Project 8: BackTesting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 8: Backtesting\n",
    "\n",
    "In this project, you will build a fairly realistic backtester that uses the Barra data. The backtester will perform portfolio optimization that includes transaction costs, and you'll implement it with computational efficiency in mind, to allow for a reasonably fast backtest. You'll also use performance attribution to identify the major drivers of your portfolio's profit-and-loss (PnL). You will have the option to modify and customize the backtest as well.\n",
    "\n",
    "\n",
    "## Instructions\n",
    "\n",
    "Each problem consists of a function to implement and instructions on how to implement the function.  The parts of the function that need to be implemented are marked with a `# TODO` comment. Your code will be checked for the correct solution when you submit it to Udacity.\n",
    "\n",
    "\n",
    "## Packages\n",
    "\n",
    "When you implement the functions, you'll only need to you use the packages you've used in the classroom, like [Pandas](https://pandas.pydata.org/) and [Numpy](http://www.numpy.org/). These packages will be imported for you. We recommend you don't add any import statements, otherwise the grader might not be able to run your code.\n",
    "\n",
    "### Install Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib==2.1.0 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 1)) (2.1.0)\n",
      "Requirement already satisfied: numpy==1.16.1 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.1)\n",
      "Requirement already satisfied: pandas==0.24.1 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 3)) (0.24.1)\n",
      "Requirement already satisfied: patsy==0.5.1 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 4)) (0.5.1)\n",
      "Requirement already satisfied: scipy==0.19.1 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 5)) (0.19.1)\n",
      "Requirement already satisfied: statsmodels==0.9.0 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 6)) (0.9.0)\n",
      "Requirement already satisfied: tqdm==4.19.5 in /opt/conda/lib/python3.6/site-packages (from -r requirements.txt (line 7)) (4.19.5)\n",
      "Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.1.0->-r requirements.txt (line 1)) (1.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.0 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.1.0->-r requirements.txt (line 1)) (2.6.1)\n",
      "Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.1.0->-r requirements.txt (line 1)) (2017.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.1.0->-r requirements.txt (line 1)) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.1.0->-r requirements.txt (line 1)) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import patsy\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import scipy.sparse\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statistics import median\n",
    "from scipy.stats import gaussian_kde\n",
    "from statsmodels.formula.api import ols\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "We’ll be using the Barra dataset to get factors that can be used to predict risk. Loading and parsing the raw Barra data can be a very slow process that can significantly slow down your backtesting. For this reason, it's important to pre-process the data beforehand. For your convenience, the Barra data has already been pre-processed for you and saved into pickle files. You will load the Barra data from these pickle files.\n",
    "\n",
    "In the code below, we start by loading `2004` factor data from the `pandas-frames.2004.pickle` file. We also load the `2003` and `2004` covariance data from the `covaraince.2003.pickle`  and `covaraince.2004.pickle` files. You are encouraged  to customize the data range for your backtest. For example, we recommend starting with two or three years of factor data. Remember that the covariance data should include all the years that you choose for the factor data,   and also one year earlier. For example, in the code below we are using  `2004` factor data, therefore, we must include `2004` in our covariance data, but also the previous year, `2003`. If you don't remember why must include this previous year, feel free to review the lessons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "barra_dir = '../../data/project_8_barra/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "covariance.2003.pickle\tpandas-frames.2003.pickle  price.2003.pickle\r\n",
      "covariance.2004.pickle\tpandas-frames.2004.pickle  price.2004.pickle\r\n",
      "covariance.2005.pickle\tpandas-frames.2005.pickle  price.2005.pickle\r\n",
      "covariance.2006.pickle\tpandas-frames.2006.pickle  price.2006.pickle\r\n",
      "covariance.2007.pickle\tpandas-frames.2007.pickle  price.2007.pickle\r\n",
      "covariance.2008.pickle\tpandas-frames.2008.pickle  price.2008.pickle\r\n"
     ]
    }
   ],
   "source": [
    "!ls '../../data/project_8_barra/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "barra_dir = '../../data/project_8_barra/'\n",
    "\n",
    "data = {}\n",
    "for year in [2004]:\n",
    "    fil = barra_dir + \"pandas-frames.\" + str(year) + \".pickle\"\n",
    "    data.update(pickle.load( open( fil, \"rb\" ) ))\n",
    "    \n",
    "covariance = {}\n",
    "for year in [2004]:\n",
    "    fil = barra_dir + \"covariance.\" + str(year) + \".pickle\"\n",
    "    covariance.update(pickle.load( open(fil, \"rb\" ) ))\n",
    "    \n",
    "daily_return = {}\n",
    "for year in [2004, 2005]:\n",
    "    fil = barra_dir + \"price.\" + str(year) + \".pickle\"\n",
    "    daily_return.update(pickle.load( open(fil, \"rb\" ) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['20040102', '20040105', '20040106', '20040107', '20040108', '20040109', '20040112', '20040113', '20040114', '20040115', '20040116', '20040120', '20040121', '20040122', '20040123', '20040126', '20040127', '20040128', '20040129', '20040130', '20040202', '20040203', '20040204', '20040205', '20040206', '20040209', '20040210', '20040211', '20040212', '20040213', '20040217', '20040218', '20040219', '20040220', '20040223', '20040224', '20040225', '20040226', '20040227', '20040301', '20040302', '20040303', '20040304', '20040305', '20040308', '20040309', '20040310', '20040311', '20040312', '20040315', '20040316', '20040317', '20040318', '20040319', '20040322', '20040323', '20040324', '20040325', '20040326', '20040329', '20040330', '20040331', '20040401', '20040402', '20040405', '20040406', '20040407', '20040408', '20040412', '20040413', '20040414', '20040415', '20040416', '20040419', '20040420', '20040421', '20040422', '20040423', '20040426', '20040427', '20040428', '20040429', '20040430', '20040503', '20040504', '20040505', '20040506', '20040507', '20040510', '20040511', '20040512', '20040513', '20040514', '20040517', '20040518', '20040519', '20040520', '20040521', '20040524', '20040525', '20040526', '20040527', '20040528', '20040601', '20040602', '20040603', '20040604', '20040607', '20040608', '20040609', '20040610', '20040614', '20040615', '20040616', '20040617', '20040618', '20040621', '20040622', '20040623', '20040624', '20040625', '20040628', '20040629', '20040630', '20040701', '20040702', '20040706', '20040707', '20040708', '20040709', '20040712', '20040713', '20040714', '20040715', '20040716', '20040719', '20040720', '20040721', '20040722', '20040723', '20040726', '20040727', '20040728', '20040729', '20040730', '20040802', '20040803', '20040804', '20040805', '20040806', '20040809', '20040810', '20040811', '20040812', '20040813', '20040816', '20040817', '20040818', '20040819', '20040820', '20040823', '20040824', '20040825', '20040826', '20040827', '20040830', '20040831', '20040901', '20040902', '20040903', '20040907', '20040908', '20040909', '20040910', '20040913', '20040914', '20040915', '20040916', '20040917', '20040920', '20040921', '20040922', '20040923', '20040924', '20040927', '20040928', '20040929', '20040930', '20041001', '20041004', '20041005', '20041006', '20041007', '20041008', '20041011', '20041012', '20041013', '20041014', '20041015', '20041018', '20041019', '20041020', '20041021', '20041022', '20041025', '20041026', '20041027', '20041028', '20041029', '20041101', '20041102', '20041103', '20041104', '20041105', '20041108', '20041109', '20041110', '20041111', '20041112', '20041115', '20041116', '20041117', '20041118', '20041119', '20041122', '20041123', '20041124', '20041126', '20041129', '20041130', '20041201', '20041202', '20041203', '20041206', '20041207', '20041208', '20041209', '20041210', '20041213', '20041214', '20041215', '20041216', '20041217', '20041220', '20041221', '20041222', '20041223', '20041227', '20041228', '20041229', '20041230', '20041231'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Barrid</th>\n",
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       "      <th>...</th>\n",
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       "      <th>DailyVolume</th>\n",
       "      <th>ADTCA_30</th>\n",
       "      <th>IssuerMarketCap</th>\n",
       "      <th>Yield</th>\n",
       "      <th>TotalRisk</th>\n",
       "      <th>SpecRisk</th>\n",
       "      <th>HistBeta</th>\n",
       "      <th>PredBeta</th>\n",
       "      <th>DataDate</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.177</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.392988e+10</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>13.959397</td>\n",
       "      <td>9.014505</td>\n",
       "      <td>-0.000189</td>\n",
       "      <td>0.156387</td>\n",
       "      <td>20040102</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.874999e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.033359</td>\n",
       "      <td>11.726327</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.147900</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA0031</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.061</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.304963e+10</td>\n",
       "      <td>2.103004</td>\n",
       "      <td>21.738218</td>\n",
       "      <td>18.047415</td>\n",
       "      <td>0.053033</td>\n",
       "      <td>0.216422</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USA0062</td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.866562e+10</td>\n",
       "      <td>2.243494</td>\n",
       "      <td>23.129496</td>\n",
       "      <td>20.830128</td>\n",
       "      <td>0.079755</td>\n",
       "      <td>0.355564</td>\n",
       "      <td>20040102</td>\n",
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       "      <th>4</th>\n",
       "      <td>USA00E2</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.967</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.720420e+10</td>\n",
       "      <td>2.167256</td>\n",
       "      <td>25.391653</td>\n",
       "      <td>21.458171</td>\n",
       "      <td>0.096238</td>\n",
       "      <td>0.412602</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USA00F1</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.025</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.742470e+11</td>\n",
       "      <td>0.392275</td>\n",
       "      <td>15.695637</td>\n",
       "      <td>11.912117</td>\n",
       "      <td>0.069464</td>\n",
       "      <td>0.346033</td>\n",
       "      <td>20040102</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.214</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.644681e+10</td>\n",
       "      <td>4.679803</td>\n",
       "      <td>21.654431</td>\n",
       "      <td>14.431033</td>\n",
       "      <td>-0.017417</td>\n",
       "      <td>0.026702</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USA00H1</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.882</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.054473e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.765658</td>\n",
       "      <td>18.313856</td>\n",
       "      <td>0.135674</td>\n",
       "      <td>0.267576</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.190</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.681907e+10</td>\n",
       "      <td>0.087623</td>\n",
       "      <td>20.410134</td>\n",
       "      <td>14.133102</td>\n",
       "      <td>-0.006358</td>\n",
       "      <td>0.278503</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USA00J1</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.198</td>\n",
       "      <td>0.863</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.335559e+10</td>\n",
       "      <td>1.670463</td>\n",
       "      <td>21.686440</td>\n",
       "      <td>19.249760</td>\n",
       "      <td>-0.010228</td>\n",
       "      <td>0.194933</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>USA00K1</td>\n",
       "      <td>-1.019</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.334</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.677209e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.867332</td>\n",
       "      <td>26.375334</td>\n",
       "      <td>-0.072812</td>\n",
       "      <td>0.475343</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USA00P1</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.098</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.377153e+10</td>\n",
       "      <td>1.413784</td>\n",
       "      <td>21.424437</td>\n",
       "      <td>16.195982</td>\n",
       "      <td>0.496970</td>\n",
       "      <td>0.853930</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>USA00R1</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.615</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.177471e+09</td>\n",
       "      <td>2.641914</td>\n",
       "      <td>18.112924</td>\n",
       "      <td>12.495233</td>\n",
       "      <td>0.258616</td>\n",
       "      <td>0.673043</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USA00S1</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.141</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4289.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.576241e+10</td>\n",
       "      <td>1.244629</td>\n",
       "      <td>21.922494</td>\n",
       "      <td>19.035684</td>\n",
       "      <td>0.477142</td>\n",
       "      <td>0.558508</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>USA00V1</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041914e+10</td>\n",
       "      <td>1.458435</td>\n",
       "      <td>12.385339</td>\n",
       "      <td>8.420332</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.083475</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USA0131</td>\n",
       "      <td>-0.609</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.440</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.359884e+10</td>\n",
       "      <td>1.613669</td>\n",
       "      <td>24.839671</td>\n",
       "      <td>21.502548</td>\n",
       "      <td>-0.121604</td>\n",
       "      <td>0.145560</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>USA0161</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.183</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.359884e+10</td>\n",
       "      <td>1.613669</td>\n",
       "      <td>28.025448</td>\n",
       "      <td>23.914479</td>\n",
       "      <td>0.457450</td>\n",
       "      <td>0.713817</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USA01I1</td>\n",
       "      <td>-0.230</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.943</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.664403e+10</td>\n",
       "      <td>0.196207</td>\n",
       "      <td>15.087449</td>\n",
       "      <td>12.143365</td>\n",
       "      <td>0.107272</td>\n",
       "      <td>0.220367</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>USA01J2</td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.851823e+10</td>\n",
       "      <td>1.782703</td>\n",
       "      <td>23.016956</td>\n",
       "      <td>20.028493</td>\n",
       "      <td>0.060553</td>\n",
       "      <td>0.355385</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>USA01L1</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.981</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.179657e+10</td>\n",
       "      <td>3.380021</td>\n",
       "      <td>23.209763</td>\n",
       "      <td>19.935156</td>\n",
       "      <td>0.089837</td>\n",
       "      <td>0.278654</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>USA01P1</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.777</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.691907e+11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.138099</td>\n",
       "      <td>23.901111</td>\n",
       "      <td>0.183841</td>\n",
       "      <td>0.410674</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USA01Q1</td>\n",
       "      <td>-0.249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.634</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.237492e+11</td>\n",
       "      <td>1.733990</td>\n",
       "      <td>22.222874</td>\n",
       "      <td>19.652249</td>\n",
       "      <td>-0.210865</td>\n",
       "      <td>0.042602</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>USA0202</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.328618e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.844475</td>\n",
       "      <td>23.970616</td>\n",
       "      <td>0.921471</td>\n",
       "      <td>1.005869</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>USA0231</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.905</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.798649e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.742676</td>\n",
       "      <td>21.356169</td>\n",
       "      <td>0.125102</td>\n",
       "      <td>0.519813</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>USA0281</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.534344e+10</td>\n",
       "      <td>3.050774</td>\n",
       "      <td>18.248930</td>\n",
       "      <td>14.319948</td>\n",
       "      <td>0.007271</td>\n",
       "      <td>0.359541</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>USA0291</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.147</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.184106e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.017693</td>\n",
       "      <td>19.557779</td>\n",
       "      <td>0.474128</td>\n",
       "      <td>0.720445</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>USA02A1</td>\n",
       "      <td>-0.339</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.140</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.248961e+09</td>\n",
       "      <td>1.947799</td>\n",
       "      <td>11.185751</td>\n",
       "      <td>7.613359</td>\n",
       "      <td>0.016464</td>\n",
       "      <td>0.268383</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>USA02B1</td>\n",
       "      <td>-2.745</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.188</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.248961e+09</td>\n",
       "      <td>1.947799</td>\n",
       "      <td>32.801110</td>\n",
       "      <td>28.797618</td>\n",
       "      <td>0.455392</td>\n",
       "      <td>0.823334</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>USA02H1</td>\n",
       "      <td>-0.560</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.215026e+10</td>\n",
       "      <td>2.398082</td>\n",
       "      <td>21.698318</td>\n",
       "      <td>18.244674</td>\n",
       "      <td>0.363409</td>\n",
       "      <td>0.574217</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USA02P1</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.085</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.117866e+11</td>\n",
       "      <td>2.582311</td>\n",
       "      <td>22.480566</td>\n",
       "      <td>19.563198</td>\n",
       "      <td>0.042230</td>\n",
       "      <td>0.340393</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12412</th>\n",
       "      <td>USAZY41</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.056250e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.208371</td>\n",
       "      <td>12.488067</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.050859</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12413</th>\n",
       "      <td>USAZY51</td>\n",
       "      <td>2.418</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.638950e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.640585</td>\n",
       "      <td>72.370109</td>\n",
       "      <td>0.279903</td>\n",
       "      <td>0.579444</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12414</th>\n",
       "      <td>USAZY61</td>\n",
       "      <td>1.618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.012930e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.334667</td>\n",
       "      <td>63.572209</td>\n",
       "      <td>0.732842</td>\n",
       "      <td>1.041313</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12415</th>\n",
       "      <td>USAZY71</td>\n",
