Analyzing Returns.ipynb

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    "Analyzing Profitability-sorted Portfolios of Returns\n",
    "\n",
    "This project is aimed at analyzing portfolios that are sorted based on firms’ profitability. First, we need to get portfolio returns data from Prof. Kenneth French’s data library: \n",
    "•\tVisit his webpage at: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html\n",
    "\n",
    "Search for the term “Portfolios Formed on Operating Profitability” and download the CSV file. \n",
    "The CSV file provides returns for portfolios that are formed with different methods. Look for the set of monthly returns for equal weighted portfolios (these data start at around line 730) and copy these returns into a separate CSV file. \n",
    "\n",
    "a)  Calculate Sharpe ratio for low- (Decile 1 or ‘Lo 10’), medium- (Decile 5), and high-profitability (Decile 10 or ‘Hi 10’) portfolios and compare them. Can you explain the differences and any potential patterns that may emerge? \n",
    "\n",
    "b)  Compute semi-deviation for low-, medium-, and high-profitability portfolios. If you compare semi-deviations with std. deviations of these portfolios, what can be said about riskiness of these portfolios? \n",
    "\n",
    "c)  Compute and plot drawdown for low-, medium-, and high-profitability portfolios. Also, determine the date and the value of the maximum drawdown of these portfolios. What does this comparison suggest about the downside risk of these portfolios? \n"
   ]
  },
  {
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   "id": "722e8237-5cb5-4971-a7be-9992571592f3",
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   "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",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ca07111-05b9-4416-8ea7-f893b1e639de",
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    {
     "data": {
      "text/plain": [
       "array([[ 0.03381385],\n",
       "       [ 0.05556121],\n",
       "       [ 0.0177915 ],\n",
       "       ...,\n",
       "       [-0.03568233],\n",
       "       [-0.03389065],\n",
       "       [-0.02994873]])"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "id": "56e30876-9d58-4d77-b74b-f4a931bfe138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009499014438627221"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(returns_norm)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "7202cce1-7ff7-4c75-88cd-901ffce7166b",
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       "      <td>0.82</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.65</td>\n",
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       "      <td>1.18</td>\n",
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       "      <td>-2.46</td>\n",
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       "      <td>-9.71</td>\n",
       "      <td>-5.16</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>-10.50</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-5.25</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>-11.69</td>\n",
       "      <td>-7.19</td>\n",
       "      <td>-5.19</td>\n",
       "      <td>-5.67</td>\n",
       "      <td>-4.33</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-6.31</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.74</td>\n",
       "      <td>-5.67</td>\n",
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       "      <td>9.41</td>\n",
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       "      <td>8.87</td>\n",
       "      <td>9.71</td>\n",
       "      <td>7.93</td>\n",
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       "      <th>202312</th>\n",
       "      <td>13.65</td>\n",
       "      <td>11.93</td>\n",
       "      <td>9.08</td>\n",
       "      <td>14.33</td>\n",
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       "      <td>12.08</td>\n",
       "      <td>10.54</td>\n",
       "      <td>9.17</td>\n",
       "      <td>15.59</td>\n",
       "      <td>10.89</td>\n",
       "      <td>9.78</td>\n",
       "      <td>11.55</td>\n",
       "      <td>12.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>12.05</td>\n",
       "      <td>8.92</td>\n",
       "      <td>8.56</td>\n",
       "      <td>9.79</td>\n",
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       "        Lo 30  Med 40  Hi 30  Lo 20  Qnt 2  Qnt 3  Qnt 4  Hi 20  Lo 10  2-Dec  \\\n",
       "196307  -1.10   -0.21  -0.41  -1.09  -0.76   0.49  -0.98  -0.38  -1.19  -0.94   \n",
       "196308   3.92    4.48   4.73   3.62   4.77   4.44   4.24   4.93   3.00   4.55   \n",
       "196309  -0.69   -1.49  -1.58  -0.48  -1.38  -1.50  -0.74  -2.16  -0.69  -0.17   \n",
       "196310   1.37    1.17   1.83   1.50   0.82   1.16   1.48   2.07   2.15   0.52   \n",
       "196311  -2.12   -0.93  -1.88  -2.46  -0.50  -1.46  -0.90  -2.24  -3.27  -1.26   \n",
       "...       ...     ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "202308  -8.47   -4.16  -3.32  -9.39  -3.55  -4.12  -4.29  -2.99 -10.56  -6.15   \n",
       "202309  -8.36   -5.29  -4.40  -9.03  -4.84  -5.53  -4.98  -3.99 -10.20  -5.79   \n",
       "202310  -9.71   -5.16  -4.80 -10.50  -5.44  -4.47  -5.25  -5.20 -11.69  -7.19   \n",
       "202311   9.41    8.70   8.87   9.71   7.93   8.62   8.64   9.66  10.12   8.56   \n",
       "202312  13.65   11.93   9.08  14.33  10.71  12.08  10.54   9.17  15.59  10.89   \n",
       "\n",
       "        3-Dec  4-Dec  5-Dec  6-Dec  7-Dec  8-Dec  9-Dec  Hi 10  \n",
       "196307  -1.14  -0.40  -0.03   1.07  -1.49  -0.48  -1.15   0.12  \n",
       "196308   4.80   4.74   4.70   4.15   4.29   4.19   4.29   5.35  \n",
       "196309  -1.27  -1.48  -0.91  -2.15  -1.50   0.00  -1.84  -2.37  \n",
       "196310   0.99   0.65   1.48   0.79   1.78   1.18   2.58   1.74  \n",
       "196311  -1.12   0.11  -1.60  -1.30  -0.94  -0.87  -0.92  -3.11  \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "202308  -3.20  -3.87  -4.19  -4.05  -4.65  -3.90  -1.71  -4.32  \n",
       "202309  -4.51  -5.14  -5.57  -5.48  -4.85  -5.12  -3.20  -4.81  \n",
       "202310  -5.19  -5.67  -4.33  -4.62  -6.31  -4.11  -4.74  -5.67  \n",
       "202311   7.75   8.09   7.90   9.45   9.70   7.51   7.41  11.94  \n",
       "202312   9.78  11.55  12.21  11.92  12.05   8.92   8.56   9.79  \n",
       "\n",
       "[726 rows x 18 columns]"
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   ],
   "source": [
    "Monthlydata = pd.read_csv(\"Week 02 assignment data.csv\",\n",
    "                    header=0, index_col=0, parse_dates=True, na_values=-99.99)\n",
    "Monthlydata"
   ]
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       "      <td>-2.15</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-2.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Lo 30  Med 40  Hi 30  Lo 20  Qnt 2  Qnt 3  Qnt 4  Hi 20  Lo 10  2-Dec  \\\n",
       "196307  -1.10   -0.21  -0.41  -1.09  -0.76   0.49  -0.98  -0.38  -1.19  -0.94   \n",
       "196308   3.92    4.48   4.73   3.62   4.77   4.44   4.24   4.93   3.00   4.55   \n",
       "196309  -0.69   -1.49  -1.58  -0.48  -1.38  -1.50  -0.74  -2.16  -0.69  -0.17   \n",
       "\n",
       "        3-Dec  4-Dec  5-Dec  6-Dec  7-Dec  8-Dec  9-Dec  Hi 10  \n",
       "196307  -1.14  -0.40  -0.03   1.07  -1.49  -0.48  -1.15   0.12  \n",
       "196308   4.80   4.74   4.70   4.15   4.29   4.19   4.29   5.35  \n",
       "196309  -1.27  -1.48  -0.91  -2.15  -1.50   0.00  -1.84  -2.37  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20783310-c95f-471c-b68b-5bd56d626c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lo 10</th>\n",
