>0.021444</td>\n",
       "      <td>0.018636</td>\n",
       "      <td>0.028724</td>\n",
       "      <td>0.026549</td>\n",
       "      <td>0.018191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.014356</td>\n",
       "      <td>0.011610</td>\n",
       "      <td>0.015464</td>\n",
       "      <td>0.015009</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>0.015915</td>\n",
       "      <td>0.011982</td>\n",
       "      <td>0.014209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014147</td>\n",
       "      <td>0.013732</td>\n",
       "      <td>0.014793</td>\n",
       "      <td>0.011685</td>\n",
       "      <td>0.012730</td>\n",
       "      <td>0.014755</td>\n",
       "      <td>0.014876</td>\n",
       "      <td>0.019012</td>\n",
       "      <td>0.014858</td>\n",
       "      <td>0.013420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soda</th>\n",
       "      <td>0.012377</td>\n",
       "      <td>0.014356</td>\n",
       "      <td>0.039983</td>\n",
       "      <td>0.016061</td>\n",
       "      <td>0.016404</td>\n",
       "      <td>0.022362</td>\n",
       "      <td>0.029317</td>\n",
       "      <td>0.023343</td>\n",
       "      <td>0.014523</td>\n",
       "      <td>0.022576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020243</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>0.020829</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.018014</td>\n",
       "      <td>0.021361</td>\n",
       "      <td>0.020625</td>\n",
       "      <td>0.034224</td>\n",
       "      <td>0.022366</td>\n",
       "      <td>0.019757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Agric      Food      Soda      Beer     Smoke      Toys       Fun  \\\n",
       "Agric  0.050516  0.011145  0.012377  0.010312  0.014338  0.019074  0.028056   \n",
       "Food   0.011145  0.016076  0.014356  0.011610  0.015464  0.015009  0.018079   \n",
       "Soda   0.012377  0.014356  0.039983  0.016061  0.016404  0.022362  0.029317   \n",
       "\n",
       "          Books     Hshld     Clths  ...     Boxes     Trans     Whlsl  \\\n",
       "Agric  0.023772  0.009855  0.017932  ...  0.021933  0.020639  0.020955   \n",
       "Food   0.015915  0.011982  0.014209  ...  0.014147  0.013732  0.014793   \n",
       "Soda   0.023343  0.014523  0.022576  ...  0.020243  0.018180  0.020829   \n",
       "\n",
       "          Rtail     Meals     Banks     Insur     RlEst       Fin     Other  \n",
       "Agric  0.014472  0.016201  0.021444  0.018636  0.028724  0.026549  0.018191  \n",
       "Food   0.011685  0.012730  0.014755  0.014876  0.019012  0.014858  0.013420  \n",
       "Soda   0.016012  0.018014  0.021361  0.020625  0.034224  0.022366  0.019757  \n",
       "\n",
       "[3 rows x 48 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the covariance monthly matrix ret[\"2002\":].cov() and this is done by simply \".cov() and MPY by 12 to have it annually\n",
    "# this \n",
    "\n",
    "# Remember, the leading diagonal of the covariance matrix is the variance of each industry as shown in the cov matrix\n",
    "# from a mathematical point of view . It also shows you the cov(Food,Beer) = 1.14%\n",
    "cov = ret[\"2002\":].cov()*12\n",
    "cov.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bbb90f81-71e3-484c-8925-de7cd0d0b860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the vector of returns\n",
    "# we take... then divide it by '12' to get number of years which is 20. use shape[0] so it should sum the index\n",
    "# instead and not the matrix since pd.shape() returns a matrix. BE CAREFUL TO AVOID ERRORS \n",
    "n_years = ret[\"2002\":].shape[0]/12\n",
    "\n",
    "# use the matrix as returns now (R) to calculate cummulative returns like the formula says < (1+R)^1/n_years -1 >\n",
    "eret = ((1+ret[\"2002\":]).prod())**(1/n_years)-1\n",
    "\n",
    "# Visualize the matrix. The coal industry has a negative average annual return of -0.04 for the last 20 years. \n",
    "eret.sort_values().plot.bar(figsize=(8,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "da950247-f544-4577-a785-d1e52d1a0bba",
   "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>Agric</th>\n",
       "      <th>Food</th>\n",
       "      <th>Soda</th>\n",
       "      <th>Beer</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>Toys</th>\n",
       "      <th>Fun</th>\n",
       "      <th>Books</th>\n",
       "      <th>Hshld</th>\n",
       "      <th>Clths</th>\n",
       "      <th>...</th>\n",
       "      <th>Boxes</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Whlsl</th>\n",
       "      <th>Rtail</th>\n",
       "      <th>Meals</th>\n",
       "      <th>Banks</th>\n",
       "      <th>Insur</th>\n",
       "      <th>RlEst</th>\n",
       "      <th>Fin</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agric</th>\n",
       "      <td>0.050516</td>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.012377</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>0.014338</td>\n",
       "      <td>0.019074</td>\n",
       "      <td>0.028056</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.009855</td>\n",
       "      <td>0.017932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021933</td>\n",
       "      <td>0.020639</td>\n",
       "      <td>0.020955</td>\n",
       "      <td>0.014472</td>\n",
       "      <td>0.016201</td>\n",
       "      <td>0.021444</td>\n",
       "      <td>0.018636</td>\n",
       "      <td>0.028724</td>\n",
       "      <td>0.026549</td>\n",
       "      <td>0.018191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.014356</td>\n",
       "      <td>0.011610</td>\n",
       "      <td>0.015464</td>\n",
       "      <td>0.015009</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>0.015915</td>\n",
       "      <td>0.011982</td>\n",
       "      <td>0.014209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014147</td>\n",
       "      <td>0.013732</td>\n",
       "      <td>0.014793</td>\n",
       "      <td>0.011685</td>\n",
       "      <td>0.012730</td>\n",
       "      <td>0.014755</td>\n",
       "      <td>0.014876</td>\n",
       "      <td>0.019012</td>\n",
       "      <td>0.014858</td>\n",
       "      <td>0.013420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soda</th>\n",
       "      <td>0.012377</td>\n",
       "      <td>0.014356</td>\n",
       "      <td>0.039983</td>\n",
       "      <td>0.016061</td>\n",
       "      <td>0.016404</td>\n",
       "      <td>0.022362</td>\n",
       "      <td>0.029317</td>\n",
       "      <td>0.023343</td>\n",
       "      <td>0.014523</td>\n",
       "      <td>0.022576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020243</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>0.020829</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.018014</td>\n",
       "      <td>0.021361</td>\n",
       "      <td>0.020625</td>\n",
       "      <td>0.034224</td>\n",
       "      <td>0.022366</td>\n",
       "      <td>0.019757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Agric      Food      Soda      Beer     Smoke      Toys       Fun  \\\n",
       "Agric  0.050516  0.011145  0.012377  0.010312  0.014338  0.019074  0.028056   \n",
       "Food   0.011145  0.016076  0.014356  0.011610  0.015464  0.015009  0.018079   \n",
       "Soda   0.012377  0.014356  0.039983  0.016061  0.016404  0.022362  0.029317   \n",
       "\n",
       "          Books     Hshld     Clths  ...     Boxes     Trans     Whlsl  \\\n",
       "Agric  0.023772  0.009855  0.017932  ...  0.021933  0.020639  0.020955   \n",
       "Food   0.015915  0.011982  0.014209  ...  0.014147  0.013732  0.014793   \n",
       "Soda   0.023343  0.014523  0.022576  ...  0.020243  0.018180  0.020829   \n",
       "\n",
       "          Rtail     Meals     Banks     Insur     RlEst       Fin     Other  \n",
       "Agric  0.014472  0.016201  0.021444  0.018636  0.028724  0.026549  0.018191  \n",
       "Food   0.011685  0.012730  0.014755  0.014876  0.019012  0.014858  0.013420  \n",
       "Soda   0.016012  0.018014  0.021361  0.020625  0.034224  0.022366  0.019757  \n",
       "\n",
       "[3 rows x 48 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the covariance monthly matrix ret[\"2002\":].cov() and this is done by simply \".cov() and MPY by 12 to have it annually\n",
    "# this \n",
    "\n",