       "      <td>2.622</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.366</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.452000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.497910</td>\n",
       "      <td>83.717198</td>\n",
       "      <td>0.833859</td>\n",
       "      <td>1.149809</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12416</th>\n",
       "      <td>USAZYI1</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.563</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>3223.94</td>\n",
       "      <td>4.740600e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.863310</td>\n",
       "      <td>73.357836</td>\n",
       "      <td>0.743190</td>\n",
       "      <td>1.041294</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12417</th>\n",
       "      <td>USAZYJ1</td>\n",
       "      <td>-1.637</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.431</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>920.22</td>\n",
       "      <td>2.525880e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.338050</td>\n",
       "      <td>69.331053</td>\n",
       "      <td>0.803832</td>\n",
       "      <td>1.162438</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12418</th>\n",
       "      <td>USAZYK1</td>\n",
       "      <td>2.077</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7950.0</td>\n",
       "      <td>769.50</td>\n",
       "      <td>6.832300e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.972597</td>\n",
       "      <td>68.149533</td>\n",
       "      <td>1.016101</td>\n",
       "      <td>1.354084</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12419</th>\n",
       "      <td>USAZYM1</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>326.41</td>\n",
       "      <td>2.270100e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.656370</td>\n",
       "      <td>76.499271</td>\n",
       "      <td>1.089179</td>\n",
       "      <td>1.282783</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>USAZYR1</td>\n",
       "      <td>-1.874</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.975</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279693.0</td>\n",
       "      <td>91477.12</td>\n",
       "      <td>1.003100e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.592276</td>\n",
       "      <td>110.419717</td>\n",
       "      <td>0.553212</td>\n",
       "      <td>0.788145</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12421</th>\n",
       "      <td>USAZYS1</td>\n",
       "      <td>2.646</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.030700e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.661249</td>\n",
       "      <td>33.805533</td>\n",
       "      <td>1.089953</td>\n",
       "      <td>1.379310</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12422</th>\n",
       "      <td>USAZYT1</td>\n",
       "      <td>-1.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.867</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13867.0</td>\n",
       "      <td>57150.03</td>\n",
       "      <td>2.763600e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.956591</td>\n",
       "      <td>70.049127</td>\n",
       "      <td>0.603339</td>\n",
       "      <td>0.850369</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12423</th>\n",
       "      <td>USAZYV1</td>\n",
       "      <td>-1.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.674</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1335300.0</td>\n",
       "      <td>10509.78</td>\n",
       "      <td>3.100469e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.257445</td>\n",
       "      <td>117.005538</td>\n",
       "      <td>0.692038</td>\n",
       "      <td>0.970822</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12424</th>\n",
       "      <td>USAZYX1</td>\n",
       "      <td>1.151</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.376</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>15628.40</td>\n",
       "      <td>4.236672e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.602659</td>\n",
       "      <td>21.212255</td>\n",
       "      <td>0.368721</td>\n",
       "      <td>0.289544</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12425</th>\n",
       "      <td>USAZZ41</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>12098.24</td>\n",
       "      <td>6.264510e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.497468</td>\n",
       "      <td>69.006331</td>\n",
       "      <td>0.327400</td>\n",
       "      <td>0.629735</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12426</th>\n",
       "      <td>USAZZ51</td>\n",
       "      <td>2.354</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.368</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.414000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.279688</td>\n",
       "      <td>30.066884</td>\n",
       "      <td>0.832962</td>\n",
       "      <td>1.114710</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12427</th>\n",
       "      <td>USAZZ61</td>\n",
       "      <td>2.481</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.798</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>281000.0</td>\n",
       "      <td>2324.45</td>\n",
       "      <td>2.224400e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.260252</td>\n",
       "      <td>111.809094</td>\n",
       "      <td>0.634920</td>\n",
       "      <td>1.013968</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12428</th>\n",
       "      <td>USAZZ71</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.741</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.684070e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.522512</td>\n",
       "      <td>63.419945</td>\n",
       "      <td>0.661152</td>\n",
       "      <td>0.900018</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12429</th>\n",
       "      <td>USAZZ81</td>\n",
       "      <td>-0.164</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.538</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40600.0</td>\n",
       "      <td>1307457.05</td>\n",
       "      <td>2.911190e+10</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>21.679651</td>\n",
       "      <td>18.778080</td>\n",
       "      <td>0.294241</td>\n",
       "      <td>0.401738</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12430</th>\n",
       "      <td>USAZZ91</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.352860e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.970832</td>\n",
       "      <td>36.773965</td>\n",
       "      <td>1.054950</td>\n",
       "      <td>1.224731</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12431</th>\n",
       "      <td>USAZZA1</td>\n",
       "      <td>2.399</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.517</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.609700e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.928704</td>\n",
       "      <td>83.337116</td>\n",
       "      <td>0.764430</td>\n",
       "      <td>1.151915</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12432</th>\n",
       "      <td>USAZZB1</td>\n",
       "      <td>2.301</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.584</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.251480e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.424176</td>\n",
       "      <td>24.267277</td>\n",
       "      <td>0.272805</td>\n",
       "      <td>0.689609</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12433</th>\n",
       "      <td>USAZZD1</td>\n",
       "      <td>-0.993</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337200.0</td>\n",
       "      <td>3295.34</td>\n",
       "      <td>4.318500e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.053340</td>\n",
       "      <td>82.523258</td>\n",
       "      <td>1.107541</td>\n",
       "      <td>1.236199</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12434</th>\n",
       "      <td>USAZZF1</td>\n",
       "      <td>2.465</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.452</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.848960e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.774107</td>\n",
       "      <td>85.172848</td>\n",
       "      <td>0.794524</td>\n",
       "      <td>1.180092</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12435</th>\n",
       "      <td>USAZZI1</td>\n",
       "      <td>2.354</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.518</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.055250e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.608398</td>\n",
       "      <td>91.998727</td>\n",
       "      <td>0.764140</td>\n",
       "      <td>0.982174</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12436</th>\n",
       "      <td>USAZZJ1</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.981</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.826553e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.764089</td>\n",
       "      <td>59.348666</td>\n",
       "      <td>0.090069</td>\n",
       "      <td>0.346851</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12437</th>\n",
       "      <td>USAZZL1</td>\n",
       "      <td>1.895</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.318</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.430560e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.542009</td>\n",
       "      <td>53.503883</td>\n",
       "      <td>0.855973</td>\n",
       "      <td>1.148298</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12438</th>\n",
       "      <td>USAZZP1</td>\n",
       "      <td>2.410</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.614480e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.293774</td>\n",
       "      <td>76.269788</td>\n",
       "      <td>0.495782</td>\n",
       "      <td>0.872594</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12439</th>\n",
       "      <td>USAZZR1</td>\n",
       "      <td>2.120</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.167</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.280000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.228029</td>\n",
       "      <td>34.504433</td>\n",
       "      <td>0.464922</td>\n",
       "      <td>0.853555</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12440</th>\n",
       "      <td>USAZZX1</td>\n",
       "      <td>-1.522</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87800.0</td>\n",
       "      <td>3580.90</td>\n",
       "      <td>2.031860e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>126.289675</td>\n",
       "      <td>123.366131</td>\n",
       "      <td>1.116599</td>\n",
       "      <td>1.320642</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12441</th>\n",
       "      <td>USAZZY1</td>\n",
       "      <td>-2.405</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.411</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86300.0</td>\n",
       "      <td>2316266.45</td>\n",
       "      <td>8.306788e+08</td>\n",
       "      <td>0.620877</td>\n",
       "      <td>32.573024</td>\n",
       "      <td>22.297109</td>\n",
       "      <td>0.813423</td>\n",
       "      <td>0.911993</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12442 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Barrid  USFASTD_1DREVRSL  USFASTD_AERODEF  USFASTD_AIRLINES  \\\n",
       "0      USA0001            -0.032            0.000               0.0   \n",
       "1      USA0011            -0.059            0.000               0.0   \n",
       "2      USA0031             0.457            0.000               0.0   \n",
       "3      USA0062            -0.128            0.431               0.0   \n",
       "4      USA00E2             0.374            0.000               0.0   \n",
       "5      USA00F1            -0.254            0.000               0.0   \n",
       "6      USA00G2            -0.317            0.000               0.0   \n",
       "7      USA00H1             0.157            0.000               0.0   \n",
       "8      USA00I1            -0.550            0.000               0.0   \n",
       "9      USA00J1            -0.169            0.000               0.0   \n",
       "10     USA00K1            -1.019            0.000               0.0   \n",
       "11     USA00P1             0.334            0.000               0.0   \n",
       "12     USA00R1             0.490            0.000               0.0   \n",
       "13     USA00S1            -0.169            0.000               0.0   \n",
       "14     USA00V1            -0.642            0.000               0.0   \n",
       "15     USA0131            -0.609            1.000               0.0   \n",
       "16     USA0161            -0.098            1.000               0.0   \n",
       "17     USA01I1            -0.230            0.000               0.0   \n",
       "18     USA01J2            -0.175            0.000               0.0   \n",
       "19     USA01L1            -0.116            0.000               0.0   \n",
       "20     USA01P1             0.701            0.000               0.0   \n",
       "21     USA01Q1            -0.249            0.000               0.0   \n",
       "22     USA0202             0.735            0.000               0.0   \n",
       "23     USA0231            -0.007            0.000               0.0   \n",
       "24     USA0281            -0.133            0.000               0.0   \n",
       "25     USA0291             0.639            0.000               0.0   \n",
       "26     USA02A1            -0.339            0.000               0.0   \n",
       "27     USA02B1            -2.745            0.000               0.0   \n",
       "28     USA02H1            -0.560            0.000               0.0   \n",
       "29     USA02P1             0.166            0.000               0.0   \n",
       "...        ...               ...              ...               ...   \n",
       "12412  USAZY41             0.715            0.000               0.0   \n",
       "12413  USAZY51             2.418            0.000               0.0   \n",
       "12414  USAZY61             1.618            0.000               0.0   \n",
       "12415  USAZY71             2.622            0.000               0.0   \n",
       "12416  USAZYI1             2.826            0.000               0.0   \n",
       "12417  USAZYJ1            -1.637            0.000               0.0   \n",
       "12418  USAZYK1             2.077            0.000               0.0   \n",
       "12419  USAZYM1             2.826            0.000               0.0   \n",
       "12420  USAZYR1            -1.874            0.000               0.0   \n",
       "12421  USAZYS1             2.646            0.000               0.0   \n",
       "12422  USAZYT1            -1.704            0.000               0.0   \n",
       "12423  USAZYV1            -1.045            0.000               0.0   \n",
       "12424  USAZYX1             1.151            0.000               0.0   \n",
       "12425  USAZZ41             2.826            0.000               0.0   \n",
       "12426  USAZZ51             2.354            0.000               0.0   \n",
       "12427  USAZZ61             2.481            0.000               0.0   \n",
       "12428  USAZZ71             1.582            0.000               0.0   \n",
       "12429  USAZZ81            -0.164            0.000               0.0   \n",
       "12430  USAZZ91             2.826            0.000               0.0   \n",
       "12431  USAZZA1             2.399            0.000               0.0   \n",
       "12432  USAZZB1             2.301            0.000               0.0   \n",
       "12433  USAZZD1            -0.993            0.000               0.0   \n",
       "12434  USAZZF1             2.465            0.000               0.0   \n",
       "12435  USAZZI1             2.354            0.000               0.0   \n",
       "12436  USAZZJ1             2.826            0.000               0.0   \n",
       "12437  USAZZL1             1.895            0.000               0.0   \n",
       "12438  USAZZP1             2.410            0.000               0.0   \n",
       "12439  USAZZR1             2.120            0.000               0.0   \n",
       "12440  USAZZX1            -1.522            0.000               0.0   \n",
       "12441  USAZZY1            -2.405            0.000               0.0   \n",
       "\n",
       "       USFASTD_ALUMSTEL  USFASTD_APPAREL  USFASTD_AUTO  USFASTD_BANKS  \\\n",
       "0                   0.0              0.0           0.0            0.0   \n",
       "1                   0.0              0.0           0.0            0.0   \n",
       "2                   0.0              0.0           0.0            0.0   \n",
       "3                   0.0              0.0           0.0            0.0   \n",
       "4                   0.0              0.0           0.0            0.0   \n",
       "5                   0.0              0.0           0.0            0.0   \n",
       "6                   0.0              0.0           0.0            0.0   \n",
       "7                   0.0              0.0           0.0            0.0   \n",
       "8                   0.0              0.0           0.0            0.0   \n",
       "9                   0.0              0.0           0.0            0.0   \n",
       "10                  0.0              0.0           0.0            0.0   \n",
       "11                  0.0              0.0           0.0            0.0   \n",
       "12                  0.0              0.0           0.0            0.0   \n",
       "13                  0.0              0.0           0.0            0.0   \n",
       "14                  0.0              0.0           0.0            0.0   \n",
       "15                  0.0              0.0           0.0            0.0   \n",
       "16                  0.0              0.0           0.0            0.0   \n",
       "17                  0.0              0.0           0.0            0.0   \n",
       "18                  0.0              0.0           0.0            0.0   \n",
       "19                  0.0              0.0           0.0            0.0   \n",
       "20                  0.0              0.0           0.0            0.0   \n",
       "21                  0.0              0.0           0.0            0.0   \n",
       "22                  0.0              0.0           0.0            0.0   \n",
       "23                  0.0              0.0           0.0            0.0   \n",
       "24                  0.0              0.0           0.0            0.0   \n",
       "25                  0.0              0.0           0.0            0.0   \n",
       "26                  0.0              0.0           0.0            0.0   \n",
       "27                  0.0              0.0           0.0            0.0   \n",
       "28                  0.0              0.0           0.0            1.0   \n",
       "29                  0.0              0.0           0.0            0.0   \n",
       "...                 ...              ...           ...            ...   \n",
       "12412               0.0              0.0           0.0            0.0   \n",
       "12413               0.0              0.0           0.0            0.0   \n",
       "12414               0.0              0.0           0.0            0.0   \n",
       "12415               0.0              0.0           0.0            0.0   \n",
       "12416               0.0              0.0           0.0            0.0   \n",
       "12417               0.0              0.0           0.0            0.0   \n",
       "12418               0.0              0.0           0.0            0.0   \n",
       "12419               0.0              0.0           0.0            0.0   \n",
       "12420               0.0              0.0           0.0            0.0   \n",
       "12421               0.0              0.0           0.0            0.0   \n",
       "12422               0.0              0.0           0.0            0.0   \n",
       "12423               0.0              0.0           0.0            0.0   \n",
       "12424               0.0              0.0           0.0            0.0   \n",
       "12425               0.0              0.0           0.0            0.0   \n",
       "12426               0.0              0.0           0.0            0.0   \n",
       "12427               0.0              0.0           0.0            0.0   \n",
       "12428               0.0              0.0           0.0            0.0   \n",
       "12429               0.0              0.0           0.0            0.0   \n",
       "12430               0.0              0.0           0.0            0.0   \n",
       "12431               0.0              0.0           0.0            0.0   \n",
       "12432               0.0              0.0           0.0            0.0   \n",
       "12433               0.0              0.0           0.0            0.0   \n",
       "12434               0.0              0.0           0.0            0.0   \n",