       "      <th>Med 40</th>\n",
       "      <th>Hi 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196307</th>\n",
       "      <td>-1.19</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196308</th>\n",
       "      <td>3.00</td>\n",
       "      <td>4.48</td>\n",
       "      <td>5.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196309</th>\n",
       "      <td>-0.69</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-2.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Lo 10  Med 40  Hi 10\n",
       "196307  -1.19   -0.21   0.12\n",
       "196308   3.00    4.48   5.35\n",
       "196309  -0.69   -1.49  -2.37"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns=['Lo 10', 'Med 40', 'Hi 10']\n",
    "Monthlydata = Monthlydata[columns]\n",
    "Monthlydata.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09d6b059-336a-4ea2-a8c0-a20ae9d8e7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LowProf</th>\n",
       "      <th>MedProf</th>\n",
       "      <th>LargeProf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196307</th>\n",
       "      <td>-0.0119</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196308</th>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.0535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196309</th>\n",
       "      <td>-0.0069</td>\n",
       "      <td>-0.0149</td>\n",
       "      <td>-0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196310</th>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196311</th>\n",
       "      <td>-0.0327</td>\n",
       "      <td>-0.0093</td>\n",
       "      <td>-0.0311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LowProf  MedProf  LargeProf\n",
       "196307  -0.0119  -0.0021     0.0012\n",
       "196308   0.0300   0.0448     0.0535\n",
       "196309  -0.0069  -0.0149    -0.0237\n",
       "196310   0.0215   0.0117     0.0174\n",
       "196311  -0.0327  -0.0093    -0.0311"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.columns = ['LowProf', 'MedProf', 'LargeProf']\n",
    "Monthlydata = Monthlydata/100\n",
    "Monthlydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d63aae1-13c8-427e-8a8c-abbbf6491271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.4234282 , -0.39287208, -0.28688015])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.skew(Monthlydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d1ac141-3bbb-4c2d-b5d3-dcc47ae05495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.79500407, 3.26038434, 2.78430471])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.kurtosis(Monthlydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c26d5c9-782b-4228-99e8-374624d27e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.010975\n",
       "MedProf      0.012327\n",
       "LargeProf    0.012171\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0f0f670-9b89-4536-a301-71000051f5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.079825\n",
       "MedProf      0.051867\n",
       "LargeProf    0.062283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcbf952-6889-46a7-8950-140c1c37f008",
   "metadata": {},
   "source": [
    "# ANNUALIZED STD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9586c56a-3ed6-46a7-9668-fd2e97e5e925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.276521\n",
       "MedProf      0.179674\n",
       "LargeProf    0.215756\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_vol=Monthlydata.std() * np.sqrt(12)\n",
    "annual_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27096c19-f552-4734-950f-3b028ca6d683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(726, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42ecf0e1-b9aa-4a78-b8fa-531b461bab9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_months=Monthlydata.shape[0]\n",
    "n_years=n_months/12\n",
    "n_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9859672c-6c8b-49c3-aefa-b42445fdc070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.098370\n",
       "MedProf      0.139895\n",
       "LargeProf    0.129685\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_Monthlydata=(1+Monthlydata).prod()**(1/n_years)-1\n",
    "annual_Monthlydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c7bd1fc-4804-4988-93a8-3b8cada4e65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.355742\n",
       "MedProf      0.778604\n",
       "LargeProf    0.601074\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_Monthlydata/annual_vol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fab5cb1-142e-47be-9790-c0422d60df4e",
   "metadata": {},
   "source": [
    "# SHARPE RATIO\n",
    "## Annual risk free rate is assume to be 3%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b3c514a-9665-4f33-bb49-546fdea5453e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.247251\n",
       "MedProf      0.611635\n",
       "LargeProf    0.462028\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=0.03\n",
    "sharpe_ratio=(annual_Monthlydata-rf)/annual_vol\n",
    "sharpe_ratio\n",
    "\n",
    "# The MedProf's sharpe ratio is the higheest meaning it is the best investment compared to SmallProf and LargeProf.\n",
    "# The MedProf has the least standard deviation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e05bd6e-78ad-4443-b5cb-9db6e126292e",
   "metadata": {},
   "source": [
    "## 1. Standard deviation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a55975b2-d7c3-4a78-b4d1-62ce43432437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.079825\n",
       "MedProf      0.051867\n",
       "LargeProf    0.062283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb897dc4-021f-4d42-b480-f6e27eed582e",
   "metadata": {},
   "source": [
    "## Semi Deviation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5835cbd-ccfd-4cb5-b025-032ec61b4bda",
   "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>LowProf</th>\n",
       "      <th>MedProf</th>\n",
       "      <th>LargeProf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196307</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196308</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196309</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196310</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196311</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202308</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202309</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202310</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202311</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202312</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>726 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        LowProf  MedProf  LargeProf\n",
       "196307     True     True      False\n",
       "196308    False    False      False\n",
       "196309     True     True       True\n",
       "196310    False    False      False\n",
       "196311     True     True       True\n",
       "...         ...      ...        ...\n",
       "202308     True     True       True\n",
       "202309     True     True       True\n",
       "202310     True     True       True\n",
       "202311    False    False      False\n",
       "202312    False    False      False\n",
       "\n",
       "[726 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata < 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89bbbe6f-a0f0-49ff-a7be-d45d8e5e81b6",
   "metadata": {},
   "source": [