    "# Remember, the leading diagonal of the covariance matrix is the variance of each industry as shown in the cov matrix\n",
    "# from a mathematical point of view . It also shows you the cov(Food,Beer) = 1.14%\n",
    "cov = ret[\"2002\":].cov()*12\n",
    "cov.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3ca1daf5-8025-4c7e-a5e7-cdb972ee9b1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_months=ret.shape[0]\n",
    "n_years=n_months/12\n",
    "n_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "37701111-1c2f-4db5-a05c-e269257e83bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agric    0.101274\n",
       "Food     0.097361\n",
       "Soda     0.114240\n",
       "Beer     0.118086\n",
       "Smoke    0.117312\n",
       "Toys     0.043099\n",
       "Fun      0.098056\n",
       "Books    0.058509\n",
       "Hshld    0.099717\n",
       "Clths    0.103400\n",
       "Hlth     0.072206\n",
       "MedEq    0.123888\n",
       "Drugs    0.118194\n",
       "Chems    0.094071\n",
       "Rubbr    0.105969\n",
       "Txtls    0.040980\n",
       "BldMt    0.096750\n",
       "Cnstr    0.094803\n",
       "Steel    0.057287\n",
       "FabPr    0.053236\n",
       "Mach     0.105523\n",
       "ElcEq    0.113967\n",
       "Autos    0.076676\n",
       "Aero     0.119808\n",
       "Ships    0.113677\n",
       "Guns     0.137400\n",
       "Gold     0.003310\n",
       "Mines    0.099702\n",
       "Coal     0.041586\n",
       "Oil      0.095618\n",
       "Util     0.092475\n",
       "Telcm    0.061249\n",
       "PerSv    0.061475\n",
       "BusSv    0.120959\n",
       "Comps    0.101505\n",
       "Chips    0.128939\n",
       "LabEq    0.123943\n",
       "Paper    0.072977\n",
       "Boxes    0.090834\n",
       "Trans    0.092884\n",
       "Whlsl    0.091539\n",
       "Rtail    0.111934\n",
       "Meals    0.111854\n",
       "Banks    0.094906\n",
       "Insur    0.113153\n",
       "RlEst    0.038661\n",
       "Fin      0.118917\n",
       "Other    0.026972\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_mret=(1+ret).prod()**(1/n_years)-1\n",
    "annual_mret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0411a70a-65e1-46f8-8f1c-c5206674b3f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annual_mret.sort_values().plot.bar(figsize=(15,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "305f260f-25a4-4b97-8998-bde8edba68b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agric    0.217134\n",
       "Food     0.141829\n",
       "Soda     0.230852\n",
       "Beer     0.164323\n",
       "Smoke    0.229568\n",
       "Toys     0.241284\n",
       "Fun      0.264395\n",
       "Books    0.205026\n",
       "Hshld    0.146771\n",
       "Clths    0.225930\n",
       "Hlth     0.226241\n",
       "MedEq    0.173951\n",
       "Drugs    0.159050\n",
       "Chems    0.200410\n",
       "Rubbr    0.204694\n",
       "Txtls    0.287333\n",
       "BldMt    0.222238\n",
       "Cnstr    0.243438\n",
       "Steel    0.301649\n",
       "FabPr    0.279365\n",
       "Mach     0.232056\n",
       "ElcEq    0.226264\n",
       "Autos    0.304512\n",
       "Aero     0.225164\n",
       "Ships    0.257873\n",
       "Guns     0.209072\n",
       "Gold     0.379665\n",
       "Mines    0.280470\n",
       "Coal     0.432059\n",
       "Oil      0.226850\n",
       "Util     0.140225\n",
       "Telcm    0.177585\n",
       "PerSv    0.204739\n",
       "BusSv    0.218894\n",
       "Comps    0.263538\n",
       "Chips    0.277209\n",
       "LabEq    0.217905\n",
       "Paper    0.177346\n",
       "Boxes    0.202983\n",
       "Trans    0.188825\n",
       "Whlsl    0.168529\n",
       "Rtail    0.177523\n",
       "Meals    0.176306\n",
       "Banks    0.217933\n",
       "Insur    0.183343\n",
       "RlEst    0.260951\n",
       "Fin      0.235946\n",
       "Other    0.215744\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_vol=ret.std() * np.sqrt(12)\n",
    "annual_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "206c6a8e-2ad3-45df-b4a0-864f588673de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annual_mret.sort_values().plot.bar(figsize=(15,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06af450-6b13-4192-a7e2-b176886cf4ce",
   "metadata": {},
   "source": [
    "## Annualizing the return and volatility"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c88ba8-59d7-40a3-995d-7c7d040e5005",
   "metadata": {},
   "source": [
    "## 95% VaR for all industries using the modified Cornish-Fisher approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "294aea0a-0863-4e1e-a9ac-fbfcb0ee51d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "614c182c-d4f6-4945-81d1-3407274b4d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6448536269514729"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = norm.ppf(0.05)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0853812d-9fd9-4866-ae80-aeaa4558ef94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.59445314, -1.65259842, -1.78042032, -1.68194653, -1.77229819,\n",
       "       -1.69469963, -1.73620556, -1.60140113, -1.67837267, -1.71254114,\n",
       "       -1.73145547, -1.7667755 , -1.69825535, -1.72892546, -1.69755338,\n",
       "       -1.82328352, -1.78240993, -1.750289  , -1.5927086 , -1.75488796,\n",
       "       -1.70326615, -1.65336763, -1.2126521 , -1.62948136, -1.72128602,\n",
       "       -1.49738751, -1.42404173, -1.63628864, -1.6584632 , -1.50177953,\n",
       "       -1.78628147, -1.71490699, -1.78387492, -1.70315901, -1.68116201,\n",
       "       -1.71858682, -1.72150043, -1.7276984 , -1.69914256, -1.73972319,\n",
       "       -1.70642384, -1.56487588, -1.69088377, -1.76003062, -1.64887758,\n",
       "       -1.7457296 , -1.68679642, -1.66149556])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = norm.ppf(0.05)\n",
    "s = scipy.stats.skew(ret[-120:])\n",
    "k = scipy.stats.kurtosis(ret[-120:])\n",
    "z_cf = (z\n",
    "    + (z**2 - 1)*s/6\n",
    "    + (z**3 - 3*z)*k/24\n",
    "    - (2*z**3 - 5*z)*(s**2)/36)\n",
    "z_cf  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d420fb34-1028-4156-ab66-0656e25a8b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agric    0.083181\n",
       "Food     0.056856\n",
       "Soda     0.068016\n",
       "Beer     0.056572\n",
       "Smoke    0.088588\n",
       "Toys     0.127301\n",
       "Fun      0.118660\n",
       "Books    0.100277\n",
       "Hshld    0.057690\n",
       "Clths    0.095557\n",
       "Hlth     0.096713\n",
       "MedEq    0.077321\n",
       "Drugs    0.062548\n",
       "Chems    0.091422\n",
       "Rubbr    0.084701\n",
       "Txtls    0.143451\n",
       "BldMt    0.105843\n",
       "Cnstr    0.113399\n",
       "Steel    0.137397\n",
       "FabPr    0.160813\n",
       "Mach     0.094883\n",
       "ElcEq    0.098222\n",
       "Autos    0.116139\n",
       "Aero     0.103893\n",
       "Ships    0.103662\n",
       "Guns     0.068284\n",
       "Gold     0.148726\n",
       "Mines    0.122490\n",
       "Coal     0.214451\n",
       "Oil      0.121653\n",
       "Util     0.065534\n",
       "Telcm    0.072706\n",
       "PerSv    0.092680\n",
       "BusSv    0.074094\n",
       "Comps    0.080436\n",
       "Chips    0.095284\n",
       "LabEq    0.076875\n",
       "Paper    0.076345\n",
       "Boxes    0.080293\n",
       "Trans    0.093310\n",
       "Whlsl    0.077460\n",
       "Rtail    0.068738\n",
       "Meals    0.072434\n",
       "Banks    0.095378\n",
       "Insur    0.062725\n",
       "RlEst    0.123393\n",
       "Fin      0.086863\n",
       "Other    0.073798\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VaR_cf = -1 * (ret.mean() + z_cf * ret[-120:].std())\n",
    "VaR_cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ccccf8c-ee41-438c-a506-38ee4c1567c8",
   "metadata": {},
   "source": [
    "##  Sorting the  95% VaR based on values, and showing the results on a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a8fb16e-3166-4d00-b2b2-6962686e76d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Beer     0.056572\n",
       "Food     0.056856\n",
       "Hshld    0.057690\n",
       "Drugs    0.062548\n",
       "Insur    0.062725\n",
       "Util     0.065534\n",
       "Soda     0.068016\n",
       "Guns     0.068284\n",
       "Rtail    0.068738\n",
       "Meals    0.072434\n",
       "Telcm    0.072706\n",