       "12435               0.0              0.0           0.0            0.0   \n",
       "12436               0.0              0.0           0.0            0.0   \n",
       "12437               0.0              0.0           0.0            0.0   \n",
       "12438               0.0              0.0           0.0            0.0   \n",
       "12439               0.0              0.0           0.0            0.0   \n",
       "12440               0.0              0.0           0.0            0.0   \n",
       "12441               0.0              0.0           0.0            0.0   \n",
       "\n",
       "       USFASTD_BETA  USFASTD_BEVTOB  ...  BidAskSpread  DailyVolume  \\\n",
       "0            -2.177           0.000  ...           NaN          NaN   \n",
       "1            -2.176           0.000  ...           NaN          NaN   \n",
       "2            -2.061           0.000  ...           NaN          NaN   \n",
       "3            -2.003           0.000  ...           NaN          NaN   \n",
       "4            -1.967           0.000  ...           NaN          NaN   \n",
       "5            -2.025           0.000  ...           NaN          NaN   \n",
       "6            -2.214           0.000  ...           NaN          NaN   \n",
       "7            -1.882           0.000  ...           NaN          NaN   \n",
       "8            -2.190           0.000  ...           NaN          NaN   \n",
       "9            -2.198           0.863  ...           NaN          NaN   \n",
       "10           -2.334           0.000  ...           NaN          NaN   \n",
       "11           -1.098           0.000  ...           NaN          NaN   \n",
       "12           -1.615           0.000  ...           NaN          NaN   \n",
       "13           -1.141           0.000  ...           NaN       4289.0   \n",
       "14           -2.176           0.000  ...           NaN          NaN   \n",
       "15           -2.440           0.000  ...           NaN          NaN   \n",
       "16           -1.183           0.000  ...           NaN        170.0   \n",
       "17           -1.943           0.000  ...           NaN          NaN   \n",
       "18           -2.045           0.000  ...           NaN          NaN   \n",
       "19           -1.981           0.000  ...           NaN          NaN   \n",
       "20           -1.777           0.000  ...           NaN          NaN   \n",
       "21           -2.634           1.000  ...           NaN          NaN   \n",
       "22           -0.176           0.000  ...           NaN          NaN   \n",
       "23           -1.905           0.000  ...           NaN          NaN   \n",
       "24           -2.160           0.000  ...           NaN          NaN   \n",
       "25           -1.147           0.000  ...           NaN          NaN   \n",
       "26           -2.140           0.000  ...           NaN          NaN   \n",
       "27           -1.188           0.000  ...           NaN        180.0   \n",
       "28           -1.387           0.000  ...           NaN          NaN   \n",
       "29           -2.085           0.000  ...           NaN      18800.0   \n",
       "...             ...             ...  ...           ...          ...   \n",
       "12412        -2.176           0.000  ...           NaN          NaN   \n",
       "12413        -1.569           0.000  ...           NaN          NaN   \n",
       "12414        -0.586           0.000  ...           NaN          NaN   \n",
       "12415        -0.366           0.000  ...           NaN          NaN   \n",
       "12416        -0.563           0.000  ...           NaN       8500.0   \n",
       "12417        -0.431           0.000  ...           NaN       1000.0   \n",
       "12418         0.029           0.000  ...           NaN       7950.0   \n",
       "12419         0.188           0.000  ...           NaN      12500.0   \n",
       "12420        -0.975           0.000  ...           NaN     279693.0   \n",
       "12421         0.190           0.000  ...           NaN          NaN   \n",
       "12422        -0.867           0.000  ...           NaN      13867.0   \n",
       "12423        -0.674           0.000  ...           NaN    1335300.0   \n",
       "12424        -1.376           0.000  ...           NaN       1100.0   \n",
       "12425        -1.466           0.000  ...           NaN      84700.0   \n",
       "12426        -0.368           0.000  ...           NaN          NaN   \n",
       "12427        -0.798           0.000  ...           NaN     281000.0   \n",
       "12428        -0.741           0.000  ...           NaN          NaN   \n",
       "12429        -1.538           1.000  ...           NaN      40600.0   \n",
       "12430         0.114           0.000  ...           NaN          NaN   \n",
       "12431        -0.517           0.000  ...           NaN          NaN   \n",
       "12432        -1.584           0.000  ...           NaN          NaN   \n",
       "12433         0.228           0.000  ...           NaN     337200.0   \n",
       "12434        -0.452           0.000  ...           NaN          NaN   \n",
       "12435        -0.518           1.000  ...           NaN          NaN   \n",
       "12436        -1.981           0.000  ...           NaN          0.0   \n",
       "12437        -0.318           0.000  ...           NaN          NaN   \n",
       "12438        -1.100           1.000  ...           NaN          NaN   \n",
       "12439        -1.167           0.000  ...           NaN          NaN   \n",
       "12440         0.247           0.000  ...           NaN      87800.0   \n",
       "12441        -0.411           0.000  ...           NaN      86300.0   \n",
       "\n",
       "         ADTCA_30  IssuerMarketCap     Yield   TotalRisk    SpecRisk  \\\n",
       "0             NaN     5.392988e+10  0.188679   13.959397    9.014505   \n",
       "1             NaN     5.874999e+09  0.000000   17.033359   11.726327   \n",
       "2             NaN     7.304963e+10  2.103004   21.738218   18.047415   \n",
       "3             NaN     2.866562e+10  2.243494   23.129496   20.830128   \n",
       "4             NaN     5.720420e+10  2.167256   25.391653   21.458171   \n",
       "5             NaN     1.742470e+11  0.392275   15.695637   11.912117   \n",
       "6             NaN     1.644681e+10  4.679803   21.654431   14.431033   \n",
       "7             NaN     8.054473e+10  0.000000   22.765658   18.313856   \n",
       "8             NaN     1.681907e+10  0.087623   20.410134   14.133102   \n",
       "9             NaN     4.335559e+10  1.670463   21.686440   19.249760   \n",
       "10            NaN     3.677209e+10  0.000000   33.867332   26.375334   \n",
       "11            NaN     2.377153e+10  1.413784   21.424437   16.195982   \n",
       "12            NaN     6.177471e+09  2.641914   18.112924   12.495233   \n",
       "13            NaN     1.576241e+10  1.244629   21.922494   19.035684   \n",
       "14            NaN     1.041914e+10  1.458435   12.385339    8.420332   \n",
       "15            NaN     3.359884e+10  1.613669   24.839671   21.502548   \n",
       "16            NaN     3.359884e+10  1.613669   28.025448   23.914479   \n",
       "17            NaN     2.664403e+10  0.196207   15.087449   12.143365   \n",
       "18            NaN     2.851823e+10  1.782703   23.016956   20.028493   \n",
       "19            NaN     9.179657e+10  3.380021   23.209763   19.935156   \n",
       "20            NaN     1.691907e+11  0.000000   27.138099   23.901111   \n",
       "21            NaN     1.237492e+11  1.733990   22.222874   19.652249   \n",
       "22            NaN     1.328618e+10  0.000000   29.844475   23.970616   \n",
       "23            NaN     8.798649e+10  0.000000   24.742676   21.356169   \n",
       "24            NaN     4.534344e+10  3.050774   18.248930   14.319948   \n",
       "25            NaN     3.184106e+10  0.000000   24.017693   19.557779   \n",
       "26            NaN     7.248961e+09  1.947799   11.185751    7.613359   \n",
       "27            NaN     7.248961e+09  1.947799   32.801110   28.797618   \n",
       "28            NaN     7.215026e+10  2.398082   21.698318   18.244674   \n",
       "29            NaN     3.117866e+11  2.582311   22.480566   19.563198   \n",
       "...           ...              ...       ...         ...         ...   \n",
       "12412         NaN     5.056250e+06       NaN   19.208371   12.488067   \n",
       "12413         NaN     6.638950e+06  0.000000   74.640585   72.370109   \n",
       "12414         NaN     3.012930e+06       NaN   67.334667   63.572209   \n",
       "12415         NaN     2.452000e+05  0.000000   87.497910   83.717198   \n",
       "12416     3223.94     4.740600e+06  0.000000   76.863310   73.357836   \n",
       "12417      920.22     2.525880e+07       NaN   73.338050   69.331053   \n",
       "12418      769.50     6.832300e+04       NaN   72.972597   68.149533   \n",
       "12419      326.41     2.270100e+06       NaN   80.656370   76.499271   \n",
       "12420    91477.12     1.003100e+07  0.000000  112.592276  110.419717   \n",
       "12421         NaN     5.030700e+01  0.000000   43.661249   33.805533   \n",
       "12422    57150.03     2.763600e+07  0.000000   72.956591   70.049127   \n",
       "12423    10509.78     3.100469e+06  0.000000  119.257445  117.005538   \n",
       "12424    15628.40     4.236672e+07  0.000000   25.602659   21.212255   \n",
       "12425    12098.24     6.264510e+06       NaN   71.497468   69.006331   \n",
       "12426         NaN     7.414000e+04       NaN   38.279688   30.066884   \n",
       "12427     2324.45     2.224400e+06  0.000000  114.260252  111.809094   \n",
       "12428         NaN     2.684070e+07  0.000000   67.522512   63.419945   \n",
       "12429  1307457.05     2.911190e+10  4.714286   21.679651   18.778080   \n",
       "12430         NaN     1.352860e+06  0.000000   45.970832   36.773965   \n",
       "12431         NaN     7.609700e+05       NaN   86.928704   83.337116   \n",
       "12432         NaN     1.251480e+06  0.000000   31.424176   24.267277   \n",
       "12433     3295.34     4.318500e+05       NaN   86.053340   82.523258   \n",
       "12434         NaN     1.848960e+06       NaN   88.774107   85.172848   \n",
       "12435         NaN     1.055250e+06  0.000000   94.608398   91.998727   \n",
       "12436         NaN     7.826553e+06  0.000000   62.764089   59.348666   \n",
       "12437         NaN     3.430560e+07  0.000000   58.542009   53.503883   \n",
       "12438         NaN     2.614480e+06       NaN   79.293774   76.269788   \n",
       "12439         NaN     8.280000e+04       NaN   40.228029   34.504433   \n",
       "12440     3580.90     2.031860e+06  0.000000  126.289675  123.366131   \n",
       "12441  2316266.45     8.306788e+08  0.620877   32.573024   22.297109   \n",
       "\n",
       "       HistBeta  PredBeta  DataDate  \n",
       "0     -0.000189  0.156387  20040102  \n",
       "1      0.000013  0.147900  20040102  \n",
       "2      0.053033  0.216422  20040102  \n",
       "3      0.079755  0.355564  20040102  \n",
       "4      0.096238  0.412602  20040102  \n",
       "5      0.069464  0.346033  20040102  \n",
       "6     -0.017417  0.026702  20040102  \n",
       "7      0.135674  0.267576  20040102  \n",
       "8     -0.006358  0.278503  20040102  \n",
       "9     -0.010228  0.194933  20040102  \n",
       "10    -0.072812  0.475343  20040102  \n",
       "11     0.496970  0.853930  20040102  \n",
       "12     0.258616  0.673043  20040102  \n",
       "13     0.477142  0.558508  20040102  \n",
       "14     0.000013  0.083475  20040102  \n",
       "15    -0.121604  0.145560  20040102  \n",
       "16     0.457450  0.713817  20040102  \n",
       "17     0.107272  0.220367  20040102  \n",
       "18     0.060553  0.355385  20040102  \n",
       "19     0.089837  0.278654  20040102  \n",
       "20     0.183841  0.410674  20040102  \n",
       "21    -0.210865  0.042602  20040102  \n",
       "22     0.921471  1.005869  20040102  \n",
       "23     0.125102  0.519813  20040102  \n",
       "24     0.007271  0.359541  20040102  \n",
       "25     0.474128  0.720445  20040102  \n",
       "26     0.016464  0.268383  20040102  \n",
       "27     0.455392  0.823334  20040102  \n",
       "28     0.363409  0.574217  20040102  \n",
       "29     0.042230  0.340393  20040102  \n",
       "...         ...       ...       ...  \n",
       "12412  0.000013  0.050859  20040102  \n",
       "12413  0.279903  0.579444  20040102  \n",
       "12414  0.732842  1.041313  20040102  \n",
       "12415  0.833859  1.149809  20040102  \n",
       "12416  0.743190  1.041294  20040102  \n",
       "12417  0.803832  1.162438  20040102  \n",
       "12418  1.016101  1.354084  20040102  \n",
       "12419  1.089179  1.282783  20040102  \n",
       "12420  0.553212  0.788145  20040102  \n",
       "12421  1.089953  1.379310  20040102  \n",
       "12422  0.603339  0.850369  20040102  \n",
       "12423  0.692038  0.970822  20040102  \n",
       "12424  0.368721  0.289544  20040102  \n",
       "12425  0.327400  0.629735  20040102  \n",
       "12426  0.832962  1.114710  20040102  \n",
       "12427  0.634920  1.013968  20040102  \n",
       "12428  0.661152  0.900018  20040102  \n",
       "12429  0.294241  0.401738  20040102  \n",
       "12430  1.054950  1.224731  20040102  \n",
       "12431  0.764430  1.151915  20040102  \n",
       "12432  0.272805  0.689609  20040102  \n",
       "12433  1.107541  1.236199  20040102  \n",
       "12434  0.794524  1.180092  20040102  \n",
       "12435  0.764140  0.982174  20040102  \n",
       "12436  0.090069  0.346851  20040102  \n",
       "12437  0.855973  1.148298  20040102  \n",
       "12438  0.495782  0.872594  20040102  \n",
       "12439  0.464922  0.853555  20040102  \n",
       "12440  1.116599  1.320642  20040102  \n",
       "12441  0.813423  0.911993  20040102  \n",
       "\n",
       "[12442 rows x 92 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['20040102']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Factor1</th>\n",
       "      <th>Factor2</th>\n",
       "      <th>VarCovar</th>\n",
       "      <th>DataDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>1.958869</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_BETA</td>\n",
       "      <td>1.602458</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_DIVYILD</td>\n",
       "      <td>-0.012642</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_DWNRISK</td>\n",
       "      <td>-0.064387</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_EARNQLTY</td>\n",
       "      <td>0.046573</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_EARNYILD</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_GROWTH</td>\n",
       "      <td>-0.133492</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_INDMOM</td>\n",
       "      <td>0.015145</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_LEVERAGE</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_LIQUIDTY</td>\n",
       "      <td>0.136076</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_LTREVRSL</td>\n",
       "      <td>-0.083329</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_MGMTQLTY</td>\n",
       "      <td>-0.034950</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_MIDCAP</td>\n",
       "      <td>-0.098919</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_MOMENTUM</td>\n",
       "      <td>0.113221</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_PROFIT</td>\n",
       "      <td>0.128059</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_PROSPECT</td>\n",
       "      <td>0.049532</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_REGMOM</td>\n",
       "      <td>-0.122587</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_RESVOL</td>\n",
       "      <td>0.189217</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_SEASON</td>\n",
       "      <td>0.035201</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_SENTMT</td>\n",
       "      <td>0.032225</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_STREVRSL</td>\n",
       "      <td>0.538114</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_SIZE</td>\n",
       "      <td>0.112223</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_VALUE</td>\n",
       "      <td>0.023840</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_OILGSDRL</td>\n",
       "      <td>-0.578034</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_OILGSEQP</td>\n",
       "      <td>-0.494304</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_OILGSEXP</td>\n",
       "      <td>-0.141746</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_OILGSCON</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_CHEM</td>\n",
       "      <td>0.867780</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_SPTYCHEM</td>\n",
       "      <td>0.792662</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_CNSTMATL</td>\n",
       "      <td>0.083748</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>USFASTD_COMMEQP</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>-9.927696</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3541</th>\n",
       "      <td>USFASTD_COMMEQP</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>0.429798</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>50.975358</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>-11.286033</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3544</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>11.475849</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>-5.647870</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>-6.781064</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>-7.607191</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>USFASTD_COMPELEC</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>8.973094</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>177.211134</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3550</th>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>32.036058</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>3.034343</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3552</th>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>3.211664</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3553</th>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>0.130603</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3554</th>\n",
       "      <td>USFASTD_TELECOM</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>-17.050183</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>218.553900</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>-6.691638</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>-1.406908</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>-10.965736</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>USFASTD_WIRELESS</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>-4.490823</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3560</th>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>62.524306</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3561</th>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>40.206928</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3562</th>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>48.669244</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>USFASTD_ELECUTIL</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>-13.706329</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3564</th>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>53.889503</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>42.766584</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>USFASTD_GASUTIL</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>-8.627009</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>222.686570</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>USFASTD_MULTUTIL</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>-9.925801</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3569</th>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>USFASTD_COUNTRY</td>\n",
       "      <td>190.504751</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3570 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Factor1           Factor2    VarCovar  DataDate\n",
       "0     USFASTD_1DREVRSL  USFASTD_1DREVRSL    1.958869  20040102\n",
       "1     USFASTD_1DREVRSL      USFASTD_BETA    1.602458  20040102\n",
       "2     USFASTD_1DREVRSL   USFASTD_DIVYILD   -0.012642  20040102\n",
       "3     USFASTD_1DREVRSL   USFASTD_DWNRISK   -0.064387  20040102\n",
       "4     USFASTD_1DREVRSL  USFASTD_EARNQLTY    0.046573  20040102\n",
       "5     USFASTD_1DREVRSL  USFASTD_EARNYILD    0.013229  20040102\n",
       "6     USFASTD_1DREVRSL    USFASTD_GROWTH   -0.133492  20040102\n",
       "7     USFASTD_1DREVRSL    USFASTD_INDMOM    0.015145  20040102\n",
       "8     USFASTD_1DREVRSL  USFASTD_LEVERAGE    0.005025  20040102\n",
       "9     USFASTD_1DREVRSL  USFASTD_LIQUIDTY    0.136076  20040102\n",