    "## VaR and CVaR "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9864f91-2431-4088-9b4c-7642915af08e",
   "metadata": {},
   "source": [
    "### Historical Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e0056c6c-3c60-4079-8289-e41b2f2e8661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11205 , 0.071525, 0.0879  ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(Monthlydata, 5,axis=0)*-1\n",
    "\n",
    "# if you invest at 95%, LowProf maximum loss will be 11% , MedProf will be 7% and LargeProf will be 8%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc633f51-a9d2-4930-b0c3-ee89fc9c14c5",
   "metadata": {},
   "source": [
    "### Parametric estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3113c9c3-305f-4e0b-a00b-08d0ca168319",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "36b4865c-6aa2-4d61-b7d9-04677d21942d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6448536269514729"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = norm.ppf(0.05)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67ee6e07-9309-495c-aab9-63ffaf356e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.120325\n",
       "MedProf      0.072988\n",
       "LargeProf    0.090277\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VaR_Gaussian = -1 * (Monthlydata.mean() + z * Monthlydata.std())\n",
    "VaR_Gaussian"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95f4125-36a6-4496-a59b-69e2734b86e7",
   "metadata": {},
   "source": [
    "### Cornish-Fisher/Semi-parametric estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c8b83a0-5c88-4b0d-a434-12c819eb00dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.50518776, -1.69081611, -1.62374273])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = norm.ppf(0.05)\n",
    "s = scipy.stats.skew(Monthlydata[-120:])\n",
    "k = scipy.stats.kurtosis(Monthlydata[-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": 25,
   "id": "b366a803-6ba2-40b1-97e9-2c225cf7f349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LowProf      0.115178\n",
       "MedProf      0.081965\n",
       "LargeProf    0.091818\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VaR_cf = -1 * (Monthlydata.mean() + z_cf * Monthlydata[-120:].std())\n",
    "VaR_cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8f233a-249b-4831-9465-9aa27eb38912",
   "metadata": {},
   "source": [
    "### Max Drawdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6e635a87-9720-434a-981f-6f9105142e17",
   "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>LowProf</th>\n",
       "      <th>MedProf</th>\n",
       "      <th>LargeProf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196307</th>\n",
       "      <td>-0.0119</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196308</th>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.0535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196309</th>\n",
       "      <td>-0.0069</td>\n",
       "      <td>-0.0149</td>\n",
       "      <td>-0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196310</th>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196311</th>\n",
       "      <td>-0.0327</td>\n",
       "      <td>-0.0093</td>\n",
       "      <td>-0.0311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LowProf  MedProf  LargeProf\n",
       "196307  -0.0119  -0.0021     0.0012\n",
       "196308   0.0300   0.0448     0.0535\n",
       "196309  -0.0069  -0.0149    -0.0237\n",
       "196310   0.0215   0.0117     0.0174\n",
       "196311  -0.0327  -0.0093    -0.0311"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d997d214-2dae-46cb-8f38-c312802d5b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([196307, 196308, 196309, 196310, 196311, 196312, 196401, 196402,\n",
       "            196403, 196404,\n",
       "            ...\n",
       "            202303, 202304, 202305, 202306, 202307, 202308, 202309, 202310,\n",
       "            202311, 202312],\n",
       "           dtype='int64', length=726)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0410aaaf-54bb-46f7-91a6-8f17cd1f11bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Monthlydata.index = pd.to_datetime(Monthlydata.index, format='%Y%m').to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4741fb1-4ca1-4cb8-a4b2-11bad28f174e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gl/sc9s01490bg0vmyvzpbkn1lm0000gn/T/ipykernel_84023/4265716391.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  Monthlydata['2020']\n"
     ]
    },
    {
     "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>LowProf</th>\n",
       "      <th>MedProf</th>\n",
       "      <th>LargeProf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01</th>\n",
       "      <td>0.0386</td>\n",
       "      <td>-0.0454</td>\n",
       "      <td>-0.0464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02</th>\n",
       "      <td>-0.0380</td>\n",
       "      <td>-0.0897</td>\n",
       "      <td>-0.0981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03</th>\n",
       "      <td>-0.1936</td>\n",
       "      <td>-0.2325</td>\n",
       "      <td>-0.2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04</th>\n",
       "      <td>0.2294</td>\n",
       "      <td>0.1612</td>\n",
       "      <td>0.2259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05</th>\n",
       "      <td>0.1368</td>\n",
       "      <td>0.0396</td>\n",
       "      <td>0.0663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06</th>\n",
       "      <td>0.1179</td>\n",
       "      <td>0.0306</td>\n",
       "      <td>0.0615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07</th>\n",
       "      <td>0.0777</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08</th>\n",
       "      <td>0.0253</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.0739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09</th>\n",
       "      <td>-0.0069</td>\n",
       "      <td>-0.0418</td>\n",
       "      <td>-0.0431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>-0.0137</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>-0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>0.2356</td>\n",
       "      <td>0.1817</td>\n",
       "      <td>0.1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>0.1194</td>\n",
       "      <td>0.0751</td>\n",
       "      <td>0.0874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         LowProf  MedProf  LargeProf\n",
       "2020-01   0.0386  -0.0454    -0.0464\n",
       "2020-02  -0.0380  -0.0897    -0.0981\n",
       "2020-03  -0.1936  -0.2325    -0.2625\n",
       "2020-04   0.2294   0.1612     0.2259\n",
       "2020-05   0.1368   0.0396     0.0663\n",
       "2020-06   0.1179   0.0306     0.0615\n",
       "2020-07   0.0777   0.0158     0.0297\n",
       "2020-08   0.0253   0.0526     0.0739\n",
       "2020-09  -0.0069  -0.0418    -0.0431\n",
       "2020-10  -0.0137   0.0403    -0.0028\n",
       "2020-11   0.2356   0.1817     0.1993\n",
       "2020-12   0.1194   0.0751     0.0874"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthlydata['2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a63ec00-60f1-4c25-8e5d-babdf168a558",
   "metadata": {},
   "outputs": [],
   "source": [
