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ngjuping/mkt_neutral.ipynb

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Created July 27, 2023 13:41
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Market Neutral Portfolio
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mkt_neutral.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Market Neutral portfolio using cvxpy optimizer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A market neutral portfolio is a portfolio which shows no correlation with Market movements <br>\n",
"This translates to having <br>\n",
"<br>\n",
"$\\beta_{mkt}=0$ <br>\n",
"<br>\n",
"It is possible to achieve this via an optimizer. In this notebook I am going to show how to leverage on cvxpy optimizer to calculate optimal weight of an (ex-ante) market neutral portfolio. <br>\n",
"To do this, I am going to use data from the Kenneth French library <br>\n",
"https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import packages\n",
"import pandas as pd\n",
"import numpy as np\n",
"import cvxpy as cvx\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) FF Factors' monthly returns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mkt-RF</th>\n",
" <th>SMB</th>\n",
" <th>HML</th>\n",
" <th>RF</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-02-29</th>\n",
" <td>0.025329</td>\n",
" <td>0.215711</td>\n",
" <td>-0.082808</td>\n",
" <td>0.004409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-03-31</th>\n",
" <td>0.048663</td>\n",
" <td>-0.158693</td>\n",
" <td>0.081433</td>\n",
" <td>0.004610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-04-30</th>\n",
" <td>-0.063736</td>\n",
" <td>-0.065641</td>\n",
" <td>0.074036</td>\n",
" <td>0.004570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-05-31</th>\n",
" <td>-0.043589</td>\n",
" <td>-0.061786</td>\n",
" <td>0.045786</td>\n",
" <td>0.005072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-06-30</th>\n",
" <td>0.046886</td>\n",
" <td>0.131117</td>\n",
" <td>-0.077924</td>\n",
" <td>0.003967</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Mkt-RF SMB HML RF\n",
"Date \n",
"2000-02-29 0.025329 0.215711 -0.082808 0.004409\n",
"2000-03-31 0.048663 -0.158693 0.081433 0.004610\n",
"2000-04-30 -0.063736 -0.065641 0.074036 0.004570\n",
"2000-05-31 -0.043589 -0.061786 0.045786 0.005072\n",
"2000-06-30 0.046886 0.131117 -0.077924 0.003967"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. FF Factors' monthly returns\n",
"factors = pd.read_excel(\"../FF_monthly.xlsx\")\n",
"factors.set_index(\"Date\", inplace=True)\n",
"factors.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date\n",
"2000-02-29 0.029738\n",
"2000-03-31 0.053274\n",
"2000-04-30 -0.059166\n",
"2000-05-31 -0.038517\n",
"2000-06-30 0.050853\n",
"Name: Mkt, dtype: float64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mkt_return = factors[\"Mkt-RF\"] + factors[\"RF\"] # get mkt return by ading back risk free to excess return\n",
"mkt_return.name = \"Mkt\"\n",
"mkt_return.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) 49 industry portfolios"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Agric</th>\n",
" <th>Food</th>\n",
" <th>Soda</th>\n",
" <th>Beer</th>\n",
" <th>Smoke</th>\n",
" <th>Toys</th>\n",
" <th>Fun</th>\n",
" <th>Books</th>\n",
" <th>Hshld</th>\n",
" <th>Clths</th>\n",
" <th>...</th>\n",
" <th>Boxes</th>\n",
" <th>Trans</th>\n",
" <th>Whlsl</th>\n",
" <th>Rtail</th>\n",
" <th>Meals</th>\n",
" <th>Banks</th>\n",
" <th>Insur</th>\n",
" <th>RlEst</th>\n",
" <th>Fin</th>\n",
" <th>Other</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-02-29</th>\n",
" <td>0.068472</td>\n",
" <td>-0.071657</td>\n",
" <td>-0.086558</td>\n",
" <td>-0.116859</td>\n",
" <td>-0.039824</td>\n",
" <td>0.004201</td>\n",
" <td>-0.033265</td>\n",
" <td>-0.002529</td>\n",
" <td>-0.116674</td>\n",
" <td>-0.105813</td>\n",
" <td>...</td>\n",
" <td>-0.127882</td>\n",
" <td>-0.052703</td>\n",
" <td>0.016575</td>\n",
" <td>-0.041647</td>\n",
" <td>-0.120569</td>\n",
" <td>-0.113040</td>\n",
" <td>-0.131426</td>\n",
" <td>0.020556</td>\n",
" <td>0.066967</td>\n",
" <td>-0.015026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-03-31</th>\n",
" <td>0.043484</td>\n",
" <td>0.104341</td>\n",
" <td>-0.003911</td>\n",
" <td>0.001821</td>\n",
" <td>0.052309</td>\n",
" <td>0.078418</td>\n",
" <td>0.111058</td>\n",
" <td>0.126672</td>\n",
" <td>-0.147599</td>\n",
" <td>0.250240</td>\n",
" <td>...</td>\n",
" <td>0.127391</td>\n",
" <td>0.127595</td>\n",
" <td>0.069853</td>\n",
" <td>0.144585</td>\n",
" <td>0.160870</td>\n",
" <td>0.150960</td>\n",
" <td>0.233528</td>\n",
" <td>0.042740</td>\n",
" <td>0.149051</td>\n",
" <td>-0.015447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-04-30</th>\n",
" <td>-0.058529</td>\n",
" <td>-0.051005</td>\n",
" <td>-0.005994</td>\n",
" <td>0.036583</td>\n",
" <td>0.038363</td>\n",
" <td>0.001111</td>\n",
" <td>0.044316</td>\n",
" <td>-0.077032</td>\n",
" <td>0.047017</td>\n",
" <td>0.036836</td>\n",
" <td>...</td>\n",
" <td>-0.082692</td>\n",
" <td>0.034616</td>\n",
" <td>-0.030021</td>\n",
" <td>-0.054818</td>\n",
" <td>0.037121</td>\n",
" <td>-0.028531</td>\n",
" <td>0.000034</td>\n",
" <td>-0.025197</td>\n",
" <td>-0.112271</td>\n",
" <td>0.088046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-05-31</th>\n",
" <td>-0.011313</td>\n",
" <td>0.173636</td>\n",
" <td>-0.073239</td>\n",
" <td>0.119248</td>\n",
" <td>0.194693</td>\n",
" <td>0.010906</td>\n",
" <td>0.013244</td>\n",
" <td>-0.064416</td>\n",
" <td>0.029127</td>\n",
" <td>-0.053616</td>\n",
" <td>...</td>\n",
" <td>-0.025352</td>\n",
" <td>-0.036809</td>\n",
" <td>0.013387</td>\n",
" <td>-0.031333</td>\n",
" <td>-0.042611</td>\n",
" <td>0.079685</td>\n",
" <td>0.062347</td>\n",
" <td>-0.032784</td>\n",
" <td>-0.065917</td>\n",
" <td>0.104813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-06-30</th>\n",
" <td>0.002221</td>\n",
" <td>0.017665</td>\n",
" <td>0.013378</td>\n",
" <td>0.060779</td>\n",
" <td>0.030101</td>\n",
" <td>-0.016968</td>\n",
" <td>-0.009577</td>\n",
" <td>-0.003258</td>\n",
" <td>-0.029174</td>\n",
" <td>-0.077753</td>\n",
" <td>...</td>\n",
" <td>-0.030232</td>\n",
" <td>-0.018448</td>\n",
" <td>-0.024008</td>\n",
" <td>-0.019798</td>\n",
" <td>-0.039315</td>\n",
" <td>-0.099044</td>\n",
" <td>-0.027761</td>\n",
" <td>-0.014854</td>\n",
" <td>0.143584</td>\n",
" <td>0.029686</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 49 columns</p>\n",
"</div>"
],
"text/plain": [
" Agric Food Soda Beer Smoke Toys \\\n",
"Date \n",
"2000-02-29 0.068472 -0.071657 -0.086558 -0.116859 -0.039824 0.004201 \n",
"2000-03-31 0.043484 0.104341 -0.003911 0.001821 0.052309 0.078418 \n",
"2000-04-30 -0.058529 -0.051005 -0.005994 0.036583 0.038363 0.001111 \n",
"2000-05-31 -0.011313 0.173636 -0.073239 0.119248 0.194693 0.010906 \n",
"2000-06-30 0.002221 0.017665 0.013378 0.060779 0.030101 -0.016968 \n",
"\n",
" Fun Books Hshld Clths ... Boxes Trans \\\n",
"Date ... \n",
"2000-02-29 -0.033265 -0.002529 -0.116674 -0.105813 ... -0.127882 -0.052703 \n",
"2000-03-31 0.111058 0.126672 -0.147599 0.250240 ... 0.127391 0.127595 \n",
"2000-04-30 0.044316 -0.077032 0.047017 0.036836 ... -0.082692 0.034616 \n",
"2000-05-31 0.013244 -0.064416 0.029127 -0.053616 ... -0.025352 -0.036809 \n",
"2000-06-30 -0.009577 -0.003258 -0.029174 -0.077753 ... -0.030232 -0.018448 \n",
"\n",
" Whlsl Rtail Meals Banks Insur RlEst \\\n",
"Date \n",
"2000-02-29 0.016575 -0.041647 -0.120569 -0.113040 -0.131426 0.020556 \n",
"2000-03-31 0.069853 0.144585 0.160870 0.150960 0.233528 0.042740 \n",
"2000-04-30 -0.030021 -0.054818 0.037121 -0.028531 0.000034 -0.025197 \n",
"2000-05-31 0.013387 -0.031333 -0.042611 0.079685 0.062347 -0.032784 \n",
"2000-06-30 -0.024008 -0.019798 -0.039315 -0.099044 -0.027761 -0.014854 \n",
"\n",
" Fin Other \n",
"Date \n",
"2000-02-29 0.066967 -0.015026 \n",
"2000-03-31 0.149051 -0.015447 \n",
"2000-04-30 -0.112271 0.088046 \n",
"2000-05-31 -0.065917 0.104813 \n",
"2000-06-30 0.143584 0.029686 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"returns = pd.read_excel(\"../49_portfolios.xlsx\")\n",
"returns.set_index(\"Date\", inplace=True)\n",
"returns.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>Beer</th>\n",
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" <th>Toys</th>\n",
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" <th>Books</th>\n",
" <th>Hshld</th>\n",
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" <th>...</th>\n",
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" <th>Whlsl</th>\n",
" <th>Rtail</th>\n",
" <th>Meals</th>\n",
" <th>Banks</th>\n",
" <th>Insur</th>\n",
" <th>RlEst</th>\n",
" <th>Fin</th>\n",
" <th>Other</th>\n",
" <th>Mkt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
" <th>2000-02-29</th>\n",
" <td>0.068472</td>\n",
" <td>-0.071657</td>\n",
" <td>-0.086558</td>\n",
" <td>-0.116859</td>\n",
" <td>-0.039824</td>\n",
" <td>0.004201</td>\n",
" <td>-0.033265</td>\n",
" <td>-0.002529</td>\n",
" <td>-0.116674</td>\n",
" <td>-0.105813</td>\n",
" <td>...</td>\n",
" <td>-0.052703</td>\n",
" <td>0.016575</td>\n",
" <td>-0.041647</td>\n",
" <td>-0.120569</td>\n",
" <td>-0.113040</td>\n",
" <td>-0.131426</td>\n",
" <td>0.020556</td>\n",
" <td>0.066967</td>\n",
" <td>-0.015026</td>\n",
" <td>0.029738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-03-31</th>\n",
" <td>0.043484</td>\n",
" <td>0.104341</td>\n",
" <td>-0.003911</td>\n",
" <td>0.001821</td>\n",
" <td>0.052309</td>\n",
" <td>0.078418</td>\n",
" <td>0.111058</td>\n",
" <td>0.126672</td>\n",
" <td>-0.147599</td>\n",
" <td>0.250240</td>\n",
" <td>...</td>\n",
" <td>0.127595</td>\n",
" <td>0.069853</td>\n",
" <td>0.144585</td>\n",
" <td>0.160870</td>\n",
" <td>0.150960</td>\n",
" <td>0.233528</td>\n",
" <td>0.042740</td>\n",
" <td>0.149051</td>\n",
" <td>-0.015447</td>\n",
" <td>0.053274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-04-30</th>\n",
" <td>-0.058529</td>\n",
" <td>-0.051005</td>\n",
" <td>-0.005994</td>\n",
" <td>0.036583</td>\n",
" <td>0.038363</td>\n",
" <td>0.001111</td>\n",
" <td>0.044316</td>\n",
" <td>-0.077032</td>\n",
" <td>0.047017</td>\n",
" <td>0.036836</td>\n",
" <td>...</td>\n",
" <td>0.034616</td>\n",
" <td>-0.030021</td>\n",
" <td>-0.054818</td>\n",
" <td>0.037121</td>\n",
" <td>-0.028531</td>\n",
" <td>0.000034</td>\n",
" <td>-0.025197</td>\n",
" <td>-0.112271</td>\n",
" <td>0.088046</td>\n",
" <td>-0.059166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-05-31</th>\n",
" <td>-0.011313</td>\n",
" <td>0.173636</td>\n",
" <td>-0.073239</td>\n",
" <td>0.119248</td>\n",
" <td>0.194693</td>\n",
" <td>0.010906</td>\n",
" <td>0.013244</td>\n",
" <td>-0.064416</td>\n",
" <td>0.029127</td>\n",
" <td>-0.053616</td>\n",
" <td>...</td>\n",
" <td>-0.036809</td>\n",
" <td>0.013387</td>\n",
" <td>-0.031333</td>\n",
" <td>-0.042611</td>\n",
" <td>0.079685</td>\n",
" <td>0.062347</td>\n",
" <td>-0.032784</td>\n",
" <td>-0.065917</td>\n",
" <td>0.104813</td>\n",
" <td>-0.038517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-06-30</th>\n",
" <td>0.002221</td>\n",
" <td>0.017665</td>\n",
" <td>0.013378</td>\n",
" <td>0.060779</td>\n",
" <td>0.030101</td>\n",
" <td>-0.016968</td>\n",
" <td>-0.009577</td>\n",
" <td>-0.003258</td>\n",
" <td>-0.029174</td>\n",
" <td>-0.077753</td>\n",
" <td>...</td>\n",
" <td>-0.018448</td>\n",
" <td>-0.024008</td>\n",
" <td>-0.019798</td>\n",
" <td>-0.039315</td>\n",
" <td>-0.099044</td>\n",
" <td>-0.027761</td>\n",
" <td>-0.014854</td>\n",
" <td>0.143584</td>\n",
" <td>0.029686</td>\n",
" <td>0.050853</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" Agric Food Soda Beer Smoke Toys \\\n",
"Date \n",
"2000-02-29 0.068472 -0.071657 -0.086558 -0.116859 -0.039824 0.004201 \n",
"2000-03-31 0.043484 0.104341 -0.003911 0.001821 0.052309 0.078418 \n",
"2000-04-30 -0.058529 -0.051005 -0.005994 0.036583 0.038363 0.001111 \n",