       "10    USFASTD_1DREVRSL  USFASTD_LTREVRSL   -0.083329  20040102\n",
       "11    USFASTD_1DREVRSL  USFASTD_MGMTQLTY   -0.034950  20040102\n",
       "12    USFASTD_1DREVRSL    USFASTD_MIDCAP   -0.098919  20040102\n",
       "13    USFASTD_1DREVRSL  USFASTD_MOMENTUM    0.113221  20040102\n",
       "14    USFASTD_1DREVRSL    USFASTD_PROFIT    0.128059  20040102\n",
       "15    USFASTD_1DREVRSL  USFASTD_PROSPECT    0.049532  20040102\n",
       "16    USFASTD_1DREVRSL    USFASTD_REGMOM   -0.122587  20040102\n",
       "17    USFASTD_1DREVRSL    USFASTD_RESVOL    0.189217  20040102\n",
       "18    USFASTD_1DREVRSL    USFASTD_SEASON    0.035201  20040102\n",
       "19    USFASTD_1DREVRSL    USFASTD_SENTMT    0.032225  20040102\n",
       "20    USFASTD_1DREVRSL  USFASTD_STREVRSL    0.538114  20040102\n",
       "21    USFASTD_1DREVRSL      USFASTD_SIZE    0.112223  20040102\n",
       "22    USFASTD_1DREVRSL     USFASTD_VALUE    0.023840  20040102\n",
       "23    USFASTD_1DREVRSL  USFASTD_OILGSDRL   -0.578034  20040102\n",
       "24    USFASTD_1DREVRSL  USFASTD_OILGSEQP   -0.494304  20040102\n",
       "25    USFASTD_1DREVRSL  USFASTD_OILGSEXP   -0.141746  20040102\n",
       "26    USFASTD_1DREVRSL  USFASTD_OILGSCON    0.260516  20040102\n",
       "27    USFASTD_1DREVRSL      USFASTD_CHEM    0.867780  20040102\n",
       "28    USFASTD_1DREVRSL  USFASTD_SPTYCHEM    0.792662  20040102\n",
       "29    USFASTD_1DREVRSL  USFASTD_CNSTMATL    0.083748  20040102\n",
       "...                ...               ...         ...       ...\n",
       "3540   USFASTD_COMMEQP  USFASTD_MULTUTIL   -9.927696  20040102\n",
       "3541   USFASTD_COMMEQP   USFASTD_COUNTRY    0.429798  20040102\n",
       "3542  USFASTD_COMPELEC  USFASTD_COMPELEC   50.975358  20040102\n",
       "3543  USFASTD_COMPELEC   USFASTD_TELECOM  -11.286033  20040102\n",
       "3544  USFASTD_COMPELEC  USFASTD_WIRELESS   11.475849  20040102\n",
       "3545  USFASTD_COMPELEC  USFASTD_ELECUTIL   -5.647870  20040102\n",
       "3546  USFASTD_COMPELEC   USFASTD_GASUTIL   -6.781064  20040102\n",
       "3547  USFASTD_COMPELEC  USFASTD_MULTUTIL   -7.607191  20040102\n",
       "3548  USFASTD_COMPELEC   USFASTD_COUNTRY    8.973094  20040102\n",
       "3549   USFASTD_TELECOM   USFASTD_TELECOM  177.211134  20040102\n",
       "3550   USFASTD_TELECOM  USFASTD_WIRELESS   32.036058  20040102\n",
       "3551   USFASTD_TELECOM  USFASTD_ELECUTIL    3.034343  20040102\n",
       "3552   USFASTD_TELECOM   USFASTD_GASUTIL    3.211664  20040102\n",
       "3553   USFASTD_TELECOM  USFASTD_MULTUTIL    0.130603  20040102\n",
       "3554   USFASTD_TELECOM   USFASTD_COUNTRY  -17.050183  20040102\n",
       "3555  USFASTD_WIRELESS  USFASTD_WIRELESS  218.553900  20040102\n",
       "3556  USFASTD_WIRELESS  USFASTD_ELECUTIL   -6.691638  20040102\n",
       "3557  USFASTD_WIRELESS   USFASTD_GASUTIL   -1.406908  20040102\n",
       "3558  USFASTD_WIRELESS  USFASTD_MULTUTIL  -10.965736  20040102\n",
       "3559  USFASTD_WIRELESS   USFASTD_COUNTRY   -4.490823  20040102\n",
       "3560  USFASTD_ELECUTIL  USFASTD_ELECUTIL   62.524306  20040102\n",
       "3561  USFASTD_ELECUTIL   USFASTD_GASUTIL   40.206928  20040102\n",
       "3562  USFASTD_ELECUTIL  USFASTD_MULTUTIL   48.669244  20040102\n",
       "3563  USFASTD_ELECUTIL   USFASTD_COUNTRY  -13.706329  20040102\n",
       "3564   USFASTD_GASUTIL   USFASTD_GASUTIL   53.889503  20040102\n",
       "3565   USFASTD_GASUTIL  USFASTD_MULTUTIL   42.766584  20040102\n",
       "3566   USFASTD_GASUTIL   USFASTD_COUNTRY   -8.627009  20040102\n",
       "3567  USFASTD_MULTUTIL  USFASTD_MULTUTIL  222.686570  20040102\n",
       "3568  USFASTD_MULTUTIL   USFASTD_COUNTRY   -9.925801  20040102\n",
       "3569   USFASTD_COUNTRY   USFASTD_COUNTRY  190.504751  20040102\n",
       "\n",
       "[3570 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariance['20040102']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Barrid</th>\n",
       "      <th>DlyReturn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>USA0001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>USA0011</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>USA0031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>USA0062</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>USA00E2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>USA00F1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>USA00G2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>USA00H1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>USA00I1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>USA00J1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>USA00K1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>USA00P1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>USA00R1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>USA00S1</td>\n",
       "      <td>-0.009555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>USA00V1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>USA0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>USA0161</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>USA01I1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>USA01J2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>USA01L1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>USA01P1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>USA01Q1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>USA0202</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>USA0231</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>USA0281</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>USA0291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>USA02A1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>USA02B1</td>\n",
       "      <td>0.083078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>USA02H1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>USA02P1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12488</th>\n",
       "      <td>USAZY41</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12489</th>\n",
       "      <td>USAZY51</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12490</th>\n",
       "      <td>USAZY61</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12491</th>\n",
       "      <td>USAZY71</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12492</th>\n",
       "      <td>USAZYI1</td>\n",
       "      <td>-0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12493</th>\n",
       "      <td>USAZYJ1</td>\n",
       "      <td>0.320755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12494</th>\n",
       "      <td>USAZYK1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12495</th>\n",
       "      <td>USAZYM1</td>\n",
       "      <td>-0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12496</th>\n",
       "      <td>USAZYR1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>USAZYS1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12498</th>\n",
       "      <td>USAZYT1</td>\n",
       "      <td>0.056604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12499</th>\n",
       "      <td>USAZYV1</td>\n",
       "      <td>0.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12500</th>\n",
       "      <td>USAZYX1</td>\n",
       "      <td>-0.002445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12501</th>\n",
       "      <td>USAZZ41</td>\n",
       "      <td>-0.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12502</th>\n",
       "      <td>USAZZ51</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12503</th>\n",
       "      <td>USAZZ61</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12504</th>\n",
       "      <td>USAZZ71</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12505</th>\n",
       "      <td>USAZZ81</td>\n",
       "      <td>0.002891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12506</th>\n",
       "      <td>USAZZ91</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12507</th>\n",
       "      <td>USAZZA1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12508</th>\n",
       "      <td>USAZZB1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12509</th>\n",
       "      <td>USAZZD1</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12510</th>\n",
       "      <td>USAZZF1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12511</th>\n",
       "      <td>USAZZI1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12512</th>\n",
       "      <td>USAZZJ1</td>\n",
       "      <td>-0.026964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12513</th>\n",
       "      <td>USAZZL1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12514</th>\n",
       "      <td>USAZZP1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12515</th>\n",
       "      <td>USAZZR1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12516</th>\n",
       "      <td>USAZZX1</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12517</th>\n",
       "      <td>USAZZY1</td>\n",
       "      <td>0.008149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12442 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Barrid  DlyReturn\n",
       "76     USA0001   0.000000\n",
       "77     USA0011   0.000000\n",
       "78     USA0031   0.000000\n",
       "79     USA0062   0.000000\n",
       "80     USA00E2   0.000000\n",
       "81     USA00F1   0.000000\n",
       "82     USA00G2   0.000000\n",
       "83     USA00H1   0.000000\n",
       "84     USA00I1   0.000000\n",
       "85     USA00J1   0.000000\n",
       "86     USA00K1   0.000000\n",
       "87     USA00P1   0.000000\n",
       "88     USA00R1   0.000000\n",
       "89     USA00S1  -0.009555\n",
       "90     USA00V1   0.000000\n",
       "91     USA0131   0.000000\n",
       "92     USA0161   0.000000\n",
       "93     USA01I1   0.000000\n",
       "94     USA01J2   0.000000\n",
       "95     USA01L1   0.000000\n",
       "96     USA01P1   0.000000\n",
       "97     USA01Q1   0.000000\n",
       "98     USA0202   0.000000\n",
       "99     USA0231   0.000000\n",
       "100    USA0281   0.000000\n",
       "101    USA0291   0.000000\n",
       "102    USA02A1   0.000000\n",
       "103    USA02B1   0.083078\n",
       "104    USA02H1   0.000000\n",
       "105    USA02P1   0.000000\n",
       "...        ...        ...\n",
       "12488  USAZY41   0.000000\n",
       "12489  USAZY51   0.000000\n",
       "12490  USAZY61   0.000000\n",
       "12491  USAZY71   0.000000\n",
       "12492  USAZYI1  -0.142857\n",
       "12493  USAZYJ1   0.320755\n",
       "12494  USAZYK1   0.000000\n",
       "12495  USAZYM1  -0.062500\n",
       "12496  USAZYR1   0.166667\n",
       "12497  USAZYS1   0.000000\n",
       "12498  USAZYT1   0.056604\n",
       "12499  USAZYV1   0.045455\n",
       "12500  USAZYX1  -0.002445\n",
       "12501  USAZZ41  -0.045455\n",
       "12502  USAZZ51   0.000000\n",
       "12503  USAZZ61   0.000000\n",
       "12504  USAZZ71   0.000000\n",
       "12505  USAZZ81   0.002891\n",
       "12506  USAZZ91   0.000000\n",
       "12507  USAZZA1   0.000000\n",
       "12508  USAZZB1   0.000000\n",
       "12509  USAZZD1   0.250000\n",
       "12510  USAZZF1   0.000000\n",
       "12511  USAZZI1   0.000000\n",
       "12512  USAZZJ1  -0.026964\n",
       "12513  USAZZL1   0.000000\n",
       "12514  USAZZP1   0.000000\n",
       "12515  USAZZR1   0.000000\n",
       "12516  USAZZX1   0.111111\n",
       "12517  USAZZY1   0.008149\n",
       "\n",
       "[12442 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return['20040102']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['20040102', '20040105', '20040106', '20040107', '20040108', '20040109', '20040112', '20040113', '20040114', '20040115', '20040116', '20040120', '20040121', '20040122', '20040123', '20040126', '20040127', '20040128', '20040129', '20040130', '20040202', '20040203', '20040204', '20040205', '20040206', '20040209', '20040210', '20040211', '20040212', '20040213', '20040217', '20040218', '20040219', '20040220', '20040223', '20040224', '20040225', '20040226', '20040227', '20040301', '20040302', '20040303', '20040304', '20040305', '20040308', '20040309', '20040310', '20040311', '20040312', '20040315', '20040316', '20040317', '20040318', '20040319', '20040322', '20040323', '20040324', '20040325', '20040326', '20040329', '20040330', '20040331', '20040401', '20040402', '20040405', '20040406', '20040407', '20040408', '20040412', '20040413', '20040414', '20040415', '20040416', '20040419', '20040420', '20040421', '20040422', '20040423', '20040426', '20040427', '20040428', '20040429', '20040430', '20040503', '20040504', '20040505', '20040506', '20040507', '20040510', '20040511', '20040512', '20040513', '20040514', '20040517', '20040518', '20040519', '20040520', '20040521', '20040524', '20040525', '20040526', '20040527', '20040528', '20040601', '20040602', '20040603', '20040604', '20040607', '20040608', '20040609', '20040610', '20040614', '20040615', '20040616', '20040617', '20040618', '20040621', '20040622', '20040623', '20040624', '20040625', '20040628', '20040629', '20040630', '20040701', '20040702', '20040706', '20040707', '20040708', '20040709', '20040712', '20040713', '20040714', '20040715', '20040716', '20040719', '20040720', '20040721', '20040722', '20040723', '20040726', '20040727', '20040728', '20040729', '20040730', '20040802', '20040803', '20040804', '20040805', '20040806', '20040809', '20040810', '20040811', '20040812', '20040813', '20040816', '20040817', '20040818', '20040819', '20040820', '20040823', '20040824', '20040825', '20040826', '20040827', '20040830', '20040831', '20040901', '20040902', '20040903', '20040907', '20040908', '20040909', '20040910', '20040913', '20040914', '20040915', '20040916', '20040917', '20040920', '20040921', '20040922', '20040923', '20040924', '20040927', '20040928', '20040929', '20040930', '20041001', '20041004', '20041005', '20041006', '20041007', '20041008', '20041011', '20041012', '20041013', '20041014', '20041015', '20041018', '20041019', '20041020', '20041021', '20041022', '20041025', '20041026', '20041027', '20041028', '20041029', '20041101', '20041102', '20041103', '20041104', '20041105', '20041108', '20041109', '20041110', '20041111', '20041112', '20041115', '20041116', '20041117', '20041118', '20041119', '20041122', '20041123', '20041124', '20041126', '20041129', '20041130', '20041201', '20041202', '20041203', '20041206', '20041207', '20041208', '20041209', '20041210', '20041213', '20041214', '20041215', '20041216', '20041217', '20041220', '20041221', '20041222', '20041223', '20041227', '20041228', '20041229', '20041230', '20041231', '20050103', '20050104', '20050105', '20050106', '20050107', '20050110', '20050111', '20050112', '20050113', '20050114', '20050118', '20050119', '20050120', '20050121', '20050124', '20050125', '20050126', '20050127', '20050128', '20050131', '20050201', '20050202', '20050203', '20050204', '20050207', '20050208', '20050209', '20050210', '20050211', '20050214', '20050215', '20050216', '20050217', '20050218', '20050222', '20050223', '20050224', '20050225', '20050228', '20050301', '20050302', '20050303', '20050304', '20050307', '20050308', '20050309', '20050310', '20050311', '20050314', '20050315', '20050316', '20050317', '20050318', '20050321', '20050322', '20050323', '20050324', '20050328', '20050329', '20050330', '20050331', '20050401', '20050404', '20050405', '20050406', '20050407', '20050408', '20050411', '20050412', '20050413', '20050414', '20050415', '20050418', '20050419', '20050420', '20050421', '20050422', '20050425', '20050426', '20050427', '20050428', '20050429', '20050502', '20050503', '20050504', '20050505', '20050506', '20050509', '20050510', '20050511', '20050512', '20050513', '20050516', '20050517', '20050518', '20050519', '20050520', '20050523', '20050524', '20050525', '20050526', '20050527', '20050531', '20050601', '20050602', '20050603', '20050606', '20050607', '20050608', '20050609', '20050610', '20050613', '20050614', '20050615', '20050616', '20050617', '20050620', '20050621', '20050622', '20050623', '20050624', '20050627', '20050628', '20050629', '20050630', '20050701', '20050705', '20050706', '20050707', '20050708', '20050711', '20050712', '20050713', '20050714', '20050715', '20050718', '20050719', '20050720', '20050721', '20050722', '20050725', '20050726', '20050727', '20050728', '20050729', '20050801', '20050802', '20050803', '20050804', '20050805', '20050808', '20050809', '20050810', '20050811', '20050812', '20050815', '20050816', '20050817', '20050818', '20050819', '20050822', '20050823', '20050824', '20050825', '20050826', '20050829', '20050830', '20050831', '20050901', '20050902', '20050906', '20050907', '20050908', '20050909', '20050912', '20050913', '20050914', '20050915', '20050916', '20050919', '20050920', '20050921', '20050922', '20050923', '20050926', '20050927', '20050928', '20050929', '20050930', '20051003', '20051004', '20051005', '20051006', '20051007', '20051010', '20051011', '20051012', '20051013', '20051014', '20051017', '20051018', '20051019', '20051020', '20051021', '20051024', '20051025', '20051026', '20051027', '20051028', '20051031', '20051101', '20051102', '20051103', '20051104', '20051107', '20051108', '20051109', '20051110', '20051111', '20051114', '20051115', '20051116', '20051117', '20051118', '20051121', '20051122', '20051123', '20051125', '20051128', '20051129', '20051130', '20051201', '20051202', '20051205', '20051206', '20051207', '20051208', '20051209', '20051212', '20051213', '20051214', '20051215', '20051216', '20051219', '20051220', '20051221', '20051222', '20051223', '20051227', '20051228', '20051229', '20051230'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['20040102', '20040105', '20040106', '20040107', '20040108', '20040109', '20040112', '20040113', '20040114', '20040115', '20040116', '20040120', '20040121', '20040122', '20040123', '20040126', '20040127', '20040128', '20040129', '20040130', '20040202', '20040203', '20040204', '20040205', '20040206', '20040209', '20040210', '20040211', '20040212', '20040213', '20040217', '20040218', '20040219', '20040220', '20040223', '20040224', '20040225', '20040226', '20040227', '20040301', '20040302', '20040303', '20040304', '20040305', '20040308', '20040309', '20040310', '20040311', '20040312', '20040315', '20040316', '20040317', '20040318', '20040319', '20040322', '20040323', '20040324', '20040325', '20040326', '20040329', '20040330', '20040331', '20040401', '20040402', '20040405', '20040406', '20040407', '20040408', '20040412', '20040413', '20040414', '20040415', '20040416', '20040419', '20040420', '20040421', '20040422', '20040423', '20040426', '20040427', '20040428', '20040429', '20040430', '20040503', '20040504', '20040505', '20040506', '20040507', '20040510', '20040511', '20040512', '20040513', '20040514', '20040517', '20040518', '20040519', '20040520', '20040521', '20040524', '20040525', '20040526', '20040527', '20040528', '20040601', '20040602', '20040603', '20040604', '20040607', '20040608', '20040609', '20040610', '20040614', '20040615', '20040616', '20040617', '20040618', '20040621', '20040622', '20040623', '20040624', '20040625', '20040628', '20040629', '20040630', '20040701', '20040702', '20040706', '20040707', '20040708', '20040709', '20040712', '20040713', '20040714', '20040715', '20040716', '20040719', '20040720', '20040721', '20040722', '20040723', '20040726', '20040727', '20040728', '20040729', '20040730', '20040802', '20040803', '20040804', '20040805', '20040806', '20040809', '20040810', '20040811', '20040812', '20040813', '20040816', '20040817', '20040818', '20040819', '20040820', '20040823', '20040824', '20040825', '20040826', '20040827', '20040830', '20040831', '20040901', '20040902', '20040903', '20040907', '20040908', '20040909', '20040910', '20040913', '20040914', '20040915', '20040916', '20040917', '20040920', '20040921', '20040922', '20040923', '20040924', '20040927', '20040928', '20040929', '20040930', '20041001', '20041004', '20041005', '20041006', '20041007', '20041008', '20041011', '20041012', '20041013', '20041014', '20041015', '20041018', '20041019', '20041020', '20041021', '20041022', '20041025', '20041026', '20041027', '20041028', '20041029', '20041101', '20041102', '20041103', '20041104', '20041105', '20041108', '20041109', '20041110', '20041111', '20041112', '20041115', '20041116', '20041117', '20041118', '20041119', '20041122', '20041123', '20041124', '20041126', '20041129', '20041130', '20041201', '20041202', '20041203', '20041206', '20041207', '20041208', '20041209', '20041210', '20041213', '20041214', '20041215', '20041216', '20041217', '20041220', '20041221', '20041222', '20041223', '20041227', '20041228', '20041229', '20041230', '20041231'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shift Daily Returns Data (TODO)\n",
    "\n",
    "In the cell below, we want to incorporate a realistic time delay that exists in live trading, we’ll use a **two day** delay for the `daily_return` data. That means the `daily_return` should be two days after the data in `data` and `cov_data`. Combine `daily_return` and `data` together in a dict called `frames`.\n",
    "\n",