    "wealth = (1+Monthlydata['LargeProf']).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c2f7898-48f5-4aa1-960e-ef07a1723d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'wealth index for Large Profit(1980-2021)'}>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HD4aXlxcAIDU1FQDQr18/l+d0/CM4bdo07NixAy+88AL69esHf39/yGQy3HHHHSgsLKzVvQcHB5fZptFoanXe0ue0p9jt58zIyIBSqSxTLiIiwul9RkYGzGYzVq1ahVWrVrm8VunmtIceegjvvvsu1Go1Hn/88RrXISsrC0IIp6Y3u8jISOn+HLkqW1MJCQm4/fbb0alTJ7z99tto3bo1tFotDh48iLlz55Z5Pq6unZGRgfDw8DLbS2+z/ywuWrTIqXnDUUXNlnZTpkzBU089BZlMBj8/P7Rr185lM17pe7V/H8v7Xtv/EXAX+/fOsT9KdY0cORJr1qzBwoUL0a5dOwBA165d8a9//Qv/+Mc/pL4yQgg89NBDGDJkCP73v/9Jx48aNQp6vR7z58/HlClT4OPjI/0+uHpumZmZCAoKAmBrXm7Tpo3T/p07dzr1+QFsTW6zZ8/Go48+itdff91pX2BgIGQyWZmfYfu1AEjXc3T06FGMHj0aHTp0wM8//+yySa26tFptrf+OUdUwiCGXWrRogY4dO2L79u1o3bo1+vbti4CAAIwcORJz5szBgQMHsH//fqkPBwCEhIQAAL7++munjnWl6fV6/Pjjj3jxxRfx7LPPStsNBoP0x8bTBAcHw2w2IyMjwymQSUlJcSoXGBgoZT7mzp3r8lyOf8zz8/MRExODjh07IjU1FQ8//DC+++67Gt1jYGAg5HI5kpOTy+yz/ydvf4Z21enQWJnNmzcjPz8f3377rdPPR1xcnMvyrq4dHBzs1MHUrvT32V6PxYsXY/LkyS7P36lTp0rvOTQ0FH379q20XOl7tf8MlPe9Lv19ri37+TIzM2sVeM6cORPTp0/HhQsXoFKp0L59eyxbtgwymQy33347AFuAmJyc7LIfU79+/fDpp5/iypUr6NatG7p37w4AOHHiBLp27SqVM5vNOHv2rJRBi4yMxKFDh5zOVfr5rFmzBg8//DBmzpyJ9957r8z33MvLC+3bt8eJEyfK3NeJEyfg5eWFtm3bOm0/evQoRo0ahVatWmHr1q3Q6XRV/VZVKCsry+3PmFxjEEPlGjVqFL788ktERUXhzjvvBAB07NgRLVu2xD//+U+YTCanTr1jx46FUqnEpUuXKmyGkMlkEEKU+Y/no48+KnfEjSPHLIg9C9TQhg8fjuXLl+Ozzz5zypZs2LDBqZy3tzeGDx+Oo0ePokePHlCr1RWe97HHHkNCQgIOHjyIs2fP4r777sNbb73l1Km4qnx8fNC/f398++23eOONN6TvndVqxfr166XAta7YP3Qcn7sQAh9++GGVzzF06FB8+eWX+OWXX5w6TW/cuNGpXKdOndChQwccO3YMS5cureWdV9/AgQPh5eWF9evX4y9/+Yu0PSkpCb/99pvUOb26yssodu7cGYCt43TpEYTVpVQq0aVLFwC2fzg++OAD3HXXXVLgGRgYCK1W69Tsabdv3z7I5XIpkOrfvz+aNWuGtWvXOnW+/vrrr5GXlycFmGq1usJgce3atXj44YfxwAMP4KOPPio3uL7nnnuwcuVKJCYmIioqCoBtsrpvv/0WkyZNglJZ8pEXFxeHUaNGoUWLFti2bRsCAwOr820q1/Xr11FUVOQUtFHdYRBD5Ro5ciTeeecd3LhxAytXrnTavmbNGgQGBjoNr27dujVefvllPPfcc7h8+TLGjRuHwMBApKam4uDBg/Dx8cFLL70Ef39/DBkyBK+//jpCQkLQunVr7N69Gx9//HGZ4aGu2P+7e+211zB+/HgoFIoqBQR1acyYMRgyZAiefvpp5Ofno2/fvvjzzz9dzmD69ttv47bbbsPtt9+Ov//972jdujVyc3Nx8eJF/PDDD9JMpB999BHWr1+PNWvWoFu3bujWrRvmzZuHZ555BoMHD5aGg1bHsmXLMHr0aAwfPhyLFi2CWq3GO++8g5MnT+Lzzz+vdeblxIkT+Prrr8ts79evH0aPHg21Wo2pU6fi6aefRlFREd59911kZWVV+fwzZ87EW2+9hQceeAD//ve/0b59e/zyyy/49ddfATg3Wb7//vsYP348xo4diwcffBDNmzdHZmYmzpw5gyNHjjhNIeBuAQEBeOGFF/CPf/wDM2bMwNSpU5GRkYGXXnoJWq0WL774Yo3O2717d+zatQs//PADmjVrBj8/P3Tq1An9+/eHl5cX9u/fX6b/1tWrV6Usx6VLlwBAekb2LCsApKWl4c0338TgwYPh5+eHs2fPYvny5ZDL5U7D/zUaDebMmYMVK1ZgxowZuP/++6FQKLB582Zs2LABs2bNkpptFAoFli9fjpiYGMyePRtTp07FhQsX8PTTT2P06NFl+ja58tVXX2HWrFno1asXZs+eXSYT17t3bykwXrRoEdatW4c777wTL7/8MjQaDV599VUUFRU5zbJ77tw56R+wV155BRcuXJBGjAElQ+ntfvnlF+Tn50vDpk+fPi19D++44w54e3tLZe3BXW1GilE1NGi3YmrUsrKyhFwuFz4+Pk4TQ9knqpo8ebLL4zZv3iyGDx8u/P39hUajEa1atRL33Xef2L59u1QmKSlJ3HvvvSIwMFD4+fmJcePGiZMnT4pWrVo5jb5wNTrJYDCIhx9+WISGhgqZTCYAiPj4eCFE+ZPdlT6vKxWNTkpPT3cqax9RYr+uEEJkZ2eLhx56SAQEBAhvb28xevRocfbsWZejGeLj48VDDz0kmjdvLlQqlQgNDRWDBg0S//73v4UQQhw/flx4eXmVueeioiLRp08f0bp16zKTmrmqi6vRQn/88YcYMWKE8PHxEV5eXmLAgAHihx9+cFm/0iNmKrteeS/79/SHH34QPXv2FFqtVjRv3lw89dRT0sgux2c8dOhQ0a1bN5fXSkhIEJMnTxa+vr7Cz89PmjUWgPjuu++cyh47dkxMmTJFhIWFCZVKJSIiIsSIESPEe++9V2mdyvtZcmT/+fzqq69c7v/oo49Ejx49hFqtFjqdTtx1113i1KlTTmWqMzopLi5ODB48WHh7ewsAYujQodK+mJgY0bVr1zL3YD+/q5fjz1dGRoYYM2aMCA0NFSqVSrRs2VLMnz+/zM++ELaRZR9++KHo27evCAgIEP7+/qJ3795i9erVLieR27Bhg/R9iIiIEI8//niZUVDlmTlzZoU/W46/g0LYJiS8++67hb+/v/D29hYjR44UsbGxVf6elP4bIITt70dVrx8TEyO6d+9epbpR7cmEKKcLOBGRh1i6dCmef/55JCQkSBP93WwOHz6Mfv36Yf/+/ejfv39D385NKScnB5GRkXjrrbfwyCOPNPTt3BQYxBCRR1m9ejUAWz8Qk8mE3377Df/5z39w//3349NPP23gu2tY999/P/Lz86s0QzW530svvYQvvvgCx48fd+p/Q3WH32Ui8ije3t546623cOXKFRgMBrRs2RLPPPOMNPPtzezNN9/Exx9/jNzcXKe5kqh++Pv7Y+3atQxg6hEzMUREROSROGMvEREReSQGMUREROSRGMQQERGRR2qyvY+sViuuX78OPz8/t06dTkRERHVHCIHc3FxERkY6TWDpSpMNYq5fvy5NO01ERESeJTExsdJ5n5psEGMfXpiYmAh/f/8GvhsiIiKqipycHERFRVVpmoAmG8TYm5D8/f0ZxBAREXmYqnQFYcdeIiIi8kgMYoiIiMgjMYghIiIij8QghoiIiDwSgxgiIiLySAxiiIiIyCMxiCEiIiKPxCCGiIiIPBKDGCIiIvJI1Q5ifv/9d0ycOBGRkZGQyWTYvHlzmTJnzpzBpEmToNPp4OfnhwEDBiAhIUHabzAYMH/+fISEhMDHxweTJk1CUlKS0zmysrIQExMDnU4HnU6HmJgYZGdnV7uCRERE1DRVO4jJz89Hz549sXr1apf7L126hNtuuw2dO3fGrl27cOzYMbzwwgvQarVSmQULFmDTpk3YuHEj9uzZg7y8PEyYMAEWi0UqM23aNMTFxWHLli3YsmUL4uLiEBMTU4MqEhERUVMkE0KIGh8sk2HTpk24++67pW1//etfoVKpsG7dOpfH6PV6hIaGYt26dbj//vsBlKw4/fPPP2Ps2LE4c+YMunbtiv3796N///4AgP3792PgwIE4e/YsOnXqVOm95eTkQKfTQa/Xc+0kIiIiD1Gdz2+39omxWq346aef0LFjR4wdOxZhYWHo37+/U5NTbGwsTCYTxowZI22LjIxEdHQ09u7dCwDYt28fdDqdFMAAwIABA6DT6aQypRkMBuTk5Di9iIiIyLN8+MelKpd1axCTlpaGvLw8vPrqqxg3bhy2bt2Ke+65B5MnT8bu3bsBACkpKVCr1QgMDHQ6Njw8HCkpKVKZsLCwMucPCwuTypS2bNkyqf+MTqdDVFSUO6tGRERE9eDn464/511xeyYGAO666y488cQT6NWrF5599llMmDAB7733XoXHCiGclt12tQR36TKOFi9eDL1eL70SExNrURMiIiJqCAazpfJCxdwaxISEhECpVKJr165O27t06SKNToqIiIDRaERWVpZTmbS0NISHh0tlUlNTy5w/PT1dKlOaRqOBv7+/04uIiIg8i9FsrXJZtwYxarUa/fr1w7lz55y2nz9/Hq1atQIA9OnTByqVCtu2bZP2Jycn4+TJkxg0aBAAYODAgdDr9Th48KBU5sCBA9Dr9VIZIiIianqqE8Qoq3vyvLw8XLx4UXofHx+PuLg4BAUFoWXLlnjqqadw//33Y8iQIRg+fDi2bNmCH374Abt27QIA6HQ6zJo1CwsXLkRwcDCCgoKwaNEidO/eHaNGjQJgy9yMGzcOjzzyCN5//30AwKOPPooJEyZUaWQSEREReSaDpepBDEQ17dy5UwAo85o5c6ZU5uOPPxbt27cXWq1W9OzZU2zevNnpHIWFhWLevHkiKChIeHl5iQkTJoiEhASnMhkZGWL69OnCz89P+Pn5ienTp4usrKwq36derxcAhF6vr24ViYiIqIG0Xfh1lT+/azVPTGPGeWKIiIg8i9Uq0Hrh10hcOaX+54khIiIiqiljdZqSwCCGiIiIGglDNTr1AgxiiIiIqJGozhwxAIMYIiIiaiQMJmZiiIiIyAOxTwwRERHVqcvpeRi38nf8cOy6W8/LTAwRERHVqae+Po6zKbmY//lRt56XfWKIiIjI7YpMFlistqnl0nKL6uQa1VlyAGAQQ0RERJXILjBi0Ku/4W9rDwGofrNPVXGINREREbnV17FJyMw34vfz6QCqH2xUFYMYIiIicptzKbnYeS5Nem8wW1Bkql7flfIkZBTg2W+O41J6HoDqNydVexVrIiIiujlcTMvFxFV7nIY+6wtNbsvEfHk4ERsPJcJHo8QLE7qyYy8RERG5x4vfnyozd0tOoclt588psp0rM98IgM1JRERE5AaFRgv+vJhRZvsd/9njtmsUGG2ZF31xYMTRSURERFRrqTm2YdQapRyPj+wgba9uoFGRwlJBDJuTiIiIqNbsQUwznRZPju6I2zuEuCxnnzumJgqMZgAlTVScsZeIiIhqLTXXAAAI89cCAAK81S7LVTd74qhMcxLXTiIiIqLaStXbMjER9iDGS+WyXFEtJr4rNJVuTmIQQ0RERLVkb04K99cAAHTlBDGFtZgzxp6JMZitKDJZYKjmuRjEEBERURn25qRwqTmpvExMzYMYe8dewNYvhs1JREREVGv25iR7EOOtdj0/bm2CGHvHXqB4Ej127CUiIqLaSs11DmIcg5XDz49Ci0Cv4u017xNT4JCJqclMwAxiiIiIqIwbxc1JoX62PjFtQn2kfSG+GnipFABQ7X4sdharcApaahLEcO0kIiIicmK2WJFfnCWxd+gd1jEU/7qrG6Kb6wAA2uIgpqiGQ6xLdwi2BTHVOxeDGCIiInKSZyjpq+KntYUKMpkMMQNbS9u1KltjTk2bkxz7wwBsTiIiIiI3yCm0BRheKgVUCtehgpSJqWFzkuPIJPs1uXYSERER1Yp9dWl7FsaVkiCmppkYV81JDGKIiIioFuxBjH85E9wBtc/EuA5iONkdERER1UJuka05qcJMjLK4T0xNO/a6CGLYnERERES1UhLEVCUT456OvTlsTiIiIqLayilekNG/wj4xthCipvPE2IdYy2W297YZe9mcRERERLVQvUxM7frEhPnZZgTW18faSb///jsmTpyIyMhIyGQybN68udyys2fPhkwmw8qVK522GwwGzJ8/HyEhIfDx8cGkSZOQlJTkVCYrKwsxMTHQ6XTQ6XSIiYlBdnZ2dW+XiIiIqqmkY2/lo5NyDeZyy1TEHsRE6EqCmDpvTsrPz0fPnj2xevXqCstt3rwZBw4cQGRkZJl9CxYswKZNm7Bx40bs2bMHeXl5mDBhAiyWkmhu2rRpiIuLw5YtW7BlyxbExcUhJiamurdLRERE1ZRrD2IqyMS0D/MFAJy8pq/RNewZnHB/27IGhSYLhKjeOao9Y+/48eMxfvz4Cstcu3YN8+bNw6+//oo777zTaZ9er8fHH3+MdevWYdSoUQCA9evXIyoqCtu3b8fYsWNx5swZbNmyBfv370f//v0BAB9++CEGDhyIc+fOoVOnTtW9bSIiIqoie3NSRX1ibmkZCAA4n5oHfaFJWp6gPFargNzeAQYlQYx9baaacHufGKvVipiYGDz11FPo1q1bmf2xsbEwmUwYM2aMtC0yMhLR0dHYu3cvAGDfvn3Q6XRSAAMAAwYMgE6nk8qUZjAYkJOT4/QiIiKi6iuZ7K78wCTUT4PWwd4AgCMJWRWer8BoxpDXd2LuhiPSNnvTkbdaWeFQ7oq4PYh57bXXoFQq8fjjj7vcn5KSArVajcDAQKft4eHhSElJkcqEhYWVOTYsLEwqU9qyZcuk/jM6nQ5RUVG1rAkREdHNqSrzxADALa1sn+VbT7n+bLY7cDkTSVmF+Ol4MixWW5uRPROjVcqdsjgqhczlOVxxaxATGxuLt99+G2vXroVMVvWbAAAhhNMxro4vXcbR4sWLodfrpVdiYmL1bp6IiIgAOAyxrqSJ6N5bWgAAPj+YiNirmeWW89GUBEMpOUUASoIYjUrhFMSolVUPTdwaxPzxxx9IS0tDy5YtoVQqoVQqcfXqVSxcuBCtW7cGAERERMBoNCIryzn1lJaWhvDwcKlMampqmfOnp6dLZUrTaDTw9/d3ehEREVH1VTUTM7h9CO7s3gwAsOtcernlTA5Dp69m5AMoaU7SlMrEaMpZcNIVtwYxMTExOH78OOLi4qRXZGQknnrqKfz6668AgD59+kClUmHbtm3SccnJyTh58iQGDRoEABg4cCD0ej0OHjwolTlw4AD0er1UhoiIiNxPCOHQsbfiTAwARAXZ+sXkG8qfL8ZxLpnEzAKnbdpSmRhVNTIx1e5Jk5eXh4sXL0rv4+PjERcXh6CgILRs2RLBwcFO5VUqFSIiIqQRRTqdDrNmzcLChQsRHByMoKAgLFq0CN27d5dGK3Xp0gXjxo3DI488gvfffx8A8Oijj2LChAkcmURERFSHDGarNOlcVTrceqtt88UUmsqfL8ZxaYIEKYgpycQ4BkuaugxiDh8+jOHDh0vvn3zySQDAzJkzsXbt2iqd46233oJSqcSUKVNQWFiIkSNHYu3atVAoFFKZzz77DI8//rg0imnSpEmVzk1DREREtWMfmSSTAT7qqgcxpVeltiswmpFVYJTeJ2QWAoC0YrVWpYCvQ7BUnT4x1Q5ihg0bBlGN