"2000-05-31 -0.011313 0.173636 -0.073239 0.119248 0.194693 0.010906 \n",
"2000-06-30 0.002221 0.017665 0.013378 0.060779 0.030101 -0.016968 \n",
"\n",
" Fun Books Hshld Clths ... Trans Whlsl \\\n",
"Date ... \n",
"2000-02-29 -0.033265 -0.002529 -0.116674 -0.105813 ... -0.052703 0.016575 \n",
"2000-03-31 0.111058 0.126672 -0.147599 0.250240 ... 0.127595 0.069853 \n",
"2000-04-30 0.044316 -0.077032 0.047017 0.036836 ... 0.034616 -0.030021 \n",
"2000-05-31 0.013244 -0.064416 0.029127 -0.053616 ... -0.036809 0.013387 \n",
"2000-06-30 -0.009577 -0.003258 -0.029174 -0.077753 ... -0.018448 -0.024008 \n",
"\n",
" Rtail Meals Banks Insur RlEst Fin \\\n",
"Date \n",
"2000-02-29 -0.041647 -0.120569 -0.113040 -0.131426 0.020556 0.066967 \n",
"2000-03-31 0.144585 0.160870 0.150960 0.233528 0.042740 0.149051 \n",
"2000-04-30 -0.054818 0.037121 -0.028531 0.000034 -0.025197 -0.112271 \n",
"2000-05-31 -0.031333 -0.042611 0.079685 0.062347 -0.032784 -0.065917 \n",
"2000-06-30 -0.019798 -0.039315 -0.099044 -0.027761 -0.014854 0.143584 \n",
"\n",
" Other Mkt \n",
"Date \n",
"2000-02-29 -0.015026 0.029738 \n",
"2000-03-31 -0.015447 0.053274 \n",
"2000-04-30 0.088046 -0.059166 \n",
"2000-05-31 0.104813 -0.038517 \n",
"2000-06-30 0.029686 0.050853 \n",
"\n",
"[5 rows x 50 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Merge datasets\n",
"data = pd.concat([returns, mkt_return], axis=1)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Code Mkt Neutral Optimization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to be able to compute market neutral weights, we need the VCV matrix of the constituents and the betas to market. <br>\n",
"Recall that portfolio beta is equal to the sum of the (weighted) beta of the constituents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VCV and Market Betas"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Agric</th>\n",
" <th>Food</th>\n",
" <th>Soda</th>\n",
" <th>Beer</th>\n",
" <th>Smoke</th>\n",
" <th>Toys</th>\n",
" <th>Fun</th>\n",
" <th>Books</th>\n",
" <th>Hshld</th>\n",
" <th>Clths</th>\n",
" <th>...</th>\n",
" <th>Trans</th>\n",
" <th>Whlsl</th>\n",
" <th>Rtail</th>\n",
" <th>Meals</th>\n",
" <th>Banks</th>\n",
" <th>Insur</th>\n",
" <th>RlEst</th>\n",
" <th>Fin</th>\n",
" <th>Other</th>\n",
" <th>Mkt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Agric</th>\n",
" <td>0.004152</td>\n",
" <td>0.000876</td>\n",
" <td>0.000803</td>\n",
" <td>0.000710</td>\n",
" <td>0.001020</td>\n",
" <td>0.001665</td>\n",
" <td>0.002323</td>\n",
" <td>0.001987</td>\n",
" <td>0.000713</td>\n",
" <td>0.001600</td>\n",
" <td>...</td>\n",
" <td>0.001681</td>\n",
" <td>0.001709</td>\n",
" <td>0.001261</td>\n",
" <td>0.001305</td>\n",
" <td>0.001776</td>\n",
" <td>0.001417</td>\n",
" <td>0.002307</td>\n",
" <td>0.002313</td>\n",
" <td>0.001283</td>\n",
" <td>0.001605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food</th>\n",
" <td>0.000876</td>\n",
" <td>0.001455</td>\n",
" <td>0.001085</td>\n",
" <td>0.001048</td>\n",
" <td>0.001353</td>\n",
" <td>0.001179</td>\n",
" <td>0.001275</td>\n",
" <td>0.001209</td>\n",
" <td>0.000961</td>\n",
" <td>0.001210</td>\n",
" <td>...</td>\n",
" <td>0.001091</td>\n",
" <td>0.001134</td>\n",
" <td>0.000924</td>\n",
" <td>0.001063</td>\n",
" <td>0.001234</td>\n",
" <td>0.001313</td>\n",
" <td>0.001388</td>\n",
" <td>0.001084</td>\n",
" <td>0.000934</td>\n",
" <td>0.000932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soda</th>\n",
" <td>0.000803</td>\n",
" <td>0.001085</td>\n",
" <td>0.003505</td>\n",
" <td>0.001378</td>\n",
" <td>0.001305</td>\n",
" <td>0.001642</td>\n",
" <td>0.002246</td>\n",
" <td>0.001702</td>\n",
" <td>0.001209</td>\n",
" <td>0.001837</td>\n",
" <td>...</td>\n",
" <td>0.001497</td>\n",
" <td>0.001492</td>\n",
" <td>0.001183</td>\n",
" <td>0.001479</td>\n",
" <td>0.001554</td>\n",
" <td>0.001575</td>\n",
" <td>0.002545</td>\n",
" <td>0.001607</td>\n",
" <td>0.001278</td>\n",
" <td>0.001276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beer</th>\n",
" <td>0.000710</td>\n",
" <td>0.001048</td>\n",
" <td>0.001378</td>\n",
" <td>0.001759</td>\n",
" <td>0.001184</td>\n",
" <td>0.000929</td>\n",
" <td>0.001090</td>\n",
" <td>0.000947</td>\n",
" <td>0.001115</td>\n",
" <td>0.001061</td>\n",
" <td>...</td>\n",
" <td>0.001072</td>\n",
" <td>0.000953</td>\n",
" <td>0.000838</td>\n",
" <td>0.001064</td>\n",
" <td>0.001034</td>\n",
" <td>0.001118</td>\n",
" <td>0.001112</td>\n",
" <td>0.000959</td>\n",
" <td>0.000950</td>\n",
" <td>0.000892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Smoke</th>\n",
" <td>0.001020</td>\n",
" <td>0.001353</td>\n",
" <td>0.001305</td>\n",
" <td>0.001184</td>\n",
" <td>0.004206</td>\n",
" <td>0.001347</td>\n",
" <td>0.001376</td>\n",
" <td>0.001383</td>\n",
" <td>0.001185</td>\n",
" <td>0.001235</td>\n",
" <td>...</td>\n",
" <td>0.001174</td>\n",
" <td>0.001399</td>\n",
" <td>0.000630</td>\n",
" <td>0.001145</td>\n",
" <td>0.001549</td>\n",
" <td>0.001374</td>\n",
" <td>0.001612</td>\n",
" <td>0.001346</td>\n",
" <td>0.001368</td>\n",
" <td>0.001119</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" Agric Food Soda Beer Smoke Toys Fun \\\n",
"Agric 0.004152 0.000876 0.000803 0.000710 0.001020 0.001665 0.002323 \n",
"Food 0.000876 0.001455 0.001085 0.001048 0.001353 0.001179 0.001275 \n",
"Soda 0.000803 0.001085 0.003505 0.001378 0.001305 0.001642 0.002246 \n",
"Beer 0.000710 0.001048 0.001378 0.001759 0.001184 0.000929 0.001090 \n",
"Smoke 0.001020 0.001353 0.001305 0.001184 0.004206 0.001347 0.001376 \n",
"\n",
" Books Hshld Clths ... Trans Whlsl Rtail \\\n",
"Agric 0.001987 0.000713 0.001600 ... 0.001681 0.001709 0.001261 \n",
"Food 0.001209 0.000961 0.001210 ... 0.001091 0.001134 0.000924 \n",
"Soda 0.001702 0.001209 0.001837 ... 0.001497 0.001492 0.001183 \n",
"Beer 0.000947 0.001115 0.001061 ... 0.001072 0.000953 0.000838 \n",
"Smoke 0.001383 0.001185 0.001235 ... 0.001174 0.001399 0.000630 \n",
"\n",
" Meals Banks Insur RlEst Fin Other Mkt \n",
"Agric 0.001305 0.001776 0.001417 0.002307 0.002313 0.001283 0.001605 \n",
"Food 0.001063 0.001234 0.001313 0.001388 0.001084 0.000934 0.000932 \n",
"Soda 0.001479 0.001554 0.001575 0.002545 0.001607 0.001278 0.001276 \n",
"Beer 0.001064 0.001034 0.001118 0.001112 0.000959 0.000950 0.000892 \n",
"Smoke 0.001145 0.001549 0.001374 0.001612 0.001346 0.001368 0.001119 \n",
"\n",
"[5 rows x 50 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_covmat = data.cov()\n",
"full_covmat.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The market beta of asset $i$ is computes as: <br>\n",
"<br>\n",
"$\\beta_{mkt,i} = \\sigma_{i, mkt} / \\sigma_{mkt}^2$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Agric 0.760898\n",
"Food 0.442068\n",
"Soda 0.604797\n",
"Beer 0.423020\n",
"Smoke 0.530625\n",
"Name: Mkt, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"betas = full_covmat[\"Mkt\"].drop(\"Mkt\") / full_covmat.loc[\"Mkt\", \"Mkt\"] # extract cov and mkt var\n",
"betas.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CVXPY Optimization Function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to setup a minimization problem. <br>\n",
"We want to minimize the portofolio variance like in a simple MV framework, but subject to an additional constrain: <br>\n",
"The sum of the constituents (weighted) betas must add up to 0, in order to achieve market neutrality"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.10616021, 0.3570373 , 0.08004185, -0.04964821, -0.05569771,\n",
" -0.06166445, -0.11043467, 0.05200452, 0.18242134, -0.03661609,\n",
" 0.02315389, 0.01938063, 0.22795245, -0.25418031, -0.06472136,\n",
" -0.02212104, -0.11358248, -0.08729581, -0.22350095, 0.11924566,\n",
" 0.18589319, 0.11868543, -0.0768249 , -0.09225611, -0.10226073,\n",
" 0.19572728, 0.06156132, 0.05906077, 0.02969392, -0.04925867,\n",
" 0.28397305, 0.02302302, 0.06068259, -0.33698112, -0.00133836,\n",
" 0.17911631, -0.01622544, -0.13153555, 0.0813863 , -0.03847363,\n",
" 0.12719394, 0.05169157, 0.22551358, 0.11862918, 0.08886352,\n",
" -0.18152509, 0.00456125, -0.08226538, 0.12575399])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Mkt neutral portfolio\n",
"covmat = data.drop(columns=[\"Mkt\"]).cov() # we need to drop mkt figures from the vcv matrix\n",
"num_weights = covmat.shape[1] # number of constituents\n",
"x = cvx.Variable(num_weights) # the variable that is allowed to change in the optimization function\n",
"port_var = cvx.QuadForm(x, covmat) # portfolio variance\n",
"objective = cvx.Minimize(port_var) # the objective function to be minimized\n",
"\n",
"# Constraints on concentration\n",
"lower_bound = -1.0\n",
"upper_bound = 1.0\n",
"\n",
"constraints = [x >= lower_bound,\n",
" x <= upper_bound,\n",
" sum(x) == 1,\n",
" x.T @ betas == 0.0]\n",
"\n",
"# solving the objective\n",
"problem = cvx.Problem(objective, constraints)\n",
"problem.solve()\n",
"\n",
"x = x.value\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Agric 0.11\n",
"Food 0.36\n",
"Soda 0.08\n",
"Beer -0.05\n",
"Smoke -0.06\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert to Pandas to ease of read\n",
"w = pd.Series(x, index=covmat.columns)\n",
"w.round(2).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot Portfolio Weights"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 8))\n",
"plt.bar(w.index, w.values)\n",
"plt.xticks(rotation = 90);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Optimization function"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def get_mkt_neutral_weights(covmat, mkt_betas, lower_bound=-1.0, upper_bound=1.0):\n",
" num_weights = covmat.shape[1]\n",
" x = cvx.Variable(num_weights)\n",
" port_var = cvx.QuadForm(x, covmat)\n",
" objective = cvx.Minimize(port_var)\n",
"\n",
" constraints = [x >= lower_bound,\n",
" x <= upper_bound,\n",
" sum(x) == 1,\n",
" x.T @ mkt_betas == 0.0]\n",
"\n",
" # solving the objective\n",
" problem = cvx.Problem(objective, constraints)\n",
" problem.solve()\n",
"\n",
" x = x.value\n",
" \n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.10616021, 0.3570373 , 0.08004185, -0.04964821, -0.05569771,\n",
" -0.06166445, -0.11043467, 0.05200452, 0.18242134, -0.03661609,\n",
" 0.02315389, 0.01938063, 0.22795245, -0.25418031, -0.06472136,\n",
" -0.02212104, -0.11358248, -0.08729581, -0.22350095, 0.11924566,\n",
" 0.18589319, 0.11868543, -0.0768249 , -0.09225611, -0.10226073,\n",
" 0.19572728, 0.06156132, 0.05906077, 0.02969392, -0.04925867,\n",
" 0.28397305, 0.02302302, 0.06068259, -0.33698112, -0.00133836,\n",
" 0.17911631, -0.01622544, -0.13153555, 0.0813863 , -0.03847363,\n",
" 0.12719394, 0.05169157, 0.22551358, 0.11862918, 0.08886352,\n",
" -0.18152509, 0.00456125, -0.08226538, 0.12575399])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_mkt_neutral_weights(covmat, betas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Rolling optimization to test Out of Sample Portfolio's Beta"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\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></th>\n",
" <th>Agric</th>\n",
" <th>Food</th>\n",
" <th>Soda</th>\n",
" <th>Beer</th>\n",
" <th>Smoke</th>\n",
" <th>Toys</th>\n",
" <th>Fun</th>\n",
" <th>Books</th>\n",
" <th>Hshld</th>\n",
" <th>Clths</th>\n",
" <th>...</th>\n",
" <th>Trans</th>\n",
" <th>Whlsl</th>\n",
" <th>Rtail</th>\n",
" <th>Meals</th>\n",
" <th>Banks</th>\n",
" <th>Insur</th>\n",
" <th>RlEst</th>\n",
" <th>Fin</th>\n",
" <th>Other</th>\n",
" <th>Mkt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2001-01-31</th>\n",
" <th>Agric</th>\n",
" <td>0.003733</td>\n",
" <td>-0.000024</td>\n",
" <td>-0.003289</td>\n",
" <td>-0.002475</td>\n",
" <td>-0.001338</td>\n",
" <td>0.000781</td>\n",
" <td>0.000168</td>\n",
" <td>0.002549</td>\n",
" <td>-0.002255</td>\n",
" <td>0.000953</td>\n",
" <td>...</td>\n",
" <td>-0.000705</td>\n",
" <td>0.001870</td>\n",
" <td>0.001935</td>\n",
" <td>-0.000540</td>\n",
" <td>0.000774</td>\n",