    "Since reporting of PnL is usually for the date of the returns, make sure to use the two day delay dates (dates that match the `daily_return`) when building `frames`. This means calling `frames['20040108']` will get you the prices from \"20040108\" and the data from `data` at \"20040106\".\n",
    "\n",
    "Note: We're not shifting `covariance`, since we'll use the \"DataDate\" field in `frames` to lookup the covariance data. The \"DataDate\" field contains the date when the `data` in `frames` was recorded. For example, `frames['20040108']` will give you a value of \"20040106\" for the field \"DataDate\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames ={}\n",
    "dlyreturn_n_days_delay = 2\n",
    "\n",
    "date_shift = zip(\n",
    "    sorted(data.keys()),\n",
    "    sorted(daily_return.keys())[dlyreturn_n_days_delay:len(data)+dlyreturn_n_days_delay]\n",
    ")\n",
    "\n",
    "for factor_date, return_date in date_shift:\n",
    "    frames[return_date] = data[factor_date].merge(daily_return[return_date], on='Barrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Daily Returns date column (Optional)\n",
    "Name the column `DlyReturnDate`.\n",
    "**Hint**: create a list containing copies of the date, then create a pandas series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Optional\n",
    "date_shift = zip(\n",
    "    sorted(data.keys()),\n",
    "    sorted(daily_return.keys())[dlyreturn_n_days_delay:len(data)+dlyreturn_n_days_delay]\n",
    ")\n",
    "for factor_date, return_date in date_shift:\n",
    "    df = frames[return_date]\n",
    "    n_rows = df.shape[0]\n",
    "    df['DlyReturnDate'] = pd.Series([return_date]*n_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>Barrid</th>\n",
       "      <th>USFASTD_1DREVRSL</th>\n",
       "      <th>USFASTD_AERODEF</th>\n",
       "      <th>USFASTD_AIRLINES</th>\n",
       "      <th>USFASTD_ALUMSTEL</th>\n",
       "      <th>USFASTD_APPAREL</th>\n",
       "      <th>USFASTD_AUTO</th>\n",
       "      <th>USFASTD_BANKS</th>\n",
       "      <th>USFASTD_BETA</th>\n",
       "      <th>USFASTD_BEVTOB</th>\n",
       "      <th>...</th>\n",
       "      <th>ADTCA_30</th>\n",
       "      <th>IssuerMarketCap</th>\n",
       "      <th>Yield</th>\n",
       "      <th>TotalRisk</th>\n",
       "      <th>SpecRisk</th>\n",
       "      <th>HistBeta</th>\n",
       "      <th>PredBeta</th>\n",
       "      <th>DataDate</th>\n",
       "      <th>DlyReturn</th>\n",
       "      <th>DlyReturnDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA0001</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.177</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.392988e+10</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>13.959397</td>\n",
       "      <td>9.014505</td>\n",
       "      <td>-0.000189</td>\n",
       "      <td>0.156387</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA0011</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.874999e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.033359</td>\n",
       "      <td>11.726327</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.147900</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA0031</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.061</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.304963e+10</td>\n",
       "      <td>2.103004</td>\n",
       "      <td>21.738218</td>\n",
       "      <td>18.047415</td>\n",
       "      <td>0.053033</td>\n",
       "      <td>0.216422</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USA0062</td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.866562e+10</td>\n",
       "      <td>2.243494</td>\n",
       "      <td>23.129496</td>\n",
       "      <td>20.830128</td>\n",
       "      <td>0.079755</td>\n",
       "      <td>0.355564</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USA00E2</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.967</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.720420e+10</td>\n",
       "      <td>2.167256</td>\n",
       "      <td>25.391653</td>\n",
       "      <td>21.458171</td>\n",
       "      <td>0.096238</td>\n",
       "      <td>0.412602</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USA00F1</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.025</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.742470e+11</td>\n",
       "      <td>0.392275</td>\n",
       "      <td>15.695637</td>\n",
       "      <td>11.912117</td>\n",
       "      <td>0.069464</td>\n",
       "      <td>0.346033</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USA00G2</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.214</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.644681e+10</td>\n",
       "      <td>4.679803</td>\n",
       "      <td>21.654431</td>\n",
       "      <td>14.431033</td>\n",
       "      <td>-0.017417</td>\n",
       "      <td>0.026702</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USA00H1</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.882</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.054473e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.765658</td>\n",
       "      <td>18.313856</td>\n",
       "      <td>0.135674</td>\n",
       "      <td>0.267576</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USA00I1</td>\n",
       "      <td>-0.550</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.190</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.681907e+10</td>\n",
       "      <td>0.087623</td>\n",
       "      <td>20.410134</td>\n",
       "      <td>14.133102</td>\n",
       "      <td>-0.006358</td>\n",
       "      <td>0.278503</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USA00J1</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.198</td>\n",
       "      <td>0.863</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.335559e+10</td>\n",
       "      <td>1.670463</td>\n",
       "      <td>21.686440</td>\n",
       "      <td>19.249760</td>\n",
       "      <td>-0.010228</td>\n",
       "      <td>0.194933</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>USA00K1</td>\n",
       "      <td>-1.019</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.334</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.677209e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.867332</td>\n",
       "      <td>26.375334</td>\n",
       "      <td>-0.072812</td>\n",
       "      <td>0.475343</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USA00P1</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.098</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.377153e+10</td>\n",
       "      <td>1.413784</td>\n",
       "      <td>21.424437</td>\n",
       "      <td>16.195982</td>\n",
       "      <td>0.496970</td>\n",
       "      <td>0.853930</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>USA00R1</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.615</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.177471e+09</td>\n",
       "      <td>2.641914</td>\n",
       "      <td>18.112924</td>\n",
       "      <td>12.495233</td>\n",
       "      <td>0.258616</td>\n",
       "      <td>0.673043</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USA00S1</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.141</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.576241e+10</td>\n",
       "      <td>1.244629</td>\n",
       "      <td>21.922494</td>\n",
       "      <td>19.035684</td>\n",
       "      <td>0.477142</td>\n",
       "      <td>0.558508</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>USA00V1</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041914e+10</td>\n",
       "      <td>1.458435</td>\n",
       "      <td>12.385339</td>\n",
       "      <td>8.420332</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.083475</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USA0131</td>\n",
       "      <td>-0.609</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.440</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.359884e+10</td>\n",
       "      <td>1.613669</td>\n",
       "      <td>24.839671</td>\n",
       "      <td>21.502548</td>\n",
       "      <td>-0.121604</td>\n",
       "      <td>0.145560</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>USA0161</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.183</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.359884e+10</td>\n",
       "      <td>1.613669</td>\n",
       "      <td>28.025448</td>\n",
       "      <td>23.914479</td>\n",
       "      <td>0.457450</td>\n",
       "      <td>0.713817</td>\n",
       "      <td>20040102</td>\n",
       "      <td>-0.026300</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USA01I1</td>\n",
       "      <td>-0.230</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.943</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.664403e+10</td>\n",
       "      <td>0.196207</td>\n",
       "      <td>15.087449</td>\n",
       "      <td>12.143365</td>\n",
       "      <td>0.107272</td>\n",
       "      <td>0.220367</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>USA01J2</td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.851823e+10</td>\n",
       "      <td>1.782703</td>\n",
       "      <td>23.016956</td>\n",
       "      <td>20.028493</td>\n",
       "      <td>0.060553</td>\n",
       "      <td>0.355385</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>USA01L1</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.981</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.179657e+10</td>\n",
       "      <td>3.380021</td>\n",
       "      <td>23.209763</td>\n",
       "      <td>19.935156</td>\n",
       "      <td>0.089837</td>\n",
       "      <td>0.278654</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>USA01P1</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.777</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.691907e+11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.138099</td>\n",
       "      <td>23.901111</td>\n",
       "      <td>0.183841</td>\n",
       "      <td>0.410674</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USA01Q1</td>\n",
       "      <td>-0.249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.634</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.237492e+11</td>\n",
       "      <td>1.733990</td>\n",
       "      <td>22.222874</td>\n",
       "      <td>19.652249</td>\n",
       "      <td>-0.210865</td>\n",
       "      <td>0.042602</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>USA0202</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.328618e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.844475</td>\n",
       "      <td>23.970616</td>\n",
       "      <td>0.921471</td>\n",
       "      <td>1.005869</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>USA0231</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.905</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.798649e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.742676</td>\n",
       "      <td>21.356169</td>\n",
       "      <td>0.125102</td>\n",
       "      <td>0.519813</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>USA0281</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.534344e+10</td>\n",
       "      <td>3.050774</td>\n",
       "      <td>18.248930</td>\n",
       "      <td>14.319948</td>\n",
       "      <td>0.007271</td>\n",
       "      <td>0.359541</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>USA0291</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.147</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.184106e+10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.017693</td>\n",
       "      <td>19.557779</td>\n",
       "      <td>0.474128</td>\n",
       "      <td>0.720445</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>USA02A1</td>\n",
       "      <td>-0.339</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.140</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.248961e+09</td>\n",
       "      <td>1.947799</td>\n",
       "      <td>11.185751</td>\n",
       "      <td>7.613359</td>\n",
       "      <td>0.016464</td>\n",
       "      <td>0.268383</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>USA02B1</td>\n",
       "      <td>-2.745</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.188</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.248961e+09</td>\n",
       "      <td>1.947799</td>\n",
       "      <td>32.801110</td>\n",
       "      <td>28.797618</td>\n",
       "      <td>0.455392</td>\n",
       "      <td>0.823334</td>\n",
       "      <td>20040102</td>\n",
       "      <td>-0.008790</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>USA02H1</td>\n",
       "      <td>-0.560</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.215026e+10</td>\n",
       "      <td>2.398082</td>\n",
       "      <td>21.698318</td>\n",
       "      <td>18.244674</td>\n",
       "      <td>0.363409</td>\n",
       "      <td>0.574217</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USA02P1</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.085</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.117866e+11</td>\n",
       "      <td>2.582311</td>\n",
       "      <td>22.480566</td>\n",
       "      <td>19.563198</td>\n",
       "      <td>0.042230</td>\n",
       "      <td>0.340393</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12405</th>\n",
       "      <td>USAZY41</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.056250e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.208371</td>\n",
       "      <td>12.488067</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.050859</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12406</th>\n",
       "      <td>USAZY51</td>\n",
       "      <td>2.418</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.638950e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.640585</td>\n",
       "      <td>72.370109</td>\n",
       "      <td>0.279903</td>\n",
       "      <td>0.579444</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12407</th>\n",
       "      <td>USAZY61</td>\n",
       "      <td>1.618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.012930e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.334667</td>\n",
       "      <td>63.572209</td>\n",
       "      <td>0.732842</td>\n",
       "      <td>1.041313</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12408</th>\n",
       "      <td>USAZY71</td>\n",
       "      <td>2.622</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.366</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.452000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.497910</td>\n",
       "      <td>83.717198</td>\n",
       "      <td>0.833859</td>\n",
       "      <td>1.149809</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12409</th>\n",
       "      <td>USAZYI1</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.563</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>3223.94</td>\n",
       "      <td>4.740600e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.863310</td>\n",
       "      <td>73.357836</td>\n",
       "      <td>0.743190</td>\n",
       "      <td>1.041294</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>USAZYJ1</td>\n",
       "      <td>-1.637</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.431</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>920.22</td>\n",
       "      <td>2.525880e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.338050</td>\n",
       "      <td>69.331053</td>\n",
       "      <td>0.803832</td>\n",
       "      <td>1.162438</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12411</th>\n",
       "      <td>USAZYK1</td>\n",
       "      <td>2.077</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>769.50</td>\n",
       "      <td>6.832300e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.972597</td>\n",
       "      <td>68.149533</td>\n",
       "      <td>1.016101</td>\n",
       "      <td>1.354084</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12412</th>\n",
       "      <td>USAZYM1</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>326.41</td>\n",
       "      <td>2.270100e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.656370</td>\n",
       "      <td>76.499271</td>\n",
       "      <td>1.089179</td>\n",
       "      <td>1.282783</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12413</th>\n",
       "      <td>USAZYR1</td>\n",
       "      <td>-1.874</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.975</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>91477.12</td>\n",
       "      <td>1.003100e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.592276</td>\n",
       "      <td>110.419717</td>\n",
       "      <td>0.553212</td>\n",
       "      <td>0.788145</td>\n",
       "      <td>20040102</td>\n",
       "      <td>-0.035714</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12414</th>\n",
       "      <td>USAZYS1</td>\n",
       "      <td>2.646</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.030700e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.661249</td>\n",
       "      <td>33.805533</td>\n",
       "      <td>1.089953</td>\n",
       "      <td>1.379310</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12415</th>\n",
       "      <td>USAZYT1</td>\n",
       "      <td>-1.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.867</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>57150.03</td>\n",
       "      <td>2.763600e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.956591</td>\n",
       "      <td>70.049127</td>\n",
       "      <td>0.603339</td>\n",
       "      <td>0.850369</td>\n",
       "      <td>20040102</td>\n",
       "      <td>-0.019608</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12416</th>\n",
       "      <td>USAZYV1</td>\n",
       "      <td>-1.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.674</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>10509.78</td>\n",
       "      <td>3.100469e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.257445</td>\n",
       "      <td>117.005538</td>\n",
       "      <td>0.692038</td>\n",
       "      <td>0.970822</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12417</th>\n",
       "      <td>USAZYX1</td>\n",
       "      <td>1.151</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.376</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>15628.40</td>\n",
       "      <td>4.236672e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.602659</td>\n",
       "      <td>21.212255</td>\n",
       "      <td>0.368721</td>\n",
       "      <td>0.289544</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12418</th>\n",
       "      <td>USAZZ41</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>12098.24</td>\n",
       "      <td>6.264510e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.497468</td>\n",
       "      <td>69.006331</td>\n",
       "      <td>0.327400</td>\n",
       "      <td>0.629735</td>\n",
       "      <td>20040102</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12419</th>\n",
       "      <td>USAZZ51</td>\n",
       "      <td>2.354</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.368</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.414000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.279688</td>\n",
       "      <td>30.066884</td>\n",
       "      <td>0.832962</td>\n",
       "      <td>1.114710</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>USAZZ61</td>\n",
       "      <td>2.481</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.798</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>2324.45</td>\n",
       "      <td>2.224400e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.260252</td>\n",
       "      <td>111.809094</td>\n",
       "      <td>0.634920</td>\n",
       "      <td>1.013968</td>\n",
       "      <td>20040102</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12421</th>\n",
       "      <td>USAZZ71</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.741</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.684070e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.522512</td>\n",
       "      <td>63.419945</td>\n",
       "      <td>0.661152</td>\n",
       "      <td>0.900018</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12422</th>\n",
       "      <td>USAZZ81</td>\n",
       "      <td>-0.164</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.538</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1307457.05</td>\n",
       "      <td>2.911190e+10</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>21.679651</td>\n",
       "      <td>18.778080</td>\n",
       "      <td>0.294241</td>\n",
       "      <td>0.401738</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12423</th>\n",
       "      <td>USAZZ91</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.352860e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.970832</td>\n",
       "      <td>36.773965</td>\n",
       "      <td>1.054950</td>\n",