       "Other    0.073798\n",
       "BusSv    0.074094\n",
       "Paper    0.076345\n",
       "LabEq    0.076875\n",
       "MedEq    0.077321\n",
       "Whlsl    0.077460\n",
       "Boxes    0.080293\n",
       "Comps    0.080436\n",
       "Agric    0.083181\n",
       "Rubbr    0.084701\n",
       "Fin      0.086863\n",
       "Smoke    0.088588\n",
       "Chems    0.091422\n",
       "PerSv    0.092680\n",
       "Trans    0.093310\n",
       "Mach     0.094883\n",
       "Chips    0.095284\n",
       "Banks    0.095378\n",
       "Clths    0.095557\n",
       "Hlth     0.096713\n",
       "ElcEq    0.098222\n",
       "Books    0.100277\n",
       "Ships    0.103662\n",
       "Aero     0.103893\n",
       "BldMt    0.105843\n",
       "Cnstr    0.113399\n",
       "Autos    0.116139\n",
       "Fun      0.118660\n",
       "Oil      0.121653\n",
       "Mines    0.122490\n",
       "RlEst    0.123393\n",
       "Toys     0.127301\n",
       "Steel    0.137397\n",
       "Txtls    0.143451\n",
       "Gold     0.148726\n",
       "FabPr    0.160813\n",
       "Coal     0.214451\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VaR_cf = -1 * (ret.mean() + z_cf * ret[-120:].std())\n",
    "VaR_cf.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9b3b7608-c8a6-4473-b7ce-da77ab9f7530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tpsZubkSfJrYMERERERGRKjEYIiIiIiIiVbIqGJoxYwayZ88OV1dXFCtWDPv37ze67d27d/Hdd98hODgYDg4O6NWrl8HtVq9ejZCQELi4uCAkJARr1661pmpERERERESKWBwMLV++HL169cKQIUNw+vRplC9fHrVq1UJsbKzB7V+/fo0MGTJgyJAhKFSokMFtDh8+jCZNmqBFixaIjo5GixYt0LhxYxw9etTS6hERERERESlicTA0efJktG3bFu3atUPevHkRFhYGf39/zJw50+D2gYGBmDp1Klq2bAlvb2+D24SFhaF69eoYNGgQ8uTJg0GDBqFq1aoICwuztHpERERERESKWJRN7s2bNzh58iQGDhyosz40NBSHDh2yuhKHDx9G7969ddbVqFHDZDD0+vVrvH79Wvv306dPrT4+ERERfb6YyY2IjLGoZejBgwdISEiAr6+vznpfX1/Ex8dbXYn4+HiL9zl+/Hh4e3trF39/f6uPT0RERERE6mNVAgWNRqPzt4jorfvQ+xw0aBCePHmiXeLi4mw6PhERERERqYtF3eTSp08PR0dHvRab+/fv67XsWMLPz8/ifbq4uMDFxcXqYxIRERERkbpZFAw5OzujWLFi2L59Oxo2bKhdv337dtSvX9/qSpQuXRrbt2/XGTe0bds2lClTxup9EhER0eeBY36I6EOxKBgCgD59+qBFixYoXrw4SpcujTlz5iA2NhadOnUCkNh97c6dO4iMjNSWiYqKAgD8888/+OuvvxAVFQVnZ2eEhIQAAHr27IkKFSpg4sSJqF+/PtavX48dO3bgwIEDdniKRERERERE+iwOhpo0aYKHDx9i1KhRuHv3LvLnz49NmzYhICAAQOIkqynnHCpSpIj2/ydPnsSSJUsQEBCAmJgYAECZMmWwbNkyDB06FD/88ANy5MiB5cuX44svvrDhqREREdGngC07RPSpsjgYAoAuXbqgS5cuBh8LDw/XWyciZvf59ddf4+uvv7amOkRERERERBazKhgiIiKi/w5bW2bYskNEnysGQ0RERB8YgxEiok+TVfMMERERERER/dcxGCIiIiIiIlViNzkiIiIT2EWNiOjzxWCIiIg+awxmiIjIGHaTIyIiIiIiVWLLEBERfdLYskNERB8KW4aIiIiIiEiVGAwREREREZEqMRgiIiIiIiJVYjBERERERESqxAQKRET0QTEBAhERfarYMkRERERERKrEYIiIiIiIiFSJ3eSIiMgkdnMjIqLPFVuGiIiIiIhIlRgMERERERGRKjEYIiIiIiIiVeKYISKizxzH/BARERnGliEiIiIiIlIltgwREX3i2LJDRET0YbBliIiIiIiIVInBEBERERERqRKDISIiIiIiUiUGQ0REREREpEpMoEBEZIKtyQuY/ICIiOjTxZYhIiIiIiJSJQZDRERERESkSgyGiIiIiIhIlRgMERERERGRKjGBAhF90pjAgIiIiD4UBkNE9EExGCEiIqJPFYMhIjKJwQwRERF9rjhmiIiIiIiIVInBEBERERERqRKDISIiIiIiUiWOGSL6hNljvA7H/BAREREZxpYhIiIiIiJSJbYMEX1AbJUhIiIi+nQxGCIygcEMERER0eeL3eSIiIiIiEiVGAwREREREZEqMRgiIiIiIiJVYjBERERERESqxGCIiIiIiIhUidnk6LPGbHBEREREZAyDIfqgbA1GGMwQERER0YfCbnJERERERKRKDIaIiIiIiEiVGAwREREREZEqccwQGWVuvA7AMTtERERE9N/FliEiIiIiIlIlBkNERERERKRKDIaIiIiIiEiVGAwREREREZEqMYHCZ4wTlhIRERERGcdg6BPGYIaIiIiI6MNhNzkiIiIiIlIlBkNERERERKRKDIaIiIiIiEiVGAwREREREZEqMRgiIiIiIiJVYjBERERERESqxGCIiIiIiIhUifMMfUCcJ4iIiIiI6NPFYMgEBjNERERERJ+vzzoYYjBDRERERETGcMwQERERERGpEoMhIiIiIiJSJQZDRERERESkSgyGiIiIiIhIlRgMERERERGRKjEYIiIiIiIiVbIqGJoxYwayZ88OV1dXFCtWDPv37ze5/d69e1GsWDG4uroiKCgIs2bN0nk8PDwcGo1Gb3n16pU11SMiIiIiIjLL4mBo+fLl6NWrF4YMGYLTp0+jfPnyqFWrFmJjYw1uf/PmTdSuXRvly5fH6dOnMXjwYPTo0QOrV6/W2c7Lywt3797VWVxdXa17VkRERERERGZYPOnq5MmT0bZtW7Rr1w4AEBYWhq1bt2LmzJkYP3683vazZs1CtmzZEBYWBgDImzcvTpw4gUmTJqFRo0ba7TQaDfz8/Kx8GkRERERERJaxqGXozZs3OHnyJEJDQ3XWh4aG4tChQwbLHD58WG/7GjVq4MSJE3j79q123T///IOAgABkzZoVdevWxenTp03W5fXr13j69KnOQkREREREpJRFwdCDBw+QkJAAX19fnfW+vr6Ij483WCY+Pt7g9u/evcODBw8AAHny5EF4eDg2bNiApUuXwtXVFWXLlsXVq1eN1mX8+PHw9vbWLv7+/pY8FSIiIiIiUjmrEihoNBqdv0VEb5257ZOvL1WqFJo3b45ChQqhfPnyWLFiBXLnzo1ff/3V6D4HDRqEJ0+eaJe4uDhrngoREREREamURWOG0qdPD0dHR71WoPv37+u1/iTx8/MzuL2TkxPSpUtnsIyDgwNKlChhsmXIxcUFLi4ullSfiIiIiIhIy6KWIWdnZxQrVgzbt2/XWb99+3aUKVPGYJnSpUvrbb9t2zYUL14cqVKlMlhGRBAVFYVMmTJZUj0iIiIiIiLFLO4m16dPH8ydOxfz58/HxYsX0bt3b8TGxqJTp04AEruvtWzZUrt9p06dcOvWLfTp0wcXL17E/PnzMW/ePPzvf//TbjNy5Ehs3boVN27cQFRUFNq2bYuoqCjtPomIiIiIiOzN4tTaTZo0wcOHDzFq1CjcvXsX+fPnx6ZNmxAQEAAAuHv3rs6cQ9mzZ8emTZvQu3dvTJ8+HZkzZ8Yvv/yik1b78ePH6NChA+Lj4+Ht7Y0iRYpg3759KFmypB2eIhERERERkT6LgyEA6NKlC7p06WLwsfDwcL11FStWxKlTp4zub8qUKZgyZYo1VSEiIiIiIrKKVdnkiIiIiIiI/usYDBERERERkSoxGCIiIiIiIlViMERERERERKrEYIiIiIiIiFSJwRAREREREakSgyEiIiIiIlIlBkNERERERKRKDIaIiIiIiEiVGAwREREREZEqMRgiIiIiIiJVYjBERERERESqxGCIiIiIiIhUicEQERERERGpEoMhIiIiIiJSJQZDRERERESkSgyGiIiIiIhIlRgMERERERGRKjEYIiIiIiIiVWIwREREREREqsRgiIiIiIiIVInBEBERERERqRKDISIiIiIiUiUGQ0REREREpEoMhoiIiIiISJUYDBERERERkSoxGCIiIiIiIlViMERERERERKrEYIiIiIiIiFSJwRAREREREakSgyEiIiIiIlIlBkNERERERKRKDIaIiIiIiEiVGAwREREREZEqMRgiIiIiIiJVYjBERERERESqxGCIiIiIiIhUicEQERERERGpEoMhIiIiIiJSJQZDRERERESkSgyGiIiIiIhIlRgMERERERGRKjEYIiIiIiIiVWIwREREREREqsRgiIiIiIiIVInBEBERERERqRKDISIiIiIiUiUGQ0REREREpEoMhoiIiIiISJUYDBERERERkSoxGCIiIiIiIlViMERERERERKrEYIiIiIiIiFSJwRAREREREakSgyEiIiIiIlIlBkNERERERKRKDIaIiIiIiEiVGAwREREREZEqMRgiIiIiIiJVYjBERERERESqxGCIiIiIiIhUicEQERERERGpEoMhIiIiIiJSJQZDRERERESkSgyGiIiIiIhIlRgMERERERGRKjEYIiIiIiIiVWIwREREREREqsRgiIiIiIiIVInBEBERERERqRKDISIiIiIiUiUGQ0REREREpEoMhoiIiIiISJUYDBERERERkSpZFQzNmDED2bNnh6urK4oVK4b9+/eb3H7v3r0oVqwYXF1dERQUhFmzZults3r1aoSEhMDFxQUhISFYu3atNVUjIiIiIiJSxOJgaPny5ejVqxeGDBmC06dPo3z58qhVqxZiY2MNbn/z5k3Url0b5cuXx+nTpzF48GD06NEDq1ev1m5z+PBhNGnSBC1atEB0dDRatGiBxo0b4+jRo9Y/MyIiIiIiIhMsDoYmT56Mtm3bol27dsibNy/CwsLg7++PmTNnGtx+1qxZyJYtG8LCwpA3b160a9cObdq0waRJk7TbhIWFoXr16hg0aBDy5MmDQYMGoWrVqggLC7P6iREREREREZliUTD05s0bnDx5EqGhoTrrQ0NDcejQIYNlDh8+rLd9jRo1cOLECbx9+9bkNsb2SUREREREZCsnSzZ+8OABEhIS4Ovrq7Pe19cX8fHxBsvEx8cb3P7du3d48OABMmXKZHQbY/sEgNevX+P169fav588eQIAePr0qXbd+9cvTD6f5NsawvKmy38KdWB50+U/hTqwPN9DtZf/FOrA8nwPP/fyn0IdWP7Teg+T/i8ipg8qFrhz544AkEOHDumsHzNmjAQHBxsskytXLhk3bpzOugMHDggAuXv3roiIpEqVSpYsWaKzzaJFi8TFxcVoXYYPHy4AuHDhwoULFy5cuHDhwsXgEhcXZzK+sahlKH369HB0dNRrsbl//75ey04SPz8/g9s7OTkhXbp0Jrcxtk8AGDRoEPr06aP9+/379/j777+RLl06aDQave2fPn0Kf39/xMXFwcvLy/QTNUDt5T+FOrA830O1l/8U6sDyfA/VXv5TqAPL8z38r5f/GHUQETx79gyZM2c2uR+LgiFnZ2cUK1YM27dvR8OGDbXrt2/fjvr16xssU7p0afz+++8667Zt24bixYsjVapU2m22b9+O3r1762xTpkwZo3VxcXGBi4uLzjofHx+zz8HLy8vqN43lP406sDzfQ7WX/xTqwPJ8D9Ve/lOoA8vzPfyvl//QdfD29jZb3qJgCAD69OmDFi1aoHjx4ihdujTmzJmD2NhYdOrUCUBii82dO3cQGRkJAOjUqROmTZuGPn36oH379jh8+DDmzZuHpUuXavfZs2dPVKhQARMnTkT9+vWxfv167NixAwcOHLC0ekRERERERIpYHAw1adIEDx8+xKhRo3D37l3kz58fmzZtQkBAAADg7t27OnMOZc+eHZs2bULv3r0xffp0ZM6cGb/88gsaNWqk3aZMmTJYtmwZhg4dih9++AE5cuTA8uXL8cUXX9jhKRIREREREemzOBgCgC5duqBLly4GHwsPD9dbV7FiRZw6dcrkPr/++mt8/fXX1lRHERcXFwwfPlyvax3L/3fqwPJ8D9Ve/lOoA8vzPVR7+U+hDizP9/C/Xv5TqQMAaETM5ZsjIiIiIiL6/Fg06SoREREREdHngsEQERERERGpEoMhIiIiIiJSJQZDRERERESkSlZlkyMiZY4dO4Y9e/bg/v37eP/+vc5jkydPNln23bt3WLx4MWrUqAE/P78PWU36yB4/fqxokmj69CQkJODs2bMICAhAmjRp/u3qENF/xMuXLyEicHd3BwDcunULa9euRUhICEJDQz/48U+dOoVUqVKhQIECAID169djwYIFCAkJwYgRI+Ds7PzB6/Cp+myzyfFC8tN5DZ4/f469e/ciNjYWb9680XmsR48eJsv+l7+848aNw9ChQxEcHAxfX19oNBrtYxqNBrt27TK7D3d3d1y8eFE7j9fH9vbtW3To0AE//PADgoKC/pU6WOPp06eKt7V15mxzJk6ciMDAQDRp0gQA0LhxY6xevRp+fn7YtGkTChUqZLL8+PHj4evrizZt2uisnz9/Pv766y8MGDDAZPlP5TwAAG/evMHNmzeRI0cOODkpvxcXERGB9OnTo06dOgCA/v37Y86cOQgJCcHSpUs/6PejV69eKFCgANq2bYuEhARUrFgRhw4dgru7OzZu3IhKlSp9sGMn+RTPg0+fPsWuXbsQHByMvHnzfvTjf2z2eA/27t2LSZMm4eLFi9BoNMibNy/69euH8uXLf+jq4+3btwgODsbGjRsREhJicfl/6zO4YcMGxdvWq1fvg9TBXkJDQ/HVV1+hU6dOePz4MfLkyYNUqVLhwYMHmDx5Mjp37vxBj1+iRAkMHDgQjRo1wo0bN5AvXz40bNgQx48fR506dRAWFvZBjw8k3kw6cOAAChYsqPhm0i+//KJ4/+auKY35bIMhwLoLyU/pIgoA4uLioNFokDVrVgCJLQ1LlixBSEgIOnToYLa8Na/BmTNnFG9bsGBBk4+fPn0atWvXxosXL/D8+XOkTZsWDx48gLu7OzJmzIgbN26YLG/Nl9eeJ09b7uT4+vpi4sSJaNWqleL6pFS5cmX06tUL9evXt3of2bNnR/PmzdGsWTPkyZPH4vI+Pj44derUfyoYcnBw0Ak+DRERaDQaJCQkmN2fLd/DoKAgLFq0CGXKlMH27dvRuHFjLF++HCtWrEBsbCy2bdtmsnxgYCCWLFmCMmXK6Kw/evQomjZtips3b5qtvz2C6itXrhht5Rw2bJjJsi9evED37t0RERGh3VdQUBB69OiBzJkzY+DAgSbLBwcHY+bMmahSpQoOHz6MqlWrIiwsDBs3boSTkxPWrFljtv6tW7dG8+bNUaVKFbOfjeSyZs2KdevWoXjx4li3bh26du2K3bt3IzIyErt378bBgwcV78ta9riIsfW3pHHjxqhQoQK6deuGly9folChQoiJiYGIYNmyZToTqZvy+PFjHDt2zODnqGXLlor2YS+WtPLZ+h4sWrQIrVu3xldffYWyZctCRHDo0CGsXbsW4eHh+O6778zWd9SoUfjf//6n/T1K8vLlS/z0009mv4dZsmTBjh07rApe7XkhvXPnTuzcudPgZ2D+/Pk6fzs4KBvN8THO5QDQpk0bTJ06FZ6enjrrnz9/ju7du+vVP7n06dNj7969