black-scholes-hedging/call-option/results_comp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib as matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import scienceplots\n",
    "\n",
    "SMALL_SIZE = 16\n",
    "FONT = {'color':  'black',\n",
    "        'weight': 'normal',\n",
    "        'size': SMALL_SIZE\n",
    "       }\n",
    "\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.style.use(['science','no-latex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notebooks paths\n",
    "BS_DELTA_ZERO = './results/bs_delta_zero.csv'\n",
    "BS_DELTA_LOW = './results/bs_delta_low.csv'\n",
    "BS_DELTA_HIGH = './results/bs_delta_high.csv'\n",
    "\n",
    "WILMOTT_DELTA_ZERO = './results/wilmott_delta_zero.csv'\n",
    "WILMOTT_DELTA_LOW = './results/wilmott_delta_low.csv'\n",
    "WILMOTT_DELTA_HIGH = './results/wilmott_delta_high.csv'\n",
    "\n",
    "TD3_DELTA_ZERO = './results/td3-bs-zero_res.csv'\n",
    "TD3_DELTA_LOW = './results/td3-bs-low_res.csv'\n",
    "TD3_DELTA_HIGH = './results/td3-bs-high_res.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bs_zero = pd.read_csv(BS_DELTA_ZERO, index_col=0)\n",
    "df_bs_low = pd.read_csv(BS_DELTA_LOW, index_col=0)\n",
    "df_bs_high = pd.read_csv(BS_DELTA_HIGH, index_col=0)\n",
    "\n",
    "df_wilmott_zero = pd.read_csv(WILMOTT_DELTA_ZERO, index_col=0)\n",
    "df_wilmott_low = pd.read_csv(WILMOTT_DELTA_LOW, index_col=0)\n",
    "df_wilmott_high = pd.read_csv(WILMOTT_DELTA_HIGH, index_col=0)\n",
    "\n",
    "df_td3_zero = pd.read_csv(TD3_DELTA_ZERO, index_col=0)\n",
    "df_td3_low = pd.read_csv(TD3_DELTA_LOW, index_col=0)\n",
    "df_td3_high = pd.read_csv(TD3_DELTA_HIGH, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the columns\n",
    "df_bs_zero = df_bs_zero.rename(columns={'E_PL_T': 'E_PL_T_bs', 'Std_PL_T': 'Std_PL_T_bs'})\n",
    "df_wilmott_zero = df_wilmott_zero.rename(columns={'E_PL_T': 'E_PL_T_wilmott', 'Std_PL_T': 'Std_PL_T_wilmott'})\n",
    "df_td3_zero = df_td3_zero.rename(columns={'E_PL_T': 'E_PL_T_td3', 'Std_PL_T': 'Std_PL_T_td3'})\n",
    "\n",
    "df_bs_low = df_bs_low.rename(columns={'E_PL_T': 'E_PL_T_bs', 'Std_PL_T': 'Std_PL_T_bs'})\n",
    "df_wilmott_low = df_wilmott_low.rename(columns={'E_PL_T': 'E_PL_T_wilmott', 'Std_PL_T': 'Std_PL_T_wilmott'})\n",
    "df_td3_low = df_td3_low.rename(columns={'E_PL_T': 'E_PL_T_td3', 'Std_PL_T': 'Std_PL_T_td3'})\n",
    "\n",
    "df_bs_high = df_bs_high.rename(columns={'E_PL_T': 'E_PL_T_bs', 'Std_PL_T': 'Std_PL_T_bs'})\n",
    "df_wilmott_high = df_wilmott_high.rename(columns={'E_PL_T': 'E_PL_T_wilmott', 'Std_PL_T': 'Std_PL_T_wilmott'})\n",
    "df_td3_high = df_td3_high.rename(columns={'E_PL_T': 'E_PL_T_td3', 'Std_PL_T': 'Std_PL_T_td3'})\n",
    "\n",
    "keys = ['Strike', 'Volatility', 'Maturity', 'Rebalancing_freq']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zero transaction resutls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.051063</td>\n",
       "      <td>0.028355</td>\n",
       "      <td>0.444859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.005124</td>\n",
       "      <td>0.106017</td>\n",
       "      <td>0.073272</td>\n",
       "      <td>0.458219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.014614</td>\n",
       "      <td>0.012899</td>\n",
       "      <td>0.089684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.001642</td>\n",
       "      <td>0.020561</td>\n",
       "      <td>0.013077</td>\n",
       "      <td>0.092480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.024382</td>\n",
       "      <td>-0.018432</td>\n",
       "      <td>0.135620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.008437</td>\n",
       "      <td>0.054875</td>\n",
       "      <td>-0.016749</td>\n",
       "      <td>0.176811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.013363</td>\n",
       "      <td>0.097282</td>\n",
       "      <td>-0.137070</td>\n",
       "      <td>1.285324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.004795</td>\n",
       "      <td>0.206135</td>\n",
       "      <td>0.185160</td>\n",
       "      <td>1.210820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.013518</td>\n",
       "      <td>0.176213</td>\n",
       "      <td>-0.304213</td>\n",
       "      <td>3.235274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.032036</td>\n",
       "      <td>0.395564</td>\n",
       "      <td>-0.312676</td>\n",
       "      <td>2.945664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.004162</td>\n",
       "      <td>0.057298</td>\n",
       "      <td>0.018664</td>\n",
       "      <td>0.479501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.030007</td>\n",
       "      <td>0.120005</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.457989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.006287</td>\n",
       "      <td>0.053737</td>\n",
       "      <td>0.031751</td>\n",
       "      <td>0.362939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.000375</td>\n",
       "      <td>0.116011</td>\n",
       "      <td>-0.060569</td>\n",
       "      <td>0.362865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.054443</td>\n",
       "      <td>0.024315</td>\n",
       "      <td>0.359150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.002007</td>\n",
       "      <td>0.117712</td>\n",
       "      <td>-0.031920</td>\n",
       "      <td>0.329331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>504</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>0.037629</td>\n",
       "      <td>0.092602</td>\n",
       "      <td>0.447998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>-0.009916</td>\n",
       "      <td>0.085893</td>\n",
       "      <td>-0.054461</td>\n",
       "      <td>0.552042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>121</td>\n",
       "      <td>0.012386</td>\n",
       "      <td>0.078027</td>\n",
       "      <td>-0.022248</td>\n",
       "      <td>0.477612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "      <td>0.014782</td>\n",
       "      <td>0.193498</td>\n",
       "      <td>0.047898</td>\n",
       "      <td>0.529128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>50</td>\n",
       "      <td>0.016029</td>\n",
       "      <td>0.117439</td>\n",
       "      <td>0.124722</td>\n",
       "      <td>0.449915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.058360</td>\n",
       "      <td>0.258304</td>\n",
       "      <td>-0.033232</td>\n",
       "      <td>0.624842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "0      100         0.2       1.0               252   0.004872     0.051063   \n",
       "1      100         0.2       1.0                52   0.005124     0.106017   \n",
       "2       80         0.2       1.0               252   0.000731     0.014614   \n",
       "3       80         0.2       1.0                52  -0.001642     0.020561   \n",
       "4       90         0.2       1.0               252   0.000616     0.024382   \n",
       "5       90         0.2       1.0                52   0.008437     0.054875   \n",
       "6      110         0.2       1.0               252   0.013363     0.097282   \n",
       "7      110         0.2       1.0                52  -0.004795     0.206135   \n",
       "8      120         0.2       1.0               252   0.013518     0.176213   \n",
       "9      120         0.2       1.0                52   0.032036     0.395564   \n",
       "10     100         0.1       1.0               252  -0.004162     0.057298   \n",
       "11     100         0.1       1.0                52   0.030007     0.120005   \n",
       "12     100         0.3       1.0               252   0.006287     0.053737   \n",
       "13     100         0.3       1.0                52  -0.000375     0.116011   \n",
       "14     100         0.4       1.0               252   0.000402     0.054443   \n",
       "15     100         0.4       1.0                52   0.002007     0.117712   \n",
       "16     100         0.2       2.0               504   0.005998     0.037629   \n",
       "17     100         0.2       2.0               104  -0.009916     0.085893   \n",
       "18     100         0.2       0.5               121   0.012386     0.078027   \n",
       "19     100         0.2       0.5                26   0.014782     0.193498   \n",
       "20     100         0.2       0.2                50   0.016029     0.117439   \n",
       "21     100         0.2       0.2                10   0.058360     0.258304   \n",
       "\n",
       "    E_PL_T_td3  Std_PL_T_td3  \n",
       "0     0.028355      0.444859  \n",
       "1     0.073272      0.458219  \n",
       "2     0.012899      0.089684  \n",
       "3     0.013077      0.092480  \n",
       "4    -0.018432      0.135620  \n",
       "5    -0.016749      0.176811  \n",
       "6    -0.137070      1.285324  \n",
       "7     0.185160      1.210820  \n",
       "8    -0.304213      3.235274  \n",
       "9    -0.312676      2.945664  \n",
       "10    0.018664      0.479501  \n",
       "11    0.001508      0.457989  \n",
       "12    0.031751      0.362939  \n",
       "13   -0.060569      0.362865  \n",
       "14    0.024315      0.359150  \n",
       "15   -0.031920      0.329331  \n",
       "16    0.092602      0.447998  \n",
       "17   -0.054461      0.552042  \n",
       "18   -0.022248      0.477612  \n",
       "19    0.047898      0.529128  \n",
       "20    0.124722      0.449915  \n",
       "21   -0.033232      0.624842  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero = pd.merge(df_bs_zero, df_wilmott_zero, how='right', on=keys).merge(df_td3_zero, how='right', on=keys)\n",
    "df_zero = df_zero.drop(columns=['E_PL_T_wilmott', 'Std_PL_T_wilmott'])\n",
    "df_zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different strikes, daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.051063</td>\n",
       "      <td>0.028355</td>\n",
       "      <td>0.444859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.014614</td>\n",
       "      <td>0.012899</td>\n",
       "      <td>0.089684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.024382</td>\n",
       "      <td>-0.018432</td>\n",
       "      <td>0.135620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.013363</td>\n",
       "      <td>0.097282</td>\n",
       "      <td>-0.137070</td>\n",
       "      <td>1.285324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.013518</td>\n",
       "      <td>0.176213</td>\n",
       "      <td>-0.304213</td>\n",
       "      <td>3.235274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "0     100         0.2       1.0               252   0.004872     0.051063   \n",
       "2      80         0.2       1.0               252   0.000731     0.014614   \n",
       "4      90         0.2       1.0               252   0.000616     0.024382   \n",
       "6     110         0.2       1.0               252   0.013363     0.097282   \n",
       "8     120         0.2       1.0               252   0.013518     0.176213   \n",
       "\n",
       "   E_PL_T_td3  Std_PL_T_td3  \n",
       "0    0.028355      0.444859  \n",
       "2    0.012899      0.089684  \n",
       "4   -0.018432      0.135620  \n",
       "6   -0.137070      1.285324  \n",
       "8   -0.304213      3.235274  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero[(df_zero['Rebalancing_freq'] == 252) & (df_zero['Volatility'] == 0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_1 = df_zero[(df_zero['Rebalancing_freq'] == 252) & (df_zero['Volatility'] == 0.2) & (df_zero['Strike'] == 100)]\n",
    "res_2 = df_zero[(df_zero['Rebalancing_freq'] == 52) & (df_zero['Volatility'] == 0.2) & (df_zero['Strike'] == 100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'red', 'red'])\n",
    "text_labels = ['BS1', 'TD3 1', 'BS5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-8, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different strikes, weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.005124</td>\n",
       "      <td>0.106017</td>\n",
       "      <td>0.073272</td>\n",
       "      <td>0.458219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.001642</td>\n",
       "      <td>0.020561</td>\n",
       "      <td>0.013077</td>\n",
       "      <td>0.092480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.008437</td>\n",
       "      <td>0.054875</td>\n",
       "      <td>-0.016749</td>\n",
       "      <td>0.176811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.004795</td>\n",
       "      <td>0.206135</td>\n",
       "      <td>0.185160</td>\n",
       "      <td>1.210820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.032036</td>\n",
       "      <td>0.395564</td>\n",
       "      <td>-0.312676</td>\n",
       "      <td>2.945664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "1     100         0.2       1.0                52   0.005124     0.106017   \n",
       "3      80         0.2       1.0                52  -0.001642     0.020561   \n",
       "5      90         0.2       1.0                52   0.008437     0.054875   \n",
       "7     110         0.2       1.0                52  -0.004795     0.206135   \n",
       "9     120         0.2       1.0                52   0.032036     0.395564   \n",
       "\n",
       "   E_PL_T_td3  Std_PL_T_td3  \n",
       "1    0.073272      0.458219  \n",
       "3    0.013077      0.092480  \n",
       "5   -0.016749      0.176811  \n",
       "7    0.185160      1.210820  \n",
       "9   -0.312676      2.945664  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero[(df_zero['Rebalancing_freq'] == 52) & (df_zero['Volatility'] == 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different volatilities, daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.004162</td>\n",
       "      <td>0.057298</td>\n",
       "      <td>0.018664</td>\n",
       "      <td>0.479501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.006287</td>\n",
       "      <td>0.053737</td>\n",
       "      <td>0.031751</td>\n",
       "      <td>0.362939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.054443</td>\n",
       "      <td>0.024315</td>\n",
       "      <td>0.359150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "10     100         0.1       1.0               252  -0.004162     0.057298   \n",
       "12     100         0.3       1.0               252   0.006287     0.053737   \n",
       "14     100         0.4       1.0               252   0.000402     0.054443   \n",
       "\n",
       "    E_PL_T_td3  Std_PL_T_td3  \n",
       "10    0.018664      0.479501  \n",
       "12    0.031751      0.362939  \n",
       "14    0.024315      0.359150  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero[(df_zero['Rebalancing_freq'] == 252) & (df_zero['Volatility'] != 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different volatilities, weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.030007</td>\n",
       "      <td>0.120005</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.457989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.000375</td>\n",
       "      <td>0.116011</td>\n",
       "      <td>-0.060569</td>\n",
       "      <td>0.362865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.002007</td>\n",
       "      <td>0.117712</td>\n",
       "      <td>-0.031920</td>\n",
       "      <td>0.329331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "11     100         0.1       1.0                52   0.030007     0.120005   \n",
       "13     100         0.3       1.0                52  -0.000375     0.116011   \n",
       "15     100         0.4       1.0                52   0.002007     0.117712   \n",
       "\n",
       "    E_PL_T_td3  Std_PL_T_td3  \n",
       "11    0.001508      0.457989  \n",
       "13   -0.060569      0.362865  \n",
       "15   -0.031920      0.329331  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero[(df_zero['Rebalancing_freq'] == 52) & (df_zero['Volatility'] != 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different maturities, daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>504</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>0.037629</td>\n",
       "      <td>0.092602</td>\n",
       "      <td>0.447998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>121</td>\n",
       "      <td>0.012386</td>\n",
       "      <td>0.078027</td>\n",
       "      <td>-0.022248</td>\n",
       "      <td>0.477612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>50</td>\n",
       "      <td>0.016029</td>\n",
       "      <td>0.117439</td>\n",
       "      <td>0.124722</td>\n",
       "      <td>0.449915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "16     100         0.2       2.0               504   0.005998     0.037629   \n",
       "18     100         0.2       0.5               121   0.012386     0.078027   \n",
       "20     100         0.2       0.2                50   0.016029     0.117439   \n",
       "\n",
       "    E_PL_T_td3  Std_PL_T_td3  \n",
       "16    0.092602      0.447998  \n",
       "18   -0.022248      0.477612  \n",
       "20    0.124722      0.449915  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero[(df_zero['Rebalancing_freq'].isin([504, 121, 50]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different maturities, weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>-0.009916</td>\n",
       "      <td>0.085893</td>\n",
       "      <td>-0.054461</td>\n",
       "      <td>0.552042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "      <td>0.014782</td>\n",
       "      <td>0.193498</td>\n",
       "      <td>0.047898</td>\n",
       "      <td>0.529128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.058360</td>\n",
       "      <td>0.258304</td>\n",
       "      <td>-0.033232</td>\n",