       "      <td>1.224731</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12424</th>\n",
       "      <td>USAZZA1</td>\n",
       "      <td>2.399</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.517</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.609700e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.928704</td>\n",
       "      <td>83.337116</td>\n",
       "      <td>0.764430</td>\n",
       "      <td>1.151915</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12425</th>\n",
       "      <td>USAZZB1</td>\n",
       "      <td>2.301</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.584</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.251480e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.424176</td>\n",
       "      <td>24.267277</td>\n",
       "      <td>0.272805</td>\n",
       "      <td>0.689609</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12426</th>\n",
       "      <td>USAZZD1</td>\n",
       "      <td>-0.993</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>3295.34</td>\n",
       "      <td>4.318500e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.053340</td>\n",
       "      <td>82.523258</td>\n",
       "      <td>1.107541</td>\n",
       "      <td>1.236199</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12427</th>\n",
       "      <td>USAZZF1</td>\n",
       "      <td>2.465</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.452</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.848960e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.774107</td>\n",
       "      <td>85.172848</td>\n",
       "      <td>0.794524</td>\n",
       "      <td>1.180092</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12428</th>\n",
       "      <td>USAZZI1</td>\n",
       "      <td>2.354</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.518</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.055250e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.608398</td>\n",
       "      <td>91.998727</td>\n",
       "      <td>0.764140</td>\n",
       "      <td>0.982174</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12429</th>\n",
       "      <td>USAZZJ1</td>\n",
       "      <td>2.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.981</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.826553e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.764089</td>\n",
       "      <td>59.348666</td>\n",
       "      <td>0.090069</td>\n",
       "      <td>0.346851</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.069643</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12430</th>\n",
       "      <td>USAZZL1</td>\n",
       "      <td>1.895</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.318</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.430560e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.542009</td>\n",
       "      <td>53.503883</td>\n",
       "      <td>0.855973</td>\n",
       "      <td>1.148298</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12431</th>\n",
       "      <td>USAZZP1</td>\n",
       "      <td>2.410</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.614480e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.293774</td>\n",
       "      <td>76.269788</td>\n",
       "      <td>0.495782</td>\n",
       "      <td>0.872594</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12432</th>\n",
       "      <td>USAZZR1</td>\n",
       "      <td>2.120</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.167</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.280000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.228029</td>\n",
       "      <td>34.504433</td>\n",
       "      <td>0.464922</td>\n",
       "      <td>0.853555</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12433</th>\n",
       "      <td>USAZZX1</td>\n",
       "      <td>-1.522</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>3580.90</td>\n",
       "      <td>2.031860e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>126.289675</td>\n",
       "      <td>123.366131</td>\n",
       "      <td>1.116599</td>\n",
       "      <td>1.320642</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12434</th>\n",
       "      <td>USAZZY1</td>\n",
       "      <td>-2.405</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.411</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>2316266.45</td>\n",
       "      <td>8.306788e+08</td>\n",
       "      <td>0.620877</td>\n",
       "      <td>32.573024</td>\n",
       "      <td>22.297109</td>\n",
       "      <td>0.813423</td>\n",
       "      <td>0.911993</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.010721</td>\n",
       "      <td>20040106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12435 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Barrid  USFASTD_1DREVRSL  USFASTD_AERODEF  USFASTD_AIRLINES  \\\n",
       "0      USA0001            -0.032            0.000               0.0   \n",
       "1      USA0011            -0.059            0.000               0.0   \n",
       "2      USA0031             0.457            0.000               0.0   \n",
       "3      USA0062            -0.128            0.431               0.0   \n",
       "4      USA00E2             0.374            0.000               0.0   \n",
       "5      USA00F1            -0.254            0.000               0.0   \n",
       "6      USA00G2            -0.317            0.000               0.0   \n",
       "7      USA00H1             0.157            0.000               0.0   \n",
       "8      USA00I1            -0.550            0.000               0.0   \n",
       "9      USA00J1            -0.169            0.000               0.0   \n",
       "10     USA00K1            -1.019            0.000               0.0   \n",
       "11     USA00P1             0.334            0.000               0.0   \n",
       "12     USA00R1             0.490            0.000               0.0   \n",
       "13     USA00S1            -0.169            0.000               0.0   \n",
       "14     USA00V1            -0.642            0.000               0.0   \n",
       "15     USA0131            -0.609            1.000               0.0   \n",
       "16     USA0161            -0.098            1.000               0.0   \n",
       "17     USA01I1            -0.230            0.000               0.0   \n",
       "18     USA01J2            -0.175            0.000               0.0   \n",
       "19     USA01L1            -0.116            0.000               0.0   \n",
       "20     USA01P1             0.701            0.000               0.0   \n",
       "21     USA01Q1            -0.249            0.000               0.0   \n",
       "22     USA0202             0.735            0.000               0.0   \n",
       "23     USA0231            -0.007            0.000               0.0   \n",
       "24     USA0281            -0.133            0.000               0.0   \n",
       "25     USA0291             0.639            0.000               0.0   \n",
       "26     USA02A1            -0.339            0.000               0.0   \n",
       "27     USA02B1            -2.745            0.000               0.0   \n",
       "28     USA02H1            -0.560            0.000               0.0   \n",
       "29     USA02P1             0.166            0.000               0.0   \n",
       "...        ...               ...              ...               ...   \n",
       "12405  USAZY41             0.715            0.000               0.0   \n",
       "12406  USAZY51             2.418            0.000               0.0   \n",
       "12407  USAZY61             1.618            0.000               0.0   \n",
       "12408  USAZY71             2.622            0.000               0.0   \n",
       "12409  USAZYI1             2.826            0.000               0.0   \n",
       "12410  USAZYJ1            -1.637            0.000               0.0   \n",
       "12411  USAZYK1             2.077            0.000               0.0   \n",
       "12412  USAZYM1             2.826            0.000               0.0   \n",
       "12413  USAZYR1            -1.874            0.000               0.0   \n",
       "12414  USAZYS1             2.646            0.000               0.0   \n",
       "12415  USAZYT1            -1.704            0.000               0.0   \n",
       "12416  USAZYV1            -1.045            0.000               0.0   \n",
       "12417  USAZYX1             1.151            0.000               0.0   \n",
       "12418  USAZZ41             2.826            0.000               0.0   \n",
       "12419  USAZZ51             2.354            0.000               0.0   \n",
       "12420  USAZZ61             2.481            0.000               0.0   \n",
       "12421  USAZZ71             1.582            0.000               0.0   \n",
       "12422  USAZZ81            -0.164            0.000               0.0   \n",
       "12423  USAZZ91             2.826            0.000               0.0   \n",
       "12424  USAZZA1             2.399            0.000               0.0   \n",
       "12425  USAZZB1             2.301            0.000               0.0   \n",
       "12426  USAZZD1            -0.993            0.000               0.0   \n",
       "12427  USAZZF1             2.465            0.000               0.0   \n",
       "12428  USAZZI1             2.354            0.000               0.0   \n",
       "12429  USAZZJ1             2.826            0.000               0.0   \n",
       "12430  USAZZL1             1.895            0.000               0.0   \n",
       "12431  USAZZP1             2.410            0.000               0.0   \n",
       "12432  USAZZR1             2.120            0.000               0.0   \n",
       "12433  USAZZX1            -1.522            0.000               0.0   \n",
       "12434  USAZZY1            -2.405            0.000               0.0   \n",
       "\n",
       "       USFASTD_ALUMSTEL  USFASTD_APPAREL  USFASTD_AUTO  USFASTD_BANKS  \\\n",
       "0                   0.0              0.0           0.0            0.0   \n",
       "1                   0.0              0.0           0.0            0.0   \n",
       "2                   0.0              0.0           0.0            0.0   \n",
       "3                   0.0              0.0           0.0            0.0   \n",
       "4                   0.0              0.0           0.0            0.0   \n",
       "5                   0.0              0.0           0.0            0.0   \n",
       "6                   0.0              0.0           0.0            0.0   \n",
       "7                   0.0              0.0           0.0            0.0   \n",
       "8                   0.0              0.0           0.0            0.0   \n",
       "9                   0.0              0.0           0.0            0.0   \n",
       "10                  0.0              0.0           0.0            0.0   \n",
       "11                  0.0              0.0           0.0            0.0   \n",
       "12                  0.0              0.0           0.0            0.0   \n",
       "13                  0.0              0.0           0.0            0.0   \n",
       "14                  0.0              0.0           0.0            0.0   \n",
       "15                  0.0              0.0           0.0            0.0   \n",
       "16                  0.0              0.0           0.0            0.0   \n",
       "17                  0.0              0.0           0.0            0.0   \n",
       "18                  0.0              0.0           0.0            0.0   \n",
       "19                  0.0              0.0           0.0            0.0   \n",
       "20                  0.0              0.0           0.0            0.0   \n",
       "21                  0.0              0.0           0.0            0.0   \n",
       "22                  0.0              0.0           0.0            0.0   \n",
       "23                  0.0              0.0           0.0            0.0   \n",
       "24                  0.0              0.0           0.0            0.0   \n",
       "25                  0.0              0.0           0.0            0.0   \n",
       "26                  0.0              0.0           0.0            0.0   \n",
       "27                  0.0              0.0           0.0            0.0   \n",
       "28                  0.0              0.0           0.0            1.0   \n",
       "29                  0.0              0.0           0.0            0.0   \n",
       "...                 ...              ...           ...            ...   \n",
       "12405               0.0              0.0           0.0            0.0   \n",
       "12406               0.0              0.0           0.0            0.0   \n",
       "12407               0.0              0.0           0.0            0.0   \n",
       "12408               0.0              0.0           0.0            0.0   \n",
       "12409               0.0              0.0           0.0            0.0   \n",
       "12410               0.0              0.0           0.0            0.0   \n",
       "12411               0.0              0.0           0.0            0.0   \n",
       "12412               0.0              0.0           0.0            0.0   \n",
       "12413               0.0              0.0           0.0            0.0   \n",
       "12414               0.0              0.0           0.0            0.0   \n",
       "12415               0.0              0.0           0.0            0.0   \n",
       "12416               0.0              0.0           0.0            0.0   \n",
       "12417               0.0              0.0           0.0            0.0   \n",
       "12418               0.0              0.0           0.0            0.0   \n",
       "12419               0.0              0.0           0.0            0.0   \n",
       "12420               0.0              0.0           0.0            0.0   \n",
       "12421               0.0              0.0           0.0            0.0   \n",
       "12422               0.0              0.0           0.0            0.0   \n",
       "12423               0.0              0.0           0.0            0.0   \n",
       "12424               0.0              0.0           0.0            0.0   \n",
       "12425               0.0              0.0           0.0            0.0   \n",
       "12426               0.0              0.0           0.0            0.0   \n",
       "12427               0.0              0.0           0.0            0.0   \n",
       "12428               0.0              0.0           0.0            0.0   \n",
       "12429               0.0              0.0           0.0            0.0   \n",
       "12430               0.0              0.0           0.0            0.0   \n",
       "12431               0.0              0.0           0.0            0.0   \n",
       "12432               0.0              0.0           0.0            0.0   \n",
       "12433               0.0              0.0           0.0            0.0   \n",
       "12434               0.0              0.0           0.0            0.0   \n",
       "\n",
       "       USFASTD_BETA  USFASTD_BEVTOB  ...    ADTCA_30  IssuerMarketCap  \\\n",
       "0            -2.177           0.000  ...         NaN     5.392988e+10   \n",
       "1            -2.176           0.000  ...         NaN     5.874999e+09   \n",
       "2            -2.061           0.000  ...         NaN     7.304963e+10   \n",
       "3            -2.003           0.000  ...         NaN     2.866562e+10   \n",
       "4            -1.967           0.000  ...         NaN     5.720420e+10   \n",
       "5            -2.025           0.000  ...         NaN     1.742470e+11   \n",
       "6            -2.214           0.000  ...         NaN     1.644681e+10   \n",
       "7            -1.882           0.000  ...         NaN     8.054473e+10   \n",
       "8            -2.190           0.000  ...         NaN     1.681907e+10   \n",
       "9            -2.198           0.863  ...         NaN     4.335559e+10   \n",
       "10           -2.334           0.000  ...         NaN     3.677209e+10   \n",
       "11           -1.098           0.000  ...         NaN     2.377153e+10   \n",
       "12           -1.615           0.000  ...         NaN     6.177471e+09   \n",
       "13           -1.141           0.000  ...         NaN     1.576241e+10   \n",
       "14           -2.176           0.000  ...         NaN     1.041914e+10   \n",
       "15           -2.440           0.000  ...         NaN     3.359884e+10   \n",
       "16           -1.183           0.000  ...         NaN     3.359884e+10   \n",
       "17           -1.943           0.000  ...         NaN     2.664403e+10   \n",
       "18           -2.045           0.000  ...         NaN     2.851823e+10   \n",
       "19           -1.981           0.000  ...         NaN     9.179657e+10   \n",
       "20           -1.777           0.000  ...         NaN     1.691907e+11   \n",
       "21           -2.634           1.000  ...         NaN     1.237492e+11   \n",
       "22           -0.176           0.000  ...         NaN     1.328618e+10   \n",
       "23           -1.905           0.000  ...         NaN     8.798649e+10   \n",
       "24           -2.160           0.000  ...         NaN     4.534344e+10   \n",
       "25           -1.147           0.000  ...         NaN     3.184106e+10   \n",
       "26           -2.140           0.000  ...         NaN     7.248961e+09   \n",
       "27           -1.188           0.000  ...         NaN     7.248961e+09   \n",
       "28           -1.387           0.000  ...         NaN     7.215026e+10   \n",
       "29           -2.085           0.000  ...         NaN     3.117866e+11   \n",
       "...             ...             ...  ...         ...              ...   \n",
       "12405        -2.176           0.000  ...         NaN     5.056250e+06   \n",
       "12406        -1.569           0.000  ...         NaN     6.638950e+06   \n",
       "12407        -0.586           0.000  ...         NaN     3.012930e+06   \n",
       "12408        -0.366           0.000  ...         NaN     2.452000e+05   \n",
       "12409        -0.563           0.000  ...     3223.94     4.740600e+06   \n",
       "12410        -0.431           0.000  ...      920.22     2.525880e+07   \n",
       "12411         0.029           0.000  ...      769.50     6.832300e+04   \n",
       "12412         0.188           0.000  ...      326.41     2.270100e+06   \n",
       "12413        -0.975           0.000  ...    91477.12     1.003100e+07   \n",
       "12414         0.190           0.000  ...         NaN     5.030700e+01   \n",
       "12415        -0.867           0.000  ...    57150.03     2.763600e+07   \n",
       "12416        -0.674           0.000  ...    10509.78     3.100469e+06   \n",
       "12417        -1.376           0.000  ...    15628.40     4.236672e+07   \n",
       "12418        -1.466           0.000  ...    12098.24     6.264510e+06   \n",
       "12419        -0.368           0.000  ...         NaN     7.414000e+04   \n",
       "12420        -0.798           0.000  ...     2324.45     2.224400e+06   \n",
       "12421        -0.741           0.000  ...         NaN     2.684070e+07   \n",
       "12422        -1.538           1.000  ...  1307457.05     2.911190e+10   \n",
       "12423         0.114           0.000  ...         NaN     1.352860e+06   \n",
       "12424        -0.517           0.000  ...         NaN     7.609700e+05   \n",
       "12425        -1.584           0.000  ...         NaN     1.251480e+06   \n",
       "12426         0.228           0.000  ...     3295.34     4.318500e+05   \n",
       "12427        -0.452           0.000  ...         NaN     1.848960e+06   \n",
       "12428        -0.518           1.000  ...         NaN     1.055250e+06   \n",
       "12429        -1.981           0.000  ...         NaN     7.826553e+06   \n",
       "12430        -0.318           0.000  ...         NaN     3.430560e+07   \n",
       "12431        -1.100           1.000  ...         NaN     2.614480e+06   \n",
       "12432        -1.167           0.000  ...         NaN     8.280000e+04   \n",
       "12433         0.247           0.000  ...     3580.90     2.031860e+06   \n",
       "12434        -0.411           0.000  ...  2316266.45     8.306788e+08   \n",
       "\n",
       "          Yield   TotalRisk    SpecRisk  HistBeta  PredBeta  DataDate  \\\n",
       "0      0.188679   13.959397    9.014505 -0.000189  0.156387  20040102   \n",
       "1      0.000000   17.033359   11.726327  0.000013  0.147900  20040102   \n",
       "2      2.103004   21.738218   18.047415  0.053033  0.216422  20040102   \n",
       "3      2.243494   23.129496   20.830128  0.079755  0.355564  20040102   \n",
       "4      2.167256   25.391653   21.458171  0.096238  0.412602  20040102   \n",
       "5      0.392275   15.695637   11.912117  0.069464  0.346033  20040102   \n",
       "6      4.679803   21.654431   14.431033 -0.017417  0.026702  20040102   \n",