yJcvH+bOnYtff/0Vp0+fxurVqzFs2DBcvHjRYLkiRYooPmeZmtPT29sbp06dQo4cOTBx4kTs2rULW7duxcGDB9G0aVPExcUZLPfVV18pOjYARediV1dXXLx4EdmzZ1e0T6XbaTQas9eURst+zsGQNReSSi6ikhj64tn7Q1O+fHl06NABLVq0QHx8PIKDg5EvXz5cuXIFPXr0MHvys+U1MPbRSHpMycmnUqVKyJ07N2bOnAkfHx9ER0cjVapUaN68OXr27Gn29bLmy2vPk6ctd3IyZcqEffv2IVeuXIrqY8jKlSsxcOBA9O7dG8WKFUPq1Kl1HjcXjAKJ3fGWLl2KkydPokiRImjRogWaNGmCTJkyKapD69atUaBAAfTp08eq52BJOUNdBxs2bKj4O5n0ndq7d6/iY1asWNHsNrZ8D93c3HDlyhX4+/ujZ8+eePXqFWbPno0rV67giy++wKNHj0we29gPx40bNxASEoJXr16Zrb+tQfVvv/2Gzp07I3369PDz89Nr5TQ3qXbPnj1x8OBBhIWFoWbNmjhz5gyCgoKwYcMGDB8+HKdPnzZZ3t3dHZcuXUK2bNkwYMAA3L17F5GRkTh//jwqVaqEv/76y+xzqFevHrZt24Z06dKhadOmaNGiBQoXLmy2nKurK65du4asWbOiQ4cOcHd3R1hYGG7evIlChQopvoH2/PlzTJgwwehFoKkfcWsvYpKz9bfEz88PW7duRaFChbBkyRIMHz4c0dHRiIiIwJw5c8y+hwDw+++/o1mzZnj+/Dk8PT31Pkd///23yfK2nktsaeWz9T3ImzcvOnTogN69e+vV87fffjN6IZyco6Mj7t69i4wZM+qsf/jwITJmzGj292zChAm4dOkS5s6da1HLLGCfzyAAjBw5EqNGjULx4sWRKVMmvXP72rVrLaqXpWz9Hhh7Dx48eAA/Pz+8e/fOaNnk57HGjRsjX758GD58OOLi4hAcHIwXL14YLDdy5EjFz2/48OFGH/Py8sLJkyeRK1cuVK9eHXXr1kXPnj0RGxuL4OBgvHz50mC51q1ba/8vIli7di28vb1RvHhxAMDJkyfx+PFjfPXVV1iwYIHZOpYoUQITJkxA1apVFT+vD+2zHjPUpUsX9OnTB3FxcYovJHfv3q39f0xMDAYOHIhWrVqhdOnSAIDDhw8jIiIC48ePN3hMb29v7f/NfWiUOHfuHEqWLAkAWLFiBfLnz4+DBw9i27Zt6NSpk9kvrjWvgZI7zUpFRUVh9uzZcHR0hKOjI16/fo2goCD8+OOP+P77782+DiKivWjYsWMH6tatCwDw9/fHgwcPDJZJeZFhi1OnTmHKlCkAgFWrVsHX11fnTo6pYKh3796YPn26TU3PSV2rkjf9WhKMAokXEH369MGVK1ewePFizJw5E/369UPlypXRvHlzs3djc+bMidGjR+PQoUMGP0PmmqVPnz6NU6dO4d27dwgODgaQ2DLg6OiIokWL6jwvQ7y9vQ1+j548eYIGDRoYLKckwLGELd/DNGnSIC4uDv7+/tiyZQvGjBkDIPGzreT98/f3x8GDB/WCoYMHDyJz5syK6m/NeSC5MWPGYOzYsWa75Bmzbt06LF++HKVKldJ5v0JCQnD9+nWz5T08PPDw4UNky5YN27Zt015Qurq6Gv0BT2nDhg14/PgxVqxYgSVLliAsLAzBwcFo3rw5vvvuOwQGBhos5+vriwsXLiBTpkzYsmULZsyYASCxtcvR0VHRsQGgXbt22Lt3L1q0aGHwItAUa86DKdn6W/LkyROkTZsWALBlyxY0atQI7u7uqFOnDvr166eoDn379kWbNm0wbtw4vdYNJWw9l6xatQrNmzcHkBiY3bx5E5cuXUJkZCSGDBlispXP1vfgxo0b+PLLL/XW16tXD4MHDzZbPqkOhp5bdHS09r0x5ejRo9i5cye2bduGAgUK6J0HTN2gtcdnEABmzZqF8PBwtGjRQnEZe7L2e/D06VOICEQEz549g6urq/axhIQEbNq0SS9ASilnzpxYt24dGjZsiK1bt2rPY/fv3zfZ08hUgGOJ4sWLY8yYMahWrRr27t2LmTNnAki85vP19TVaLnmAM2DAADRu3BizZs3Snv8SEhLQpUsXxb2lxo4di//9738YPXq0wd+jj9HrSo98xjQajd7i4OCg/decKlWqyJIlS/TWL168WCpWrGi2fP/+/aVdu3by7t077bp3795Jhw4d5H//+5+i55A6dWq5efOmiIh8+eWXMmHCBBERuXXrlri6upotb+trYKv06dPL5cuXRUQkd+7csmXLFhERuXjxori5uZktX7lyZWnZsqVERkZKqlSp5OrVqyIismfPHgkICPhg9U7i5uYmt27dEhGRb775RkaMGCEiIrGxsWbrn5CQIDVr1pSgoCCpW7euNGzYUGdRIiYmxuRircOHD0vhwoUVfQYCAwONLtmzZzdb/ueff5Yvv/xS/v77b+26v//+W+rXry+TJk0yW97W79HmzZtl//792r+nTZsmhQoVkm+//VanTqbY8j3s2rWrBAQESLVq1SRdunTy7NkzERFZtmyZFClSxOyxJ0yYIOnSpZP58+dr3/d58+ZJunTpZNy4cYrqb+t5wNPTU65fv67oWIa4ublpy3t4eGj/HxUVJV5eXmbLf/fdd1K0aFFp27atuLu7y4MHD0REZP369ZIvXz6r6hQXFyc//vij5MmTRxwdHY1uN3z4cPH29pY8efJItmzZ5NWrVyIiMm/ePClVqpTi43l7e8uBAwesqqs9zoO2/pbkypVLli9fLv/8849kyJBBdu7cKSKJ72G6dOkU1cHd3d2mz5Gt5xIXFxeJi4sTEZH27dtLz549RUTkxo0b4unpabKsre9Bjhw5ZNasWXrrZ82aJTlz5jRZ1sfHR9KkSSMODg7a/yctXl5e4uDgIF26dDFbh1atWplcTLHXb3HatGnl2rVrirefOnWqvHz5Uvt/U4sS1n4Pks6VxhZHR0cZM2aMyWOvXLlSUqVKJQ4ODlK9enXt+nHjxknNmjUV1V9E5NGjR/Lbb7/JwIED5eHDhyIicvLkSbl9+7bJctHR0ZI/f37x8vLSXsuIiHTr1k2+/fZbRcdOnz69XLp0SW/9pUuXJG3atIr2kfJ3KGlR+nsUFxcn06dPlwEDBkjv3r11Fmt91sGQrReSbm5ucuXKFb31ly9fVnQhb48PTcmSJWXAgAGyb98+cXV1laioKBFJvJjNkiWL2fLWvAbr16+XN2/eaP9vajGnevXqsnjxYhER6dixo5QsWVIWLVokNWrUkJIlS5otb82X154nzwIFCsjUqVMlNjZWvLy85NChQyIicuLECfH19TVZtkuXLuLi4iI1a9aU77//3qIfng/l6NGj0rNnT/Hz8xM3Nzdp3LjxBz9m5syZ5dy5c3rrz549K5kyZTJb3tbvUf78+eWPP/4QEZEzZ86Is7OzDBo0SL744gvF74Mt38M3b97ITz/9JD169JBTp05p10+ZMkV+++03s8d+//699O/fX1xdXbU/Gu7u7jJy5EhFdRex/VzYpk0bmTlzpuLjpVShQgX55ZdfRCQxGLpx44aIJAaKNWrUMFv+0aNH0rVrV6lXr55s3rxZu37YsGFmL0AMefPmjaxdu1YaNWokrq6ukjlzZpPbr1y5UiZPnqy9kBYRCQ8Pl3Xr1ik+ZmBgoFy4cMHiuorY5yLG1t+S6dOni5OTk/j4+EihQoUkISFBRER++eUXqVSpkqI6NGzYUJYvX65oW0NsPZdky5ZNtm7dKu/evRN/f3/5/fffRUTk3Llz4uPjY7Ksre/BjBkzxNnZWTp16iSRkZGycOFC6dixo7i4uBgMkpILDw+XBQsWiEajkalTp0p4eLh2WbJkifZ36UOyx2dQJPHm1qhRoxRvHxgYqL35YeuNORHrvwd79uyR3bt3i0ajkTVr1siePXu0y6FDh+TOnTuKjn/37l05deqU9vsjkvi7fPHiRUXlo6OjJUOGDJIzZ05xcnLS3lwYOnSotGjRQtE+Unr58qX2ms8cHx8fWbt2rd76tWvXmv0OJUn+2hlaTNmxY4e4u7tLvnz5xMnJSQoXLiw+Pj7i7e0tlStXVnR8Qz7rYMhWuXPnlj59+uit79Onj+TOndtseXt8aHbv3i0+Pj7i4OAgrVu31q4fNGiQ4tYFS2k0Grl37572/8YWJRH88ePHZdeuXSIicv/+falVq5Z4enpKkSJFtCcha5j68trz5GnLnRwPDw/ZuHGjBc/KsMjISClTpoxkypRJe+E6ZcoUxRdily9flmHDhmlPntWrV5fw8HB5+vSpRfV4/fq1XLp0Sd6+fWtROQ8PD+1d5OR27twpHh4eZsvb+j1Kfidw+PDh0qhRIxFJvJNmLqBN8m98D1N69uyZHDt2TM6ePattnfhYxo0bJ+nTp5fvv/9eJk2aZPFNhYMHD4qnp6d06tRJXF1dpWfPnlKtWjVJnTq1nDhx4iM8g0S7du2Sdu3aae+ot2rVSrZv365zYfKhLFy4UL7++mt5/vy53fZpyUWMPT7Dx48flzVr1mhbN0VENm7caLLFK/nNs7lz50q2bNlk+PDhsmrVKotvrtl6LrFXK19ylrwHa9askbJly0ratGklbdq0UrZsWYsC6j179ig+liGVK1eWR48e6a1/8uSJ1ReSljx/EZEePXqIj4+PVKhQQbp162bVnf379+9rf+MtZev3ICYmRt6/f2/VsRcsWCAvXrywqmySqlWrSr9+/UREt5X94MGDH6W3TO/evSVNmjTy008/yf79+2X//v3y008/Sbp06RS9f+/fv5crV67I+fPnLb6WEBEpUaKE/PDDDyLyf8//2bNnUq9ePZkxY4bF+0vyWSdQAICFCxdi1qxZuHnzJg4fPoyAgACEhYUhe/bsZgcTb9q0CY0aNUKOHDlQqlQpAMCRI0dw/fp1rF69GrVr1zZZvk+fPggPD8fgwYN1yk+YMAEtW7Y0O89MkoSEBDx9+lQn201MTIw2I5spkZGRJh//2Nl7rPXmzRuDg46zZcv2wY8dHx+Pu3fvolChQtrkDMeOHYOXl5fJ7GwBAQHYunWrVRncksycORPDhg1Dr169MHbsWJw7dw5BQUEIDw9HRESEzhg3YxwcHFC8eHF89913aNq0qcXplW3NBNayZUvs3bsXP//8s873oF+/fqhQoYJ2v8bY+j1KmzYtDhw4gJCQEJQrVw4tW7ZEhw4dEBMTg5CQEKODVlOy5Xt4+fJl/Prrr9qUunny5EH37t214x4+BlvqYCqbj9IMPmfPnsWkSZNw8uRJvH//HkWLFsWAAQO0qXrNefXqFc6cOaN3HtBoNAbHYqSUNWtWPHz4EDVq1ECzZs3w5Zdf6vT7N8WS7FfJpcwCde3aNYgIAgMDkSpVKp1tzSWhAIATJ07ovH9JY+iUsuUzbC17JrSx9VwCJI4biouLwzfffKPNKBYREQEfHx9FCUaseQ/evXuHsWPHok2bNvD39ze7/Yfi4OCA+Ph4vff6/v37yJIlC96+fWt2HydPntRJDZ58rJYSlStXNvqYqSknHj9+jCFDhmD58uXapDNp0qRB06ZNMXbsWJ3x2ubY8j3YsmULPDw8UK5cOQDA9OnT8dtvvyEkJATTp083mZUwU6ZMeP78Ob755hu0bdtWL0OoEskTWXh6eiI6OhpBQUG4desWgoOD9RLqpEmTRvH4RHMJTIDEMdmTJk3C1KlTcffuXe3z6tmzJ/r27WtyHGVMTAzq16+Pc+fOAUgcb7ZmzRqLPkOenp6IiopCjhw5kCZNGhw4cAD58uVDdHQ06tevj5iYGMX70mF1GPUfMGPGDEmfPr2MGTNGp8/6ggULFDfrx8XFae8YNGjQQAYPHiyxsbGKyiYkJMjEiRMlc+bM2taUzJkzy8SJE3XGP3xIPj4+Okvq1KlFo9GIi4uLpEmTxmz5iIgIg3ehX79+LRERER+iyjouX74s5cqV0+uf+7HGPNli/vz50rhxY5vuBOfNm1fbKpL8LtDZs2cV99NPGrNlrR49ekixYsVk//79kjp1am0d1q9fL4ULFzZb/vnz59K5c2dxcXHRvn/Ozs7SuXNn+eeff8yWt/V79OWXX0qNGjVk1KhRkipVKm2/6q1bt0quXLnMlk/y9u1b2b59u8yaNUvbqnbnzh2du+SGrFy5UpycnKRUqVLau5+lS5cWJycnWbFihcEyDRs2lCdPnmj/b2pRwpo62FN0dLTRxwy1+qW0efNmSZ8+vdUt1CIis2fPVjxGLLkRI0aIg4ODlCxZUurXry8NGjTQWcyVVbqYEhcXJ+XKlRONRqMdK6LRaKRs2bKKf48WLlxo9DElY+/evXsnc+fOlW+//VaqVq0qlStX1lk+BlvPJaZeq8OHD5ssa+t7kLyF2lKmxqokX4yJjo6W6Oho0Wg0snv3bu3f0dHRcurUKRk3bpzZVoV79+5JpUqVtM/fx8dHNBqNVKlSRe7fv2/V81Lq4cOHkjt3bkmdOrV06NBBpkyZIpMnT5b27dtL6tSpJU+ePBZ9t609l4vY1u363bt3sn79emnYsKE4OztLcHCwTJgwQe7evau47hkzZtR2t05+TbB161bJmjWr3vbJu1SaWyz15MkT7e+UEo0bN5bcuXPL4sWLZfXq1VKqVCkpUaKERcf09fWV8+fPi4hISEiItkU5KipKUqdObdG+kvusW4ZCQkIwbtw4NGjQQCeCPnfuHCpVqmRRBhRbJaVftTRLhrH88hqNBq6ursiZMydatWpl8m5LSlevXkXnzp3Rr18/1KhRw+S21qTytFdOfAAoW7YsnJycMHDgQIMZmMxNWAkAt2/fxoYNGwxO+mqoVcFe6dGLFCmC69ev23Qn2M3NDZcuXUJAQIDOZ/jq1asoWLCg4kxaSV69eoXly5fj+fPnqF69uqK03wEBAdpMYMnrcO3aNRQtWtSi1MJJr0fOnDn1MsgoYc33KDY2Fl26dEFcXBx69OiBtm3bAkjM9peQkKBoQrdbt26hZs2aiI2NxevXr7WtY7169cKrV68wa9Yso2WDgoLQvHlzjBo1Smf98OHDsXDhQoOtKq1bt8Yvv/wCT09PnbSmhihJZWpNHewpU6ZMOHjwoN5cVatXr0bLli3x/Plzk+Vz5syJGjVqYNiwYSazHlni1q1beP78OfLkyWOy9SJTpkz48ccf/7XsV0Biiv+nT58iIiJC25J3+fJltGnTBqlTpzY7VxWQOF/YokWLtFnAkvTu3RvLli3T3uU1plu3bggPD0edOnUMnouTsm6aEhkZiSZNmsDFxUVn/Zs3b7Bs2TLFPRWsPZfkyZMHBw8eRLp06XTWHzx4EHXq1MHjx4+NlrX1PWjQoAEaNGhg1bxzDg4OCAgIwPfff48iRYoY3c5Yy1byKUMMXfK5ubnh119/1ZvYObkmTZrg+vXrWLhwoXaeogsXLuD7779Hzpw5sXTpUkuekkV69eqFnTt3YseOHXrf//j4eISGhqJq1aqKPoO2nMuBxMyW586dQ2BgIEaMGIFz585h1apVOHXqFGrXro34+HhFz+n+/ftYtGgRwsPDcenSJdSsWRNt27bFl19+afJ81KFDB/z1119YsWIF0qZNizNnzsDR0RENGjRAhQoVPsrEqdbKnDkzli5dqs32evv2bQQEBOCff/6Bm5ubon00aNAAderUQfv27dG/f3+sXbsWrVq1wpo1a5AmTRrs2LHDqrp91qm1b968afDE4eLiYvbHN8njx48xb948bbNwSEgI2rRpY1GTLGB9qsCaNWti5syZKFCgAEqWLAkRwYkTJ3DmzBm0atUKFy5cQLVq1bBmzRrFc4jkypULEyZMQPPmzXHp0iWT24qRVJ63b982+ho0aNBA+/9Xr15hxowZCAkJ0aYnP3LkCM6fP48uXbqYrWtUVBROnjxpdVeznTt3ol69esiePTsuX76M/PnzaycKNNY0a+l7a0zy18Fa2bNnR1RUlN5kmZs3bzY7i3i/fv3w5s0bTJ06FUDiBUfp0qVx/vx5uLu7o3///ti+fbv2fTHmr7/+Mth14Pnz5xalB06dOrVeCuf79+8b7ZZg6LHk36N3797h1KlT2jSpxmTLlg0bN27UW6/khzNJz549Ubx4cURHR+tcSDVs2BDt2rUzWTY+Pt7gRV7z5s3x008/GSyTFOCICEaMGIEMGTJYlYrYljr06dMHo0ePRurUqc3O72Kuq2Lnzp1RtWpVHDp0SDu/1fLly9GmTRuEh4ebrf/9+/fRp08fqwKhiIgIPHr0CL169dKu69ChA+bNmwcgcULXrVu3Gu2+9ObNG6u6s6R0/PhxvH//Hl988YXO+qNHj8LR0dFkd6v9+/fj0KFDOl0ag4OD8euvv6Js2bKKjr9s2TI0bdoUGzZsQIUKFQAA3bt3x5o1axR1t122bBlWrFhhtnu4Ka