       "      <td>0.624842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "17     100         0.2       2.0               104  -0.009916     0.085893   \n",
       "19     100         0.2       0.5                26   0.014782     0.193498   \n",
       "21     100         0.2       0.2                10   0.058360     0.258304   \n",
       "\n",
       "    E_PL_T_td3  Std_PL_T_td3  \n",
       "17   -0.054461      0.552042  \n",
       "19    0.047898      0.529128  \n",
       "21   -0.033232      0.624842  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero[(df_zero['Rebalancing_freq'].isin([104, 26, 10]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Low transaction results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.617632</td>\n",
       "      <td>0.258913</td>\n",
       "      <td>-0.494779</td>\n",
       "      <td>0.263058</td>\n",
       "      <td>0.165846</td>\n",
       "      <td>0.749428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.271625</td>\n",
       "      <td>0.161219</td>\n",
       "      <td>-0.114218</td>\n",
       "      <td>0.668894</td>\n",
       "      <td>0.099517</td>\n",
       "      <td>0.732453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.099966</td>\n",
       "      <td>0.072096</td>\n",
       "      <td>-0.093986</td>\n",
       "      <td>0.064879</td>\n",
       "      <td>0.090757</td>\n",
       "      <td>0.181319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.040901</td>\n",
       "      <td>0.035402</td>\n",
       "      <td>-0.030095</td>\n",
       "      <td>0.136520</td>\n",
       "      <td>0.057643</td>\n",
       "      <td>0.161313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.317394</td>\n",
       "      <td>0.133345</td>\n",
       "      <td>-0.260803</td>\n",
       "      <td>0.168427</td>\n",
       "      <td>0.126441</td>\n",
       "      <td>0.281713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.134857</td>\n",
       "      <td>0.079479</td>\n",
       "      <td>-0.061264</td>\n",
       "      <td>0.278445</td>\n",
       "      <td>0.032836</td>\n",
       "      <td>0.310006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.076932</td>\n",
       "      <td>0.557806</td>\n",
       "      <td>-0.927918</td>\n",
       "      <td>0.500694</td>\n",
       "      <td>0.268663</td>\n",
       "      <td>2.191919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.471162</td>\n",
       "      <td>0.309397</td>\n",
       "      <td>-0.130789</td>\n",
       "      <td>1.119580</td>\n",
       "      <td>0.756748</td>\n",
       "      <td>1.524757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.524328</td>\n",
       "      <td>1.148313</td>\n",
       "      <td>-1.379671</td>\n",
       "      <td>1.419537</td>\n",
       "      <td>0.139938</td>\n",
       "      <td>6.068710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.718183</td>\n",
       "      <td>0.640199</td>\n",
       "      <td>-0.389655</td>\n",
       "      <td>2.651437</td>\n",
       "      <td>0.413195</td>\n",
       "      <td>4.781828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.310209</td>\n",
       "      <td>0.463384</td>\n",
       "      <td>-1.189151</td>\n",
       "      <td>0.493833</td>\n",
       "      <td>0.492511</td>\n",
       "      <td>0.788484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.561350</td>\n",
       "      <td>0.261862</td>\n",
       "      <td>-0.300800</td>\n",
       "      <td>0.391117</td>\n",
       "      <td>0.248433</td>\n",
       "      <td>0.677436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.403907</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>-0.278491</td>\n",
       "      <td>0.558589</td>\n",
       "      <td>0.296017</td>\n",
       "      <td>0.556225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.170615</td>\n",
       "      <td>0.133034</td>\n",
       "      <td>0.038371</td>\n",
       "      <td>0.767355</td>\n",
       "      <td>0.050444</td>\n",
       "      <td>0.702178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.310627</td>\n",
       "      <td>0.122985</td>\n",
       "      <td>-0.182789</td>\n",
       "      <td>0.722203</td>\n",
       "      <td>0.211325</td>\n",
       "      <td>0.596840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.113961</td>\n",
       "      <td>0.134518</td>\n",
       "      <td>-0.015781</td>\n",
       "      <td>0.790565</td>\n",
       "      <td>-0.039474</td>\n",
       "      <td>0.626294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>504</td>\n",
       "      <td>-0.609074</td>\n",
       "      <td>0.255213</td>\n",
       "      <td>-0.525328</td>\n",
       "      <td>0.372037</td>\n",
       "      <td>0.429611</td>\n",
       "      <td>0.579392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>-0.275566</td>\n",
       "      <td>0.128932</td>\n",
       "      <td>-0.048317</td>\n",
       "      <td>0.647440</td>\n",
       "      <td>0.068634</td>\n",
       "      <td>0.630368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>121</td>\n",
       "      <td>-0.617245</td>\n",
       "      <td>0.225774</td>\n",
       "      <td>-0.504427</td>\n",
       "      <td>0.457158</td>\n",
       "      <td>0.128094</td>\n",
       "      <td>0.927855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "      <td>-0.262446</td>\n",
       "      <td>0.188253</td>\n",
       "      <td>-0.013231</td>\n",
       "      <td>0.655518</td>\n",
       "      <td>0.083446</td>\n",
       "      <td>0.787131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.643196</td>\n",
       "      <td>0.271016</td>\n",
       "      <td>-0.361152</td>\n",
       "      <td>0.369329</td>\n",
       "      <td>0.285235</td>\n",
       "      <td>0.956450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.254707</td>\n",
       "      <td>0.299575</td>\n",
       "      <td>0.006294</td>\n",
       "      <td>0.633387</td>\n",
       "      <td>0.248786</td>\n",
       "      <td>0.713144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "0      100         0.2       1.0               252  -0.617632     0.258913   \n",
       "1      100         0.2       1.0                52  -0.271625     0.161219   \n",
       "2       80         0.2       1.0               252  -0.099966     0.072096   \n",
       "3       80         0.2       1.0                52  -0.040901     0.035402   \n",
       "4       90         0.2       1.0               252  -0.317394     0.133345   \n",
       "5       90         0.2       1.0                52  -0.134857     0.079479   \n",
       "6      110         0.2       1.0               252  -1.076932     0.557806   \n",
       "7      110         0.2       1.0                52  -0.471162     0.309397   \n",
       "8      120         0.2       1.0               252  -1.524328     1.148313   \n",
       "9      120         0.2       1.0                52  -0.718183     0.640199   \n",
       "10     100         0.1       1.0               252  -1.310209     0.463384   \n",
       "11     100         0.1       1.0                52  -0.561350     0.261862   \n",
       "12     100         0.3       1.0               252  -0.403907     0.172415   \n",
       "13     100         0.3       1.0                52  -0.170615     0.133034   \n",
       "14     100         0.4       1.0               252  -0.310627     0.122985   \n",
       "15     100         0.4       1.0                52  -0.113961     0.134518   \n",
       "16     100         0.2       2.0               504  -0.609074     0.255213   \n",
       "17     100         0.2       2.0               104  -0.275566     0.128932   \n",
       "18     100         0.2       0.5               121  -0.617245     0.225774   \n",
       "19     100         0.2       0.5                26  -0.262446     0.188253   \n",
       "20     100         0.2       0.2                50  -0.643196     0.271016   \n",
       "21     100         0.2       0.2                10  -0.254707     0.299575   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "0        -0.494779          0.263058    0.165846      0.749428  \n",
       "1        -0.114218          0.668894    0.099517      0.732453  \n",
       "2        -0.093986          0.064879    0.090757      0.181319  \n",
       "3        -0.030095          0.136520    0.057643      0.161313  \n",
       "4        -0.260803          0.168427    0.126441      0.281713  \n",
       "5        -0.061264          0.278445    0.032836      0.310006  \n",
       "6        -0.927918          0.500694    0.268663      2.191919  \n",
       "7        -0.130789          1.119580    0.756748      1.524757  \n",
       "8        -1.379671          1.419537    0.139938      6.068710  \n",
       "9        -0.389655          2.651437    0.413195      4.781828  \n",
       "10       -1.189151          0.493833    0.492511      0.788484  \n",
       "11       -0.300800          0.391117    0.248433      0.677436  \n",
       "12       -0.278491          0.558589    0.296017      0.556225  \n",
       "13        0.038371          0.767355    0.050444      0.702178  \n",
       "14       -0.182789          0.722203    0.211325      0.596840  \n",
       "15       -0.015781          0.790565   -0.039474      0.626294  \n",
       "16       -0.525328          0.372037    0.429611      0.579392  \n",
       "17       -0.048317          0.647440    0.068634      0.630368  \n",
       "18       -0.504427          0.457158    0.128094      0.927855  \n",
       "19       -0.013231          0.655518    0.083446      0.787131  \n",
       "20       -0.361152          0.369329    0.285235      0.956450  \n",
       "21        0.006294          0.633387    0.248786      0.713144  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low = pd.merge(df_bs_low, df_wilmott_low, how='right', on=keys).merge(df_td3_low, how='right', on=keys)\n",
    "df_low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different strikes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.617632</td>\n",
       "      <td>0.258913</td>\n",
       "      <td>-0.494779</td>\n",
       "      <td>0.263058</td>\n",
       "      <td>0.165846</td>\n",
       "      <td>0.749428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.099966</td>\n",
       "      <td>0.072096</td>\n",
       "      <td>-0.093986</td>\n",
       "      <td>0.064879</td>\n",
       "      <td>0.090757</td>\n",
       "      <td>0.181319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.317394</td>\n",
       "      <td>0.133345</td>\n",
       "      <td>-0.260803</td>\n",
       "      <td>0.168427</td>\n",
       "      <td>0.126441</td>\n",
       "      <td>0.281713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.076932</td>\n",
       "      <td>0.557806</td>\n",
       "      <td>-0.927918</td>\n",
       "      <td>0.500694</td>\n",
       "      <td>0.268663</td>\n",
       "      <td>2.191919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.524328</td>\n",
       "      <td>1.148313</td>\n",
       "      <td>-1.379671</td>\n",
       "      <td>1.419537</td>\n",
       "      <td>0.139938</td>\n",
       "      <td>6.068710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "0     100         0.2       1.0               252  -0.617632     0.258913   \n",
       "2      80         0.2       1.0               252  -0.099966     0.072096   \n",
       "4      90         0.2       1.0               252  -0.317394     0.133345   \n",
       "6     110         0.2       1.0               252  -1.076932     0.557806   \n",
       "8     120         0.2       1.0               252  -1.524328     1.148313   \n",
       "\n",
       "   E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "0       -0.494779          0.263058    0.165846      0.749428  \n",
       "2       -0.093986          0.064879    0.090757      0.181319  \n",
       "4       -0.260803          0.168427    0.126441      0.281713  \n",
       "6       -0.927918          0.500694    0.268663      2.191919  \n",
       "8       -1.379671          1.419537    0.139938      6.068710  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.271625</td>\n",
       "      <td>0.161219</td>\n",
       "      <td>-0.114218</td>\n",
       "      <td>0.668894</td>\n",
       "      <td>0.099517</td>\n",
       "      <td>0.732453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.040901</td>\n",
       "      <td>0.035402</td>\n",
       "      <td>-0.030095</td>\n",
       "      <td>0.136520</td>\n",
       "      <td>0.057643</td>\n",
       "      <td>0.161313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.134857</td>\n",
       "      <td>0.079479</td>\n",
       "      <td>-0.061264</td>\n",
       "      <td>0.278445</td>\n",
       "      <td>0.032836</td>\n",
       "      <td>0.310006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.471162</td>\n",
       "      <td>0.309397</td>\n",
       "      <td>-0.130789</td>\n",
       "      <td>1.119580</td>\n",
       "      <td>0.756748</td>\n",
       "      <td>1.524757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.718183</td>\n",
       "      <td>0.640199</td>\n",
       "      <td>-0.389655</td>\n",
       "      <td>2.651437</td>\n",
       "      <td>0.413195</td>\n",
       "      <td>4.781828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "1     100         0.2       1.0                52  -0.271625     0.161219   \n",
       "3      80         0.2       1.0                52  -0.040901     0.035402   \n",
       "5      90         0.2       1.0                52  -0.134857     0.079479   \n",
       "7     110         0.2       1.0                52  -0.471162     0.309397   \n",
       "9     120         0.2       1.0                52  -0.718183     0.640199   \n",
       "\n",
       "   E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "1       -0.114218          0.668894    0.099517      0.732453  \n",
       "3       -0.030095          0.136520    0.057643      0.161313  \n",
       "5       -0.061264          0.278445    0.032836      0.310006  \n",
       "7       -0.130789          1.119580    0.756748      1.524757  \n",
       "9       -0.389655          2.651437    0.413195      4.781828  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-20, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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snDvOvAEAEOTyb2w484aHfE6nUxs2bNA333yjlStXaujQoRo9erQyMjL8XSb+h/AGAECQS09Pl5R3A8PZdOjQQVOnTtXrr7+uv//97/4oDYUgvAEAEOTy71Jt3br1n0575ZVXSpLmz5+vpKSkcq0LhbN1ePN4PDIMQ2632+pSAACwra+++kqSNHjwYG/bmDFjVLt2bW3cuNFn2tq1a0vKe1PDnj17/Faj1dxutwzDkMfjsboUe4c3p9Op+Pj4oHyvGQAAZeHbb7/V119/rbZt2+rqq6/2ti9evFhHjx4tcAdq/iu3pN9vdAgGLpdL8fHxcjqdVpfC3aYAAASrU6dOafz48YqOjtZbb73lc8dqu3btdOzYMY0dO9Znnk2bNknKe+vAhRde6Nd6kcfWZ94AAAgWKSkphf67KJmZmd63IWRlZRXo//zzz9WrVy/t379fH330kVq2bOnTP2fOHNWtW1chIb9HhaSkJM2dO1exsbF69dVXS7Uehw8flqRSPVAYeTjzBgBAgPr+++81evRopaamav/+/d72Tp06KTY2VtWqVdPMmTPVt29fb9/+/fs1fPhw7dq1y/voj+7du3vPkmVnZyspKUlRUVEaOnSoJk+erOjo6AJjd+jQQQsXLtSTTz6pXbt2yeFwKCUlRUOHDtX999+v6tWrF3s9tm3bpsmTJ+vAgQPe74zdeOONevDBB1W7dm19/PHHioiIKM0mCkqENwAAAlTLli0LfXDu2TRo0ECff/55mYzfqFEjzZ8//5yX06lTpzKrCYQ3AABsZdcu6ZdfpOrVpY4dpdBQqyuCv/GdNwAAbOCbb6TLL5eaNZMGDJC6dpUaNZLefNPqyuBvnHkDACDAff+91L27dPq0b/uBA9JNN0knTkgTJlhTG/yPM28AAAS4e+7JC245OYX3T50qHT/u35pgHcIbAAAB7PBhafXqooObJKWnS0uX+q8mWCtgLptmZmbqn//8p5YuXaoqVaqoWrVq6tmzp6ZOnerz0EAAAILJf/8rmebZpwkLk854kggquIAIbxkZGRowYIBCQkK0Zs0a1alTR6+99ppuvfVWNWjQQMOHD7e6RAAALPG/V4meVU6OdP755V8LAkNAhLcJEybowIED+uqrr1S5cmVJeU9gzsrKUkZGhsXVAQBgnfr1pR49pC1bpNzcwqcJCZGGDfNvXbCO5d95+/rrr7Vo0SLdeeed3uAmSf/4xz906NAh3XTTTRZWBwCA9Z56Ki+gFfUtonvukerU8W9NsI7l4W3u3LkyTVP9+/cv0Fe3bl0LKgIAILD06CGtWiU5nXk/54e4qCjpkUekxx6zrDRYwPLLpmvWrJEknX/++Zo4caJ++OEHHTlyRH379tX06dNL9O40AAAqqrg4ad8+6aOPfn/DwtVXS9WqWV0Z/M3S8Jaenu590e7MmTN111136aKLLtKpU6d0zTXXqE+fPtq8ebMqVapkZZkAAASE0NC8EIfg5jDNP7sBufz897//lfN/54Bffvll3Xrrrd6+jRs3qnfv3rrvvvv0xBNPFDp/y5YtVatWLe/PcXFxivPTb3VycrJq1Kjhl7EQWNj3wY39H7zY98Fp3bp1WrVqlcLDwyVJp06d0o4dOyytydLwduLECVWtWlWStG/fPsXGxnr7jh07plq1aumCCy7QwYMHC53fMAzFx8f7pdY/SkhIUOfOnS0ZG9Zi3wc39n/wYt8HrzP3vZXZI5+lNyxER0d7k2xMTIxPX36oO3TokI4cOeL32gAAAAKR5XebNmvWTFLeGxbOFBLye2n5AQ8AACDYWR7errjiCkl53387U0pKiiSpQYMG3HEKAADwP5aHt7FjxyokJEQJCQk+7fk/n3kTAwAAQLCzPLxdeumluvvuu/XYY495v9t28uRJPfXUU+rRo4fuvvtuiysEAAAIHJY/pFeSZsyYoZiYGA0cOFCVKlXSqVOnNHDgQP3jH/9QRESE1eUBAAAEjIAIb5J0xx136I477rC6DAAAgIBm+WVTAAAAFB/hDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI4Q3AAAAGyG8AQAA2Iitw5vH45FhGHK73VaXAgAAKjC32y3DMOTxeKwuJXBej1UaTqdT8fHxVpcBAAAqOJfLJZfLJcMwrC7F3mfeAAAAgg3hDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI4Q3AAAAGyG8AQAA2AjhDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI4Q3AAAAG7F1ePN4PDIMQ2632+pSAABABeZ2u2UYhjwej9WlKMzqAs6F0+lUfHy81WUAAIAKzuVyyeVyyTAMq0ux95k3AACAYEN4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARmwd3jwejwzDkNvttroUAABQgbndbhmGIY/HY3UpCrO6gHPhdDoVHx9vdRkAAKCCc7lccrlcMgzD6lLsfeYNAAAg2BDeAAAAbITwBgAAYCOENwAAABshvAEAANgI4Q0AAMBGCG8AAAA2QngDAACwEcIbAACAjRDeAAAAbITwBgAAYCOENwAAABshvAEAANgI4Q0AAMBGCG8AAAA2QngDAACwEVuHN4/HI8Mw5Ha7rS4FAABUYG63W4ZhyOPxWF2Kwqwu4Fw4nU7Fx8dbXQYAAKjgXC6XXC6XDMOwuhR7n3kDAAAINoQ3AAAAGyG8AQAA2AjhDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgIwET3l544QWtXr1ap06dkiTl5ORo586devjhh3XixAmLqwMAAAgMARPeli1bpoEDB6py5cqqUaOGIiMjdemll6pSpUqKjo62ujwAAICAEDDhTZKaNWumiIgIhYaG6pprrtGmTZv0j3/8w+qyAAAAAkZAvZj+p59+sroEAACAgBZQZ94AAACCQXJysh566CHt3LlTOTk5kqSTJ09q9erVmjdv3lnnDagzbwAAAIXp2LGjtm/frlq1anm/ZnX48GH9+OOPkqSGDRsqNjZWWVlZ2rt3rw4ePKjzzz9fiYmJ2r9/v4YNG6Zdu3bp2LFjkqTGjRsrJiZGkpSdna2jR48qKipKw4YN06RJk1SlSpVC68jMzNRdd92lHTt2SJKOHz+u7t2766677lLDhg2LvT6pqal67LHH9Nhjjyk0NFRVqlRRamqq6tSpo48++uis8wZUeNu5c6fuueceJScn6/Tp0+rfv78efvhhnXfeeVaXBgAALJSYmKinn35ad911l0JDQyVJWVlZioiIkCStXbtWzZs3907/9ttva/To0crNzVWDBg20detWZWZmKiYmRkeOHNHmzZtVt25dnzE2b96siRMnau7cuVq