2WiuXLlSZptWq8WqVauwatWqco8LCgrC+vXrq3t7REREVAs5hcX9YTRKp3ldyuNVQRBjNFvR++VtTjPxJhT3ibFnYrQqhVPGR+2Q0KgM104iIiIiSW4V5ohxZM/WFLoIYlJzisosJZCWawDg2CdGDl+H0UvVaU5iEENEREQSqVNvJcOr7UoyMWX7xNibjBxl5hshhHAYnaRw6hPTYEOsiYiIyLOVzNZbtR4nFfWJySlyFdhYUWiywOCQiXFqTlI20GR3RERE5NmqM7waqDiIyXURxAC2bEyRuaRPjGPHXo2SfWKIiIioBkpWsK5aJsZLZSvnOogxuTwmK99UMmOvUu7U/0bdUJPdERERkWeTRidVszmp0KFPjH0Uc3mZmKwCo9QnRqtSOHXsZZ8YIiIiqhEpE1PFjr3emuLmJJMFQgi88tNp9F+6A2m5ReVmYtJyDdJCkFqlwinrU6fzxBAREVHTlVPFdZPsvIuHWAth67T74R/xAIBVOy4iwNt1IJScXSh9rVHJnQIXazXmomMQQ0RERJKSPjFVHGKtKumI69gv5mpmAeQyb6eyWpUcRSYrkotXsgbKzgtjMDnPK1MRNicRERGRpCQTU7UgRiGXSYGI41wxSVkFZfrEROq8AAAbDiQAsAUwMpkMMlnJsGrHFa8rwyCGiIiIJDmF1ZsnBnDs3FuSiUnKKiwzT0yETuv0XqsqO5zayCCGiIiIasLeJOSjqU4QYyv7vz/jpW1Gs1UKiOyCfNRO710tMcAghoiIiGqkJIip+qRz9kzM5wcTnbafTclxej+oXYjTe5WLOWFM5qp37GUQQ0RERBJ7vxb7wo5VYV8/qbTSzUndIv2x99kR0vvsAqP0detgWyfgUV3Dqnxdjk4iIiIiAIDVKqRMjHc5gYkr17OLKi8E23DqyAAv6X2+Qx+ab+cMxtGELNzSTIvHqnhdZmKIiIgIAFBoKgkqvKuRibmRZ6hSOW0F6yIF+agxsks4FHIuAElERETVlF/clCST2eZ0qSpZFeMO+2ik6py7IgxiiIiICABQYCju1KtWOs3dUpm1f7u1SuXswUuEv7aSklXDIIaIiIgAoEb9YQBgaMdQPDm6Y6Xl7JmY/0ztDT+NEksmdq3+TTpgx14iIiIC4DAyqRpzxNh5uZi4rjR18ZDqHi0CcOzFMZBXo/+LK8zEEBEREYCS0UJVCUhKq0o/F8egpbYBDMBMDBERERUrMNgzMdUPYjTlBD6hfhq8cnc0ArzVLvfXBoMYIiIiAlCSianO8Gq78rI3GqUcY7pF1Oq+ysPmJCIiIgLg2CemJs1J5QcxdYVBDBEREQFwHJ1U/UxMeX1iygtu3IFBDBEREQEo6RNT3SHWQMXNSXWFQQwREREBqF2fmPKbk5iJISIiojpWsoK1+4ZYa9y0xIArDGKIiIgIAJBfvOyAdw0muysv48LmJCIiIqpz9o69NcnEeDkc4ziPHTv2EhERUZ2zNyd51ag5qeQYtUP2pVFlYn7//XdMnDgRkZGRkMlk2Lx5s7TPZDLhmWeeQffu3eHj44PIyEjMmDED169fdzqHwWDA/PnzERISAh8fH0yaNAlJSUlOZbKyshATEwOdTgedToeYmBhkZ2fXqJJERERUuRR9EQAg2EdT7WO1DsGKSuEYxDSiTEx+fj569uyJ1atXl9lXUFCAI0eO4IUXXsCRI0fw7bff4vz585g0aZJTuQULFmDTpk3YuHEj9uzZg7y8PEyYMAEWi0UqM23aNMTFxWHLli3YsmUL4uLiEBMTU4MqEhERUWVMFisSMgsAAG1Dfap9vNIhcFEr6icTU+2eO+PHj8f48eNd7tPpdNi2bZvTtlWrVuHWW29FQkICWrZsCb1ej48//hjr1q3DqFGjAADr169HVFQUtm/fjrFjx+LMmTPYsmUL9u/fj/79+wMAPvzwQwwcOBDnzp1Dp06dqnvbREREVIGEzAKYrQJeKgUi/LW1OpdSUdIpxqNHJ+n1eshkMgQEBAAAYmNjYTKZMGbMGKlMZGQkoqOjsXfvXgDAvn37oNPppAAGAAYMGACdTieVKc1gMCAnJ8fpRURERFVzOT0fANAmxKfWK0w7NidpG1NzUnUUFRXh2WefxbRp0+Dv7w8ASElJgVqtRmBgoFPZ8PBwpKSkSGXCwsLKnC8sLEwqU9qyZcuk/jM6nQ5RUVFurg0REVHTdSk9DwDQLsy31udy6tjriZkYk8mEv/71r7BarXjnnXcqLS+EgExWEvk5fl1eGUeLFy+GXq+XXomJiTW/eSIiopvM5eIgpm1I9fvDlKaSN9KOvVVhMpkwZcoUxMfHY9u2bVIWBgAiIiJgNBqRlZXldExaWhrCw8OlMqmpqWXOm56eLpUpTaPRwN/f3+lFREREVZOSYwAAtAj0qvW5VEqHPjGNaYh1ZewBzIULF7B9+3YEBwc77e/Tpw9UKpVTB+Dk5GScPHkSgwYNAgAMHDgQer0eBw8elMocOHAAer1eKkNERETuY1/80U9b/dl67aKCbAHQ3b2aS9vqsjmp2neal5eHixcvSu/j4+MRFxeHoKAgREZG4r777sORI0fw448/wmKxSH1YgoKCoFarodPpMGvWLCxcuBDBwcEICgrCokWL0L17d2m0UpcuXTBu3Dg88sgjeP/99wEAjz76KCZMmMCRSURERHWgNos/2v04/3ZcSs9DM50W//7pDIC67dhb7Ts9fPgwhg8fLr1/8sknAQAzZ87EkiVL8P333wMAevXq5XTczp07MWzYMADAW2+9BaVSiSlTpqCwsBAjR47E2rVroVCUVPSzzz7D448/Lo1imjRpksu5aYiIiKj2pMUfNTUPOnReKtzSMhCZ+UZpm+NIJXerdhAzbNgwCCHK3V/RPjutVotVq1Zh1apV5ZYJCgrC+vXrq3t7REREVAPS4o+1yMTYOY5OKmc8jltw7SQiIiIqycS4IYipy868jhjEEBER3eSsVoFCU3EmphbNSXZKueOUKbU+XbkYxBAREd3kiswW2HuDuCMT4zTvG+ouimEQQ0REdJOz94eRyQCtm4ZE+6htGZ0eLXRuOZ8rtQ+3iIiIyKPZ+8N4qxTlzoxfXQefG4UCowXBvhq3nM8VBjFEREQ3OWlkksZ9YYGPRgkfN57PFTYnERER3eRKRibV3cR0dYFBDBER0U3OHbP1NgQGMURERDc5+7pJtZmttyEwiCEiIrrJMRNDREREHskd6yY1BAYxRERENzl3rptUnxjEEBER3eQ4OomIiIg8Ul3ME1MfGMQQERHd5JiJISIiIo9UUDw6SatiEENEREQexGSxAgA0Ss8KCzzrbomIiMjt7EGMmkEMEREReRKD2RbEqBSeFRZ41t0SERGR29kzMQxiiIiIyKOYLAIAm5OIiIjIwxiLm5PUzMQQERGRJ2FzEhEREXkkoxTEyBr4TqqHQQwREdFNjkOsiYiIyCMZOcSaiIiIPBFHJxEREZFHMnF0EhEREXkig71jLzMxRERE5CmEEA5DrDk6iYiIiDyExSogbF1imn5z0u+//46JEyciMjISMpkMmzdvdtovhMCSJUsQGRkJLy8vDBs2DKdOnXIqYzAYMH/+fISEhMDHxweTJk1CUlKSU5msrCzExMRAp9NBp9MhJiYG2dnZ1a4gERERlc/eqRe4CTr25ufno2fPnli9erXL/cuXL8eKFSuwevVqHDp0CBERERg9ejRyc3OlMgsWLMCmTZuwceNG7NmzB3l5eZgwYQIsFotUZtq0aYiLi8OWLVuwZcsWxMXFISYmpgZVJCIi8kxCCCz+9gSe/voYLFZR+QE1YB9eDXjeEGuIWgAgNm3aJL23Wq0iIiJCvPrqq9K2oqIiodPpxHvvvSeEECI7O1uoVCqxceNGqcy1a9eEXC4XW7ZsEUIIcfr0aQFA7N+/Xyqzb98+AUCcPXu2Svem1+sFAKHX62tTRSIiogZzLatAtHrmR9HqmR/FOzsv1sk10nKKpGtYrdY6uUZ1VOfz260hV3x8PFJSUjBmzBhpm0ajwdChQ7F3714AQGxsLEwmk1OZyMhIREdHS2X27dsHnU6H/v37S2UGDBgAnU4nlSnNYDAgJyfH6UVEROTJrmYUSF+/t/tSnVzDcbZemewm7tibkpICAAgPD3faHh4eLu1LSUmBWq1GYGBghWXCwsLKnD8sLEwqU9qyZcuk/jM6nQ5RUVG1rg8REVFDSsjMl77WF5pgtlgrKF0znrqCNVBHo5NKR3JCiEqju9JlXJWv6DyLFy+GXq+XXomJiTW4cyIiosbDMRMDAAUmSzkla85Th1cDbg5iIiIiAKBMtiQtLU3KzkRERMBoNCIrK6vCMqmpqWXOn56eXibLY6fRaODv7+/0IiIi8mSlg5hCY/WCGJPFiqJKAp+SFaxv8kxMmzZtEBERgW3btknbjEYjdu/ejUGDBgEA+vTpA5VK5VQmOTkZJ0+elMoMHDgQer0eBw8elMocOHAAer1eKkNERNTUXXVoTgKAfIO5yscKITBx1R6MfHM3DObyAxlPXTcJAJTVPSAvLw8XL16U3sfHxyMuLg5BQUFo2bIlFixYgKVLl6JDhw7o0KEDli5dCm9vb0ybNg0AoNPpMGvWLCxcuBDBwcEICgrCokWL0L17d4waNQoA0KVLF4wbNw6PPPII3n//fQDAo48+igkTJqBTp07uqDcREVGjJoQo25xUjUzMjTwjzqbYpje5llWItqG+Lst5cp+Yagcxhw8fxvDhw6X3Tz75JABg5syZWLt2LZ5++mkUFhZizpw5yMrKQv/+/bF161b4+flJx7z11ltQKpWYMmUKCgsLMXLkSKxduxYKhUIq89lnn+Hxxx+XRjFNmjSp3LlpiIiImpqzKbnILTJDq5Ij2EeDa9mF1QpiHDsFp+Uayg1iTB7cnFTtIGbYsGEQovwJd2QyGZYsWYIlS5aUW0ar1WLVqlVYtWpVuWWCgoKwfv366t4eERFRk7DzXBoAYFC7EKTmFBUHMWWbk8ob9OKYxUnNKSr3OkaHIdaexvPumIiI6Caw86wtiBneKRTealtLRelMzPbTqeixZCu2nEwuc7xjEJOeayj3OvbmpJt+dBIRERHVXpHJgqMJ2QCAoR3D4K22NZzYg5hT1/W49ZXtePjTw8g1mDFvw1EIIZCZb5TOkZBZEsSkuQhisguMeHv7BVxOtzU73RTNSURERFS3Tl7Tw2wVCPHVICrIS8rEFBY3J63cfsEpMJHJgFmfHMZvZ9Pwzd8Hok+rIFzNcOgT46I5acn3p7A57rr0ns1JREREVGtxidkAgF5RAZDJZFImJr84E1M64DBZBH4rbn6yZ3Aqy8TsuZjh9N4TRyd53h0TERE1MVcz8rF8y1nkFc8DUxLE6ACgTJ8YX3X5DSnZBSbkGcy4kVfStOQqiPFSO4cAbE4iIiKiahv55m6YrQImixXP3dkVx5KyAQC9omzrDHprioOY4iAnu9Do8jwAkFlgREKp+WVcNSd5q5xDABWbk4iIiKg6copMMFttU5ecvJaDjDwDEjMLAQA97JmY4oDDvnZSVr6p3PNl5RulOWLahvgUX8NcZtZerVrh9J7NSURERFQtu86lS18rFTKpKaldqA/8tSoAgE+pTExmQfmZmIx8ozS8uktkyTqC+QbnIMZQak0ltZJDrImIiKga7PPBALa5XQ7GZwIoaUoCAK9SfWKyi4OY9x64pcz5DsZnYtkvZwHYMjFale2jvvS6SzmFztkcT+wT43l3TERE1EQIIbDn4g3pfUJmAd7//TKAkk69gHPHXqtVIKvAFoC0CPSu8Pwtg7zhI41scg5i9AxiiIiIqKbOp+YhPdcAjYtOtcM6hUlfl0x2Z0ZukRmW4j40UUEVBzFtQnzgoykOYhyak0wWqzRc247zxBAREVGV/VmchenfNthp+5mXxzkFKI6ZmKzipiQftQL+2vIHGT82tB1uaRkoHevYnFQ6CwMAXipFmW2NHYdYExERNZD4G7ZRRD2a66CUy/Db2TT838gOUh8YO3sm5mxKLuZuOAIACPBWu1z4EQCaB3jh2fGdAQC+mpIsjl12cXOUn1aJVyf3wI4zqbi7V3M31qx+MIghIiJqIPaFGcP8NZgxsDsOX83CuG4RZcoFequkr09dzwEABPmoyz1vqJ9G+trbRXOSPRMT4K3CnT2a4c4ezWpRi4bD5iQiIqIGkpZrm4Qu1FeDMH8t7ujeDHJ52exKmxAfTO/f0mmbzktVppxd31YlI5t87M1JDpkY+8ikis7hCRjEEBERNZD0vJJMTEVkMhkeHdLWaZu3umwfljV/64f7+rTAE6M7Sttcdey1z/gb4FV+NscTsDmJiIioAQghpOakUF9tpeVL95Mp/R4AhncKw3CHUU2AQybGoWNvao7tuhU1SXkCZmKIiIgaQK7BjCKTFYBzH5bylB495CoT44qUiXFoTjqfkgsA6BDmW6VzNFYMYoiIiBqAPQvjp1G6zKqUpi0VxHipqtaYYg9iChyak86l2oKYjhF+VTpHY8UghoiIqAFITUlVyMIAthl1VYqSTr/2TMxr93aHXAa8H9PH5XH25qS84kyM2WLFhbQ8AEBnDw9i2CeGiIionukLTVj01TEAQEgVgxjA1qRkstiCEXv25v5+LXFXr+ZlMjV23lImxnbc1cwCGM1WeKkUiKpk2YLGjpkYIiKievb9setIyioEAIRVJ4hxaHZy7BNTXgADoGTtpOLmpAvFTUkdwn1dDuf2JAxiiIiI6llCRr70dXVmynXs3FvVZQJ8NM7zxNhHJrUI9KrydRsrBjFERET17Fq2LQvz4sSuGNU1vMrHOWZcqtIZGHCcJ8YWxNwonpsm2KfqGaDGin1iiIiI6okQAr+dTcPB+EwAtjWOqsPbqTmpiqOT7M1JxatW24OYEF8GMURERFRF3x+7jv/bGCe9b17NJp3y+sRURGpOkjIxttl6Q/w8e6I7gEEMERFRnbJaBT7ZdwUB3iq8s/OS074WAdUbHeRVg+YkjdJWzmi2TazH5iQiIiKqkNUqcO97e3E0IbvcMv5e1fsYduwTU9VMjLJ4bhmzVUAIIQUxoU0gE8OOvURERHXgdHJOhQEMYFvYsTocA5eqjk5SyUs+6k0WgRu5xc1JTaBPDIMYIiKiOrD7fLrL7e9OvwUAMGNgq2qfsyajk1TKkkApp8iEQpOtg29wEwhi2JxERERUB3adS5O+7tMqEJNvaY42wT4Y1D4EB/8xEgHe1W/OcczbVHV0ktIhE5OiLwIAaFVyaTkCT+b2TIzZbMbzzz+PNm3awMvLC23btsXLL78Mq9UqlRFCYMmSJYiMjISXlxeGDRuGU6dOOZ3HYDBg/vz5CAkJgY+PDyZNmoSkpCR33y4REZHbbTiQgENXsiCTAX88PRzf/H0QpvdvhUHtQwAAYf5aqJXV/wgWDl9XuTnJYb0lexAT4qupdlNWY+T2IOa1117De++9h9WrV+PMmTNYvnw5Xn/9daxatUoqs3z5cqxYsQKrV6/GoUOHEBERgdGjRyM3N1cqs2DBAmzatAkbN27Enj17kJeXhwkTJsBisbi6LBERUaOQkFGAl36w/WO+aEwnRAXVzfpEiiouGSCTyaSyyTm2