1bt0bNmjX1vtfPnj1D69atFQdDlp5LkpQvXx6hoaHYs2ePdtLMffv2oW7dumbncrH1PahVqxYGDRqEc+fOGUwnbGoC8KNHj2L+/PmYOnUqsmfPjjZt2qBZs2ZmJ4pNcvPmTYgIgoKCcOzYMWTIkEH7mLOzMzJmzGg2TfyWLVv0JmxN6hpmbgLy5CpXrmzyd8NQN7l169Zh9uzZBr//fn5++PHHH9GpUydF53RbzuVA4uuV1LV6x44d2s9s2rRpFd8YBICMGTOibNmyuHz5Mq5cuYKzZ8+iVatW8PHxwYIFC4zOeTVp0iTUrl0bGTNmxMuXL1GxYkXEx8ejdOnSGDt2rMljWjtPlb1ucMfHx+tcy2XNmhVubm64d++e0akNUpo8eTL++ecfAMCIESPwzz//YPny5ciZM6dFv+kpfdbBkC0XkkBi3+AaNWrAzc1NG4hMnjwZY8eOxbZt2wxeTNuzVQRInMirb9+++OGHH3TWjxkzBrdu3cK2bdswfPhwjB492qIJFR0dHfHnn38afTzpeWg0GlStWlVngraEhATcvHkTNWvWNFg2eU78du3aoUePHhg9erTeNkomaQsJCbGpBW/QoEHo27cvRo0aBU9PT6xevRoZM2ZEs2bNjNZfyZ12JewxN0C/fv3QtWtXvHr1CiKCY8eOYenSpRg/fjzmzp1rsuzmzZsxbtw47d+LFy/GrVu3cPXqVWTLlg1t2rTBmDFj8Mcff5jcT4kSJfDHH3+ge/fuAP5vDo/ffvvNZCDl7u6OW7duaX94a9asiQULFmgvhu/du4fMmTMbPQFnypRJ58SdN29ebN26VTtO7OHDhyhdurTB8pb8KCm5UXHgwAEcPHgQzs7OOusDAgJw584dk2UrVaqE/fv3I2fOnHr7LF++vMmyIoJcuXLh/PnzNk3ea00dTp8+jUuXLqFIkSImJ9RUcr4bNmwYHj58iGrVqmH//v3YsmUL2rVrh4ULF6JRo0Zmy3/99dfYs2cPcuTIYXbblGbNmqUzs/yWLVuwYMECREZGIm/evOjWrRtGjhxp9PvUrl07LFmyRO8cbKmuXbuif//+esHQnTt3MHHiRBw9etRo2WzZshkcz/Hu3TtkyZJF0fFr1qyJWbNmoUGDBti2bRvmz5+P9evXY/fu3cidO7fZ8s7OznqfH0tZc3MNsP1ckmTOnDn45ptvUKdOHWzbtg2HDx9GvXr1MGbMGPTs2dNkWVvfg6Q56QzdODA3XqpEiRIoUaIEpkyZglWrVmHBggUYMGAAvvzyS7Rt2xbVq1c3eeykayBb5uB7//69Xu8GAEiVKpVF+0050fHbt28RFRWFc+fO4fvvvzdY5u7du8iXL5/RfebPn19xi4wt53IAKFeuHPr06YOyZcvi2LFjWL58OYDEsbRJY9BMuXfvHhYuXIgFCxbgxo0baNCgATZu3Ihq1arh5cuXGDp0KL7//nvcunXLYHkvLy8cOHAAu3btwqlTp7TjL6tVq2b22MY6gr1+/Vrv9UjOHjd2gcTPecpWLwcHB6P1MiR57wJ3d3ftvG+2+qyDIVsuJIHE7gP16tXDb7/9pg0G3r17h3bt2qFXr17Yt2+fXhl7fWiSrFixAidPntRb37RpUxQrVgy//fYbvv32W6N3Zjds2KDzt4jg7t27mDZtmsm7WQ0aNICIICoqCjVq1ICHh4f2MWdnZwQGBiq6iFm5ciVOnDiht7558+YoXry4ycHHADBx4kT0798f48aNQ4ECBfROxuYuZC9evKhtvndycsLLly/h4eGBUaNGoX79+iYnTbWVLXeCk7Ru3Rrv3r1D//798eLFC3z33XfIkiULpk6diqZNm5osGxsbqxP0b9u2DV9//bX2h7Fnz56K7vKOHz8eNWvWxIULF/Du3TtMnToV58+fx+HDh7F3716j5ZK+d0kOHjyo163P1Ekw5WO3b9/Wm93bWHkfHx+zF+lJF2ZKJj59//69we1u376tvcNsTL169TBgwACcPHlSZ9D3ypUrMXLkSJ3vaMq7ww4ODsiVKxcePnxocTCUcr+m6mDI7t27tXcSk1oOmjRpgl9++cWqFpqpU6eiRYsWKFWqFO7cuYOlS5cqvoEzbdo0fPPNN9i/f7/B84CpiX+vXLmi811bv3496tWrh2bNmgEAxo0bZ7Ir4qtXrzBnzhzs2LEDBQsW1Du20kQ4Fy5cMHoD7cKFCybL/vjjj+jevTumT5+OYsWKQaPR4MSJE+jZsycmTZqk6PhA4u/Go0ePUK5cOWTIkAF79+5VHOD07dsXU6dOxbRp0yxqEQZsu7kG2H4uSaLRaLB06VLUqVMHVatWxZkzZzB+/Hh069bNbFlb3wN7TAbu6uqK5s2bo3nz5rh58ybatm2LmjVr4q+//lI06WpERATSp0+POnXqAAD69++POXPmICQkBEuXLtW7cZxclSpV0LNnTyxdulQ72fOdO3fQu3dvVK1aVfFzMHb3PukuvyHp06dHTEyM0WDj5s2bel0fjbHlXA4knou6dOmCVatWYebMmdpAePPmzSY/wwDw5ZdfYuvWrcidOzfat2+Pli1b6rxvbm5u6Nu3r6IWjipVqqBMmTJwcXEx+31M6gqu0Wgwd+5cneu5hIQE7Nu3z2TvG3tN+ioiyJ07t059//nnHxQpUkQnSFKSyCF5Io+QkBCT3UeV+KzHDAGJd6/HjBmjbYXIkiULRowYoR03YIqbmxtOnz6t9yG5cOECihcvrjgLlS18fX3x008/6XUfiIyMRL9+/XDv3j1cuHABFSpUMNiCkjIK12g0yJAhA6pUqYKff/5Ze2fNGGN9vJXy8/PD+PHj9S42FixYgIEDB+LevXsmyyfVP+WXXemFrJ+fH3bt2oWQkBDky5cP48ePR7169RAdHY2yZcsaPfkmt2rVKqxYscLgmCNTrXslS5ZE//798fXXX+usX7Nmjdk7wYY8ePAA79+/V5z1ycfHB8ePH9deRGfPnh0//PCDtl94TEwM8ubNq2jckTWZwFJmLko+3ggwfzfXlvKmgrSUkvovm9KkSRN4e3tjzpw58PT0xJkzZ5AhQwbUr18f2bJlM9maaGs2rT/++AMTJkzAzJkzkT9/fkX7ssdxk/aR/D3w8vJCVFSU3tgfQ1LeiAES7wL37t0boaGhOoGfqS5CADB37lx06tQJbm5uSJcunc75wFw2O3d3d1y8eFF7oVeoUCG0adNG2xIQGxuL4OBgo98Da7NfpZQuXTps3LhRrzX10KFDqFOnjjZDliFp0qTBixcv8O7dO50bc05OTnrdrZJfSBjr3rhq1SoUKVJEp6VNyfjJXbt2IW3atMiXL59eUGhq/GRSwD1y5Ej07dvX6M01Y3enbTkXnDlzRm/ds2fP8O2336JOnTo6N8RSdr1Lztr3wJBXr14pzmSY0u3btxEeHo7w8HC8fPkSLVq0wJgxY3QCTGOCg4Mxc+ZMVKlSBYcPH0bVqlURFhaGjRs3wsnJyeR7GBcXp80E5u/vD41Gg9jYWBQoUADr169X1CpiyrVr11CyZEmDr13btm1x7do1bN++Xe8z8vr1a9SoUQM5cuTQdn01xZZzua3atm2Ldu3amexRISKIjY01Gpi+f/8eY8eOxaxZs3Dv3j3tmKcffvgBgYGBBq9tk7KB3rp1C1mzZtXpEpn0/Rs1apTejVtTrAlGlGR7BGC0hRBI7A7btGlT7NmzBz4+PhARPHnyBJUrV8ayZct0uoBa4rNuGQKA9u3bo3379hZfSAKJP/yxsbF6wVBcXJyiOwhJbEkL3b17d3Tq1AknT55EiRIloNFocOzYMcydOxeDBw8GAGzdutXoB9Hau1HJB1wmD2S8vLwQHByM/v37K0o00KtXL3Tu3FnvjvT8+fMxbNgws+WV9GU3pVSpUjh48CBCQkJQp04d9O3bF2fPnsWaNWu09THll19+wZAhQ/D9999j/fr1aN26Na5fv47jx4+ja9euJsvacifYkPTp01u0fZ48efD777+jT58+OH/+PGJjY3Uu7G7duqX4Dn+BAgUUn8g+BUoCHEtMmTIFlStXRkhICF69eoXvvvsOV69eRfr06c0OHLb1jnDz5s3x4sULFCpUCM7OznoDTY1deNnjTnRKltw7M9VKPn/+fG2rsJKbGkOHDsWoUaMwcOBAxUFekoCAAJw8eRIBAQF48OABzp8/r02LCyT2YzfVRcvWc1CS6tWrY9CgQVi/fr32eI8fP8bgwYPNdnOydlC0se6NOXLkwNOnT7WPG7uznPJ1adiwoVX1SLqzHBgYiCZNmlgdCFijcOHC0Gg0Op/dpL9nz56NOXPmKLq5ZuvA9ISEBIwbN86ii9gkb968wdq1azFv3jzs378ftWrVQlhYGGrXrm3R9yEuLk7bErhu3Tp8/fXX6NChA8qWLWt0jEoSf39/nDp1Ctu3b8elS5cgIggJCVHUPUuJw4cPG/1cjBw5EsWLF0euXLnQtWtX7TXZhQsXMGPGDLx+/RoLFy5UdBxrz+UrVqxAgwYNtMFYTEwM/P39tYHFixcvMG3aNPTv39/oPpQEaxqNxmQL3ZgxYxAREYEff/wR7du3164vUKAApkyZYvBzdPPmTQCJN3aSEg1Yy5ZgxFSQo1T37t3x9OlTnD9/Xi+RR48ePaxP5GF1Hrr/CFtSKHbv3l2yZs0qy5Ytk9jYWImLi5OlS5dK1qxZpWfPnmbL2yst9KJFi6RUqVLadJ6lSpWSxYsXax9/8eKFvHz5UtG+3r17J6dPnzabhnLt2rWybt06vSU8PFy6dOkibm5uilPyLl++XMqUKaOtf5kyZWyahdwS169f16b1TUrLWqBAAWnYsKF2AlNTgoODZcmSJSKim8byhx9+kK5du5osmzZtWoMzgx88eFDxpLvx8fHSvHlzyZQpkzg6OipOpSoismrVKkmVKpVUqVJFfH19pW7dujqP9+/fX7755htF9Xj37p2sXLlSRo0aJaNHj5ZVq1aZnTDNwcFBJ+Wqp6en3LhxQ+e5mXoODg4Ocu3aNXny5Ik8fvxYPD09JTo6WpvO88qVK4q/R48ePZKtW7fKwoULJSIiQmdR6sWLFzJ//nzp2rWrdO7cWX777TebJ9BTwt7pUC2R8j308PDQeQ8/ljRp0si1a9esKjtu3Djx8/OTUaNGSaVKlSRfvnw6j0+ZMkWqVq1qj2qadPv2bQkKChJvb2+pVKmSVKpUSXx8fCQ4OFhxemy1suVcEhMTo3j5kEaOHClBQUGyaNEinak+li9fbnbC17Rp00pAQIAMGzZMrl69qj0HplzMyZAhgzYtc+HChbXnv2vXrplNS2yvaTZSTg3QoEED+eKLL8TR0dFkivkbN25IzZo1tddQSddRNWrUkKtXryo+vkjiuXzevHkWncsdHBy0k9GLJH4Gk95DEfO/Z0n27NkjdevWlRw5ckjOnDnlyy+/lH379imue44cOWTHjh0iontNcvHiRbPXFaae459//qno+I0bN5ZixYrJhQsXtOvOnz8vxYsXl6ZNmyraR5Ljx49LZGSkLFy4UI4fP66ojJeXlxw7dkxv/dGjR8Xb29ui4yf3WXeTszWF4ps3b9CvXz/MmjVLO1YhVapU6Ny5MyZMmGC265g90kLbqlevXihQoADatm2LhIQEVKhQAYcPH4a7uzs2btxo9m6QMdOnT0dkZKTFXb0sZWhcVnJJWZE+lORdbDJmzIjt27ejUKFCuHr1KkqVKoWHDx8aLdu0aVPEx8fr3Qlu0KABMmbMiBUrVpg9fq1atRAbG4tu3boZ/AyZG3OxY8cO/PHHH/Dz80P37t11MpKNHDkSFStWNPsZOHfuHOrXr4/4+HhtJqUrV64gQ4YM2LBhg9Gucg4ODvD29tbW+fHjx/Dy8tLeyRQRPH361GQXreTPV1IMvhaFXSV///13NGvWDM+fP4enp6deFysl/ZPv3btntBXtzJkzJrvXAImT9O7Zs8dgC7HSMSe22rlzJ6ZMmaIzYWSvXr1M3tl1cHBArVq1tOe633//HVWqVNHrFmSqe4099O7dGxkyZNC2hlvi/fv3GD58ODZu3Ag/Pz9MnjxZJyPWN998o01ra8zx48excuVKg11lLXnuz58/x+LFixEdHQ03NzcULFgQ3377rcGB6U+fPtWOiTSXEMTabKWWqFKlCtasWQMfHx+d9U+fPkWDBg2Mdhe0x6SPtp5LrGXP9yBnzpyYPXs2qlatqtPN79KlSyhdurTJbpLJW38MvZZKz4XNmjXTJkVZunQpYmNjkS5dOmzYsAGDBw/WToZpiLWZyJLcuHHDYAuYg4ODtuu+kqx0jx49wtWrVwEkvqZKxkrZg63dvgFg0aJFaN26Nb766iuULVsWIoJDhw5h7dq1CA8Px3fffWe2Hsam27hw4QJKlixpsut/njx5sGTJEr0eK6tWrULnzp3x119/mT2+t7c3duzYgRIlSuisP3bsGEJDQ02mp09y+/ZtfPvttzh48KD2fPL48WOUKVMGS5cuNTkxsaenJ/bv36+XiOP06dOoWLGiRcmTkvusu8nZI4Xi1KlTMX78eJ05DZSmuLU1LbQ9rFq1Cs2bNweQeCETExODS5cuITIyEkOGDMHBgwet2m9oaCiGDh2qeHtrB7sZulBP/mNg7gQcFBSE48eP6w2ufPz4MYoWLWp2fhU/Pz88fPgQAQEBCAgIwJEjR1CoUCFtqlJTfv75Z1SoUAEBAQHa5xsVFQVfX1/FTfoHDhww+MVXqlq1akYvdpUOimzXrh3y5cuHEydOaJvXHz16hFatWqFDhw44fPiwwXK29r22V/ekvn37ok2bNhg3bpzV6akLFCiAuXPn6o1tmTRpEn744QeT467GjRuHoUOHIjg4GL6+vnrBmDnGTu4ajQYuLi4mswAlmTZtGnr37o2vv/5aO1bmyJEjqF27NiZPnmx0AHnKbg1J5xJr7N27F5MmTdKZvb5fv35mM+oBid/zH3/8EVu3brU4iYGDgwNGjBiBatWqoUCBAnoXTytXrjR57KT5b0JDQ7F9+3aEhobi6tWriI+Pt7jbWOrUqXUy25mSJk0a7cWnsYQgSi+CgcRAbMKECdi5c6fBoNzcuXDPnj16gSCQOP5l//79RsvZY94Te47juH79OsLCwnQ+hz179jSYqdCe78GdO3cMJqt4//69wSx1ydnrXDh9+nQMHToUcXFxWL16tfZ38eTJk/j2229Nlk15MyqJuUyASXLlyoW7d+9q30trk7GkSZPG7HQK5ly5csXozSkl3fetNXbsWPz444/o3bu3dl3Pnj0xefJkjB49WlEwlC9fPuzfv1+vK93KlSvNXldVr14dZcqUwYgRIzBgwAA8f/4c3bp1w8qVKzFhwgRFz8EeWQXbtGmDt2/f4uLFi3pzdrVt29bknF32SuShx+o2pf+AdOnSyaVLl0REtznx5s2b4ubmZtG+4uLitDPXK1W8eHHZv3+/RWVSSmoKtnTG6SQuLi4SFxcnIiLt27fXdu+7ceOGeHp6Wl2v6Oho8fPzM7vdvXv3pHLlylbPWv348WOd5a+//pJt27bJF198oW0qNkWj0eg0bSeJj48XZ2dns+Xbtm2rbbqfOXOmuLm5SbVq1cTHx0fatGljtvw///wjs2fPli5dukjfvn0lIiJC3rx5Y7Zckrx582q7NVhr8+bNOp/DadOmSaFCheTbb79VNGu3q6urnDt3Tm/92bNnxdXV1aa6fQzu7u463RmsMWnSJHF1dZWOHTvKixcv5Pbt21K5cmXJmDGjdgZsYzJmzCgLFiyw+tjmzgHZsmWTYcOGSUJCgtF9ZM6cWX799Ve99dOmTZNMmTJZXTelFi5cKE5OTtK4cWOZOnWqhIWFSePGjSVVqlQ6XX6NSepWZmipXLmyojq4uLhY1cWvQIECMm3aNBH5v9+R9+/fS/v27WXYsGEW7+/8+fOyefNmWb9+vc6S0p49e7RdUffs2WNyUaJp06aSKVMm6d+/v0yZMkXCwsJ0FmOio6MlOjpaNBqN7N69W/t3dHS0nDp1SsaNGycBAQEmj22sW5el3bxstWXLFnF2dpaSJUtK7969pVevXlKyZElxcXGRbdu26W1vz/egWLFisnDhQhHRvR4ZMWKElC1b1o7P0r4KFy4sRYoUEQcHBylQoIAUKVJEuxQsWFA8PT0VdbdO+VucspvZxzJnzhxxdHQUX19fKVSokBQuXFi7FClSxGi5lPVP/h6KKOsm5+zsbLBL39WrV8XFxUVR/Tds2CDe3t4yYcIEcXd3l59++knatWsnzs7OBj/DKW3evFn8/PykXLlyEhQUJIULF5bz588rOraISL169aRChQpy584d7brbt29LxYoVpUGDBor24erqavC65uTJk2avKWJjY6VIkSKSKlUqCQoKkhw5ckiqVKmkaNGi2mtda3zWwVCaNGm0b3LyD+7+/fslY8aMZssnJCTIyJEjxcvLS3vh4e3tLaNGjTJ64ZH8xL5z504pXbq07N69Wx48eGDViT/lmJ2VK1fK4MGDJUuWLDJ37lyz5bNlyyZbt26Vd+/eib+/v/z+++8iInLu3DnF41YM6datm9SqVcvsdvbsX5rc3r17pWjRokYfT7rA0Gg0EhkZqXPRsWbNGunatavkzp3b7HESEhJ0xsYsX75cunfvLlOnTpXXr19bXX+ltm7dKqGhoXLz5k2r95E/f375448/RETkzJkz4uLiIoMGDZIvvvhCWrVqZbZ8oUKFZOfOnXrrd+7cKfnz51dcD0v6Byu9eFLyPWrYsKFdxqhFRUVJ/vz5JWfOnJI2bVqpXbu2xMfHmy3n5+cnV65csfq4ERERkjVrVhk6dKhs2LBB1q9fL0OHDhV/f3+ZPXu2jBkzRnx8fGTs2LFG9+Hh4WHwR/jKlStmxwrYQ548eWTy5Ml663/++WfJkyfPBz++SOLNKSU3UFJyd3fXfv/SpUsnZ86cERGRCxcuKLohlOT69etSsGBBbXCbfNyDJWNIreXt7S0HDhywuFzyOibVOfni7u4u8+bNU7wPW27uHTt2TI4cOaK3/siRI4rGHBQuXFgGDBigt37AgAEmL4TtwdhFbKpUqUxexNrzXChifPxkZGSkwe1HjBghI0aMEI1GI//73/+0f48YMULGjRsnS5YsUfRbaC6Y+FiyZcsmEyZMsLhcymsJd3d3mTNnjvbviIgIs5/hHDlyyKxZs/TWz5o1S3LmzKm4Llu2bJEKFSpI6tSpxc3NTcqWLStbt25VVDYhIUG6dOkiGo1GUqVKJVu2bFF8XBHDwYiTk5NFwUju3Lnl6NGjeuuPHj0qOXLkULSPbdu2yS+//CJTp06V7du3W/QcDPmsg6HGjRtL+/btReT/Bv4+e/ZMqlSpougicODAgZIhQwaZMWOGREdHS1RUlEyfPl0yZMgggwcPNlgm5Unf0I+ApQkUDFm8eLHUq1fP7HbDhw8Xb29vyZMnj2TLlk07AHLevHkmB2327t3b4NKmTRspWLCgpE6dWlGLxYca7HbhwgWTF3HJLzRS/ng7OztL7ty5tYGhPaW822tqMcbHx0ebbCJNmjTi7OwsDg4O4uHhobM+TZo0iuqUOnVq7cXc8OHDpVGjRiKSeBfG19fXbPk//vhD8uXLJytXrpS4uDiJi4uTlStXSoECBeSPP/4w+2McFxcn5cqV07YOpkmTRjQajZQtW9bowHGlF0/GvkfJX+e5c+dKtmzZZPjw4bJq1SrF70NKT58+lSZNmoiTk5M4OTkpTl4wceJERQlXjKlSpYrBYG758uVSpUoVERGJjIyU4OBgo/v47rvv5Mcff9Rb/9NPP9l0U0Ipe9wRTdp+y5Yt2oHA79+/V1x269atUrhwYfn999/lzz//VHwhmTVrVm0AVLBgQW1ClUOHDomXl5fi49etW1fq168v9+/fFw8PD7lw4YLs379fSpYsqWgAta1JQAIDA3VuSikVExMjN2/eFI1GI8ePH9dJOPDnn3/Ku3fvzO4jeQvK7t27xc3NTRYvXmxx60qJEiVk5cqVeutXr14tJUuWNFvexcXF4I2Jy5cvK/oc2voepLyILVeunGzbtk1Onz5ttIyt58LkNmzYIJ6entobuz4+PtrF3O9JeHi44kRNhnwqyVisbZEydCMg5WLsPWjdurU8ffpUZsyYIc7OztKpUyftjcGOHTuKi4uLwSDJ3q5duyYlS5aUbNmyybZt22TIkCHi4uIi/fr1s6jHiojI9u3brQ5G1q1bJyVLlpTjx49rz+HHjx+XUqVKydq1aw2W2blzp+TNm9fgufrx48cSEhJiUSKKlD7rBAp//vknKleuDEdHR1y9ehXFixfH1atXkS5dOuzfv99smu3MmTNj1qxZeuME1q9fjy5duhicrdje85sYc/36dRQsWBDPnz83u+2qVasQFxeHb775RjsXQEREBHx8fIwOwDc2t4aXlxfy5MmDLl26mEz/mMTWwW4p54iQ/z9p7IQJE/D27VuzY56yZ8+O48ePG0xLfefOHUUzh+/fvx+zZ8/G9evXsWrVKmTJkgULFy5E9uzZdVL0AvaZ28WSFNZKUlWmTZsWBw4cQEhICMqVK4eWLVuiQ4cOiImJQUhIiNn5sgwN3k06bST/29hzCg0NxdOnTxEREaHXPzh16tQG+wcn/x7FxMRg4MCBaNWqlXZ+hsOHDyMiIgLjx483+BrY431I7uDBg2jevDnSpUuHhQsX4uDBg+jTpw9q1qyJ2bNnm0xV+v79e9SpUwdXrlxBSEiIRfOzAIlJPKKjo/UmXb169SoKFSqEFy9e4ObNm8iXL5/Oe5k00R6QOO5o0qRJKFu2rPY1PHLkCA4ePIi+fftaNP7PGjlz5kS/fv3QsWNHnfWzZ8/GpEmTtAOijXn48CEaN26M3bt3Q6PR4OrVqwgKCkLbtm3h4+ODn3/+2WwdjA1CN/XZBYDvvvsOxYsXR58+fTB27FhMnToV9evXx/bt21G0aFHFCRTSp0+PXbt2oWDBgvD29saxY8cQHByMXbt2oW/fvkbTYAP2SQKyaNEirF+/HhEREVaPnbOXlIPPlfLw8MCZM2f0yt28eRMFCxbEs2fPTJb39/fH5MmT8c033+isX7FiBf73v/8hNjbWaFl7vAfJPX78GIsXL8a8efMQHR1t9PNn67kwudy5c6N27do2jZ+01qeSjKVt27YoUaIEOnXq9EGPk1zy5BNr167Fzz//jIsXLwKAduyk0gmobRkH7enpiTp16mDWrFnaxAWHDh1Cy5Yt4enpafIc9PLlS+zcuRN169YFAAwaNAivX7/WPu7k5IRRo0YZTY+eMpHK8+fPjc7ZZei7VK9ePVSuXFlnvFVyv/zyC3bv3o21a9cafQ6mfNbBEJD4Bi5duhSnTp3SThbZrFkzvbk6DHF1dcWZM2eQO3dunfWXL19G4cKFFU1W+SG8fPkSgwYNwubNm3H58uV/pQ5K1a9fH48fP9Yb7NasWTOkSZPG7Ac3KaNYyo9pqVKlMH/+fKuSU8THx2PcuHH47bffzL6Hq1evRosWLdCsWTMsXLgQFy5cQFBQEGbMmIGNGzdi06ZNFh//Y6tXrx7evHmDsmXLYvTo0bh58yayZMmCbdu2oVu3brhy5YrJ8rYG+G5ubjh06JDe4M5Tp06hbNmyZt+DqlWrol27dnoDfJcsWYI5c+Zgz549BstdvXpVL4CwlouLC3r37o3Ro0drg5nr16+jRYsWiI2Nxe3bt42W7dq1K+bNm4fKlSvrJVAAzA8Oz507N7766iu9Aa4DBw7E2rVrcfnyZZw4cQL169fXuUGTNNGeOeYmLbWHmTNnolevXmjTpg3KlCkDjUaDAwcOIDw8HFOnTtULklJq2bIl7t+/j7lz5yJv3rzaC+lt27ahd+/eOH/+vNk6mPscG7s59ffff+PVq1fInDkz3r9/j0mTJuHAgQPImTMnfvjhB8VzdqRJkwYnT55EUFAQcuTIgblz56Jy5cq4fv06ChQoYPKmhD0uYosUKaJNBBQYGKgXlBuaQNrQxLnGmJs4NzlrgyFbJq4FgFGjRmHKlCkYOHCgzudw4sSJZm8K2CuQ2LVrF+bPn481a9YgICAAjRo1QqNGjRQlFbL2XJgkderUOHv2rMWvO6Cf3TMlczeVUk68bsyHnPQUAMaPH4/JkyejTp06KFCggN73oEePHnY/ZspMdB9iX/fu3UO2bNl0ApSUFi5ciBYtWuitf/bsGXr16mVyHqTZs2dj48aN+P333wEkfofz5cunvZa+dOkS+vfvbzRYsfUmb0BAALZs2aKTCTS5S5cuITQ01OQNDZOsblP6D3jw4IH2/7du3ZIffvhB/ve//yluSitZsqR0795db323bt3kiy++MFve1oHrIvpdpnx8fMTR0VE8PT0Vd/HZsWOHDBo0SNq2bSutW7fWWT40W/uXppwHIjY2VlFT/aNHj+S7776T9OnTS6ZMmWTq1KmSkJAgP/zwg7i5uUnx4sW13V1MST4XQ/I+zqdPn1bUxSyJtd0LUs5tkOTBgweKu1reunVL6tSpIwULFtQZZ9arVy+Dn297s7V/sJubm9GuLaYSoWg0GsmaNau0bNlSFixYYNM8Isa68CQkJMioUaNMlvXw8JCNGzdafez169eLs7OzFCxYUNq2bSvt2rWTQoUKibOzs7ar54wZM6R3795WH+NjWLNmjZQtW1bSpk0radOmlbJly8q6desUlfX19ZWoqCgR0f0e3rhx44OOeXr79q2Eh4fL3bt3bd5XuXLltF1Avv32W6lZs6YcOHBAWrZsqTf3UUr2SAKSfKyHocUQJV2DrOn2be14kSZNmkjFihXl8ePH2nWPHj2SihUrKhrE//79e5k8ebJkyZJFW/csWbJIWFiY2S6XtrwHcXFxMnr0aMmePbtkzJhRunXrJk5OThYNXBex/lyYxJbxkynnHrR0/PKnIjAw0OiSPXt2g2Vs7f6u0WgUJYwyxZZx0End9GxRvnx5WbNmjfbvlN/hhQsXmp0vyxYuLi4m55O6evWqTQmdPstg6MyZMxIQECAODg4SHBysvXD18PAQLy8vcXR0NNovMbk9e/ZI6tSpJW/evNKmTRtp27at5M2bVzw8PBQFVCkHrjs7O1s0cF1Ef8LFyMhI2bx5s+JgasSIEeLg4CAlS5aU+vXrS4MGDXSWjyX5YDclg5hfvHihM6Zn4MCBOmOX+vXrZzLA6Ny5s2TNmlX69u0r+fLlEwcHB6lVq5ZUrlxZcfYlkcQfn6TxNsm//NevXzfbx/zdu3cyatQoyZw5szg6OmrLDh06VPGPh7FseHfu3PnomdyeP38uFy9e1MkmlTShrSnW9A9OLnfu3NKnTx+99X369DGZBGPfvn0yevRoqVq1qri7u4uDg4MEBgZKmzZtZOHChRZnhxQRuX//vuzfv18OHDig+MctW7ZscvHiRYuPlVxMTIwMGDBAO0nhwIED5ebNmybHGnxOPDw8tBeByb+Hx44dk7Rp0yrez759+6RZs2ZSunRp7fsfGRlpMuunm5ubXSbk3LJli6xevVpEEs8fefPmFY1GI+nTpzd7TrRXEpBPhbXjRew5ce3Tp08tukC09j2oVauWeHp6yrfffisbN27UjrGyJhiy9lyYxF7jJ5NTOn75v8zWmwIajUbvxrahRUkdrBkHbeymqiV8fX11ssqmT59eJ7HT5cuXLRpDmeTFixeKxm8GBQXpBGMprV692mgwq8Rn2U2uVq1acHJywoABA7Bo0SJs3LgRoaGhmDt3LgCge/fuOHnyJI4cOWKw/I0bN5A9e3ZoNBr8+eefmDFjBi5evAgRQUhICLp06aLt8mWKh4cHzp07h8DAQIwYMQLnzp3DqlWrcOrUKdSuXRvx8fEmy7979w5jx45FmzZtTE5CZUqmTJnw448/Gmwa/ZCOHj2Kv//+G7Vq1dKui4iIwPDhw/HixQs0aNAAv/76q9GJa21tkg0ICMC8efNQrVo13LhxAzlz5kSPHj0snvMiR44cmD17NqpVq6bTtSMyMhITJkzAhQsXjJYdNWoUIiIiMGrUKLRv3x7nzp1DUFAQVqxYgSlTphidnwf4v/EeSV2zPDw8tI8lJCRg3759iImJMdnHN4m5ZuNs2bKZfPyvv/5C69atsXnzZoOPG+oeYWv/4OQ2bdqERo0aIUeOHChVqhSAxPEu169fx+rVq1G7dm2T5QHg7du3OHz4MPbs2YM9e/bgyJEjeP36NXLmzKmoq+mLFy/QrVs3LFy4UPt8HR0d0bJlS/z6668mu80sWLAAW7ZswYIFC+zSTz9prMH8+fMRFRWlaMxTmzZtTD4+f/58m+ul1D///KM3H4W5CSvr1KmDokWLYvTo0fD09MSZM2cQEBCApk2b4v3791i1apXZ41rb5bVy5cro2bMnGjRooPg5KvX3338bnZQ0eRe1v/76C6NGjULr1q0Ndu2xpIuapXbt2oVu3brhyJEjeu/TkydPUKZMGcyaNcvkfFFfffWVzt+2jBexZOJaY/766y9cvnwZGo0GwcHBBseUAvZ5D5ycnNCjRw907txZp9tuqlSpEB0djZCQEMX1tvVcaGospdLxkylZMn75c3f79m3tuOzkHBwcEBYWZnY+JiVjgE2NgzbGHt303NzcEBUVpR33m9KlS5dQuHBhvHr1yuy+nj9/jgEDBmDFihUGJ6439Dns3r079uzZg+PHj+uNS3r58iVKliyJypUr64yVtcRnGQwlH6j6zz//wMvLC8eOHUPx4sUBJL5ppUqVMjpTbsqZlq2dHMzWgetAYhBw9uxZBAYGWnTsJOnSpcOxY8cMTij3IdWqVQuVKlXCgAEDAABnz55FsWLF8P333yNv3rz46aef0LFjR4wYMcJg+QoVKqB3797aSQ1T9jFftGgRpk+fbjSgSJUqFW7duqUNWt3d3XHs2DHkz5/foufx448/IiIiAvPnz0f16tWxadMm3Lp1C71798awYcOMTlYJ2DbjeNJ4j1u3biFr1qxwdHTUPubs7IzAwECMGjUKX3zxhdnnYGtf72bNmiEmJgZhYWGoXLky1q5di3v37mHMmDH4+eefUadOHb0y9k4CERcXh5kzZ+LSpUvamxKdOnWy+CbBy5cvceDAAWzduhW//fYb/vnnH0UXAB07dsSOHTswbdo0lC1bFkDihLg9evRA9erVMXPmTKNlrRmrYYgtYw1STg769u1bnDt3Do8fP0aVKlU++KDlmzdvolu3btizZ4/Oj6UonLDywoULqFSpEooVK4Zdu3ahXr16OH/+PP7++28cPHhQ0fmtSJEi6N27t3awcNL3MSoqCjVr1jR6c2rlypUYOHAgevfujWLFiuldwBcsWNDkcc0FoklSBqT2TgKSkJCAKVOmYMWKFYiNjdWbQNXYTQl7DFz+VMaLPH/+HN27d0dkZKQ2IDd1U8Me78Hhw4cxf/58rFixAnny5EGLFi3QpEkTZM6c2eJgCLDfudAe/ivjl/v06YPRo0cjderU6NOnj8ltTU3gbIy5ccj2CEYM3WCOjIzE8OHD8fz5c5M3mB0cHHDv3j1kyJDB6uPnypULEyZMQKNGjQw+vmLFCgwePBjXrl0zu6+uXbti9+7dGDVqFFq2bInp06fjzp07mD17NiZMmIBmzZrplbl37x6KFi0KR0dHdOvWDcHBwdBoNLh48SKmT5+OhIQEnDp1yuLrdC2r25Q+YbZOjmWvycG+/PJLqVGjhowaNUpSpUql7ZaxdetWyZUrl6J91K9f36YJG/v37292TMOH4OfnpzPvw+DBg3UmlluxYoXkzZvXaHlbm2TtmcZz8ODB4ubmpm2SdnV1laFDh5ot5+rqqu1ek/wzeP78ecXjHCpVqiR///233L9/X2cMnCWioqJ0luPHj8ucOXMkT5482m47pvj5+WnH/Hh6esrly5dFJLEP86c8WaBI4litnTt3ytChQ6VcuXLi4uIiefLkkY4dO8rixYsVd5VLly6d7N69W2/9rl27JH369CbLWjNWI4m9xhoYkpCQIB07dpSJEyfavC9zSpcuLaVLl5Zly5bJ7t27rZo09O7duzJs2DCpU6eO1KpVS4YMGSJ//vmn4jpY2uW1devW8uTJE6PdYZSOldFoNBIYGKjt4mhs+dB++OEHyZQpk/z000/i6uoqo0ePlrZt20q6dOlk6tSpRstly5bNZEruixcvir+//4eosojYZ7qCJB06dJCgoCDZtGmTtkvOH3/8ITly5JBOnTp9sOcgktjNeN68eVK2bFlJlSqVODg4SFhYmM1jOZSwR1piY+OXPTw8rO5i97FUqlRJHj16pP2/qcUYW8Yh26ObWo0aNXTmRzpz5ow4OTlJu3bt5OeffxY/Pz8ZPny4wbL26KbXo0cPCQkJMTg84cWLFxISEiI9evRQ9Fz8/f21v6eenp7asUCRkZEm56+MiYmRWrVq6c3TVqtWLZvmYhT5TMcMpRyslvJC2NJgyNrBnvYYuD5r1izx8/OTvn37ypIlSyw++ffo0UN8fHykQoUK0q1bN715gz4UFxcXnT7cZcuWldGjR2v/vnnzpnh4eBgt7+rqKpcuXTL6+MWLF02O2dFoNFK7dm1p2LChNGzYUJycnCQ0NFT7d9Ki1PPnz+X48eNy9OhRefbsmaIypmYcL1eunNnyjx49ks6dO0u6dOm080ikS5dOunbtqj2x22Ljxo1SsWJFs9t5enpqTzQBAQHaiRtv3LihaNBuckr7B6dkzViPChUqiJubm+TPn1+6dOkiy5cvVzRJqiFubm4GLwjPnTsn7u7uVu3THHuONTDm0qVLFk0caq3UqVOb/D5/DEFBQdr5MJJ/HyMiIgzemEm6gEmZxCXlYk7nzp0lTZo0UqhQIZk6dao8fPhQcZ3tObdGUFCQNpGHh4eHXLt2TUREpk6dKt9++63