+fLnatWvn05+enq6EhATdfffdmj17treu9u3ba8mSJdq0aZNPDX+mdu3acjgcSk1NVe3atTVu3DhNmzZNtWrVOut8ARXennnmGb311luqUaOGUlNT1bNnT23btk0ffvihd0edaffu3erZs6f357i4OMXFxfml1uTkZCUkJPhlLAQW9n1wY/8HL/a9tcLDw9WrVy9t37690P7vv/9eKSkp3p+bNGmi9u3ba926dapRo4a3PT9PfPvtt9q3b5/PMsLCwvTPf/5Tt956q/r06aOFCxfqhx9+0KpVqxQeHq6jR48qJSVFU6ZM0bfffitJqlOnjoYOHapnn31Wc+fO1bPPPlvsdRo4cKBee+21Yk/vrbPEc5ST22+/XYMHD1Z4eLgkqVq1anrggQc0fPhwLViwQOPHjy8wT5MmTRQfH+/vUiVJCQkJ6ty5syVjw1rs++DG/g9e7HvrpKWlqWPHjmfd/m3bti1w2bJPnz6qX7++WrVq5W2LjIyUJHXo0EHVq1cvdFlvvPGGunfvrueff16bNm1SXFycOnfurB9//FFt2rRRgwYNfKbPf0Zt5cqVS7F2JRcwNywMHz7cG9zytW7dWpL0+uuvW1ESAAAIAEeOHFGjRo1KPF/Dhg115MiREs/XrVs3NWnSRJ9++qk+/fRTb/vFF1+suLg4rVq1ymf6rVu3KjIyUqNGjSrxWKUREOEtKytLiYmJBdqjoqIkyXtqEgAABJ9GjRrpiSeeKPF8t956q3r16lWqMdu3by8p7yUCZzN79mzt27dP7777rpo1a1bicebPn68uXbqoR48e6tKli1asWPGn8wREeLv22mt14YUXasuWLT7taWlpklTgjBwAAAguDoejVPOUZj5J3jtRt23bVqDP4/GoT58+at26tWbNmqUlS5Zo0KBBJR4jPj5eISEh2rp1qz777DM9/vjjGjJkiF599dWzzhcQ4S0xMVHVqlVT1apVfdo9Ho+k39MvAACAP1SqVEmSdPTo0QJ9TqdTGzZs0DfffKOVK1dq6NChGj16tDIyMoq9/Fq1aunVV1/VLbfc4m3r16+f4uLiNGXKFCUlJRU5b0CEt379+um9997TJZdc4tO+Zs0aSdKUKVMsqAoAAASr9PR0SSpwYumPOnTooKlTp+r111/X3//+92IvPzo6utCzda1bt9aJEyf0r3/9q8h5AyK8TZs2Tffff782btzobfvuu++0aNEiTZs2TQMHDrSwOgAAEGzyv4uff/Pk2Vx55ZWS8r6/drYzZmc6evSoTp8+XaC9ON/3D4hHhdSsWVPvv/++pk2bpocfflghISEyTVOvvfaarr/+eqvLAwAAQearr76SJA0ePNjbNmbMGK1du1YbN270uRGidu3akvLe1LBnzx7vz0XZt2+fWrRooYYNG+qHH37w6SvO9/0DIrxJUt26dbVo0SKrywAAAEHu22+/1ddff622bdvq6quv9t60sHjxYmVlZWnZsmU+4S3/lVvS7zc6nM3x48eVkZEhp9NZoK843/cPiMumAAAAgeDUqVMaP368oqOj9dZbb/ncrdquXTtVqVJFY8eO9Zln06ZNkvLe9HThhRf+6RgtW7bU5ZdfrtWrV/u0Z2dn68MPP1T9+vU1ZMiQIucnvAEAAFs683VYZ/67KJmZmd63IWRlZRXo//zzz9WrVy/t379fH330kVq2bOnTP2fOHEVGRiok5Pf4lJSUpLlz5yo2NvZPH/GRLywsTJMmTdLQoUN9ztrNmDFDOTk5Wrp0qfe7b4XOX6xRAAAAAsD333+v0aNHKzU1Vfv37/e2d+rUSbGxsapWrZpmzpypvn37evv279+v4cOHa9euXd5Hf3Tv3t17liw7O1tJSUmKiorS0KFDNXnyZEVHRxcYu0OHDmrTpo2ee+457dq1Sw6HQykpKRo6dKjuv//+Il+3VZghQ4YoOjpaN954o7Kzs5Wenq7mzZtrx44duuiii846L+ENAADYRsuWLQt9cO7ZNGjQQJ9//nmZjF+pUiXNnz+/TJYVFxenuLi4Es/HZVMAAGB/R45I+/dLhVwOrWgIbwAAwL7+/W+pc2epTh0pNjbv73vvlf73yI2KiPAGAADsad486dprpe3bf29LSZGeeUbq00c6edKy0soT4Q0AANjPwYPSpEl5/87N9e3LyZG++EKaPdv/dfmBrcObx+ORYRhyu91WlwIAAPxp0aKCoe1MubnSSy9Jplkmw7ndbhmG4X2IrpVsfbep0+lUfHy81WUAAAB/+/FH6YwH6Bbq8GHpxAnpT14uXxwul0sul0uGYZzzss6Vrc+8AQCAIFWlyp+Ht5AQ6SwPu7UrwhsAALCfIUOk7Oyi+8PC8m5miIjwX01+QngDAAD207ev1KWLFBpasC//jNx99/m3Jj8hvAEAAPtxOPKe8datW97PYWFSeHjevytXlpYtkzp1sq6+cmTrGxYAAEAQq1VL2rhR2rpVWrFCOn1auvRS6cYb874TV0ER3gAAgH05HFLXrnl/ggSXTQEAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjtg5vHo9HhmHI7XZbXQoAAKjA3G63DMOQx+OxuhR7v9vU6XQqPj7e6jIAAEAF53K55HK5ZBiG1aXY+8wbAABAsCG8AQAA2AjhDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI4Q3AAAAGyG8AQAA2AjhDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI7YObx6PR4ZhyO12W10KAACowNxutwzDkMfjsboUhVldwLlwOp2Kj4+3ugwAAFDBuVwuuVwuGYZhdSn2PvMGAAAQbAhvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYiK3Dm8fjkWEYcrvdVpcCAAAqMLfbLcMw5PF4rC5FYVYXcC6cTqfi4+OtLgMAAFRwLpdLLpdLhmFYXYq9z7wBAAAEG8IbAACAjRDeAAAAbITwBgAAYCOENwAAABshvAEAANgI4Q0AAMBGCG8AAAA2QngDAACwEcIbAACAjQRkeMvJyVG3bt302WefWV0KAABAQAnI8DZjxgx9/vnnys7OtroUAACAgBJw4e2rr77SwoULrS4DAAAgIAVUeMvMzNRjjz2mqVOnWl0KAABAQAqo8DZ9+nRNnTpVlSpVsroUAACAgBQw4W3Lli0KCQlR165drS4FAAAgYIVZXYAknTp1Sv/85z/1zjvvlGi+3bt3q2fPnt6f4+LiFBcXV9blFSo5OVkJCQl+GQuBhX0f3Nj/wYt9H5zWrVunVatWKTw8XFJeZrFaQIS3Bx54QA899JAiIiJKNF+TJk0UHx9fTlWdXUJCgjp37mzJ2LAW+z64sf+DF/s+OHXu3FlxcXHefW8YhsUVBcBl0w8//FB169ZV69atrS4FAAAg4Fl65i01NVULFiwo8eVSAACAYGVpePvoo4/0yy+/6IorrvBp/+233yRJU6ZMUfXq1XXHHXdo6NChVpQIAAAQUCwNb4MHD9bgwYMLtD/yyCOaPn26nn32WfXu3dv/hQEAAAQoy7/zBgAAgOILyPCWlJQkSTp69KjFlQAAAASWgApvDz30kJo0aaL/+7//kyT95S9/UatWrbRixQqLKwMAAAgMAfGct3yPPvqoHn30UavLAAAACFgBdeYNAAAAZ0d4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARmwd3jwejwzDkNvttroUAABQgbndbhmGIY/HY3UpgfWGhZJyOp2Kj4+3ugwAAFDBuVwuuVwuGYZhdSn2PvMGAAAQbAhvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYiK3Dm8fjkWEYcrvdVpcCAAAqMLfbLcMw5PF4rC5FYVYXcC6cTqfi4+OtLgMAAFRwLpdLLpdLhmFYXYq9z7wBAAAEG8IbAACAjRQZ3l5++WV/1gEAAIBiKDK8zZw50591AAAAoBiKvGFh7969euqpp9S9e/eSLTAsTN26dTvnwgAAAFDQWe823bRpkzIzM8+6ANM0tXDhQv33v/+VJF177bVavnx5mRUIAACA3xUZ3mrXrq1Vq1YpJKToexr27NmjUaNGyePxKDo6WnPmzNGYMWPKpVAAAACc5Ttv11133VmD28svv6w2bdpo8+bNuvzyy/XNN98Q3AAAAMpZkels/vz5hbYfPHhQAwYM0IQJE5Sdna3Zs2drw4YNio2NLbciAQAAkKdEb1hwu9264447lJycrHbt2unNN99UixYtyqs2AAAA/EGxHtJ77NgxDR8+XCNHjtSJEyf00EMPaevWrQQ3AAAAP/vTM2+rVq3SuHHjdOjQIV188cV644031KFDB3/UBgAAgD8o8szbyZMnNXbsWBmGocOHD2vy5Mn64osv/jS4vfLKK2VeJAAAAPIUGd4uvfRSLVq0SPXr19fHH3+sOXPmKCoq6k8X+PTTT5dpgQAAAPhdkZdN9+3bp5YtW2rGjBlyOBzatGnTny7s008/1b59+8qyPgAAAJyhyPBWs2ZN3XDDDdqxY0exF/b555+XSVEAAAAoXJHhrUmTJnr44YdLtLDs7GxdeOGF51xUcXk8HhmGIZfLJZfL5bdxAQBAcHG73XK73fJ4PFaXUnR4a9euXckXFham66677pwKKgmn06n4+Hi/jQcAAIJT/okiwzCsLqXoGxZeeumlUi2wqDczAAAA4NwV6w0LaWlp8ng8at68uU/7o48+qj179qhp06Zq2bKlWrRooaZNmyo0NLRcigUAAAh2RYa3IUOGaN++fUpLS1OHDh3UuXPnAuHtoYceUkZGhrZs2aK5c+fqvffeU0REhNLT08u9cAAAgGBUZHh7//33NXToUL3xxhtnfb5bZGSk+vTpoz59+ugf//iHZs2aVS6FAgAA4CzfeXM4HJo7d26xHsyb795771VYWInedQ8AAIASKDJpXXTRRapVq5b359dff10Oh6PAdDfddJP339WrV9dFF11UxiUCAAAgX5Fn3s4MbpJ04YUXyuFwaNGiRbr55pu1YcMG1a1bt8B8NWvWLPsqAQAAIOksZ94qVark8/OVV14pSbruuusUExOjBQsWFHpXaWRkZBmXCAAAgHxFnnkrStWqVdWyZUseBwIAAGCBIsNbbm5ukTP98axccecDAADAuSnysun27dt1//33F3oZdN++fXr00UcLtGdnZ+vLL78s2woBAADgVWR4O3XqlGbMmFFon2mamj59eqHthd2RCgAAgLJx1oeyGYah6tWrF3thycnJWrly5bnWBAAAgCIUGd5iY2P1/vvvl3iBjRo1OqeCAAAAULQib1ho3bp1qRZY2vkAAADw54oMbytWrCjWAhITE5WVlVXi+QAAAFByRV42ffLJJ5Wdne3TFhERoWnTpkmSXnzxRc2YMUMHDx5UaGiorrnmGj333HOqX79++VYMAAAQxIo889a3b1/961//0mOPPaadO3fqkksu0ZAhQyRJ99xzjyZPnqzjx4/rzjvv1PPPP6+oqCh17txZBw8e9FvxHo9HhmHI7Xb7bUwAABB83G63DMOQx+OxupSiz7w1atRIVapU0ZYtW9SxY0dv+1dffaVZs2apUqVK2rx5sy699FJJ0m233ab58+frwQcf1IIFC8q/cklOp1Px8fF+GQsAAAQvl8sll8slwzCsLqXoM2+PPPKIZs6c6RPcJGnZsmWSpBEjRniDW75x48bp+++/L4cyAQAAIJ0lvH3//ffq1q1bgfbNmzfL4XDo8ssvL3S+KlWqlF11AAAA8FFkeAsLK3hFNScnR//5z38kSV27di10vpycnDIqDQAAAH9UZHhLT09Xenq6T9vHH3+skydPyul06qKLLiowz5YtW9S2bduyrxIAAACSzhLeBg8erPHjx3vPpB04cECTJk2Sw+HQrbfeWmD6EydO6P7779eUKVPKrVgAAIBgV2R4mzJlihITE+V0OtW5c2c1b95cP//8s/r166d7773XO11mZqZWrlypLl26KDQ0VJUrV/ZL4QAAAMGoyEeFhIaGatWqVVq0aJHWrFmj2NhY9e/fXzfffLNCQvIy3y233KLffvtNmZmZqlOnjnJycnTHHXfo7bff9tsKAAAABJMiw5skhYSE6JZbbtEtt9xSaP/ChQvLpSgAAAAUrsjLpgAAAAg8Zz3z5i8HDhzQo48+ql27dsk0TaWlpal///668847Vbt2bavLAwAACBiWh7fDhw/ruuuu06uvvqo2bdpIknbt2qW2bdvqvffe02effaY6depYWyQAAECAsPyy6eLFi/Xll1/qwQcf9LY1bdpUV155pXbt2qV33nnHwuoAAAACi+XhrXXr1qpevbpiYmJ82k+dOiVJPHoEAADgDJZfNu3du7eSk5N92jIzM/XFF1+oVq1aGjJkiEWVAQAABB7Lz7z9UU5Oju6++26FhYUpPj5eNWvWtLokAACAgGH5mbd8O3bs0F133aVDhw4pOztbK1euVIcOHawuCwAAIKA4TNM0rS7ij/79739r6NChmjx5smbMmCGHw1HodC1btlStWrW8P8fFxSkuLs4vNSYnJ6tGjRp+GQuBhX0f3Nj/wYt9H5zWrVunVatWKTw8XFLed/J37NhhaU0Bc+btTNdcc41cLpdmzpypWrVq6Z577il0uiZNmig+Pt7P1eVJSEhQ586dLRkb1mLfBzf2f/Bi3wenzp07Ky4uzrvvDcOwuKIA/M5bviuvvFKS9PTTTys3N9fiagAAAAKDpeEtNzdXcXFxiomJ0Q8//ODTl/9mhWPHjikpKcmK8gAAAAKOpeHt6NGj+vDDD+XxeLRmzRqfvmPHjkmSoqKifL7XBgAAEMws/c7b+eefr/r168vpdGrYsGE+fZs2bZIk3XTTTQoNDbWiPAAAgIBj+Xfe5s6dq9q1ays7O9vb9ssvv+idd95R27Zt9cwzz1hYHQAAQGCx/G7TgQMHqn79+rr//vvl8XjkcDiUmpqqqVOnaurUqYqKirK6RAAAgIBheXiT8t5v+vbbb1tdBgAAQMCz/LIpAKB8PPHEE9q4caMyMjIkSVlZWdq+fbsefPBBiysDcC4IbwBgkVmzZqlp06ZyOBxyOBwKCwtT27Zt9eGHH0qS/vWvf+myyy5TSEiId5pWrVrp5Zdf9i7jk08+UWxsrLc/JiZGy5YtkyTNnz9fvXv31nnnnacaNWooKipK3bp108UXX2zJ+gIoG4Q3ALDI1KlTtWvXLo0YMUKSdNttt+nLL7/0PqR88ODB+vrrrzVz5kxJUt++fbVz506NHz/eu4zevXvr119/1fjx4zVhwgQdOHBAQ4YMkZT3qKXY2FiFhYUpKipKLpdLX3zxhXc8APZEeAMAi40ePVqS9MEHHxTaP3LkSIWEhGjz5s06fvx4odPk5OTowQcf9HkX9AUXXKB9+/YpMzNTBw8e1FtvvaVLLrmkzOsH4F+ENwCwWJ8+fVSnTh3t3r1bX3zxRYH+06dPKzc3VxkZGVqxYkWB/qysLB07dkwXXHCBP8oFYDHCGwBYLDQ01Hupc/HixQX6lyxZoptvvrnI/nXr1ikuLq58iwQQMAhvABAAhg8fLikvqP3R1q1b9cwzzyg8PFzr1q1TcnKyT//777/vDX9/9Nlnn6lv377q3r272rZtq5kzZyo3N7fsVwCA3xDeACAA9OzZU/Xq1dO+ffuUkJDgbd+9e7caN26sWrVqqV+/fsrKytL777/v7c/MzFRaWppq165dYJmHDh3Se++9p1WrVmnz5s3697//rWeffdZ7Fg+APRHeACAAhISE6IYbbpDke2l0yZIl3rNy+X+f2b927Vr179+/0GU+8MADevbZZ3XeeedJkpxOp6ZMmaI33nijyJsjAAQ+W4c3j8cjwzDkdrutLgUAzll+OFu6dKlM05Qkbd++XR07dpQkDRo0SBEREfr444+VlJQkSVqxYoWuv/76Qpc3cuTIAm2tW7eWJL3++utlXj9QkbndbhmGIY/HY3Up9g5vTqdT8fHxcrlcVpcCAOesa9euql+/vn777Tdt2bJFP//8s5o3b+7tr1atmvr376/s7GwtW7ZMGRkZSk9PV/Xq1Qss6/Tp0zp27FiB9vz3RX/77bflth5AReRyuRQfHy+n02l1KfYObwBQkTgcDg0bNkxS3qXRMy+Z5jvz0umaNWs0YMCAQpfVtm1bxcTEaN++fT7taWlpkqTw8PAyrh6AvxDeACCAnHnp9Msvv1SbNm18+g3DUFRUlDZu3Kj58+fruuuuK3Q5R44cUc2aNb3fd8uXf8mnffv2ZV88AL8gvAFAAOnYsaMaNWqkQ4cOqVWrVgX6o6OjNWDAAOXm5ioyMlLR0dGFLqd///7asGGD6tat69O+Zs0ahYWF6Y477iiX+gGUP8IbAASYP95dWtJ+SXrsscf017/+1ee7bRs2bNCaNWv0/PPP67LLLivDigH4U5jVBQAAfLlcLm3YsKHQM2+SdM0116hx48a65pprilxG48aN9dZbb+nee+/13pkaFRWlDz74QL179y6PsgH4CeENAAJM69attXXr1iL7K1eurN27d//pcpo0aaKlS5eWZWkAAgCXTQEg0GzfLt18s9SypdS2rTR9unTwoNVVAQgQnHkDgEDy1FPSffdJYWFSdnZe2zffSLNmSWvWSD16WFsfAMtx5g0AAsWaNXnBTfo9uElSbq506pQ0cKCUmmpNbQACBuENAALFP/8phYYW3pebK504Ib3xhn9rAhBwCG8AEAhMU/rkEykn5+zTbdjgl3IABC7CGwAEiv+9jP6cpwFQoRHeACAQOBxSt25FXzbN17Onf+oBELAIbwAQKO68s+jLpg6HVKmSNHq0X0sCEHgIbwAQKAYN+v1u07AznuQUGipFRkrLl0s1a1pRGYAAQngDgEDyxBN5NyUMGiTVry81aSL9/e/S999L/fpZXR2AAGDrh/R6PB4ZhiGXyyWXy2V1OQBQNnr3zvsDIGC43W653W55PB6rS7F3eHM6nYqPj7e6DAAAUMHlnygyDMPqUrhsCgAAYCeENwAAABshvAEAANgI4Q0AAMBGCG8AAAA2QngDAACwEcIbAACAjRDeAAAAbITwBgAAYCOENwAAABshvAEAANgI4Q0AAMBGCG8AAAA2QngDAACwEcIbAACAjRDeAAAAbMTW4c3j8cgwDLndbqtLAQAAFZjb7ZZhGPJ4PFaXojCrCzgXTqdT8fHxVpcBAAAqOJfLJZfLJcMwrC7F3mfeAAAAgg3hDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI4Q3AAAAGyG8AQAA2AjhDQAAwEYIbwAAADZCeAMAALARwhsAAICNEN4AAABshPAGAABgI4Q3AAAAG7F1ePN4PDIMQ2632+pSAABABeZ2u2UYhjwej9WlKMzqAs6F0+lUfHy81WUAAIAKzuVyyeVyyTAMq0ux95k3AACAYEN4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANhIw4e3o0aOaNGmS4uLi1K5dO7Vu3VozZ85UZmam1aUBAAAEjIB4PdbRo0dlGIaee+45dejQQZK0cuVK3XDDDVq2bJk+/vhjVa5c2eIqAQAArBcQZ94ef/xxTZkyxRvcJOnaa6/VHXfcoW3btmn69OkWVgcAABA4AiK8rV+/XmPGjNH69et92q+//npJ0pIlS6woCwAAIOAERHhr3ry50tLSlJyc7NN+/vnnS5IOHTpkRVkAAAABJyC+87Z48WIlJiaqXr16Pu2//vqrJKlJkyZWlAUAABBwAuLMW2hoaIHgJklvv/22JGncuHH+LgkAACAgOUzTNK0uojC7du1Sy5Yt1b17d61fv15hYQVPErZs2VK1atXy/hwXF6e4uDi/1JecnKwaNWr4ZSwEFvZ9cGP/By/2fXBat26dVq1apfDwcEnSqVOntGPHDktrCsjwlpWVpe7duyskJEQffPCBqlWrVuh0hmEoPj7ez9XlSUhIUOfOnS0ZG9Zi3wc39n/wYt8HrzP3vZXZI19AXDb9o9tvv13Vq1fX+vXriwxuAAAAwSjgwtsjjzyi9PR0rVq1SlWqVJGUd4oSAAAAARbeFi5cqIMHD+qNN97wXluWpJtvvtnCqgAAAAJHQDwqRJLWrFmj77//XvPmzZPD4fC2Z2Vl6fTp0xZWBgAAEDgCIrzt2LFDw4cP14UXXqhVq1b59J08eVIDBgywqDIAAIDAEhDhbdSoUTpx4oR++umnQvubN2/u54oAAAACU0CEt++++87qEgAAAGwhoG5YAAAAwNkR3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI3YOrx5PB4ZhiG32211KQAAoAJzu90yDEMej8fqUgLj9Vil5XQ6FR8fb3UZAACggnO5XHK5XDIMw+pS7H3mDQAAINgQ3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhLcAM2vWLDVt2lQOh0MOh0NhYWFq27atPvzwQ0nSv/71L1122WUKCQnxTtOqVSu9/PLL3mV88sknio2N9fbHxMRo2bJl3v709HRt2LBBAwcO1OOPP+73dQQAAKVHeAswU6dO1a5duzRixAhJ0m233aYvv/xSV155pSRp8ODB+vrrrzVz5kxJUt++fbVz506NHz/eu4zevXvr119/1fjx4zVhwgQdOHBAQ4YM0YEDB3TllVfqxhtv1OLFi7V69WplZ2f7fyUBAECpEd4C1OjRoyVJH3zwQaH9I0eOVEhIiDZv3qzjx48XOk1OTo4efPBBORwOSVL9+vX14Ycfavny5brxxhvLpW4AAFC+CG8Bqk+fPqpTp452796tL774okD/6dOnlZubq4yMDK1YsaJAf1ZWlo4dO6YLLrjAH+UCAAA/IbwFqNDQUA0ZMkSStHjx4gL9S5Ys0c0331xk/7p16xQXF1e+RQIAAL+zdXjzeDwyDENut9vqUsrF8OHDJeUFtT/aunWrnnnmGYWHh2vdunVKTk726X///fe94Q8AAJwbt9stwzDk8XisLsXe4c3pdCo+Pl4ul8vqUspFz549Va9ePe3bt08JCQne9t27d6tx48aqVauW+vXrp6ysLL3//vve/szMTKWlpal27dpWlA0AQIXjcrkUHx8vp9NpdSn2Dm8VXUhIiG644QZJvpdGlyxZ4j0rl//3mf1r165V//79/VgpAADwF8JbgMsPZ0uXLpVpmpKk7du3q2PHjpKkQYMGKSIiQh9//LGSkpIkSStWrND1119vTcEAAKBcEd4CXNeuXVW/fn399ttv2rJli37++Wc1b97c21+tWjX1799f2dnZWrZsmTIyMpSenq7q1atbVzQAACg3hLcA53A4NGzYMEl5l0bPvGSa78xLp2vWrNGAAQP8XicAAPAPwpsNnHnp9Msvv1SbNm18+g3DUFRUlDZu3Kj58+fruuuus6BKAADgD4Q3G+jYsaMaNWqkQ4cOqVWrVgX6o6OjNWDAAOXm5ioyMlLR0dEWVAkAAPyB8GYTf7y7tKT9f3T48GFJ0pEjR8qgOgAA4C+EN5twuVzq3LlzoWfeJOmaa65R48aNdc011xS5jMzMTHXv3l3NmzfXX/7yF0nSSy+9pPr166tbt27atm1budQOAADKTpjVBaB4Wrdura1btxbZX7lyZe3evfusy4iIiNDmzZvLujQAAOBHhDebSE2V3nxT+uILKSJCGjhQuvpqKTTU6soAAIA/Ed5sYOVK6cYbpdOnfw9rL78sNW8uffCBFBtrbX0AAMB/+M5bgPvyS2nw4LzgZppSdnbeH0n65RfpiiukzExrawQAAP5DeAtwzzyT9/f/3ozlIzs7L8Cd8U56AABQwRHeAtzy5b+faStMaCjhDQCAYEJ4C2CmKWVknH2anJy8S6oAACA4EN4CmMMhXXxx3t9FCQ2VLrnEfzUBAABr2Tq8eTweGYYht9ttdSnl5o47zt5vmtKtt/qnFgAAgpXb7ZZhGPJ4PFaXYu9HhTidTsXHx5d6/m3btmnixIn66aefdOLECUnSJZdcolq1anmnOXnypCpXrqxRo0bppptuUugfHqyWlJSk6dOna/v27YqKilJ2drauuuoqTZ06VXPmzFH37t3Vs2dPn3lyc3P13XffacGCBfr555+1du3aImscN07697+lNWt8b1oIDc27ZPr88zwqBACA8uZyueRyuWQYhtWl2PvM27nq1KmT/vOf/2j//v2SpEqVKunrr7/WJ5984v3zn//8R8OGDdOYMWPUv39/nTp1yjv/wYMH1b59ezVv3lxbtmzRhg0btHHjRtWtW1eGYeiJJ55QTk6Oz5jDhg3TwIED9f777+uFF15Qenr6WWsMC8u7aWHWLN+Q1r27tHq1NHFimW0OAABgA0Ed3vJVr15dkhQeHq6QkIKbZMKECbruuuv00Ucf6cknn/S233PPPWrTpo3uuOMOOf73xbSQkBCNHTtW/fv3V1paWoFlLVmyRGvWrNHDDz9c7PrCw6U775T27pWSk6WTJ6WNG6UBA0q4ogAAwPYIb8XUt29fSdLq1au9batXr1azZs0KnX7y5Mm64IILyrQGh0OqXl2qVKlMFwsAAGyE8FZMJ0+elJT3cvd8ISEhWrt2bYFLo5IUGhqqoUOH+q0+AAAQHAhvxXDixAm98sorkqQ7zrj9s3///vruu+9kGIa++eabAvO1bt1a4eHhfqsTAABUfIS3P7F7924NHDhQqampev311zVy5Ehv34wZM1S/fn2tXr1al112merWratBgwZp9uzZ8ng8GjdunLp3725h9QAAoKIhvJ0hLS1NvXv39v7p1q2bunXrpqSkJM2bN08jRozwmT4mJkYJCQkaO3asqlSposTERK1YsUJTp05V06ZNtXTpUovWBAAAVFSEtzNUqVLF5zEhW7ZsUWJiov7v//5PEydOVPv27bVr1y6feerVq6f58+crOTlZO3bs0Isvvqhrr71Wp0+f1o033qivvvrKmpUBAAAVEuGtGPr06aN58+bp66+/Vr9+/bw3L4wZM8Y7TVhYmNq1a6eJEycqPj5eCxcuVG5urmbMmGFV2QAAoAIivBXToEGDVLlyZe3fv1+LFy+WlPd9uKKMGTNG7du358wbAAAoU4S3YgoNDVVkZKSk3x8bkpCQoMTExCLnady4sWrXru2X+gAAQHAgvBXTxo0bdezYMYWHh+uqq66SJGVmZurOO+8sdPqsrCxt3bpVQ4YM8WeZAACggiO8SUpJSZGkQh+2K+WdYRsxYoRCQkL00ksvqWnTpt6+kydPatiwYdqzZ4+3LSMjQ7fddpsaNGjg81y4Mx0+fFiSlJycrOzs7DJaEwAAUNGFWV2AlbZt26ZJkybpxx9/lJT3qJDWrVurZs2a3mmOHj2qY8eOqW/fvpo6daouu+wyb19cXJzef/99bd26VQ8//LAOHjyo3NxcpaWl6eqrr9ZLL71U4CG9t912mz7//HP99NNPkqRvvvlGderUUZMmTXTLLbdo/PjxflhzAABgV0Ed3jp16qSEhIRSz//BBx9Ikrp06aIuXboUa5558+aVejwAAICgDm9nk5sr7d8vmabUoIEUGmp1RQAAADb/zpvH45FhGHK73WW2zNxc6fnnpUaN8v5cdJFUv740c6bEV9MAAAhObrdbhmHI4/FYXYq9z7w5nU7Fx8eX2fJMU7r1VmnhQt/2gweladOk7duld9+VQmwdeQEAQEm5XC65XC4ZhmF1KfY+81bWPv64YHDLZ5rS0qXS8uV+LQkAAMAH4e0M8+ZJYWc5FxkaKs2d6796AAAA/ojwdoadO8/+vbacHOmHH/xXDwAAwB8R3s5QrZrkcJx9muho/9QCAABQGMLbGW688ez9ISHSX/7in1oAAAAKQ3g7w+jRUr16hT/TLTRUqllT4gUIAADASoS3M1SrJm3cKDVpkvdzWJiU/3armBhpwwapTh3r6gMAALD1c97KQ5Mm0vffSx9+mBfWTFPq2VMaMIC3LAAAAOsR3goREiL175/3BwAAIJBw2RQAAMBGAiq8ZWVlKSEhQX/96181btw4q8sBAAAIOAFx2TQzM1NXX321oqKi1KJFC7311lsaNWqU1WUBAAAEnIAIbxEREVq/fr0kad++fZo1a5bFFQEAAASmgLpsCgAAgLMjvAEAANgI4Q0AAMBGAuI7b6W1e/du9ezZ0/tzXFyc4uLi/DJ2cnKyEhIS/DIWAgv7Prix/4MX+z44rVu3TqtWrVL4/165dOrUKYsrsnl4a9KkieLj4y0ZOyEhQZ07d7ZkbFiLfR/c2P/Bi30fnDp37qy4uDjvvjcMw+KKuGwKAABgK4Q3AAAAGyG8AQAA2AjhDQAAwEYIbwAAADYScOHt8OHDkqSkpCSZpmlxNQAAAIElYMLbddddp1atWqlHjx6SpFWrVumCCy5Qly5dLHscCAAAQKAJmOe8rVixwuoSAAAAAl7AnHkDgMK8+eabWrJkiVJTUyVJpmlqz549evrpp7V3716LqwMA/wuYM28AAt+2bds0ceJE/fTTTzpx4oQk6ZJLLlGtWrW805w8eVKVK1fWqFGjdNNNNyk0NNRnGUlJSZo+fbq2b9+uqKgoZWdn66qrrtLUqVM1Z84cde/e3ee1dx999JFef/11SVLVqlWVnp6uzMxMTZgwQY0aNfLDWgNAYCG8ASi2Tp066T//+Y9SUlJUo0YNVapUSV9//bVCQnxP4r/00ksaM2aM3n77bcXHx6tSpUqSpIMHD6pLly66++679fzzz8vhcCg3N1evvvqqDMPQli1btHLlygLjNm7cWL/99pscDod69eqlSZMm6dprr/XLOgNAoCG8ASix6tWrS5LCw8MLBDdJmjBhgtatW6cVK1boySef1OOPPy5Juueee9SmTRvdcccd3mlDQkI0duxYHT9+XOvWrSt0vPXr16thw4Zlvh4AYEd85w1Auejbt68kafXq1d621atXq1mzZoVOP3nyZF1wwQV+qQ0A7IzwBqBcnDx5UpIUERHhbQsJCdHatWuVk5NTYPrQ0FANHTrUb/UBgF0R3gCUuRMnTuiVV16RJJ9LpP3799d3330nwzD0zTffFJivdevWCg8PL9B+4MABDR8+XF26dFH79u1122236ejRo+W3AgAQwAhvAMrU7t27NXDgQKWmpur111/XyJEjvX0zZsxQ/fr1tXr1al122WWqW7euBg0apNmzZ8vj8WjcuHHq3r17gWU+9dRTmj17trZu3aotW7Zoz5496tWrl/eOVwAIJrYObx6PR4ZhyO12W10KEJTS0tLUu3dv759u3bqpW7duSkpK0rx58zRixAif6WNiYpSQkKCxY8eqSpUqSkxM1IoVKzR16lQ1bdpUS5cuLTDG4MGD5Xa7FRMTI0mKjIzU448/rp07d+rJJ5/0y3oCgNvtlmEY8ng8Vpcih2njF4gahmHZq7MSEhLUuXNnS8aGtdj3eRwOh6pVq6aUlJQCfRs2bNCwYcPkdDq1dOlSNW3atMA02dnZ+uabb/T555/rgw8+0MqVKxUSEqIdO3aoTZs2Zx07PT1d5513nurVq6f//ve/ZbRGxcP+D17s++B15r63Mnvks/WZNwCBqU+fPpo3b56+/vpr9evXz3vzwpgxY7zThIWFqV27dpo4caLi4+O1cOFC5ebmasaMGd5pcnNzCw1nUVFRkvKeG8d33wAEG8IbgHIxaNAgVa5cWfv379fixYsl5X0frihjxoxR+/bt9dVXX3nbJkyYIKfT6Z0/X1pamvffhd3gAAAVGeENQLkIDQ1VZGSkpN8fG5KQkKDExMQi52ncuLFq167t/TkxMVGVKlVSzZo1fabL/85JkyZNVLVq1bIuHQACGuENQLnYuHGjjh07pvDwcF111VWSpMzMTN15552FTp+VlaWtW7dqyJAh3rZ+/frpxRdf1JVXXukz7Zo1ayRJf//738upegAIXIQ3ACWWf5NCYQ/blfLOsI0YMUIhISF66aWXfG5YOHnypIYNG6Y9e/Z42zIyMnTbbbepQYMGPs+Fu+WWW/TWW2/pvffe87b99ttvmjNnjlwul26//fYyXjMACHy82xRAsW3btk2TJk3Sjz/+KCnvu2etW7f2uax59OhRHTt2TH379tXUqVN12WWXefvi4uL0/vvva+vWrXr44Yd18OBB5ebmKi0tTVdffbVeeukln++wRUZGasWKFXrggQf0wgsvKDQ0VJmZmXrwwQc1duxY/604AAQQwhuAYuvUqZMSEhJKPf8HH3wgSerSpYu6dOlSrHmqVKmiZ599ttRjAkBFQ3gDUDYyM6WEBOnUKallS6l+fasrAoAKie+8ATg3pinNmSNdeKF0+eXSVVdJsbHSNddI+/dbXR0AVDiENwDn5t57pTvvlM58WK5pSmvXSp07S35+AwIAVHSENwClt2ePNHNm4X05OVJSkvTUU/6tCQAqOMIbgNJ7/XUp5Cz/GcnOll59Ne9vAECZILwBKL39+yWH4+zTnDolpab6px4ACAKENwCld/75fz5NeLgUHV3+tQBAkCC8ASi9ESPOfkk0LEy68UYpIsJ/NQFABUd4A1B6l10muVyFf+8tNFSKjJTuv9//dQFABWbr8ObxeGQYhtxut9WlAMHrtdek22/PO8sm/f4duCZNpI0bpebNLSsNAMqK2+2WYRjyeDxWl2LvNyw4nU7Fx8dbXQYQ3CIipBdflB56KO/ZbqdOSZdcInXv/uc3MwCATbhcLrlcLhmGYXUp9g5vAAJInTrSTTdZXQUAVHi2vmwKAAAQbAhvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhDcAAAAbIbwBAADYCOENAADARghvAAAANkJ4AwAAsBHCGwAAgI0Q3gAAAGyE8AYAAGAjhLdSWrdundUlwCLs++DG/g9e7PvgFWj73tbhzePxyDAMud1uv48daDsS/sO+D27s/+DFvg9e69atk9vtlmEY8ng8Vpdj7/DmdDoVHx8vl8vl025FmCsv/lgXf22virQu/lCRtldFWhd/YL8E7jjlrSJtr4q0LpLkcrkUHx8vp9Ppl/HOxtbhrSgV5SCWOMACcQx/qUjbqyKtiz+wXwJ3nPJWkbZXRVqXQOMwTdO0uojSatWqlRo3blyg3ePxlHsy9scY/hqHdQm8Mfw1DusSmOOwLoE5TkUZw1/jVNR1+eWXX7Rz585yHe/P2Dq8AQAABJsKedkUAACgoiK8AQAA2AjhDQAAwEYIbwAAADZCePOzF154QatXr9apU6ckSTk5Odq5c6cefvhhnThxwuLqcDZZWVlKSEjQX//6V40bN67E8+fk5Ojxxx9X69at1atXL7Vv314PPPCA0tPTy6FaBBqO/cBTFsckx3Vws+y4NlEimZmZ5tatW82RI0eaY8eOLfH8vXr1MiWZkszq1auboaGhpsPhMGfMmFEO1aIsZGRkmFdccYU5cOBAc+rUqaYkc9SoUSVezm233WY6nU7zt99+M03TNFNTU802bdqYAwYMMHNzc8u4apSl7Oxs87HHHjMvvfRS8/LLLzfbtWtn3n///ebp06eLvQyO/cBTFsckx7V92fm4JrwVU1l9gPfq1cts1qyZGRERYdaqVcu87rrrzE8//bTsC0a52Lt3b6n2/WeffWZKMp9//nmf9hUrVpiSTLfbXYZVoqyVxQc0x35gKYtjkuPa3ux8XBPeSqG0H+CmmbejYV+l3fe33HKLKck8cOCAT3tmZqYZGRlpXnHFFWVYJcpSWX1Ac+wHlrI4Jjmu7cvuxzXfeQP8YOPGjYqOjlZMTIxPe3h4uJo2baotW7YoJyfHoupwNosWLZIkXX/99T7tAwYMUGRkpBYsWGBFWThHZXFMclzbl92Pa8IbUM6ys7O1d+9eRUdHF9ofHR2t06dP6/Dhw36uDMXBB3TFUxbHJMe1vdn9uCa8WWDnzp0aOHCgunXrprZt22ratGk6ffq01WWhnKSkpCgnJ+es/5GXpGPHjvmzLBRDWX9Ac+wHhrI4Jjmu7asiHNdh5bp0FOqZZ57RW2+9pRo1aig1NVU9e/bUtm3b9OGHHyo0NNTq8lDG8h8ZEBUVVWh/eHi4JCk1NdVvNaF4SvIBfeGFF/7p8jj2A0NZHJMc1/ZVEY5rzrz52e2336758+erRo0akqRq1arpgQce0IYNGwL+GjtKp3r16mftT0tLk1T0hwCsU5Yf0Bz7gaMsjkmOa/uqCMc14c3Phg8f7v3FyNe6dWtJ0uuvv25FSRVaSkqKLr74YjVs2LBEfz7++OMyq6FKlSqqVKmScnNzC+3Pf5Bj/sGPwFGWH9Ac+4GjLI5Jjmv7qgjHdVBcNk1JSVGXLl1K/MTrV199VX379i2zOrKyspScnKw6der4tOf/gnz77bdlNhbyVK9eXT/++KPVZahRo0ZKTk4utC8pKUmRkZGKjY31c1UV37ke+2X1Ac2xH3jK4pjkuLaninBcB0V4C5QP8GuvvVbr16/Xpk2b1K1bN297fsr/Y3pHxTFgwADNmjVLycnJPv9BSElJ0f79+9W7d2++81QOyuLYL4sPaI79wFMWxyTHtX3Z/bjmsqkfJSYmqlq1aqpatapPu8fjkSS1b9/eirJQxk6dOlXgDrNBgwZJkpYtW+bTvnTpUknSTTfd5JfaUHIDBgzQb7/9VuA/9Pkf0F27dv3TD2iO/cBT0mOS47pisf1xbcmjgW2uOE/ZP3nypHn06FGftrvvvtv8+OOPC0w7efJkU5L573//u6xLRRnbunWrKckcOHBgoa9POXnypFmvXj0zIiLC3L59u0+fYRim0+k09+3bZ5qmaR4+fNi86KKLzN69e/MOxACW/yT2+fPn+7S/8sorpiTz1Vdf9Wnn2LeP4h6THNcVj92Pa8JbKZT2A/zo0aNm165dzU8++cTb9u2335pVq1Y1p02b5pfaUTqGYZgtW7Y0w8LCvC8hrlOnjtm5c2dzxYoV3ukyMjLMTp06mRdccIH5448/+iwjPT3dvO+++8xLLrnE7N69u9msWTNzwoQJZmpqqr9XByV0rh/yHPuBqbjHJMd1xWTn49phmqZZfuf1KpbrrrtOu3fv1s8//6zs7GxJUp06ddSoUSPdd999MgxDkpSZmamePXtq//79+uSTT9S8eXPvMg4fPqxp06Zp7969CgkJkWma+tvf/lbgFR0AAkdGRoYeffRRxcfHq1q1ajpy5Ij69eunp556yueSCcc+YB92Pq4JbwAAADbCDQsAAAA2QngDAACwEcIbAACAjRDeAAAAbITwBgAAYCOENwAAABshvAEAANgI4Q0AAMBGCG8AAAA2QngDEFD279+vLl26qFatWnI4HHI4HHrqqaf+dL60tDTVrl1bDodDoaGhatWqlR588EGNHz9ezZo18y4rPDxcPXr0UO/evdW7d2916dJFLVq0UFhYmBwOh9q2betd5vTp09W1a1fVq1dPDodDNWrU0IEDBwqM/dBDD8npdHrHcDqdeuihh8p0uwBAPl6PBSAgZWZmqmvXrjpy5IhCQ0O1Z88eORyOIqdfsGCBli9frlWrVmnOnDmaMmWKT/+UKVP03HPPafjw4Xr33XcLzH/ixAlNmzZN77zzjpKTk336XnvtNc2cOVM//PCDrrjiCn344YcFajFNU+PGjZPD4dArr7xy1loB4Fxw5g1AQIqIiFB0dLRGjx6tffv2af369WedfteuXWrSpIkkqXr16gX689uioqIKnT86OlovvviinE6nUlNTC/Tfeeed6tChgz766CO9+OKLBfodDod69Oih7t27E9wAlCvCG4CANmbMGDkcDi1YsKDIab777jtdeuml5zyWw+HQtddeq19//bVAX1hYmN544w1FRUVp2rRp+vnnn895PAAoDcIbgIDWsGFDXXHFFVq+fLmOHj1a6DTvvfeehgwZUqrl//zzz3rkkUe8P3fs2FGHDh0qdNoWLVroqaee0qlTp3TTTTcpJyenVGMCwLkgvAEIeLfccosyMzP1xhtvFOjLyMhQTk6OzjvvvFIt+49BbfDgwYqLiyty+smTJ6t3795KSEjQjBkzSjUmAJwLwhuAgHf99derZs2aWrhwYYG+5cuXa9CgQaVabkZGhubOnVuieRwOh1577TVVrVpV06dP11dffVWqsQGgtAhvAAJeZGSkRo4cqZ07d+rzzz/36duxY4fat29f7GWtXbtWvXv3Vrdu3VSvXr1C7zz9M7GxsXr22WeVlZWlm266SRkZGSVeBgCUFuENgC2MHTtWknxuXNi3b58aNmxYouVcddVV+uSTT7RlyxYlJSXp/vvvL1U9N998swzD0Lfffssz3QD4FeENgC1ceuml6tixo5YsWaK0tDRJ0jvvvCOXy1XqZYaEhGjEiBEF2qdPn16s+V955RXVrl1bs2bN0ubNm0tdBwCUBOENgG3ccsstSktLk9vtVm5urlJTU1WjRo1zWmaLFi187jaVpJ07dxZr3rp16+rll19Wbm6uRo0apZMnT55TLQBQHIQ3ALbhcrlUqVIlLViwQGvXrlX//v3LfIw9e/YoOjq62NMPHjxYI0eO1C+//KInn3yyzOsBgD8ivAGwjapVq+qGG27Qtm3btGDBAvXp06fMx5g7d65atmxZonleeOEFxcTE6L///W+Z1wMAf0R4AxCQfvnlF+3YsUO//PKLT3v+jQvt27cv8BqqlJQUn78L68vOzi50vFOnTmn27NmaM2eOWrRo4W03TVOfffaZNm/erKJeBV29enUtWrSI12IB8AteTA8goOzfv1+DBg3St99+q+zsbIWFhenSSy/V8uXL1aBBA0lShw4dtGLFCjmdTknSiBEjlJCQ4A16ISEhatGiha6//nolJibqk08+8b7OKiIiQh06dFB4eLikvDCXkpKiXbt2KTMzU5K0d+9eNWzYUA899JAWLlzoPaN24YUX6pZbbtGjjz5aaO1/+9vf1KlTJ40cObL8NhCAoEd4AwAAsBEumwIAANgI4Q0AAMBGCG8AAAA2QngDAACwEcIbAACAjRDeAAAAbITwBgAAYCP/DxN+KVH5P0UwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 120)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 120)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 120, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 80)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 80)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 80, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 90)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 90)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 90, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 110)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 110)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 110, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different volatilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.310209</td>\n",
       "      <td>0.463384</td>\n",
       "      <td>-1.189151</td>\n",
       "      <td>0.493833</td>\n",
       "      <td>0.492511</td>\n",
       "      <td>0.788484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.403907</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>-0.278491</td>\n",
       "      <td>0.558589</td>\n",
       "      <td>0.296017</td>\n",
       "      <td>0.556225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.310627</td>\n",
       "      <td>0.122985</td>\n",
       "      <td>-0.182789</td>\n",
       "      <td>0.722203</td>\n",
       "      <td>0.211325</td>\n",
       "      <td>0.596840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "10     100         0.1       1.0               252  -1.310209     0.463384   \n",
       "12     100         0.3       1.0               252  -0.403907     0.172415   \n",
       "14     100         0.4       1.0               252  -0.310627     0.122985   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "10       -1.189151          0.493833    0.492511      0.788484  \n",
       "12       -0.278491          0.558589    0.296017      0.556225  \n",
       "14       -0.182789          0.722203    0.211325      0.596840  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] != 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.561350</td>\n",
       "      <td>0.261862</td>\n",
       "      <td>-0.300800</td>\n",
       "      <td>0.391117</td>\n",
       "      <td>0.248433</td>\n",
       "      <td>0.677436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.170615</td>\n",
       "      <td>0.133034</td>\n",
       "      <td>0.038371</td>\n",
       "      <td>0.767355</td>\n",
       "      <td>0.050444</td>\n",
       "      <td>0.702178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.113961</td>\n",
       "      <td>0.134518</td>\n",
       "      <td>-0.015781</td>\n",
       "      <td>0.790565</td>\n",
       "      <td>-0.039474</td>\n",
       "      <td>0.626294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "11     100         0.1       1.0                52  -0.561350     0.261862   \n",
       "13     100         0.3       1.0                52  -0.170615     0.133034   \n",
       "15     100         0.4       1.0                52  -0.113961     0.134518   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "11       -0.300800          0.391117    0.248433      0.677436  \n",
       "13        0.038371          0.767355    0.050444      0.702178  \n",
       "15       -0.015781          0.790565   -0.039474      0.626294  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] != 0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z6U7JJUJDQ9WmTRtt2rRJhYWFZxyjWbNm2rBhgx566CGdPHnS3X7ixAlt3rxZV111VZXUDgAAUJYTJ05IKj6zeSaXXXaZpkyZotdff1333HNPldfl9wBYUFCgPXv2lHmRRmRkpHJzc3Xw4MEzjjNixAg1b95cc+bMUbdu3fTVV18pPz9f48eP18SJE93n4AEAAKpLydXDcXFxZ+179dVXS5IWLlyo9PT0Kq3L7wEwMzNThYWFZwyAktx33C5L3bp1lZSUpNjYWG3fvl29evVSx44d5XA4NHXqVJ/XDQAAcDbffvutpOIDVae2NWrUSMnJyR59GzVqJKn44Nju3burtC6/3wam5NBoWbd6Kbk5Y1ZW1lnHuvjiizVhwgS99dZb2r59u3bu3Kk77rhDr732mgYOHFjmert27XJfmi0V3/gx0G6ImZGRoZSUFH+XEVCYE0/Mhyfmwxtz4on58MR8FPvmm2/cZx3PNCcl39PbunVrmQexdu3ape+++07NmjXT7NmzNWfOHEnFF38UFRVpxYoVHlcXn3qw6/Svxfma3wNg/fr1z7g8OztbUtkBscTx48c1cuRI9e3bV9u2bdP333+v22+/XVu2bNGQIUP04Ycflvk9wNjY2IB/FnBKSop69Ojh7zICCnPiifnwxHx4Y048MR+emI9iXbp0cT+L90xzUvIEkMsuu6zULJOTk6N77rlHkZGR+vjjj9WhQwf3soYNG6pp06YaP368xzobNmyQVHwg6oILLvDB3pTN76eAIyIiVKdOHRUVFZW6/NixY5Kk6OjoM45z3333qV69enr44YdlsVh06aWX6ssvv9Q//vEPWSwWTZo0yee1AwCAmi8zM7PUv5clPz/ffeeRUy88LfHll1+qX79+Sk1N1SeffOIR/iTpkksuUZMmTRQU9L8Ylp6ervnz56tly5Z69dVXK7UfJUcuy3PTar8fAZSKb8yYkZFR6rL09HRZrVa1bNmyzPVPnjypJUuWaP369R7tQUFBuueee5SXl6eHHnpIR44cUYMGDXxaOwAAqHl+/PFHjR07VllZWUpNTXW3d+/eXS1btlS9evV0yy23eBwBTE1N1Y033qidO3e6b+vSp08f99G6goICpaenKzw8XKNGjdKkSZNKPT1cv359Pffcc3ryySe1c+dOWSwWZWZmatSoUXr44YfPenb0VFu2bNGkSZP0+++/y+VySZL+/Oc/65FHHlGjRo306aefKiwszGu9gAiACQkJmjt3rjIyMjyO9GVmZio1NVXx8fEeD20+3dGjR5Wfn1/mJdbXXHON/u///s99Lx4AAGBuHTp0KPXmzKc6/ft/LVq00JdffumT7bdu3VoLFy4853G6d+9eqZr8fgpYkvuu16c/JmX58uWSpJtvvtmjPScnx+OLkg0bNlSHDh30/vvvlzr+Dz/8oKFDh5b7mcIAAMB8/vhDWrdOSk6WquFezH4VEAGwT58+stvtmj59un777TdJxffNmTNnjuLj4zV27Fh335ycHMXGxqpZs2batm2bu33RokWaN2+eFi1a5PF9wlWrVmn27Nl68cUXq21/AABAzfHHH9J110nNm0uDB0vx8VLTptLOnVIZlyjUeAFxCliSli1bppkzZ2rYsGGqV6+eDh06pCFDhmj27Nkez98LCQlR8+bNZRiGIiIi3O29evXS1q1bNXv2bL388suqU6eO8vLy1L17d3388cfue+sAAACUOHRI6tVLcrk8w15mpvTf/0p//7v0wgt+K6/KBEwAtFqtmjVrlmbNmnXGfmFhYWXek6d169Z65ZVXqqI8AABQCz37bHH4K+uJsy++KP3tb9Ill1RvXVUtIE4BAwAAVDfDkF55pezwJ0khIdKSJdVXU3UhAAIAAFPKy5PKuAudW1GRdMpdYmoNAiAAADAlq1U62x3igoKk88+vnnqqEwEQAACYksUijRlTfJq3LAUF0l//Wn01VRcCIAAAMK3775ciIqTSnjdhsUh2u1QbH5FMAAQAAKbVurX0+edS+/bF/y6581xwcPF9AZcu/V9bbRIwt4EBAADwh44dpe+/l1JSpG+/lcLDi28InZpa/PfaiAAIAABMz2KRevYs/lOiNl79W4JTwAAAACZDAJTkcrlkt9vldDr9XQoAAKilnE6n7Ha7XC6Xv0vhFLAk2Ww2JSYm+rsMAABQizkcDjkcDtntdn+XwhFAAAAAsyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEASnK5XLLb7XI6nf4uBQAA1FJOp1N2u10ul8vfpSjE3wUEApvNpsTERH+XAQAAajGHwyGHwyG73e7vUjgCCAAAYDYEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQEkul0t2u11Op9PfpQAAgFrK6XTKbrfL5XL5uxSF+LuAQGCz2ZSYmOjvMgAAQC3mcDjkcDhkt9v9XQpHAAEAAMyGAAgAAGAyBEAAAACTIQACAACYDAEQAADAZAiAAAAAJkMABAAAMBkCIAAAgMkQAAEAAEyGAAgAAGAyBEAAAACTIQACAACYDAEQAADAZAiAAAAAJkMABAAAMBkCIAAAgMkQACW5XC7Z7XY5nU5/lwIAAGopp9Mpu90ul8vl71IU4u8CAoHNZlNiYqK/ywAAALWYw+GQw+GQ3W73dykcAQQAADAbAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJBMSj4AoLCzV79mwtW7ZM0dHRys7OVkJCgqZNm6bw8PCzrv/HH3+odevWatasmcLCwmSxWGSxWDz6XH755fr3v/9dVbsAAABQYwREAJw4caI++OADpaSkyGaz6ejRo+rXr5++/vprrV692ivMnW779u3Kz8/Xb7/9Vupyi8WiF198sSpKBwAAqHH8fgp448aNWrBggaZOnSqbzSZJioqK0owZM5SUlKSlS5eedYwffvhB7733nnJzc1VQUKCioiIZhiHDMLRy5Urdd999uuqqq6p6VwAAAGoEvwfAJUuWSJKuu+46j/aEhARZrVYtWrTorGP89NNPSkhIUHh4uIKDg91HDP/44w+9+OKLmjVrlu8LBwAAqKH8HgCTk5MVGRmpmJgYj/bQ0FC1adNGmzZtUmFh4RnHmDlzptd3BQ3D0IQJE/T8888rLCzM53UDAADUVH4NgAUFBdqzZ48iIyNLXR4ZGanc3FwdPHjwjOM0a9bMq23x4sXq3r27Lr74Yp/UCgAAUFv4NQBmZmaqsLDwjAFQko4cOVKhcdPT0/XMM89o8uTJ51oiAAAIMBkZGXr00Ue1Y8cO91nC48ePa82aNVqwYIGfq6sZ/HoV8IkTJySpzFu9hIaGSpKysrIqNO4//vEP9enTRxEREeXqv2vXLvXt29f970GDBmnQoEEV2mZVy8jIUEpKir/LCCjMiSfmwxPz4Y058WSm+bjlllv03//+V/Xq1VOLFi0UEhKiI0eOuO+e0axZMzVq1EiStH//fqWnpys6OlpJSUk6cOCAHn74Yf3+++86evSoJCkmJkbnn3++pOJbuWVlZclqteqqq67S9ddfrzp16pRaR1ZWll577TX99NNPkopDW1xcnP7yl7/oggsuKPf+/PHHH3r88cf1+OOPKzg4WOedd56ys7MVHR2tF154wWc/V1+9RtatW6d169a5/52Tk3POY54zw4+OHTtmSDIuvfTSUpf369fPkGRs3bq13GNmZ2cb0dHRxuuvv17uda655ppy9/WXzZs3+7uEgMOceGI+PDEf3pgTT2aajxYtWhhPPfWUUVBQ4G7Lz883JBmSjJ9++sljPt58800jJCTEKCwsdLfl5eUZ559/viHJOHDggNc2vvjiC+PSSy81mjdvbmzbts1reW5urtG9e3fj448/drcdPHjQiImJMRo3bmz89NNP5d6fPXv2GI0aNTLOP/98IywszLjooouMKVOmGOnp6eUeozyq6jUSCLnDr6eAIyIiVKdOHRUVFZW6/NixY5Kk6Ojoco+5cuVKZWRkqG3btj6pEQCAmi4qKkoPPPCAgoOD3W0lZ9kkyWq1evS/6aabNGDAAB0+fNjdFhYW5j6yd3p/SerTp482bNig+vXra+DAgdq1a5fH8nXr1mnLli0eX89q3LixRo0apbS0NM2fP79C+zR06FClpaUpLy9Pu3bt0jPPPKOGDRtWaAwz8/tVwK1bt1ZGRkapy9LT02W1WtWyZctyj1dyiLVp06Y+qQ8AgJosOztbHTt2rPB6Xbt2VVpaWoXWiYqK0oIFC5SRkaFx48Z5LGvbtq2aNGmiFi1aeLSXnA6tW7duhWtE5fk9ACYkJGjfvn1eITAzM1Opqanq1auXx/+xnE1ycrKkih01BACgtjp06JBat25d4fVatWqlQ4cOVXi93r17KzY2Vp9//rk+//xzd/vFF1+sAwcOaPXq1R79N2/eLKvVqjFjxlR4W6g8vwfA4cOHS5JWrFjh0b58+XJJ0s033+zRnpOTU+ZVwUVFRXK5XJLEvf8AAFDxmbbKPBDh9ttvV79+/Sq1zW7dukny/mw/3bPPPqu9e/fqnXfeqdRXtxYuXKiePXvqiiuuUM+ePbVq1apK1WtGfg+Affr0kd1u1/Tp091XI6WlpWnOnDmKj4/X2LFj3X1zcnIUGxurZs2aadu2bV5jpaenuy8HL+37CQAAmFHJE7Iquk5l1pPkfrjDli1bvJa5XC71799fcXFxmjt3rpYtW+Y+GFQRiYmJCgoK0ubNm/XFF1/oiSee0MiRI/Xqq69Wqmaz8ettYEosW7ZMM2fO1LBhw1SvXj0dOnRIQ4YM0ezZsz1efCEhIWrevLkMwyj1Fi/16tVThw4d1KxZMwUF+T3bAgBgSiUXi5x6EUkJm82m9evXS5K2bt2q/v37a+TIkXr55ZfLffCmYcOGevXVVz2C48CBAzVo0CBNnjxZdrvdfVsblC4gAqDVatWsWbPOeog6LCzsjPfjsVqt2rFjh6/LAwAAFVByn9+oqKgz9rvssss0ZcoUTZ8+XXXq1NFLL71UrvEjIyNLPWoYFxenpKQkvffee7r99tsrXLeZcJgMAAD4VMnVw3FxcWfte/XVV0sq/j5fenp6ucY/fPiwcnNzvdpLHiyxffv28pZqWgRAAADgU99++60kacSIEe62cePGqVGjRu67dZQoOVVbUFCg3bt3n3XsvXv3KiYmRl27dvValp2dLcnzHocoHQEQAAD4zPbt2/Xdd9+pS5cu+tOf/uRuX7p0qQ4fPux1ZfCpd/YouXjkTI4ePaq8vDzZbDavZSV3Aim5ChllC4jvAAIAgJovJydHd9xxhyIjI/Xmm296XMjZtWtXHTlyROPHj/dYZ8OGDZKkQYMGlet5wB06dNCVV16pNWvWeLQXFBToo48+UvPmzTVy5Egf7E3txhFAAABMKDMzs9S/lyU/P9/91I6TJ096Lf/yyy/Vr18/paam6pNPPlGHDh08ls+bN09NmjTxuEtHenq65s+fr5YtW5b79i0hISG6++67NWrUKI+jh3PmzFFhYaGWL1/u/i4gysYRQAAATOLHH3/U2LFjlZWVpdTUVHd79+7d1aRJEzVp0kRPP/20BgwY4F6WmpqqG2+8UTt37nTf1qVPnz7uo3UFBQVKT09XeHi4Ro0apUmTJikyMtJr25dddpkWL16sJ598Ujt37pTFYlFmZqZGjRqlhx9+WPXr1y/3fowcOVKRkZH685//rIKCAp04cULt2rXTtm3bdOGFF1ZydsyFAAgAgEl06NCh1JszS1JKSop69Ojh1d6iRQt9+eWXPtl+69attXDhQp+MNWjQIA0aNMgnY5kRp4ABADCrL76QRoyQoqKkNWukIUOktWv9XRWqAQEQAAAzevFFqW9f6YMPpGPHpIIC6eOPpYQEacYMf1eHKkYABADAbH74Qbr77uK/FxT8r72wsPi/06dLn31W3VWhGhEAVXzfILvdLqfT6e9SAACoevPnS8HBZS8PCZFeeKH66jEJp9Mpu93uvl+hP3ERiIofTJ2YmOjvMgAAqB4bN3oe+TtdQYG0aVP11WMSDodDDodDdrvd36VwBBAAANMpz6PSeJxarUYABADAbIYNO/sp4GHDqq8eVDsCIAAAZnP77ZLVKgWVEgNKHt82cWL11oRqRQAEAMBsmjWTVq+WzjvPMwQGBxef+l2+XGrf3n/1ocpxEQgAAGYUHy/99pv06qvF9/9r1Eh69FFp/Hjp/z/mDbUXARAAALNq2FC6//7iPykpUimPgkPtxClgAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBUJLL5ZLdbpfT6fR3KQAAoJZyOp2y2+1yuVz+LoVnAUuSzWZTYmKiv8sAAAC1mMPhkMPhkN1u93cpHAEEAAAwGwIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQKgJJfLJbvdLqfT6e9SAABALeV0OmW32+VyufxdikL8XUAgsNlsSkxM9HcZAACgFnM4HHI4HLLb7f4uhSOAAAAAZkMABAAAMBkCIAAAgMkQAAEAAEyGAAgAAGAyBEAAAACTIQACAACYDAEQAADAZAiAAAAAJkMABAAAMBkCIAAAgMkQAAEAAEyGAAgAAGAyBEAAAACTIQACAACYDAEQAADAZAiAklwul+x2u5xOp79LAQAAtZTT6ZTdbpfL5fJ3KQrxdwGBwGazKTEx0d9lAACAWszhcMjhcMhut/u7FI4AAgAAmA0BEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQC5lFwhYWFmj17tpYtW6bo6GhlZ2crISFB06ZNU3h4eLnHyc/P1z/+8Q8tX75cERERqlevnvr27aspU6bIYrFU4R4AAADUDAETACdOnKgPPvhAKSkpstlsOnr0qPr166evv/5aq1evLld4y8vLU0JCgoKCgpSUlKTGjRvrtdde0+23364WLVroxhtvrIY9AQAACGwBcQp448aNWrBggaZOnSqbzSZJioqK0owZM5SUlKSlS5eWa5w777xTv//+u1atWqXGjRtLkg4ePKiTJ08qLy+vyuoHAACoSQIiAC5ZskSSdN1113m0JyQkyGq1atGiRWcd47vvvtOSJUt07733qm7duu72qVOn6sCBA7r55pt9WzQAAEANFRABMDk5WZGRkYqJifFoDw0NVZs2bbRp0yYVFhaecYz58+fLMAwNHjzYa1mTJk18Wi8AAEBN5vcAWFBQoD179igyMrLU5ZGRkcrNzdXBgwfPOE5SUpIk6fzzz9ddd92lAQMGqFOnTpo0aZIyMzN9XTYAAECN5fcAmJmZqcLCwjMGQEk6cuRImWOcOHFCqampkqSnn35a9913nz799FOlpKRo+/bt6t+/v3JycnxfPAAAQA3k96uAT5w4IUll3uolNDRUkpSVlVXmGKeGw+bNm+vCCy+UJNWpU0ePPfaY4uPjNWvWLM2aNavU9Xft2qW+ffu6/z1o0CANGjSoYjtSxTIyMpSSkuLvMgIKc+KJ+fDEfHhjTjwxH56YD2++mpN169Zp3bp17n8HwkEpvwfA+vXrn3F5dna2pLIDoiSPo4enfwewU6dOkqRXX321zAAYGxurxMTE8pTrNykpKerRo4e/ywgozIkn5sMT8+GNOfHEfHhiPrz5ak569OihRx55xP1vu91+zmOeK7+fAo6IiFCdOnVUVFRU6vJjx45JkqKjo8scIzIy0n2k8PQLSaKioiRJBw4c0KFDh3xRMgAAQI3m9wAoSa1bt1ZGRkapy9LT02W1WtWyZcszjtG2bVtJxU8COVVQ0P92sSQkAgAAmFlABMCEhATt27fPKwRmZmYqNTVVvXr1UnBw8BnHuOqqqyRJf/zxh9cYktSiRYuznm4GAAAwg4AIgMOHD5ckrVixwqN9+fLlkuR1E+ecnByvq4LHjx+voKAgry9rlvz79ttv92XJAAAANVZABMA+ffrIbrdr+vTp+u233yRJaWlpmjNnjuLj4zV27Fh335ycHMXGxqpZs2batm2bu71Tp066//779fjjj7u/63f8+HHNnj1bV1xxhe6///5q3ScAAIBA5fergEssW7ZMM2fO1LBhw1SvXj0dOnRIQ4YM0ezZs2WxWNz9QkJC1Lx5cxmGoYiICI8x5syZo5iYGA0dOlR16tRRTk6Ohg4dqqlTpyosLKy6dwkAACAgBUwAtFqtZ7xXX4mwsLAz3pNn4sSJmjhxoq/LAwAAqDUC4hQwAAAAqg8BEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgJJcLpfsdrucTqe/SwEAALWU0+mU3W6Xy+XydymB8yxgf7LZbEpMTPR3GQAAoBZzOBxyOByy2+3+LoUjgAAAAGZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgJJcLpfsdrucTqe/SwEAALWU0+mU3W6Xy+XydykK8XcBgcBmsykxMdHfZQAAgFrM4XDI4XDIbrf7uxSOAAIAAJgNARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwmTID4Msvv1yddQAAAKCalBkAn3766eqsAwAAANUkpKwFe/bs0ezZs9WnT5+KDRgSot69e59zYQAAAKgaZQZASdqwYYPy8/PPOIBhGFq8eLH++OMPSdI111yj999/32cFAgAAwLfKDICNGjXS6tWrFRRU9nUiu3fv1pgxY+RyuRQZGal58+Zp3LhxVVIoAAAAfKPMdHfttdeeMfy9/PLL6ty5szZu3Kgrr7xS33//fY0Nfy6XS3a7XU6n09+lAACAWsrpdMput8vlcvm7lLKPAC5cuLDU9v3792vcuHFat26drFarnn32WU2ePFkWi6XKiqxqNptNiYmJ/i4DAADUYg6HQw6HQ3a73d+lnPk7gKdzOp2aOHGiMjIy1LVrV/373/9W+/btq6o2AAAAVIFy3Qj6yJEjuvHGGzV69GgdO3ZMjz76qDZv3kz4AwAAqIHOegRw9erVuu2223TgwAFdfPHFeuONN3TZZZdVR20AAACoAmUeATx+/LjGjx8vu92ugwcPatKkSfr666/PGv5eeeUVnxcJAAAA3ykzAHbq1ElLlixR8+bN9emnn2revHkKDw8/64BPPfWUTwsEAACAb5V5Cnjv3r3q0KGD5syZI4vFog0bNpx1sM8//1x79+71ZX0AAADwsTIDYIMGDXT99ddr27Zt5R7syy+/9ElRAAAAqDplBsDY2Fg99thjFRqsoKBAF1xwwTkXBQAAgKpT5ncAu3btWuHBQkJCdO21155TQQAAAKhaZQbAl156qVIDlvUEEQAAAASGcj0JJDs7Wy6XS+3atfNonzlzpnbv3q02bdqoQ4cOat++vdq0aaPg4OAqKRYAAADnrswAOHLkSO3du1fZ2dm67LLL1KNHD68A+OijjyovL0+bNm3S/Pnz9e677yosLEwnTpyo8sIBAABQOWUGwJUrV2rUqFF64403znj/P6vVqv79+6t///6aOnWq5s6dWyWFAgAAwDfK/A6gxWLR/Pnzy3Xz5xIPPfSQQkLKdVYZAAAAflJmWrvwwgvVsGFD979ff/11WSwWr34333yz++/169fXhRde6OMSAQAA4EtlHgE8NfxJ0gUXXCCLxaIlS5bolltu0fr169WkSROv9Ro0aOD7KgEAAOAzZR4BrFOnjse/r776aknStddeq5iYGC1atKjUq32tVquPS6x6LpdLdrtdDodDDofD3+UAAIBayOl0yul0yuVy+buU8t0G5lRRUVHq0KFDrbrVi81mU2Jior/LAAAAtVjJgSa73e7vUso+BVxUVFTmSqcfHSzvegAAAPC/Mo8Abt26VQ8//HCpp3T37t2rmTNnerUXFBTom2++8W2FOKu5c+fq5Zdf1q5duyRJwcHB6tSpk55++mldffXVeu+99zRjxgxt375dhmFIkjp06KC///3vuuOOOyRJn332mcaMGaPU1FRJxUdFn3vuOY0cOVKSdOLECX355ZeaO3euevXqpWnTpvlhTwEAgC+UGQBzcnI0Z86cUpcZhqEZM2aU2l7alcKoWlOmTNGUKVM0evRovfXWW5owYYJeeOEF9/IRI0ZoxIgRmjt3ru6//34NGDBAn3zyiccY8fHx+u233zRhwgQFBwfrhRdekMVi0e+//65x48apbt26atq0qdasWaPLL7+8uncRAAD40Bm/A2i321W/fv1yD5aRkaEPPvjgXGtCJY0dO1ZvvfWWPvzww1KXjx49WlOnTtXGjRt19OhRRUVFefUpLCzU9OnT3UG+efPm+uijjyQVHyV8+eWXq24HAABAtSgzALZs2VIrV66s8ICtW7c+p4JQef3791fjxo21a9cuff311+ratavH8tzcXBUVFSkvL0+rVq3SX//6V4/lJ0+e1JEjR9S0adPqLBsAAFSzMi8CiYuLq9SAlV0P5y44ONj9nb2lS5d6LV+2bJluueWWMpevW7dOgwYNqtoiAQCA35UZAFetWlWuAdLS0nTy5MkKr4eqceONN0oqDnun27x5s5555hmFhoZq3bp1ysjI8Fi+cuVKd4AEAAC1V5mngJ988kkVFBR4tIWFhenBBx+UJL3wwguaM2eO9u/fr+DgYA0bNkzPPfecmjdvXrUV44z69u2rZs2aae/evUpJSVGPHj0kSbt27dJFF12khg0bauDAgUpKStLKlSs1btw4SVJ+fr6ys7PVqFEjf5YPAACqQZlHAAcMGKD33ntPjz/+uHbs2KGOHTu6jw498MADmjRpko4ePap7771X//rXvxQeHq4ePXpo//791VY8vAUFBen666+X5Hmad9myZe6jgyX/PXX52rVrNXjw4GqsFAAA+EuZAbB169aKiIjQpk2btHTpUo0YMUJt2rTRt99+q7lz5+q8887Txo0b9cwzz2jChAl6++23NWPGDD3yyCPVWT9KURLwli9f7r7v39atW923bxk+fLjCwsL06aefKj09XVLxqfvrrrvOPwUDAIBqVWYAnD59up5++mmve76tWLFCknTTTTepU6dOHstuu+02/fjjj1VQZvk8//zzWrNmjXJyciQV39Jkx44deuyxx3Ts2DG/1VXdevXqpebNm2vfvn3atGmTfvnlF7Vr1869vF69eho8eLAKCgq0YsUK5eXl6cSJExW65Q8AAKi5ygyAP/74o3r37u3VvnHjRlksFl155ZWlrhcREVGpQgoLC/XEE08oLi5O/fr1U7du3TRt2jSdOHGi3GOsWLFCQ4cOVd26dRUdHS2r1apOnTqpTp06ioyMrFRdNZHFYtENN9wgqfg076mnf0uceho4KSlJCQkJ1V4nAADwjzIDYEiI9/UhhYWF+uqrryQVH2UqTWFhYaUKmThxohYsWKCkpCQlJydr/fr1Wr16tUaMGOE+jVkebdu2VVhYmPvClA0bNmjq1KmVqqkmO/U08DfffKPOnTt7LLfb7QoPD1dycrIWLlyoa6+91g9VAgAAfygzAJ44ccLr6Nunn36q48ePy2az6cILL/RaZ9OmTerSpUuFi9i4caMWLFigqVOnymazSZKioqI0Y8YMJSUllXrPurL8/PPPysvLU3p6ut5//31dccUVFa6nNrj88svVunVrHThwQJdcconX8sjISCUkJKioqEhWq9VUR0gBADC7MgPgiBEjdMcdd7iP6P3++++6++67ZbFYdPvtt3v1P3bsmB5++GFNnjy5wkUsWbJEkrwuQkhISJDVatWiRYsqPCbkddVvRZef7uDBg5KkQ4cO+aA6AADgL2UGwMmTJystLU02m009evRQu3bt9Msvv2jgwIF66KGH3P3y8/P1wQcfqGfPngoODlbdunUrXERycrIiIyMVExPj0R4aGqo2bdpo06ZNlT61bGYOh0M9evQo9QigJA0bNkwXXXSRhg0bVuYY+fn56tOnj9q1a6e//OUvkqSXXnpJzZs3V+/evbVly5YqqR0AAFSdMm8EHRwcrNWrV2vJkiVKSkpSy5YtNXjwYN1yyy0KCirOjbfeeqv27dun/Px8NW7cWIWFhZo4caLeeuutchdQUFCgPXv2qEmTJqUuj4yMVG5urg4ePKgLLrjgrOPt2LFDDzzwgDIyMpSbm6vBgwfrscce03nnnVfummqLuLg4bd68uczldevW1a5du844RlhYmDZu3Ojr0gAAgB9ZjIpcYVEF0tPTdf7556tdu3b66aefvJYPHjxY69at0/bt29WxY8czjhUfH69WrVpp3rx5io6OVlZWlvr27atGjRrpo48+UnBwcKnrdejQQQ0bNnT/e9CgQQH3TNyMjAxFR0dXat2iIunECSkkRAoL83FhfnQuc1IbMR+emA9vzIkn5sMT8+HNV3Oybt06rVu3zv3vnJwcbdu27ZzHPSeGn/3++++GJOPSSy8tdfnQoUMNScYXX3xx1rHeeecdIz8/36Nt6dKlhiRjwYIFZa53zTXXVKhmf9i8eXOF18nMNIwpUwyjXj3DkIr/9OljGGvX+r4+f6jMnNRmzIcn5sMbc+KJ+fDEfHirqjkJhNxR5ncAq8vZbj6cnZ0tSQoPDz/rWDfeeKNCQ0M92uLi4iRJr7/+euUKrKGOHpX69pXmzZOysv7X/uWXUkKC9NprfisNAAD4md8DYEREhOrUqaOioqJSl5c8weNsh2BPnjyptLQ0r/aS4Lh9+/ZzrLRmmT1b+vFH6fRrZ4qKio8F3nGHdPiwf2oDAAD+5fcAKBU/dzgjI6PUZenp6bJarWrZsuUZx7jmmmt0wQUXaNOmTR7tJUcQTz8yWJsVFEgLFniHv9P7/Pvf1VcTAAAIHAERABMSErRv3z6vEJiZmanU1FT16tWrzAs4SqSlpalevXqKioryaHe5XJKkbt26+bboAHb4sJSZeeY+wcHSf/9bLeUAAIAAExABcPjw4ZKKn+V7quXLl0uSbr75Zo/2nJwcHTlyxKNt4MCBevfdd72uFE5KSpKkSt2guqaqU+fsfQxDquRjmwEAQA0XEAGwT58+stvtmj59un777TdJxUf05syZo/j4eI0dO9bdNycnR7GxsWrWrJnHJdQPPvigHn74YSUnJ7vbfvjhBy1ZskQPPvighg4dWm3742+RkdLAgcVH+cpSUCCNGlV9NQEAgMBR5o2gq9uyZcs0c+ZMDRs2TPXq1dOhQ4c0ZMgQzZ49WxaLxd0vJCREzZs3l2EYijjlEFaDBg20cuVKPfjgg3rssccUFBQkwzD02muveT1izgweeUT69FPJYik+2neq4ODiK4R79vRPbQAAwL8CJgBarVbNmjVLs2bNOmO/sLAwpaSklLqsSZMm7ucKm92VV0rvvCONHSvl5hbfBNowio/89esnrVhRHA4BAID5BEwAhO9df700ZIj09tvSDz9IdetKI0dKl1/u78oAAIA/EQBrucjI4nv+AQAAlAiIi0AAAABQfTgC6ANbtmzRXXfdpZ9//tn95JKOHTuqYcOG7j7Hjx9X3bp1NWbMGN18881e9zVMT0/XjBkztHXrVoWHh6ugoEBDhgzRlClTNG/ePEVHR6tHjx4e6xQVFemHH37QokWL9Msvv2jt2rVVv7MAAKDGIwD6QPfu3fXVV18pMzNT0dHRqlOnjr777jsFBXkeYH3ppZc0btw4vfXWW0pMTFSd/3/Dvv3796tnz566//779a9//UsWi0VFRUV69dVXZbfbtWnTJj311FMeY91www06duyYevbsqeeff179+vWrtv0FAAA1G6eAfah+/fqSih87d3r4k6Q777xT1157rT755BM9+eST7vYHHnhAnTt31sSJE923vAkKCtL48eM1ePBg9+PsTrVs2TIlJSXpscceq5qdAQAAtRYBsJoNGDBAkrRmzRp325o1a9S2bdtS+0+aNElNmzatltoAAIA5EACr2fHjxyUV38+wRFBQkNauXavCwkKv/sHBwRrFIzsAAIAPEQCr0bFjx/TKK69IkiZOnOhuHzx4sH744QfZ7XZ9//33XuvFxcUpJISvawIAAN8gAEpyuVyy2+1yOp1Vto1du3Zp6NChysrK0uuvv67Ro0e7l82ZM0fNmzfXmjVrdOmll6pJkyYaPny4nn32WblcLt1222269NJLq6w2AABQ9ZxOp+x2u1wul79L4SpgSbLZbEpMTPTZeNnZ2YqPj3f/Oz8/X7t27VKjRo20YMECjRw50qN/TEyMUlJS9Oijj+qdd95RWlqaVq1apVWrVumRRx7R66+/rhYtWvisPgAAUP0cDoccDofsdru/S+EIYFWIiIjQZ5995v6zadMmpaWl6cUXX9Rdd92lbt26aefOnR7rNGvWTAsXLlRGRoa2bdumF154Qddcc41yc3P15z//Wb/88ouf9gYAANQ2BMBq1L9/fy1YsEDfffedBg4c6L4gZNy4ce4+ISEh6tq1q+666y4lJiZq8eLFKioq0htvvOGvsgEAQC1DAKxmw4cPV926dZWamqqlS5dKKv5+YFnGjRtX6hFDAACAyiIAVrPg4GBZrVZJ/7slTEpKitLS0spc56KLLlK9evWqpT4AAFD7EQCrWXJyso4cOaLQ0FANGTJEUvFFIvfee2+p/U+ePKnNmzerf//+1VkmAACoxQiAPpSZmSlJpd7QWSo+0nfTTTcpKChIL730ktq0aeNedvz4cd1www3avXu3uy0vL08TJkxQixYtdP3115c65sGDByVJGRkZKigo8NGeAACA2ozbwPjAli1bdPfdd+unn36SVHwbmLi4ODVo0MDd5/Dhwzpy5IgGDBigKVOmeNzXb9CgQVq5cqU2b96sxx57TPv371dRUZGys7P1pz/9SS+99JK+/fZbj21OmDBBX375pX7++WdJ0vfff6/GjRsrNjZWt956q+64446q33EAAFAjEQB9oHv37kpJSan0+h9++KEkqWfPnurZs2e51lmwYEGltwcAAMyNU8BVxDCkTz6R/vxnqUsX6eqrpddek06c8HdlAADA7DgCWAUKC6UxY6S33pJCQqSCAikoSPr4Y+mpp6T166WmTf1dJQAAMCuOAFaBp56S3n67+O8l12UUFRX/d9cu6YYb/FMXAACARAD0ufx86R//KD4FXJqCAunzz6Wvv67eugAAAEoQAH3sxx+lw4fP3Cc4uPh0MAAAgD8QAH2sjFsAVrofAACArxEAfax9eyky8sx9CgulPn2qpx4AAIDTEQB9rE4d6fbbi6/6LU1IiHTJJVLfvtVbFwAAQAkCoCSXyyW73S6n0+mT8R5/XLryyuK/nxoEg4Kkhg2lFSski8UnmwIAADWE0+mU3W6Xy+XydykEQEmy2WxKTEyUw+HwyXjnnSd9+KH06qvSZZcVh76LLpIee0z6/nupXTufbAYAANQgDodDiYmJstls/i6FG0FXlbAw6ZZbiv8AAAAEEo4AAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEASnK5XLLb7XI6nf4uBQAA1FJOp1N2u10ul8vfpSjE3wUEApvNpsTERH+XAQAAajGHwyGHwyG73e7vUjgCCAAAYDYEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYAKMnlcslut8vpdPq7FAAAUEs5nU7Z7Xa5XC5/l6IQfxcQCGw2mxITE/1dBgAAqMUcDoccDofsdru/S+EIIAAAgNkQAAEAAEyGAAgAAGAyBEAAMIklS5YoOTlZeXl5kqSTJ09q69ateuSRR/xcGYDqRgAEgAAxd+5ctWnTRhaLRRaLRSEhIerSpYs++ugjSdJ7772nSy+9VEFBQe4+l1xyiV5++WX3GJ999platmzpXh4TE6MVK1ZIklatWqX4+Hidd955io6OVnh4uHr37q2LL77YL/sLwH8IgAAQIKZMmaKdO3fqpptukiRNmDBB33zzja6++mpJ0ogRI/Tdd9/p6aefliQNGDBAO3bs0B133OEeIz4+Xr/99pvuuOMO3Xnnnfr99981cuRISZLValXLli0VEhKi8PBwORwOff311+7tATAPAiAABJixY8dKkj788MNSl48ePVpBQUHauHGjjh49WmqfwsJCPfLII7JYLO62Bg0aaO/evcrPz9f+/fv15ptvqmPHjj6vH0DgIwACQIDp37+/GjdurF27dunrr7/2Wp6bm6uioiLl5eVp1apVXstPnjypI0eOqGnTptVRLoAaiAAIAAEmODjYfdp26dKlXsuXLVumW265pczl69at06BBg6q2SAA1GgEQAALQjTfeKKk47J1u8+bNeuaZZxQaGqp169YpIyPDY/nKlSvdAfJ0X3zxhQYMGKA+ffqoS5cuevrpp1VUVOT7HQAQ0AIiABYWFuqJJ55QXFyc+vXrp27dumnatGk6ceLEOY3Zu3dvffHFFz6sFACqR9++fdWsWTPt3btXKSkp7vZdu3bpoosuUsOGDTVw4ECdPHlSK1eudC/Pz89Xdna2GjVq5DXmkSNH9O6772r16tXauHGj/vOf/+if//yn+2giAPMIiAA4ceJELViwQElJSUpOTtb69eu1evVqjRgxQoZhVGrMOXPm6Msvv1RBQYGPqwWAqhcUFKTrr79ekudp3mXLlrmPDpb899Tla9eu1eDBg0sdc+zYsfrnP/+p8847T1Lxc9AnT56sN954o8wLTgDUTn4PgBs3btSCBQs0depU2Ww2SVJUVJRmzJihpKSkUr/fcjbffvutFi9e7OtSAaBalQS85cuXu/9neOvWrbr88sslScOHD1dYWJg+/fRTpaenSyq+1991111X6ngJCQlebXFxcZKk119/3ef1Awhcfg+AS5YskSSvN6yEhARZrVYtWrSoQuPl5+fr8ccf15QpU3xWIwD4Q69evdS8eXPt27dPmzZt0i+//KJ27dq5l9erV0+DBw9WQUGBVqxYoby8PJ04cUL169f3Gis3N1dZWVle7eHh4ZKk7du3V9l+AAg8fg+AycnJioyMVExMjEd7aGio2rRpo02bNqmwsLDc482YMUNTpkxRnTp1fF0qAFQri8WiG264QVLxad5TT/+WOPU0cFJSUqlH+SSpS5custvt2rt3r0d7dna2pOL3XADm4dcAWFBQoD179igyMrLU5ZGRkcrNzdXBgwfLNd6mTZsUFBSkXr16+bJMAPCbU08Df/PNN+rcubPHcrvdrvDwcCUnJ2vhwoW69tprSx3n0KFDioqKcn//r4TL5ZIkdevWzffFAwhYfg2AmZmZKiwsPGMAlIqvXDubnJwc/eMf/+Ch5gBqlcsvv1ytW7fWgQMHdMkll3gtj4yMVEJCgoqKimS1Wst8Px08eLBefPFFNWnSxKM9KSlJISEhmjhxYpXUDyAwhfhz4yW3eSn5DsrpSk5JlPa9ldNNmzZNjz76qMLCwipcx65du9S3b1/3vwcNGhRwN1HNyMjwuBUEmJPTMR+eatN89O3bV3v27FH79u1L3aeuXbtq5cqV6tq1a5n7PHLkSD388MPKz89XbGysJGnbtm1as2aN7rnnHp04caLWzFd51abXiC8wH958NSfr1q3TunXr3P/Oyck55zHPlV8DYGlfVD5VyXdTygqIJT766CM1adLEfTVbRcXGxioxMbFS61aXlJQU9ejRw99lBBTmxBPz4ak2zcd5552nn3/+WQ6Ho9TlHTt21GuvvaZ77rlHdevWLbVPjx49ZBiGli5d6r5iODw8XOvWrVN8fHxVlR7QatNrxBeYD2++mpMePXp4nKG02+3nPOa58msAjIiIUJ06dcq8C/2xY8ckSdHR0WWOkZWVpUWLFuntt9+ukhoBwN/i4uK0efPmMpfXrVtXu3btOus4zZs31/Lly31ZGoAayq8BUJJat27t9RijEunp6bJarWrZsmWZ63/yySf69ddfddVVV3m079u3T5I0efJk1a9fXxMnTtSoUaN8VzgAVKfcXGn5cmn7dum886Thw6WuXf1dFYAayu8BMCEhQXPnzlVGRobHkb7MzEylpqYqPj5ewcHBZa4/YsQIjRgxwqt9+vTpmjFjhv75z3+a9vQGgFoiKUn6y1+kzEwpNFQyDOnxx6WBA4tD4Vm+TgMAp/P7fQCHDx8uSVqxYoVHe8lpiptvvtmjPScnp1xXBQNArbB1q2S3SyUXw508KZU84nL9eunaa4sDIQBUgN8DYJ8+fWS32zV9+nT99ttvkqS0tDTNmTNH8fHxGjt2rLtvTk6OYmNj1axZM23btu2M45Z8yfnw4cNVVjsAVLknnyz+b2khr7BQ2rBB+vzz6q0JQI3n9wAoFT/cfMyYMRo2bJiuuOIK9e3bV0OGDNGqVatksVjc/UJCQtS8eXM1aNBAERERpY716KOPKjY2Vi+++KIk6S9/+YsuueQSrVq1qlr2BQB8Jj9fWrXqf0f8ShMSUnwaGAAqwO/fAZQkq9WqWbNmadasWWfsFxYWdtb78cycOVMzZ870ZXkA4B+5uVIZd0nwcPRo1dcCoFYJiCOAAIBSREZKjRqduY9hSG3bVk89AGoNAiAABKqgIOlvfyv+75ncckv11AOg1iAAAkAgu/9+6ZJLpNNvh1USCufNky64oPrrAlCjEQABIJBFRhZf5Xv33dKpF7916iStWFHcDgAVFBAXgQAAzqBeveIjfbNnS/v2FT8JxGbzd1UAajACIADUFOHhUmysv6sAUAtwChgAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAJblcLtntdjmdTn+XAgAAaimn0ym73S6Xy+XvUrgKWJJsNpsSExP9XQYAAKjFHA6HHA6H7Ha7v0vhCCAAAIDZEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAqAkl8slu90up9Pp71IAAEAt5XQ6Zbfb5XK5/F2KQvxdQCCw2WxKTEz0dxkAAKAWczgccjgcstvt/i6FI4AAAABmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwCU5HK5ZLfb5XQ6/V0KAACopZxOp+x2u1wul79LUYi/CwgENptNiYmJ/i4DAADUYg6HQw6HQ3a73d+lcAQQAADAbAiAAAAAJkMABAAAMBkCIAAAgMkQAAEAAEyGAAgAAGAyBEAAAACTIQACAACYDAEQAADAZAiAAAAAJkMABAAAMBkCIAAAgMkQAAEAAEyGAAgAAGAyBEAAAACTIQACAACYDAFQksvlkt1ul9Pp9HcpAACglnI6nbLb7XK5XP4uRSH+LiAQ2Gw2JSYm+rsMAABQizkcDjkcDtntdn+XwhFAAAAAsyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmEzAPAqusLBQs2fP1rJlyxQdHa3s7GwlJCRo2rRpCg8PL9cYv//+u2bOnKmdO3fKMAxlZ2dr8ODBuvfee9WoUaMq3gMAAICaIWAC4MSJE/XBBx8oJSVFNptNR48eVb9+/fT1119r9erVslgsZ1z/4MGDuvbaa/Xqq6+qc+fOkqSdO3eqS5cuevfdd/XFF1+ocePG1bAnAAAAgS0gTgFv3LhRCxYs0NSpU2Wz2SRJUVFRmjFjhpKSkrR06dKzjrF06VJ98803euSRR9xtbdq00dVXX62dO3fq7bffrrL6AQAAapKACIBLliyRJF133XUe7QkJCbJarVq0aNFZx4iLi1P9+vUVExPj0Z6TkyNJqlu3ro+qBQAAqNkC4hRwcnKyIiMjvcJbaGio2rRpo02bNqmwsFDBwcFljhEfH6+MjAyPtvz8fH399ddq2LChRo4cWSW1AwAA1DR+PwJYUFCgPXv2KDIystTlkZGRys3N1cGDBys0bmFhoe6//36FhIQoMTFRDRo08EW5AAAANZ7fjwBmZmaqsLDwjAFQko4cOaILLrjgrONt27ZN9913nw4cOKCCggJ98MEHuuyyy3xaMwAAQE3m9wB44sQJSSrzVi+hoaGSpKysrHKN161bN3322WeSpP/85z+64oorNGnSJM2ZM6fMK4l37dqlvn37uv89aNAgDRo0qLy7UC0yMjKUkpLi7zICCnPiifnwxHx4Y048MR+emA9vvpqTdevWad26de5/l1yf4E9+D4D169c/4/Ls7GxJZQfEMxk2bJgcDoeefvppNWzYUA888ECp/WJjY5WYmFjh8atTSkqKevTo4e8yAgpz4on58MR8eGNOPDEfnpgPb76akx49enjcpcRut5/zmOfK798BjIiIUJ06dVRUVFTq8mPHjkmSoqOjKzX+1VdfLUl66qmnytwGAACAmfg9AEpS69atva7gLZGeni6r1aqWLVuWuX5RUZEGDRqkmJgY/fe///VYVvIEkCNHjig9Pd13RQMAANRQAREAExIStG/fPq8QmJmZqdTUVPXq1euMt4A5fPiwPvroI7lcLiUlJXksO3LkiKTiU8gNGzb0ffEAAAA1TEAEwOHDh0uSVqxY4dG+fPlySdLNN9/s0Z6Tk+MOdpJ0/vnnq3nz5urZs6duuOEGj74bNmxwj3GmEAkAAGAWAREA+/TpI7vdrunTp+u3336TJKWlpWnOnDmKj4/X2LFj3X1zcnIUGxurZs2aadu2be72+fPnq1GjRiooKHC3/frrr3r77bfVpUsXPfPMM9W2PwAAAIHM71cBl1i2bJlmzpypYcOGqV69ejp06JCGDBmi2bNne9y+JSQkRM2bN5dhGIqIiHC3Dx06VM2bN9fDDz8sl8sli8WirKwsTZkyRVOmTKnUVcQAAAC1UcAEQKvVqlmzZmnWrFln7BcWFlbmPXni4uL01ltvVUV5AAAAtUZAnAIGAABA9SEAAgAAmAwBEAAAwGQIgAAAACZDAARgCv/+97+1bNkyZWVlSZIMw9Du3bv11FNPac+ePX6uDgCqV8BcBQzAPLZs2aK77rpLP//8s/t53x07dvR4Ws/x48dVt25djRkzptQbuaenp2vGjBnaunWrwsPDVVBQoCFDhmjKlCmaN2+eoqOjPR7i/sknn+j111+XJEVFRenEiRPKz8/XnXfeqdatW1fDXgNA4OAIIIBq1717d3311VdKTU2VJNWpU0ffffedPvvsM/efr776SjfccIPGjRunwYMHKycnx73+/v371a1bN7Vr106bNm3S+vXrlZycrCZNmshut2vWrFkqLCz02u5FF10kq9Uqi8Wifv36KTExUS+++GK17TcABAqOAALwm/r160uSQkNDFRTk/f+jd955p9atW6dVq1bpySef1BNPPCFJeuCBB9S5c2dNnDjR3TcoKEjjx4/X0aNHtW7dulK39/HHH6tVq1Y+3w8AqGk4AgggoA0YMECStGbNGnfbmjVr1LZt21L7T5o0SU2bNq2W2gCgpiIAAghox48fl1T8FKASQUFBWrt2bamneYODgzVq1Khqqw8AaiICoCSXyyW73S6n0+nvUgCc4tixY3rllVckyeN07+DBg/XDDz/Ibrfr+++/91ovLi5OISHe33D5/fffdeONN6pnz57q1q2bJkyYoMOHD1fdDgDAKZxOp+x2u1wul79LIQBKks1mU2JiohwOh79LAfD/7dq1S0OHDlVWVpZef/11jR492r1szpw5at68udasWaNLL71UTZo00fDhw/Xss8/K5XLptttu06WXXuo15uzZs/Xss89q8+bN2rRpk3bv3q1+/fq5r0QGgKrkcDiUmJgom83m71IIgAD8Lzs7W/Hx8e4/vXv3Vu/evZWenq4FCxbopptu8ugfExOjlJQUjR8/XhEREUpLS9OqVas0ZcoUtWnTRsuXL/faxogRI+R0OhUTEyNJslqteuKJJ7Rjxw49+eST1bKfABAoCIAA/C4iIsLjFjCbNm1SWlqaXnzxRd11113q1q2bdu7c6bFOs2bNtHDhQmVkZGjbtm164YUXdM011yg3N1d//vOf9csvv3j0t9vtqlevnkdbXFycJLnvDwgAZkEABBCw+vfvrwULFui7777TwIED3ReEjBs3zt0nJCREXbt21V133aXExEQtXrxYRUVFeuONN9x9ioqK9Mcff3iNHx4eLqn4voJ8FxCAmRAAAQS04cOHq27dukpNTdXSpUslFX8/sCzjxo3zOmJ45513ymazudcvkZ2d7f57aGiojysHgMBFAAQQ0IKDg2W1WiX975YwKSkpSktLK3Odiy66yON0b1pamurUqaMGDRp49Cu5Ei82NlZRUVG+Lh0AAhYBEEBAS05O1pEjRxQaGqohQ4ZIkvLz83XvvfeW2v/kyZPavHmz+vfv724bOHCgXnjhBV199dUefZOSkiRJ99xzTxVVDwCBiQAIwG8yMzMlqdQbOkvFR/puuukmBQUF6aWXXlKbNm3cy44fP64bbrhBu3fvdrfl5eVpwoQJatGiha6//np3+6233qo333xT7777rrtt3759mjdvnhwOh/72t7/5eM8AILDxLGAA1W7Lli26++679dNPP0kq/i5eXFycxynaw4cP68iRIxowYICmTJnicV+/QYMGaeXKldq8ebMee+wx7d+/X0VFRcrOztaf/vQnvfTSS/r222/d/a1Wq1atWqVp06bp+eefV3BwsPLz8/XII49o/Pjx1bbfABAoCIAAql337t2VkpJS6fU//PBDSVLPnj3Vs2fPcq0TERGhf/7zn5XeJgDUJgRAAIHDMKTvv5f275eaNZPi4iSLxd9VAUCtw3cAAQSGTz4pDnydO0sJCcX/7dRJ+ugjf1cGALUOARCA/330kTR4sPTjj57tP/4oDRkirV3rn7oAoJYiAALwL8OQ/vY3qaio+M/pywxDuvNO72UAgEojAALwry+/lH79tTjolcYwpD17pI0bq7cuAKjFCIAA/Ov338vXLzW1ausAABMhAALwr/PP920/AMBZEQBV/DxQu90up9Pp71IA87nySqlp0zP3adxYOuXRbgBQEzmdTtntdvdzyP2JACjJZrMpMTFRDofD36UA5hMSIj3zzJn7PPWUFBpaPfUAQBVxOBxKTEyUzWbzdykEQAABYPRoackSqX794n+X3Py5fn1p0SJp7Fg/FQYAtRNPAgEQGMaOlRwOKSlJ+uOP4ieB/OlPktXq78oAoNYhAAIIHFarNHy4v6sAgFqPU8AAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAEpyuVyy2+1yOp3+LqVM69at83cJAYc58cR8eGI+vDEnnpgPT8yHN1/PidPplN1ul8vl8um4lUEAlGSz2ZSYmCiHw+HvUsrEL6Y35sQT8+GJ+fDGnHhiPjwxH958PScOh0OJiYmy2Ww+HbcyCIBSpZN4ZY4YVvdRxuqssbrXq85tVed6vEZ8t151bqsmzGN1bqsmzGNl1YSfNa8R32yrsgL5jGIJAqAIgL5Yxx/rVee2eKM693X8sV51bqsmzGN1bqsmzGNl1YSfNa8R32yrsmpCALQYhmH4uwh/i4mJUdeuXSu8nsvlqvBh3MqsU93r1YQaK7seNfpmPWr0zXrU6Jv1qNE369WEGiu7XqDV+Ouvv2rHjh0VHteXCIAAAAAmwylgAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDADSh559/XmvWrFFOTo4kqbCwUDt27NBjjz2mY8eO+bm6/zl58qRSUlL017/+VbfddluF1y8sLNQTTzyhuLg49evXT926ddO0adN04sSJc+prBjXlNXIuzLCPVcnf8+eL31neIyrP3z//6lKr99NAQMjPzzc2b95sjB492hg/fnyF109PTzcmTpxoXH311UaXLl2MTp06GU899ZSRl5fn1bdfv36GJEOSUb9+fSM4ONiwWCzGnDlzfLEr5ywvL8+46qqrjKFDhxpTpkwxJBljxoyp8DgTJkwwbDabsW/fPsMwDCMrK8vo3LmzkZCQYBQVFVW6rz8UFBQYjz/+uNGpUyfjyiuvNLp27Wo8/PDDRm5ubrnWd7lcRlhYmNGyZUujTZs2Rtu2bY127dp5/Bk9erS7f6C/Rs51PgyjYvvoi+1VNV/UWJPeR3zxO8t7hKea9PMvD94nzowA6Ge+CDvp6elG7969ja+++srdlpiYaFitVqN79+5Gdna2R/9+/foZbdu2NcLCwoyGDRsa1157rfH555/7Ynd8bs+ePZWaky+++MKQZPzrX//yaF+1apUhyXA6nZXq6y/n+uGzdu1a95tYaX8sFovx8ccfu/sH+mvEFx/GFdnHQP/wN4xzr7EmvY/44neW9whPNennX168T5wZATCAVDbsTJ482Vi2bJlX+3333WdIMu6//36P9n79+p1DldWrsnNy6623GpKM33//3aM9Pz/fsFqtxlVXXVWpvv7giw+fuXPnGu+9956Rm5trFBQUeLwZrVy50pgyZYpH/0B+jfjqw7i8+1gTPvx9UWNNeh/xxe8s7xGeatLPvzx4nzg7AmAAqWzY6dixoxEREWF89NFHHu0lL8iWLVt6tAf6L+6pKjsnsbGxRmRkZKnLOnbsaJx33nlGQUFBhfv6gy8+fMaPH1/qaQiXy2UMHDjQ6xRPIL9GfPVhXN59DPQPf8PwTY016X3EF7+zvEd4qkk///LgfeLsuAikFmjXrp2ys7OVkZHh0X7++edLkg4cOOCPsvymoKBAe/bsUWRkZKnLIyMjlZubq4MHD1aor78kJycrMjJSMTExHu2hoaFq06aNNm3apMLCwjOOMXPmTIWHh3u0GYahCRMm6Pnnn1dYWJjP664qvpiPQN5eZfiixpryPuKL31neI7zVlJ9/efE+cXYEwFpg6dKl+uOPP3T99dd7tP/222+SpNjYWK91duzYoaFDh6p3797q0qWLHnzwQeXm5lZLvVUtMzNThYWFZ3zDlqQjR45UqK8/+OrDp1mzZl5tixcvVvfu3XXxxReXuk4gvkZ8/WF8tn2sCR/+vqqxpryP+OJ3lvcIbzXl518evE+UT4i/C8C5Cw4OLvUD/q233pKkUm+h8swzz+jNN99UdHS0srKy1LdvX23ZskUfffSRgoODq7zmqlRyW4bTj3iVCA0NlSRlZWWpfv365e7rDxX58LngggvKPW56erqeeeYZbdu2rcw+gfga8fV8nG0fq2r+fclXNdaU95GK/H77YgyzvEfUlJ9/efA+UT4cAayldu7cqbfeekv9+vXTXXfd5bHsb3/7mxYuXKjo6GhJUr169TRt2jStX79eixYt8ke5PlXyhl2W7OxsScVv6BXp6w+++LArzT/+8Q/16dNHERERpS4P1NeIL+ejPPtYVfPvS1VZYyC+j/jid5b3iPIJxJ9/efA+UT4EwFro5MmTuummm9StWzetWrVKISGeB3pvvPFG9wuyRFxcnCTp9ddfP6dtZ2Zm6uKLL1arVq0q9OfTTz89p+2eKiIiQnXq1FFRUVGpy0tu3hkdHV2hvv5QFR8+x48f14IFCxQfH19mn6p8jZwLX85HefYx0D/8paqr0Z/vI2fii99Z3iPOLlB//uXB+0T5cAr4HGRmZqpnz54VvhP8q6++qgEDBlRRVcX/x1K/fn299957Xkd4Tp48qYyMDDVu3NijveSFuX379nPadv369fXTTz+d0xi+0Lp1a68vM5dIT0+X1WpVy5YtK9y3os71NVIVHz4rV65URkaG2rZtW+ryqnyNBMp8lHcfq+PDP1Dm5HT+fB85G1/8zgbKe8S5MuPP/2xq4/tElfD3Zcj4n8re8uRUjz32mHHTTTcZ+fn57rbjx4+7/z548GAjODjY2Lhxo8d627dvNyQZ0dHRld52VajsnJTcVPvIkSMe7RkZGYYkIz4+vlJ9/eGSSy4xYmJiSl3WokULw2q1VugWFH/9618NScaePXtKXR7orxFfzEdF9tHX818VfF1joL+P+OJ3lveIsgX6z788eJ84O04B1yKLFy/W/v379cYbb3gcsr7lllvcf09LS1O9evUUFRXlsa7L5ZIkdevWrXqK9aGcnByvK/CGDx8uSVqxYoVH+/LlyyVJN998c6X6+kNCQoL27dvndQQiMzNTqamp6tWrV4W+cJ2cnCyp7P8bDfTXiC/moyL76Ov5rwq+rLEmvI9U9HeW94ja9fMvD94nysHfCRT/U56jXcePHzcOHz7s1b5mzRrj3nvv9XrcTH5+vnHNNde4/33//fcbn376qdf6kyZNMiQZ//nPfyq/A1Vg8+bNhiRj6NChpT5K5/jx40azZs2MsLAwY+vWrR7L7Ha7YbPZjL179xqGYRgHDx40LrzwQiM+Pt5rrIr0rW4lN2JduHChR/srr7xiSDJeffVVj/ayXiOGYRiFhYVGcHCwIcnIyckptU+gv0Z8MR8V2ceKbs8ffPUaqUnvI+X9neU9onb+/M+G94mzIwAGkMqGna1btxqRkZFGu3btvP7ExMQYt912m7vv4cOHjV69ehmfffaZu2379u1GVFSU8eCDD1btDlaA3W43OnToYISEhLifV9u4cWOjR48exqpVq9z98vLyjO7duxtNmzY1fvrpJ48xTpw4Yfzf//2f0bFjR6NPnz5G27ZtjTvvvNPIysry2l5F+vqDLz7sStYrmc/CwsJSt1UTXiPnOh8V3cdA/vAvca5zUtPeR8r7O8t7RO38+ZcH7xNnRgAMAOcadi655BL3eqX9mTt3rsf2Dhw4YIwdO9bo16+f0b9/fyM+Pt547733qm1/UXG++LArGadDhw5nfSxRoL9GfDEfFdnHQP/wN4xznxPeR2o2fv7eeJ84M4thGIaPzyoDAAAggHERCAAAgMkQAAEAAEyGAAgAAGAyBEAAAACTIQACAACYDAEQAADAZAiAAAAAJkMABAAAMBkCIAAAgMkQAAHUSqmpqerZs6caNmwoi8Uii8Wi2bNnn3W97OxsNWrUSBaLRcHBwbrkkkv0yCOP6I477lDbtm3dY4WGhuqKK65QfHy84uPj1bNnT7Vv314hISGyWCzq0qWLe8wZM2aoV69eatasmSwWi6Kjo/X77797bfvRRx+VzWZzb8Nms+nRRx/16bwAgCTxKDgAtVp+fr569eqlQ4cOKTg4WLt375bFYimz/6JFi/T+++9r9erVmjdvniZPnuyxfPLkyXruued044036p133vFa/9ixY3rwwQf19ttvKyMjw2PZa6+9pqefflr//e9/ddVVV+mjjz7yqsUwDN12222yWCx65ZVXzlgrAFQWRwAB1GphYWGKjIzU2LFjtXfvXn388cdn7L9z507FxsZKkurXr++1vKQtPDy81PUjIyP1wgsvyGazKSsry2v5vffeq8suu0yffPKJXnjhBa/lFotFV1xxhfr06UP4A1BlCIAATGHcuHGyWCxatGhRmX1++OEHderU6Zy3ZbFYdM011+i3337zWhYSEqI33nhD4eHhevDBB/XLL7+c8/YAoKIIgABMoVWrVrrqqqv0/vvv6/Dhw6X2effddzVy5MhKjf/LL79o+vTp7n9ffvnlOnDgQKl927dvr9mzZysnJ0c333yzCgsLK7VNAKgsAiAA07j11luVn5+vN954w2tZXl6eCgsLdd5551Vq7NPD3ogRIzRo0KAy+0+aNEnx8fFKSUnRnDlzKrVNAKgsAiAA07juuuvUoEEDLV682GvZ+++/r+HDh1dq3Ly8PM2fP79C61gsFr322muKiorSjBkz9O2331Zq2wBQGQRAAKZhtVo1evRo7dixQ19++aXHsm3btqlbt27lHmvt2rWKj49X79691axZs1KvCD6bli1b6p///KdOnjypm2++WXl5eRUeAwAqgwAIwFTGjx8vSR4Xg+zdu1etWrWq0DhDhgzRZ599pk2bNik9PV0PP/xwpeq55ZZbZLfbtX37du75B6DaEAABmEqnTp10+eWXa9myZcrOzpYkvf3223I4HJUeMygoSDfddJNX+4wZM8q1/iuvvKJGjRpp7ty52rhxY6XrAIDyIgACMJ1bb71V2dnZcjqdKioqUlZWlqKjo89pzPbt23tcBSxJO3bsKNe6TZo00csvv6yioiKNGTNGx48fP6daAOBsCIAATMfhcKhOnTpatGiR1q5dq8GDB/t8G7t371ZkZGS5+48YMUKjR4/Wr7/+qieffNLn9QDAqQiAAEwnKipK119/vbZs2aJFixapf//+Pt/G/Pnz1aFDhwqt8/zzzysmJkZ//PGHz+sBgFMRAAHUar/++qu2bdumX3/91aO95GKQbt26eT1yLTMz0+O/pS0rKCgodXs5OTl69tlnNW/ePLVv397dbhiGvvjiC23cuFFlPYK9fv36WrJkCY+AA1DlLEZZ70QAUIOlpqZq+PDh2r59uwoKChQSEqJOnTrp/fffV4sWLSRJl112mVatWiWbzSZJuummm5SSkuIOi0FBQWrfvr2uu+46paWl6bPPPnM/ui0sLEyXXXaZQkNDJRUHwszMTO3cuVP5+fmSpD179qhVq1Z69NFHtXjxYveRvQsuuEC33nqrZs6cWWrtf//739W9e3eNHj266iYIgKkRAAEAAEyGU8AAAAAmQwAEAAAwGQIgAACAyRAAAQAATIYACAAAYDIEQAAAAJMhAAIAAJjM/wPor6+HJ4/wzwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.1) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.1) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.1, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.3) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.3) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.3, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 252) & (df_low['Volatility'] == 0.4) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 52) & (df_low['Volatility'] == 0.4) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.4, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different maturities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>504</td>\n",