" <td>-0.000433</td>\n",
" <td>0.000742</td>\n",
" <td>0.003591</td>\n",
" <td>-0.000638</td>\n",
" <td>0.001272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food</th>\n",
" <td>-0.000024</td>\n",
" <td>0.005363</td>\n",
" <td>-0.000616</td>\n",
" <td>0.003445</td>\n",
" <td>0.003840</td>\n",
" <td>0.000572</td>\n",
" <td>-0.001896</td>\n",
" <td>0.000644</td>\n",
" <td>0.000635</td>\n",
" <td>0.001889</td>\n",
" <td>...</td>\n",
" <td>0.000386</td>\n",
" <td>0.000590</td>\n",
" <td>0.001186</td>\n",
" <td>0.002147</td>\n",
" <td>0.002850</td>\n",
" <td>0.004430</td>\n",
" <td>-0.000406</td>\n",
" <td>-0.001709</td>\n",
" <td>0.000087</td>\n",
" <td>-0.000877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soda</th>\n",
" <td>-0.003289</td>\n",
" <td>-0.000616</td>\n",
" <td>0.007725</td>\n",
" <td>0.002260</td>\n",
" <td>-0.000172</td>\n",
" <td>-0.001548</td>\n",
" <td>-0.000812</td>\n",
" <td>-0.001440</td>\n",
" <td>0.001586</td>\n",
" <td>0.001784</td>\n",
" <td>...</td>\n",
" <td>0.002929</td>\n",
" <td>-0.002411</td>\n",
" <td>-0.000206</td>\n",
" <td>0.001730</td>\n",
" <td>-0.000631</td>\n",
" <td>0.000483</td>\n",
" <td>0.000651</td>\n",
" <td>-0.002849</td>\n",
" <td>-0.002904</td>\n",
" <td>-0.001551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beer</th>\n",
" <td>-0.002475</td>\n",
" <td>0.003445</td>\n",
" <td>0.002260</td>\n",
" <td>0.005686</td>\n",
" <td>0.001194</td>\n",
" <td>-0.001130</td>\n",
" <td>-0.002209</td>\n",
" <td>-0.002034</td>\n",
" <td>0.001963</td>\n",
" <td>-0.000238</td>\n",
" <td>...</td>\n",
" <td>0.000903</td>\n",
" <td>-0.001881</td>\n",
" <td>-0.000419</td>\n",
" <td>0.001353</td>\n",
" <td>0.000210</td>\n",
" <td>0.002581</td>\n",
" <td>-0.000909</td>\n",
" <td>-0.005209</td>\n",
" <td>-0.000480</td>\n",
" <td>-0.002513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Smoke</th>\n",
" <td>-0.001338</td>\n",
" <td>0.003840</td>\n",
" <td>-0.000172</td>\n",
" <td>0.001194</td>\n",
" <td>0.009348</td>\n",
" <td>0.001998</td>\n",
" <td>-0.001225</td>\n",
" <td>0.000372</td>\n",
" <td>0.004368</td>\n",
" <td>0.001493</td>\n",
" <td>...</td>\n",
" <td>-0.000220</td>\n",
" <td>0.001516</td>\n",
" <td>-0.000977</td>\n",
" <td>0.001465</td>\n",
" <td>0.002929</td>\n",
" <td>0.002579</td>\n",
" <td>-0.000703</td>\n",
" <td>0.000252</td>\n",
" <td>0.002289</td>\n",
" <td>0.000353</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" Agric Food Soda Beer Smoke Toys \\\n",
"Date \n",
"2001-01-31 Agric 0.003733 -0.000024 -0.003289 -0.002475 -0.001338 0.000781 \n",
" Food -0.000024 0.005363 -0.000616 0.003445 0.003840 0.000572 \n",
" Soda -0.003289 -0.000616 0.007725 0.002260 -0.000172 -0.001548 \n",
" Beer -0.002475 0.003445 0.002260 0.005686 0.001194 -0.001130 \n",
" Smoke -0.001338 0.003840 -0.000172 0.001194 0.009348 0.001998 \n",
"\n",
" Fun Books Hshld Clths ... Trans \\\n",
"Date ... \n",
"2001-01-31 Agric 0.000168 0.002549 -0.002255 0.000953 ... -0.000705 \n",
" Food -0.001896 0.000644 0.000635 0.001889 ... 0.000386 \n",
" Soda -0.000812 -0.001440 0.001586 0.001784 ... 0.002929 \n",
" Beer -0.002209 -0.002034 0.001963 -0.000238 ... 0.000903 \n",
" Smoke -0.001225 0.000372 0.004368 0.001493 ... -0.000220 \n",
"\n",
" Whlsl Rtail Meals Banks Insur RlEst \\\n",
"Date \n",
"2001-01-31 Agric 0.001870 0.001935 -0.000540 0.000774 -0.000433 0.000742 \n",
" Food 0.000590 0.001186 0.002147 0.002850 0.004430 -0.000406 \n",
" Soda -0.002411 -0.000206 0.001730 -0.000631 0.000483 0.000651 \n",
" Beer -0.001881 -0.000419 0.001353 0.000210 0.002581 -0.000909 \n",
" Smoke 0.001516 -0.000977 0.001465 0.002929 0.002579 -0.000703 \n",
"\n",
" Fin Other Mkt \n",
"Date \n",
"2001-01-31 Agric 0.003591 -0.000638 0.001272 \n",
" Food -0.001709 0.000087 -0.000877 \n",
" Soda -0.002849 -0.002904 -0.001551 \n",
" Beer -0.005209 -0.000480 -0.002513 \n",
" Smoke 0.000252 0.002289 0.000353 \n",
"\n",
"[5 rows x 50 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roll_covmat = data.rolling(window=12).cov().dropna()\n",
"roll_covmat.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Rolling optimization\n",
"date_list = set(roll_covmat.index.get_level_values(\"Date\"))\n",
"weights = pd.DataFrame(index=pd.Index(date_list).sort_values(), columns = returns.columns)\n",
"betas = pd.DataFrame(index=pd.Index(date_list).sort_values(), columns = returns.columns)\n",
"\n",
"for date in date_list:\n",
" # print(\"Optimizing weights at time \", date)\n",
" tmp_full_cov = roll_covmat.loc[date]\n",
" tmp_cov = tmp_full_cov.drop(\"Mkt\").drop(columns=[\"Mkt\"])\n",
" tmp_betas = tmp_full_cov[\"Mkt\"].drop(\"Mkt\") / tmp_full_cov.loc[\"Mkt\", \"Mkt\"]\n",
" \n",
" weights.loc[date, :] = get_mkt_neutral_weights(tmp_cov, tmp_betas)\n",
" betas.loc[date, :] = tmp_betas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To calculate the portfolio rolling realized beta, we have to multiply the realized beta times the optimal weights"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2001-02-28 0.049634\n",
"2001-03-31 0.083870\n",
"2001-04-30 -0.004589\n",
"2001-05-31 0.001909\n",
"2001-06-30 0.001888\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"portfolio_beta = (weights.shift(1) * betas).dropna().sum(axis=1)\n",
"portfolio_beta.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Mkt Neutral Portfolio Rolling Beta to Market')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1296x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"portfolio_beta.plot(figsize=(18, 8))\n",
"plt.axhline(portfolio_beta.mean(), color=\"red\")\n",
"plt.title(\"Mkt Neutral Portfolio Rolling Beta to Market\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now calculate portfolio return and compare performance to Market"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Backtest of Market Neutral Portfolio Return')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rraxfv77dmH/84x+sWrWKt956C29vb55++mkeeeQR5s2b1+k5ExMTWbx4MS+88AIXX3xxy/ZVq1Zx6NAhNm3ahGmaXHLJJXz22WckJiZ2ep6rrrqKf/zjHx2u5ePjw+effw5AUVER3/rWtwD4xS9+wdNPP833vve9Ht83wMaNG3Fzc6OhoYGf/OQnbN26ldDQUJYtW8Zbb73FZZddRlVVFdOnT+fBBx8E4JlnnmHNmjVEREQA8Omnn7bE96c//QmAXbt2sX//fpYtW8bBgwe7vP5dd93FTTfdxM0338wzzzzD3XffzVtvveVQ7CIiIiIiIjKwRl0FwYwZMzh+/DgrVqzgwgsv7LD/hRde4IMPPuCNN97A29vb4fP+7Gc/4+GHH25Xvr9q1SpWrVrF7NmzmTNnDvv37+fQoUO9jvmaa65p+Xr37t2cfvrppKam8tJLL7Fnz54ej//LX/7CrFmzuPfee3nllVfYsmULS5YsITIyEg8PD2644QY+++wzwJouceWVVzoU1+eff97S02Hy5MkkJSV1myDYsGED119/PQA33nhjS9JDREREREREXM8lFQSOPOkfSJdccgn33nsva9eupaioqN2+6dOns2PHDjIzM0lJSXH4nOPHj2fWrFm8+uqrLdtM0+SnP/0p3/72t9uNzczMbJdI6GlpP39//5avb7nlFt566y1mzpzJc889x9q1a3uM7Z577uHee+9t+b67p/Y+Pj64u7v3eE6w3l9/aNUCERERERGRoWPUVRAA3Hrrrdx///2kpqZ22Dd79mwef/xxLrnkkpZmgYGBgVRUVPR43p///OftVhI477zzeOaZZ6isrAQgKyuL/Px8oqKiyM/Pp6ioiLq6OlauXNlyTE/XqqioICYmhoaGBl566SWH33NbCxcuZN26dRQWFtLU1MSKFSs488wzOx3bXTxnnHFGSwwHDx7kxIkTTJo0qcvrnnrqqbz88suA1Ztg8eLFfYpfREREREREnG9UJgji4+P5/ve/3+X+xYsX88gjj3DRRRdRWFjILbfcwh133NFlk8Jm06ZNY86cOS3fL1u2jOuvv55FixaRmprKVVddRUVFBZ6eni2NAJcvX87kyZNbjunpWr/5zW9YuHAh5557brvjeiMmJoY//OEPLF26lJkzZzJnzhwuvfTSTsfefvvtXHDBBZ0ua3jnnXfS1NREamoq11xzDc8991y30zIeffRRnn32WWbMmMELL7zA3/72tz7FLyIiIiIiIs5n9LdMvDPz5s0zt2zZ0m7bvn37mDJlitOvJUNbRUUFgYGBDo/X74mI861du5YlS5a4OgyRUU1/hyKupb9BkVaGYWw1TbPTDvyjsoJARERERERERqEBeEA+kihBICIiIiIiIqPD/+6A125xdRRDlktWMRAREREREREZVA21sO8d8Al2dSRDlhIEIiIiIiIiMvKd+BIaqu3/1ICnr6sjGnI0xUBERERERERGvsOftH5dku66OIYwJQhERERERERk5Du0GvwirK9Ljrk2liFqVCUIDMPgxhtvbPm+sbGRyMhIli9fDsADDzzAI4880uG45557juzs7EGLU0RERERERJyo9AQUHoC5N1vflxx3aThD1ahKEPj7+7N7925qamoAWL16NXFxcT0epwSBiIiIiIjIMNDUCLteh8b69tsPf2y9zrgGvAKhWBUEnRlVCQKACy64gPfeew+AFStWcN1113U67sknn+SCCy7ghRdeYMuWLdxwww3MmjWrJbkgIiIiIiIiQ8yxtfDGbbDh7+23H/4EghMhYiKEJmuKQRdcs4rBB/dB7i7nnjM6FS54qMdh1157LQ8++CDLly8nLS2NW2+9lfXr17cb849//INVq1bx1ltv4e3tzdNPP80jjzzCvHnznBuziIiIiIiIOE/REet1/Z9h9o0QMMaqJji6FlKvBsOAsGTI3+/KKIesUVdBMGPGDI4fP86KFSu48MILO+x/4YUX+OCDD3jjjTfw9vZ2QYQiIiIiIiLSJ8XHwN0LGmthze+sbRkbob4SJpxrfR+aAqXpYLO5Ls4hyjUVBA486R9Il1xyCffeey9r166lqKio3b7p06ezY8cOMjMzSUlJcVGEIiIiIiIi0mslx6xpBClnwMZ/w4Lb4fBqcPOwtgGEpUBTPVRkQ3C8a+MdYkZdBQHArbfeyv33309qamqHfbNnz+bxxx/nkksuaWlMGBgYSEVFxWCHKSIiIiIiIr1RfNTqMXDGj8A7CD76ORz6GBIXgXegNSY02T5WfQhONioTBPHx8Xz/+9/vcv/ixYt55JFHuOiiiygsLOSWW27hjjvuUJNCERERERGRocpmg5J0q0LALwzO/AkcXQP5e2D8Oa3jQu2V4mpU2IFrphi4SGVlZYdtS5YsYcmSJQA88MADLdvPO+88zjvvPACuvPJKrrzyysEIUURERERERPqiIhua6iBsrPX9/G/C5ietqoK2CYLgBGvKQclxl4Q5lI2qBIGIiIiIiIiMUM1TBporBDy84NLHYPebEDWtdZy7h5Uk0BSDDpQgEBERERERkeHj09+Clz8svqf99uKj1mtYm2bzSada/5wsNFlTDDoxKnsQiIiIiIiIyDBUXw1f/gO2PtdxX8kxa+pAkAMrE4SlaIpBJwY1QWCa5mBeToYZ/X6IiIiIiEi3jq6Bxhrr5r6mpP2+4mMQkmRNIehJaIp1fE3pQEQ5bA1agsDHx4eioiLdBEqnTNOkqKgIHx8fV4ciIiIiIiJD1f73Wr/O3tF+X8mx9tMLuhOmlQw6M2g9COLj48nMzKSgoGCwLilDQG1trcM3/T4+PsTHO1AOJCIiIiIio4+tCQ5+CBOWwaFVkLMDxi219pmmVUEQv8Cxc4UmW6/FxyB29kBEOywNWoLA09OTlBQHszkyYqxdu5bZs/UHJyIiIiIi/ZSxEaqLYOZ1UHgQsre37qsuhrry1iUOe9KcIFAfgnbUpFBERERERESGvv3vgZsnjD/HeurfNkHQPFXA0SkG3oHgH6kpBidRgkBERERERESGNtO0EgRjzwSfIIiZBaUnrMoBaF3iMLQXVeuhKdYUA2mhBIGIiIiIiIgMbQX7raf9ky60vm/uG9BcRVB8DDBapw44IjQZStKdGOTwpwSBiIiIiIiIDG37V1qvzQmCmJnWa84O67XkGATFgmcvVkULS4HyTGis73y/rWnU9ShQgkBERERERESGtv3vQ9xcCIqxvvcNsRoStq0g6M30ArDGmzZrqkJnvvonPLYQ6ir6HPZwowSBiIiIiIiIDF3l2ZC9rbV6oFnMLMjeaX1dfNTxBoXNmsd31ahw79vQWAvlOb077zCmBIGIiIiIiIgMXXvfsV4nX9R+e+wsKDth9RGoyu99gqC7pQ4r8iBzi/V1ZV7vzjuMKUEgIiIiIiIiQ1NJOqz5PcQvgMjJ7fc1Nyrc+5b12tspBgFR4BMMmZs77jv0EWBaXytBICIiIiIiIuJCTQ3wxm2ACVc+CYbRfn9zo8Ldb1qvYWN7d37DgOlXWVMJakra7zvwAfiFW18rQSAiIiIiIiLiQmt+bz3dv/ivnS9f6BMMYeNaVzLo7RQDgLk3W30G0l5r3dZQA0fWwPQrwd1bCQIRERERERERlzmyBj7/C8y5ybpR70rsLOvVN8xKGPRWzExrqsLW58C0Tyk4ug4aa2DSBdY0hAolCEREREREREQGX1UR/O/bEDERzv9j92Ob+xD0pXqg2ZybIX8PZG21vj/wPngFQtJiCBijCgIRERERERERl9jxknVTfuVT4OXX/diYWdZrb/sPtJV6FXj6w9ZnwWaDgx/C+LPBw8uqIKjM7/u5hxklCERERERERGTo2P0GxM2FmBk9j42ZCW6eEDGp79fzDoTUK61mh8c/s5ITky609gWMgcrcvp97mFGCQERERERERIaGoiNW08FpVzg23icIvvUpnHJH/6475xZoqIZ37gbDDSaca20PjIbqImtFhVFACQIREREREREZGpqXLJx2uePHxMywqgD6I24ORKVCaTokLgK/MGt7wBjrtaqgf+cfJpQgEBERERERkaFh9xuQeCoExw3udQ3DWvIQrNULmgVEWa8Vo2OagYerAxAREREREREhby8U7IMLH3HN9WddD6UnYNYNrdsCoq3XUdKoUAkCERERERERcb3db1jz/6de5prre/nDst+039Y8xWCULHWoKQYiIiIiIiLiWqYJe96ElDMgINLV0bRSgkBERERERERkEOXsgOKjMP1KV0fSnoc3+IYqQSAiIiIiIiIyKHa/AW6eMHm5qyPpKCBq1CQI1INAREREREREXKOuEo6tg7TXYPzZrcsLDiUBY6BCCQIRERERERER59v/Hmx6AtK/hKZ68A6CRXe5OqrOBURDxkZXRzEolCAQERERERGRwfXevYAJC26HiedBwing4eXqqDoXMMZa5tA0wTBcHc2AUoJAREREREREBk9FLlRkw3l/gEV3ujqangVEQWMN1JWDT7CroxlQalIoIiIiIiIigyd7h/UaO9ulYTgsMNp6rcx3bRyDQAkCERERERERGTw5O8Bwg+hUV0fimIAx1usoWMlACQIREREREREZPNnbIWIieAe4OhLHBERZrxW5ro1jEChBICIiIiIiIoMne8fwmV4ArQkCTTEQERERERERcZLyHKjMhZhZro7Ecb6h4OapKQYiIiIiIiIiTpOzw3odThUEhmFVEYyCBIFDyxwahnEcqACagEbTNOcNZFAiIiIiIiIyAmVvH14NCpsFKkFwsqWmaRYOWCQiIiIiIiIysmVvh8jJ4OXn6kh6JyAKSk+4OooBpykGIiIiIiIiMvBM02pQOJz6DzQLGDMqVjFwtILABFYZhmECj5um+cTJAwzDuB24HSAqKoq1a9c6LUgZviorK/W7IOJi+jsUcT39HYq4lv4Ghwbv2kIWVeVzqCqArGH280gurCWpuojPPv0Y0603hfhDiGkSnftxt0McfWenmaaZbRjGGGC1YRj7TdP8rP21zCeAJwDmzZtnLlmypA8Ry0izdu1a9Lsg4lr6OxRxPf0diriW/gaHiP3vwVcw4cyrmZCwwNXR9I7/EUh/mTPnTYOgGFdH03s1pbDyHjjwZrfDHJpiYJpmtv01H/gfMMx+miIiIiIiIuJS2dvBcIeo6a6OpPcCoqzXymE4zSBjEzx+Oux9G86+v9uhPSYIDMPwNwwjsPlrYBmw2ymBioiIiIiIyOiQvWN4NigECIy2XivzXRtHb216Ep453/r61o/g9P/rdrgjUwyigP8ZhtE8/r+maX7YvyhFRERERERk1DBNq4Jg4vmujqRvAsZYrwO01GFBRR0RAV7Y77ud48u/w6pfWJ/5FU+AT3CPh/RYQWCa5lHTNGfa/5lmmubvnBKsiIiIiIiIjA7lWVBdCLGzXB1J3/jbEwQVzk8QlFTVc9pDn/LmtiznnfSzR6zkwNTL4JoXHUoOgJY5FBERERERkYGWvd16jZ3t2jj6ytMHfEIGpIIgt7yW+iYbq/Y6ob+BacKa38Onv4EZ18CVT4O7p8OHK0EgIiIiIiIiAytrm71B4TRXR9J3AVG9SxDYmhwaVlJdD8CXh4toaLL1JbJWBz+CdX+E2V+Hy/4F7r1bklEJAhERERERERlYBz+ChAXg6evqSPouYAzk7rJWBbD1cCN/8CP4Y4pDTQ3LqhsAqKhrZEdGac9x1JZDfXXn+3a8ZE2HWP43cHPv+VwnUYJAREREREREBk7BQcjfY82HH84mXWj1Unj6XPhrKnz0cyjL7Hxs9naoK4Mjn/Z42hJ7ggDgs4MFPcfx4pXw+jc6bq8pgYMfQupVva4caKYEgYiIiIiIiAycvW8BBky9xNWR9M+iO+FHh+HyxyE6Fb76F3z2cOdjmxMHDiQISmusKQbTYoOsBIFpQlVh54NtTZCz00oE5O1tv2/v29BUDzO+5ug76kAJAhERERERERk4e96CxFMgKNbVkfSfTzDMvBaufxliZkLpic7HlWdbr0c+7XE6Qml1A94ebiybGk1aVhkVaSvhT5M6P3dZBjTVWV9/9c/2+9JehYiJEDOrd++pDSUIREREREREZGCMlOkFnQlJgNKMzveVZ4G7F1QVQN7ubk9TWl1PqJ8XZ0yMwDShIO0jsDValQInKzxsvUZNtxIClfYpCaUnIP0Lq3rAMPr8lpQgEBERERERkYGx9y3rdbhPL+hMcII1lcA0O+4ry4JJF1hf9zDNoKS6gRA/T2bEhxDi54lb9jZrR/7+joOLDlmvF/w/q5Jgy9PW97tes15Tr+7DG2mlBIGIiIiIiIgMjD1vQeKikTG94GTB8dBYA9XF7bfXlkF9BcTNgzFTe0wQlNkTBO5uBmeMCyam5qC1o6CTBEHhIfAOhqRTYcJ5sPkpaKiFna9Yn3Nocr/ekhIEIiIiIiIi4nwjeXoBWBUEAGUn9Qooy7Lvj4NxZ8GJDV0vSwiUVNcT4usFwMVjivCmAZu7NxQc6Di46BBEjLemESy605rCsPqXUHigX80JmylBICIiIiIiIs43kqcXgFVBAB2XOiy3JwiC4mHcUmtlgfQvuzxNaU0Dof6eACzwOgrAsfAzofCgtWpBW4WHIXyC9XXKmVYvgk1PWP0Opl3e77ekBIGIiIiIiIg43563IGGErF7QmZBE6/XkRoXNCYPgOEg6Ddy9u5xmYJompdX1BNsrCIKLdlFkhPJJw3Srx0DJ8dbBdZVQkW1VEIBVRXDKndbXE5aBb2i/35ISBCIiIiIiIuJchYes6QVOeKo9ZPmGgqdf5xUEhhsERIOnr9Uv4MgnnZ6iqr6JhiaTUD+rgoCsLRQGp7KqwH6z37YPQfER67W5ggAg9SqYdgWc9n2nvCUlCERERERERMS50l4BjJE7vQB4Jy2HuoC4TnsQNPpH85sPDlLX2GT1ISjY39qboI3S6noAQvw8oaYEig7jFj+PfY32qou2CYJC+woGEW0SBB7ecPWzkLDAKe9JCQIRERERERFxHlsT7FgB488esdMLiirruOeVHRypC+mkgiCTfCOcpz8/xlvbs6wEAcDRNR3OU1rdAECInxdkbQXAb+wCqvCl2je6/VKHRYcBA8LGDsA7sihBICIiIiIiIs5zdC2UZ8KsG1wdyYD5YHcuTTaTw/WhHRMEZVlk2cIAePyzo9gip4L/mE77ELQkCHw9IWsbYBA5aRHubgZ53skdKwiCE6xpCwNECQIRERERERFxnh0vWfPzJ1/k6kgGzMq0bAAO1IRYSw021Fg7TBPKszhUE0REgBdHC6pYvT8fUk6HExs7nKfEPsUg1N8LMrdA5CS8/ENICPXlqJHQfiWD5iUOB5ASBCIiIiIiIuIcNSWwbyWkXm3Njx+B8str2XismNmJIWSb4dbG5v4CNSXQWMvhuhC+efpYEsJ8+fe6I5ihKVCR02HZwtIaewWBjwdkbYG4uQCkRPizqy4GGmuh9ISVeCg60r5B4QBQgkBEREREREScY9fr1vJ8I3h6wfu7cjBNuO/8yWSZEdbG5kaF9ukG2WY4M+NDuP30sWw/UcrxhhAwm6Ait925SqusCoLg+myoLmqTIAhgY0WkNahgv5VcqK9s36BwAChBICIiIiIiIs6x4yWISoWYma6OZMCsTMthcnQgC8eG0xgYZ21s7kNQblUS5JhhTIsL4qq5CYT5e/H2Udrtb1Za04CflzveudutDfHzAEiJ9Gd3Q4y1rWB/6woG4ZpiICIiIiIiIkNd3h7I3g6zbwDDcHU0AyK7tIYt6SUsn2HdvEfFpdCEW2uCwP7qHhJPkI8nvl7u3HJqMh9meFj7T0oQlFTXE9q8goGHD4yZCsDYCH8q8KPOLxoKDlj9B0AVBCIiIiIiIjIMbH8J3Dwh9WuujsQpbDaTYvsUgGbv78oBYPkMa/nGKfHh5JkhNBSlWwPKs2jAg7j4pJZjblqURKmnfbpAWfsEQVl1A8G+nlaDwphZ4O4JwNhIfwCKfFPsFQSHwdMPAgd22UglCERERERERKT3Vv8Knj4P/nMp/Pda2P4iTLoA/MNdHZlTvL4tk7m/Xc3DH+2nockGwLtpOUyPCyI5wrqBT40LJsuMoLrQShDUFWWQawtlWnxoy3lC/LxYPH081fh0WkEQ4Qfk7GyZXgAQFeiDr6c7J9wTrQqCwoMQPg7cencL/9b2LG55dpPD45UgEBERERERkd6pr4IN/4DKPKivhvJMCI6HU7/n6sicZk9WGQCPrTnC1x7fwJeHC9mZUdpSPQAwPS6YbDMCoywDgNqiE2QTTmpccLtzjR0TQI4tlMaSjHbbS2samOBeYDV2jJ7Rst3NzSA5wp/9TbHQUA0nNvRpBYPXt2ay9kAB5bUNDo336PUVREREREREZHTL3AK2RrjwYZhwrqujGRAZJTVMjg7iu0vH8dM3d3H9UxsBuCg1pmVMZKA3ZZ5R+NVsApsNozyLXDOJM2OD2p0rPtSPbDOc2JLMdjfhpdUNJIfnWd+c1IBwbIQ/WzOjuAWsJEEv+w/UN9rYkl5svZfiaqbFBvdwhCoIREREREREpLfSvwQMSFjg6kgGzIniahLDfFk+I5b37z6dRWPDOWdKFAlhfu3GuYUm4EEjVObiV5tHpXcUIX5e7cYkhPqSY4ZjVLROMbDZTEqr64k3rb4GhKW0OyYlwp/Py9pM1+hlBUFaZim1DdbUiIziGoeOUQWBiIiIiIiI9M6JLyF6Ovj0/FR6ODJNk8ySapZMtJoLJoT5seL2UzodGxCVAkVQk74ZXxpxD4nvMCYhzI/PCMe7pgCaGsDdk4q6RmwmRDVmg08I+IW1OyYlwp8Smz+NflF4VOdBRO+WOPzqaFHL15kl1Q4dowoCERERERERcVxjPWRshqTTXB3JgCmorKO2wUZiuF+PY6MTrCf7+bvXARAUldxhTLi/F0VuERiYUGFVDJRVW30BwuoyIWxsh2NS7CsZlAfa94WN69V7+OpoMZOjAwn08eBEsRIEIiIiIiIi4mw5O6GxBhIXuTqSAZNhv6FOCO05QTBu/GQAGo9vACAqoeONvGEYNATYexeUZwPWCgYAQTUZnSYIxtpXSjjiPweiU8EnqMOYrtQ32tiaXsIpY8NJCPVreT89UYJAREREREREHHfiS+s16VTXxjGAmufsn9xvoDORkZGU40987UEAksdO7HSce0iC9UVZJmCtYOBJI77V2Z0mCEL8vAjz9+LNgOvgjs97Ff+urFJqGpo4ZWwYCWG+ZJQ41oNACQIRERERERFxXPoGq9w9YIyrIxkwzSX58aG+Do0v84zC22ikDi/CI2M7HeMXkWh9UW41KiytrifeKMAwbZ0mCMDqQ3CssLKX0VvTCwAWpLRWEJim2eNxShCIiIiIiIiIY2w2OLFhRFcPgDXFYEygNz6e7g6Nrw+IA6DUIxIMo9MxkRGRVJi+1BVlWGOrG0gycq2d3SYIqnoZvdWgcHJ0IGH+XiSG+1HXaKOgoq7H45QgEBEREREREccU7IPa0hGfILCWOOx5ekEzz3CrOqDeP7rLMQlhvuSYYdQWWwmCkup6ko08a2c3CYK88jqq6hodjqWhycaW41b/AWjto5DhwEoGShCIiIiIiIiIY9Lt/QdGcINCgMySGof6DzSLjLMaEwaOSepyTHyoHzlmeGsPguoGJnjkg1cg+Ed0ekxzo8LeVBGkZZa19B8AKzEBrX0VuqMEgYiIiIiIiDjmxAYIjIXQZFdHMmDqG23klPUuQeAbmQxASHRKl2MSwvzINsPxrLKWOSytrmesez6EpXQ5LWFsZADQuwTBV0eLAKv/AFiJCcChlQyUIBAREREREZGemaZVQZC0qMsb2pEgu7QGmwkJDjYoBCDYvkJBUFzXQ3w9KfaIwK++CBrrKK1pIJHcLqcXACSF+2EYcLSgdwmC5v4DAD6e7owJ9G5pvNgdJQhERERERESkZyXHoSJnxE8vaJ6r35seBETPgFk3wIRl3Q6r94uxvqjIoayqlihbXrcJAh9Pd2KDfR1eyaC5/8DClLB22xPC/NSDQERERERERJzkxAbrdRQ0KAR6NcUATx+47J8QktDtMCPYXmFQloVPVRYeNHWbIAAYG+n4Sgat/QfC221PCPVVDwIRERERERFxkoyN4BMMkVNcHcmAyiiuwdPdICrIx+nn9g6zVjswyzIJrrGaFfaUIJgaE8Tu7HLWHyro8fxb04sBmJfcsYIgp6yGhiZbt8crQSAiIiIiIiI9K8u0bmbdRvZtZEZxNfGhfri7Ob/PQvMqB1UFJ4hsyLI29pAguOus8UwYE8CdL25jf255t2O3pZeSGOZHZKB3u+0JYX7YTKu/QndG9k9WREREREREnKMiFwKiXR3FgMsoqSa+Nw0KeyFmTARlph+lecdJMnJpdPOBwO4/00AfT579xnz8vN35xrObySuv7XScaZpsPVHC3KTQDvsSWlYyUIJARERERERE+qsiFwKjXB3FgMsoru5dg8JeaF7qsL7oBElGHlX+iQ6tCBET7Mszt8ynvKaBbzy7mcq6xg5jMktqKKioY05iSCfXtRIePTUqVIJAREREREREutfUANWFI76CoKK2gZLqht41KOyFuBBfcsxw3CuzSTbyqA9OcvjYabHB/OOGORzIq+C3K/d22L/tRAkAsxM7VhDEBPvi4WaQ0cNSh0oQiIiIiIiIDCG1DU08tf4odY1Nrg6lVWW+9TrCKwiaS/CbS/Kdzd/bgxKPSELqc0k08rGFdt9/4GRLJ43h8tlxvLcrh/rG9g0Ht58oxc/LncnRgR2Oc3cziA3xJaNEUwxERERERESGjZVpOfz2vX2sP1jo6lBaVeZaryO8gqB5icOBmmIAUOMbTTCVeBsNuIeP6/Xx502LpqK2kY3Hitpt35pewsz4EDzcO7/NTwzza3l/XVGCQEREREREZAhZs996Wp/ew83coKrIs157aKg33GXa5+g3z9kfCGZgbMvXPlHje3386RMi8PV0Z9WevJZtNfVN7MspZ05SSJfHJYT5kqkEgYiIiIiIyPDQ0GTjM/t69z3NFx9UFTnW6whPEJworibQx4NgX88Bu4ZHaGLL177RE3p9vI+nO2dMjGD13jxsNhOAtMxSGm0mczrpP9AsPtSPoqr6bs+tBIGIiIiIiMgQsTW9hIraRgwD0ouqXB1Oq8o8wAD/Ma6OZEBlFFeTEOqH4cDKAn3lPyYBgDo8cQ+O79M5zpsWTW55LWlZZQBs7aZBYTNHGi8qQSAiIiIiIjJErDmQj6e7weLxEUNsikEu+EeAu4erIxlQGSU1A9p/ACAsOgWAHCMK3Pp2S37W5DG4uxms2mP1htiWXsrYCH/C/L26PMaR96UEgYiIiIiISD/ZbCaPrTnc76f+a/bnMz85jKkxQWQW19BkLyF3ucq8Ed+g0DRNq4JgAPsPAMSNCafYDCDfM67P5wjx82JhShir9uZhmibbT5R0Wz0AkBDa8/tSgkBERERERKSf0rLKePijAzzz+bE+nyOzpJqDeZWcNXkMieF+1DfZyCuvdWKU/VCRO+KXOCyoqKOu0eZQKX5/xIb48MfG6/g09Kp+nWfZ1CgO51ey9kABRVX13TYoBAjz98LPy73bMUoQiIiIiIiI9NPqvVap9/rDfV+acM0Bqznh0sljSArzByC9aIhMM6jMG/ENCo8UWNUfSeH+A3odbw93vgq5iNIxp/TrPMumWT+PP364H6DbBoUAhmGQENp98mNkTyAREREREREZBKv25GEYcLSgiqzSGuJCel+mvmZ/PolhfoyN8MfLvpb9ieIqFo0Ld3a4vWNrGhVTDHZllQIwPTZowK/1/DcWEOjTv9vx2BBfUuOC2ZVVRoC3BxOjAns85roFCazqZr8qCERERERERByw7UQJz33RcQrB8cIqDuVXcu18qzv95/ZlCnujtqGJL48UctbkMRiGQUywDx5uxtCoIKgqBNM24isI0jLLiAvxJTzAe8CvlRzh75TrLJtqTfuYlRCCu1vPKy/cclpKt/uVIBAREREREXHAE+uO8sC7e9mXU95u++q9eQDcuWQ8UUHefHao99MMNhwtorbBxtLJ1jKCHu5uxIX6Do2VDCqt6RMEjOweBLuyypgRH+zqMHrlvOlW0mZOUvfTCxylBIGIiIiIiIgDdtnXnH/ys6Pttq/em8fk6EASwvxYPD6SLw4X9nr1gTX78/H1dGdhSljLtsQwPzKGQoKgwkqAjOQKgtLqetKLqpkRH+LqUHplYlQg//76HG49Ldkp51OCQEREREREpAdFlXVkldYQ4ufJOzuzySqtadm+Jb24pdT79AkRlFY3sCe7zOFzm6bJmgP5nDY+HB/P1i7zSeF+Q2OKwSioIGhO/gy3CgKA86fHEOLn5ZRzKUEgIiIiIiLSg+YbyF9cNBUTWpYz/GR/PjaztaP8aeMjAFjfi2kGB/IqyCiu4azJ7W/AE8P8KKtpoKy6wQnvoB+GWAVBVV0jNfVNTj1nWqb1850eN/wSBM6kBIGIiIiIiEgPdtsTBMumRXHJzFhe3nSCsuoGVu/NIzbYh2n2zveRgd5MiQlifS8aFX6021oB4dypJycI7EsdFlc56V104pnzYf2fux9TkQO+oeAx8M37epJRXM2yv3zG91/e7tTzpmWWkhLhT7Cvp1PPO9woQSAiIiIiItKDXVllpET4E+Tjye1njKWqvomnPj/K+kMFnDM1CsNo7SB/+oQItqaXUF3f6NC5P9yTy7ykUCID29+AJ4Vba9afGKg+BLVlcGIDbH0WzG56JgyRJQ4ziqu59omvyCqtaano6K2/rD7Ir9/d02H7rswyUkd59QAoQSAiIiIiItKjXZllLeXnU2KCOGNiJI+tOUxtg63Dk//TJ0TQ0GSy8Whxj+c9UVTNvpxyzpvW8QY8McxKEAxYH4KCg9Zr6QnITet6XEUuBLq2/0BmiZUcqKhtYPmMGHLKaqmscywB09ab2zP5z4Z08itqW7YVVNSRXVY7LPsPOJsSBCIiIiIiIt0orLTfQLZ5wnzHGWOxmRDo48HClPB24+cnh+Hl4eZQH4KP9lgNADtLEPh7exAR4MWJgUoQFB5o/Xrfu12Pc3EFQXZpTUty4KVvnsLFM2MBOJJf2avzlNU0kFFcQ5PN5H/bslq278oqBRh2KxgMBCUIRERERETEIftyynlhw/EBOXdNfRNmd2XuLtRczt62gd2iceGcOi6cK2bH4eXR/rbKx75c4eeHC6iub+TLw4U8+skhHltzuMN7/GhPLlNjgkiwVwucLDHMb+B6EBTsB3dvSDqt6wSBaVoJAhdWEPz8f7sorW7gxW8uJDU+mPFjAgA41MsEwd7scgD8vNx5bWtmy88iLbMMw6Clj8Ro5uHqAEREREREZHj419ojvLMzm4tmxBLm75xl1QAam2yc/ae1TIkJ4t83zsXTfWg9x9xt73A/La71BtIwDP77rVO6PGbx+Aj+8MF+Uh9YRZOtNSkQF+LLZbPjAMivqGXriRJ+cPbELs+TFO7PpmM9T1Xok4IDEDEBpl4GH/zImnIQeVIsNSXQVA+BMQMTQw82Hi1izYECfnrB5JYn/Elhfni6GxzuZYKgeenJO5eM45FVB9mZWcashBB2ZZYxPjIAf2/dHg+tvzwRERERERmSTNNkw9EiALamlzj13PtyKsguq+WT/fn86LWd2GxDq5JgV1YZY+0NCh112ew4LkyN5s4l43j2G/PZ/stzmZMYwq/f3UNhZR0Aq/fmYZpw/vSuy/cTw/zILquhrtG5y/oBVgVB5CSYfJH1/b53Oo6pyLFeAwaugqC2oYmz/rSWF75Kb7fdNE3++OF+ooK8ufnU5JbtHu5uJIf79zpBsDe7vOVcPp5uvLYlA9M0ScsqI1X9BwAlCERERERExAFHCqooqLBubLccd+4T7U32891yajJv7cjmwZV7h9R0g11ZZe2mFzgiKsiHf94wl/9bNomlk8YQ6u/FH6+cQVVdE79+dy8AH+7OJTncj4lRAV2eJzHMD9OEzJKafr2HDuqrrOaEkZMhOA7i5nU+zaDC6pFA4MD1INiVVcbRgioeeGcPXxxu7duwem8e206U8oNzJuLj6d7umAlRARwp6G0FQTnTYoMJ9PHkgukxvLMzm/Siagoq6pip/gOAEgQiIiIiIuKAr+zVAzHBPmx2doLgWBGJYX786uKp3LY4hee+PM7fPz3s1Gv0VUFFHTlO6nA/ISqQ7501nnd3ZvPG1kw2HCnivOnR7ZZIPFnLUofOblRYeMh6jZxkvU69BHJ2WEmDtirzrNcBrCDYmVEKQHyoL9/97zZOFFXTZDN5+KMDjI3w5+q58R2OGR8ZQHpRFbUNjlVW1DY0cbigkqkx1jSRq+fGU1HbyMOrrEaNqiCwKEEgIiIiIiI92nC0iOggHy6ZFcuurDKHb8x6YpomW46XMD85DMMw+PmFU7hyTjx/Xn2QN7ZmOuUa/bG7kwaF/fHtM8cxOTqQH7+RRqPN7HT1grYSmxMExU5OEBTYVzCInGy9Tl5uve5b2X7cIFQQ7MgoJS7El+e/sQCbzeT2F7bw0sZ0DuVXcu95k/DopCfFuDEB2Ew4XuRYA8cDuRU02cyWRoSnjA0nPtSX99Jy8HAzWhIHo53DCQLDMNwNw9huGMbKnkeLiIiIiMhIYZomG48WsWhcOPOTwmhoMlue+vbXkYIqiqrqWZASCoCbm8Efr0xl0dhwfva/XS2N5VxlV5ZzO9x7ebjxxytnYJomYwK9mdVDaXtkgDe+nu6kO7uCoGA/uHlA2Fjr+/BxEDW9Yx+CyjzwCgQvf+dev40dGaXMSgghOcKff1w/h4N5Fdz/9h5mxAdzQRf9GZpXMnC0D8Ee+woG02KtRI+bm8GVc6zKhIlRgR2mMIxWvakg+D6wb6ACERERERGRoelwfiWFlfWcMjaMuUnWjfwWJzUqbO7QvyAlvGWbh7sbj143mxA/T77z4jbKqhuccq2+2JVVRkqEP4G9aFDYk5kJIfzhilR+uXwqbm5dTy8Aa7WExDA/Tjh7qcOCAxA2DtzbvK8pl8CJr6Air3VbRe6ALnFYWFlHZkkNMxOsG/czJkbyswun4OFmcN/5k7ucfjEuMgDD6E2CoIxAHw8Swnxbtl1ln7rgjOkjI4VDCQLDMOKBi4CnBjYcEREREREZappXL1g0NoJQfy/GjwlwWqPCzceLiQjwJtleSt8sMtCbf94wl5yyGn746g6XrWywK7OMVCdNL2jrmvmJXDwz1qGxieF+A1NB0Nx/oNnUSwETtjzduq0id0CXOEzLLAVo1yTwm6ePZdv953Lq+Iguj/PxdCch1I9DvaggmBoT1C7hkBDmxz+un813lozrU+wjkaMVBH8FfgzYBi4UEREREREZir46WkRciG/L09f5yaFsSS9xyk37pmPFLEgJ7fRJ8dykUH5x0VQ+2Z/PY2sGpmlhk81kV2YZT352lG8+v5l5v/2Yrz2+gcfWHOazgwXkltcOSIKgN5LC/DhRXO28lR0a66DkWGv/gWZjJsO0K+CLR6E0w9pWmTugDQp3nCjFzejYJNCRJSXHjwngiAMJgiabyf7c8pbpBW0tnxFLUvjATZ8Ybjx6GmAYxnIg3zTNrYZhLOlm3O3A7QBRUVGsXbvWSSHKcFZZWanfBREX09+hiOvp71CGM5tpsn5/NTMiPVi3bh0AATUNVNQ28tJ7a0gI7Hvf86IaG1mlNSyJaerybyTRNDklxp0/rz5IQkMGIT69v153f4O/31jDwRLrOWi0n8GEEDeyi0p5+KPWCommgmOsXXui0+MHQ1VhA3WNNt5bvZYAr+6nJDjCv/I4800bewuayD/pc/EOvJAFtpUU/vc77Jvyf5xelk22fwNHBujfYZ+m1RIX4MamLz/v9bFetfUczm/g0zVrcOtmJYisShu1DTbcy7NYuza/P+GOeD0mCIDTgEsMw7gQ8AGCDMN40TTNr7cdZJrmE8ATAPPmzTOXLFni7FhlGFq7di36XRBxLf0dirie/g5lONufW07FR+u57NSpLJmXAMDYomqe3LUGIsex5JSkPp/7re1ZwA6uP3dBp093m0VNKufCR9fTEDGhJYbe6OpvsKymgYMfruLa+Qncc+5EooJ8WvYVVNSx/lABWSU13LZkXKed9AdL2Y4sVuzfweRZ81ua8/XL7jdgC0w983KmRqd23O99iKh1fyTqnLvBVk/ClHkknLak/9c9iWmafH/dai5MjWbJkhm9Pj4/IIMPj6cxNnUByRFdVwE0/55defZCJkdrtYLu9PhbbprmT03TjDdNMxm4Fvj05OSAiIiIiIiMTF8dsfoPnDK2tYlgQpgvkYHebO1nH4JNx4sJ9Pbo8aZtSkwgkYHerDtY0K/rnWyHfSWGi2fGtksOgNUD4Yo58Xzv7AkuTQ4AhPt7A1BUWeecExYcAMMNwsd3vv+070NQHLx7t/X9AC1xeLyomrKahnb9B3qjOVnSUx+CPdlleHm4MS7SCcmVEc61v+kiIiIiIjKkbThaRHyoLwlhrU0EDcNgfnIom487vpJBcVU9n+7PazePftOxYuYlh+LuQCf/MydGsv5QIU1ObFa4/UQJhmGtKjCUhQd4AVBUVe+cExbsh9Bk8PTtfL+XP5zza6jIsb4foB4EzUtl9vXz72ypw/yKWn751m7Si1pXfdiTXc6kqEA8XZzoGQ569QmZprnWNM3lAxWMiIiIiIgMHTabycZjxSxqUz3QbF5SGFmlNWSX1vR4nvpGG7c+t5lbn9vC3S/voLq+keKqeg7nVzI/JcyhWJZMiqSspqHlqb8zbD9RyqSoQAK8HZl57ToRAc6uIDjYsUHhyVKvgoSF1tcDtIrBjoxS/LzcmRgV2Kfjg3w8GRPo3S5BcP9be3jhq3Su/NcG9maXY5ome7LLmRarqQWOUApFREREREQ6ME2TlbtyKK1uaDe9oNn8ZOvGfkt6z1UEf/xwPzsySrl0VizvpWVz+WNf8vpWq0v+gmTHEgSLx0fgZuC0aQY2m8n2EyXMTgx1yvkGUqifJ4YBBZVOqCBoaoCiwx2XODyZYcDFj8L8b0LY2P5ftxM7MkqZHhfcYwVJdyZEBXC4wEoQfLg7hw/35HLjKUl4uhtc88QG3t6RTVlNgxIEDlKCQERERERE2tmfW85Nz2zi7hXbGRvpz9lTxnQYMyUmED8vdzYf674Pwao9uTz9+TFuXpTE366dzfO3LiCvopbfv78fbw+3DsvbdSXEz4tZCSGsO+CcLvRHC6sor21kdmKIU843kDzc3Qj183JOBUHxMbA19FxBANayhxf9CdydX2FR32hjb3Y5s/o5vWN8pLXUYVlNA/e/vYdpsUH86uKpvP6dU4kM9OYHr+wAYGo3TTCllRIEIiIiIiICWFUDD7yzhwv/tp60zDLuXz6VD79/BiF+Xh3Geri7ccaESF7dksH2E51XEWQUV3PvaztJjQvmZxdNAeD0CZG8e9di5iSGcN60aLw93B2Ob8mkMaRllTnlRrk55jnDIEEAEO7vRZEzKggK9luvERP7f65+2JdTTn2Trf8JgjEBVNY18sNXdlBUVc8fr5yBh7sbcSG+vPbtRcyID8bPy50pMX2bxjDaKEEgIiIiIiIAfHW0mOe+PM5Vc+NZ96Ml3Lo4BS+Prm8Zfnf5dKKCfPjWf7aQWVLdbl9VXSN3/XcbJvDY9XPaJQISwvx4887TePS62b2K78yJkZgmfH64sFfHdWbbiVKCfDwYGzE8OtuHB3hRVOWECoKCA9arixMEOzNLgf43iBxnb1T4yf58vrk4helxrZUC4QHevPrtRaz+4Zn4eQ3tPhNDhRIEIiIiIiICwH83nSDIx4MHL53eadXAycIDvHnmlnnUNdq47bktVNQ2ALB6bx7n/nkdaVllPHzVTBLD/Xo4k2NS44IJ8/di7YH+9yHYfqKEWYmhuPVj/vtgigjwdl4FQXACePc+MWKaJnWNTf2PAav/QESAN7HBPj0P7saEMVZlQGKYHz84p2PSw8fTnbiQLlZrkA6URhEREREREYoq6/hody7XL0zEx9Pxsv/xYwL51w1zufnZTdz50jb8vTz4cE8uk6IC+fv1c5ib5LwmgG5uBmdMiOCzgwXYbGafb+4r6xo5mFfBedOinRbbQIsI8KbAGT0IMjdDbO8qN5r9/K3dvLI5g0lRgcxMCGFWQjDLpkYT6t9zMqkt0zTZfLyY2YkhGEb/EjQRAV7cfdZ4zpkaha+X47+30jlVEIiIiIiICG9uy6K+ycb1CxN7feziCRH85tLprD9UyNqD+fzk/MmsvHuxU5MDzc6cFElRVT27s8v6fI60jFJsJswZgPgGSri/FxW1jf17gl+RC6XprcsXAv/vw/08+skhahu6P29BRR2vb8lkdkII4QFerEzL5idv7OK37+3rdRiH8ivJKK5hyaTIXh97MsMw+OGyScyID+n3uUQVBCIiIiIio55pmqzYdIJ5SaF9XpP++oWJRAd7M2FMIAlhzplS0JkzJkRiGLDuQEGfbwq3Z5QCMGsY3VSGB3gDUFxVT0xwH0vmMzZar4mnAFDX2MS/1x3BZsJbO7L4/eWpnS5pCfDfjSeob7Lxx6tmMC4yAJvN5IanNnI4v6LXYazemwfAOVOi+vY+ZMCogkBEREREZJT76mgxRwuruG5B76sH2jprctSAJgfAulFOjQvm3bTsPj9N336ihHGR/gT7eTo5uoETEWCV8ferD8GJjeDhA9EzADheWI3NhK+fkkhDk41rn/iK+95Io7q+sd1h9Y02XtyYzpJJkYyLtHoXuLkZjBvjz9HCKkzT7FUYH+/LY2Z8MFFB/es/IM6nBIGIiIiIyCjX3Jzwohkxrg7FIXcuGcfBvEp++uauXt+cmqbJthOlzEkcPtMLoLWCoLA/fQgyvoLYOeBhJRuOFFQCcO38RFb94Ey+feZYXt2SwX1vtP9c39uVTUFFHd84LaXd6VIiAqiobaS4yvGkRX5FLTsySlU9MEQpQSAiIiIiMoo1Nye8Yk58r5oTutL502P4wTkTeHNbFk98drRXx54orqa4qp7ZwyxB0FxBUNjXCoKGGsjZCYmt/QcO51sJgrGR/vh6ufPTC6bwf8sm8c7ObFZsygCshMqzXxxnXKQ/Z0yIaHfKlAirWuR4UZXDYXy6Lx/ThHOmKkEwFClBICIiIiIyijU3J+zv9ILB9v2zJ3DRjBge+nA/H9vntDti+4lSAGYnhgxMYAOkuYKgqK8VBFnbwNYICae0bDpSUElciC9+Xq2t6b5z5jjOmBjJA+/uYW92OdtOlJCWWcYtpyZ3WHEgOdwfgGOF1Q6H8fG+POJCfJkc3bdeFzKwlCAQERERERmlCivreOrzo8xNCmXSMLthMwyDR66aSWpcMN9/eTsHch1rlrfpeDH+Xu59bsboKv5e7nh7uFHUi3L+djK+sl4TFrRsOpxfybgxAe2GubkZ/OVrMwn18+S7/93GP9ccIdDHgyvmxHc4ZUKYH+5uBscLHasgqK5vZP2hQs6dGtXv5Q1lYChBICIiIiIyCjU02fjuS9sorW7g15dMc3U4feLr5c4TN87Dzc1waKpBTX0TK3dmc9aUKNzdhtcNqmEYRAR4970HQcYmiJgIfmEA2GwmRwuqGB8Z0GFoeIA3f79uDulFVXyyP59r5yfg791xATxPdzfiQ3055uAUg88PFVLXaONcTS8YspQgEBEREREZhf7w/n42HivmoStTmR4X7Opw+iw62IfZiaHsyynvcezKtGzKaxv5+sLhNZ2iWUSAV996ENhs1hKHCa39B7LLaqhpaGLcGP9OD1mQEsZ9F0wmxM+TmxYld3nqlAh/hysIPt6XR6CPBwtSwnoVvgyejmkgEREREREZ0d7ekcUzXxzjllOTuXx2x9Lx4WZKdCDPHimiocmGp3vXz0Bf3HiCCWMCht4NalMDvHw9FB2xvm6qt1YauPldCE1uGRYe4E1eeW3vz190CGpK2iUIjhRYN/WdVRA0u/2McXzjtJRuP9PkcH82HSvGNM1upw002Uw+2ZfP0kljuj2fuJZ+MiIiIiIio8je7HJ+8kYaC1LC+PlFU1wdjlNMjgmkvsnGsW6eZO/OKmNnRik3LEwcevPf9/wPDq2C8PGQfBokL4bSE5D+Zbth4f5eFPWlgiBjo/Wa2NqgsHkFg5N7EJysp5v5lAh/quubKKjofurDjowSiqrqtXrBEKcKAhERERGRUcJmM/nJG2kE+Xjy2PVzRsyT3MnRQQDsz63osvngSxvT8fF04/JOmu25lGnCF3+DiElw3cvg5gZNjbD3bSg40G5oRKA3RVV1PT6t7+DERvANsxIQdkcKKgnx8yTc36tf4SdHNK9kUMWYIJ8ux63em4+Hm8GZEyP7dT0ZWCPj3wgiIiIiItKjN7dnsSurjJ9eOJnIQG9Xh+M04yID8HAz2N9FH4LqBpO3d2RzycxYgn09Bzm6Hhz5FPJ2w2l3W8kBAHcP62b+pARBuL8XDU0m5bWNvbtGc/+BNkmFI/mVjIsM6Hc1RYp9qcPjPTQqXL03l1PGhg+9z1/aUYJARERERGQUqK5v5OGP9jMzPphLZ8a5Ohyn8vJwY1xkAPu7WOpwQ04j1fVN3LAwaZAjc8AXf4OAaEi9uv32yElQeFIFQYCV1OnVSgZVRVYPgsSF7TYfKajstv+Ao2JDfPB0NzhWWN3lmKMFlRwpqOKcKWP6fT0ZWEoQiIiIiIiMIKXV9Xz9qY28vOlEu+3/XneUvPI67r94Km7DbIk/R0yOCey0gsA0TdacaCA1LpiZCSGDH1h3snfAsXVwynfA46SKjshJUHIcGlqbEoYHWNMBetWHoLn/QEJr/4HS6noKK+u7XMGgNzzc3UgM8+t2JYNP9uUDcPYU9R8Y6pQgEBEREREZQf7+6WE+P1zIfW/u4v63d9PQZCO7tIYnPjvC8hkxzE0aYh38nWRydBDZZbWU1TS02741vYTMSpMbhuLShl8+Cl6BMO8bHfdFTgLTBkWHWzaF+1tJhKIeKgi2nyjBZjOtb7Y8DT4hEDu7Zf+RAnuDQidUEIDVqLC7BpGr9+UxOTqQhDA/p1xPBo6aFIqIiIiIjBAniqr5z4bjXDU3nnB/Lx7/7CgHcisI9vXEZsJ9F0x2dYgDZnKM1ZzwQG5Fu2UMV6bl4OkGF8+MdVVonSs5bq1esOi74BPccX/EJOu1YD9ET7c2BVoVBIVVXVcQfHm4kOuf2sjTN8/jbO/9cPhjWPZb8GxtIHgk377EYQ8rGDgqOdyf9YcKsdnMDtUpxVX1bDlezF1Lx3dxtAwlShCIiIiIiIwQf/xoPx5ubvzovElEBfkwJSaIn7yRRl2jje8uHUd86Mh9gjulZSWD8pYEgWmafLwvj6nh7vh7D7Fbnw3/BMMdFn6n8/3h48Fwg8KDLZvC/JqnGHRdQfBuWjYAh/IqOPvgryAoHuZ/q92YwwWVeHm4Oe33ITnCn7pGG7nltcSG+Lbbt2Z/PjYTLW84TAyxvxIREREREemLreklvJeWw/fPnkCUfbm5y2bHMTbSnze3ZfGdJSP7CW5UkDfBvp7sy2ltVHggr4LMkhrOmda/pfycrrEO0l6GaZdBcBcNIz19IDTZqiCw83B3I9TPs8smhY1NNj7akwdA4NH3IHs7XPavdtUDYK1gMDbCH3cn9aJIsS91eLywqkOC4ON9eUQFeTM9tpMqCRly1INARERERGSYeWNrJnf9dxubjhVjmiamafL79/cRGejN7WeMbTd2RnwID1wyjYCh9gTdyQzDYHJ0IAdyWxsVfrzXulmeFenuqrA6d/gTqC2D1K91Py5yMhQcbLcpPMC7yyaFXx0tpriqHl/3Js7K+jeMmQozrul4+YJKp/UfAKuCAODYSUsd1jY0se5gAedMiRqRjTFHopH9bwkRERERkRGmrLqBB97dQ0VtIyvTcpiVEMLClDC2ppfwhytSh14p/SCaEhPEa1syWubCr96bx6yEEEJ8Gno+eDDtfh18w2Dc0u7HRUyEQ6uhqRHcrZ9ruL9XlwmC93Zl4+/lzi+jNxGTnw3n/BXc2idHahuayCiu5tJZzlvqMibIB28Ptw4rGWw4WkR1fZOmFwwjqiAQERERERlGnlx/lIraRt74zqn85tJplFTX8/hnR5kYFcDVc+NdHZ5LTY4OpKq+icySGvLKa9mZWca5Q+3mtL4KDnwAUy8Fd8/ux0ZOBlsDlBxr2RQR6E1hVccpBs3TC86fFMTFJS+w0TaZ2uSzO4w7XlSFzYRxkf1f4rCZm5tBcrg/xwqr223/eG8efl7uLBob7rRrycAavelFEREREZFhpqiyjme+OMZFM2KYmxTK3KRQrl+YxLqD+YyNCMDDfXQ//5scYzUq3Jdb3vKU/ZwpUeTsz3RlWO0d+AAaqiH1qp7HRk60Xgv2Q8QEACL8vSis6JggaJ5ecENELv6Hini08XbuL65hUnRgu3HOXsGgWXKEH4fzK1u+t9msBpFnTozEx3OITfGQLo3uf4OIiIiIiAwj/153hNqGJu45Z0LLNnc3g7MmR7XMAx/NJkYFYBjWUocf78sjIcyXiVHOvRHut12vQ2AsJJ7a89iI5gTBgZZN4QHelNc2Ut9oazf0vV05+Hm5M6MhDZuHL5tsUzh+Uk8AgMP5lRgGjI1wdoLAn4ziGppsJgCr9uaRV17HOVOGWAWHdEsJAhERERGRYSCvvJb/bEjnstlxjB8T2PMBo5CflwdJYX5sO1HC54cLOWdKFIYxhJrjVRfD4Y9h+hXg5sCtmHegtUxhuwSBtSJDcVVrHwJrekEuZ0+JwuPE5zTFL6QBD9I7SxAUVBIX4ouvl3Of6qeE+1PfZGNnZik/eHk7d7y4lZQIf/UfGGY0xUBEREREZBh4bM1hmmwm3z97Qs+DR7HJ0UF8uCcXgHOH2tPrfe9aPQUcmV7QLHISFLYmCCICvAEorKwjOthavrB5esHlEzxh5V48z76asAyvDj0BAHZnlTHVPhXDmZorWL727w24GQZ3nzWeO5eO1/SCYUYVBCIiIiIiQ9yJompWbDrB1fMSSArXVILuTI6xqiuCfDyYnxLm4mhOsvt1CBsHMbMcPyZykrXUoc2aUhBhryAoalNB0Dy9YLHnfmtDyhkkhft1qCAoq2ngWGEVMxNC+vMuOjUpKhB/L3cWjg3jgx+czg+XTVJyYBhSBYGIiIiIiIOabCa1DU2DupRgRnE1X396I17ubnzvrPGDdt3hanK09XR86eQxeA6lpo0VuXBsPZz5Y+jNtIfISdBYA2UZEJpEuL+9gsDeqDC3rJb3d+Vw1uQxeGV8BF6BEDOLlPDdfHW0qN2pdmWWATAjPtg576mNUH8vtv7yXLw93IbWtA7plSH0FyMiIiIiMjRV1DbwzOfHWPrIWk596FPKaxsG5bqH8yu5+t8bKKtp4MVvLiQ2xHdQrjuczUoIwdvDjUtnxbo6lPbSXgFMmN6L6QUAEZOsV3sfgvCWCoI6ahuauP2FLTQ02fjeWROsBETSqeDuQVK4P9lltdQ2NLWcamdmKQAz4kL6+WY65+PpruTAMKcKAhERERGRLtQ1NvHIRwdYsSmDyrpGJkcHcqK4mk/25XH57HinXuvOl7ZSWFnPqePCOXVcBN4ebtz63GYMA16+/RSmDMC88ZEoOtiHtAeW4e0xhMrby7Nh3cMw7qzWpQsdFWlPEBQegInLCPD2wMvDjcLKen70ehq7ssp44sZ5TPKrhKJDMPdmwFp2EKwKlAlR1rSLnRmlJIf7Eezn6bS3JiOLEgQiIiIiIl149ovjPLn+GBfPjOW2xSnMiAtm8R8/ZeXOHKcmCDKKq3l/Vy7RQT5sPl7MXz8+BEBssA8vfnMhYyOH2FJ9Q9yQSg6YJrx3L9ga4aI/9/54vzDwj4QCq7+AYRhEBnjz8qYTlNc28uPzJ3Hu1ChIe9Uan3y69WLvVXGssKolQZCWWcaCodaXQYYUJQhERERERDpRUdvA4+uOcObESP5+3eyW7RemxvD8huOU1TQQ7OucJ7FrDxYA8NK3FhLh781Xx4o4mFvBFXPjidO0gqGrphTcvcDLr+sx+96BA+/Bub+BsJS+XSdystWo0C48wIus0hounx3Hd84cZ208tg58QiA6FWhNEKQXWSsZ5JfXklteOyD9B2TkUA8CEREREZFOPPvFcUqqG/jhue1LwpfPjKWhyWSVfSk9Z1h3IJ+EMF/GRvgT7OfJedOi+d7ZE5QcGMpyd8Ojs+GFy1tWGOigpgTe/xHEzIRT7uz7tSImWj0ITBOw+iycMjaMP1yR2jrn/9h6SF4Mblb1RLCfJ6F+nhy3r2Sw096gcNYArGAgI4cSBCIiIiIiJymrbuDJ9Uc5Z0pUhyXhZsYHEx/qy3u7cpxyrbrGJr48UsSZEyPV4G24yN0Fz18MjXWQ8RXserXzcavvh6pCuOTv4N6P4u2wsVBXBrWlADx46XRWfOuU1mUES9KhNB1Szmh3WFK4f0uCIC2zFHc3g2mxqiCQrilBICIiIiJykqc+P0pFbWOH6gGw5oBfNCOGzw8VUtJmLfq+2nK8hOr6JpZMHNPvc8kgyN0Fz18Cnr5wx3qIm2slAuoq2o/b/z5s+w+cepdVQdAfQfYVGcqzWza1SyYdX2+92vsPNEsO9+N4oTXFYGdmGRPGBODrNYT6M8iQowSBiIiIiEgbxVX1PPP5MS5KjWFqbOcrByxPjaXRZrJqb/+nGaw9kI+XuxuLxoX3+1zST411sPKHUHi48/15e63KAU8/uGUlhI+DCx6GyjxY9/9ax534Cl7/BsTOgTPv639cLQmCLqpWjq0HvwgYM6Xd5uQIf7LLaqhtaCIts5SZ8SH9j0VGNCUIRERERETaeHzdEWoamrjn3AldjpkeF0RSuB8r0/o/zWDdwQLmp4Ti763+4S538CPY8jSkvdz5/s//bPUBuOVdq+wfIH4uzPo6fPUvKDwE+fvgv1+D4Hi44bXuGxg6KjDGeq3I7nz/8c8h5XQ4aYpKcrg/pgkbjhRRWt3AjARNL5DuKUEgIiIiImJXVdfIfzakc+msOMaPCexynGEYXJQaw5dHiiiqrOvz9bJLaziYV8mZEyP7fA5xorRXrNesrZ3vz9xs3Yg3JweanfMra8rBO3fDC1eAhy98/U3wj3BOXM0JgvJOEgR1lVCe2bJ6QVtJ4VZy4u0dWQCqIJAeKUEgIiIiImL32cECahqauGZ+Qo9jl8+Ipclm8mE/VjNYZ1/ecMkk9R/olmlCU8PAXqOmBA6tAsPNShDYVwxoUVUIJcchbl7HYwPGwJL74MSXUF8FX38DQpOcF5uHF/hHdp4gKMu0XoMTO+xKibCWOly1Nw8vDzcmRXed9BIBUB2TiIiIiIjdqr15hPp5Mi8ptMexU2ICGRvhz7NfHGfZ1GgiA717fb21B/KJCfZhwpiAvoQ7ctlssO1564l9wX5riT93T/jBbvAeoM9q79vQVA/zbrOmGRQftXoMNGuuKojvJEEAsOB26wZ+6qUQPd358QXGQEUnU1rKMqzXkI5JrRA/L4J9PSmraWBWQgie7no+LN3Tb4iIiIiICNDQZOOTfXmcPSUKDwdupAzD4FeXTCOzpJqr/v0l6fbl5HpzvS8OF7FkkpY37GDPm7DyB3BoNXj5W935a0ogN23grpn2KkRMhHm3Wt9nbmm/P3OLVV0QM6vz49094bzfQcKCgYkvKK6LCgJ7giC486qXZHsVwcx49R+QnilBICIiIiICbDpWTHltI8umRjl8zJkTI/nvt06hvKaBK/75JWmZpQ4fuzW9hMq6RvUf6MymJyE0Bf7vANz8Liz/i7U9Z4ASBKUZkP4FpH7NWgnA079jH4KsLTBm6sBVMPQkKKbzBEFpBrh5QGB0p4cl2/sQzFD/AXGAEgQiIiIiIsCqPbn4erpzRi9v2OckhvL6d07Fx9Oda5/4ik3Hins8xmYzeWt7Fh5uBqeOd1Iju5EidxdkfAXzbwM3++1KYLQ1Bz9n58Bcc9dr1mvqVeDmDrGz2ycIbDbr+7i5A3N9RwTGQk0xNNS0316WYS2D6Obe6WFJ4fYKAq1gIA5QgkBERERERj3TNFm1N48zJkbg49n5jVZ3xkUG8L87TyXM34vfrNyLeXKDuzYO5VXwtcc38PLmDC6dFUeQj2d/Qh95Nj8FHj4w64bWbYYBMTMHZoqBaVrTCxJOgbAUa1vcHOtajfYVKoqPQG0ZxM93/vUdFRRrvZ7ch6Ass9MGhc2umhPPj86bxLhI9bmQnilBICIiIiKj3q6sMnLKalk2tfMybUeMCfLhrqXj2ZVVxheHizrsr2+08edVB7jw0fUcLqjk4atm8MjVM/oT9shTU2rdrKdeBX5h7ffFzIT8fdBQ69xr5u2Ggn0w4+rWbXFzrYaFebut75v7EXTVoHAwBDUvdXhSgqA0o9MGhc0Sw/347tLx6nMhDlGCQERERERGvVV78nB3Mzhrcv+WG7x8ThxjAr3517rDHfb9+t09PPrpYZbPiOWTH57J1fMSdNN2sp0roKEa5n+r477oGWA2Qf5e514z7RVrDv+0K1q3NU8lyNpmf90CXoFWE0NXCYqzXtv2IWhqgIpsCI53TUwy4ihBICIiIiKj3qq9uSxIDiPU36tf5/H2cOebp6fwxeEidmaUtmz/4nAhL208wTcXp/CXa2YRHtD7JRFHPJvNml4QPx9iZ3XcHzPTenVmH4KDq2DLszDhvPYVC8HxEBDV2ocgcwvEze5ynv+gCLRXEFS0SRBU5IBp63IFA5HeUoJAREREREa1Y4VVHMyrZNk0x1cv6M51CxIJ9PHg3+uOAFBZ18iPX09jbIQ/9543ySnXGJGOrYOiw51XDwCEJoN3sHP6EJgmfPUvWHENhI2Fix5pv98wrCqCzC1WU8C83RDnwukFAD5B4BXQfopBqX2Jw26mGIj0hhIEIiIiIjKqrd6bC8C5vVjesDuBPp7ctCiJD/fkcrSgkj9+sJ/sshr+31Uz+tQAcdTY/BT4RcC0yzrfbxgQM6P/FQRNDfDeD+HD+2DShXDrh60NANuKmwNFh+DYerA1urb/QLOgWCjPav2+zJ4g6KZJoUhvKEEgIiIiIqPa+7tymRoTRHyon9POecupKXi5u/HDV3fywlfp3HpaCvOSw3o+cLQqPgoH3oc5N4FHN9MvomdA3h5oauz7tT76OWx5Bk77AXztBfDy73xcc8XA5ifbf+9KgTHtVzForiAIjnNNPDLiKEEgIiIiIqPW4fwKdmSUctnsTp4g90NkoDdXz4tnR0YpyeF+3LtMUwu6teExq1Hgwm93Py5mJjTWQuHBvl2nqgi2PQ+zb4Rzfw1u3dwOxc62Xg+tsub4BzqnwqRfgmLbTzEoywD/SPD0dV1MMqIoQSAiIiIio9ZrWzNxdzO4bLbzn8DeceY4FiSH8edrZuHrNUKmFpRlwsGPnHvOqkLY/hLMuAYCe1hmMsa+LGRf+xBse85KMCy6q+exviEQPsH6unlVA1cLirUqCGxN1vdlGWpQKE6lBIGIiIiIjEqNTTbe3JbF0kmRjAn0cfr540P9ePWORcxJDHX6uV1mze/hv9dA0RHnnXPTk9BYA6d+r+ex4RPAw7dvfQiaGqxrjV0KYyY7dkxzYmAo9B8Aa4qB2QRVBdb3pRla4lCcSgkCERERERmVPjtUQEFFHVfN1RNYh5gmHP4YMK2Ggs5QXw2bnrCaBUY6MA3D3QOip0POSRUEDTVWfN3Z+7b19P2UOx2PrzkxMBT6D0BrM8XybOv9lmVCiBoUivMoQSAiIiIio9JrWzIJ8/firMljXB3K8JC7CyrzwDcMtr8IdZX9P+eOl6CmGE692/FjomdYUwxsNuv7knT420z49DfdH/fVvyB8PIw/x/FrzbwWLvoTJCx0/JiB1DZBUF1kVV5oioE4kRIEIiIiIjLqFFfV8/G+PC6bFYeXh/6X2CGHP7ZeL/k71JXDzhX9O19TI3z5d4hfAImnOH5czEzr+qXHoa4CVlxrJS4yN3d5SFDZAcjaAgvv6L4x4cm8A2H+N3t3zEAKtCcIKnKg9IT1dYgSBOI8Q+Q3XURERERk8Ly9I4uGJpOr52n+tsMOfwLRqTD5IoidY00NaH6K3xd734LSdDjtbjAMx49rblSYtQ1evw0KDkDUdMjf3+Uh8ZnvgHcwzLyu7/EOBf6R1moP5dnW9AJQDwJxKiUIRERERGTUeW1LJtPjgpgSE+TqUIaH2nLI+ArGn2vdzC+8w1pq8Oia3p2nsd7qBfDiVfDmtyBiotV/oDfGTLVukj/8KRz6CC74o7UCQlU+VBd3HF+WRWTBlzDnRvAO6N21hho3N6tRYXm2tYIBaIqBOJUSBCIiIiIyquzJLmNvTjlXqzmh446tA1tj6/z9aZeB/xirisBRhz+BP0+GV2+CvD2w+Idw0zvg1sslID28IXKKlRBYcDss+BZE2lclKDjQcfy+dzCwwfzbenedoSowBiqyrRUMvALAdwStkiEu5+HqAEREREREBtP/tmXh5e7GpbNiXR3K8HH4Y/AKhIQF1vce3jDvG7Du/1lLHoaP6/74+mp49/tWg8PLH4dxZ/U+MdDWrOshZwec9wfr++ZlCwv2Q9Ki9mOzd1DnFYp32Ni+X28oCYqBvL3gHWRNL+jN9AyRHqiCQERERERGDZvN5P1dOZwxMYIQPy9XhzM8mCYc+hjGngnunq3b591q3eRv/HfP5/jy71ZJ/MV/hQnn9i85ALDoTrjiCWvZQ4CgePD077yCIGcHFYHj+3e9oSQornWKgaYXiJMpQSAiIiIio8b2jFKyy2q5MDXG1aEMHwUHoDzTurFvKzAaZt0Am5+CjK5XEKAsEz7/C0y9DJIXD0yMbm4QOREK9rXfXl8FhQepDBgh1QNgTTFoqIKCg1rBQJxOCQIRERERGTXe35WDl7sb50yNcnUow0fz8objzu64b9lvrKf3b34L6io7P371rwATzn1wwEIErD4EJ1cQ5O4G00ZFYA9TIIaTIPvUmMYaVRCI0/WYIDAMw8cwjE2GYew0DGOPYRi/HozAREREREScyWYz+WBXDqdPiCDIx7PnA8RyeLV1893Z02qfYLjicSg5Dh/9rOP+9A2w+3U49XsQmjSwcUZOgoocqClt3ZazE4DKgBGYIAAlCMTpHKkgqAPOMk1zJjALON8wjFMGNCoRERERESfbkanpBb1WXwXpX7auXtCZpFPhtO/DtufhwAet22tK4cP7IDAWFt8z4KESOcV6LTzYui1nJ/hFUOcdPvDXHyyBbX5/NcVAnKzHVQxM0zSB5nohT/s/5kAGJSIiIiLibO+naXpBr9SUwns/hKb6jv0HTrb0Z9Yyhm/fBbO/DsfXQ/Z2MG1wxVPg5T/w8UZOsl4L9reutpCzA2JnjaxO/20TBKogECdzqAeBYRjuhmHsAPKB1aZpbhzQqEREREREnMg0TT7YncvpEyII9tX0gh4dWw//Og32vAVLfw4pZ3Y/3sMbrnwS6ithwz/AzRPO+BHcugpmXD0oIROSCB6+rX0IGmohfx/EzByc6w8WTx/wCwc3D6tRpIgT9VhBAGCaZhMwyzCMEOB/hmFMN01zd9sxhmHcDtwOEBUVxdq1a50cqgxHlZWV+l0QcTH9HYq4nv4OXe9IaRNZpbVcmNCkn0V3TJOUYy+SeOINanyj2Tf7ISrMibBunUOHe83/F03uvjR5+FobjtbA0bUDF+9J5vrE0LD/C9K81xJYfpC5ZhO7iz2o9B1Zf4PzjCDcvdzZ+Nl6V4ciI4xDCYJmpmmWGoaxFjgf2H3SvieAJwDmzZtnLlmyxEkhynC2du1a9Lsg4lr6OxRxPf0dut7nK/fi6X6cu65YogqC7hxaDeteh5nX43fhw8z1DnB1RL1TNA/