Rch964LI59kziYM1NjSNHjsimTZt01kVEREhgYKBkyJBB2rdvr52/T6lLly5Jv379xM/PT1xdXeXLL780um3KMZqmFmO+/PJLg5MeJ5k6darZgNzU+GU1jF20ZRyysbG/lrDlBrNGo5GpU6fqvYcpF1Pi4+PFz89PsmXLJj/++KOsW7dO1q9fLxMnThR/f3/JlCmT4t/X1KlTa8+dWbJk0d5sVZoQ5++//5Zjx47J0aNHFY+fN8fJuvakT1+rVq20zYWvXr1Cp06dtN0bTKUeBBKbvFN2KzLVzSilpLlzfHx8sGTJEr31I0eOVLyvzp07AzDcdKuke8SZM2e0c/ycO3dO8XFt5evri5s3b8Lf3x9v3rzBqVOndJ73s2fPTPbxzpo1K86dO2e0f+qZM2cM9s1NkrLrVfPmzS2qv7VdW5LKTp06FcOHD0eLFi1w584dvH//HmvWrMHly5cRGRmJjRs3mtzv33//jdKlS2vTkOfNmxcigosXLyI8PBw7d+7EoUOHFKf1NSR37tw4fvy42e2Cg4Nx+fJlBAYGonDhwpg9ezYCAwMxa9YsZMqUyWx5a/oHJ5d8rMepU6e0399nz55h3LhxRsd6HDp0CJkyZULlypVRqVIlVKhQwaJ+1smVLl0aw4cPR2RkpLa/8suXLzFy5Ei9NL/GnDx5EhcvXoRGo0FISIjZ7m3btm0zONbAnq5fv4537959kH0nV6JECcTFxRn9Pivx+PFjHDt2DPfv39cbc9SyZUuz5Tt27IiePXti/vz52vGghw8fxv/+9z8MGzZMb3v5/z3IlcynZsqMGTMwZcoUrFmzBvPnz8egQYNQp04dtG3bFqGhoSZ/W8LCwtC+fXuDY6q8vb3RsWNHTJ482eR4nSTx8fEoUKAAgMTxrE+ePAEA1K1bFz/88IPRclmyZMHZs2eRM2dOg4+fOXNG0XnAWinfa1u8ePHCYDeajBkzGu22Pnz4cFSuXFnbPens2bNo27YtWrVqpe3ynTlzZqNdvg0JDg7Gjz/+iPHjx+P33383OWavcOHCBqeXSMnU9UB0dDQmTpxotGxoaCgmTZpkcv8pf1OfPHmCxYsXY8iQIYrHLv6blPymazQao+ml//jjDyxYsADVqlVDly5dkDNnTuTOnVvROGR7fIYfPXqk89ndu3cvatasqf076RxrTNOmTW3qpufr64tDhw6hc+fOGDhwoM5cg9WrV8eMGTMUd1ELCgpCTEwMAgICEBISghUrVqBkyZL4/ffftfMfmZImTRqUKFHC6udikF1Cqk9Mq1atFC3G2NqqoHTG6M9Zhw4dpHTp0rJv3z7p06ePpEuXTl6/fq19fNGiRVK8eHGj5e3ZJGsNW7q2JG8STznjeNmyZWXr1q1mj9+zZ0/Jnz+/wTstd+/elQIFCkivXr0UPZeULTGPHz+WixcvSpMmTaRQoUJmyy9atEjbVfPUqVOSIUMGcXBwEFdXV1m2bJnZ8l26dJG8efPKypUrxc3NTebPny+jR4+WrFmzyqJFi8yWtza9+T///CObN2+WAQMGSMmSJcXZ2Vny588vXbt2lZUrV1qU6vTs2bOSJUsWSZcunVSpUkWqVq0q6dKlk8yZM+t05zTk3r17UrlyZdFoNNruJRqNRqpUqWKyDocOHZJ27dqJl5eXlCxZUn799Ve5f/++VS1DKVuEe/XqJU2aNJHUqVNL165dLdqXNa5duybVqlWT8PBwOXHihMUZCTds2CCenp7i4OAg3t7e4uPjo13Mde9IbsiQIYq7vNojHa4hMTExMmLECAkKChJ/f3+TLc327KKWO3duOXLkiIgkpvkeP368iIgsW7ZMMmTIYLRct27dJH/+/EbPxfnz5//gKfpr1aqlk057zJgxOq3jDx48MNntOkmVKlXkm2++0XkuL168kG+++UaqVq1qsIytXb5tZa5lUkkrpT1b93bu3CnNmjUTNzc3yZMnjwwZMkROnTpl8fP62Gztrurk5CR37tzR/u3m5iZnz579GFUXkcRzwd69e0VE5PXr1+Lm5qaThfLMmTNGz4X26KaX3MOHD+Xo0aNy9OhRi1q6r1+/LgkJCTJ58mRt19xdu3aJm5ubODs7a7uO/hs+ywQKtrJ1sGfyyf1EBLVr18bcuXORJUsWne2MTfKX5P379wgPD8eaNWsQExMDjUaDoKAgNGrUCC1atDB5RzFl9h5DNBoNVq9ebXY7a/z111/46quvcPDgQXh4eCAiIkJnEHfVqlVRqlQpjB071mD5e/fuoXDhwnB2dka3bt2QO3duaDQaXLp0CdOmTcO7d+9w+vRp6wfLmdGlSxcsW7YM2bJlQ5s2bdC8eXOkTZtWUVl7DJYMDAzE7NmzUaNGDYOPb9myBZ06dUJMTIyi+qT8rIgI/P39sWzZMqMtGy9evEC/fv2wbt06vH37FtWqVcMvv/wCd3d3XLp0CdmyZVPU0pItWzZERkaiUqVK8PLywqlTp5AzZ04sXLgQS5cuNTtxrbu7Oy5cuIDAwECdge83btxASEiIouw1QGJL0oEDB7B7927s2bMH0dHRyJUrl+IW05cvX2LRokU6A5eVTODcpEkTXL9+HQsXLtROGHfhwgV8//33yJkzJ5YuXWqy/IsXL7Bs2TLMnz8fx44dQ0JCAiZPnow2bdrA09NTUd0rVaqk8xlwcHBAhgwZUKVKFdSpU0fv3GRvR44cwXfffafzeU26262khduWCS9Tfo4rV66Mvn37AgBCQkJ0MjUm5+DgAG9vb7O9AsxlQ0wpNjYW4eHhCA8Px5s3b3Dp0iWjdXB1dcW5c+eMtspcu3YNBQoUUDQB+MCBA+Hl5YXBgwdj1apV+PbbbxEYGIjY2Fj07t1bb1LfJB984LICKc+pXl5eiIqK0ibUuXfvHjJnzmz2c3Tu3DnUrFkTr169QqFChaDRaBAVFQUXFxds27YN+fLl0yvj6uqKq1evahMUlCtXDjVr1tRO0BoTE4MCBQrg2bNn9nzKBj18+BDp0qUDkJhIIWnAfr169Uy2DubIkQOTJk3SS6SSZM2aNfjf//5ndPLl27dvIzw8HPPnz8fz58/RuHFjzJo1y6oEEP8WW37TgcREG/Hx8dokBElZLZVObm2rjh074uzZs5g4cSLWrVuHiIgI/Pnnn3B2dgYALF68GGFhYQZ7e9hz0ldbGEtO9vr1a5w4cQI5cuRAoUKF/p3K/SshmMpYM+bo/fv3UqdOHdFoNFK4cGFp2rSpNGnSRAoWLCgajUbq169vsrytrWP28vjxY+1Yh+QePnyo01JkyI0bN6RGjRp6g+Vq1Khh8wSESrx69UqWLFki1apVE3d3d/nmm29ky5YtZifns8cdZWdnZ5OT0sbFxZmd5yhJygHr+/btk4sXL8rbt29Nlvvf//4n7u7u0r59e+nRo4ekT59evv76a4ueh4jt/YMtHethTEJCghw5ckTGjx8voaGh2rmHlLBlAmcvLy85duyY3vqjR4+Kt7e34vqLWDbWwJy7d+9Kt27dPsp8VXnz5pWvvvpKjhw5Ijdv3rR43I0tE15a+zm2Rz/7JMnPJa6urvL111/LH3/8IQkJCSbLfci5NY4cOSI///yzorGnH3LgshK2JkVK7sWLFzJnzhzp06eP9O7dW3777Td58eKF0e1tuSNvL7bOnWhL697HGLv4sVj7my5i/3HIlrp//76UK1dONBqNeHp66p0XqlSpIoMHD/5gx7cHe43H/xAYDH0E1rzh8+fPF09PT9m1a5feYzt37hRPT09t16HPnbVNsvZkSdcWe2RuyZw5s8nkAPv27ZPMmTMrqnvyC/nY2FjFF/JBQUGydOlS7d9Hjx4VJycng8GtKQUKFNAOMK1evbr07dtXRBIH7Sp5DhMnTpSQkBA5cuSIeHp6yv79+2XRokWSIUMG+fXXX42WS0hIkKNHj8rEiROlZs2a2m5W/v7+0rJlS1mwYIHZC3F7TODs4eFhcIDxqVOnxNPT0+zzN+Tdu3eydu1as8FQ8gxImTNn1mZAGjZsmLi7u5vMgGRP7u7uJrvpmGPLpKPWfo7tMehZRDeBQlhYmEVZIe3ZRc3YeSDpQl+JDzFwWQl7BUPW3NSwtcu3PdSsWVPq1q0r+/fvl44dO0qWLFmkdevWkpCQIAkJCdKlSxf54osvjJaPj4+XzJkzi7+/v0ycOFE7+H3ChAni7+8vmTNnNjr43dHRUXr37q2d9DjJfzEYSs6S33SRz+MG87+NwZDKWfOGV69eXdun25CxY8dKaGiorVUjhW7duiUjR46U7NmzS5YsWcwGQ7beUW7Tpo1UqFDB4Mnt1atXUrFiRWnTpo3Jfdh6IZ88HXwSV1dXi2d6t0f/YGvSmycFP1myZJFmzZrJb7/9ps2gpZStFyEiIvXq1ZMKFSro9De/ffu2VKxY8YOnVE6eASkkJMSiDEj2VLduXVm1apVFZZJnzZw7d65ky5ZNhg8fLqtWrbIoq6a1n2N79bPXaDQSEBAgDRo00LuLbO6Osi0XsUnsEdD/28xNlWAuGLLlNfgU7sinS5dOO7bu2bNnotFodMYxXbx40Wwrs7Wte/Ycu/gpseQ3nezDnlOe2BvHDH0E1vQt9fPzw5YtW7SZ4FI6ffo0atWqZXSiQLLd69evtRmgDhw4gLp166J169aoWbOmycn47NE/9/bt2yhevDhcXFzQtWtX5MmTB0DiWJMZM2Zo+9iammivVq1acHJywoABA7Bo0SJs3LgRoaGhmDt3LoDEGZ1PnjyJI0eOGCyfso80YJ9+0rGxsThx4gQyZMiABQsWmMyklNyLFy9w4cIFvH//3uRYjySzZ89G5cqVkTt3bqvrausEzkBi3/769evj3Llz8Pf3h0ajQWxsLAoUKID169ebzIpoq4CAAMybNw/VqlXDjRs3kDNnTvTo0UNRBiR7mjNnDsaMGYM2bdqgQIECepkk69Wrp1fGXpOOWvs5tlc/+1atWinKRmpsDOqtW7fQuXNnbN26VSeDU40aNTBjxgyzE3Lbeh74FDg4OKBWrVraDLG///47qlSpopMhdsuWLUY/B/Z4DZ48eQIPDw+dCbCBxDFjHh4e2rEbH0LKz2LKSciVjpkCErOSXbt2DSKCXLlyKc5Iao+xi/82a3/TyT7MfY+TfOhJwA1hMPQBpExeYM0b7uzsjFu3bhlNWfrnn38ie/bsZtOEk3WSD7Zs3bo1mjdvrh24ak7KQYLWunnzJrp06YJt27bppbGcNm2a0UHVSWy9kE954gIMf5atPXFFR0ejaNGiRn/AbUlvbi/2vAjZvn27TvKFatWqfbB6J0mVKhVu3bqFzJkzA0hMRnHs2DHkz5//gx87OVMXGkoSKNh67A/5Of5YrL2ItUdA/2+zNanRf/01cHBwwL1794wO3rfkPGQPly9fxrx587Bw4UI8fvwY1atXx4YNGz7Ksa1ly2862Yet3+MPicHQB2CPN9zQ3czkPvbJT20cHByQLVs2FClSxORdXUMXUPbO3PLo0SNcvXoVAJAzZ06rs9pZeiH/oU9c5oIhBwcHBAQEoEiRIibn2Fi7dq1Vx1fClouQXbt2oVu3bjhy5IjePDFPnjxBmTJlMGvWLEVzxFjr386AZCt7vIaf8g/wx2DPgP6/6r/+GtjaMvahJCQkaOdJ+tSDIVt+0+nz99lOuvpvssePqojoTBybEluEPqyWLVtaNNFucvacJBBInGCsZMmSVpW1ZfLgf/visFOnTli2bBlu3LhhVSpUe7F2Amd7TphprZTnkZT1T/KhLgCOHj2Kv//+WzthJQBERkZi+PDheP78ORo0aIBff/3V6HnOHq/hv/05/hTYch74XPyXXwMlk4grmXjY3hwdHdGgQQM0aNDgox/bUrb8ptPnjy1Dnyi1380k232qdxOTmGsZAnT7eB86dAh16tRB27ZtERoa+lF+2Gz5HgYEBGDLli3auYVSunTpEkJDQxEbG2tTHU35t88jtWrVQqVKlTBgwAAAwNmzZ1G0aFG0atUKefPmxU8//YSOHTtixIgRBst/Cq/hf92nfh74GPgaEJEpDIaIPlP/9oWwuYl/Hz9+jL179yq+ALl16xbCw8MRGRmJt2/f4sKFC2aTKPyb7Dlh5n9VpkyZ8Pvvv2vHZgwZMgR79+7FgQMHAAArV67E8OHDceHCBYPl+Rra7t8+D3wK+BoQkSnsJkf0mfq3f9i9vb3NPm5J1w6NRgONRgMRsXtXxA8hS5YsOHv2rNEL+TNnzhhNkPK5ePToEXx9fbV/7927FzVr1tT+XaJECcTFxRktz9fQdv/2eeBTwNeAiExhMEREH4Q9LkAMpUKdNm3afyIVau3atTFs2DDUqlULrq6uOo+9fPkSw4cPR926df+l2n0cvr6+uHnzJvz9/fHmzRucOnUKI0eO1D7+7NkzvTTbyfE1JCKiD43d5Ijok/RfT4V67949FC1aFI6OjujWrRuCg4Oh0Whw8eJFTJ8+HQkJCTh16pROy8nnpmPHjjh79iwmTpyIdevWISIiAn/++ad2TpbFixcjLCwMx48fN1ieryEREX1oDIaI6JP0OaRCtXXCzP+6v/76C1999RUOHjwIDw8PREREoGHDhtrHq1atilKlSmHs2LFG96H215CIiD4sBkNE9Elq1aqVooxx/4XxANZOmPm5ePLkCTw8PODo6Kiz/u+//4aHh4e2pcgUtb+GRET0YTAYIiIiIiIiVfq0RyATERERERF9IAyGiIiIiIhIlRgMERERERGRKjEYIiIiIiIiVWIwREREREREqsRgiIiIiIiIVInBEBERERERqRKDISIiIiIiUqX/B0mts4GEsIgIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VaR_cf = -1 * (ret.mean() + z_cf * ret[-120:].std())\n",
    "VaR_cf.sort_values().plot.bar(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6177df-f1e6-45b7-a6ee-612e3b9d8482",
   "metadata": {},
   "source": [
    "## Calculate the Sharpe Ratio using annualized return and volatilities (The Annual Risk free rate is assummed to be 3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "10a64a8d-f5cf-46b6-89ac-e7890e5b7300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agric    0.328249\n",
       "Food     0.474946\n",
       "Soda     0.364910\n",
       "Beer     0.536054\n",
       "Smoke    0.380331\n",
       "Toys     0.054289\n",
       "Fun      0.257404\n",
       "Books    0.139050\n",
       "Hshld    0.475003\n",
       "Clths    0.324880\n",
       "Hlth     0.186551\n",
       "MedEq    0.539739\n",
       "Drugs    0.554506\n",
       "Chems    0.319700\n",
       "Rubbr    0.371133\n",
       "Txtls    0.038215\n",
       "BldMt    0.300354\n",
       "Cnstr    0.266201\n",
       "Steel    0.090461\n",
       "FabPr    0.083173\n",
       "Mach     0.325452\n",
       "ElcEq    0.371102\n",
       "Autos    0.153281\n",
       "Aero     0.398856\n",
       "Ships    0.324489\n",
       "Guns     0.513700\n",
       "Gold    -0.070298\n",
       "Mines    0.248520\n",
       "Coal     0.026816\n",
       "Oil      0.289257\n",
       "Util     0.445533\n",
       "Telcm    0.175964\n",
       "PerSv    0.153731\n",
       "BusSv    0.415539\n",
       "Comps    0.271326\n",
       "Chips    0.356910\n",
       "LabEq    0.431120\n",