       "      <td>-0.609074</td>\n",
       "      <td>0.255213</td>\n",
       "      <td>-0.525328</td>\n",
       "      <td>0.372037</td>\n",
       "      <td>0.429611</td>\n",
       "      <td>0.579392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>121</td>\n",
       "      <td>-0.617245</td>\n",
       "      <td>0.225774</td>\n",
       "      <td>-0.504427</td>\n",
       "      <td>0.457158</td>\n",
       "      <td>0.128094</td>\n",
       "      <td>0.927855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.643196</td>\n",
       "      <td>0.271016</td>\n",
       "      <td>-0.361152</td>\n",
       "      <td>0.369329</td>\n",
       "      <td>0.285235</td>\n",
       "      <td>0.956450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "16     100         0.2       2.0               504  -0.609074     0.255213   \n",
       "18     100         0.2       0.5               121  -0.617245     0.225774   \n",
       "20     100         0.2       0.2                50  -0.643196     0.271016   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "16       -0.525328          0.372037    0.429611      0.579392  \n",
       "18       -0.504427          0.457158    0.128094      0.927855  \n",
       "20       -0.361152          0.369329    0.285235      0.956450  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low[(df_low['Rebalancing_freq'].isin([504, 121, 50]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>-0.275566</td>\n",
       "      <td>0.128932</td>\n",
       "      <td>-0.048317</td>\n",
       "      <td>0.647440</td>\n",
       "      <td>0.068634</td>\n",
       "      <td>0.630368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "      <td>-0.262446</td>\n",
       "      <td>0.188253</td>\n",
       "      <td>-0.013231</td>\n",
       "      <td>0.655518</td>\n",
       "      <td>0.083446</td>\n",
       "      <td>0.787131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.254707</td>\n",
       "      <td>0.299575</td>\n",
       "      <td>0.006294</td>\n",
       "      <td>0.633387</td>\n",
       "      <td>0.248786</td>\n",
       "      <td>0.713144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "17     100         0.2       2.0               104  -0.275566     0.128932   \n",
       "19     100         0.2       0.5                26  -0.262446     0.188253   \n",
       "21     100         0.2       0.2                10  -0.254707     0.299575   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "17       -0.048317          0.647440    0.068634      0.630368  \n",
       "19       -0.013231          0.655518    0.083446      0.787131  \n",
       "21        0.006294          0.633387    0.248786      0.713144  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low[(df_low['Rebalancing_freq'].isin([104, 26, 10]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l8PDwQmOdOXNGKSkphdpDQkIkSd98802F7QeA6osAqPz3+TkcDrlcLrNLAWABNptNN998s6T807znn/4tcP5p4E2bNmno0KFFjtW1a1dFRETo4MGDXu0ZGRmSpMDAQB9XD6C8XC6XHA6H3G632aUQAKX862USEhKq/2tdAFQb558G/uKLL9SlSxevfofDoZCQEG3fvl3Lli3TddddV+Q4x48fV/369T3X/xUo+AcmJibG98UDKBen06mEhATZ7XazSyEAAoAZunfvrlatWuno0aPq1KlTof6wsDANHTpUeXl5Cg4OVlhYWJHjDBkyRNu2bVPjxo292jdt2qSAgABNmTKlQuoHUL0RAAHAJL+/67es/ZL02GOP6c9//rPXtX7btm3Tpk2b9Mwzz+iKK67wYcUAaooAswsAAKtyOp3atm1bkUcAJWn48OFq06aNhg8fXuwYbdq00auvvqoHH3zQc8dwSEiI3n33XcXFxVVE2QBqAAIgAJgkOjpau3btKra/du3a2rdv3wXHiYqK0po1a3xZmqWlpqbqySef1KhRo3TZZZfJ399fp0+f1vbt23Xo0CFNmjTJ7BKBi0YABABclO7du+vzzz9XgwYN1K5dOwUFBenYsWP64YcfJEktW7ZUZGSkzp07pwMHDujIkSO69NJLlZSUpEOHDunmm2/W3r17PY+zadOmjSIiIiRJOTk5OnHihEJCQnTzzTdr6tSpCg0NLbKOlJQUzZs3T7t375YknTx5Un379tW9996rli1blnp/0tPT9dhjj+mxxx6Tv7+/QkNDlZ6erkaNGmnr1q0XMVNA1UEABACz5OZKGzdKy5dLv/wiNW0qjRkj3XijFBRkdnWllpSUpH/84x+699575e/vL0k6d+6cgv5vH9555x2v5xyuWrVKY8eOVV5enlq0aKFdu3YpOztbEREROn78uHbs2FHoppYdO3Zo8uTJWrJkid566y1169bNqz8rK0tDhw7V448/rsWLF3vqiomJUXx8vD788EOvGi6kYcOGstlsSk9PV8OGDTVx4kQ98MADatCgQbnmCKhquAkEAMyQlSX98Y/SdddJb78tff21tGWL9Kc/SX37SmlpZldYanXq1NHf/vY3T/iTvJ8/GBwc7LX86NGjNXDgQJ04ccLTFhQUpFq1ahW5vCT17dtXH374ocLDw3XVVVcVOjW+efNmffrpp5o+fbqnrVGjRho5cqSSkpK0ZMmSMu3TsGHDlJSUpLNnz2rfvn164oknCH+oUQiAAGCGv/1Neu+9/N/n5ub/mpeX/+sXX0gTJphTVxllZGTo8ssvL/N63bp1U1JSUpnWqVOnjpYuXarU1FSNGzfOq69du3Zq3LixWrRo4dWemZkpKf96SgD/QwAEgMqWliYtW/a/wPd7ubnSm2/mnxau4o4fP65WrVqVeb2WLVvq+PHjZV6vT58+ioqK0kcffaSPPvrI037ZZZfp6NGj2rhxo9fyu3btUnBwsMaMGVPmbQE1GQEQACpbYmL+KeCSGIb0wQeVUs7FaNWqlebNm1fm9e644w7FxsaWa5sFbzdZu3ZticstXrxYBw8e1Ouvv6527dqVeTvLli1Tr1691K9fP/Xq1Uvr168vV71AVcRNIABQ2Yo78lfe5Uxms9kqZZ0CBXcIf/rpp4X63G63br31Vp04cULHjx9XfHy8hgwZUuZtJCQkqH///p7H9Lz33nu65ppr9OKLLxY6/QxURxwBBIDKFhMjBZTi/9+9e1d8LdVQwc0i599EUsBut2vbtm36+uuvtWHDBo0cOVJjx47V2bNnSz1+gwYN9NJLL2n8+PGetquuukqDBw/W9OnTPQ/cBqozAiAAVLZGjSSnUzrvrlkvAQHSoEHSZZdVbl3VRNb/nT6vU6dOictdeeWVmjFjhl5++WX99a9/LfX4YWFhGjFiRKH26OhonTp1Sm+++WaZ6gWqIgIgAJjhmWekK66QbLb8rwJ+flKLFtIrr5hXWxVXcPdwdHT0BZe9+uqrJeVfz1faI3cnTpzQmTNnCrWHhIRIktd7l4HqigAIAGYID5c+/lh6/nmpSxepYUOpUyfpiSekPXukZs3MrrDK+vLLLyVJN9xwg6dt3LhxatiwobZv3+61bMOGDSXlv1Fk//79Fxz74MGDioiIKPSgaSn/kTeS9zMOgeqKm0AAwCyXXCJNmpT/hVL55ptv9NVXX6lr16764x//6GlfvXq1MjMztXbtWq+7iwteLyf97+aRkpw8eVJnz56V3W4v1Od2uyX97y5koDojAAIAqoXMzEzdeeedCgsL06uvvup1J3G3bt2UkpKiCb97gPaHH34oSRo8eLCaleKoaseOHfWHP/xBb7/9tld7Tk6OtmzZoubNm+vGG2/0wd4A5uIUsPL/V+dwOORyucwuBQBqhLTzXmWXVorX2mVnZ3ve2nHu3LlC/Z988oliY2N16NAhbd26VR07dvTqf/LJJ9W4cWP5+f3vn7Xk5GQtWbJEkZGReumll0pVd0BAgKZOnaqRI0d6HT1csGCBcnNztWbNGs+1gEBZuVwuORwOz9FkM3EEUPmPDUhISDC7DACo1r7//nuNHTtW6enpOnTokKe9R48eioyMVN26dbVw4UINHDjQ03fo0CGNGjVKe/fu9TzWpW/fvp6jdTk5OUpOTlZISIhGjhypadOmKSwsrNC2r7zySq1YsUKPP/649u7dK5vNprS0NI0cOVIPPfSQwsPDS70fN954o8LCwnTLLbcoJydHWVlZat++vXbv3q3WrVuXc3YAyel0yul0yuFwmF0KARAA4BsdO3Ys8uHMJWnRooU++eQTn2y/VatWWrZsmU/GGjx4sAYPHuyTsYCqiFPAAAAAFsMRQABAxcjKkuLjpc2bpdzc/Deb3HZb/iNwAJiKAAgA8L2vv5aGDJGOHs1/44lhSKtXS3//u7R2bX4fANNwChgA4Ftpafmvsjt+PP/73FwpLy8/BGZmStddJ/34o6klAlZHAAQA+NYrr0gnTuQHv98zjPz2Z56p/LoAeBAAAQC+tX59ftArTk5O/mlgAKYhAAIAfOv/HuhcorNnK74OAMUiAAIAfCsmRgoo4R5Df3+pa9fKqwdAIQRAAIBvTZqUf5q3OLm50tSplVcPgEIIgAAA37r8cmnhwvzf+/v/r73gPb3jx0sjRlR6WQD+hwAIAPC9++6TNm6U+vWTbLb8ts6dpZUrpWXL/tcGwBQ8CBoAUDH++Mf8r5yc/OcABgWZXRGA/0MABABUrJJuCAFgCk4BAwAAWAwBEAAAwGIIgAAAABZDAJTkdrvlcDjkcrnMLgUAANRQLpdLDodDbrfb7FK4CUSS7Ha7EhISzC4DAADUYE6nU06nUw6Hw+xSOAIIAABgNQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAi6kSr4LLzc3V/PnzFR8fr3r16ikjI0NDhw7VzJkzFRISUupxsrOz9c9//lNr1qxRaGio6tatq/79+2vGjBmy2WwVuAcAAADVR5UIgFOmTNGGDRuUmJgou92ukydPKjY2Vnv27NHGjRtLFd7Onj2roUOHys/PT5s2bVKjRo3073//W3fccYdatGihUaNGVcKeAAAAVH2mnwLesWOHli5dqvvvv192u12SVKdOHc2ZM0ebNm3S6tWrSzXO3XffrV9//VXr169Xo0aNJEnHjh3TuXPndPbs2QqrHwAAoLoxPQCuXLlSknT99dd7tQ8dOlTBwcFavnz5Bcf46quvtHLlSt1zzz2qXbu2p/3+++/X0aNHddttt/m2aAAAgGrM9AC4fft2hYWFKSIiwqs9MDBQbdu21c6dO5Wbm1viGEuWLJFhGBoyZEihvsaNG/u0XgAAgOrO1ACYk5OjAwcOKCwsrMj+sLAwnTlzRseOHStxnE2bNkmSLr30Uk2ePFkDBw5U586dNW3aNKWlpfm6bAAAUE11795dNptNDRs2VJ8+fRQXF6cOHTrIZrPJZrOpVatWiouLU9++fdWsWTPZbDbPpWWHDh1Sr1691KBBA8/yUVFRiouLU1xcnPr166cOHTqoa9eumj9/vjIyMoqtIyUlRffee69n3W7dumnq1Kk6ePBgufYrLS1Nr7/+ujp27KiPP/74gsubehNIWlqacnNzSwyAUv4kNWvWrMhlsrKydOjQIUnSwoULde+996p169bKzMzU8OHDNWDAAO3YsUO1atWqmJ0AAADVRlJSkv7xj3/o3nvvlb+/vyTp3LlzCgoKkiS98847at++vWf5VatWaezYscrLy1OLFi20a9cuZWdnKyIiQsePH9eOHTsKnW3csWOHJk+erCVLluitt95St27dvPpzc3M1dOhQPf7441q8eLGnrpiYGMXHx+vDDz/0qqEkGzZs0LPPPqtGjRopJSVF//3vf5WTk3PB9UwNgFlZWZJU7KNeAgMDJUnp6enFjpGSkuL5ffPmzdW6dWtJUq1atfTII48oLi5O8+bN07x584odY9++ferfv7/n+8GDB2vw4MGl35FySE1NVWJiYoVuA0Vj7s3D3JuDeTcPc2+e4uY+MDBQsbGx+vzzz4tc7/vvv/c6exgVFaWYmBht3rxZ9erV87QXhMdvvvmm0FG7gIAA/fOf/9Qdd9yhAQMG6Pbbb9fu3bs9/b/99pv279+v6dOn65tvvpEkNWrUSCNHjtRTTz2lJUuW6KmnnirVfl577bW69tprJUmzZ8/2nBW9EFMDYHh4eIn9BYdOS3oW4PlHD39/DWDnzp0lSS+99FKJATAqKkoJCQkXKtenEhMT1bNnz0rdJvIx9+Zh7s3BvJuHuTdPUXOfkZGh7t27l/hn0rVrV7Vs2dKrbcCAAWrevLk6derkaQsODpYkXXnllcXmmVdeeUV9+/bVnj179NFHH3mNd/r0abVo0cJr+czMTEnyuqG1oph6DWBoaKhq1aqlvLy8IvtPnTolSV6J+/fCwsI8Rwp/fyNJnTp1JElHjx7V8ePHfVEyAACopo4fP65WrVqVeb2WLVuWK0f06dNHUVFR+uijj7wCYFhYmI4ePaqNGzd6Lb9r1y4FBwdrzJgxZd5WWZl+F3CrVq2UmppaZF9ycrKCg4MVGRlZ4hjt2rWTlP8mkPP5+f1v9wpCIgAAsKZWrVqVeEawOHfccYdiY2PLtc2YmBhJ0tq1a0tcbvHixTp48KBef/11T66pSKYHwKFDh+rw4cOFQmBaWpoOHTqk3r17e86zF2fQoEGS8s+p/34MSWrRosUFTzcDAICarzyvhi2447c8Cs5Ofvrpp4X63G63BgwYoOjoaC1atEjx8fEaMWJEubZTVqYHwIId/X0yXrNmjSQVeohzZmam140fkjRhwgT5+fkVutiz4Ps77rjDlyUDAACUSsFTSE6cOFGoz263a9u2bfr666+1YcMGjRw5UmPHjq2UN5iZHgD79u0rh8Oh2bNn65dffpGUfyv0ggULFBcXp7Fjx3qWzczMVFRUlJo2bep1N03nzp1133336bHHHvOcoz99+rTmz5+vfv366b777qvUfQIAAJD+98STgvsSinPllVdqxowZevnll/XXv/61wusyPQBKUnx8vMaMGaPhw4erX79+6t+/v6655hqtX7/e65BrQECAmjdvrvr16ys0NNRrjAULFmjy5MkaNmyY4uLiNGDAAA0aNEhbtmzxPNsHAACgMiUlJUmSoqOjL7js1VdfLUlatmyZkpOTK7QuUx8DUyA4OPiCz+qTpKCgoBKfpzRlyhRNmTLF1+UBAACUy5dffilJuuGGG7zaGjZsqLVr13rdXNKwYUNJ+W9K279/v+f7ilAljgACAADUNN98842++uorde3aVX/84x897b/99ptOnDhR6P6H8+9x+P2j7XytShwBBAAAqEkyMzN15513KiwsTK+++qrXJW1169ZVkyZNNGHCBK91PvzwQ0n5byQr7hW4vsIRQAAAYGnnv/rt/N8XJzs72/PWjnPnzhXq/+STTxQbG6tDhw5p69at6tixo1d/p06d1LhxY6/nFScnJ2vJkiWKjIzUSy+9VK79OHbsmCSV6qHVHAEEAACW8/3332vs2LFKT0/XoUOHPO09evRQZGSk6tatq4ULF2rgwIGevkOHDmnUqFHau3ev57Euffv29Ryty8nJUXJyskJCQjRy5EhNmzbN65W1BcLDw/X000/r8ccf1969e2Wz2ZSWlqaRI0fqoYceKtOziz/99FNNmzZNv/76q9xutyTplltu0axZs9SwYUO9//77Rd4MSwAEAACW07FjxyIfzlySFi1a6JNPPvHJ9lu1aqVly5Zd9Dg9evQoV00EQAAAYHkpKdIbb0jHjkl2u3TjjVLdumZXVXEIgAAAwLIMQ1qwQJo9Wzp3TvL3l3JzpcmT89unTTO7worBTSAAAMCy/vlP6e9/l7Kz88NgTk7+r1lZ0vTpkg/O0lZJBEAAAGBJZ85Ijz5a8jIzZ+YfGaxpCIAAAMCSNm+WTp4seZmkJOnjjyunnspEAJTkdrvlcDjkcrnMLgUAAFSS1FTfLnchLpdLDofD87gWM3ETiCS73a6EhASzywAAAJWoTRvfLnchTqdTTqdTDofDNwNeBI4AAgAAS+rXT4qKkvyKSUN+flKXLtIVV1RqWZWCAAgAACzJZsu/y9ffP//rfP7+UlCQ9MIL5tRW0QiAAADAsuLipO3bpb59vdsHDJB27JB69DClrArHNYAAAMDSevfOD4GHDuW/CaRZs/y3gdRkBEAAAABJLVrkf1kBp4ABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBUJLb7ZbD4ZDL5TK7FAAAUEO5XC45HA653W6zS1GA2QVUBXa7XQkJCWaXAQAAajCn0ymn0ymHw2F2KRwBBAAAsBoCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCoCS32y2HwyGXy2V2KQAAoIZyuVxyOBxyu91ml6IAswuoCux2uxISEswuAwAA1GBOp1NOp1MOh8PsUjgCCAAAYDUEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYAKMntdsvhcMjlcpldCgAAqKFcLpccDofcbrfZpSjA7AKqArvdroSEBLPLAAAANZjT6ZTT6ZTD4TC7FI4AAgAAWA0BEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGJqVAB89tln9fbbbyszM1OSlJubq++++06PPPKITp06ZXJ1AAAAVUOVCYC5ubmaO3euoqOjFRsbq5iYGM2cOVNZWVmlHmPt2rUaNmyYateurXr16ik4OFidO3dWrVq1FBYWVoHVAwAAVB8BZhdQYMqUKdqwYYMSExNlt9t18uRJxcbGas+ePdq4caNsNlupxmnXrp0OHjwof39/DR8+XDNmzFC/fv0quHoAAIDqo0oEwB07dmjp0qV65plnZLfbJUl16tTRnDlzdN1112n16tW65ZZbSjXWjz/+WJGlAgAAVHtV4hTwypUrJUnXX3+9V/vQoUMVHBys5cuXm1EWAABAjVQlAuD27dsVFhamiIgIr/bAwEC1bdtWO3fuVG5urknVAQAA1CymB8CcnBwdOHCg2Js0wsLCdObMGR07dqxU43333XcaNmyY+vTpo65du+qBBx7QmTNnfFkyAABAtWb6NYBpaWnKzc0tMQBKUkpKipo1a3bB8Z544gm9+uqrqlevntLT09W/f399+umn2rJli/z9/YtcZ9++ferfv7/n+8GDB2vw4MHl2JvSS01NVWJiYoVuA0Vj7s3D3JuDeTcPc2+eqjT3mzdv1ubNmz3fFzyuzkymB8CCx7yEhIQU2R8YGChJSk9Pv+BYd911l2644QbPOnXr1tXMmTM1atQoLV++XHfeeWeR60VFRSkhIaE85ZdbYmKievbsWanbRD7m3jzMvTmYd/Mw9+apSnPfs2dPzZo1y/O9w+EwsZp8pp8CDg8PL7E/IyNDUvEB8XyjRo3yhL8C0dHRkqSXX365fAUCAADUMKYHwNDQUNWqVUt5eXlF9he8waNevXoljnPu3DklJSUVai8Ijt98881FVgoAAFAzmB4AJalVq1ZKTU0tsi85OVnBwcGKjIwscYxrr71WzZo1086dO73aC44g/v7IIAAAgFVViQA4dOhQHT58uFAITEtL06FDh9S7d+9ib+AokJSUpLp166pOnTpe7W63W5IUExPj26IBAACqqSoRAEeMGCEp/12+51uzZo0k6bbbbvNqz8zMVEpKilfbVVddpTfeeEOXX365V/umTZskSdOnT/dhxQAAANVXlQiAffv2lcPh0OzZs/XLL79Iyj+it2DBAsXFxWns2LGeZTMzMxUVFaWmTZtq9+7dnvYHHnhADz30kLZv3+5p+/bbb7Vy5Uo98MADGjZsWKXtDwAAQFVm+mNgCsTHx+vRRx/V8OHDVbduXR0/flzXXHON5s+fL5vN5lkuICBAzZs3l2EYCg0N9bTXr19f69at0wMPPKBHHnlEfn5+MgxD//73vwu9Yg4AAMDKqkwADA4O1rx58zRv3rwSlwsKCir2wY6NGzf2vFcYAAAARasSp4ABAABQeQiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAktxutxwOh1wul9mlAACAGsrlcsnhcMjtdptdStV5FZyZ7Ha7EhISzC4DAADUYE6nU06nUw6Hw+xSOAIIAABgNQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRASW63Ww6HQy6Xy+xSAABADeVyueRwOOR2u80uRQFmF1AV2O12JSQkmF0GAACowZxOp5xOpxwOh9mlcAQQAADAagiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAktxutxwOh1wul9mlAACAGsrlcsnhcMjtdptdigLMLqAqsNvtSkhIMLsMAABQgzmdTjmdTjkcDrNL4QggAACA1RAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAJbndbjkcDrlcLrNLAQAANZTL5ZLD4ZDb7Ta7FAWYXUBVYLfblZCQYHYZAACgBnM6nXI6nXI4HGaXwhFAAAAAqyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyk2AL7wwguVWQcAAAAqSbEBcOHChZVWRG5urubOnavo6GjFxsYqJiZGM2fOVFZW1kWN2adPH3388cc+rBQAAKD6K/ZdwAcOHND8+fPVt2/fsg0YEKA+ffqUaZ0pU6Zow4YNSkxMlN1u18mTJxUbG6s9e/Zo48aNstlsZRpPkhYsWKBPPvlEOTk5ZV4XAACgJis2AErShx9+qOzs7BIHMAxDK1as0G+//SZJuvbaa/XWW2+VuoAdO3Zo6dKleuaZZ2S32yVJderU0Zw5c3Tddddp9erVuuWWW0o9niR9+eWXWrFiRZnWAQAAsIpiA2DDhg21ceNG+fkVf5/I/v37NWbMGLndboWFhenJJ5/UuHHjylTAypUrJUnXX3+9V/vQoUMVHBys5cuXlykAZmdn67HHHtOMGTM0efLkMtUCAABgBcWmu+uuu67E8PfCCy+oS5cu2rFjh/7whz/o66+/LnP4k6Tt27crLCxMERERXu2BgYFq27atdu7cqdzc3FKPN2fOHM2YMUO1atUqcy0AAABWUGzCW7ZsWZHtR44c0dChQ3X33XcrJydHixcv1rZt2xQZGVnmjefk5OjAgQMKCwsrsj8sLExnzpzRsWPHSjXezp075efnp969e5e5FgAAAKso8RrA33O5XJoyZYpSU1PVrVs3/ec//1GHDh3KvfG0tDTl5uaWGAAlKSUlRc2aNStxrMzMTP3zn//Ua6+9Vu56AAAArKBUATAlJUV33XWX3njjDfn7++vhhx/WzJkzFRBQpvxYSMFjXkJCQorsDwwMlCSlp6dfcKyZM2fq4YcfVlBQUJnr2Ldvn/r37+/5fvDgwRo8eHCZxymL1NRUJSYmVug2UDTm3jzMvTmYd/Mw9+apSnO/efNmbd682fN9ZmamidXku2CC27hxoyZOnKijR4/qsssu0yuvvKIrr7zSJxsPDw8vsT8jI0NS8QGxwJYtW9S4cWNFR0eXq46oqCglJCSUa93ySkxMVM+ePSt1m8jH3JuHuTcH824e5t48VWnue/bsqVmzZnm+dzgcJlaTr9hrAE+fPq0JEybI4XDo2LFjmjZtmvbs2XPB8Pfiiy+WeuOhoaGqVauW8vLyiuw/deqUJKlevXrFjpGenq7ly5drxowZpd4uAACAlRV7BLBz58765Zdf1Lx5c7388suKjY0t1YD/+Mc/dMcdd5S6gFatWik1NbXIvuTkZAUHB5d4g8nWrVv1888/a9CgQV7thw8fliRNnz5d4eHhmjJlikaOHFnqugAAAGqqYgPgwYMH1bFjRy1YsEA2m00ffvjhBQf76KOPdPDgwTIVMHToUC1atEipqaleR/rS0tJ06NAhxcXFyd/fv9j1b7jhBt1www2F2mfPnq05c+boqaeeUlxcXJlqAgAAqMmKPQVcv3593XTTTdq9e7e2bdtWqq+PPvqozAWMGDFCkrR27Vqv9jVr1kiSbrvtNq/2zMxMpaSklHk7VrVo0SK1bdtWNptNNptNAQEB6tq1q7Zs2SJJevPNN3XFFVfIz8/Ps0ynTp30wgsveMb44IMPFBkZ6emPiIjw+vPKysrStm3bNGzYMM2dO7fS9xEAAJRNsUcAo6Ki9Mgjj5RpsJycnAs+ruX3+vbtK4fDodmzZ+vqq69WZGSkkpKStGDBAsXFxWns2LGeZTMzMxUVFaUTJ05o586diomJKXbc5ORkSdKJEyfKVE9NM2PGDM2YMUO33nqrVq1apUmTJum5557z9BccQV20aJHuu+8+DRw4UFu3bvUaIy4uTr/88osmTZokf39/Pffcc7LZbPr11181btw41a5dW02aNNHbb7+t7t27V/YuAgCAMir2CGC3bt3KPFhAQICuu+66Mq8XHx+vMWPGaPjw4erXr5/69++va665RuvXr5fNZvMav3nz5qpfv75CQ0OLHOvhhx9WVFSU/vWvf0mS/vSnP6lTp05av359meuqSQqC9Lvvvltk/6233io/Pz/t2LFDJ0+eLHKZ3NxczZo1y/Nn0rx5c23ZskVvvfVWmd/XDAAAzFPsEcDnn3++XAMW9waRkgQHB2vevHmaN29eicsFBQVd8Jk+jz76qB599NEy11DTDRgwQI0aNdK+ffu0Z8+eQgH/zJkzysvL09mzZ7V+/Xr9+c9/9uo/d+6cUlJS1KRJk8osGwAAVIDiX/Z7noyMDP3444+F2h999FGNHTtW8+bN07p16/TDDz+U6b29qDz+/v668cYbJUmrV68u1B8fH6/bb7+92P7NmzdX+MOxAQBA5Sg2AN54442KiYlR+/btdeeddxZ56vDhhx/WCy+8oD59+sjlcqljx46qXbt2hRaM8hs1apSk/LD3e7t27dITTzyhwMBAbd68udCjedatW+cJkAAAoHorNgCuW7dObdq00VdffaVVq1bpL3/5S5HLBQcHa8CAAYqPj9d9992nc+fOVVixuDj9+/dX06ZNdfDgQa9T6fv27VObNm3UoEEDXXXVVTp37pzWrVvn6c/OzlZGRoYaNmxoRtkAAMDHig2ANptNS5YsueBr2M734IMPXvT7gVFx/Pz8dNNNN0nyPs0bHx/vOTpY8Ov5/e+8846GDBlSiZUCAICKVGxaa926tRo0aOD5/uWXX/a6I7fA+c/pCw8PV+vWrX1cInxp1KhReuaZZ7RmzRotXrxYNptNn3/+uf7+979Lyn8uY1BQkN5//30lJyerYcOGWr9+vRYvXmxy5QAAwFeKPQJ4fviTpGbNmslms2nlypW6/fbbtW3bNjVu3LjQevXr1/d9lfCZ3r17q3nz5jp8+LB27typn376Se3bt/f0161bV0OGDFFOTo7Wrl2rs2fPKisrS+Hh4eYVDQAAfKrYI4C1atXy+v7qq6+WJF133XWKiIjQ8uXLi3xFW3BwsI9LhC/ZbDbdfPPNWrx4sVavXq1GjRp5TvsWGDVqlDZs2KDVq1ercePGGjp0qEnVAgCAilCqx8Ccr06dOurYsWOJ7+dF1VYQ+NasWaMvvvhCXbp08ep3OBwKCQnR9u3btWzZsnI93BsAAFRdxQbAvLy8Ylf6/dHB0q6HqqF79+5q1aqVjh49qk6dOhXqDwsL09ChQ5WXl6fg4GCFhYWZUCUAAKgoxZ4C/vzzz/XQQw8VeUr34MGDRb5tIycnR1988YVvK0SFGDVqlBYsWFDo9O/5/evWrSu2//eOHTsmSTp+/LjPagQAABWj2ACYmZmpBQsWFNlnGIbmzJlTZHtRdwpXdW63Ww6HQ06nU06n0+xyKoXT6dS2bduKPAIoScOHD1ebNm00fPjwYsfIzs7WgAEDlJycrH379knKf4VgQkKCmjdvrqeeeko9evSokPoBAKhuXC6XXC6X3G632aUUHwCl/GvBynL3Z2pqqjZs2HCxNVU6u92uhIQEs8uoVNHR0dq1a1ex/bVr1/aEuuIEBQVpx44dvi4NAIAaqeBAk8PhMLuU4gNgZGSk19sgSqtVq1YXVRAqh2FI27dLW7dKublS377SNddI3NsDAEDNV2wAjI6OLteA5V0PleeXXySHQ/r6aykgQLLZpPnzpZYtpfXrJf4IAQCo2Yq9C3j9+vWlGiApKcnr/b+lXQ/mOH1aiouTvv8+//ucHKngj+/XX/P7jhwxqzoAAFAZij0C+PjjjysnJ8erLSgoSA888IAk6bnnntOCBQt05MgR+fv7a/jw4Xr66afVvHnziq0YF2XVqvwjgIZRuC83Vzp5UlqyRCriJm8AAFBDFHsEcODAgXrzzTf12GOP6bvvvtPll1+uG2+8UZL0t7/9TdOmTdPJkyd1zz336JlnnlFISIh69uypIxw+qtJcrpL7c3PzQyIAAKi5ij0C2KpVK4WGhmrnzp3q3r27p/3LL7/UokWLVKtWLe3YsUOdO3eWJE2aNEnLli3TrFmztHz58oqvHOWSmlr00b/znTxZObUAAABzFHsEcPbs2Vq4cKFX+JOktWvXSpJGjx7tCX8FJk6cqO8LLi5DldSpU/6NH8Xx85Muu6zy6gEAAJWv2AD4/fffq0+fPoXad+zYIZvNpj/84Q9FrhcaGuq76uBzd96Zf+NHcfLypEmTKq8eAABQ+YoNgAFFHCbKzc3VZ599Jknq3bt3kevl5ub6qDRUhP79pYkTi+6z2aRhw6RSvv0NAABUU8UGwKysLGVlZXm1vf/++zp9+rTsdrtat25daJ2dO3eqa9euvq8SPmOzSUuXSk8/LZ1/w3bDhtLs2dK6dSWfIgYAANVfsQHwhhtu0J133uk5ovfrr79q6tSpstlsuuOOOwotf+rUKT300EOaPn16hRUL3/Dzk/7yF+ngQWnfPumnn6TffpMeflgKDDS7OgAAUNGKDYDTp09XUlKS7Ha7evbsqfbt2+unn37SVVddpQcffNCzXHZ2tjZs2KBevXrJ399ftWvXrpTCcfH8/KQ2baS2bQl+AABYSbEn+/z9/bVx40atXLlSmzZtUmRkpIYMGaLbb79dfn75uXH8+PE6fPiwsrOz1ahRI+Xm5mrKlClaxYPkAAAAqqwSr/by8/PT+PHjNX78+CL7V6xYUSFFAQAAoOIUewoYAAAANRMBEAAAwGIIgAAAABZDAJTkdrvlcDjkcrnMLgUAANRQLpdLDodDbrfb7FJKvgnEKux2uxISEswuAwAA1GBOp1NOp1MOh8PsUjgCCAAAYDUEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQElut1sOh0Mul8vsUgAAQA3lcrnkcDjkdrvNLkUBZhdQFdjtdiUkJJhdBgAAqMGcTqecTqccDofZpXAEEAAAwGoIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBUJLb7ZbD4ZDL5TK7FAAAUEO5XC45HA653W6zS1GA2QVUBXa7XQkJCWaXAQAAajCn0ymn0ymHw2F2KRwBBAAAsBoCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMVUiVfB5ebmav78+YqPj1e9evWUkZGhoUOHaubMmQoJCSnVGL/++qseffRR7d27V4ZhKCMjQ0OGDNE999yjhg0bVvAeAAAAVB9VIgBOmTJFGzZsUGJioux2u06ePKnY2Fjt2bNHGzdulM1mK3H9Y8eO6brrrtNLL72kLl26SJL27t2rrl276o033tDHH3+sRo0aVcKeAAAAVH2mnwLesWOHli5dqvvvv192u12SVKdOHc2ZM0ebNm3S6tWrLzjG6tWr9cUXX2jWrFmetrZt2+rqq6/W3r179dprr1VY/QAAANWN6QFw5cqVkqTrr7/eq33o0KEKDg7W8uXLLzhGdHS0wsPDFRER4dWemZkpSapdu7aPqgUAAKj+TD8FvH37doWFhRUKb4GBgWrbtq127typ3Nxc+fv7FztGXFycUlNTvdqys7O1Z88eNWjQQDfeeGOF1A4AAFAdmXoEMCcnRwcOHFBYWFiR/WFhYTpz5oyOHTtWpnFzc3N13333KSAgQAkJCapfv74vygUAAKgRTD0CmJaWptzc3BIDoCSlpKSoWbNmFxxv9+7duvfee3X06FHl5ORow4YNuvLKK31aMwAAQHVnagDMysqSpGIf9RIYGChJSk9PL9V4MTEx+uCDDyRJ/+///T/169dP06ZN04IFC0q8k3jfvn3q37+/5/vBgwdr8ODBpdpmeaWmpioxMbFCt4GiMffmYe7Nwbybh7k3T1Wa+82bN2vz5s2e7wvuUTCTqQEwPDy8xP6MjAxJxQfEkgwfPlxOp1MLFy5UgwYN9Le//a3YZaOiopSQkFDmbVyMxMRE9ezZs1K3iXzMvXmYe3Mw7+Zh7s1Tlea+Z8+eXk8qcTgcJlaTz9RrAENDQ1WrVi3l5eUV2X/q1ClJUr169co1/tVXXy1J+sc//lHsNgAAAKzG9MfAtGrVqtAdvAWSk5MVHBysyMjIYtfPy8vT4MGDFRERof/+979efQVvAElJSVFycrLvigYAAKjGTA+AQ4cO1eHDhwuFwLS0NB06dEi9e/cu8REwJ06c0JYtW+R2u7Vp0yavvpSUFEn5p5AbNGjg++IBAACqIdMD4IgRIyRJa9eu9Wpfs2aNJOm2227zas/MzPQEO0m69NJL1bx5c/Xq1Us333yz17IffvihZ4ySQiQAAICVmB4A+/btK4fDodmzZ+uXX36RJCUlJWnBggWKi4vT2LFjPctmZmYqKipKTZs21e7duz3tS5YsUcOGDZWTk+Np+/nnn/Xaa6+pa9eueuKJJyptfwAAAKo6098EIknx8fF69NFHNXz4cNWtW1fHjx/XNddco/nz53s9viUgIEDNmzeXYRgKDQ31tA8bNkzNmzfXQw89JLfbLZvNpvT0dM2YMUMzZswo113EAAAANVWVCIDBwcGaN2+e5s2bV+JyQUFBxT7TJzo6WqtWraqI8gAAAGoU008BAwAAoHIRAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGACjJ7XbL4XDI5XKZXQoAAKihXC6XHA6H3G632aVUjXcBm81utyshIcHsMgAAQA3mdDrldDrlcDjMLoUAeLE+/fRTTZ48WT/++KNOnTolSbr88svVoEEDzzKnT59W7dq1NWbMGN12223y9/f3GiM5OVlz5szR559/rpCQEOXk5Oiaa67RjBkz9OSTT6pv377q37+/1zp5eXn69ttvtXz5cv3000965513Kn5nAQBAjUAAvEg9evTQZ599prS0NNWrV0+1atXSV199JT8/77Przz//vMaNG6dVq1Z5HW08cuSIevXqpfvuu0/PPPOMbDab8vLy9NJLL8nhcGjnzp3asGGD11g333yzTp06pV69eunZZ59VbGxspewrAACoGbgG0EfCw8MlSYGBgYXCnyTdfffduu6667R161Y9/vjjnva//e1v6tKli6ZMmSKbzSZJ8vPz04QJEzRkyBBlZGQUGis+Pl6bNm3SI488UjE7AwAAajQCYCUaOHCgJOntt9/2tL399ttq165dkctPmzZNTZo0qZTaAACAdRAAK9Hp06clSUFBQZ42Pz8/vfPOO8rNzS20vL+/v0aOHFlp9QEAAGsgAFaSU6dO6cUXX5QkTZkyxdM+ZMgQffvtt3I4HPr6668LrRcdHa3AwMBKqxMAANR8BMBKsG/fPg0bNkzp6el6+eWXdeutt3r6FixYoObNm+vtt9/WFVdcocaNG2vEiBFavHix3G63Jk6cqL59+5pYPQAAqGkIgD6WkZGhuLg4z1efPn3Up08fJScna+nSpRo9erTX8hEREUpMTNSECRMUGhqqpKQkrV+/XjNmzFDbtm21Zs0ak/YEAADUVDwGxsdCQ0P1wQcfFGrftm2bbr75Zj3++OOFQl3Tpk21bNkyLVmyRF9//bU++eQTvfvuu9qwYYNuueUWtW3bVl26dKmcHQAAADUeRwAryYABA7R06VJ99dVXuuqqq3TmzBlJ0rhx4zzLBAQEqFu3bpo8ebISEhK0YsUK5eXlacGCBWaVDQAAaiACYCUaMWKEateurUOHDum9996TlH99YHHGjRunmJgYffnll5VUIQAAsAICYCXy9/dXcHCwJHmOACYmJiopKanYddq0aaOGDRtWSn0AAMAaCICVaPv27UpJSVFgYKB69eolScrOztY999xT5PLnzp3Trl27dOONN1ZmmQAAoIYjAPpIWlqaJBX5QGcp/0jf6NGj5efnp+eff14tWrTw9J0+fVo333yz9u/f72k7e/asJk2apBYtWng9N/B8x44dkySlpqYqJyfHR3sCAABqOu4Cvkiffvqppk6dqh9++EFS/mNgoqOjVb9+fc8yJ06cUEpKigYOHKgZM2boiiuuUGJioiRp8ODBWrdunXbt2qVHHnlER44cUV5enjIyMvTHP/5Rzz//fKEHQU+aNEmffPKJfvzxR0nS119/rUaNGikqKkrjx4/XnXfeWUl7DwAAqiMC4EXq0aOHJ8yVx7vvvitJ6tWrl+e08IUsXbq03NsDAADgFHAFO35cOnBA+r97PgAAAExHAJTkdrvlcDjkcrl8NubWrVL//lKjRlLr1lLDhtKUKdKJEz7bBAAAqEZcLpccDofcbrfZpXAKWJLsdrsSEhJ8Np7LJY0eLdls/2vLzJSWLpXeeUfatctnmwIAANWE0+mU0+mUw+EwuxSOAPraqVPShAn5v8/L8+7LzZUOHpRmz67sqgAAAP6HAOhjLlf+9X6GUXR/bq60cmX+rwAAAGYgAPrYjz9KARc4sZ6ZKWVlVU49AAAAv0cA9LGwsMKnfotyoZAIAABQUQiAPnbDDSWf3vXzk/r0kf7vlcAAAACVjgDoY9HR0nXX5Qe9ohiG9MgjlVsTAADA+QiAFWDVKqngDu+AACkwMP+RMJdcIv3nP9LgwebWBwAArI0r0SpA7drSunXS119Lb7yR/2iY9u2lP/1JqlPH7OoAAIDVEQArUHR0/hcAAEBVwilgAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRASW63Ww6HQy6Xy+xSAABADeVyueRwOOR2u80uhTeBSJLdbldCQoLZZQAAgBrM6XTK6XTK4XCYXQpHAAEAAKyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGACjJ7XbL4XDI5XKZXQoAAKihXC6XHA6H3G632aUowOwCqgK73a6EhASzywAAADWY0+mU0+mUw+EwuxSOAAIAAFgNARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiqkwAzM3N1dy5cxUdHa3Y2FjFxMRo5syZysrKKvUYJ06c0NSpUzV48GB169ZN0dHRWrhwobKzsyuwcgAAgOqlyrwLeMqUKdqwYYMSExNlt9t18uRJxcbGas+ePdq4caNsNluJ6584cUIOh0NPP/20rrzySknShg0bdNNNN2nt2rV6//33Vbt27crYFQAAgCqtShwB3LFjh5YuXar7779fdrtdklSnTh3NmTNHmzZt0urVqy84xty5czV9+nRP+JOka6+9VlOmTNGnn36qOXPmVFj9AAAA1UmVCIArV66UJF1//fVe7UOHDlVwcLCWL19+wTHee+89jRs3Tu+9955Xe8GY8fHxPqoWAACgeqsSAXD79u0KCwtTRESEV3tgYKDatm2rnTt3Kjc3t8Qx2rdvr4yMDKWmpnq1X3rppZKko0eP+rZoAACAasr0awBzcnJ04MABNW7cuMj+sLAwnTlzRseOHVOzZs2KHWf16tVKSkpS06ZNvdp/+eUXSVJUVJTvigYAAKjGTD8CmJaWptzcXIWFhRXZX9CekpJS4jj+/v6Fwp8krVq1SpI0ceLEi6wUAACgZjD9CGDBY15CQkKK7A8MDJQkpaenl3nsvXv3atWqVYqNjdXkyZOLXW7fvn3q37+/5/vBgwdr8ODBZd5eWaSmpioxMbFCt4GiMffmYe7Nwbybh7k3T1Wa+82bN2vz5s2e7zMzM02sJp/pATA8PLzE/oyMDEnFB8TinDt3TqNHj1ZMTIzWr1+vgIDidzUqKkoJCQllGv9iJSYmqmfPnpW6TeRj7s3D3JuDeTcPc2+eqjT3PXv21KxZszzfOxwOE6vJZ/op4NDQUNWqVUt5eXlF9p86dUqSVK9evTKNe9dddyk8PFzvvfee6tate9F1AgAA1BSmB0BJatWqVaG7dwskJycrODhYkZGRpR5v9uzZysrK0saNGxUaGiqpahxuBQAAqAqqRAAcOnSoDh8+XCgEpqWl6dChQ+rdu7f8/f1LNdaKFSt05MgRvfLKK57rByXp9ttv92nNAAAA1VWVCIAjRoyQJK1du9arfc2aNZKk2267zas9MzOzyLuCN23apO+//15Lly6Vn9//du3cuXM6c+aMj6sGAAConqpEAOzbt68cDodmz57teW5fUlKSFixYoLi4OI0dO9azbGZmpqKiotS0aVPt3r3b0757926NGjVKGzduVIcOHXTZZZd5vlq3bq0mTZpU9m4BAABUSabfBVwgPj5ejz76qIYPH666devq+PHjuuaaazR//nzZbDbPcgEBAWrevLkMw/Bc3ydJY8aM0alTp/Tjjz8WOX779u0rfB8AAACqgyoTAIODgzVv3jzNmzevxOWCgoKKfK7Pt99+W1GlAQAA1ChV4hQwAAAAKg8BEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgJLcbrccDodcLpfZpQAAgBrK5XLJ4XDI7XabXUrVeRewmex2uxISEswuAwAA1GBOp1NOp1MOh8PsUjgCCAAAYDUEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQElut1sOh0Mul8vsUgAAQA3lcrnkcDjkdrvNLkUBZhdQFdjtdiUkJJhdBgAAqMGcTqecTqccDofZpXAEEAAAwGoIgAAAABZDAAQAALAYAiAAAIDFEAABlNp//vMfxcfHKz09XZJkGIb279+vf/zjHzpw4IDJ1QEASou7gIFq6tNPP9XkyZP1448/6tSpU5Kkyy+/XA0aNPAsc/r0adWuXVtjxozRbbfdJn9/f68xkpOTNWfOHH3++ecKCQlRTk6OrrnmGs2YMUNPPvmk+vbtq/79+3uW37p1q15++WVJUp06dZSVlaXs7GzdfffdatWqVSXsNQDAFwiAQDXVo0cPffbZZ0pLS1O9evVUq1YtffXVV/Lz8z6w//zzz2vcuHFatWqVEhISVKtWLUnSkSNH1KtXL91333165plnZLPZlJeXp5deekkOh0M7d+7Uhg0bCm23TZs2Onz4sGw2m2JjYzV16lRde+21lbLPAADfIAAC1Vx4eLgkKTAwsFD4k6S7775bmzdv1vr16/X4449r7ty5kqS//e1v6tKli6ZMmeJZ1s/PTxMmTNDJkye1efPmIrf33nvvqWXLlj7fDwBA5eEaQMACBg4cKEl6++23PW1vv/222rVrV+Ty06ZNU5MmTSqlNgBA5SMAAhZw+vRpSVJQUJCnzc/PT++8845yc3MLLe/v76+RI0dWWn0AgMpFAARquFOnTunFF1+UJK/TvUOGDNG3334rh8Ohr7/+utB60dHRCgwMLNT+66+/atSoUerVq5diYmI0adIknThxouJ2AADgcwRAoAbbt2+fhg0bpvT0dL388su69dZbPX0LFixQ8+bN9fbbb+uKK65Q48aNNWLECC1evFhut1sTJ05U3759C405f/58LV68WLt27dLOnTu1f/9+xcbGeu5EBgBUfQRAoIbIyMhQXFyc56tPnz7q06ePkpOTtXTpUo0ePdpr+YiICCUmJmrChAkKDQ1VUlKS1q9frxkzZqht27Zas2ZNoW3ccMMNcrlcioiIkCQFBwdr7ty5+u677/T4449Xyn4CAC4eARCoIUJDQ/XBBx94vnbu3KmkpCT961//0uTJkxUTE6O9e/d6rdO0aVMtW7ZMqamp2r17t5577jlde+21OnPmjG655RZ9+eWXXss7HA7VrVvXqy06OlqSPM8HBABUfQRAoIYbMGCAli5dqq+++kpXXXWV54aQcePGeZYJCAhQt27dNHnyZCUkJGjFihXKy8vTggULPMvk5eXpt99+KzR+SEiIpPznCnItIABUDwRAwAJGjBih2rVr69ChQ1q9erWk/OsDizNu3DjFxMR4HQG8++67ZbfbPesXyMjI8Py+qJtGAABVDwEQsAB/f38FBwdL+t8jYRITE5WUlFTsOm3atFHDhg093yclJalWrVqqX7++13Jut1uSFBUVpTp16vi6dABABSAAKv8fMIfDIZfLZXYpQIXYvn27UlJSFBgYqGuuuUaSlJ2drXvuuafI5c+dO6ddu3bpxhtv9LRdddVVeu6553T11Vd7Lbtp0yZJ0l//+tcKqh4AagaXyyWHw+H5j7OZCICS7Ha7EhIS5HQ6zS4FKLO0tDRJKvKBzlL+kb7Ro0fLz89Pzz//vNq2bevpO336tG6++Wbt37/f03b27FlNmjRJLVq08Hpu4Pjx4/Xqq6/qjTfe8LQdPnxYTz75pJxOp+666y4f7xkA1CxOp1MJCQmy2+1ml8K7gIHq6tNPP9XUqVP1ww8/SMq/Fi86OtrrFO2JEyeUkpKigQMHasaMGbriiis8fYMHD9a6deu0a9cuPfLIIzpy5Ijy8vKUkZGhP/7xj3r++ee9rukLDg7W+vXrNXPmTD377LPy9/dXdna2Zs2apQkTJlTejgMALhoBEKimevToocTExHKv/+6770qSevXqpV69epVqndDQUD311FPl3iYAoGrgFDAAAIDFEACBmsQwpPfek26+WbriCmnQIGnFCikz0+zKAABVCKeAgZoiN1caM0ZatUoKCJByciQ/P+n996V//EP64AOpWTOzqwQAVAEcAQRqioULpddey/99Tk7+r3l5+b8eOCCd90gXAIC1EQCBmuDcOenJJ/NPARclJ0fatUv67LPKrQsAUCURAIGa4L//lY4fL3kZf//86wMBAJZHAARqgoJTvSWx2Uq3HACgxiMAAjXBZZdJF3oPb06O1Ldv5dQDAKjSCIBATRASIt11V/5dv0UJCJA6dJBiYyu3LgBAlUQABGqK2bOlAQPyf39+EPTzkxo0kNatyz8NDACwPAIgUFOEhEibNkkvvyz17CldeqnUtm1+MPzmG6l9e7MrBABUETwIGqhJAgOl227L/wIAoBgcAQQAALAYAiAAAIDFEAAlud1uORwOuVwus0sBAAA1lMvlksPhkNvtNrsUrgGUJLvdroSEBLPLAAAANZjT6ZTT6ZTD4TC7FI4AAgAAWA0BEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDADTJ5s2bzS7Bsph78zD35mDezcPcm4e5LxkB0CR8MM3D3JuHuTcH824e5t48zH3JCIDFcLlcZpdQbhVde3UfvyJV97lh7mvu+BWpus8Nc19zx69I1bl2iQBYrOr8B1vd/8Iy9zV3/IpU3eeGua+541ek6j43zL15bIZhGGYXYbZOnTqpTZs2Xm1ut1t2u73CtlmR41fn2qv7+NW59uo+fnWuvaLHr861V/fxq3Pt1X38qlz7zz//rO+++87HFZUNARAAAMBiOAUMAABgMQRAAAAAiyEAAgAAWAwBsJLk5uZq7ty5io6OVmxsrGJiYjRz5kxlZWWVaZzs7GwtWLBAMTExio2NlcPh0BNPPCEu5Syer+b+92P26dNHH3/8sQ8rrXl8MfcnTpzQ1KlTNXjwYHXr1k3R0dFauHChsrOzK7Dyqs8Xc1sRfzesgM+1efh57kMGKsWkSZMMu91uHD582DAMw0hPTze6dOliDB061MjLyyvVGFlZWcaAAQOMQYMGGceOHTMMwzBWrlxpBAYGGq+//nqF1V7d+WLuf2/u3LmGJGPbtm0+rLTmudi5T05ONvr06WN89tlnnraEhAQjODjY6NGjh5GRkVFhtVd1vvhcV8TfDSvgc20efp77DgGwEnz88ceGJOOZZ57xal+/fr0hyXC5XKUaZ9y4cUZUVJTXD4cFCxYYkoyXX37ZpzXXFL6a+/N98cUXRqtWrSz5A6MsfDH306dPN+Lj4wu133vvvYYk47777vNZvdWJL+a2Iv5uWAGfa/Pw89y3CICVYPz48YYk49dff/Vqz87ONoKDg41BgwZdcIwvv/zSsNlsxvPPP1+o7+jRoz6rtabxxdyf7+zZs8YNN9xg/Otf/7LkD4yy8MXcX3755UZoaKixZcsWr/aCfwgiIyN9WXK14Yu59fXfDavgc20efp77FtcAVoLt27crLCxMERERXu2BgYFq27atdu7cqdzc3BLHWLJkiQzD0JAhQwr1NW7c2Kf11iS+mPvzzZkzRzNmzFCtWrV8XWqN44u5b9++vTIyMpSamurVfumll0qSjh496tuiqwlfzK2v/25YBZ9r8/Dz3LcIgBUsJydHBw4cUFhYWJH9YWFhOnPmjI4dO1biOJs2bZKU/wNi8uTJGjhwoDp37qxp06YpLS3N12XXCL6a+wI7d+6Un5+fevfu7csyayRfzf3q1av122+/6aabbvJq/+WXXyRJUVFRvim4GvHF3Pr674ZV8Lk2Dz/PfY8AWMHS0tKUm5tb4odWklJSUoodIysrS4cOHZIkLVy4UPfee6/ef/99JSYm6ptvvtGAAQOUmZnp++KrOV/MfYHMzEz985//1KxZs3xaY03lq7n39/dX06ZNC7WvWrVKkjRx4sSLrLT68cXc+vLvhpXwuTYPP899jwBYwQpuTQ8JCSmyPzAwUJKUnp5e7Bjnf6CbN2+u1q1bS5Jq1aqlRx55RF9++aXmzZvnq5JrDF/MfYGZM2fq4YcfVlBQkO8KrMF8Ofe/t3fvXq1atUqxsbGaPHly+YuspnwxtxX551OT8bk2Dz/PfY8AWMHCw8NL7M/IyJBU/Idaktf/eH5/DWDnzp0lSS+99FI5K6y5fDH3krRlyxY1btxY0dHRviqtxvPV3P/euXPnNHr0aMXExGj9+vUKCAgob4nVli/mtqL+fGo6Ptfm4ee57/Epu4C0tDT16tWrzA+ZfOmllzRw4ECFhoaqVq1aysvLK3K5U6dOSZLq1atX7FhhYWEKDAzUuXPnCl38WqdOHUn5Fw0fP37ccxFxTVAV5j49PV3Lly/Xa6+9VqYaqruqMPdFueuuuxQeHq4333xToaGhZVq3pvDF3FbUn09Nx+faPPw89z0C4AWEh4frhx9+uKgxWrVqVehurwLJyckKDg5WZGRkiWO0a9dO3333nbKzs3XJJZd42v38/ncQt+AQeE1RFeZ+69at+vnnnzVo0CCv9sOHD0uSpk+frvDwcE2ZMkUjR468qFqrkqow9783e/ZsZWVlaePGjZ7PemZmpiXv4PPF3Pr6z8cq+Fybh5/nPmb2c2isYMaMGYYkIyUlxas9NTXVkGTExcVdcIy//OUvhiRj3759Xu0nTpwwJBktWrTwac01hS/mviiPPPKIJZ8bVRa+nPvly5cbd9xxh5Gbm+vVfvPNN/uk1urGF3NbUX83ajo+1+bh57lvcQ1gJRgxYoQkae3atV7ta9askSTddtttXu2ZmZmF7mSaMGGC/Pz8lJiY6NVe8P0dd9zhy5JrDF/MPcrHV3O/adMmff/991q6dKnXEe9z587pzJkzPq66evDF3JZ1DOTjc20efp77mNkJ1CocDodht9uNgwcPGoZhGMeOHTNat25txMXFeb2/8PTp00bTpk2NoKAg4/PPP/ca4/777zcuu+wyIykpyTAMw8jIyDD69+9v9OvXzzh79mzl7Uw144u5/73Jkycbkow33nijQmuv7i527j///HMjLCzMaN++faGviIgIY+LEiZW+T1WFLz7XpR0D3vhcm4ef575DAKwkWVlZxt///nfj8ssvN/r27Wu0a9fOuPvuu4309HSv5c6ePWv06NHDaNKkifHDDz8UGufZZ581unfvbsTGxhrdu3c3Zs+ebZw5c6aydqNa8tXcG4ZhzJo1y2jTpo0hyZBkBAUFGR07djTeeuutytiVaudi575Tp06euS7qa9GiRZW9S1WGLz7XpR0D3vhcm4ef575jMwzDqNxjjgAAADAT1wACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQQI106NAh9erVSw0aNJDNZpPNZtP8+fMvuF5GRoYaNmwom80mf39/derUSbNmzdKdd96pdu3aecYKDAxUv379FBcXp7i4OPXq1UsdOnRQQECAbDabunbt6hlzzpw56t27t5o2bSqbzaZ69erp119/LbTthx9+WHa73bMNu92uhx9+2KfzAgCSxKvgANRo2dnZ6t27t44fPy5/f3/t379fNput2OWXL1+ut956Sxs3btSTTz6p6dOne/VPnz5dTz/9tEaNGqXXX3+90PqnTp3SAw88oNdee02pqaleff/+97+1cOFC/fe//9WgQYO0ZcuWQrUYhqGJEyfKZrPpxRdfLLFWACgvjgACqNGCgoIUFhamsWPH6uDBg3rvvfdKXH7v3r2KioqSJIWHhxfqL2gLCQkpcv2wsDA999xzstvtSk9PL9R/zz336Morr9TWrVv13HPPFeq32Wzq16+f+vbtS/gDUGEIgAAsYdy4cbLZbFq+fHmxy3z77bfq3LnzRW/LZrPp2muv1S+//FKoLyAgQK+88opCQkL0wAMP6Keffrro7QFAWREAAVhCy5YtNWjQIL311ls6ceJEkcu88cYbuvHGG8s1/k8//aTZs2d7vu/evbuOHj1a5LIdOnTQ/PnzlZmZqdtuu025ubnl2iYAlBcBEIBljB8/XtnZ2XrllVcK9Z09e1a5ubm65JJLyjX278PeDTfcoMGDBxe7/LRp0xQXF6fExEQtWLCgXNsEgPIiAAKwjOuvv17169fXihUrCvW99dZbGjFiRLnGPXv2rJYsWVKmdWw2m/7973+rTp06mjNnjr788stybRsAyoMACMAygoODdeutt+q7777TJ5984tW3e/duxcTElHqsd955R3FxcerTp4+aNm1a5B3BFxIZGamnnnpK586d02233aazZ8+WeQwAKA8CIABLmTBhgiR53Qxy8OBBtWzZskzjXHPNNfrggw+0c+dOJScn66GHHipXPbfffrscDoe++eYbnvkHoNIQAAFYSufOndW9e3fFx8crIyNDkvTaa6/J6XSWe0w/Pz+NHj26UPucOXNKtf6LL76ohg0batGiRdqxY0e56wCA0iIAArCc8ePHKyMjQy6XS3l5eUpPT1e9evUuaswOHTp43QUsSd99912p1m3cuLFeeOEF5eXlacyYMTp9+vRF1QIAF0IABGA5TqdTtWrV0vLly/XOO+9oyJAhPt/G/v37FRYWVurlb7jhBt166636+eef9fjjj/u8HgA4HwEQgOXUqVNHN910kz799FMtX75cAwYM8Pk2lixZoo4dO5ZpnWeffVYRERH67bfffF4PAJyPAAigRvv555+1e/du/fzzz17tBTeDxMTEFHrlWlpamtevRfXl5OQUub3MzEwtXrxYTz75pDp06OBpNwxDH3/8sXbs2KHiXsEeHh6ulStX8go4ABXOZhT3kwgAqrFDhw5pxIgR+uabb5STk6OAgAB17txZb731llq0aCFJuvLKK7V+/XrZ7XZJ0ujRo5WYmOgJi35+furQoYOuv/56JSUl6YMPPvC8ui0oKEhXXnmlAgMDJeUHwrS0NO3du1fZ2dmSpAMHDqhly5Z6+OGHtWLFCs+RvWbNmmn8+PF69NFHi6z9L3/5i3r06KFbb7214iYIgKURAAEAACyGU8AAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBi/j9Um0Eoi4N6ZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 504) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 104) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 2.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 50) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 10) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 0.2$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN',  fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_low[(df_low['Rebalancing_freq'] == 121) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "res_2 = df_low[(df_low['Rebalancing_freq'] == 26) & (df_low['Volatility'] == 0.2) & (df_low['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 0.5$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High transaction resutls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-3.179776</td>\n",
       "      <td>1.107326</td>\n",
       "      <td>-3.201862</td>\n",
       "      <td>1.251742</td>\n",
       "      <td>1.696863</td>\n",
       "      <td>0.765794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.281686</td>\n",
       "      <td>0.490345</td>\n",
       "      <td>-0.999273</td>\n",
       "      <td>0.532805</td>\n",
       "      <td>0.864241</td>\n",
       "      <td>0.647094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.513890</td>\n",
       "      <td>0.367200</td>\n",
       "      <td>-0.572396</td>\n",
       "      <td>0.382615</td>\n",
       "      <td>0.266314</td>\n",
       "      <td>0.118841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.226612</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>-0.224317</td>\n",
       "      <td>0.145728</td>\n",
       "      <td>0.105470</td>\n",
       "      <td>0.089601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.542040</td>\n",
       "      <td>0.661163</td>\n",
       "      <td>-1.608369</td>\n",
       "      <td>0.685119</td>\n",
       "      <td>0.670254</td>\n",
       "      <td>0.262217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.698181</td>\n",
       "      <td>0.280408</td>\n",
       "      <td>-0.528852</td>\n",
       "      <td>0.251133</td>\n",
       "      <td>0.299984</td>\n",
       "      <td>0.211625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-5.548541</td>\n",
       "      <td>2.920519</td>\n",
       "      <td>-5.507856</td>\n",
       "      <td>2.698322</td>\n",
       "      <td>4.291693</td>\n",
       "      <td>2.295564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-2.432443</td>\n",
       "      <td>1.152548</td>\n",
       "      <td>-1.679675</td>\n",
       "      <td>1.062955</td>\n",
       "      <td>2.094416</td>\n",
       "      <td>1.686929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-8.628951</td>\n",
       "      <td>5.638791</td>\n",
       "      <td>-7.931971</td>\n",
       "      <td>5.358684</td>\n",
       "      <td>9.672870</td>\n",
       "      <td>6.603595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-3.908757</td>\n",
       "      <td>2.705555</td>\n",
       "      <td>-2.522668</td>\n",
       "      <td>1.986643</td>\n",
       "      <td>5.832255</td>\n",
       "      <td>5.080264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-6.244052</td>\n",
       "      <td>2.221014</td>\n",
       "      <td>-6.404046</td>\n",
       "      <td>2.122349</td>\n",
       "      <td>3.107585</td>\n",
       "      <td>0.903637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-2.740028</td>\n",
       "      <td>0.913328</td>\n",
       "      <td>-2.577313</td>\n",
       "      <td>0.900344</td>\n",
       "      <td>1.320448</td>\n",
       "      <td>0.883725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-2.023858</td>\n",
       "      <td>0.719476</td>\n",
       "      <td>-2.092943</td>\n",
       "      <td>0.705339</td>\n",
       "      <td>1.427431</td>\n",
       "      <td>0.577799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.859556</td>\n",
       "      <td>0.345447</td>\n",
       "      <td>-0.346787</td>\n",
       "      <td>0.443058</td>\n",
       "      <td>0.691861</td>\n",
       "      <td>0.562149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.520854</td>\n",
       "      <td>0.606139</td>\n",
       "      <td>-1.420447</td>\n",
       "      <td>0.498862</td>\n",
       "      <td>0.914120</td>\n",
       "      <td>0.646855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.701880</td>\n",
       "      <td>0.281909</td>\n",
       "      <td>-0.202671</td>\n",
       "      <td>0.548996</td>\n",
       "      <td>0.481034</td>\n",
       "      <td>0.550701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>504</td>\n",
       "      <td>-3.251773</td>\n",
       "      <td>1.232440</td>\n",
       "      <td>-3.190116</td>\n",
       "      <td>1.196275</td>\n",
       "      <td>2.019728</td>\n",
       "      <td>0.836070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>-1.384359</td>\n",
       "      <td>0.527870</td>\n",
       "      <td>-1.012320</td>\n",
       "      <td>0.434218</td>\n",
       "      <td>0.873667</td>\n",
       "      <td>0.542169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>121</td>\n",
       "      <td>-3.126790</td>\n",
       "      <td>1.075376</td>\n",
       "      <td>-2.722059</td>\n",
       "      <td>1.117226</td>\n",
       "      <td>1.674434</td>\n",
       "      <td>0.909251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "      <td>-1.342410</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>-0.846998</td>\n",
       "      <td>0.427003</td>\n",
       "      <td>0.812656</td>\n",
       "      <td>0.782751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>50</td>\n",
       "      <td>-3.212788</td>\n",
       "      <td>1.099713</td>\n",
       "      <td>-3.201199</td>\n",
       "      <td>0.930251</td>\n",
       "      <td>2.029560</td>\n",
       "      <td>0.842430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>-1.135463</td>\n",