IKYpNCUBddCctG/fPtx111248847AQCtW7fG559/jsOHDwOwZWFWrlyJ5557DpMnTwYAfPLJJwgPD8eGDRswe/Zs6PV6fPzxx1i3bh1GjRoFAFi/fj2ioqKwfft2jB071t23TURE5BavbTkLg9mKQe2CMWdYu4a+HQCAUi6DxSqQWpyJCfX1/JFJQB1kYm677Tbs2LED58+fBwAcO3YMe/bswR133AEAiI+PR0pKCsaMGSMdo9FoMHToUOzduxcAEBsbC5PJ5FQmMjIS0dHRUpnSDAYDcnJynF5ERET17cQ1PQBg3oj2bm+yEaLyMq6oFLaP++t623IHTWFkElAHmZhnnnkGer0enTt3hkKhgMViwSuvvIKpU6cCAFJSUgAA4eHOa0WEh4fj6tWrUhm1Wo3AwMAyZezHl7Zs2TK89NJL7q4OERHdJMwWK+ISsyEA9GsdVOPzZBfYhjCH+WnddGclBGoWxdjnirH3iQlmJsa1L774AuvXr8eGDRtw5MgRfPLJJ3jjjTfwySefOJUrHZ0KISqNWCsqs3jxYuj1eumVmJhYu4oQEdFNIyPPgL+8vw/3vbcPf3lvHz7Ze6VG57FYBXKKbNP7B3qr3HiHNl2a+dfoOHsmxrFjb1Pg9kzMU089hWeffRZ//etfAQDdu3fH1atXsWzZMsycORMREREAbNmWZs2aScelpaVJ2ZmIiAgYjUZkZWU5ZWPS0tIwaNAgl9fVaDTQaJrGQyEiovpzPbsQ0z7cjysZBdK2f/90GoPbB6N9mF+1zqUvNElf67zcH8Tc3zcKOYVmDGoXXK3jVMUde1NymlYQ4/ZMTEFBAeRy59MqFAppiHWbNm0QERGBbdu2SfuNRiN2794tBSh9+vSBSqVyKpOcnIyTJ0+WG8QQERHVxEd/xONKRgFaBHph+5NDMaxTKEwWgXd3Xa72uexNSX4aJZQK988nq1TI8fdh7dAzKqDaxwFAgdE+0V3TaE5yeyZm4sSJeOWVV9CyZUt069YNR48exYoVK/DQQw8BsDUjLViwAEuXLkWHDh3QoUMHLF26FN7e3pg2bRoAQKfTYdasWVi4cCGCg4MRFBSERYsWoXv37tJoJSIiIne4nm3r7ProkLZoH+aLBaM6Yte5dHwXdw3Pju+MUL+qZS1yi0zYfzkTAKCrg6ak2lAqnLtihDaRTIzbg5hVq1bhhRdewJw5c5CWlobIyEjMnj0b//znP6UyTz/9NAoLCzFnzhxkZWWhf//+2Lp1K/z8StJ2b731FpRKJaZMmYLCwkKMHDkSa9euhULh+TMMEhFR/TqXkgtvtcJpzhaTxYojV7OQXDxiJ8jHlp3oFRWAtiE+uHwjHxdSc6sUxOw+n455G44gV+oP07gyHapSLSRNpTnJ7UGMn58fVq5ciZUrV5ZbRiaTYcmSJViyZEm5ZbRaLVatWuU0SR4REVF1peUUYezK3wEAE3tGYnC7YIyLjsDUDw/gTHLJdBzBPiUf7BE6LS7fyEdarqHS81usAv/87qQUwABAQCPOxCjksjrpr9MQuHYSERE1STvOpCI1x4Agn5IP7B+OXccPx67jWnahUwADOPcTsWdf0qsQxGw/k4qrDp2Cgbrp1FsbKof+OcE+asirONtvY8cghoiImpy03CLM+sQ2U/yYruFl9n91uOxafPbmJAAIKw5i0nKLKr2WfaHHFoFeSMqyNU01uuYkh0yMn7bpfPQ3nZoQEdFN72JaLvZdynBqBtp6OrVMOftQYzuZzDnwqE4mJi3HVmZM1wj87894AI0vE+O4krW2igtHegIGMURE1GSMWvF7lcsO7xSKnefSAQAKh0USgZLZdqvSJ+ZGnq1M39aBUhBjKp5WpLFw7BPTlIIY9w9iJyIiagD2odJV1a9NydICZqvzdP7VycTYy0QGeEnb9AWm8oo3CMc+MRpl0/nobzo1ISKim9pvZ9Oc3nupFFh+Xw/c2b0ZWgR6lSnfLVJX7rlK+sRUHMQIIZBenIkJ9dPg7l6RAICZg1pX59brnNIhy8QghoiIqJH540K60/uoIC9M6RuF/06/BQvHdCxTvltk+esQ2TMx+kITikyWcsvpC00wWWxZnBBfNVZM6YWjL4yu8RpHdcU5E8PmJCIiokblYlqe0/uWDhPb+WnKdrQN8dUguHhEUumOuDovFdTFGQt7nxdX7E1JOi8VNEoF5HIZAn0a18gkwHl0kkbVdD76m05NiIjopmW2WJGQ6TxXS4vAkiBmaKdQjOoSjhkDW6FdqA8WFWdmPnnoVgxqF4xPHrrV6ViZTCZNzX/6ek65fWPs26u6NEFDUTbRPjEcnURERB7vWnYhTBYBlUImNe+oHT6sVQo5PprZt8xx0c112PDIAJfnDPXT4Fp2IR5dFwulXIYnx3TEnGHtncpI/WEa+TT+TpkYNicRERE1Hpdv5AMA2ob4StsiddpanTPMIbtitgqs2HoellKjmFL0tvlmGn0mRs5MDBERUaN0pTiIaRPigznD22Hn2TT89daWtTpn6cDEbBW4kWdAuL8WVqvA+79fxn92XAAAtA72dnWKRqOpzhPDIIaIiDxevD2ICfXBXb2a465ezWt9TvuEd45S9EUI99fi6yNJeG3LWQDA4PbBmHVb21pfry5xnhgiIqJGKt4hE+MurpqIkoubj34+kQwAmNK3BdY91B+6RrZqdWkcnURERNRIXU53fxAT5iKISc0pQr7BjL0XMwAAj9ze1iNWhHbuE9N0mpMYxBARkUcrMllwXW9bcqCuMjH2+WSS9UXYfzkDRosVLYO80T7Mt7zDGxXn0UlN56O/6dSEiIhuSgmZBRAC8NMqpWDDHcL8S4KY3i0DAdgyMfY1mro084NM1vizMECpeWLYnERERNQ42JuS2ob4uDWoCPF1DGICAADJ+kJkFS/uGNQIZ+Ytj/PaSU2nOYmjk4iIyKNdybAFMa3d2JQE2Eb0vHV/T+QVmdEh3A8AkJpjQGa+EQAQ4O05QUxTHZ3EIIaIiDyafY6Y1sHuDWIA4J7eLQAAl9Nt6zKl5xqQVWALYoI8NIhpSvPENJ1wjIiIbkqJWbY1kxwXfHS3YB9b01KewYy0HNtSA41xocfyKNmxl4iIqPFJzLR1tI2qwyDGT6uEorhfyeUbtqxMkE/jnhvGEddOIiIiakSy8o3453cnpdWrWwR61dm15HIZAosntEu1Z2I8qDnJaZ4Yjk4iIiJqWMt+OYNP910FYMs0hPvXbsHHypTuyOtJQQzniSEiImpELqblSV+H+Wml5p66Urojr0f1ieGMvURERI2H44y66bmGOr9eoEMfGIVcBn+t5wzwdYhhmIkhIiJqaBl5RunrVsF116nXznFyu0BvtcfM1gsAVmvJ1xxiTURE1MDS82zZF3+tEv+Z2rvOr+fYByawka9aXZpFCOlrdRPKxHhOLoyIiMiBvQnp+3m3uX22XlccMzFtQ+v+eu5ktZYEMXXdd6g+NZ1wjIiIbhr5BjMKjBYAzn1j6pJjJmZ4p7B6uaa7OGZimhIGMURE5HHsWRhvtQI+mvppVHCc9XZ4Z88KYjxpscrqYHMSERF5lAKjGfM+PwKg/rIwAHBrmyBolHL0aKGr8zlp3G1g22A8PqI9Okb4NfStuFWdZGKuXbuGBx54AMHBwfD29kavXr0QGxsr7RdCYMmSJYiMjISXlxeGDRuGU6dOOZ3DYDBg/vz5CAkJgY+PDyZNmoSkpKS6uF0iIvIgP59IwclrOQCAIpOl3q7bTOeFfYtH4tOH+tfbNd1FJpPhyTGdMKFHZEPfilu5PYjJysrC4MGDoVKp8Msvv+D06dN48803ERAQIJVZvnw5VqxYgdWrV+PQoUOIiIjA6NGjkZubK5VZsGABNm3ahI0bN2LPnj3Iy8vDhAkTYLHU3w8sERE1Po6T3A1uH1Kv1w7yUcNL3XSGKHs6mRDu7e3z7LPP4s8//8Qff/zhcr8QApGRkViwYAGeeeYZALasS3h4OF577TXMnj0ber0eoaGhWLduHe6//34AwPXr1xEVFYWff/4ZY8eOLXNeg8EAg6FksqOcnBxERUVBr9fD39/fnVUkIqIGNPezI/jpRDK6NvPHmr/187imHapYTk4OdDpdlT6/3Z6J+f7779G3b1/85S9/QVhYGHr37o0PP/xQ2h8fH4+UlBSMGTNG2qbRaDB06FDs3bsXABAbGwuTyeRUJjIyEtHR0VKZ0pYtWwadTie9oqKi3F01IiJqBOJv5AMAFo7pyADmJuf2IOby5ct499130aFDB/z666947LHH8Pjjj+PTTz8FAKSkpAAAwsPDnY4LDw+X9qWkpECtViMwMLDcMqUtXrwYer1eeiUmJrq7akRE1MCEELiaYQtiWgV71lwt5H5uH51ktVrRt29fLF26FADQu3dvnDp1Cu+++y5mzJghlSs9XbMQotIpnCsqo9FooNHUXy91IiKqfzfyjMg3WiCXAVFBXg19O9TA3J6JadasGbp27eq0rUuXLkhISAAAREREAECZjEpaWpqUnYmIiIDRaERWVla5ZYiI6OZzJtk2KikywKtJrcZMNeP2IGbw4ME4d+6c07bz58+jVatWAIA2bdogIiIC27Ztk/YbjUbs3r0bgwYNAgD06dMHKpXKqUxycjJOnjwplSEiopuLwWzBv348DcA27wmR25uTnnjiCQwaNAhLly7FlClTcPDgQXzwwQf44IMPANiakRYsWIClS5eiQ4cO6NChA5YuXQpvb29MmzYNAKDT6TBr1iwsXLgQwcHBCAoKwqJFi9C9e3eMGjXK3bdMREQeIPZKFi6k5SHQW4XFd3Rp6NuhRsDtQUy/fv2wadMmLF68GC+//DLatGmDlStXYvr06VKZp59+GoWFhZgzZw6ysrLQv39/bN26FX5+JTMJvvXWW1AqlZgyZQoKCwsxcuRIrF27FgoF04dERDejS8Wjkvq0Cmyy0+hT9bh9npjGojrjzImIqPH794+n8dGeeDx8Wxs8P6Fr5QeQR2rQeWKIiIjqwhX70OoQDq0mGwYxRETkEeyT3LXh/DBUjEEMERE1eharQGJmIQCgdYh3A98NNRYMYoiIqNGLv5EHo8UKtVKOSB0nuSMbBjFERNTofbznCgCgf5sgyOUVz+5ONw8GMURE1KjpC0346rBtPbzHR3Zo4LuhxoRBDBERNWpXM/JhtgqE+WnQr3VQQ98ONSIMYoiIyO2y8o3SaKLaup5dBMC2XhKRIwYxRETkVkIITP1wP0a+uQs7zqTW+nzJetuopMgAba3PRU0LgxgiInKrA/GZOJuSC6sA5m04irScolqdL1lvOz7Cn5kYcsYghoiI3EYIgf/tiZfeF5oseHPr+Vqd0x7EMBNDpTGIISIit1n920VsPZ0KmQz4113dAABfxiYitZxsTHaBEat/u4AbeYZyz5mcbWtOasb5YagUBjFEROQ2Gw/ZhkIvmdgNMQNbo1dUAIQAdp5Nc1n+pR9O442t5zFn/ZFyzyk1J+mYiSFnDGKIiMgtUvRFuJZdCLkMuLdPCwDAyM5hAIAdLoKYPIMZm45eAwAcvJKJk9f0ZcpYrELK4rA5iUpjEENERG5xJCELANA5wh++GiUAYEQXWxCz58INmC1Wp/I/H092er/lZEqZcx6Mz4TZKuCnVSLMj0EMOWMQQ0REtWYwW7B27xUAQJ9WgdL2LhH+UMhlKDRZcCPP6HTMr6dsQYuPWgEAuF48lBqwTXC399INbDqaBAC4s3szKLjcAJWibOgbICIiz7d8yzkcjM8EAAzvHCptl8tlCPPTIFlfhJScIqlfS6HRgj0XbwAAYga2xnu7LyGluO+L1SoQ8/FBJGQWSOe5p3fz+qoKeRBmYoiIqNZOFPdnmdK3BUZ0DnfaF+ZvC1wcRyj9efEGDGYrWgR6YWhHW9BjD2KOJWU7BTAjO4dxuQFyiZkYIiKqtYQMW9Ax9daWZfZF+GtwDM5BzLnUXADArW2CpA67l2/k4/VfzyKn0CyVe3pcJzxye1uuXE0uMYghIqJaKTJZkFIcoLQK9imzP9xFJuZa8dwvLQK9pf0A8N+dl6Sv35l+C+7o3qxO7pmaBjYnERFRrSRl2bIwvholAr1VZfbbg5QUfcmEdteLg5jmAVpoVQqoFc4fRzovFUYUD88mKg+DGCIiqpWrxU1JLYO8IZOVbfaxBzFpuSWZmOulZuE1lhp+PaZrOLQqRZ3cLzUdDGKIiKhKvj92XZoLxpE9iGkV7O3yuAgXzUnXs+0T2JVdSmBg22A8NbZTre+Xmj72iSEiokodT8rG458fBQBcefVOp30X0/MA2DIxroT7awDYRh+ZLVbkGczIM9g679o79b47/Ra8ue08Vk/rjc4R/nVSB2p6GMQQEXmAPy/eQFaBESM6h8FbXf9/uo8nlSwJoC80Qedl6/tisQpsO50KwDbSyJVQP1sQk1NkRo+XtkrvA71VUl3Gd2+G8ezES9XEIIaIqJFL1hdixv8OwmIVuKVlAL6dM7je78E+mggArtzIR8+oAAC2ZQHScw3w1ypxe4dQl8f6a1VQyGWwWAUKjBap+clVUxJRdbBPDBFRI3fkajYsVgEAOJqYjSKTpd7v4XxKrvT1lYx86eutp21LB4zpFgG10vVHilwuQ5CPusz22zqEuPku6WbDIIaIqJE7lpQtfS2EcxBRX+yT0wFA/I2S6/9ZvHRAZcOhg0sFMc/f2QVPj+3sxjukmxGDGCKiRi4uMdvp/cW0vHq9fp7BjKSskuYkexCTllOE86l5kMlsI4oq4piJ+fuwdnj49rZc0JFqjUEMEVEjZrZYcbJ4XaJexf1QLqXVbybmyo18l+//vGTLwkRH6hDoornIUbCvRvo6xOFrotpgEENE1IidvJ6DAqMF/lolxnSzLax4KT0PsVczMfT1nfjycGKd34N9Rl6tyvaRYV+c0R5M9YzSVXoOx+Yk++gkotqq8yBm2bJlkMlkWLBggbRNCIElS5YgMjISXl5eGDZsGE6dOuV0nMFgwPz58xESEgIfHx9MmjQJSUlJdX27RESNyt7ibMeAtsHoEOYHwLZi9OOfx+FqRgE2HblW5/dgD1rsTUZZBSb0X7odq3deBACE+2nLPdbOMYgJ8a04a0NUVXUaxBw6dAgffPABevTo4bR9+fLlWLFiBVavXo1Dhw4hIiICo0ePRm5uScexBQsWYNOmTdi4cSP27NmDvLw8TJgwARZL/ffKJyJqKPsuZQAABrULRp9WgdAo5Yi/kS8NebYHGHXJfo2ukf7S/DCpOSXrIFUlsxLkELiEMRNDblJnQUxeXh6mT5+ODz/8EIGBgdJ2IQRWrlyJ5557DpMnT0Z0dDQ++eQTFBQUYMOGDQAAvV6Pjz/+GG+++SZGjRqF3r17Y/369Thx4gS2b99eV7dMRNRoCCGw71IGDlzOBAAMbBeCIB81pt7a0qlcsr4QRrPV1SncJjHTFjC1DPJGi8Cyc7tUJYjx1ZRMS8Y+MeQudRbEzJ07F3feeSdGjRrltD0+Ph4pKSkYM2aMtE2j0WDo0KHYu3cvACA2NhYmk8mpTGRkJKKjo6UypRkMBuTk5Di9iIg81ZzPjmDqh/thtFhxe4cQdAz3BQDMHd4eA9sG47k7ukCrksMqShZTrCuJxZmYqEDXQUxYFZqTHNmzOUS1VSdBzMaNG3HkyBEsW7aszL6UFNvESOHh4U7bw8PDpX0pKSlQq9VOGZzSZUpbtmwZdDqd9IqKinJHVYiI6l16rgG/nLT9reveXIe3/9pbWh061E+Dzx8dgEeGtJXWKqrLJiWrVUjDq6OCvF3OsluVTIy9P02LQC+XK10T1YTbg5jExET83//9H9avXw+ttvzovPQPsRCi0h/sisosXrwYer1eeiUm1n2PfSKiurDrXBoAILq5P36Yf5vL2W4B1EsQcyUjH0aLFRqlHM10WshQ9m9wcBU66ob5a3HwHyOx9YkhdXGbdJNyexATGxuLtLQ09OnTB0qlEkqlErt378Z//vMfKJVKKQNTOqOSlpYm7YuIiIDRaERWVla5ZUrTaDTw9/d3ehEReZpCowVfxdpGYo7o7PrvnV1UcRATf6Pu5o05UTxHTddIfygVcnSNLPu3VaWo2kdJmL+2QRavpKbL7UHMyJEjceLECcTFxUmvvn37Yvr06YiLi0Pbtm0RERGBbdu2SccYjUbs3r0bgwYNAgD06dMHKpXKqUxycjJOnjwplSEiaoqe/DIOB+MzoZDLMKFHxas692hhm5/l84MJThPSpeUWlZnltybe2nYe/7cxznat5rZr3dO7ORaP53IB1Di4PST28/NDdHS00zYfHx8EBwdL2xcsWIClS5eiQ4cO6NChA5YuXQpvb29MmzYNAKDT6TBr1iwsXLgQwcHBCAoKwqJFi9C9e/cyHYWJiJqKcym5+OVkCmQyYO3f+qFjuF+F5Sf1bI4vDiVi/+VMfLwnHv+62/Y3dsJ/9iAt14DNcwdLs/xWl8Fswds7Lkjvu7ewnUchl2H20HbYHHcdZ5I5gIIaVoPk9Z5++mkUFhZizpw5yMrKQv/+/bF161b4+ZX8wr711ltQKpWYMmUKCgsLMXLkSKxduxYKhaIhbpmIqM6t3RsPABgfHYHbO4RWWl4hl2HmwNbYfzkTh67YhmIXGM1Iy7XN4fL7+fQaBzFXbjj3s7FnfeyWTe6O6R/uxxOjO9bo/ETuUC9BzK5du5zey2QyLFmyBEuWLCn3GK1Wi1WrVmHVqlV1e3NERI2EfU6Yv/St+ujKPq1tozjPpeZCX2jCWYfsiMlS/fljTl7TY/f5dKeh1DEDWqFDmK9TuV5RATi+ZCwXcaQGxR5WRET1rMhkwdexSRjcPgRtQnwAADlFJlwu7tfSs7jppirC/LRoGeSNhMwC/HEhHdccVpuuyailCav2AChZJ+n+vlFSM1VpDGCooTGIISKqRxarwLwNR7H9TCq6NPPHz4/fBplMJq1U3SLQq9wh1eW5tU0QEjIL8OSXxxDuXzJnS3WDGIO5ZFmXIpMti9Mh3Le84kQNjqtYExHVo22nU7H9TCoA4ExyjrQ20vEkWxBTuu9JVTwxuiPahPjAaLZKSwQAtuUCYq9m4s+LN6p0nvMpeWW2daikczFRQ2IQQ0RUj345mez0fvb6WGw/nYovDtkm6KxOU5Jd8wAvvDP9Fum9vZnnRp4B9767D9M/OoDsAmOl57HPCePolpbVvx+i+sIghoioHlisAk9/fQzfxV0HAPx32i3oGO6L3CIzHv70MOJv5CNSp8WUanTqddSlmT/aFveveXJ0xzLrE1Wlaal0EPPvu6Php+U6R9R4sU8MEVE9OHQlE18ets3EG+anwfjoCAzvHIphr+9CWq4BKoUM/51+CwKr2R/G0ScP3Yqd59Iw9daWMJgsWLXzIoSw7UvWF6FHi4qPP5pgmyV98fjO6BThh6EdKx/mTdSQmIkhIqoHJx2yHO/H9IFcLoO3WomX7+oGf60S/747Gr1bBlZwhspFBXljxsDWUCnkeHJMJ/y2cBjahdqyM8mVrHStLzThXGouAOCeW5pjWKcwLtRIjR4zMURE9cDecXfRmI5Owcq46GYYF13x8gI11SbEB0M7huFSejyS9UUVlj1yNQtCAK2DvRHmV/7ivUSNCTMxRET1wJ6J6V6Djru1ERlgC0iuVxLE2Gf87dc6qM7vichdGMQQEbmZwWxBevHU/wCQoi+SJrLr3rz6Q6hro5nONvNuZc1J54ubknrUcJkCoobAIIaIyM3mfnYEg17dIc3P8s/vTgKwDVeu7kR2tRWhs2ViKmtOupZt298iwKvCckSNCfvEEBG5UUaeAdvPpAEApn90AL2iAhCXmA2FXIalk7vX+/3Ym5NScopgsYpylwpI1hcWl2cQQ56DmRgiIjfaeS7d6X1cYjYAoH+bIHSO8K/3+wn2sS1DYLEK5BWZXZbJN5iRXWACUBL0EHkCBjFERG60o3hJgceGtnOaZ2VE57AGuR+1Ug6N0vanPqfI5LKMPQvjp1FycjvyKAxiiIhqwGoV+OVEMi6k5mLFtvNI0RfBYLbg9/O2TMwd3SOcmo9GdglvqFuFf/HsvbnlZGKuF/eHYVMSeRr2iSEiqoHPDyXguU0npfexVzPx6JB2yDdaEOanQXSkDnK5DO890AeFJjPaFC8J0BD8tEqk5xrKzcRcLx651IxNSeRhGMQQEdWAfcFGuz8vZiApyxYMjOwSDnlxB9px0RH1fm+l2ZuIXGVirmcX4tlvTwBgJoY8D5uTiIiqKVlfKM3A661WIMTXNmz6aoZtkcVRXRqm/0t5/LW2/1dzXWRi1vwZL33dJcKv3u6JyB0YxBARVZO930ufVoE4/fI4fDijr7RPIZdhcPuQhro1l/yLMzE5hWWDmLMptknuxnWLwLT+rer1vohqi0EMEVE1nb6eA8A2eR0A9HRYSsBfq4RWpWiAuyqfn5SJKducdCE1DwDwyJA25c4hQ9RYMYghIqqm08m2IKZbpG0JAblchjnD2gEAljXAhHaVkUYnGZyDmJwiE1JybCOT2oexKYk8Dzv2EhFVg9UqcCbZ1gTTNbJk8ronR3fEtP4t0SLQu6FurVx+Gtd9YuxZmHB/DXRenB+GPA8zMURELgghYDRbIYTAmj/jseFAAgDgamYB8gxmaJRytHUYNq1UyBtlAAOUNCflFDpnYi6m2YKxjuHMwpBnYiaGiKiUvRdv4LnNJ5FVYMSknpH4dN9VAECvqAA8881xAECPFjooFZ7xf6B9iHXpeWKuFQ8JbxnUOIMvosowiCEicnDlRj4e/vQwCowWAJACGACY+uF+6AtNCPJRY8mkbg11i9VW3oy9N/KNAIAQX0293xORO3jGvxFERPXkuc0nUGC0oFukP/q0CnTapy8eovzEqA5Sp15PIDUnlcrEZOQZAECa54bI0zATQ0RU7FhiNv68mAFl8XIBUUHesFgFTBYr7nlnL84k5yDAW4V7+7Ro6FutlvKGWGfk2TIxwczEkIdiEENEVOz93y8BACb1ikRUcT8RhVwGhVyB7+YOxo4zqWgT6gNvtWf96bRPdqcvNEEIAZnMNh9MRnFzUrAPMzHkmTzrN5GIqI5cuZGPX06mAABmD2lXZr9aKcf47s3q+7bcItxfC7kMMJqtSM81IMzfttDjjeLmJGZiyFOxTwwREYAP/7gMIYARncPQqYmtIaRWyqXFHa9m2tZ3MpqtUvMS+8SQp2IQQ0Q3vfRcA76KTQIAzB7StoHvpm7Yh1HbF6nMLG5KUsplUnMTkadxexCzbNky9OvXD35+fggLC8Pdd9+Nc+fOOZURQmDJkiWIjIyEl5cXhg0bhlOnTjmVMRgMmD9/PkJCQuDj44NJkyYhKSnJ3bdLRIT1+6/CaLaiV1QAbm0T1NC3UydaBduCmEVfHcNnB65KTUlBPmrIuWYSeSi3BzG7d+/G3LlzsX//fmzbtg1msxljxoxBfn6+VGb58uVYsWIFVq9ejUOHDiEiIgKjR49Gbm6uVGbBggXYtGkTNm7ciD179iAvLw8TJkyAxWJx9y0T0U1MCIHv4q4BAB4c1Frq9NrUtAwqmV34uU0nkVDcrMT+MOTJ3N6xd8uWLU7v16xZg7CwMMTGxmLIkCEQQmDlypV47rnnMHnyZADAJ598gvDwcGzYsAGzZ8+GXq/Hxx9/jHXr1mHUqFEAgPXr1yMqKgrbt2/H2LFj3X3bRHQTEkLgoz/icSWjABqlHKO6hjf0LdUZeybGzr6MAvvDkCer8z4xer0eABAUZEvRxsfHIyUlBWPGjJHKaDQaDB06FHv37gUAxMbGwmQyOZWJjIxEdHS0VKY0g8GAnJwcpxcRUUX+/dMZvPLzGQC2Dr2+mqY7YLONwzpPALDn4g0AQDOdtiFuh8gt6jSIEULgySefxG233Ybo6GgAQEqKbQhjeLjzfzzh4eHSvpSUFKjVagQGBpZbprRly5ZBp9NJr6ioKHdXh4iakD8upOPjPfEAgH6tA7FgVMcGvqO61aWZP167tzv+OaGr0/auzfzLOYKo8avTIGbevHk4fvw4Pv/88zL7Src7O07AVJ6KyixevBh6vV56JSYm1vzGiahJM5qtePF722CCGQNb4avHBjW5YdWu3N+vJWYOau2UcerqQcsnEJVWZ0HM/Pnz8f3332Pnzp1o0aJkiu6IiAgAKJNRSUtLk7IzERERMBqNyMrKKrdMaRqNBv7+/k4vImrahBD4/GACJr/zJz47cBVCiCod98Ox67icno8QXzUWje1Ux3fZuCjkMqf+MZ2bNf3gjZoutwcxQgjMmzcP3377LX777Te0adPGaX+bNm0QERGBbdu2SduMRiN2796NQYMGAQD69OkDlUrlVCY5ORknT56UyhARfR2bhMXfnsCRhGw8t+kkPj9YtQzspqO20UgzB7a+KedIsa+lBOCmrD81HW4PYubOnYv169djw4YN8PPzQ0pKClJSUlBYWAjA1oy0YMECLF26FJs2bcLJkyfx4IMPwtvbG9OmTQMA6HQ6zJo1CwsXLsSOHTtw9OhRPPDAA+jevbs0WomImr4beQZczcgvd/93cded3n95uPIgJi2nCHsv2Tq13tWree1u0EM9f2dXKOUyzLqtTeWFiRoxt3fFf/fddwEAw4YNc9q+Zs0aPPjggwCAp59+GoWFhZgzZw6ysrLQv39/bN26FX5+JWnNt956C0qlElOmTEFhYSFGjhyJtWvXQqFQuPuWiagR2ngwAUt+OAWD2Yrp/VvipUnRUMhlEELg+2PXcSE1Txph88WjA/DXD/cjLjEb17IL0bx4in1Xvj92HVYB3NIyAC1LDTu+WUQ31yHuxTHwUvHvKXk2mahqI7KHycnJgU6ng16vZ/8YIg+TllOE217bCaPFKm17dEhb/OOOLvj2SBKe/PKYtL1tqA9+WzgMU97fh4PxmXhxYlf8bXD5GYaJq/bgxDU9Xr6rG2YMbF2X1SCiGqjO5zfXTiKiRkEIAX2BCQDw8Z/xMFqs6BkVgLf/2gsA8NEfl5GRZ8DK7RcAAP5aJVoFe2POsPYAgGGdQgEA+y9nlHuNS+l5OHFND4Vchjs9dEVqIirRdGd2IiKPsvq3i3hz23n8bXBrfFO8GOO84e0xums43th6DomZhXj5x9NIyCxAiK8avz89HN7qkj9hA9oGAwAOxGfCahWQy2VYsfUcTifnYtHYjugc4Y/vijv0DukQwun2iZoABjFE1CDMFiuUipJk8PfHbJ101/x5BQDQPMALIzqHAQC6N9chMbNQ6sj72NB2TgGMvYy3WoHsAhNi/ncAS+/pjv/8dhEAsP1MKib3bo4D8ZkAgLt735wdeomaGjYnEVGd0xeakFNkkt7HXs1Cn39vx7wNR2AwW2CxCiRlFTod85e+LaAoXl05unnJhGxhfho8MKBVmWuoFHL0L16B+s+LGfjbmkNO+789eg3XsgvhrVZgdBNeI4noZsIghojqVLK+ECPe2IVRb+7GjTwDTl7TY9Ynh6AvNOHH48mY/M5e7D6fhkKTBVqVHK/d2x1juoZjpkOn2x7NA6Sv5w5vD205o2r+dXc0Oob7AgAu37ANzX5mXGd8OKMvFHIZNEo5Xr23R5ksDhF5Jo5OIiKJwWxBZr4RzXTlD1Guqht5BhyKz8T7v19GXGJ2lY7pFRWAzXMHl9meW2TC8Dd2IcBbjR/n31ZuEAPYsj59/rUNZqvtT9vvTw1Hy2BvXE7Pg1alQGQFw6+JqOFV5/Ob/44QEQBg17k0PPPNcaTlGvDOtFswvhqjd5KyCiAEEBVkm3fFbLFiyvv7cDnd9UR1LYO88eLErugQ5ocRb+6SAo6uka7/YPlpVdj11HDIZagwgAEAnZcKz9/ZBdvPpOGJ0R2kuWDahvpWuT5E5BkYxBARziTn4O/rj6DQZAEAPP31cfhqlbi9Q2iZsvkGM7xUCsjlMsRezcI7Oy9ix9k0AMCs29rg+Tu74OeTKVIAc2ePZnhiVAckZhbiiS/j0CHMF+sf7g+N0haMTOoZiW+PXoNSLsO9t5Tf4dZx0cLKPDi4DR6sYK4YImoa2JxEdJNJyirAY+tj0bWZPx6+vS1aB/vgnnf+xKnrOejbKhDxN/KRkW8EAEy9NQpL7+kurR7/+/l0PLY+Fj1bBGBq/5Z4/POjZc6/aExHfH/sOs6n5uGJUR3xf6M6SPtMFivkMpnUYRcAcopM+PJQIkZ0DmO2hIiq9fnNIIboJvPEF3HSAoiO/LVK/LZoGLILTPjPjgv48bhtev5v/j4QfVoF4eQ1Pe5/fx/yjRan425tE4Sl90Rj17l0/PunM9J2nZcKu58ahgBvdZ3XiYiaDvaJISKXtp1Oxea4kgBGrZDDaLFCKZfhX3dHI8RXgxBfDf4ztTcKjGZsP5OG40l6tA/1w9/WHkK+0YIWgV7ScOgAbxX+92A/+GqUaB/mh5wiM1b9dgFCAE+M6sAAhojqFIMYopvEd3HX8OSXxyAEcF+fFnjjLz1hNFtx6roebUJ8ygQc3SJ12H4mDSev5SA15xLScw1oG+qD7+YOxu7z6fgu7jru69PCqa/Kk6M74s7uzRB/Ix9ju3EuFiKqWwxiiJogi1WgyGSBT3GA8fnBBPxj0wkIAUy+pTlendwdAKBWytG7ZaDLc9gnmPvmSBLUxTPr/mN8F/hpVZjQIxITekS6PK5ThB86Rfi53EdE5E4MYog8kBACJ67p0TrEB/5aldO+IpMFD645iENXsnB3r+Z4ZEgbPFccwMwY2ApLJnaD3KFjbXm6OQx3NlqsuKN7BEZ2CXN7XYiIaopBDJEHWv7rOby76xLC/TX4cEZfGMxWfLb/KrILTdh1Ll0q982RJHxzxLaY4u0dQvDSpG7SSKPKNNNpERXkhcTMQky9NQov3xVd5WOJiOoDgxgiD7PxYALe3XUJAJCaY8Ck1X+WKeOvVWLmoNZY8+cV5BnMAIDZQ9pVKwiRyWT44tGByC4wlTsJHRFRQ2IQQ9RI5RSZUGCwIEKnBQBk5huxfMtZbDyUCAB4dEhbfHEoEfpC28KKHcJ8cWubIEzoEYneLQOgVSlwT+/m+HTfVei8VBjcPrja9xAZ4MVp+omo0WIQQ9TIXErPw6odF/DzyRQYzVZoVXJE+GuRmmOQZtS9q1cknh3XGZ3C/bDwq2OY0rcFXp3co0xfl7ahvlgyqVtDVIOIqM4xiCFqRG7kGTDtw/1IzTFI24pMVlzJKAAAdI7ww7/ujka/1kEAgHv7tMCIzmEI9OF8LER082EQQ9RIXEzLxf9tjENqjgHtQn2wYkovHLqSiaSsQgxqF4zIAC90i/Qv06+FAQwR3awYxBC5UWJmAfZcvIFgHzVGdgl3WiMIsM3fciEtF838vaDzVuFEkh6f7ruCw1ezEH/DtmBisI8a7z3QBx3C/dAzKqABakFE5BkYxBDVgsFswVeHk/DtkSQo5XIcTcyCyWJbjqxPq0B0b65Dx3A/tAv1wWtbzuLktRwYLVb4qBVoH+aLY0l66VxyGTCicxhenNgNUUHeDVUlIiKPwSCGqBJxidlI0RdBIZch2FeNnEITErMKcSktD1/HJklDmO06R/jhcno+Yq9mIfZqlstz5hstOJakh0wG3N2rOSb1isQtUYHQeatcliciorIYxNBNyWyxYt/lDAR6q132MwGA86m5eGvbefxyMqXCc4X7azCpZySigrzRp1UgukXqcDk9D7+dTUN6ngE/xF3HjXwjRnUJw+MjO6BtiC/2Xc7AtaxC3NomCO3DfOuqmkRETZpMCCEa+ibqQnWW8qabS3quAQ+tPYQT12xNOb2iAjA+OgK3tApEZr4R4f5aHL6SiTe2nkORyQoA8NUooVHKkV1ogr9WiX6tgxDko8aIzmEY1SW8wmn8hRCwCpTpH0NERGVV5/ObmRhq8jLyDLh8Ix+Z+UZYrQLv/35ZCmCUchniErMRl5jt8tjb2ofg+Qld0DnC9otkMFsgl8mgKl4QsSpkMhkUjF+IiNyOQQw1GkUmC/IMZoT4asrdn5FvRLCPGlqVwmWZa9mF+On4dey5mIHr2YXwUilw6roe1lL5Rn+tEpvmDoavRokvDiVi76UbOBifiQh/LfKNFnQM98W46GaYObAVlA4Bi0bp+rpERFT/2Jx0ExJC4Fp2IbLyTegY4evygzktpwgH4jNhFQIR/lrcyDPiXEoOzqXmQgYZ7uvTAqO6htf4+qk5BsQlZmH/5UwUGM04GJ+Jq5kFEMLWMdZfq0Kwrxq9ogJwOT0fqblFOHwlC3kGMzRKOWYOao1nx3V2asb58fh1PPvNiTIdbQGgZZA3An3USM8pQusQHzx3Zxd0i9SVuS8ucEhE1LCq8/nNIOYmYTBb8NuZNGyOu4bjSXok64sAAGF+GvRvG4yeLXRQyGXwVitw4poeXx5KgtFiLfd8vhol/nx2BHReKgghcOp6Dk4n58BotiK3yIzL6XkI9dNAJrPNONsyyBu5Rabiphs9buQZyj13VU3sGYm5w9vhcno+dp5Nw1exttWauzfXYWy3cIT6aWC2CgztGIoWgRyyTETkCRjEoHEGMem5BvxyMhmxV7OgUcrRJsQXvlolukX6QymXIchHjQh/rVPzRXmEEDiamI2jCdlQKWRQK+RIyCyAwWyFxSrgpVagSzN/HEvMxoH4DJxPyXMKSpRyGQRsk6+Vp0szf/hqFMjIM8LPS4XO4X7oEO6L5VvOwWixYninUHSL1GHb6VScS82t1vdCLgPah/miX+sgeKsV6NMqCLe0CoAQwOErtmHJf1xIx8W0PNzSKhDh/lpEBXphVJdwfHE4Ef/YdAKufnIfub0Nnh3fhZ1oiYg8FIMY1D6IsVpFhSNOKqMvNOFMcg5OXc/BwfgMnLyWg2vZhZUep5DLEOGvRfNAL7QI9IKvRgmLVeBiWh5ScorgrVbCR62AwWyVOqdWVYS/Fvfc0hzDOoYiurkOFiGw+eg1XMsqxIH4TIT5aaAvNCHfaMbcYe0xLjrCZfPKd3HX8H8b45y2eakUuKVVALzVSpgtVkQ310FfaILZKuClUiAxswAalQI9W+jQu2UAujbTwUtd8/4lRxKysHzLWRyIz0S7UF/0jgrA/f2i0Ld4TSEiIvJMTSqIeeedd/D6668jOTkZ3bp1w8qVK3H77bdXelx53wSD2YLzKXm4fCMPV24U4HhSNjQqOQK91TCarTiflofLaXnINZjhpVKgWYAWg9uFwEejRGpOEVQKGYJ9NQj2USPAW430XAPUSjkCvVXwViuQkW9E7NUs/HQ8GQZz2eaY7s11GN01HFYhcDWjADfyDDgYnwk/rQr6QqM022tVqBVyDO0UCpVChnyDBa2CveGtVkIhB65nF+FKRj5aBXljVNdw9GwRgBaBXm7p8yGEwFeHk/DdsWsI89NiYNtgjO0W0SATtVmsglkXIqImpMkEMV988QViYmLwzjvvYPDgwXj//ffx0Ucf4fTp02jZsmWFx9q/CSOX/Yzo1hHILDAhVV+EyzfyqhUo1EbzAC+0CfHB7R1C0KNFALo194e/tvwPeqtVID3PgKSsQiRlFeBadiEKDBaYrFb4a1Xo0yoQBrMVBQYziswW9G0VxOnpiYioSWkyQUz//v1xyy234N1335W2denSBXfffTeWLVtW4bH2b0LUgi8h1zh/0Ad6q9Ah3A9Rgd7oFukPuQzIKjBBIZehXagvOob7ItBHjXyDGedScvH7hXQYTFa0D/OF2SpwI8+AjDwjsgpsw30tAsguMCLPYEawjwYtAr0wqVckekcFcLQLERFRNTSJye6MRiNiY2Px7LPPOm0fM2YM9u7dW6a8wWCAwVAy4iUnJwcA8Mrd0dBblAjz1yLCX4s2IT5VblYJ8dWgVbAPxnSLqGVtiIiIyN0abRBz48YNWCwWhIc7z0USHh6OlJSya9ksW7YML730Upntd/Vu3mhGJxEREZH7VH3u9AZSOmNS3oRkixcvhl6vl16JiYn1dYtERETUABptJiYkJAQKhaJM1iUtLa1MdgYANBoNNBrX09UTERFR09NoMzFqtRp9+vTBtm3bnLZv27YNgwYNaqC7IiIiosai0WZiAODJJ59ETEwM+vbti4EDB+KDDz5AQkICHnvssYa+NSIiImpgjTqIuf/++5GRkYGXX34ZycnJiI6Oxs8//4xWrVo19K0RERFRA2vU88TURmNcO4mIiIgqVp3P70bbJ4aIiIioIgxiiIiIyCMxiCEiIiKPxCCGiIiIPBKDGCIiIvJIDGKIiIjIIzGIISIiIo/UqCe7qw379Dc5OTkNfCdERERUVfbP7apMY9dkg5jc3FwAQFRUVAPfCREREVVXbm4udDpdhWWa7Iy9VqsVHTt2RGxsLGQyWZWO6devHw4dOtRgZXNychAVFYXExMQqzzLc0Pdc3fLVrWNjuOfqlOUzrN198BnWT1k+w/q5j7os25SfoRACubm5iIyMhFxeca+XJpuJkcvlUKvVlUZxjhQKRZV/4OuqLAD4+/s3+H1U957rqo6N5Z75DMviM6z7+6jL+gF8hvVxH3yGNStb1c/uJt2xd+7cuXVWvq7KVldjuee6qmNjuWc+w5prLPfc1J+hJ9avrs/dGO6jqdevrs9dmSbbnOSJboZFK5t6HZt6/YCmX8emXj+g6dexqdcPuDnqWBVNOhPjaTQaDV588UVoNJqGvpU609Tr2NTrBzT9Ojb1+gFNv45NvX7AzVHHqmAmhoiIiDwSMzFERETkkRjEEBERkUdiEENEREQeiUEMEREReSQGMUREROSRGMS42e+//46JEyciMjISMpkMmzdvdtqfmpqKBx98EJGRkfD29sa4ceNw4cIFpzIpKSmIiYlBREQEfHx8cMstt+Drr792eT2DwYBevXpBJpMhLi6ujmrlrL7qeOTIEYwePRoBAQEIDg7Go48+iry8vLqunlvqd+nSJdxzzz0IDQ2Fv78/pkyZgtTUVJfX89RnWJU6NtQzXLZsGfr16wc/Pz+EhYXh7rvvxrlz55zKCCGwZMkSREZGwsvLC8OGDcOpU6ecyhgMBsyfPx8hISHw8fHBpEmTkJSU5PKa9fkc67N+DfEM3VW/Dz74AMOGDYO/vz9kMhmys7PLvWZ9/x7WZx0b6vewPjCIcbP8/Hz07NkTq1evLrNPCIG7774bly9fxnfffYejR4+iVatWGDVqFPLz86VyMTExOHfuHL7//nucOHECkydPxv3334+jR4+WOefTTz+NyMjIOq1TafVRx+vXr2PUqFFo3749Dhw4gC1btuDUqVN48MEHG3398vPzMWbMGMhkMvz222/4888/YTQaMXHiRFit1jLn9MRnWJU6NuQz3L17N+bOnYv9+/dj27ZtMJvNGDNmjNPP4PLly7FixQqsXr0ahw4dQkREBEaPHi0tHgsACxYswKZNm7Bx40bs2bMHeXl5mDBhAiwWS5lr1udzrK/6NdQzdFf9CgoKMG7cOPzjH/+o9Jr1/XtYX3VsyN/DeiGozgAQmzZtkt6fO3dOABAnT56UtpnNZhEUFCQ+/PBDaZuPj4/49NNPnc4VFBQkPvroI6dtP//8s+jcubM4deqUACCOHj1aJ/WoSF3V8f333xdhYWHCYrFI+48ePSoAiAsXLtRRbcqqSf1+/fVXIZfLhV6vl8pkZmYKAGLbtm1O5/fUZ1iVOjaWZyiEEGlpaQKA2L17txBCCKvVKiIiIsSrr74qlSkqKhI6nU689957QgghsrOzhUqlEhs3bpTKXLt2TcjlcrFlyxan8zf0c6yr+jWWZ1iT+jnauXOnACCysrJcnr+hn58QdVfHxvIM6wozMfXIYDAAALRarbRNoVBArVZjz5490rbbbrsNX3zxBTIzM2G1WrFx40YYDAYMGzZMKpOamopHHnkE69atg7e3d73VoTLuqqPBYIBarXZawdTLywsAnM5T36pSP4PBAJlM5jSTplarhVwud7p3T36GValjY3qGer0eABAUFAQAiI+PR0pKCsaMGSOV0Wg0GDp0KPbu3QsAiI2NhclkcioTGRmJ6OhoqQzQOJ5jXdWvsTzDmtSvqhrD8wPqro6N5RnWFQYx9ahz585o1aoVFi9ejKysLBiNRrz66qtISUlBcnKyVO6LL76A2WxGcHAwNBoNZs+ejU2bNqFdu3YAbOn+Bx98EI899hj69u3bUNVxyV11HDFiBFJSUvD666/DaDQiKytLSpc6nqe+VaV+AwYMgI+PD5555hkUFBQgPz8fTz31FKxWq1TG059hVerYWJ6hEAJPPvkkbrvtNkRHRwOw9ckCgPDwcKey4eHh0r6UlBSo1WoEBgaWW6YxPMe6rF9jeIY1rV9Vz93Qz89+H3VVx8bwDOsSg5h6pFKp8M033+D8+fMICgqCt7c3du3ahfHjx0OhUEjlnn/+eWRlZWH79u04fPgwnnzySfzlL3/BiRMnAACrVq1CTk4OFi9e3FBVKZe76titWzd88sknePPNN+Ht7Y2IiAi0bdsW4eHhTuepb1WpX2hoKL766iv88MMP8PX1lRZpu+WWW6Qynv4Mq1LHxvIM582bh+PHj+Pzzz8vs08mkzm9F0KU2VaaY5nG8Bzrsn6N4Rm6u36OGsPzA+q2jo3hGdaphmjDulmgVF8DR9nZ2SItLU0IIcStt94q5syZI4QQ4uLFi2X6IwghxMiRI8Xs2bOFEELcddddQi6XC4VCIb0ACIVCIWbMmFF3FXKhruroKCUlReTm5oq8vDwhl8vFl19+6d5KVKAm9XOUnp4utVGHh4eL5cuXCyE8/xk6Kq+OjhrqGc6bN0+0aNFCXL582Wn7pUuXBABx5MgRp+2TJk2Svv87duwQAERmZqZTmR49eoh//vOfQoiGf451XT9HDfEMa1M/R+X1F2no5ydE3dfRUUP+La0rDGLqUEUfDnbnz58Xcrlc/Prrr0IIIY4fPy4AiNOnTzuVGzNmjHjkkUeEEEJcvXpVnDhxQnr9+uuvAoD4+uuvRWJiYp3UpTx1VUdXPv74Y+Ht7V3hL6m71aR+ruzYsUPIZDJx9uxZIYTnP0NXStfRlfp6hlarVcydO1dERkaK8+fPu9wfEREhXnvtNWmbwWBw2fH1iy++kMpcv37dqeNrQz3H+qqfK/XxDN1RP0flfcA35O9hfdXRlYb4W1pXGMS4WW5urjh69KjU+3vFihXi6NGj4urVq0IIIb788kuxc+dOcenSJbF582bRqlUrMXnyZOl4o9Eo2rdvL26//XZx4MABcfHiRfHGG28ImUwmfvrpJ5fXjI+Pr9ce9fVVx1WrVonY2Fhx7tw5sXr1auHl5SXefvvtRl8/IYT43//+J/bt2ycuXrwo1q1bJ4KCgsSTTz5Z7jU97RkKUbU6NtQz/Pvf/y50Op3YtWuXSE5Oll4FBQVSmVdffVXodDrx7bffihMnToipU6eKZs2aiZycHKnMY489Jlq0aCG2b98ujhw5IkaMGCF69uwpzGazy+vW13Osz/o1xDN0V/2Sk5PF0aNHxYcffigAiN9//10cPXpUZGRkuLxuff4e1mcdG+r3sD4wiHEzezRc+jVz5kwhhBBvv/22aNGihVCpVKJly5bi+eefFwaDwekc58+fF5MnTxZhYWHC29tb9OjRo8xwZEf1/QFYX3WMiYkRQUFBQq1WV/o9aGz1e+aZZ0R4eLhQqVSiQ4cO4s033xRWq7Xca3riM6xKHRvqGbqqGwCxZs0aqYzVahUvvviiiIiIEBqNRgwZMkScOHHC6TyFhYVi3rx5IigoSHh5eYkJEyaIhISEcq9bX8+xPuvXEM/QXfV78cUXKz2Po/r8PazPOjbU72F9kAkhRGX9ZoiIiIgaG45OIiIiIo/EIIaIiIg8EoMYIiIi8kgMYoiIiMgjMYghIiIij8QghoiIiDwSgxgiIiLySAxiiIiIyCMxiCEiIiKPxCCGiIiIPBKDGCIiIvJI/w/N8K/ojag1cgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wealth['1980':].plot(title=\"wealth index for Large Profit(1980-2021)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "312b2d84-205b-4d8d-96e6-c2346881b2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'wealth Lastpeak'}>"
      ]
     },
     "execution_count": 36,
     "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": [
    "lastpeak = wealth.cummax()\n",
    "lastpeak['1980':].plot(title=\"wealth Lastpeak\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "23925b81-e4bf-4fc2-a1b9-6308c103480b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "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": [
    "drawdown = (wealth - lastpeak)/lastpeak\n",
    "drawdown['1980':].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f97e0c07-8624-4daa-aa81-702ee70c9717",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}