       "7      0.000000   22.765658   18.313856  0.135674  0.267576  20040102   \n",
       "8      0.087623   20.410134   14.133102 -0.006358  0.278503  20040102   \n",
       "9      1.670463   21.686440   19.249760 -0.010228  0.194933  20040102   \n",
       "10     0.000000   33.867332   26.375334 -0.072812  0.475343  20040102   \n",
       "11     1.413784   21.424437   16.195982  0.496970  0.853930  20040102   \n",
       "12     2.641914   18.112924   12.495233  0.258616  0.673043  20040102   \n",
       "13     1.244629   21.922494   19.035684  0.477142  0.558508  20040102   \n",
       "14     1.458435   12.385339    8.420332  0.000013  0.083475  20040102   \n",
       "15     1.613669   24.839671   21.502548 -0.121604  0.145560  20040102   \n",
       "16     1.613669   28.025448   23.914479  0.457450  0.713817  20040102   \n",
       "17     0.196207   15.087449   12.143365  0.107272  0.220367  20040102   \n",
       "18     1.782703   23.016956   20.028493  0.060553  0.355385  20040102   \n",
       "19     3.380021   23.209763   19.935156  0.089837  0.278654  20040102   \n",
       "20     0.000000   27.138099   23.901111  0.183841  0.410674  20040102   \n",
       "21     1.733990   22.222874   19.652249 -0.210865  0.042602  20040102   \n",
       "22     0.000000   29.844475   23.970616  0.921471  1.005869  20040102   \n",
       "23     0.000000   24.742676   21.356169  0.125102  0.519813  20040102   \n",
       "24     3.050774   18.248930   14.319948  0.007271  0.359541  20040102   \n",
       "25     0.000000   24.017693   19.557779  0.474128  0.720445  20040102   \n",
       "26     1.947799   11.185751    7.613359  0.016464  0.268383  20040102   \n",
       "27     1.947799   32.801110   28.797618  0.455392  0.823334  20040102   \n",
       "28     2.398082   21.698318   18.244674  0.363409  0.574217  20040102   \n",
       "29     2.582311   22.480566   19.563198  0.042230  0.340393  20040102   \n",
       "...         ...         ...         ...       ...       ...       ...   \n",
       "12405       NaN   19.208371   12.488067  0.000013  0.050859  20040102   \n",
       "12406  0.000000   74.640585   72.370109  0.279903  0.579444  20040102   \n",
       "12407       NaN   67.334667   63.572209  0.732842  1.041313  20040102   \n",
       "12408  0.000000   87.497910   83.717198  0.833859  1.149809  20040102   \n",
       "12409  0.000000   76.863310   73.357836  0.743190  1.041294  20040102   \n",
       "12410       NaN   73.338050   69.331053  0.803832  1.162438  20040102   \n",
       "12411       NaN   72.972597   68.149533  1.016101  1.354084  20040102   \n",
       "12412       NaN   80.656370   76.499271  1.089179  1.282783  20040102   \n",
       "12413  0.000000  112.592276  110.419717  0.553212  0.788145  20040102   \n",
       "12414  0.000000   43.661249   33.805533  1.089953  1.379310  20040102   \n",
       "12415  0.000000   72.956591   70.049127  0.603339  0.850369  20040102   \n",
       "12416  0.000000  119.257445  117.005538  0.692038  0.970822  20040102   \n",
       "12417  0.000000   25.602659   21.212255  0.368721  0.289544  20040102   \n",
       "12418       NaN   71.497468   69.006331  0.327400  0.629735  20040102   \n",
       "12419       NaN   38.279688   30.066884  0.832962  1.114710  20040102   \n",
       "12420  0.000000  114.260252  111.809094  0.634920  1.013968  20040102   \n",
       "12421  0.000000   67.522512   63.419945  0.661152  0.900018  20040102   \n",
       "12422  4.714286   21.679651   18.778080  0.294241  0.401738  20040102   \n",
       "12423  0.000000   45.970832   36.773965  1.054950  1.224731  20040102   \n",
       "12424       NaN   86.928704   83.337116  0.764430  1.151915  20040102   \n",
       "12425  0.000000   31.424176   24.267277  0.272805  0.689609  20040102   \n",
       "12426       NaN   86.053340   82.523258  1.107541  1.236199  20040102   \n",
       "12427       NaN   88.774107   85.172848  0.794524  1.180092  20040102   \n",
       "12428  0.000000   94.608398   91.998727  0.764140  0.982174  20040102   \n",
       "12429  0.000000   62.764089   59.348666  0.090069  0.346851  20040102   \n",
       "12430  0.000000   58.542009   53.503883  0.855973  1.148298  20040102   \n",
       "12431       NaN   79.293774   76.269788  0.495782  0.872594  20040102   \n",
       "12432       NaN   40.228029   34.504433  0.464922  0.853555  20040102   \n",
       "12433  0.000000  126.289675  123.366131  1.116599  1.320642  20040102   \n",
       "12434  0.620877   32.573024   22.297109  0.813423  0.911993  20040102   \n",
       "\n",
       "       DlyReturn  DlyReturnDate  \n",
       "0       0.000000       20040106  \n",
       "1       0.000000       20040106  \n",
       "2       0.000000       20040106  \n",
       "3       0.000000       20040106  \n",
       "4       0.000000       20040106  \n",
       "5       0.000000       20040106  \n",
       "6       0.000000       20040106  \n",
       "7       0.000000       20040106  \n",
       "8       0.000000       20040106  \n",
       "9       0.000000       20040106  \n",
       "10      0.000000       20040106  \n",
       "11      0.000000       20040106  \n",
       "12      0.000000       20040106  \n",
       "13      0.000259       20040106  \n",
       "14      0.000000       20040106  \n",
       "15      0.000000       20040106  \n",
       "16     -0.026300       20040106  \n",
       "17      0.000000       20040106  \n",
       "18      0.000000       20040106  \n",
       "19      0.000000       20040106  \n",
       "20      0.000000       20040106  \n",
       "21      0.000000       20040106  \n",
       "22      0.000000       20040106  \n",
       "23      0.000000       20040106  \n",
       "24      0.000000       20040106  \n",
       "25      0.000000       20040106  \n",
       "26      0.000000       20040106  \n",
       "27     -0.008790       20040106  \n",
       "28      0.000000       20040106  \n",
       "29      0.000000       20040106  \n",
       "...          ...            ...  \n",
       "12405   0.000000       20040106  \n",
       "12406   0.000000       20040106  \n",
       "12407   0.000000       20040106  \n",
       "12408   0.000000       20040106  \n",
       "12409   0.166667       20040106  \n",
       "12410   0.142857       20040106  \n",
       "12411   0.000000       20040106  \n",
       "12412   0.000000       20040106  \n",
       "12413  -0.035714       20040106  \n",
       "12414   0.000000       20040106  \n",
       "12415  -0.019608       20040106  \n",
       "12416   0.000000       20040106  \n",
       "12417   0.000000       20040106  \n",
       "12418  -0.100000       20040106  \n",
       "12419   0.000000       20040106  \n",
       "12420  -0.250000       20040106  \n",
       "12421   0.000000       20040106  \n",
       "12422   0.004320       20040106  \n",
       "12423   0.000000       20040106  \n",
       "12424   0.000000       20040106  \n",
       "12425   0.000000       20040106  \n",
       "12426   0.000000       20040106  \n",
       "12427   0.022222       20040106  \n",
       "12428   0.022222       20040106  \n",
       "12429   0.069643       20040106  \n",
       "12430   0.068966       20040106  \n",
       "12431   0.000000       20040106  \n",
       "12432   0.000000       20040106  \n",
       "12433   0.058824       20040106  \n",
       "12434   0.010721       20040106  \n",
       "\n",
       "[12435 rows x 94 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames['20040106']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Winsorize\n",
    "\n",
    "As we have done in other projects, we'll want to avoid extremely positive or negative values in our data. Will therefore create a function, `wins`, that will clip our values to a minimum and maximum range. This process is called **Winsorizing**. Remember that this helps us handle noise, which may otherwise cause unusually large positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wins(x,a,b):\n",
    "    return np.where(x <= a,a, np.where(x >= b, b, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Density Plot\n",
    "\n",
    "Let's check our `wins` function by taking a look at the distribution of returns for a single day `20040102`. We will clip our data from `-0.1` to `0.1` and plot it using our `density_plot` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb52479e588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def density_plot(data): \n",
    "    density = gaussian_kde(data)\n",
    "    xs = np.linspace(np.min(data),np.max(data),200)\n",
    "    density.covariance_factor = lambda : .25\n",
    "    density._compute_covariance()\n",
    "    plt.plot(xs,density(xs))\n",
    "    plt.xlabel('Daily Returns')\n",
    "    plt.ylabel('Density')\n",
    "    plt.show()\n",
    "    \n",
    "test = frames['20040108']\n",
    "test['DlyReturn'] = wins(test['DlyReturn'],-0.1,0.1)\n",
    "density_plot(test['DlyReturn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor Exposures and Factor Returns\n",
    "\n",
    "Recall that:\n",
    "\n",
    "$r_{i,t} = \\sum_{j=1}^{k} (\\beta_{i,j,t-2} \\times f_{j,t})$  \n",
    "where $i=1...N$ (N assets),   \n",
    "and $j=1...k$ (k factors).\n",
    "\n",
    "where $r_{i,t}$ is the return, $\\beta_{i,j,t-2}$ is the factor exposure, and $f_{j,t}$ is the factor return. Since we get the factor exposures from the Barra data, and we know the returns, it is possible to estimate the factor returns. In this notebook, we will use the Ordinary Least Squares (OLS) method to estimate the factor exposures, $f_{j,t}$, by using $\\beta_{i,j,t-2}$ as the independent variable, and $r_{i,t}$ as the dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_formula(factors, Y):\n",
    "    L = [\"0\"]\n",
    "    L.extend(factors)\n",
    "    return Y + \" ~ \" + \" + \".join(L)\n",
    "\n",
    "def factors_from_names(n):\n",
    "    return list(filter(lambda x: \"USFASTD_\" in x, n))\n",
    "\n",
    "def estimate_factor_returns(df): \n",
    "    ## build universe based on filters \n",
    "    estu = df.loc[df.IssuerMarketCap > 1e9].copy(deep=True)\n",
    "  \n",
    "    ## winsorize returns for fitting \n",
    "    estu['DlyReturn'] = wins(estu['DlyReturn'], -0.25, 0.25)\n",
    "  \n",
    "    all_factors = factors_from_names(list(df))\n",
    "    form = get_formula(all_factors, \"DlyReturn\")\n",
    "    model = ols(form, data=estu)\n",
    "    results = model.fit()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "facret = {}\n",
    "\n",
    "for date in frames:\n",
    "    facret[date] = estimate_factor_returns(frames[date]).params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dates = sorted(list(map(lambda date: pd.to_datetime(date, format='%Y%m%d'), frames.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2004-01-06 00:00:00'),\n",
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       " Timestamp('2004-01-09 00:00:00'),\n",
       " Timestamp('2004-01-12 00:00:00'),\n",
       " Timestamp('2004-01-13 00:00:00'),\n",
       " Timestamp('2004-01-14 00:00:00'),\n",
       " Timestamp('2004-01-15 00:00:00'),\n",
       " Timestamp('2004-01-16 00:00:00'),\n",
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       " Timestamp('2004-01-22 00:00:00'),\n",
       " Timestamp('2004-01-23 00:00:00'),\n",
       " Timestamp('2004-01-26 00:00:00'),\n",
       " Timestamp('2004-01-27 00:00:00'),\n",
       " Timestamp('2004-01-28 00:00:00'),\n",
       " Timestamp('2004-01-29 00:00:00'),\n",
       " Timestamp('2004-01-30 00:00:00'),\n",
       " Timestamp('2004-02-02 00:00:00'),\n",
       " Timestamp('2004-02-03 00:00:00'),\n",
       " Timestamp('2004-02-04 00:00:00'),\n",
       " Timestamp('2004-02-05 00:00:00'),\n",
       " Timestamp('2004-02-06 00:00:00'),\n",
       " Timestamp('2004-02-09 00:00:00'),\n",
       " Timestamp('2004-02-10 00:00:00'),\n",
       " Timestamp('2004-02-11 00:00:00'),\n",
       " Timestamp('2004-02-12 00:00:00'),\n",
       " Timestamp('2004-02-13 00:00:00'),\n",
       " Timestamp('2004-02-17 00:00:00'),\n",
       " Timestamp('2004-02-18 00:00:00'),\n",
       " Timestamp('2004-02-19 00:00:00'),\n",
       " Timestamp('2004-02-20 00:00:00'),\n",
       " Timestamp('2004-02-23 00:00:00'),\n",
       " Timestamp('2004-02-24 00:00:00'),\n",
       " Timestamp('2004-02-25 00:00:00'),\n",
       " Timestamp('2004-02-26 00:00:00'),\n",
       " Timestamp('2004-02-27 00:00:00'),\n",
       " Timestamp('2004-03-01 00:00:00'),\n",
       " Timestamp('2004-03-02 00:00:00'),\n",
       " Timestamp('2004-03-03 00:00:00'),\n",
       " Timestamp('2004-03-04 00:00:00'),\n",
       " Timestamp('2004-03-05 00:00:00'),\n",
       " Timestamp('2004-03-08 00:00:00'),\n",
       " Timestamp('2004-03-09 00:00:00'),\n",
       " Timestamp('2004-03-10 00:00:00'),\n",
       " Timestamp('2004-03-11 00:00:00'),\n",
       " Timestamp('2004-03-12 00:00:00'),\n",
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       " Timestamp('2004-03-16 00:00:00'),\n",
       " Timestamp('2004-03-17 00:00:00'),\n",
       " Timestamp('2004-03-18 00:00:00'),\n",
       " Timestamp('2004-03-19 00:00:00'),\n",
       " Timestamp('2004-03-22 00:00:00'),\n",
       " Timestamp('2004-03-23 00:00:00'),\n",
       " Timestamp('2004-03-24 00:00:00'),\n",
       " Timestamp('2004-03-25 00:00:00'),\n",
       " Timestamp('2004-03-26 00:00:00'),\n",
       " Timestamp('2004-03-29 00:00:00'),\n",
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       " Timestamp('2004-03-31 00:00:00'),\n",
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       " Timestamp('2004-04-02 00:00:00'),\n",
       " Timestamp('2004-04-05 00:00:00'),\n",
       " Timestamp('2004-04-06 00:00:00'),\n",
       " Timestamp('2004-04-07 00:00:00'),\n",
       " Timestamp('2004-04-08 00:00:00'),\n",
       " Timestamp('2004-04-12 00:00:00'),\n",
       " Timestamp('2004-04-13 00:00:00'),\n",
       " Timestamp('2004-04-14 00:00:00'),\n",
       " Timestamp('2004-04-15 00:00:00'),\n",
       " Timestamp('2004-04-16 00:00:00'),\n",
       " Timestamp('2004-04-19 00:00:00'),\n",
       " Timestamp('2004-04-20 00:00:00'),\n",
       " Timestamp('2004-04-21 00:00:00'),\n",
       " Timestamp('2004-04-22 00:00:00'),\n",
       " Timestamp('2004-04-23 00:00:00'),\n",
       " Timestamp('2004-04-26 00:00:00'),\n",
       " Timestamp('2004-04-27 00:00:00'),\n",
       " Timestamp('2004-04-28 00:00:00'),\n",
       " Timestamp('2004-04-29 00:00:00'),\n",
       " Timestamp('2004-04-30 00:00:00'),\n",
       " Timestamp('2004-05-03 00:00:00'),\n",
       " Timestamp('2004-05-04 00:00:00'),\n",
       " Timestamp('2004-05-05 00:00:00'),\n",
       " Timestamp('2004-05-06 00:00:00'),\n",
       " Timestamp('2004-05-07 00:00:00'),\n",
       " Timestamp('2004-05-10 00:00:00'),\n",
       " Timestamp('2004-05-11 00:00:00'),\n",
       " Timestamp('2004-05-12 00:00:00'),\n",
       " Timestamp('2004-05-13 00:00:00'),\n",
       " Timestamp('2004-05-14 00:00:00'),\n",
       " Timestamp('2004-05-17 00:00:00'),\n",
       " Timestamp('2004-05-18 00:00:00'),\n",
       " Timestamp('2004-05-19 00:00:00'),\n",
       " Timestamp('2004-05-20 00:00:00'),\n",
       " Timestamp('2004-05-21 00:00:00'),\n",
       " Timestamp('2004-05-24 00:00:00'),\n",
       " Timestamp('2004-05-25 00:00:00'),\n",
       " Timestamp('2004-05-26 00:00:00'),\n",
       " Timestamp('2004-05-27 00:00:00'),\n",
       " Timestamp('2004-05-28 00:00:00'),\n",
       " Timestamp('2004-06-01 00:00:00'),\n",
       " Timestamp('2004-06-02 00:00:00'),\n",
       " Timestamp('2004-06-03 00:00:00'),\n",
       " Timestamp('2004-06-04 00:00:00'),\n",
       " Timestamp('2004-06-07 00:00:00'),\n",
       " Timestamp('2004-06-08 00:00:00'),\n",
       " Timestamp('2004-06-09 00:00:00'),\n",
       " Timestamp('2004-06-10 00:00:00'),\n",
       " Timestamp('2004-06-14 00:00:00'),\n",
       " Timestamp('2004-06-15 00:00:00'),\n",
       " Timestamp('2004-06-16 00:00:00'),\n",
       " Timestamp('2004-06-17 00:00:00'),\n",
       " Timestamp('2004-06-18 00:00:00'),\n",
       " Timestamp('2004-06-21 00:00:00'),\n",
       " Timestamp('2004-06-22 00:00:00'),\n",
       " Timestamp('2004-06-23 00:00:00'),\n",
       " Timestamp('2004-06-24 00:00:00'),\n",
       " Timestamp('2004-06-25 00:00:00'),\n",
       " Timestamp('2004-06-28 00:00:00'),\n",
       " Timestamp('2004-06-29 00:00:00'),\n",
       " Timestamp('2004-06-30 00:00:00'),\n",
       " Timestamp('2004-07-01 00:00:00'),\n",
       " Timestamp('2004-07-02 00:00:00'),\n",
       " Timestamp('2004-07-06 00:00:00'),\n",
       " Timestamp('2004-07-07 00:00:00'),\n",
       " Timestamp('2004-07-08 00:00:00'),\n",
       " Timestamp('2004-07-09 00:00:00'),\n",
       " Timestamp('2004-07-12 00:00:00'),\n",
       " Timestamp('2004-07-13 00:00:00'),\n",
       " Timestamp('2004-07-14 00:00:00'),\n",
       " Timestamp('2004-07-15 00:00:00'),\n",
       " Timestamp('2004-07-16 00:00:00'),\n",
       " Timestamp('2004-07-19 00:00:00'),\n",
       " Timestamp('2004-07-20 00:00:00'),\n",
       " Timestamp('2004-07-21 00:00:00'),\n",
       " Timestamp('2004-07-22 00:00:00'),\n",
       " Timestamp('2004-07-23 00:00:00'),\n",
       " Timestamp('2004-07-26 00:00:00'),\n",
       " Timestamp('2004-07-27 00:00:00'),\n",
       " Timestamp('2004-07-28 00:00:00'),\n",
       " Timestamp('2004-07-29 00:00:00'),\n",
       " Timestamp('2004-07-30 00:00:00'),\n",
       " Timestamp('2004-08-02 00:00:00'),\n",
       " Timestamp('2004-08-03 00:00:00'),\n",
       " Timestamp('2004-08-04 00:00:00'),\n",
       " Timestamp('2004-08-05 00:00:00'),\n",
       " Timestamp('2004-08-06 00:00:00'),\n",
       " Timestamp('2004-08-09 00:00:00'),\n",
       " Timestamp('2004-08-10 00:00:00'),\n",
       " Timestamp('2004-08-11 00:00:00'),\n",
       " Timestamp('2004-08-12 00:00:00'),\n",
       " Timestamp('2004-08-13 00:00:00'),\n",
       " Timestamp('2004-08-16 00:00:00'),\n",
       " Timestamp('2004-08-17 00:00:00'),\n",
       " Timestamp('2004-08-18 00:00:00'),\n",
       " Timestamp('2004-08-19 00:00:00'),\n",
       " Timestamp('2004-08-20 00:00:00'),\n",
       " Timestamp('2004-08-23 00:00:00'),\n",
       " Timestamp('2004-08-24 00:00:00'),\n",
       " Timestamp('2004-08-25 00:00:00'),\n",
       " Timestamp('2004-08-26 00:00:00'),\n",
       " Timestamp('2004-08-27 00:00:00'),\n",
       " Timestamp('2004-08-30 00:00:00'),\n",
       " Timestamp('2004-08-31 00:00:00'),\n",
       " Timestamp('2004-09-01 00:00:00'),\n",
       " Timestamp('2004-09-02 00:00:00'),\n",
       " Timestamp('2004-09-03 00:00:00'),\n",
       " Timestamp('2004-09-07 00:00:00'),\n",
       " Timestamp('2004-09-08 00:00:00'),\n",
       " Timestamp('2004-09-09 00:00:00'),\n",
       " Timestamp('2004-09-10 00:00:00'),\n",
       " Timestamp('2004-09-13 00:00:00'),\n",
       " Timestamp('2004-09-14 00:00:00'),\n",
       " Timestamp('2004-09-15 00:00:00'),\n",
       " Timestamp('2004-09-16 00:00:00'),\n",
       " Timestamp('2004-09-17 00:00:00'),\n",
       " Timestamp('2004-09-20 00:00:00'),\n",
       " Timestamp('2004-09-21 00:00:00'),\n",
       " Timestamp('2004-09-22 00:00:00'),\n",
       " Timestamp('2004-09-23 00:00:00'),\n",
       " Timestamp('2004-09-24 00:00:00'),\n",
       " Timestamp('2004-09-27 00:00:00'),\n",
       " Timestamp('2004-09-28 00:00:00'),\n",
       " Timestamp('2004-09-29 00:00:00'),\n",
       " Timestamp('2004-09-30 00:00:00'),\n",
       " Timestamp('2004-10-01 00:00:00'),\n",
       " Timestamp('2004-10-04 00:00:00'),\n",
       " Timestamp('2004-10-05 00:00:00'),\n",
       " Timestamp('2004-10-06 00:00:00'),\n",
       " Timestamp('2004-10-07 00:00:00'),\n",
       " Timestamp('2004-10-08 00:00:00'),\n",
       " Timestamp('2004-10-11 00:00:00'),\n",
       " Timestamp('2004-10-12 00:00:00'),\n",
       " Timestamp('2004-10-13 00:00:00'),\n",
       " Timestamp('2004-10-14 00:00:00'),\n",
       " Timestamp('2004-10-15 00:00:00'),\n",
       " Timestamp('2004-10-18 00:00:00'),\n",
       " Timestamp('2004-10-19 00:00:00'),\n",
       " Timestamp('2004-10-20 00:00:00'),\n",
       " Timestamp('2004-10-21 00:00:00'),\n",
       " Timestamp('2004-10-22 00:00:00'),\n",
       " Timestamp('2004-10-25 00:00:00'),\n",
       " Timestamp('2004-10-26 00:00:00'),\n",
       " Timestamp('2004-10-27 00:00:00'),\n",
       " Timestamp('2004-10-28 00:00:00'),\n",
       " Timestamp('2004-10-29 00:00:00'),\n",
       " Timestamp('2004-11-01 00:00:00'),\n",
       " Timestamp('2004-11-02 00:00:00'),\n",
       " Timestamp('2004-11-03 00:00:00'),\n",
       " Timestamp('2004-11-04 00:00:00'),\n",
       " Timestamp('2004-11-05 00:00:00'),\n",
       " Timestamp('2004-11-08 00:00:00'),\n",
       " Timestamp('2004-11-09 00:00:00'),\n",
       " Timestamp('2004-11-10 00:00:00'),\n",
       " Timestamp('2004-11-11 00:00:00'),\n",
       " Timestamp('2004-11-12 00:00:00'),\n",
       " Timestamp('2004-11-15 00:00:00'),\n",
       " Timestamp('2004-11-16 00:00:00'),\n",
       " Timestamp('2004-11-17 00:00:00'),\n",
       " Timestamp('2004-11-18 00:00:00'),\n",
       " Timestamp('2004-11-19 00:00:00'),\n",
       " Timestamp('2004-11-22 00:00:00'),\n",