       "      <td>0.523035</td>\n",
       "      <td>-0.602196</td>\n",
       "      <td>0.528863</td>\n",
       "      <td>0.842800</td>\n",
       "      <td>0.907893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "0      100         0.2       1.0               252  -3.179776     1.107326   \n",
       "1      100         0.2       1.0                52  -1.281686     0.490345   \n",
       "2       80         0.2       1.0               252  -0.513890     0.367200   \n",
       "3       80         0.2       1.0                52  -0.226612     0.167000   \n",
       "4       90         0.2       1.0               252  -1.542040     0.661163   \n",
       "5       90         0.2       1.0                52  -0.698181     0.280408   \n",
       "6      110         0.2       1.0               252  -5.548541     2.920519   \n",
       "7      110         0.2       1.0                52  -2.432443     1.152548   \n",
       "8      120         0.2       1.0               252  -8.628951     5.638791   \n",
       "9      120         0.2       1.0                52  -3.908757     2.705555   \n",
       "10     100         0.1       1.0               252  -6.244052     2.221014   \n",
       "11     100         0.1       1.0                52  -2.740028     0.913328   \n",
       "12     100         0.3       1.0               252  -2.023858     0.719476   \n",
       "13     100         0.3       1.0                52  -0.859556     0.345447   \n",
       "14     100         0.4       1.0               252  -1.520854     0.606139   \n",
       "15     100         0.4       1.0                52  -0.701880     0.281909   \n",
       "16     100         0.2       2.0               504  -3.251773     1.232440   \n",
       "17     100         0.2       2.0               104  -1.384359     0.527870   \n",
       "18     100         0.2       0.5               121  -3.126790     1.075376   \n",
       "19     100         0.2       0.5                26  -1.342410     0.470356   \n",
       "20     100         0.2       0.2                50  -3.212788     1.099713   \n",
       "21     100         0.2       0.2                10  -1.135463     0.523035   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "0        -3.201862          1.251742    1.696863      0.765794  \n",
       "1        -0.999273          0.532805    0.864241      0.647094  \n",
       "2        -0.572396          0.382615    0.266314      0.118841  \n",
       "3        -0.224317          0.145728    0.105470      0.089601  \n",
       "4        -1.608369          0.685119    0.670254      0.262217  \n",
       "5        -0.528852          0.251133    0.299984      0.211625  \n",
       "6        -5.507856          2.698322    4.291693      2.295564  \n",
       "7        -1.679675          1.062955    2.094416      1.686929  \n",
       "8        -7.931971          5.358684    9.672870      6.603595  \n",
       "9        -2.522668          1.986643    5.832255      5.080264  \n",
       "10       -6.404046          2.122349    3.107585      0.903637  \n",
       "11       -2.577313          0.900344    1.320448      0.883725  \n",
       "12       -2.092943          0.705339    1.427431      0.577799  \n",
       "13       -0.346787          0.443058    0.691861      0.562149  \n",
       "14       -1.420447          0.498862    0.914120      0.646855  \n",
       "15       -0.202671          0.548996    0.481034      0.550701  \n",
       "16       -3.190116          1.196275    2.019728      0.836070  \n",
       "17       -1.012320          0.434218    0.873667      0.542169  \n",
       "18       -2.722059          1.117226    1.674434      0.909251  \n",
       "19       -0.846998          0.427003    0.812656      0.782751  \n",
       "20       -3.201199          0.930251    2.029560      0.842430  \n",
       "21       -0.602196          0.528863    0.842800      0.907893  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high = pd.merge(df_bs_high, df_wilmott_high, how='right', on=keys).merge(df_td3_high, how='right', on=keys)\n",
    "df_high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different strikes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-3.179776</td>\n",
       "      <td>1.107326</td>\n",
       "      <td>-3.201862</td>\n",
       "      <td>1.251742</td>\n",
       "      <td>1.696863</td>\n",
       "      <td>0.765794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.513890</td>\n",
       "      <td>0.367200</td>\n",
       "      <td>-0.572396</td>\n",
       "      <td>0.382615</td>\n",
       "      <td>0.266314</td>\n",
       "      <td>0.118841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.542040</td>\n",
       "      <td>0.661163</td>\n",
       "      <td>-1.608369</td>\n",
       "      <td>0.685119</td>\n",
       "      <td>0.670254</td>\n",
       "      <td>0.262217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-5.548541</td>\n",
       "      <td>2.920519</td>\n",
       "      <td>-5.507856</td>\n",
       "      <td>2.698322</td>\n",
       "      <td>4.291693</td>\n",
       "      <td>2.295564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-8.628951</td>\n",
       "      <td>5.638791</td>\n",
       "      <td>-7.931971</td>\n",
       "      <td>5.358684</td>\n",
       "      <td>9.672870</td>\n",
       "      <td>6.603595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "0     100         0.2       1.0               252  -3.179776     1.107326   \n",
       "2      80         0.2       1.0               252  -0.513890     0.367200   \n",
       "4      90         0.2       1.0               252  -1.542040     0.661163   \n",
       "6     110         0.2       1.0               252  -5.548541     2.920519   \n",
       "8     120         0.2       1.0               252  -8.628951     5.638791   \n",
       "\n",
       "   E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "0       -3.201862          1.251742    1.696863      0.765794  \n",
       "2       -0.572396          0.382615    0.266314      0.118841  \n",
       "4       -1.608369          0.685119    0.670254      0.262217  \n",
       "6       -5.507856          2.698322    4.291693      2.295564  \n",
       "8       -7.931971          5.358684    9.672870      6.603595  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.281686</td>\n",
       "      <td>0.490345</td>\n",
       "      <td>-0.999273</td>\n",
       "      <td>0.532805</td>\n",
       "      <td>0.864241</td>\n",
       "      <td>0.647094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.226612</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>-0.224317</td>\n",
       "      <td>0.145728</td>\n",
       "      <td>0.105470</td>\n",
       "      <td>0.089601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.698181</td>\n",
       "      <td>0.280408</td>\n",
       "      <td>-0.528852</td>\n",
       "      <td>0.251133</td>\n",
       "      <td>0.299984</td>\n",
       "      <td>0.211625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-2.432443</td>\n",
       "      <td>1.152548</td>\n",
       "      <td>-1.679675</td>\n",
       "      <td>1.062955</td>\n",
       "      <td>2.094416</td>\n",
       "      <td>1.686929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-3.908757</td>\n",
       "      <td>2.705555</td>\n",
       "      <td>-2.522668</td>\n",
       "      <td>1.986643</td>\n",
       "      <td>5.832255</td>\n",
       "      <td>5.080264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "1     100         0.2       1.0                52  -1.281686     0.490345   \n",
       "3      80         0.2       1.0                52  -0.226612     0.167000   \n",
       "5      90         0.2       1.0                52  -0.698181     0.280408   \n",
       "7     110         0.2       1.0                52  -2.432443     1.152548   \n",
       "9     120         0.2       1.0                52  -3.908757     2.705555   \n",
       "\n",
       "   E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "1       -0.999273          0.532805    0.864241      0.647094  \n",
       "3       -0.224317          0.145728    0.105470      0.089601  \n",
       "5       -0.528852          0.251133    0.299984      0.211625  \n",
       "7       -1.679675          1.062955    2.094416      1.686929  \n",
       "9       -2.522668          1.986643    5.832255      5.080264  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 120)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 120)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 120, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 80)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 80)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 80, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 90)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 90)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 90, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 110)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 110)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 110, \\sigma = 0.2, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different volatilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-6.244052</td>\n",
       "      <td>2.221014</td>\n",
       "      <td>-6.404046</td>\n",
       "      <td>2.122349</td>\n",
       "      <td>3.107585</td>\n",
       "      <td>0.903637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-2.023858</td>\n",
       "      <td>0.719476</td>\n",
       "      <td>-2.092943</td>\n",
       "      <td>0.705339</td>\n",
       "      <td>1.427431</td>\n",
       "      <td>0.577799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252</td>\n",
       "      <td>-1.520854</td>\n",
       "      <td>0.606139</td>\n",
       "      <td>-1.420447</td>\n",
       "      <td>0.498862</td>\n",
       "      <td>0.914120</td>\n",
       "      <td>0.646855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "10     100         0.1       1.0               252  -6.244052     2.221014   \n",
       "12     100         0.3       1.0               252  -2.023858     0.719476   \n",
       "14     100         0.4       1.0               252  -1.520854     0.606139   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "10       -6.404046          2.122349    3.107585      0.903637  \n",
       "12       -2.092943          0.705339    1.427431      0.577799  \n",
       "14       -1.420447          0.498862    0.914120      0.646855  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] != 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-2.740028</td>\n",
       "      <td>0.913328</td>\n",
       "      <td>-2.577313</td>\n",
       "      <td>0.900344</td>\n",
       "      <td>1.320448</td>\n",
       "      <td>0.883725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.859556</td>\n",
       "      <td>0.345447</td>\n",
       "      <td>-0.346787</td>\n",
       "      <td>0.443058</td>\n",
       "      <td>0.691861</td>\n",
       "      <td>0.562149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-0.701880</td>\n",
       "      <td>0.281909</td>\n",
       "      <td>-0.202671</td>\n",
       "      <td>0.548996</td>\n",
       "      <td>0.481034</td>\n",
       "      <td>0.550701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "11     100         0.1       1.0                52  -2.740028     0.913328   \n",
       "13     100         0.3       1.0                52  -0.859556     0.345447   \n",
       "15     100         0.4       1.0                52  -0.701880     0.281909   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "11       -2.577313          0.900344    1.320448      0.883725  \n",
       "13       -0.346787          0.443058    0.691861      0.562149  \n",
       "15       -0.202671          0.548996    0.481034      0.550701  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] != 0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.1) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.1) & (df_high['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.1, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.3) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.3) & (df_high['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.3, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 252) & (df_high['Volatility'] == 0.4) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 52) & (df_high['Volatility'] == 0.4) & (df_high['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.4, T = 1.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different maturities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>504</td>\n",
       "      <td>-3.251773</td>\n",
       "      <td>1.232440</td>\n",
       "      <td>-3.190116</td>\n",
       "      <td>1.196275</td>\n",
       "      <td>2.019728</td>\n",
       "      <td>0.836070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>121</td>\n",
       "      <td>-3.126790</td>\n",
       "      <td>1.075376</td>\n",
       "      <td>-2.722059</td>\n",
       "      <td>1.117226</td>\n",
       "      <td>1.674434</td>\n",
       "      <td>0.909251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>50</td>\n",
       "      <td>-3.212788</td>\n",
       "      <td>1.099713</td>\n",
       "      <td>-3.201199</td>\n",
       "      <td>0.930251</td>\n",
       "      <td>2.029560</td>\n",
       "      <td>0.842430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "16     100         0.2       2.0               504  -3.251773     1.232440   \n",
       "18     100         0.2       0.5               121  -3.126790     1.075376   \n",
       "20     100         0.2       0.2                50  -3.212788     1.099713   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "16       -3.190116          1.196275    2.019728      0.836070  \n",
       "18       -2.722059          1.117226    1.674434      0.909251  \n",
       "20       -3.201199          0.930251    2.029560      0.842430  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high[(df_high['Rebalancing_freq'].isin([504, 121, 50]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### weekly rebalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Strike</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Rebalancing_freq</th>\n",
       "      <th>E_PL_T_bs</th>\n",
       "      <th>Std_PL_T_bs</th>\n",
       "      <th>E_PL_T_wilmott</th>\n",
       "      <th>Std_PL_T_wilmott</th>\n",
       "      <th>E_PL_T_td3</th>\n",
       "      <th>Std_PL_T_td3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>104</td>\n",
       "      <td>-1.384359</td>\n",
       "      <td>0.527870</td>\n",
       "      <td>-1.012320</td>\n",
       "      <td>0.434218</td>\n",
       "      <td>0.873667</td>\n",
       "      <td>0.542169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "      <td>-1.342410</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>-0.846998</td>\n",
       "      <td>0.427003</td>\n",
       "      <td>0.812656</td>\n",
       "      <td>0.782751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>-1.135463</td>\n",
       "      <td>0.523035</td>\n",
       "      <td>-0.602196</td>\n",
       "      <td>0.528863</td>\n",
       "      <td>0.842800</td>\n",
       "      <td>0.907893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Strike  Volatility  Maturity  Rebalancing_freq  E_PL_T_bs  Std_PL_T_bs  \\\n",
       "17     100         0.2       2.0               104  -1.384359     0.527870   \n",
       "19     100         0.2       0.5                26  -1.342410     0.470356   \n",
       "21     100         0.2       0.2                10  -1.135463     0.523035   \n",
       "\n",
       "    E_PL_T_wilmott  Std_PL_T_wilmott  E_PL_T_td3  Std_PL_T_td3  \n",
       "17       -1.012320          0.434218    0.873667      0.542169  \n",
       "19       -0.846998          0.427003    0.812656      0.782751  \n",
       "21       -0.602196          0.528863    0.842800      0.907893  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_high[(df_high['Rebalancing_freq'].isin([104, 26, 10]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 504) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 104) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 2.0$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 50) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 10) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 0.2$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_1 = df_high[(df_high['Rebalancing_freq'] == 121) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "res_2 = df_high[(df_high['Rebalancing_freq'] == 26) & (df_high['Volatility'] == 0.2) & (df_high['Strike'] == 100)]\n",
    "\n",
    "x_1 = res_1[[col for col in res_1.columns if col.startswith('E')]].values[0]\n",
    "y_1 = res_1[[col for col in res_1.columns if col.startswith('Std')]].values[0]\n",
    "x_2 = res_2[[col for col in res_2.columns if col.startswith('E')]].values[0]\n",
    "y_2 = res_2[[col for col in res_2.columns if col.startswith('Std')]].values[0]\n",
    "\n",
    "x = np.concatenate((x_1, x_2))\n",
    "y = np.concatenate((y_1, y_2))\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.scatter(x, y, c=['blue', 'blue', 'blue', 'red', 'red', 'red'])\n",
    "text_labels = ['BS1', 'W1', 'TD3 1', 'BS5', 'W5', 'TD3 5']\n",
    "\n",
    "for i, txt in enumerate(text_labels):\n",
    "  plt.annotate(txt, (x[i], y[i]), xytext=(-7, 5), textcoords=\"offset points\", size=SMALL_SIZE)\n",
    "\n",
    "plt.title(r'$K = 100, \\sigma = 0.2, T = 0.5$', fontdict=FONT)\n",
    "plt.xlabel('MEN', fontdict=FONT)\n",
    "plt.ylabel('SEN', fontdict=FONT)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
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