Sv7T+3jYfAWD6OTdQuPPYyPobrL0A6itH1nuSIcGRVQwi7ZUDGIbhC5wD7B/guEREREREnKJ1ekGkkgPdsdngkwchJAku/hsMt+QAwJjJUJ4JteVWg0KfEGvqwUhz/h/gkr+7OgoZgRzpQRADrDEMIw3YjNWDYOXAhiUiIiIi4hxpmWVkldZo9YKe7HsbctOshoMeXq6Opm8iJ1uvhYdGZoNCkQHmyCoGacDsQYhFRERERMTpvjhSCMDSSZEujmQIa2qENb+3brBTB6mp4EBoThDkpkHeXlh0p2vjERlmetWDQERERERkuNl0rJjxYwIID/B2dShDV9orUHgQrnkR3NxdHU3fhSSBuzfsfQtsDSNvBQORAebQMociIiIiIsNRk81k6/ESFqSEuTqUoauxDtY+BLGzYfJyV0fTP+4eEDEBjtpXXYiZ5dJwRIYbJQhEREREZMTal1NORV0jC5Ug6Nq2/0DZCTj7/pExXz9yMmCCdxCEprg6GpFhRQkCERERERmxNh0rBmB+shIEnSrLgjW/g+TTYexSV0fjHM19CGJmgptud0R6Q38xIiIiIjJibT5eTHyoL7Ehvq4OZeixNcGb34KmBmtZw5FQPQAQOcl6Vf8BkV5TgkBERERERiTTNNl0rFj9B7ry2SOQ/gVc9CcIH+fqaJwnZia4eUDSqa6ORGTY0SoGIiIiIjIiHSmooqiqngWaXtBR+pew7iGYcS3MvNbV0ThXaBLcsxcCxrg6EpFhRwkCERERERmRmvsPqILgJNXF8Ma3IDQZLnrE1dEMjMAoV0cgMiwpQSAiIiIiI9Lm48VEBHiTEuHv6lCGlvf+Dypz4bbV4B3o6mhEZAhRDwIRERERGRQFFXU8vu4INps5KNfbdKyYhSlhGCOl+Z4z7Pkf7HkTltwHcXNcHY2IDDFKEIiIiIjIoHjmi2P84YP97MwsHfBrZZZUk1Vaw/zk0AG/1rBRWWBVD8TOhtPucXU0IjIEKUEgIiIiIoNi1Z5coLU3wEDafLy5/0D4gF9rWDBNeO+HUFcBl/0b3DXTWEQ6UoJARERERAbc4fxKjhRUAYOTINh0rJggHw8mRWuOPQC734B978DSn8GYya6ORkSGKKUORURERGTArdprVQ+cOTGSzceLsdlM3NwGrjfAxmPFzEsOw30ArzGkNdZDaToUH4OS47D29xA3DxZ9z9WRicgQpgSBiIiIiAy4j/bkMTM+mEtmxrLuYAEH8iqYEhM0INfanVXG0YIqrp2fMCDnH/Iyt8KLV0Btaeu2gGi47F+aWiAi3dK/IURERERkQOWW1bIzo5QfnTeJBSlhgNUjYCASBKZp8rv39hHm78U18xOdfv4hr+AAvHQl+IbABX+E0BQISwH/SNBqDiLSA/UgEBEREZEBtdo+veC8aVHEh/oSG+zDxgHqQ/Dxvnw2HC3innMmEOzrOSDXGLJKM+CFy8HdC258C2ZeC4kLIWCMkgMi4hAlCERERERkQK3am8fYSH/GjwnEMAzmp4Sx6Vgxpmk69Tr1jTZ+//4+xo8J4LoFo6x6oKrQSg7UVcLX37CqBkREekkJAhEREREZMGXVDWw4UsSyqdEt2xakhFFQUUd6UbVTr/XSxnSOFVbx8wun4OE+Sv4319ZkrVDwzHlQlgHXvwzRqa6OSkSGKfUgEBEREZEBs+ZAPo02k2XTolq2LbT3Idh0rJjkCH+nXKe0up6/fnyI0ydEsGRSpFPOOaQ1NcCu12D9n6DoMERMgutfgaRTXR2ZiAxjShCIiIiIyID5aE8uYwK9mRUf0rJtXGQAYf5ebDxWzNectNLAXz8+REVtAz+/aArGSJ9vf/xzeO//oGA/RKXC1c/DlEvAbZRUTYjIgFGCQEREREQGRFVdI+sOFnD57Djc3Fpv2g3DYH5yKJuPO6dR4f+2Z/Lcl8e5aVESk6MHZunEIaEiD1b/EtJegZBEuOZFmLxcDQhFxGmUIBARERERpzNNk5/9bxc1DU1cOTe+w/4FKeF8tCePnLIaYoJ9+3ydjUeL+MnruzhlbBi/uGhqf0IeumxNsPlp+PS30FANp98Lp/8fePm5OjIRGWGUIBARERERp3vuy+O8vSObe5dNZE5iaIf9C5Jb+xBcOiuuy/PUNjRxOL+SfTnl5FfUcebESKbFBmEYBkcKKrn9ha0khPny+Nfn4eUxAkvsM7fAez+EnJ0wdglc+AhETHB1VCIyQilBICIiIiJOtfFoEb97bx/nTo3iziXjOx0zJSaQAG8PNh/vPEFQWl3PN5/fwvaMUppsrcshPvzRAcZG+LN8Rgxv78zGw83g2VsWEOznOWDvZ9DUlkPBAajMhYpcyNoGO1dAQBRc9QxMu0LTCURkQClBICIiIiJOk1dey3f/u53EMD/+9LWZ7XoPtOXh7sbcpFDWHiigtqEJH0/3dvt/994+tmeU8u0zxjItNpjJMYEE+3qyem8e7+7M5u9rDuPl7saK208hMXwElNrXVcK/TrWWKmzm7gWn3AlL7gOfEdxbQUSGDCUIRERERMQpGptsfPelbVTXN7LiWwsJ8un+qf6ti1O4+ZlNPLhyL7+/PLVl+5eHC3ltaybfWTKOH58/ud0x1y1I5LoFieSX11Jd3+S0ZRJdbsNjVnLg0scgOhUCY8AvHNzcez5WRMRJlCAQEREREad4/LOjbEkv4W/XzmJCVGCP48+cGMm3zxzL4+uOsmhsOBfPjKW2oYmf/W8XSeF+fP/srufajwnycWborlVVCF8+ClMuhtlfd3U0IjKKjcBOLiIiIiLiLFV1jTy25jCFlXXdjtuXU85fPz7IRakxXDIz1uHz37tsEnOTQvnpm7s4XljF3z89xPGian5/eWqHaQcj1mePWKsTnHW/qyMRkVFOCQIRERER6dJDH+zn4Y8O8JPX0zBNs9Mx9Y02fvjqToJ9vfjNZdMxetFIz9PdjUevm427m8Gtz2/m8XVHuXJOPKeNj3DWWxjaStJhy9NW5UDkRFdHIyKjnBIEIiIiItKpLw8X8sJX6UwYE8An+/N5Y1tWp+Me/eQQ+3LKeeiKVML8vXp9nbgQX/509UyOFlQR5OvJLy6a0t/Qh481vwfDDZb81NWRiIgoQSAiIiIy2pimSXltQ7djquoa+fEbaaRE+PPWd09jfnIov353DzllNe3GbTtRwj/XHubqufGcMzWqzzGdMzWKv107i6dunkdoH5IMw1Lubkh7BRZ+G4Icn5YhIjJQlCAQERERGUWq6xu57fktzPvNx7zwVXqX0wYe+mA/WaU1PHzVDPy9PXj4qpk0Npnc98YuTNOkrrGJv39yiOue+IqYYF9+efHUfsd26aw45iSG9vs8w0JtGbxzl7V84eJ7XB2NiAigVQxERERERo2Cijpue34zu7PKmB4XzC/f2s3Go0X84YpUAtssSdg8teC2xSnMSw4DIDnCn/sumMyv3tnDb1buY+3BfI4WVHFRagy/XD61xyUNpY26CnjxKquC4JoXwHeUJEVEZMhTgkBERERkFDhaUMktz24mv6KWJ26cx1mTx/Dvz47wp1UH2ZNdzk2LkjhWWMX+3Ap2Z5WREuHPvcsmtTvHjack8eHuXJ754hhJ4X489435LJk0xkXvaJiqq4SXroasrfC152HSBa6OSESkhRIEIiIiIiPcvpxyrn/yK9wMg5dvX8SshBAA7lwynnlJYXxvxTZ+/e5eAr09mBgdyKWz4rhtcQq+Xu2XGXRzM3j0utms2Z/PJbNiR88yhM5SXw0rroWMTXDV0zDlYldHJCLSjhIEIiIiIiNYRnE1Nz+zCR9Pd1Z86xSSI/zb7V+QEsbae5dSUl1PTLBPj0sURgZ687X5CQMZ8shUUwL/vRYyN8EVT8K0y10dkYhIB0oQiIiIiIxQhZV13PTMJuoabbx+x6IOyYFmvl7u+Hr5DnJ0o0h5NrxwBRQfgaufg6mXujoiEZFOKUEgIiIiMgJV1jXyjWc3k1NWw0vfPIUJUYGuDml0KjwEL1wONaXw9Tcg5QxXRyQi0iUlCERERERGmPyKWu5esZ29OeU8edNc5iapS75LlOfAM+eB4Qa3rITYWa6OSESkW0oQiIiIiIwQpmny6pYMfvfePmobbfzp6pmcNTnK1WGNXpuftHoPfGcDjJns6mhERHqkBIGIiIjICJBRXM1P3kjjyyNFLEgJ46ErUhkbGeDqsEavhhrY8ixMulDJAREZNpQgEBERERnmmmwmNz+7iYLyOn5/eSrXzk/Aza371QhkgO1+A2qKYeEdro5ERMRhShCIiIiIDHMf7s7laEEVj10/h4tmxLg6HDFN+OrfMGYaJC92dTQiIg5TgkBERERkGDNNk3+uPczYCH/Onx7t6nBGNtOE4qOQvR1K06EkHUpPWM0Hz/oluLlb49K/hLxdcPGjYKiSQ0SGDyUIRERERIaxzw4Vsie7nP935QzcNa3A+Ww22PUqHP4Ejn8OFdmt+/wiICAKPv8LlGfDpf8Edw/Y+G/wDYXUq10Xt4hIHyhBICIiIjKMPbbmMDHBPlw2O87VoYxMW56G9+8F/zHWdIHkxZCwAEJTwNveBPKzR+DT34Ct0aok2L8STr0bvPxcG7uISC8pQSAiIiIyTG05XsymY8Xcv3wqXh5urg5n5KmrgLUPQdJpcMt7XU8XOONecPeE1ffD0XXWtvnfHLw4RUScRAkCERERkWHqn2uPEOrnybULElwdysj05d+huhDOfbXnXgKnfR/cPOGjn8KUSyBEPxMRGX6UIBAREREZhramF/Pp/nx+eO5E/Lz0v3ROV5ELX/4Dpl4G8XMdO2bRnRA7GyInDWhoIiIDRf81ERERERkm8spreXdnNu+m5bAzo5QQP09uXpTs6rBGprUPQVMdnH1/745LWjQw8YiIDAIlCERERESGgQ1Hirjx6Y002kymxwXxswsnc8nMOIL9PF0d2shTeAi2/Qfm3Qrh41wdjYjIoFGCQERERGSIM02Thz7cT1SQDy/ctoCxkQGuDqlzTY2Q9opVYh8/r+P+vD1w5FM45U5wcx/8+LpydC1kbLb6DBhucGgVePrCmT9xdWQiIoNKCQIRERGRIe7T/fnszCjloStSXZMcqCkFn+DuG/VlbIb37oHcXdZN9uIfWjfYHl5gmrDpCVj1S6tsP3IyTDh30MLvkmnCZw/Dmt+dtMOA8x+CgEiXhCUi4ipKEIiIiIgMYaZp8ufVB0kM8+PKufGDH0D2DnjyLEhYCGf/EpJObb+/Ig/W/h62Pg+BMXDFU3BsLax/BA6vhvP+AF/81XoqP2EZZGyCtFd7nyCoq4SaYghJdM77aqiBt+6EPW/CjGth+Z+tVQhMm7Xf08c51xERGUaUIBAREREZwj7ak8ue7HL+dPVMPN3dBj+Abf8Bd08oPgrPXgDjzobZN0D2djiyFvJ2geEOi74LS+4D70CYcTVMvADevRueuxDcveGCh2HBt2DlD6wEQV0lePdQDZG7C/a/B0fXQeZmsDXAOQ/A4nv6954KD8Ob37SSH+f82lqisKdlDEVERgElCERERESGKJvN5C+rDzE20p/LZscNfgANtbD7dZhyCVz8N9j8FHz+FzjyCbh7WVUFZ/3S2h85sf2xU5ZD/HzY+C9IvRqiplnbU78GW5+DA+/DjK91fe3iY/DEUrA1QuwsKwFRcgw+fsDqdXDmj3r3XmxNcPAj6z0c+QS8AuDa/8LkC3t3HhGREUwJAhEREZEh6r1dORzIq+DR62bj7uaCJ9wH3oPaMph1PXj5wWl3w9xbIH8vRKeCl3/3xwdGWU/820pcBMEJVhVBdwmCA+9bFQPf3WQ1PQTrJv/t78Ka31qJgyX3OfbkvyQdnl8OpScgMBaW/hzm3ASB0T0fKyIyiihBICIiIjIEFVXW8ZfVB5kYFcDy1BjnnryqCPavtL529wQ3D0hYAKHJ7cdtfwmC4iHlzNZtPkGQeErfr+3mBqlXwRePQmVB140AD3wAY6a2JgfAWvng0ses13UPASYs/VnP19z6LJRlwdXPw+Tl4K7/BRYR6Yz+7SgiIiLiIu+l5fDB7hxuW5zC7MTQlu1fHinkBy/voLSmgadumoebM6sHakqtp+n5e9tv9w2D73wBQbHW9+XZcHQNnP5/1k29M6V+zZqqsOd/sPD2jvuriyH9y857Dbi5w8V/h8Z6awWCube0xtwZ04Tdb8LYM2HaZc56ByIiI5ILOt2IiIiISElVPT/73y5WpuVw+T+/5OZnNrE1vZg/rz7IDU9tJMDHg7fuPI0zJjpxqb2GWnj5eig8BNe9Avfshe/vhG98CI218ObtVhk/wM4VVkf/Wdc77/rNoqZCVCqkvdL5/kOrwWyCSV30B3Bzg6U/teLb8VL318reBqXpMO2K/sUsIjIKKEEgIiIi4gJ/+fggFbUNvHnnqfzk/MmkZZZy5b828Ognh7hyTjzv3rWYqbFBzrugrQn+dzukfwGX/xsmnQ/Bcda0gqRFcOHDcHy99WTfNGHHfyHxVAgb67wY2ppxNWRtgaIjHfcdeB8CoiF2dtfHh42F5NNh+4tgs3U9bveb1vKFU5b3P2YRkRFOUwxEREREBtn+3HJe/Cqdr5+SxJzEUOYkhnLToiRe3pxBVJA3y2d0UzLfF6YJH/4U9r4Ny35n9QA42awb4MgaWPN78PCGosP9X06wO9OvgtW/gl2vw5KftG5vrIPDH1sx9jS1Yc5N8Oa3IP1zSDmj436bzZrGMP5s8A3tuF9ERNpRBYGIiIjIIDJNkwff3Uugjyf3nNO6NKC/twe3LU7pe3Kg5Lj1T2fWPwKbHodFd8Gpd3U+xjBg+Z8hOB5W/QI8/WDqpX2LxRHBcZC8GLa/ALXlrduPr4f6yq6nF7Q15WLwDoZtL3S+P3MTlGdpeoGIiIOUIBAREREZRKv25vHlkSJ+eO5EQv29nHPS+ip45nz45yLY9277fRufgE9/CzOuhXN/0/15fILhqmesVQ2mXQHegc6JrytLf27dwK+8x6pyAGv1Ak+/9isndMXT15qqsPdtqCnpuH/3G+DuDZMucG7cIiIjlBIEIiIiIoOktqGJ3763l4lRAdywMNF5J/7iUajIgZBEeOXrsP5P9j4CK+CDH1lL+136mGOrEcTPg+9ssHoSDLSkRdYyhbtftyoJTNNKEIw7Czx9HDvHnJugqc6aqtCWrclKHExcZi3NKCIiPVIPAhEREZFB8srmDDKKa3jhtgV4uDvpOU1ZFnzxN5h2OVz2L3j7u/DJg3BsPRz7zHoSf+XT4N6L/+2LnNjzGGdZ/EMr1vd/bDUTLM+Cs37h+PExMyF6Bmz7Dyz4Vuv29C+gMk/TC0REekEVBCIiIiL9YJomxwur+HhvHv9ed4QfvbaT1XvzOoxrspk888UxZieGcPqEPixduG8l/DXVeire1ie/tpb7O+fXVsn9lU9bpftH11irAFz7X8efxruCmztc8SR4B8Bb3wHDDSac17tzzLkJctMgZ2frtt1vWlMVJvbyXCIio5gqCERERET6qK6xie+8uI1P9+e3bPNyd+PjfXms/dFSgn09W7Z/vC+P9KJqfnze5N5f6Nh6eP1W6+tXb4Zlv4VF34WsbZD2irXaQGiStd8w4Mwfw+SLrCUMvfz78Q4HSWAUXPEEvHAFJJwC/uG9Oz71Kvjo5/DS18A/0ko6FByweg8Mh/cvIjJE9JggMAwjAfgPEA3YgCdM0/zbQAcmIiIiMpQ1NNm467/b+XR/PvecM5HFEyIYPyaAjOJqlv/9c/619gj3XdCaDHhq/VHiQnw5b1pU7y6UvQNWXAdhKXDj/+DD+2DVz6HkGOTuBv8xVpn+yaKm9e8NDrZxZ8HXnrf6KPSWbyhc8BAcXQtNjWBrhIAxcOr3nB6miMhI5kgFQSPwf6ZpbjMMIxDYahjGatM09w5wbCIiIiJDUpPN5J5XdrB6bx6/vmQaN5+a3LIvOC6Yy2fH8cwXx7hxURJxIb4cLW1i8/ESfnHRlN71Hig6Ai9eCb4h8PU3ISgWrnoOPnnA6jsAcPGjI6cJX3+WVZx3q/WPiIj0WY//hTJNM8c0zW32ryuAfUDcQAcmIiIiMhTZbCY/fj2NlWk5/PSCye2SA83+b5nV5O9PHx0A4KPjDQR6e3DN/ATHL1RwEP5zGWBalQPB9v/9cnODcx+ES/5hzb2f/fX+vSERERE7w2xec9aRwYaRDHwGTDdNs/ykfbcDtwNERUXNffnll50YpgxXlZWVBAQEuDoMkVFNf4cizvXhsQZePlDPZeM9uWy8V5fjXj1QzwfHGvjebG/+sb2WZcmeXDvZ26FrhBZvZ9qeh7G5eZA2434qA8c7K3yRUUn/LRRptXTp0q2mac7rbJ/DCQLDMAKAdcDvTNN8s7ux8+bNM7ds2dLrQGXkWbt2LUuWLHF1GCKjmv4ORZynsLKOpQ+vZW5yKM/eMh/DMLocW1bTwJKH11BZ10iTzeSzHy8lPtSv/SBbEzTWtm+kt+lJ+OAnEDkZrn+5b3PyRaQd/bdQpJVhGF0mCBxaxcAwDE/gDeClnpIDIiIiIiPVn1YdoKahiV8un9ptcgAg2NeT7501gQdX7mVBtLuVHGiogS3PWkvy5e+1Ou031oKnv9VUzzsAcnfBxPPhyqfAO3CQ3pmIiIhjqxgYwNPAPtM0/zzwIYmIiIgMPbuzynh5cwa3npbCuEjHSpW/fkoSeeW1jDVzrA2f/hY2/AMCY2DMFJh3G/hHQFUhVOZZ/yz5KZzxI2upPhERkUHkSAXBacCNwC7DMHbYt/3MNM33BywqERERkSHENE0eXLmXUD8v7j57QtcDbTb47P9B5CSYdjleHm789MIprF2bB8XHYNMTVlPBSx8bvOBFREQc1GOCwDTNz4Hua+hERERERrD3d+Wy6Vgxv7t8OsG+nl0PXP1Lq0LAzRNCkiBuTuu+jx8ANw9Y+osBj1dERKQverEQr4iIiMjQdTi/kpVp2aRlllJSVU9vVmrqTklVPb9/fx+TowO5dn43DQO/+JuVHJh9IwRGw2u3QE0pAEFl+2DvW3Da9yEoxilxiYiIOJtDTQpFREREhrKymgauf/Ir8ivqWrYFenswJTaIeUmhzE8OY05iKMF+nT/9f2dnNnuyyrhz6fh2FQLpRVV849nNFFTW8ddrZ+Hu1kVR5Y7/wur74f+3d99xUVxbAMd/s/TeOwJ2QaxgN7EbjV1jiSYaNTExvZeX+l56782YqNGosSW2qLHGFit2RUQRKRYQpdfdeX9cS4ygoMACnu/nsx91d3b2DO6wO+fee07jwdDvC0jaAVN6w8JHYNh06sX+pOoOtH+sXI9bCCGEKE+SIBBCCCFEtffO0kOkZuXz3T0RGDQ4kZbDibQc9iSmM2n9Mb5ZdxQrC41JoyPp0tD7iuemZObz0vy9ZBcYWbAridf7hdGniR+7E85z/7QdGHWdmfe3ITLEvfgXj1kBCx+FOp1h0HdgMECt1tD9DfjzFZg1AufMGBjwzZXtDIUQQogqRhIEQgghhKjWNhxJ4dcdCTzYqQ69wn2vejy3wMjuhPO8tnA/r/y2n5VP34699eWvQJ+vjiG/yMRXI1vw/V/HeHTmLmbUiWd3wnm8nWyZOrYVdUrqWnByL8wdC77hMHwGWNpcfqzdo3B8E8QsI9OxNk7N7i7vQxdCCCHKldQgEEIIIUS1lZ1fxIvz91Hb04Gnujcodhs7awva1fXg7UFNSDqfy5drYi89Fnsmi1nbEhjVJoi+Tf357eH2vNo3jL2J6TT0dWbBw+1LTg5knIRZI8DOFUbOARunKx/XNBj4DYQPIabBI2pmgRBCCFGFyQwCIYQQQlRbH644THJ6LnMebIetlcU1t21d252hEYH8sP4Yg1oE0MDHifeWRWNvZXGpdaGlhYHxHWszLDIQOysLLC1KuKgvyFbJgbx0GLdcFSUsjr073PUTmevW3cRRCiGEEJVDUtlCCCGEqJY2x6Yy7e/jjG4bTKuS6gP8y0t3huJoa8krv+3n76NnWXXoNA91rouHo80V2znZWpWcHDCZYMEEOLUXhvwIvk1u9lCEEEKIKkESBEIIIYSodjbFpjJ+2g5qezrwfK9GpX6eu4M1L/VuxLbjaTw0Yyd+LraM71i7jC/+GUQvgZ5vQ8NeZXuuEEIIUYVJgkAIIYQQ1cqa6NOMnbqdIHd7Zk9oi4NN2VZMDo2oRUSwG+m5hTzbs+F1lyZc4Uw0rHsXQvtD24lljFwIIYSo2qQGgRBCCCGqjT/2neTxWbsI9XPm53GtcXOwLvM+DAaNz0c0Z+XB0wxqEVD6JxqLYOHDYO0IfT5RRQiFEEKIGkQSBEKIW46u65xMz+PQyQzO5RTSt6lf2UYQhRBmsS0ujUdnRtEiyI0pY1vhbGt1w/sKdLNnbIcyLi34+ytI2gl3/QSOXjf82kIIIURVJQkCIcQtw2jSeW7eHtZEn+F8TuGl+9fHpPD5iOZoMhooRKUwmXQMhrKdbwVFJl7+bR9+LnZMG9caxzIuK7hpKYdh7TsQ2g8aD67c1xZCCCEqiSQIhBC3jAVRiSyISqJfM39ahbgR5ufMxthUPlt1hCYBLjxwex1zhyhEjVZoNPHRn4eZ/nc8X41sQddGPqV+7uSNxzhyJouf7ous/OSAsQgWPgLW9rK0QAghRI0mCQIhxC0hr9DIZ6uO0DTQhS/+MVsgItiNw6cyeXfZIUL9nOlY39PMkQpRMyWk5fDYrF3sTjiPh4M1j83cxfyH29PI17lUz/1i9RF6NfYtU1KhTArzwFQINk5XP7buHUjcrloaOnpXzOsLIYQQVYB0MRBC3BJmbIkn6XwuL/RqdMVSAk3T+GhoM+p5O/LYrCgS0nLMGKUQNY+u6yzZm8ydX2zg6Jksvh7ZkqWP34ajrSXjp+4gNSv/iu0LjSYKikxXPP+1hfux0DRe7x9WcYHOHgmfN4OTe6+8P+ZP2PAxtLgXmtxVca8vhBBCVAGSIBBC1HiZeYV8vTaWjvU86VDv6hkCDjaWfH9vJEUmnfun7SAuNdsMUQpR8+xOOM+ISVt4dOYu6no58scTt9GnqR++LrZMHt2Ks9n5TPh5B3mFRmLPZPLfxQeIeHMlzf/3JxNn7GRBVCJzdyay9nAKT/VogJ+LXcUEemwdHF0N+VkwrZ8qRAhwPgF+mwA+TeDODyvmtYUQQogqRJYYCCGqlZPpuTw/by+dG3ozrkNIqQoL/rD+GOdyCnm+V8MSt6nt6cA3o1ry8C9R9PpsPc/0bMC4DrWxtCifPOr8nYmE+jkT5n/96dRCVFeFRhMpmfkknc9l6qbjLN13Eg8Ha/43oDF3tw7C6h/nU5NAFz4Z1pyHf4mi60frSE7Pw8pCo1e4H862lqw8eJpl+08BEObnzH3tQyomaF2H1f8D50AY/TvMGAI/D4QRM2HVG2AywrBpYFVByQkhhBCiCpEEgRCi2khIy2Hk5C0knctlw5FUtsWd5YO7muFiV3Krs5TMfCZvjKNPEz+aBrpec/+31fdi1dOdeOX3/bzzRzRL957kw6HNaOBTzJrkMli4O4ln5u4hItiN+RPb39S+hKiKpv99nM9XH+FsdgG6ru6zt7bg8W71mXB7nRKLCt7ZxI+X7wxlzo4EXujViKGRgXg62gDw5oBw9ialsyEmhd5N/MotWXeVw3+oGQP9vwLP+jB2mZpFMK2venzYdPCoWzGvLYQQQlQxkiAQQlQLcanZjPphC9kFRhY83IEdx9N4b1k0/b7cyDejWhIe4HLF9rquE3XiHF+uiSW/yMQzPRuU6nV8nG2ZdG8ES/ed5PWFBxj8zWamjm1FZIj7DcV9NCWL/yzYh52VBTvjz3HibA5BHvY3tC8hqqLtx9N4Y/FBWga5MqpNML4utvg429A00PXSxf61PHB7nWI7iBgMGs1rudK8lmsFRH2ByQhr3gKPetDsbnWfS4BKEswZDXU6QVj/int9IYQQooqRBIEQosqLPZPJyB+2UmTSmfVAW8L8nWley5UWQW48OjOKAV9voo6nAw18nKjv40iRUWfRnmROpOVga2XgmZ4NqOPlWOrX0zSNvk39iQx2Z+TkLYz+aRuTx0TSvm7ZOhzkFhh55JcobKws+GF0JEO+3czvu5N4vFv9sv4IhKhUhUYTn62KIfFcLrU9Hajt6UBdL0fC/JwxGC4v6zmXXcDjs3YR6GbHT/e1wsm25Nk8ZpeXDn9/AyEd1U3TYP98OHMQ7poCFv/4SuTkA+NXmC9WIYQQwkwkQSCEqNLO5xQw+sdt6MCvE9pS/x/T/SOC3Vj6+G1M3RTHwZOZ7E9O54/9JwHoUNeTx7vV547GPjd80eLrYsvsCW25Z/JWxk7ZzqTRkXRq4FXq57+x6ACHT2cydWxrIoLdaFPbnd93JfFY13qlqp0ghDlk5xfx8C9R/BWTgp+LLYv2JF9aNhAZ7Ma7g5tQ38cJXdd5bt4eUrPyWTCxQ9VODmSnwvRBcGov/AV4NoRW98OWr8G3CYQNNHeEQgghRJUgCQIhRJWl6zovzN9LSlY+8ye2vyI5cJG7gzVP97xcfDC3wEh+kRFXe+tyicHbyZbZE9pxz+StPDBtB0/1aMCQiAC8nWyv2jYrv4hjKVkcTckiKv48v+5I4NEu9S4lFQa1CODFBfvYm5hOs4qcNi3EDTqblc+4qdvZl5TOe4ObMKJ1EHmFRuLP5rDteBof/3mYO7/YwEOd6mJvbcmqQ2d4vV8YTQJdrr9zc8lIhp8HwPkTMHwG5GXA9smw7Dn1+Kh5YJCmTkIIIQRIgkAIUYXN2HqCFQdO80qf0OsWGLzIztoCO2uLco3D3cGaWQ+05dFZUby/PJqP/jxMpwZe3NnEj9SsfPYlpbM/KZ34szmXnmNh0Ogd7suT3S8vJ+jdxI/XFh7g991JJSYIzmUXMGdHAnmFJh7tWg8Lg8w0EJUjIS2He3/cyqmMPCbdG0n3MB8AbK0saOjrRENfJ+4M9+XtpYf4ck0sAD3CfCquu0B5SDumkgM55+Ce+WppAUCLUZAUBWdjoV5388YohBBCVCGSIBBCVEnRpzJ4c8lBOjXwYlyH2uYOBxd7K6aPb8PRlCzm70xkQVQSa6LPABDoZkeTABeGRgRSz9uJet6OBLnbY2155aiki50V3UK9WbwnmZfvDL2iKvuhkxlM23yc33YlkV9kAiD+bDYf3NW04qq3C3HB6Yw8RkzaQnZBEb/c35aIYLdit/NwtOGT4c0Z3DKQJXuTebF3o6q5XKYgG7b9AJs+U/8esxACIq7cJqClugkhhBDiEkkQCCGqnNwCI4/O3IWLnRUfD2t2RVE0c6vr5cjzvRrxTM+GHDqZQYCrHW4OpV/OMLBFAMv2n2JjbCqdG3pTUGTi7aUHmfZ3PLZWBga3DGRM+2BWHjjNxytjyDea+Gx48yv6xwtxI3RdZ/PRszQNdLmiXkB6TiGjf9zG+ZwCZk9oV6rlAh3re9KxftmKdlaKwlzYMQU2fgLZKVC3G/R6F7waXv+5QgghhJAEgRDCPHRdL3bkcVNsKu8vj+ZoShbTx7UpVZs0c7AwaFe1ViyNzg29cLa15PddSTTydebhX3YSdeI84zrU5vFu9S7VTmjk64y1pYF3l0VTWGTiy5EtsLEs36UT4tbyzbqjfLjiMB4O1jzdswHDI2tRaNQZN207canZTB3bqmrXErieonyY1g8St0Pt26HLDAhqa+6ohBBCiGpFEgRCiEq3eE8yr/y+Hzd7K9rX86RDXU/cHaz5au0RNsWexd/Fls9HtKiaI5Q3ycbSgj5N/fltVyIbY1PJKTDy9ciW9Gnqd9W2D3aqi42lgTcWH+SjFYd5uU+YGSIWNcGSvcl8uOIwPcJ8SM8p5OXf9vPz5njcHayJOnGOb0a2pH29an6+LXtBJQcG/wBNh5k7GiGEEKJakgSBEKLSFBlNvLcsmskb42heyxVPR2sW7U5m5tYTgCoG+GrfMEa1CcLWquaOlg9uGcCsbSfwd7Vi1gNti+3OcNF9HWqzOvoMm2LPVmKEoiaJOnGOp+fsITLYjS/vboGNpYEVB07xzh/R/H3sLG8PCqd3k6sTVNXKrhmwcwp0eFKSA0IIIcRNkASBEKJSpGTm8+jMKLbGpTGmXTAv9wnD2tJAkdHE3qR04s9m0yPMF0ebmv9rqVWIO7MeaEt4gHOpesc3CXBh0vpj5BUaa3TiRJS/hLQcHpi2A19nWyaNjrz0/ukV7keXRt7En82hwTUSVNVC8i5Y8jTU7gRdXzV3NEIIIUS1VvO/iQshzCo1K59pm4/z89/x5BcZ+XR4Mwa1CLz0uKWFgZZBbrQMKr5qek3Vrq5HqbdtEuBCkUkn5nRmqds9iltbodHEb7uS+HzVEQqNJn66rxXu/yqmaWNpUf2TAxnJ8OtocPCCu34CC/laI4QQQtwM+SQVQlSIpPO5fLfuKHN2JFBgNNEzzIenejSgka+zuUOrdi4WQ9yflCEJAnFNhUYTC6IS+WptLAlpuYQHOPPVyBbU83Y0d2jlx2SCY2th51Q4/AdoFjBuGThU8xoKQgghRBUgCQIhRLnKzCvk23VHmbwxDl3XGdwikAmd6lDXqwZdoFSyQDc7nG0t2Z+cbu5QRBWm6zoTZ0Sx6tBpmga68Ea/xnRt5F1st5BqKy0OZgyGtGNg5w5tHoLIceBR19yRCSGEEDWCJAiEEOXCaNL5dXsCn6w8TGpWAYNaBPDsHQ0JcLUzd2jVnqaplooHkiRBIEq2eO9JVh06zXN3NOThznVrVmIAQNfhj+cgKwWG/Aih/cCyarZBFUIIIaorSRAIIW5aQloOz8zZw7bjabQKcePHMa1oVsvV3GHVKE0CXJiy+TiFRhNWFgZzhyPKUW6BkSNnMgn2cMDF7vpFK4tzLruA/y46QLNAFx7qVAOTAwDRSyB2JdzxLjS5y9zRCCGEEDWSJAiEEDdM13XmRyXxxqIDaMDHQ5sxuGVAzbw4MbPGAS4UFJk4cjqLMH+p41AT6LrO0n0neWfpIZLT8wDwc7Glvo8TfZv6MSyyVrHPiz2TSW1PRywMl8+zt/84xPncQqaPb3PF/TVGQTYsexG8G0PrCeaORgghhKixJEEghLghpzPyeGPRAZbtP0Xr2u58MqwZgW725g6rxgq/kBTYn5wuCYIaIPpUBm8sOsCWY2mE+Tnz7B0NOZ2Rz5HTmexJPM/z8/ZyPqeACbdfXltvMum8tzyaSeuP0cDHkZd6h9K5oRebj55l3s5EJnauW3PfG+s/goxEGDJZOhUIIYQQFUg+ZYUQZVJoNDFlU9yF9mk6L/RqxITb69TMUcsqJMTDAUcbS1WHoISRZVE9LN9/kkdm7sLJ1pK3BoZzd+ugK86fIqOJJ3/dzTt/RGPQNO6/rQ6FRhMvzN/Lgqgk+jb1Y39SOmOnbqdtHXeSz+cR4mHPE93qm/GoKlBKDGz+EpqNhOB25o5GCCGEqNEkQSCEKLW/j57l1YX7iT2TRddG3rzWN4wQTwdzh3VLMBg0wvyd2SeFCqu17cfTeHz2bpoGuvDTmFa4OVhftY2lhYHPhjdH1+GtpYcoMulsi0tjTfQZnu7RgMe61qPIpDNr2wk+X3WEs9kFzHygDbZWFmY4ohtQlA/H1kHt28HqGkVMdR2SomD5i2BtDz3+V2khCiGEELcqSRAIIa5L13V+2HCMd5dFU8vNnh/HRNIt1MfcYd1ywv1dmLktHqNJlxkb1dCR05ncP20HgW52JSYHLrK0MPDZiOaYdJ33lkWjafD2oHBGtQkGwMpCY3S7EAa1COBEWg6N/V0q6zBuzvkEmDMakqPA0RduewYixlzuRmAshFN7IXop7J8P546DhTX0/xIcvcwauhBCCHErkASBEOKa8gqN/GfBPhbsSqJPEz8+HNoUe2v51WEO4QHO5BWaOJaSRX0fJ3OHYzb5RUYsNA3LatTN4VR6HmN+2oa1pYFpY1tfMzlwkZWFgS/ubsEnK2OICHKje9jVSTknW6vqkxw4uhbmj4eiArjjHZUEWPYcbPocQvvCqf2QtBOKckEzQO1OcPtz0Kgv2LmaO3ohhBDiliDf8oUQJTqTkceE6TvZnXD+0tRm6VBgPuEB6kJwf3L6LZsgWHf4DBNnRJFbaMTJ1hI3e2vqejnw8bDmuJfiotsctsWl8Z/f9pGeW8ivD7ajlnvpi3laWRh4oVejCoyuBCYT7PgRslMhpAMEtrr2coDr7WvTp7DmLfBsAMN/Ac960PZhtdRg7Tuw/UfwawoR90Gt1hDSERy9y/OIhBBCCFEKkiAQQhQrv8jIfVO2c/xsNt/dE0GvcF9zh3TLq+vliK2VgX2JGQxqYe5oKt/WY2d5aMZO6ng6ckdjX87lFHAup4Bl+07x7Nw9TB4diaEKLb04mpLFe8uiWXnwNH4utvwwJvJSkqdKK8yDhQ+rKf4Af6Gm+Qe2hj4fg3cZEhYZJ+H3h1QioPFgtVTAxlE9pmlQt4u6mUxgqD4zQoQQQoiaShIEQohifbj8MAdPZjB5dGSxU5tF5bMwaIT5ObM/uWYVKjSZdE5n5uHnUvII9d7E84yftoMAVzumj2+Nh6PNpcdaBh3n9UUH+HFjHA/cXueK56XnFmJrZcDGsvIK+BUUmfhwRTQ/bTqOnZUFz93RkPEda1ePIoI5aTB7JJz4G7r/V43on9gC8Rth1wxY/ASMW64u7q8neiksfBSK8qDf59ByTMnPk+SAEEIIUSVIgkAIcZX1MSlM3hjHvW2Da3ZywFgIiTugVptqc4ESHuDCgqgkTCa9So2W36gio4knZu/mj/0neaRzPZ7sXv+q2gIxpzMZ/dM23Bys+OX+tlckBwBGtwtm89FU3l8eTava7jSv5YrJpPPL1njeWxZNmL8zMx9oi1Ul1CxIOp/LI79EsTvhPHe3DuKZng3w/Fe8VVZaHPxyF5w/AXf9BOFD1P0Ne6mbRz2VIDi4EBoPvPr5hXmqwGDiDji+EQ4vBd+mal+eNbQFoxBCCFHDVI9vxEKISnM2K59n5u6hvrcjL/cJNXc4FefoGviuI0zpBavfMHc0pRbu70JWfhHxaTkYTTpns/I5k5Fn7rBuiMmk88L8fSzdd5JWwe58tTaWUZO3Xjqe5PO5vLXkIIO+3oS1hYFfxrfF18X2qv1omsYHQ5rh42zLozOj2Jt4nhE/bOHVhQeo7eXA9uPneG9ZdKnj0nWdlQdPM+CrjTwyM4q8QmOpnrfu8Bn6frGB2DNZfDuqJe8OblJ8csBYCLGrIetMqWOqcNlnYfpAVXNg9KLLyYF/anEveDeGla+pVoUX6Tqs/h+8Gwg/9oAVL8HJPdDxabh/lSQHhBBCiGpEZhAIIS7RdZ3n5+0lPbeQn8e1rrpTos8cAmd/sL2B9dxpx2DFy3D4D3ALgYZ9VBV136bQ5K5yD7W8XVzD3u/LjWQXFKHr6v4Bzf15qXdosRfQVZGu67yx+ADzoxJ5qnsDnuhen/k7E3nl9/3c+cUG2tbxYPn+U+hA36Z+PN2jAUEeJRf3c7G34suRLRj23d/0/2oTTraWfDCkKUMjA/nv4oP8uDGOyGA3ejfxu2ZMf8Wk8OnKGPYkphPgasfepHRSMvL5YUwkLnZWl7bdGZ/G9L/jScspJCuvkKz8Io6cyaKhjxPf3hNBbU+H4l8k8xTMGQMJWy5U6r8dwu+C0H7mq9RflK+WFWSegjFLoFar4rczWMAdb8H0QbD1e+jwuLp/7Tuw4WNVYyB8CARGgpPULBFCCCGqI0kQCHELOJ2Rx5ZjZ9lyLI2d8Wm42lkTGeJGqxB3Qv2cOXQyg02xqWyMTSX6VCav9Q0j1M/Z3GEXb8cUWPIkWDtCsxHQegJ4NSzdc4+ugdn3qL93ex3aPQJo8PMAtVbasz74NauoyMtFQ18n7u9Ym9xCIx4O1rg7WHMyI48pm46z8uBpHutan3EdQ0q15v5Ueh6frYrh5T6hONlaXXf78qLrOu8vP8zPf8fz4O11eLxbPQCGRATSNNCFh3+JYk30GUa3C2FcxxAC3UpX9b9lkBvvDG7C1mNpPN+rIT7OKlnynztD2Z1wnufm7aWhrxN1vByveu7m2FQ+XhnDzvhzBLja8cGQpgxuGcDyA6d46tfdDP/+b6aNa02h0cT7yw+zeE8ybvZWBHk44GRjibeTLT3CfHi0S33srEv42cdvhrn3QX4m3PmRuiDfPw8WPQp/PKfW+7d/DFwCbuTHemN0HRY9phIWd00pOTlwUd2uUL8nrP8Qmo+EqJ9h/QfQ4h7o92W1WaojhBBCiOJp+sXhp3IUGRmp79ixo9z3K6qfdevW0blzZ3OHcUs5mJzB+iMpHE/NJi41m/izOZy6MGXbycaSiBA3zuUUciApnSLT5fPf2sJARLAbPcJ8GNshpGq2M9w6SfVNr9cdHLzVxZWxAOp0UdXRXWuV/NxDi2HeOPCoD6PmXnkRlpUCkzqpEd0H1oKjV8UfSzk7cTaHN5ceZOXB09TxcuCjoc1oGeQGlHwePjIziqV7T/L9vRHc0bjyRnxXHzrN+Gk7uKdtEG8OCL/qvabrOoVGHWvL8rvYTDqfS98vNuDjbMt/+zfGwcYSBxtLTqXn8fnqGLYcS8PX2ZZHu9ZjWGStK15745FUJkzfgaONJem5hQA82KkuD3Wqg731NfLshXmQnqDW9Cdsgw0fgWswDJ8BPmEXDxaSolRLwb2/Aho0v1tNz3evXW7HX6K/PoC1b0PXV+D250r3nDPR8G178A6F0/vVDIjBk9QMA1Ei+TwUwrzkHBTiMk3Tduq6HlnsY5IgEBVJfhlXjIIiEwVGExpg0DQy8gpZvCeZeTsTiT6VCYCnozXBHg6EeDjQyNeJtnU8CPN3xuJCYbucgiJ2J5zn0MlMGvo4ERniVnWXFABs/gr+fBka9VUjnZbWar30zqmw6Quwtod75oNP46ufu+sXNUobEAmj5oCd29XbJO+Cn3qpWQSh/VVBNs/64NUILCpvdP1mrTt8hpd/28/J9Fwm3F6Xp3rU5++NG646D7ceO8vwSVsALk3xrwxGk07vz9dTaNT586nbK6Vw4EV/xaRw35Rt/Ptjz9PRhke61OXu1kElngN7E8/z8C9RRAS78XyvRgS4ltxxgbx0lYyKXXXl/Q37wKBvS14ac/6Eei9H/az+fftz0OEJ9V6vCAd+h7ljoNndMPDb0nUmuGjpM7B9sjofh06tVueIucjnoRDmJeegEJdJgkCYjfwyLh1d10k6n3vdqdTHU7OZuvk4c3ckkF1wdeG0ZrVcGdIygD5N/K6q9F6tbfgEVv8XwgbCkMlXX4ycPgAzhkBBDtw9E0I6qvvPHoWoaarGQJ3OMPyXyz3Yi3NwIax4BdJPXL4vsBWMXQ4W1WdFVmZeIW8vPcTs7Qk08HFkVN0ixvTvdulxo0mn/1cbOZddgKZpNA104dt7Iioltjk7Enh+3l6+GdWSO69RD6CixJ/NJvFcLln5ReQUFGHQNHqG+Za8LKCsMk+r92LKIWj3qBpldw1SN+eA0l2EZ5xUhf4O/KYSVP2+gKA25RPfRSkx8EMX8A6D+5aAZRl/X+RnqvOlydCyP/cWJZ+HQpiXnINCXHatBEH1+cYrRDWl6zrZBUbSsgqwt7HAw8H60pTq3AIjC3YlMmXTcWLPZHF/x9q8dGfopVH+i6JOnOObtUdZHX0aS4NGv6b+NPJzQtfBpIOlQaNLIy/qeTuZ4xArjq6rKdDr3lEXIgO/K/5C3acxjF8JMwarAmptHoL4TZC0Uz3eZBgM+Or6FzJhA9StIAfSjsKRlSoxsW0StHu4/I+vgjjZWvHekKbcEe7Li/P38vaWfHzrnrq0jGDezgQOJGfw+YjmLNt36tKsk4qWV2jk05UxNKvlSu9w8xSxC/ZwINijhAKCNyvtmHr/ZZ2Bu3+F+t1vbD/OfmpUvukINVL/U09Vs6D1A+UTZ34WzLkXLG3V69zIBb6Nk6o7IIQQQogaRRIEQpST7PwiVh48TVxqNglpOZxIy+Fkeh6pWfnkF5kubedmb0V9HycCXe1Ye/gM53IKaezvTP9m/kzeGEd8Wg6fj2iOvbUlWflFfLg8mp+3xONmb81jXepxT7tgvJ2qR6X6m6LrsOYttW672Uh1gX+tNc6utWDcCpg5HDZ/oboS9HgTwgeDS2DZXtvaHnybgE+4Kiy39m3V993Z/6YOqbJ1aejNksduY/hXa3hoxk5evjOU4a1q8eGKw0QEu9G/mT9xqdmsOHiKnIKia6+nLwfTNh/nZHoenwxrXjVrXNyMo2thwQNgMsKYxaqS/81q2EvNhpk7Bv58Bep1A/c6N7dPXYfFT0BqDNz7W+UWRBRCCCFElScJAiFuUn6RkVlbT/DV2lhSswrQNPBztqWWuz1tarvj6WRzqdp8Rl4RR05ncuRMFn/FpNAqxJ3xHWvTurY7mqbRIsiVN5ccZPj3W7j/ttq8vyyakxl53Nc+hGd7NsTBpoacsgU5ELtSXYB71L36cV2Hla/C5i+h5Wjo+3npqqPbu8O45ao2gZPPzcepaXDnh/BNW1j+EgybdmP7OX1A1VBo2EvNUKhEXk42vNDalt+SnXlr6SFmbTtBalYBP45phaZpNPJ1Rtch5nQWzWu5ltvrHj6VSUpm/qXaFuk5hXy9NpYuDb1oV9ej3F7H7I5vUm3+4jeqAoSj5oFXg/Lbv42jKsD5dRtY8hTc+3vZagX827YfVHHPbq+pZTdCCCGEEP9QQ642hKhcuq6TnJ7HhpgUvlobS+K5XNrWceebUQ1pVsulVC3mijO2Q21qudnz+OxdPDF7N/W8HZn3UHsigospqlcd5Z5Thc22fAc5qeq+Op0hchzUvwNO7VOF3Y78CclR0Op+6P1h2VqnGSzKJzlwkXttuO1ZWPsWHFlVtmnjKTGw7l21lhxdtVls0KvS12zbWGh8M6ol7y2PZtL6YwxpGUizC8mAsAvtLKNPZpRLgiDxXA4frTjM77uTAbC1MtC+ricakJlfxPO9Gt30a1QJWSmw4H44tg4cfaD3B9ByDFhVwOweZ3/o/rpabrBntupyUJLopar4oL0HOHiqPzNPqXPr9D5VCLFBb+jwVPnHKYQQQohqTxIEQpSCruvEnsli3eEUth9PY1fCeVIy8wEID3DmnUFNuK2+Z7lMm+4e5sP8ie3ZfjyN4a1q3XCyocowFsGJv+HQItg9EwqyVB/11hPg5G7YOQ3mjFYtBnUToEFAS7jjXWg78eZGS8tLh8dVC7o/noGHt4DVNSrYX7T2XdUf3tIOOj4FvuGqsv2eWarffSUzGDT+c2covcN9Cb2QFAAIdLPDwdrihuoQnMnIIyUrn9wCI9kFRjbHpjJl83E04OHOdYkIdmN9TAprD6dwIi2HuyICr3jtam35i2r5yR3vqARXad4TNyNiHOydAyv+A/V7qIv/fzOZYNmLkHNWnU8FF/9PNdWRI7AVtHoAIseWLekmhBBCiFuGJAiEuIaDyRn8sjWedYdTSDqfC0CIhz0d63nSIsiV5rVcCfd3wWAo34vYUD/n6n8hFb8ZoqZDzDI1c8DCBsL6Q4cn1cUyqAudjk+rWQNx61VioE4XtVSgKrG0gT4fw8/94bcHYcDXqkhbSbZOgr/eg6bDoefb4Oillk1s+kJ1VGhxr9l6xrcIunI2isGg0dDXiUMnM8q0nxUHTjFxxk5M/2iEo2kwuEUgz/RsgP+FNoDdQn1440KXDi+nGlLt/ugaNU2/04vQ7pHKeU2DQXUz+K6jWu4y5Iert4lbpzpw3PUThA+BwjyVLLBzBesKKswohBBCiBpFEgTillZkNLH8wClaBrlduqABNWNgyqbjvLvsENYWBjrU8+SRLvXo1NDr2v3PBSRsV9Pxj61T/d4b9IJGfaBut+JbDBosoMEd6laV1ekE3V6HNW+q6dp3/QT+La7eLnopLH8BGt6pestfTARomppJMHeMag8XPrhy47+GRn7OLN17El3XSzUL5mxWPv9ZsI9Gvs483q0+9tYWONhY4ONsW2yrTk3TrtvCs9oozFNT/d3rqP/PyuTdCG57Gv56H1qMurqGQNR0sHODRn3Vv61spQihEEIIIcpEEgTilnUmM4/HZu5ia1walgaN/s38mdCpDj5Otjw3bw+rDp2he6gPH97VFDcHa3OHW/Wdi4c/noMjK9S6555vQeR41RGgprjtaajVBubfD5N7QPc3oOW9KhECkLgT5o1XiYMhP149SyC0H3jUg42fQuNBVWP5BBDq68TMrSc4lZGHn8u1E2C6rvPK7/vJzCti5gPNaehbw1prXs+mz1Q7w3t/q5h6A9fT8Wm1VGfl6zBh3eX3UE4aRC9Ryx0qucaFEEIIIWoOSRCIW9KWY2d5bNYuMvMKeXNAY+JSc5i9/QQLdiXhZGtJXqGR1/qGMbZDSM1rx1YRErbBrLvBWKCqo7d+sPjZAjVBSAeYuAl+fxj+fFndXINUR4aEbeDoDXf/WnxixGChllgsehSOroZ6ZSh4WIEaXSpUmHndBMGiPcks23+K53s1vPWSA2ePwoaPIfwuqNvVPDFY2UKX/8DvE+Hg7yrRBKo+gbFALV8RQgghhLhBkiAQtxSTSef79cf4cEU0IR4OzBjf5tJFzuPd6jFjSzy7E9J5olt9mgS6mDnaamLfPHWx7OwPo+aqYmg1nb073D0Ljq2F5F2qjeHpA2rmxIhfVM2BkjQdrtribfi0+ARBymHYNV0VN+z6csUdwz9cPAcOncqgSyPvErc7k5HHawsP0CLIlQm31amU2Mwu97xKDKQdhW2TwNJWFSY0p6bDVT2L1W+q5QQGS/We8W9xub6HEEIIIcQNkASBuGWk5xTyzNzdrDp0hj5N/Xh/SFMcbS6fAq721jza9Ra4uC0vxkI1mrruXQhqD8NngEMN6m9/PZqmRpHLOpJsaQ3tH1XV6GePAo+64BaiihjumQ2J2y5vG9oX/JqVa9jFcba1IsDVjuiTJXcyOJqSxWsL95NXaOSjoc2wtKgiVfCNRZB1ClwCy3e/WSkw517VgeMig6UqFFiebTRvhMFCzdSZffflxMDp/aqQphBCCCHETZAEgbgl7EtMZ+IvOzmdkccb/cIY0/4WXTqQew6SosC3iZoKfyNMRjVrYN27cC4Omt0N/T6Xdc9lEXEfnNwLyVFwZCUYVctMPBtAjzdVgcNJnWDzlzBkcqWEFOrnRPSpKzsZ5BQUsWTvSeZsT2BH/DksDBpvDginrlcVWD5SkAO7f1E/o/QEuH8VBESUz77PHoUZgyHzNHR5BXzCVO0It5Cq8z5v2FvVw1j3vkpSWdqqpQ9CCCGEEDdBEgSiWtB11UutrBf16TmFTN9ynC9Wx+LpaM2vD7aj5b/avNVoJhPE/QUxy+H4JjXKiK4qnff/So1Ql5auw6FFsOZtSD2skgx3z1ZdCm7FZMvNsHaAwd+rv5tMkHkS8jPBq+Hln2XLMbD1OzVS7BpU4SE18nVm7eEU8ouM2FhakF9kZOh3f3MgOYM6ng682LsRg1sG4O1khsJ8/2QyqiKPW75RLfwCW0FBliraN2bxzb8XE3fAzGHq7/ctgcDIm4+5ImiaKpI5pTfsmamWHdi5mjsqIYQQQlRzkiAQlSY9p5Ddiec5kZZDwoVbQZEJg0HDoIFB08gtNJKTbyS7oIicAiPZ+Rf+LCiiVYg7U8e2wt76+m/b46nZTNkUx5wdieQWGuke6sMHdzXF/VbpRpCTpkZXd0xRa6ct7aBWK1XczCdctUn7dZSqeN7z7et3Goj/G/58BZJ2gGdDGDoNQvur3uzi5hgMxbeiaztRJQi2fAe9Kn7NeyM/J4wmnSOnswgPcOHjP2M4kJzB5yOa07+Zf9WZcbPxE1jzFtTrobpKBLWDbT/AsufUbIwGPW9838f+gpnD1RKCexao5R9VWXB7qH+H6hwixQmFEEIIUQ4kQSAqxYYjKTw5ezdnswsAsLY0UMvNDlsrC4wmHZOuY9LB3toCe2sLfJ1tsbexxNHGAntrS3Qdpm6O4/FZu/j+3kgsDMVfrKTnFvL+8mhmbTtxoXVhAOM71ibM37kyD7dyxa5SxcryM0E3qhHWrDNq2nqtttD5RXUx/8+WbPW6w5o3YfMXELcefJteeEBX66zt3MHBUxXdO7pGtU9z8lOzDpqPvLp9nyh/rrUgfAhETYNOz1f46HAj3wudDE5lkpFbyA8bjjGqTRADmheTvDCX+L9VgcfwIaqN5MWkRcR9akbBqtehXrcbe38ai2Dp0ypZM3bZjS/BqWx9PoZDnSCko7kjEUIIIUQNIAkCUaFMus6nK2P4Ys0R6ns78vmIFtT3ccTL0QZDCRf5Jantac+rCw/wv8UHeKN/4ytGNHVdZ+m+k/x38UHOZuVzX/sQJnaqi7ezmadDVyRjobrI3/Q5eNRXhcoMFhcu8N1UbYCSKppbWkPPN9Xa5VWvw8k9Fy62NDAVqRkI+elqW2sn6PoqtH34+jMNRPnq8DjsmwM7flKj5RUoxMMeG0sDW4+dZcORVGp7OvBKn7AKfc0yyUmD+ePBNRj6fnblUgJLa7UUY95YVeixxaiy73//PDgbC8OmV5/kAKhEUrtHzB2FEEIIIWoISRCICnM2K5+Pd+Rx4OwRBrcM4K2B4aVaHlCSe9uFcCIthx82xFHL3Z7xHWuTeC6XrXFpLNqTzPqYFJoEuDDlvlaEB9SwFoW6rhICpiJ1yzyl+qAn7YCIsdDrXbC6dv/6YtXtom7FKSpQa7xtHMHmFut3X1X4NoE6XdRSg3aPVGiBPEsLAw18nJi7MxFLg8ZvoztgZ11FZoroOix8RM2MGf8n2BYzI6jxIFWwcO3bED645PPBWAgWVv+6r0gtu/FpotoGCiGEEELcoiRBICpEdn4RY6ZsI+acifeHNGFYZK1yWcP8Uu9QEs/l8vYfh/hpYxzJ6XkAuNlb8UqfUO5rH1J12q+Vl/QkmDsGErdfeb+NMwydqi6MKoKlNTj7Vcy+Rel1eBymD4K9c6Blxa4zb+TrxL6kdJ7q0YAmgZWYZEtPUu/v0H7FLw/Y8i0c/gPueBcCWha/D02DHv+DaX1h6/fQ8cmrtzm1H6bcCZH3Qff/Xp6FsG8OpB2D4b9IXQ0hhBBC3NKumyDQNO0noC9wRtf1EuYrC3FZkdHEozOjOJicwRMtbRjeqvwqsBsMGp8Ob45h7h50XefB2h60qeNOA2+nMi9ZqHRH18C546qvvXfjyzUB8jMhNUaNjgZ3uHJ0NGG7KiZYkA23P6eq3xsswcJatTmrhOr2wszqdAGvRrD31wpPEIxoXQtXeyse6lRJxfkKc9Wo/8ZPoTAHat8OgyZdTkwV5sLK12DbJNX6se3Ea++v9m2qaN9fH6haBL5N/vFaebDgASjMVstyLGyg68sXZg98oOpwNOpTcccqhBBCCFENlGYGwVTgK+Dnig1F1AS6rvPqwgOsPZzCO4Oa4J97rNxfw9bKgq9HljCKWBXpOqx7D/567/J9BkvV8z73PGQmX77f0g7C+kPzUZCeCEueBGd/GL0QvEMrO3JRFWga1O+plhkUZKskUQWJCHYnIti9wvZ/hYMLYcUrkH4CwgaobgSr/wfftoeB34JLIMy/H1IOQdtHVI2B0sxC6v8FTOoCM0fAA2tURwKAVW/AmYMwap567fUfqCSdow+ci1MtO6tKpwYhhBBCCDO5boJA1/X1mqaFVEIsVV5+kZG10Sm42lvRto6HucOpkr796yiztp3g4c51GdkmiHXryj9BUK0U5cOix9Tob7ORcPuzcHo/JO+G0wfUqKVXA9U60NYZDvwG++ar7UGNqA6dBvaVdNEmqqa6XVTHifjNUL+HuaO5eZu/gj9fVi03By5RI/8AdbupQoOzhoPBSr3v75mvum6UlpMvjJwNP/VSs2/GLIH4jbD1W2jzkPr51e0KRXkqIWHtpAp8NuhVMccqhBBCCFGNSA2CUog5ncmv2xNYEJXIuZxCAIZFBvJq3zCcbK2u8+xbx9RNcXyw/DADmvvzbM+G5g6ndIyFajQxuL0aqS8vhXlq2cCyF+DEZtUF4LZn1AilR101Ylqc2rfDHe9A9FLIOw8tx1xdUE3ceoLaqSnxR9dWnwSBrqs//z0qv3eOSg6EDYAhP4HFPz6GvBrA/atVd46sM6r4poNn2V/brxkM+h7m3KuWFSRsBa9Q6P6GetxgAQO/Uwm8Q4ug839k9oAQQgghBKDpF7/EXWsjNYNgybVqEGiaNgGYAODl4xcxZ/bM8orRLEy6zt4UI8uPFxKdZsJCgxbeFtweaEnMORNLjxXibqsxvokNYR5VpNK3mei6zryYQpbGFdLS24KJzW2wulAPICsrC0dHRzNHWDy7nGRCD32Mc2YsRoM1iYH9SKg1mCIrRzRTIV4pf+OfvALrgjRSvNpz2qcTOQ4lr/l3zDxKyPFfcciOxzbvNBo6Js2KQ6FPkOJ9WyUemaiJmu55HZv8NLa3/rLMz63s89AmL4XGB97HuiCd4yHDOe3TBd1ggVvaLprse5N0l1D2NXkdk4V1hcYRFD+XOnEzMGmW7Iz4mGzHkCse10xF2OckXnW/EBWhKn8eCnErkHNQiMu6dOmyU9f1yOIeK7cEwT85BTbUjx7ag7dT9etBbzTpzN+ZyKQNx4g9k4W/iy1j2odwV0QgHo6XW4ztjD/Hs3P3EJeazVsDw7mnbbAZozafQqOJF+bvZUFUEqPaBPG/AeFY/KNY4Lp16+jcubP5AiyOrsOu6bDsRTU63+N/cHwj7JsLti4Q2hcOL4ecVHALUbe4DaAb1ZTolqPV7WIbNV1XfeqXvwi2rhDSQdUX8GwAga3A7dZ8b4hytulzVbDv6egyd5eo1PMwbgPMvU+NzrvXhlN7waM+RNwHa98B9zowdqk61yqarqsChJ71VetDIcyoSn4eCnELkXNQiMs0TSsxQVAhSwyKTDrjp+5g9oS2ONiU/BK5BUYMBrCxLH4E3mTSK7UyfaHRxNNz9rB4TzJhfs58PqI5dzbxw6qYtnkRwW788fhtTJi+g7eWHqRjPU9CPCuueFhVFJeazWsL97PhSCrP9GjAo13rlUsrwwpVmAu/T1Rr/UNuU9OQXQIgYoxqJ7fqv7B7luoQEDkW6nRVbc+yzsCB32HPLFj2PKz/SPWlb3a3mi69b65aJz1oEjhIfQpRAep2VQmCY+ug+d3mjuZquq7aC674j1pGM/wXdWEevQTWvK3OE9cguGde5SQHQC0b6PxC5byWEEIIIUQNUJo2h7OAzoCnpmmJwOu6rv94recEudtxIDmdR2ZG8cPoyEsX2Lquc+RMFusOn2FtdAo74tMwmnQC3Oyo7elILTc7zuUUcCIth4S0XHILjPRu4svI1kG0ru1eoRefeYVGHp25i1WHTvNCr0Y81KnOdV/PztqCD+9qRo9P/+L5+XuZ/UDbqt9qrxwkn8/li9VHmLszEWsLAx8MacqwVrXMHdb15aTBrBGQsA26vQ4dnryy57lvE3XxYjJd3Qvd0RvaTFC345tg/Yew6nV10wzQ9RXo+Iz0UBcVx7sxOHipdplVLUGQkQxLn4XDS1U7wkHfX27XGdpP3RezAvyaqiKCQgghhBCiSipNF4MyfxN1srXimUFNeGnBPh6cvhM3e2uOpmRxNCWLzLwiABr5OjGuQ21srCyIS83meGo2exPP425vTaC7Pc1ruWLSYfGeZBbuTqaetyMTO9VlSETgDRzmteUUFDHh551sjE3lzQGNubddSKmf6+tiy6t9w3h+3l6mb4lnTPvSP7eqycgrZOGuJLYdP0f3UG96hfteMbvjYHIGM7fFM2d7IgCj2wXzcOd6eDnZlLTLquNcPMwYAudPwNCp0Hhgydte7yI/pIO6Je6EqKnQZKgqLihERTIYoE5nNYOguCSWOZhMsPMnNfPGWAA93oR2j14dm8ECGt1pnhiFEEIIIUSpVVgXg7tbB5GSmc/nq4/g6WhNXS9HBjbzJ8Ijj9ucTuKRsVW1e3P2hw4DILB9sV94X+0TxuK9yUz/O55n5u7BaNLLZbRa13WOpmSx8Ugqc3cmcuhkBh8NbcZdN5CAGBoRyJK9J3lvWTRdGnoT5GF/0/FVpv1J6Uz/O55Fe5LJLTTibGvJ4j3JuDtYMzQikGAPB+bsSGB3wnmsLQ0MbhnAY93qE+BqV7oXOBevRj2PrQWTEYLaqqrsfs2uX6E/Lx0OLlJT+O1c4c6P1Gh+aRUVQNxfsPAR1dbs3t/UxX15CIxQNyEqS92u6lw4c0DNeDGn7LMweyQkbIHanaDfZ6q+gBBCCCGEqLYqJkGgmwB4vFt9Jnaue3kN/8JHYM2My9u5BsPhZbDlG3D0VcXhQvtDcIdLra/srC0YFlmLQS0CGDd1Oy/9tg8fF1s6NfAqc1gpmflsik1lY2wqG4+kciojD4BgD3u+GdWSXuFlK/x1kaZpvDe4CT0/Xc/z8/fwybDm+DjbXlGsr6raHJvKPT9uxcbSgv7N/BnZJogmAS5sPnqWX7bGM3ljHEaTTn1vR17rG8bglgG42pei8vj5BIj6mdbbZ8C6ZHWfc4BKCEQvUf+2tFMjoo0Hqh7kdq5qHfO5ODixBY78qd4fRXnqwiNhq7r/rp8gpGPJr52dCkdWQswyiF0DBZngHAjjFoJ36E3+xIQwozqd1Z9H15o3QaDrsPBhSI6CAd9A85HSJlAIIYQQogaomARBeqL6Aqlpl5MD++bBrhkQMRaajQCfxmDjBHkZ6kLw4ELY9Qtsnwz2HtCozxVTt60sDHwzqiXDvt/CwzN28uuD7QgPuHahq9wCI1vjzrIpNpUNR1KJPpUJgKu9FR3qetKxvicd63lSy/3mR/z9Xe14pU8oLy7YR/v31mBp0PB1saVZoCvvDmmCs23V62V/JiOPx2fvoo6XI/MeanfFhX/H+urncyYjjzOZ+TT2d75+DQhdh9hVsP1HOLICdJ08t2bYd3pcjXx6NlAXEZmn1IV+/CaIXqou5A1WEBgJZ49C9hm1P3sPaHGvKgQY0BJOH4C5Y2BaP+jysnovGfNVtfTcc2qWQswKSNwO6ODoA+GDoEFvdWFlXb1mdghxFWd/8GqkZuN0eNx8cWz9DmKWQ6/3ocUo88UhhBBCCCHKVanaHJZVpL+FvmPRZFUFHlQBq2/aqgvEscsvzQ64SkG2usA8uEh9+SzIUlPKWz9waZNT6XkM+mYTRpPOc3c0JC41m5jTmcSeycKgaTjYWOJgY4HRpLMnIZ0CowlrCwORIW6XEgKN/V0qbHR/x/E0Yk5nkXReFVr8Y99JIoLdmDauNbZWxXdrMIcio4lRk7eyNzGdRY92oL6P083tMC8DFj2qEj0O3pdaAa7bE3ftljImkxqFPPi7ajXo2eDyEgTPhlcvO8nPhMVPwP75xe/Pr7nqQNDgDvBtVjXWaQtRnpa/pNpqvhAPVqVrJVuurZ1O7oHJ3aFuN7h7lswcEKKUpMWaEOYl56AQl1V6m0NsnFUrON+mauR34SNgLFSVrUtKDgBYO0DYAHUrzFO9tP94TlW9Du0HqKKAU8e25q7vNvPcvL1YGjTqeDnQ2N8FTYPs/CKy842YdBjTPpiO9b1oHeKOnXUpL851XY1wl7HP+EWRIe5Ehrhf+ne3UG+emL2bx2bt4ttRLbG8MKMir9DI0r0naRnsRu0Kbo+4Mz6NrXFp9A73u/Ran66KYWtcGp8Ma3bzyYHTB+DXe+Hccej+X9X+71JtgbhrP9dgUDMHAot9f17NxgmG/AiNB6uZKpY26mZlD7Xa3PD/mxDVRp0uallW/EbVWrMy5WfB3LFg7wkDvpbkgBBCCCFEDVMxCQK3YHAywpzRqr/80TXQ52PVG7u0rGzVWvNp/WDeeBi9EILbAdDQ14m1z3YmNSufOp6OWFuWMEqs65ASDVt/hoyTEBABQW3ArXbxX2zzM2Hxk7B/nmqB1/2Nm/4CPKB5AOeyC3hj8UH+89s+XuvXmJlb4/lhQxwpmfmEeNiz9PHbcLCpmP+KoylZ3DdlO5l5RXyw/DDNAl1oXdudHzbEMaJVLQa3vImuEDlpcGABrHhF9TW/bwkEty+/4EuiaapehRC3opCOapbOn6+qei1WpSwWeiNy0uDUPshNU3+PWa5qhIxZDA4eFfe6QgghhBDCLCrmqtRgCcOmwk93wNq31VTUyPFl34+1PYycAz/2UP3rx60A70YAeDra4OlYQnu93POqT330EjWqDWDlANt/UH939FExNR+pvmAbDHBqv1rfnnYMQm6DTZ9B1mno/+X1K+1fx30dapOWXcAXa2JZtCeZvEITHep58EjnuvxvyUHeWHSAD4c2u6nXKE5GXiEP/LwDKwsD8ye2Jyr+HAv3JPHDhjhC/Zx5o3/jsu80aaeqJ3F8g/qZoauf15Afwcmn3I9BCPEv1vYw6DuYMRj+fEUlXytC4k6YNRyyU/5xpwbdX792kVAhhBBCCFFtVVibQ/ybqymom7+EAV/d+Ei8gwfcuwAm94CZw2DiZrBxLHl7Y5FamnB8gyqM1+EJVSHf0UfNJjixBU78rZIHe2aCa5BKFuyZBbauamQsuAP89QGse0dVxB82TS1/uAlP9WhAkUknLjWbCbfXoUWQGwBnswv4ck0snRp60bep/029xj8ZTTpPzt7NibM5zLi/DRHBbkQEu/HA7XWIP5uNq7112WsixPwJv44CzQC1WkOX/6gLhVptVJ9zIUTlqNcN2j0Kf3+lfs816lO++z+0BObfD45eMGqeKo5o7wF2bmpJjxBCCCGEqJEqLkEA0OQudbtZbiHqIn1KbzUjode7JW+7+g1V4bv/l6pQ3j/5NFa3VuOhIEdV0N/9C+ycqqrcD/5BfSEG6PyCGhFf8hR830mt9Q1oCf4tVcu9Mha/0zSN53s1uur+x7vVZ2NsKi8t2EfzWq4EupVPpf2P/zzMmugzvDkwnLZ1rpwKHOxxA8mOi8kB71C493ewd7/uU4QQFajb66qw58JHVHFOlwDISlGFXjVNdYu5EVu+VYUQA1rC3bPB0btcwxZCCCGEEFVXxSYIylNwe2j1gPry2ngw1Gp19TZ756gZC60euDo58G/W9tB0qLoV5Kh1vP+e5RBxHzj5wYZPVBJh67fq/pDb4J755TKSZmVh4PPhLbjziw089etupo9vc1PdDlKz8vly9RGm/R3P3a2DuKdNUNl2kJUCCVtUEsSzoSoqKckBIaoeS2tVp+W722DmcPXvpCjgQmcaa4dLxV1LbfOXatlCo74qYSqtQYUQQgghbinVJ0EAau3r4WWqnd6D66+8QE/eDYseU8sDrjXDoDjX+hLc4A51MxapJQpH/oTV/4VFj6t1wOVQxTvIw563Bobz5K+7afXWKno09qFfM3861vPEyqJ0MxWy84uYvCGOSeuPkldkYlSbIF7v1xitNPEZi9So467pqgiZqUjdb2kL3mFwer8kB4SoijzqQt9P4PeJ4N8COr+kZjstfVr9PvRvqWYWlEZKDKx+UyUHhv0sy4aEEEIIIW5B1StBYOMEfT+FmUNhw8dqDXx+FuydDX99qFpvDZ1200UFi2VhCb7h6mYsVPUJvBrAbc+Uy+4HtgjA28mG33YlsfzAKRZEJeFoY0mYvzNhfs6E+TvTvq5HsUsQ9ielM27qds5k5tOrsS/P3tGQet7XqNNwUWos7J4Bu2dB1ilw8IK2E9UFwvkEOLlb9Tyv10PVkZDkgBBVT7MRED7kyt97Q36E72+H3x5UHWCux2RSCQUrO+jziSQHhBBCCCFuUdUrQQDQoCc0Ha4SBJkn4cDvkJ8Bvk1h4DeXawhUpE7PQ2oMrP4feNSHsP7lstv29TxpX8+TtwaFsyEmlXUxZziYnMGv2xPILTRia2XgnUFNrmhNuON4GmOnbMfZzor5E9sTEexW8gvoOqQcVv3T982HE5tBs4D6PaHFPWqmxMWLjKC2avmFEKLq+3dS1LMe3PmBqk+w6TMg4trP3/GjWlo08FvpRiKEEEIIcQurfgkCgDvehaNr1Mh340HQ+gEIbFUu0/1LRdPUiPq547BgguqE4N+83HZvY2lB9zAfuoepL+pGk86xlCxeXbifp+fsYWf8OV7rF8a2uDQm/LwTPxdbZtzfBn/XEvqhH/sLtn6vujfkpqn73OtC9zeg2d3g5FtusQshqojmoyB2Nax5G6cW7wKdi9/u/AlY9YbqhtDs7koMUAghhBBCVDXVM0Hg4AEPbVSj35UxY6A4VnYwYib80BVmj4IJ6yosFguDRn0fJ2aMb8NHf8bw3V9H2XH8HHGp2dTxcmD6+DZ4OZVQMHH3TDV12NEHGt4Jwe0gqJ0qQlhZCRUhROXTNLUkK3E7oYc+g95jwMr2ym10XXVq0XXo+5n8ThBCCCGEuMWVrVdfVeLka77kwKUYfGDEL5CTCnPHqNoEFcjSwsCLvRvx/b0RJJ/PJdTfmdkT2hafHNB1WP+RKl4Wchs8shUGfq2WEnjUlQsBIW4Fdq7Q/wvsc5Nh/QdXP759sipQ2v11cAuu9PCEEEIIIUTVUn0TBFWFf3Po/xXEb1K9w2/U2aNquULC9utuekdjXza/1JX5D7XD1d766g1MRvjjWVjzpqrXMHKOKvAohLj11O3KKZ+usOlzOLXv8v3HN8HyF6FBL9UaVgghhBBC3PIkQVAemg6F9o/B9h8g6mc1ep+TpgoCZpy8/vOPb4TJ3WDvrzC1D+z59bpPcbK1wrK4Foi6DkueVCODHZ6Egd+p/uhCiFtWbL2xYOemlhuZjJCeqGY9uYXA4ElgkI8CIYQQQghRXWsQVEXd3oBT+2HxE7DkaTBdWG5gsIL7V6oe5cWJmq7WALvXhlHzYdXr8NsESDkEXV8r+xf3v95XSYrbnoFur93UIQkhaoYiK2fo/T7MGwcbP4XopVCYB/ctBVsXc4cnhBBCCCGqCEkQlBcLSxg6BTZ+pv7t6A0OXrDydfjtIZjw15UFwnRdJQM2fa6qh981Ra0XvmcBLHtOfYk/fQDaPw7B7UvXl3znNFj3LjQbCV1frYijFEJUV40Hw945aukRqCKrXg3NG5MQQgghhKhSJEFQnuzcoMd/r7zP3h1mDIG1b0HPt9R9ug4rXoYtX0PkeOj9gUowgFoO0Pcz8G6sWo8d+ROc/CBsIIT2g4CIqyuRAxxermYi1OsO/b+QIoRCiCtpGvT5RNUhiBwHjfqYOyIhhBBCCFHFSIKgotXrDhFjYfNXqs1gUDt14b/la2jzEPR67+qLeU2DNhOgxSiIWQH758OOH2Hrt2BhA4GRaj8AKdGQGgNnY8G3KQydBhZWlX6YQohqwCUAntwvNQeEEEIIIUSxJEFQGXq+BcfWqpaDYQNh02dqBK+45MA/WTtA+GB1y0tXxQzjN6uOCRs/ATRVu8CrEYQNUAkHG8dKOighRLUkyQEhhBBCCFECSRBUBhtHGPgtTLlTJQda3AN3fly2ZQC2LmpK8MVpwQXZYLAES5sKCVkIIYQQQgghxK1FEgSVJbi9mjGQmQzdXr/5UTxrh/KJSwghhBBCCCGEQBIElavtQ+aOQAghhBBCCCGEKJYsRhVCCCGEEEIIIYQkCIQQQgghhBBCCCEJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQiAJAiGEEEIIIYQQQgCaruvlv1NNSwHiy33H5csFSDd3EGVUHWP2BFLNHUQZVcefM1TPuCXmyiHnYeWQmCtPdYxbzsPKITFXjuoYc3U8B6F6/qwl5spxMzEH67ruVdwDFZIgqA40TZuk6/oEc8dRFtU05h26rkeaO46yqI4/Z6iecUvMlUPOw8ohMVee6hi3nIeVQ2KuHNU05mp3DkK1/VlLzJWgomK+lZcYLDZ3ADegOsZcHVXXn3N1jFtiFiWpjj9nibnyVNe4q5vq+HOWmCtHdYy5uqqOP2uJuXJUSMy37AwCUTmqa7ZWiJpEzkMhzE/OQyHMS85BIUrnVp5BICrHJHMHIISQ81CIKkDOQyHMS85BIUpBZhAIIYQQQgghhBBCZhAIIYQQQgghhBBCEgSijDRNq6Vp2lpN0w5pmnZA07QnLtzvrmnaSk3Tjlz40+0fz3lJ07RYTdMOa5p2RzH7XKRp2v7KPA4hqrPyPA81TRuuadreC/v5wBzHI0R1VNbzUNM0jwvbZ2ma9lUJ+5TPQyFKqTzPQfksFOIySRCIsioCntF1PRRoCzyiaVoY8CKwWtf1+sDqC//mwmMjgMZAL+AbTdMsLu5M07TBQFblHoIQ1V65nIeapnkAHwLddF1vDPhomtat8g9HiGqpTOchkAe8Cjxb3M7k81CIMiuXc1A+C4W4kiQIRJnoun5S1/WoC3/PBA4BAcAAYNqFzaYBAy/8fQAwW9f1fF3X44BYoDWApmmOwNPAW5V2AELUAOV4HtYBYnRdT7mw3SpgSKUchBDVXFnPQ13Xs3Vd34i6SLmCfB4KUXbleA7KZ6EQ/yAJAnHDNE0LAVoAWwEfXddPgvqFDXhf2CwASPjH0xIv3AfwJvAxkFMZ8QpRE93keRgLNNI0LUTTNEvUl6halRO5EDVHKc/Da5HPQyFuwk2eg/JZKMQ/SIJA3JALox3zgSd1Xc+41qbF3KdrmtYcqKfr+m8VEZ8Qt4KbPQ91XT8HTAR+BTYAx1FTNoUQpVSG87Ck5zdHPg+FuGE3ew7KZ6EQV5IEgSgzTdOsUL+If9F1fcGFu09rmuZ34XE/4MyF+xO5MgsbCCQD7YAITdOOAxuBBpqmrav46IWoGcrpPETX9cW6rrfRdb0dcBg4UhnxC1ETlPE8LIl8Hgpxg8rpHJTPQiH+QRIEokw0TdOAH4FDuq5/8o+HFgFjLvx9DLDwH/eP0DTNRtO02kB9YJuu69/quu6v63oI0BG19qtzZRyDENVdeZ2HF/blfeFPN+BhYHLFH4EQ1d8NnIfFks9DIW5MeZ2DF/Yln4VCXKDpum7uGEQ1omlaR9T0q32A6cLd/0Gt+ZoDBAEngKG6rqddeM7LwDjUdK0ndV1f9q99hgBLdF0Pr4xjEKK6K8/zUNO0WUCzC/v4n67rsyvrOISozm7wPDwOOAPWwHmgp67rB/+xzxDk81CIUinPc1A+C4W4TBIEQgghhBBCCCGEkCUGQgghhBBCCCGEkASBEEIIIYQQQgghkASBEEIIIYQQQgghkASBEEIIIYQQQgghkASBEEIIIYQQQgghkASBEEIIIYQQQgghkASBEEIIIYQQQgghkASBEEIIIYQQQgghgP8DR43TXYTpSfIAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1296x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"portfolio_ret = (weights.shift(1) * returns).dropna().sum(axis=1)\n",
"(1 + portfolio_ret).cumprod().plot(figsize=(18, 8), label=\"Mkt Neutral Portfolio\")\n",
"(1 + data.loc[portfolio_ret.index].Mkt).cumprod().plot()\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.title(\"Backtest of Market Neutral Portfolio Return\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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