       "Paper    0.242334\n",
       "Boxes    0.299701\n",
       "Trans    0.333030\n",
       "Whlsl    0.365156\n",
       "Rtail    0.461542\n",
       "Meals    0.464273\n",
       "Banks    0.297825\n",
       "Insur    0.453539\n",
       "RlEst    0.033191\n",
       "Fin      0.376855\n",
       "Other   -0.014034\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=0.03\n",
    "sharpe_ratio=(annual_mret-rf)/annual_vol\n",
    "sharpe_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0c7ddf1b-7bce-48ba-bc57-aef2f158db66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gold    -0.070298\n",
       "Other   -0.014034\n",
       "Coal     0.026816\n",
       "RlEst    0.033191\n",
       "Txtls    0.038215\n",
       "Toys     0.054289\n",
       "FabPr    0.083173\n",
       "Steel    0.090461\n",
       "Books    0.139050\n",
       "Autos    0.153281\n",
       "PerSv    0.153731\n",
       "Telcm    0.175964\n",
       "Hlth     0.186551\n",
       "Paper    0.242334\n",
       "Mines    0.248520\n",
       "Fun      0.257404\n",
       "Cnstr    0.266201\n",
       "Comps    0.271326\n",
       "Oil      0.289257\n",
       "Banks    0.297825\n",
       "Boxes    0.299701\n",
       "BldMt    0.300354\n",
       "Chems    0.319700\n",
       "Ships    0.324489\n",
       "Clths    0.324880\n",
       "Mach     0.325452\n",
       "Agric    0.328249\n",
       "Trans    0.333030\n",
       "Chips    0.356910\n",
       "Soda     0.364910\n",
       "Whlsl    0.365156\n",
       "ElcEq    0.371102\n",
       "Rubbr    0.371133\n",
       "Fin      0.376855\n",
       "Smoke    0.380331\n",
       "Aero     0.398856\n",
       "BusSv    0.415539\n",
       "LabEq    0.431120\n",
       "Util     0.445533\n",
       "Insur    0.453539\n",
       "Rtail    0.461542\n",
       "Meals    0.464273\n",
       "Food     0.474946\n",
       "Hshld    0.475003\n",
       "Guns     0.513700\n",
       "Beer     0.536054\n",
       "MedEq    0.539739\n",
       "Drugs    0.554506\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=0.03\n",
    "sharpe_ratio=(annual_mret-rf)/annual_vol\n",
    "sharpe_ratio.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "44fd883c-144c-423a-bf67-59a3d854f91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf=0.03\n",
    "sharpe_ratio=(annual_mret-rf)/annual_vol\n",
    "sharpe_ratio.sort_values().plot.bar(figsize=(10,5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eb1c6e62-43e9-4d5d-b856-d59ff4fe8b01",
   "metadata": {},
   "source": [
    "\n",
    "## There is a negative relationship between the 95% VaR values and the sharpe ratio values of the industries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "500788d4-1682-4a99-ba2a-f44f8fc48496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CVaR_cf =  2.062712807818914\n"
     ]
    }
   ],
   "source": [
    "portfolio_losses = pd.Series(scipy.stats.norm.rvs(size=1000))\n",
    "VaR_cf = scipy.stats.norm.ppf(0.95)\n",
    "CVaR_cf = (1/(1-0.95))*scipy.stats.norm.expect(lambda x: x, lb=VaR_cf)\n",
    "print(\"CVaR_cf = \", CVaR_cf)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7f7b4cda-d98e-4622-84ee-f67e4f3f5f6e",
   "metadata": {},
   "source": [
    "### Part B. Efficient Frontier Analysis\n",
    "Use the same file that you used in the previous problem, that is the Fama-French 48 Industry Portfolios, monthly value-weighted returns, since January 1990. \n",
    "i.\tPick your favorite two industries and plot the two-asset efficient frontier using annual returns and volatilities. To do so, create portfolios of the two assets with weights in the increments of 0.10, for example (0, 1), (0.1, 0.9), (0.2, 0.8), ... . \n",
    "ii.\tAmong these portfolios, characterize the minimum-variance portfolio. That is, determine the asset weights, portfolio return, and portfolio volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0b5f4b5e-7cab-4760-9e2e-5d7399e77736",
   "metadata": {},
   "outputs": [],
   "source": [
    "def portfolio_ret(weights, returns):\n",
    "    \"\"\"\n",
    "    weights + returns --> portfolio return\n",
    "    \"\"\"\n",
    "    return weights.T@returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5dcd541e-aefd-4d91-88a3-0df3738643b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def portfolio_vol(weights, covmat):\n",
    "    \"\"\"\n",
    "    weights + covmat --> portfolio volatility\n",
    "    \"\"\"\n",
    "    return (weights.T@covmat@weights) ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7e2c99d7-ed6f-4733-b617-73b021fdd233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hlth     0.072206\n",
       "Drugs    0.118194\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [\"Hlth\", \"Drugs\",]\n",
    "annual_mret[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c0963ecc-fd4d-4937-8478-622433e39886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 48)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov = ret[\"2002\":].cov()*12\n",
    "cov.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "eaaec12a-b63c-40ed-8740-83c29d5c3994",
   "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>Hlth</th>\n",
       "      <th>Drugs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hlth</th>\n",
       "      <td>0.042216</td>\n",
       "      <td>0.014756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugs</th>\n",
       "      <td>0.014756</td>\n",
       "      <td>0.020504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hlth     Drugs\n",
       "Hlth   0.042216  0.014756\n",
       "Drugs  0.014756  0.020504"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov.loc[l,l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "306272cd-7d8a-428f-b4fb-f6f6b67ec1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1, 0.9])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = np.array([.10, .90])\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1e08f0a6-0b28-4abe-9a99-f4f5bfc85590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0., 1.]),\n",
       " array([0.1, 0.9]),\n",
       " array([0.2, 0.8]),\n",
       " array([0.3, 0.7]),\n",
       " array([0.4, 0.6]),\n",
       " array([0.5, 0.5]),\n",
       " array([0.6, 0.4]),\n",
       " array([0.7, 0.3]),\n",
       " array([0.8, 0.2]),\n",
       " array([0.9, 0.1]),\n",
       " array([1., 0.])]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_points = 11\n",
    "weights = [np.array([w, 1-w]) for w in np.linspace(0,1,n_points) ]\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e9b278b5-867f-4670-a9bc-44a2326d7b7c",
   "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>Returns</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.118194</td>\n",
       "      <td>0.143192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.113595</td>\n",
       "      <td>0.140308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.108996</td>\n",
       "      <td>0.139761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.104398</td>\n",
       "      <td>0.141577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.099799</td>\n",
       "      <td>0.145668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Returns      Risk\n",
       "0  0.118194  0.143192\n",
       "1  0.113595  0.140308\n",
       "2  0.108996  0.139761\n",
       "3  0.104398  0.141577\n",
       "4  0.099799  0.145668"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[\"Hlth\", \"Drugs\"]\n",
    "returns=[portfolio_ret(w, annual_mret[l]) for w in weights]\n",
    "vols=[portfolio_vol(w, cov.loc[l,l]) for w in weights]\n",
    "ef = pd.DataFrame({\"Returns\": returns, \"Risk\": vols})\n",
    "ef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f96619e8-2761-4976-9d6d-c2cf9b9ca322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Risk'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ef.plot.line(x=\"Risk\", y=\"Returns\", style=\".-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b705d7b2-539d-466e-80f8-461ddb15a7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11359520988397016"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_ret(w,annual_mret[l])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d916db27-10c5-4c03-9def-b97c899f3dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14030846712678202"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_vol(w, cov.loc[l,l])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}