       " Timestamp('2004-11-23 00:00:00'),\n",
       " Timestamp('2004-11-24 00:00:00'),\n",
       " Timestamp('2004-11-26 00:00:00'),\n",
       " Timestamp('2004-11-29 00:00:00'),\n",
       " Timestamp('2004-11-30 00:00:00'),\n",
       " Timestamp('2004-12-01 00:00:00'),\n",
       " Timestamp('2004-12-02 00:00:00'),\n",
       " Timestamp('2004-12-03 00:00:00'),\n",
       " Timestamp('2004-12-06 00:00:00'),\n",
       " Timestamp('2004-12-07 00:00:00'),\n",
       " Timestamp('2004-12-08 00:00:00'),\n",
       " Timestamp('2004-12-09 00:00:00'),\n",
       " Timestamp('2004-12-10 00:00:00'),\n",
       " Timestamp('2004-12-13 00:00:00'),\n",
       " Timestamp('2004-12-14 00:00:00'),\n",
       " Timestamp('2004-12-15 00:00:00'),\n",
       " Timestamp('2004-12-16 00:00:00'),\n",
       " Timestamp('2004-12-17 00:00:00'),\n",
       " Timestamp('2004-12-20 00:00:00'),\n",
       " Timestamp('2004-12-21 00:00:00'),\n",
       " Timestamp('2004-12-22 00:00:00'),\n",
       " Timestamp('2004-12-23 00:00:00'),\n",
       " Timestamp('2004-12-27 00:00:00'),\n",
       " Timestamp('2004-12-28 00:00:00'),\n",
       " Timestamp('2004-12-29 00:00:00'),\n",
       " Timestamp('2004-12-30 00:00:00'),\n",
       " Timestamp('2004-12-31 00:00:00'),\n",
       " Timestamp('2005-01-03 00:00:00'),\n",
       " Timestamp('2005-01-04 00:00:00')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose Alpha Factors\n",
    "\n",
    "We will now choose our alpha factors. Barra's factors include some alpha factors that we have seen before, such as:\n",
    "\n",
    "* **USFASTD_1DREVRSL** : Reversal\n",
    "\n",
    "* **USFASTD_EARNYILD** : Earnings Yield\n",
    "\n",
    "* **USFASTD_VALUE** : Value\n",
    "\n",
    "* **USFASTD_SENTMT** : Sentiment\n",
    "\n",
    "We will choose these alpha factors for now, but you are encouraged to come back to this later and try other factors as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [20, 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wjC2macbd/EqWywEAAAAAANSo5duP6VJp1Z/67uoomXCNoUOHyuFwVPipbB8lAAAAAABQ0Ypvj+mXKdv0jy1HrI5S59iTCddYtmyZ1REAAAAAAHA5pwqK9buPvlNsiK8S4tpZHafOMZMJAAAAAACgmkzT1MSPduh8cZmmPx4rN3vjq1wa3zsGAAAAAACoYcu3H9NnO0/qvwZ2UmSrZlbHsQTL5QAAAAAAAG5TcWmZ3v3yoP6yNlOOdn4a26+D1ZEsw0ymWpaVlaWuXbtWOJacnKzp06crPT1dvXv3lsPhUJcuXZScnCxJmjNnjoKCgpybbo8ePdp5b2lpqQIDA/XKK69UGHPFihXq3r27YmNjFR0drdmzZ2vKlCnOMex2u/P3mTNnKjk5WcHBwXI4HIqMjNSwYcO0a9eu676P5OTka14zIyNDXbp0cf69bds2GYZxzSbhPj4+14z31FNPafHixZVel5WVJS8vrwobj3/wwQfXzQYAAAAAgBW++P6UHv7Tl3rjs73q36ml3hnVU3abYXUsyzCTyUJjxozRokWLFBsbq7KyMu3du9d5LiEhQbNmzbrmntTUVEVFRWnRokV6/fXXZRiGSkpKNG7cOG3atEkhISEqLi5WVlaWoqKiNGHCBEmXC5yMjAznOMnJyUpKStKLL74oSVq4cKHi4+O1Y8cOBQUFXfO6iYmJevjhh/X73//eeWzBggV64oknnH+npKSob9++SklJ0YMPPlitzyYiIqJCXgAAAAAA6otj+Rc1eeUufbrjhMICvDXn6TvVP6ql1bEsx0wmC+Xk5KhNmzaSJLvdrujo6Jvek5KSovHjx6t9+/ZKT0+XJBUUFKi0tFQBAQGSJA8PD0VFRVUpS0JCggYOHKj58+dXej4qKkp+fn765ptvnMcWLVqkkSNHSrq8wdnixYs1Z84cpaamqqioqEqvDwAAAABAfXeptFxvr9+v+/74udbuydGLAzvps6R7KZiuaDQzmaZumqo9Z/bU6Jid/Tvr171+fdv3JyUlKSoqSv3799dDDz2kMWPGyNPTU9LlmUUbNmyQJI0fP15PP/20Ll68qLS0NM2ePVv5+flKSUlRnz595O/vr8GDBys0NFT33XefBg0apMTERNlsVesQe/TooT17rv8ZJSYmasGCBerdu7fS09MVEBCgyMhISdLGjRsVHh6uiIgI9e/fX59++qmGDRt2m5+MtH//fjkcDufff/nLX9SvX7/bHg8AAAAAgOrYuO+0Xv34O+0/dV4Do1vpd4Oi1c7f2+pY9QozmWqZYVS+FtMwDL366qvavHmzcwbRQw895DyfkJCgjIwMZWRk6Omnn5Z0ed+lAQMGyNvbW8OHD9eyZctUVlYmSXr33XeVlpamXr16afr06XrmmWeqnNU0zRueHzlypBYvXqzy8nItWLBAiYmJznMpKSnOWU0jR45USkrKDceq7HO5+tgPy+V++KFgAgAAAABY5ZPtx/Tku9+opMzU35+K0zuj4yiYKtFoZjJVZ8ZRdQQEBCgvL6/CsTNnzig8PFzS5TLlueee09ixYxUUFKTc3NzrjpWSkqKNGzcqLCxMkpSbm6t169bp/vvvlyTFxMQoJiZGo0aNUnh4uObMmVOlrNu2bVNcXNx1z7dr105hYWH6/PPPtWTJEn399deSpLKyMi1ZskTLly/XlClTZJqmcnNzVVBQoGbNKn9s448/lzNnzigwMLBKeQEAAAAAqG25hcWatHynHO38tGDcXfJ0t1sdqd5iJlMt8/HxUZs2bZSWlibpcpmyatUq9e3bVytXrnTOHsrMzJTdbpefn1+l45w7d04bNmzQ4cOHlZWVpaysLL311ltKSUlRYWGh1q9f77w2IyNDoaGhVcq5ZMkSpaamVpidVJnExEQlJSUpIiJCISEhkqQ1a9YoNjZWR44cUVZWlg4dOqThw4fro48+uu44/fv318KFC3Xp0iVJl5+oN2DAgCplBgAAAACgtv3Pil0qKCrRtBHdKJhugpKpDnzwwQeaPHmyHA6H4uPjNWnSJEVEROjDDz9UVFSUHA6HRo0apXnz5slur/wLu3TpUsXHx8vDw8N5bMiQIVq+fLnKyso0bdo051iTJk26pVlMM2bMkMPhUGRkpObOnau1a9dW+mS5qz3++OPauXOnc2mcdHmG1dChQytcN3z4cOcm4hcuXFBISIjz580339SgQYPUr18/9ezZUw6HQxs3btTUqVOd9/+wJ9MPPzNnzrzp+wEAAAAAoCat3XNSH2cc0y8GdFSnVpWv1MH/Z9xsHx5XERcXZ27evLnCsd27d6tLly4WJYKr4fsCAAAAAPjB+eJS3f/m52ru6a5P/rOvmrg1znk6hmFsMU3z+nvrXKXR7MkEAAAAAABwq2Z/cUDHzxZp1hPdG23BVFWUTLjG0KFDdfDgwQrHpk6dqgcffNCiRAAAAAAA1J0TZ4v0zhf79W/d2qhnqL/VcVwGJROusWzZMqsjAAAAAABgmempe1VeLv3moc5WR3EpzPcCAAAAAAC4Yuexs1qyNVtP3ROmdv7eVsdxKZRMAAAAAAAAV/z+0z3y83LXLwZ0tDqKy6FkAgAAAAAAkLTlUJ427Dut5/t3lK+Xu9VxXA4lEwAAAAAAgKS31++Tn7e7nujd3uooLomSqZZlZWWpa9euFY4lJydr+vTpSk9PV+/eveVwONSlSxclJydLkubMmaOgoCA5HA45HA6NHj3aeW9paakCAwP1yiuvVBhzxYoV6t69u2JjYxUdHa3Zs2drypQpzjHsdrvz95kzZyo5OVnBwcFyOByKjIzUsGHDtGvXrhu+l8pe44f388NYP/zk5+dr/fr1MgxDn3zyiXOMQYMGaf369Ro6dKgcDoc6duwoX19f531fffWV+vfvr/bt28s0Ted9jz32mHx8fLRjxw7ntf7+/goPD5fD4dD9999/W/99AAAAAACQpN3Hz2nN7hw9fXe4mnrwnLTbwadmoTFjxmjRokWKjY1VWVmZ9u7d6zyXkJCgWbNmXXNPamqqoqKitGjRIr3++usyDEMlJSUaN26cNm3apJCQEBUXFysrK0tRUVGaMGGCJMnHx0cZGRnOcZKTk5WUlKQXX3xRkrRw4ULFx8drx44dCgoKuuZ1r/caP7h6rKuFhIRoypQpevTRRysc/+EJduvXr9f06dO1YsWKCuf9/Py0ceNG9e3bV/n5+Tp+/LgkKSYmxvk+nnrqKQ0aNEgjRoy4/ocMAAAAAMAteHv9fjVtYteYu0OtjuKymMlkoZycHLVp00aSZLfbFR0dfdN7UlJSNH78eLVv317p6emSpIKCApWWliogIECS5OHhoaioqCplSUhI0MCBAzV//vxKz9/ua8TGxsrX11erV6+uUp6RI0dqwYIFkqSlS5dq2LBhVbofAAAAAIBblXX6vFZ8e0z/fleo/LybWB3HZTWamUwnXn9dxbv31OiYHl06q/Vvf3vb9yclJSkqKkr9+/fXQw89pDFjxsjT01PS5ZlFGzZskCSNHz9eTz/9tC5evKi0tDTNnj1b+fn5SklJUZ8+feTv76/BgwcrNDRU9913nwYNGqTExETZbFXrEHv06KE9eyr/jG72GjNmzNDcuXMlSS1atNC6deuc906cOFETJ07UAw88cMtZ7rvvPo0dO1ZlZWVasGCB3nnnHb322mtVej8AAAAAANyKd748IDe7Tc/2Dbc6iktjJlMtMwzjusdfffVVbd682TmD6KGHHnKeT0hIUEZGhjIyMvT0009Lurwn0oABA+Tt7a3hw4dr2bJlKisrkyS9++67SktLU69evTR9+nQ988wzVc569R5IlbnRayQlJTnzXl0wSVK/fv0kSV9++eUtZ7Hb7erbt68WLlyoixcvKiws7NbfCAAAAAAAt+jsxRIt23pUjznaqmVzT6vjuLRGM5OpOjOOqiMgIEB5eXkVjp05c0bh4Zfb0YiICD333HMaO3asgoKClJube92xUlJStHHjRmfhkpubq3Xr1jk3vY6JiVFMTIxGjRql8PBwzZkzp0pZt23bpri4uBtec7uvMWHCBE2ZMkVubrf+lRs5cqSGDh3q3BAdAAAAAICatnhLti6WlGl0nzCro7g8ZjLVMh8fH7Vp00ZpaWmSLhdMq1atUt++fbVy5Urn7KHMzEzZ7Xb5+flVOs65c+e0YcMGHT58WFlZWcrKytJbb72llJQUFRYWav369c5rMzIyFBpatY3KlixZotTUVCUmJlZ6vrqvMXDgQOXl5Wn79u23fE+/fv30yiuvXDcTAAAAAACSVFxapq/2ndbUVXs08aMdWr3rpC5eKrvpfeXlpj78Oks9Q1uoa7Bv7Qdt4BrNTCYrffDBB/rFL36hX/3qV5KkSZMmKSIiQhMmTFBSUpK8vb3l5uamefPmyW63VzrG0qVLFR8fLw8PD+exIUOG6OWXX9abb76padOm6ec//7m8vLzUtGnTW5ph9MM+SufPn1fXrl21du3aSp8sJ11eSnej17h6TyZJ+uijj64ZY8KECRoyZMhNc/3AMIxKn1gHAAAAAMAPth7O06h3v9H5S2VysxnycLNpbvphebjZ1LdjoO6PbqX7OresdCncl/tOKyv3gpIe6GRB8obHuNk+PK4iLi7O3Lx5c4Vju3fvVpcuXSxKBFfD9wUAAAAAXM8rS3fok+3H9KcEh+6KCFATu02bDp7Rmt0ntXrXSR3NvyhJig3x1f1dWun+6Fbq3LqZDMPQz97/lzKO5Gvjb+Ll4Vb5pI/GzjCMLaZp3nhvnSuYyQQAAAAAAFySaZr64vtTujsiQPdHt3Ie7xsZqL6RgZr0aLT2nizQml0ntWZ3jv64+nv9cfX3Cvbz0r2dgpS2J0cvDOhIwVRDKJlwjaFDh+rgwYMVjk2dOlUPPvigRYkAAAAAALjWwdPndTT/ov6jf0Sl5w3DUOfWzdW5dXO9EB+pnIIirduTo9W7crRsW7bc7TY90bt9HaduuCiZcI1ly5ZZHQEAAAAAgJv6MvO0JOknkZXvL/xjLZt5KuHO9kq4s70uXirT2Yslau177V5NuD0NvmQyTVOGYVgdA/VcQ9mbDAAAAAAaky8zTyk0wFvtA7yrfK9XE7u8mrBMribZrA5Qmzw9PZWbm0uBgBsyTVO5ubny9KS9BgAAAABXcam0XF/vz1W/yECro+CKBj2TKSQkRNnZ2Tp16pTVUVDPeXp6KiQkxOoYAAAAAIBbtO1wns5fKlO/W1wqh9rXoEsmd3d3hYeHWx0DAAAAAADUsC8yT8luM9QnIsDqKLiiQS+XAwAAAAAADdOXmafVvZ2fmnu6Wx0FV1AyAQAAAAAAl5JbWKwdR8+yVK6eoWQCAAAAAAAu5aOMYzJN6YHoVlZHwVUomQAAAAAAgMswTVPzvzkkRzs/RbdtbnUcXIWSCQAAAAAAuIxvDp7R/lPn9WTv9lZHwY9QMgEAAAAAAJcx75vDau7ppkHd2lodBT9CyQQAAAAAAFzC6cJirfruuIb1CJFXE7vVcfAjlEwAAAAAAMAlLN6SrZIyk6Vy9ZSb1QEAAAAAAABupKzc1KrvTuh/NxxUr3B/RbZqZnUkVIKSCQAAAAAA1EslZeX6OOOY/u/6fTpw6rw6BDbVbx/pYnUsXAclEwAAAAAAqFeKSsr0j81H9NfPD+ho/kV1adNcs57oroe7tpHdZlgdD9dByQQAAAAAAOqFwuJSzUs/pL99eVCnC4vVo72fXnvsDg2IainDoFyq7yiZAAAAAABAnco5VySvJnY183R3Hlv13XH9ZukO5V8o0T0dAzRzgEN9OgRQLrkQSiYAAAAAAFBnCotL9dCfv5Qk/ebhzhreI0R/WZupP63JVGw7P733VLS6t29hcUrcDkomAAAAAABQZ+amH9KZ85d0R9vmennxt5r+2V7lFBRreI8QTRnaVZ7udqsj4jZRMgEAAAAAgDpRVFKmd788oH6RgXr/6V5auu2o/u/6fRp3bwc92zecpXEujpIJAAAAAADUiQWbDut04SX9Z3ykbDZDI3qGaETPEKtjoYbYrA4AAAAAAAAavkul5Zr9xQH1CvNXr3B/q+OgFlAyAQAAAACAWrd4S7aOny3SC/EdrY6CWkLJBAAAAAAAatUX35+9suAKAAAgAElEQVTSf3+yUz1DW6hfZKDVcVBLKJkAAAAAAECtWbvnpH72/mZFBPnob6Pj2Ny7AWPjbwAAAAAAUCvW7jmpn3+4RZ1bN9eHz/aSn3cTqyOhFlEyAQAAAACAGrftcJ6en7dVnVs319yf9Zavl7vVkVDLWC4HAAAAAABq1MHT5/Xs+5vVspmn3nv6TgqmRoKSCQAAAAAA1JhTBcUa8/dNkqT3n+mlQB8PixOhrlAyAQAAAACAGnG+uFTPvv8v5RQU6X/HxCk8sKnVkVCHKJkAAAAAAEC1lZaV64X5W/Xd0bOaldhD3du3sDoS6hgbfwMAAAAAgGoxTVMTln2ndXtPacrQrro/upXVkWABZjIBAAAAAIBq+XNaphZuPqIXBnTUk71DrY4Di9RqyWQYxkOGYew1DGOfYRi/qeS8h2EYC6+c/8YwjLArx90Nw3jfMIwdhmHsNgzjldrMCQAAAAAAbs/Cfx3Wn9ZkaniPEP1qYCer48BCtVYyGYZhl/SWpIclRUtKNAwj+keXPSspzzTNjpJmSJp65fjjkjxM04yR1FPSz38ooAAAAAAAQP2wbk+OfrvsO/WLDNQfhsfIMAyrI8FCtTmTqZekfaZpHjBN85KkBZKG/OiaIZLev/L7Ykn3GZe/kaakpoZhuEnyknRJ0rlazAoAAAAAAKpg7Z6T+o+5W9S5dTO9/e895W5nR57Grja/AcGSjlz1d/aVY5VeY5pmqaSzkgJ0uXA6L+m4pMOSppumeaYWswIAAAAAgFv0yfZjGvfBFnVq1UwfPttbPh48Vwy1WzJVNkfOvMVrekkqk9RWUrikXxmG0eGaFzCMcYZhbDYMY/OpU6eqmxcAAAAAANzEpzuO65cLtqlH+xaaP7a3/Js2sToS6onaLJmyJbW76u8QSceud82VpXG+ks5IekLSKtM0S0zTzJG0UVLcj1/ANM13TNOMM00zLigoqBbeAgAAAAAAuNrfNxxUxyAfvf9MLzXzdLc6DuqR2iyZ/iUp0jCMcMMwmkgaKWn5j65ZLmnMld9HSFprmqapy0vk4o3Lmkq6S9KeWswKAAAAAABuorC4VBlH8vVAdCt5NbFbHQf1TK2VTFf2WHpB0meSdktaZJrmTsMw/scwjMFXLvtfSQGGYeyT9F+SfnPl+FuSfCR9p8tl1XumaX5bW1kBAAAAAMDNbTqYq9JyU307BlodBfVQre7MZZrmp5I+/dGxV6/6vUjS45XcV1jZcQAAAAAAYJ0NmbnycLOpR2gLq6OgHuL5ggAAAAAA4JZs3HdavcL95enOUjlci5IJAAAAAADcVE5BkfaeLNDdESyVQ+UomQAAAAAAwE19vT9XktiPCddFyQQAAAAAAG5qQ+Zp+Xm7K7ptc6ujoJ6iZAIAAAAAADdkmqY27jutuyMCZLcZVsdBPUXJBAAAAAAAbigr94KOnS3SPSyVww1QMgEAAAAAgBtatydHknQPm37jBiiZAAAAAADADS3ekq2YYF+FBTa1OgrqMUomAAAAAABwXTuPndWu4+f0eFyI1VFQz1EyAQAAAACA6/rH5mw1sds0OLat1VFQz1EyAQAAAACAShWXlumjjKN64I5W8vNuYnUc1HOUTAAAAAAAoFJpu3OUf6FEP41rZ3UUuABKJgAAAAAAUKl/bD6iNr6e6tuRp8rh5iiZAAAAAADANXLOFenz709pWI9g2W2G1XHgAiiZAAAAAADANT7bdVLlpvSYI9jqKHARlEwAAAAAAOAaqTtPKDywqTq29LE6ClwEJRMAAAAAAKjgXFGJ0g/kamB0KxkGS+VwayiZAAAAAABABev3nlJJmakHoltZHQUuhJIJAAAAAABUsHrXSQX6NFH39i2sjgIXQskEAAAAAACcLpWWa/2eHN3XuRVPlUOVUDIBAAAAAACn9AO5Kigu1cA7WCqHqqFkAgAAAAAATqm7TsjL3a57OgZaHQUuxs3qAAAAAAAAwHolZeX6dMdxrfz2uH7SKUie7narI8HFUDIBAAAAANCIFRaXasGmw/r7hoM6drZIEUFN9UJ8R6tjwQVRMgEAAAAA0AidOFuk9746qPnfHFZBUal6h/vrtce6akBUS9nY8Bu3gZIJAAAAAIBGZPfxc/rblwe0POOYyk1Tj8S00dh+HRTbzs/qaHBxlEwAAAAAADQS7355QJNX7pZ3E7tG9QnVM/eEq52/t9Wx0EBQMgEAAAAA0Ah8mH5Ik1fu1iMxrfX60Bj5eTexOhIaGEomAAAAAAAauMVbsvW7j77T/V1a6s8ju8vdbrM6EhogvlUAAAAAADRgB04V6uXF29UvMlCznuhBwYRawzcLAAAAAIAGLGXTYdkMQ3/8aaw83e1Wx0EDRskEAAAAAEADVVxapsVbsvVAdCu1bOZpdRw0cJRMAAAAAAA0UKk7TyrvQokSe7W3OgoaAUomAAAAAAAaqJRNhxXSwkt9OwZaHQWNACUTAAAAAAAN0MHT5/XV/lyNvLOdbDbD6jhoBNysDgAAAAAAAKrGNE2VlJkqKzdVWl4ud7vtmk29F2w6LLvN0ONx7SxKicaGkgkAAAAAgHpqX06Bfrv0O+05cU6l5ebln7JylZsVrzMMqWOQj2JCfCVJWw/lKSv3ggZGt1Kr5mz4jbpByQQAAAAAQD1TXm5qzldZmrpqj7yb2DW0e7Dc7Ta52W1ysxmy2wy52w3ZbZf/Ligu1c6jZ/Vl5mmZpqke7VsosVd7JdzJLCbUHUomAAAAAADqkaP5F/XSP7brq/25uq9zS/1+eIxaNmM2Euo/SiYAAAAAAG7TexsPKiLIR/d2Cqr2WKZpatm2o5r08U6Vm6b+MCxGCXe2k2GwaTdcAyUTAAAAAAC34Wj+Rf33J7tktxma/ng3De0eUq3xpq7aq79+vl93hrXQHx93qH2Adw0lBeoGJRMAAAAAALdh1XcnJEl3tG2upIXbVVhUqlF9wm5rrL9vOKi/fr5fib3aa/JjXWW3MXsJroeSCQAAAACA27Dqu+Pq3LqZFv28j16Yv1W/+3inyk1pzN1hlV5fVm7q6/25WrotW6k7Tyos0Fs/jWsnTze7Xlu5SwOjW1EwwaVRMgEAAAAAUEU5BUXafChP4++LlKe7XW//e089P2+rkj/ZqRZNm2hwbFvntXtPFGjptmx9vO2YTpwrUjNPNw28o5X2nijQqx/vlCTFhbbQzMTuFExwaZRMAAAAAABU0Wc7T8o0pYe7tpEkudtt+ktid43+30361aIMGZJOnivSsm1HtfPYObnZDP2kU5B+Nyha93VpKU93uyTpu6NntWHfaSXe2d55DHBVlEwAAAAAAFTRqu+Oq0NgU3Vq5eM85ulu19/GxClh9tf6z5RtkqRuIb6a9Gi0Ho1tq0Afj2vG6Rrsq67BvnWWG6hNlEwAAAAAAFRB3vlLSj9wRj+/t4MMo+LyNl8vd334bG99tO2oBnQOUseWzSxKCdQ9SiYAAAAAAG4gp6BIU/+5V1sP56lfZKCa2G0qKzedS+V+LKiZh8be26GOUwLWo2QCAAAAAKASJWXlev+rLP1pTaYulZarV7i/Fm0+oqKScgX7ealrcHOrIwL1CiUTAAAAAAA/snHfaU1avlP7cgo1ICpIkx69Q2GBTXXxUpm+zDyltn5e1yyVAxo7SiYAAAAAAK44mn9RU1bu0qc7Tqi9v7f+d0yc7uvSynneq4ldA+9obWFCoP6iZAIAAAAANCqmaV4zC6mopEx/++KA3lq/T5L0qwc6aey9HeTpbrciIuCSKJkAAAAAAI3Gh+mH9GbqXiUPvkNDHMGSpO1H8pW0MEMHTp/XIzGtNeHfohXs52VxUsD1UDIBAAAAABqF3MJiTfvnHl0qK9f4BRnakHlaoQHe+tOaTLVs5qEPn+2lfpFBVscEXBYlEwAAAACgUfjj6u91oaRMn/6ynz7Zfkxvrd8n05T+rVsbvf5YjHy93a2OCLg0SiYAAAAAQIO3+/g5Ldh0WKP7hCmqdTNFtY7ST6KClFt4SQ/e0YonxQE1gJIJAAAAANCgmaap11bsUnMvd/2f+yOdx+8M87cwFdDw2KwOAAAAAABAbdpyKE9f7c/V/7kvUn7eTayOAzRYlEwAAAAAgAZtw77TMgxpaI8Qq6MADRolEwAAAACgQUs/kKs72jaXrxcbewO1iZIJAAAAANBgFZWUaevhfN0VHmB1FKDBo2QCAAAAADRYGUfydam0XHd1oGQCahslEwAAAACgwUo/kCvDkO4M50lyQG2jZAIAAAAANFjsxwTUHUomAAAAAECDxH5MQN2iZAIAAAAANEjsxwTULUomAAAAAECDlH4gVzb2YwLqDCUTAAAAAKBBurwfky/7MQF1hJIJAAAAANDgLN9+TFsO5emuDsxiAuqKm9UBAAAAAACoKYXFpUpevlOLt2Sre3s/je3XwepIQKNByQQAAAAAaBC2H8nX+AXbdPjMBf0yvqN+eV+k3Ows4AHqCiUTAAAAAMCllZebeufLA5r+2V61bOahlLF3qTdPlAPqHCUTAAAAAMBlnThbpP9alKGv9ufqkZjW+v3QbvL1ZqNvwAqUTAAAAAAAl5S684R+veRbFZWUa+rwGP00rp0Mw7A6FtBoUTIBAAAAAFzOxxlHNX5Bhu5o21wzE7srIsjH6khAo0fJBAAAAABwKRculer1T3crNsRXi/6jjzzc7FZHAiCJbfYBAAAAAC7lb18c1MlzxfrdoGgKJqAeoWQCAAAAALiMnHNFmv3Ffj3ctbXiwvytjgPgKiyXAwAAAADUW7uPn9PKb4/Lq4ldXYN99fG2oyopK9dvHu5sdTQAP0LJBAAAAACod9btzdG0VXu1+/g52W2GyspN57mf9Q1XaEBTC9MBqAwlEwAAAACgXjFNU79dukPudpv+Z8gdGtStrew2QzuPndXB0+c1tHuw1REBVIKSCQAAAABQr3x39JyOny3S9MdjNaJniPP43RGBujsi0MJkAG6Ejb8BAAAAAPVK6q4TshlSfOeWVkcBUAWUTAAAAACAeiV150ndGeYv/6ZNrI4CoAoomQAAAAAAdeJQ7nkdP3vxptfsPVmggXe0rqNUAGoKezIBAAAAAGqdaZoa8devdeb8JT3ctbWe6RuuHu1bXHPd6l0nJUkDo1vVdUQA1UTJBAAAAACodYfPXNCpgmL1DvfX59+f0opvj8vRzk/P9A3Xw11by91+eaFN6s6T6tKmudr5e1ucGEBVUTIBAAAAAGpdxpF8SdKrj0YrLKCplmzN1nsbs/TLlG1q3dxTo+8O1cDo1tp86IxeiI+0OC2A20HJBAAAAACodd9mn5Wnu02dWjWTu92m0X3C9O+9Q7X++xz9fUOWpq3aqzc+2yvTZKkc4KoomQAAAAAAtW77kXx1bevrXBYnSTabofjOrRTfuZX2nDinORuzVFhcqjvaNrcwKYDbRckEAAAAAKhVpWXl+u7YWT3ZO/S613Ru3Vx/GN6tDlMBqGm2m18CAAAAAMDt+/5koYpKytUtxNfqKABqESUTAAAAAKBWbc++vOm3o52fxUkA1CZKJgAAAABArdp+JF9+3u5q7+9tdRQAtYiSCQAAAABQqzKO5KtbiJ8Mw7A6CoBaRMkEAAAAAKg1Fy6VKjOnUA72YwIaPEomAAAAAECt2XnsnMrKTcWyHxPQ4FEyAQAAAABqzfYjlzf97hZCyQQ0dJRMAAAAAIBa8/X+XAX7eSmomYfVUQDUMkomAAAAAECt2HnsrNL25Gh4j2CrowCoA5RMAAAAAIBaMWP192ru6aZn+3WwOgqAOkDJBAAAAACocRlH8rVmd47G3dtBvl7uVscBUAcomQAAAAAANe6PqXvl37SJnron3OooAOoIJRMAAAAAoEZtOnhGX2ae1n/8pIN8PNysjgOgjlAyAQAAAABq1PxvDsnXy12j7gqzOgqAOkTJBAAAAACoMRcvlWn1rpN6JKa1vJrYrY4DoA5RMgEAAAAAaszaPTk6f6lMj3Zra3UUAHWMkgkAAAAAUGM+2X5MgT4e6t0hwOooAOoYJRMAAAAAoEYUFJVo7d4cDerWRnabYXUcAHWMkgkAAAAAUCNSd57UpdJyPRrLUjmgMaJkAgAAAADUiE++PaZgPy/1aO9ndRQAFnCzOgAAAAAAoH7al1Ogd744oCZuNrXwbiJfL3f5eTeRn5e7/Lwv/zTzdNex/Iv6/mSBNmSe1rP9wmUYLJUDGiNKJgAAAADANQqLSzX2gy06cbZIXk3syr9wSeXmje/xb9pECXHt6iYggHqHkgkAAAAAGjnTNPXfn+xSaIC3RvcJk82Qfrt0hw7lnteCcX3UK9xf5eWmCopLdfZCifIvXlL+hRLlXbikc0Wlat3cU5EtfdTO35sNv4FGjJIJAAAAABq5708Was5XWZKkFd8eV9+OgVq+/ZheHNhJvcL9JUk2myFfL3f5ermrvbwtTAugvmLjbwAAAABo5NbsPilJmvhvXbQvp1B/TstUv8hAPd+/o8XJALgSZjIBAAAAQCOXtvukYoJ99bN+HTQ4tq0WbT6iJ3qHysbSNwBVwEwmAAAAAGjEThcWa9uRfN3XpaUkqWVzT70QHyn/pk0sTgbA1VAyAQAAAEAjtm5PjkxTur9LK6ujAHBxlEwAAAAA0Iil7c5R6+aeuqNtc6ujAHBxlEwAAAAA0EgVl5bpy8xTiu/SUobB/ksAqoeSCQAAAAAaqfQDZ3T+Upnuv7IfEwBUB0+XAwAAAIBG5nxxqfacOKcPv86Sp7tNd0cEWh0JQANAyQQAAAAAjUB5uanPvz+lv288qA37Tss0Lx8f3iNEnu52a8MBaBAomQAAAACgAbtwqVRLth7VexsP6sCp82rd3FO/6N9RjnZ+im7bXG18Pa2OCKCBoGQCAAAAgAboWP5FffD1IaVsOqyzF0vULcRXfx7p0CMxbeRuZ3teADWPkgkAAAAAGgjTNLX1cJ7mfHVIn+44LtM09VDX1nrmnnD1DG3BE+QA1CpKJgAAAABwcacLi/Xh14f0UcZRHcq9oGYebnr67jCNuTtM7fy9rY4HoJGgZAIAAAAAF/fy4m+1bm+O7o4I0C8GdNQjMW3k48E/9wDULf6vAwAAAAAurKzc1DcHcvVk7/aa/FiM1XEANGLs9gYAAAAALmzviQKdv1SmnqEtrI4CoJGjZAIAAAAAF7b1cJ4kqUd7SiYA1qJkAgAAAAAXtvVwngKaNlF7NvgGYDFKJgAAAABwYdsO56t7+xYyDMPqKAAaOUomAAAAAHBRZ85f0sHT59Uj1M/qKABAyQQAAAAArmrblf2YerIfE4B6gJIJAAAAAFzU1sN5crMZ6hbCTCYA1qNkAgAAAAAXtfVQvrq0aS6vJnarowAAJRMAAAAAuKLSsnJtz85Xj/bMYgJQP1AyAQAAAIAL2nuyQBculalHKPsxAagf3KwOAAAAAAC4NWXlplbvOqldx85q4/5cSVIPNv0GUE9QMgEAAACAi1i7J0f/MXeLbIYUFthUo/uEKqSFl9WxAEASJRMAAAAAuIzNWWfUxG7T1lcfkI8H/5wDUL+wJxMAAAAAuIgth/J0R3BzCiYA9RIlEwAAAAC4gEul5fr26Fn1ZA8mAPUUJRMAAAAAuICdx87qUmk5T5MDUG9RMgEAAACAC9h6OF+S1JOSCUA9RckEAAAAAC5g66E8Bft5qVVzT6ujAEClKJkAAAAAwAVsPZzHUjkA9RolEwAAAADUc8fyL+r42SL1aO9ndRQAuC5KJgAAAACo57YezpPEfkwA6jdKJgAAAACo57Yeypenu01d2jS3OgoAXBclEwAAAADUc1sO56lbsJ/c7fwTDkD95WZ1AAAAAAC4VeXlprLzLiozp0CZOYVq3dxTj3UPdp4vLi3TT2enq62vp8be20E92tff5WXZeRdkMwz5eburid2mnIJiHT97ee+lE2eLdCy/SCfOXdSx/CLtyM7XuHsjrI4MADdEyQQAAACgXjpVUKyth/O0L6dQmScvl0r7TxWqqKTceY2bzdC9nYLk37SJJOmL709r+5F87Tlu0z+/O6FeYf6aPaqnWlw5X19sP5KvIW9tvOE1Xu52tfHzVFtfL43oGaInerWvo3QAcHtqtWQyDOMhSX+WZJf0rmmaf/jReQ9JH0jqKSlXUoJpmllXznWTNFtSc0nlku40TbOoNvMCAAAAqB9OFRTr/jc/19mLJZKkYD8vdWzpoz4dAhTZykcdWzZTWbmpn87+Wiu/PaZRfcIkSSu+PSY/b3d9/uIALd6arddW7NKSrdn6Wb8OFr6ba/3zuxNysxlKHnyHCopKVVxaplbNPdXG11NtfL3U2tdTzT3dZBiG1VEB4JbVWslkGIZd0luSHpCULelfhmEsN01z11WXPSspzzTNjoZhjJQ0VVKCYRhukuZKGmWa5nbDMAIkldRWVgAAAAD1y4w13+t8cak+fLaXurdvIR+Pa//pYpqmOrXy0UcZl0umopIyrdl1Uo/GtpWvt7ue7RuupVuztXLH8XpXMq3ZfVK9wv3173eFWh0FAGpMbe4a10vSPtM0D5imeUnSAklDfnTNEEnvX/l9saT/x959h1dd3v8ff94nm+xBQgaBEMIeYcpUca+KG1u11r2qrXbaafX3rXaotY5ate69Vx11gYLMsHdCgCQEyJ5kn/v3R0IMkHEScnKS8Hpc17k45/O5P5/zOqWXCe9z3+/7ZNNYqj8NWG+tXQdgrS201ja4MauIiIiIiPQS2/eX8+qKLC6fMYS5KQNbLTABGGOYnxpP2u5isosOsHBbHpW1DZw9IbZ5zFnjY1mTVUJuSVVPxe/QroJKMvIqOGV0jKejiIh0K3cWmeKB7Bavc5qOtTrGWlsPlAKRwAjAGmM+NcasNsb8srU3MMZcb4xZZYxZlZ+f3+0fQEREREREet6fP9pCoJ83t52c0uHY+alxALy/LpcP1u8lMtCXmcMim8+fOW4QAJ9s3OeesF3w+Zb9ACoyiUi/486eTK0tHrYujvEG5gDTgAPAF8aYNGvtF4cMtPYJ4AmAqVOnHn5vERERERHpA8qq61i1qwg/by9yig+wcFs+vzlrVHMz7/YkhA9g2tBw3kzLYV9pNRdMjsfb67vv0ocNDGLUoGA+3riXq+ckufNjuOyLLXmMiAkiMXKAp6OIiHQrdxaZcoDBLV4nALltjMlp6sMUChQ1HV9krS0AMMZ8BEwGvkBERERERHqVfaXVDAr17/L1D/xvO89+u6v59eCIAK6cNdTl6+enxvO7dzcCHLJU7qCzxsfy4Ofb2V9WTUxI13N2h9IDdazYVcQNx/euHlEiIt3BncvlVgIpxpgkY4wvcCnw/mFj3geubHp+EfCltdYCnwITjDEDmopPJwCbERERERGRXuXJrzOZce8XPNeiSNRZX23LY+awSF6/YSbPXjWNV6+fiZ+3l8vXnz0+Fm+HISrIj+OSIo84f9b4WKztHUvmFm7Po8FpOVlL5USkH3LbTCZrbb0x5sc0Foy8gKettZuMMXcDq6y17wP/AV4wxmTQOIPp0qZri40xD9BYqLLAR9ba/7orq4iIiIiIdN6i7fnc+/EWgvy8uefDzYyLD2HKkIhO3WN3YSW7Cw9w1ayhTE/q3LUHhQf68pOTU4gM8sPLcWRHjuHRQYyICeKDdbl8f3oivt7u/K69fZ9vySMqyJfUwWEeyyAi4i7uXC6HtfYj4KPDjv2hxfNq4OI2rn0ReNGd+UREREREpGt2FVRy68urGRETzDNXTePSJ5Zx80ur+fDWuQwM9nP5Pl+nFwBw/IiBR5Xn1g6ahM9Pjedvn25j3F2fMiY2hIkJoUxICGPi4DCGRQXiaKU41d2qahtYuC2PM8YOarUYJiLS17m1yCQiIiIiIr1bQUUNhRW1OAwYYw7502EMpunPg88z8ipYnlnI22v24HAYnvzhVGJDA/jXZVO44F9L+PHLq3n+mukuL3f7ens+CeEBJEUFuvVz3nhCMklRgazNLmFddglvpOXw3NLdAAT7eTMuPpRJiWHccHwyoQN83JLhpeW7Ka+uZ8G0wR0PFhHpg1RkEhERERE5RtXWOzn5/kWUVtV16jqHgbFxoTxwSSqDIxp3SBsTF8J9F0zgp6+t5aYXV/Ovyyd3WGiqa3CydEch56bGYYx7Z/Z4OQxnjY/lrPGNjcEbnJYd+RWsyy5hXU4J63NKeXzRDkqq6vjz+eO7/f2r6xr499eZzEqOZOrQri0LFBHp7VRkEhERERE5Rm3YU0ppVR23zEtmdGwITgvWWqwFp7U4m/60LZ7HhQYwZWg4If5HzvY5b1I8lbX1/Padjdzy0moeu2xKu/2PVu8upqKmnuNTjm6pXFd4OQwjYoIZERPMxVMbZxb9+q31vJWWwx2njiAqqP0lf9+k55OZX0njvkWNjWQPajpEUlQg80ZFA/Dqiizyy2t4+PuTuv2ziIj0FioyiYiIiIgco1buKgLgqtlJHRZVXHXZcUNwOi2/f28TP355NY9eNhkfr9YLTV+n5+PlMMwafuSOcJ5w7dxhvLoym+eX7uaOU0cAkFtSRdruYs4cNwhvLwfWWh75MoP7P9vu0j0vmZrAb88ew78W7WB6UgQzhvWOzyoi4g4qMomIiIiIHKNW7ixi2MDAbiswHXTFzKE4Lfzx/U3c+vIaHv7BpFYLTV9vL2ByYlirs6I8YXh0EKeMjuaFpbu46YRkymvqWPDEUrKLqkiJDuJ354xh4bY8nlmyiwsmxfObs0fjaFrm13KxnzGNs5meXrKTh7/M4LPN+yk+UMcDl6R65HOJiPQUz+3dKSIiIiIiHuN0WlbuKmK6m/oDXTlrKH84ZwyfbNrHT19dS32D85DzhRU1bMwt9chSufZcf3wyxQfqePbbXVz1zEoKymv53dmjqal3cuXTK3hmyS6unp3E3y+eSFSQHxGBvkQE+hLe4hE2oPHPn502kmeumoYFjkRdYe8AACAASURBVEuKYFayZjGJSP+mmUwiIiIiIsegbfvLKauuZ5obm1BfPScJp7X8v/9uwRj4x4LU5iVnD3y2HWtp7lnUW0wbGs7EwWH85ZOteDsMT145lXkjo7li5hBeXJaFr7eDy49LdLlR+byR0Sz+1UnNu/aJiPRnKjKJiIiIiByDDvZjmp7k3p3Orp07jAan5d6Pt+IwhgcumcgjX2Xw0vIsbjhhGOPiQ936/p1ljOHWecO58cU07rtwAvNGNhbB/Ly9uGZOUpfuGeSnf3aJyLFB/7UTERERETkGrdhZRGyoPwnhAW5/rxtOSMZp4S+fbGVXYSXrc0q5aEoCvz5jlNvfuytOGRPDhrtOJ8DXy9NRRET6FBWZRERERESOMdY29mM6Limyx5Zw3XRiMk5r+dun2zh5VDT3XTC+Vy8fU4FJRKTzVGQSERERETnGZBdVsb+shmluXip3uFvmDeeEEQNJiQnCu5Xd5kREpG9TkUlERERE5Biz4mA/Jjc2/W5Lb+vBJCIi3UdfH4iIiIiIHGNW7iwiNMCHlOggT0cREZF+REUmEREREZFjiLWWb9LzOS4pAoej9/ZEEhGRvkdFJhERERGRY8jGPWXkllZz6pgYT0cREZF+RkUmEREREZFjyP8278Nh4JTRKjKJiEj3UpFJREREROQY8ummfUxPiiA80NfTUUREpJ9RkUlERERE5Bixs6CS7fsrOH3sIE9HERGRfkhFJhERERGRY8Rnm/cBqB+TiIi4hYpMIiIiIiLHiE837WdsXAgJ4QM8HUVERPohFZlERERERI4BeeXVrM4q1lI5ERFxGxWZRERERESOAR9v2Ie1cNpYLZUTERH3UJFJRERERKSfW5xewP99tIVJiWGMjAn2dBwREemnVGQSEREREelnKmvqqa13ArAss5Brn19JUmQg/7lyGsYYD6cTEZH+ytvTAUREREREpHtYa3l+6W7u+XAzDdYSE+xPSVUtCeEDeOm644gI9PV0RBER6cdUZBIRERER6Qdq6hv4/bsbeX1VDieOHMiEhDD2FFfhtJY7zxxFVJCfpyOKiEg/pyKTiIiIiEgfl1dWzQ0vprEmq4QfzxvOHaeOwOHQsjgREelZKjKJiIiIiPRSW/eVMSQikABfrzbHrMkq5oYX0qioqeexyyZz1vjYHkwoIiLyHTX+FhERERHphXYWVHLWQ9/w/SeXUVZd1+qY11dls+Dfy/DzcfD2zbNUYBIREY9SkUlEREREpBd6b+0eLLApt5TLn1pO6YHvCk11DU7uen8Tv3xzPdOSwnn/ljmMGhTiubAiIiJouZyIiIiISK9jreX9tbkclxTBtXOGcfNLq1nwxFJOGzuIqCBfPtm4j293FHLNnCTuPHMU3l767lhERDxPRSYRERERkV5m454yMgsque74YZwyJoZ//3AKv3l7Aw9/mY614Ovt4P6LJ3LhlARPRxUREWmmIpOIiIiISC/z3to9+HgZzhrX2GNp3sholt55MvUNTooP1OHr5SB0gI+HU4qIiBxKRSYRERERkV6kwWl5f10uJ46MPqKQ5O3lYGCwn4eSiYiItE+Lt0VEREREepHlmYXkldcwPzXO01FEREQ6RUUmERERERE3qK130uC0Lo+31rK7sJJnv91FoK8XJ4+KcWM6ERGR7qflciIiIiIi3ay23skF/1pCUlQQD39/Uofjn168k0e/yqCwshaAq2YPJcDXy90xRUREupWKTCIiIiIi3ezJbzLZuKeMXQUHqGtw4uPV9gKCTzbu4+4PNzN7eCRnjY9lcmI4I2OCezCtiIhI91CRSURERESkG2XmV/DQF+nEhvqzt7Sa9TklTBkS0erYbfvK+dnra5k4OIz/XDkNfx/NXhIRkb5LPZlERERERLqJtZbfvLMBP28Hz109HWNgSUZhq2NLDtRy3fOrCPTz5okrpqjAJCIifZ6KTCIiIiIi3eTlFVksyyziN2eNZkRMMGNiQ1iSUXDEuPoGJz9+eQ37Sqt5/IopxIT4eyCtiIhI91KRSURERESkGyzPLOSu9zcxZ3gUC6YOBmD28CjWZJVQVdtwyNj7Pt7K4owC/t/545icGO6JuCIiIt1ORSYRERERkaO0q6CSG15MY3DEAB79wWQcDgPArORIahucrNxV1Dz2rbQcnlq8kx/NGsolTcUoERGR/kBFJhERERGRo1BaVcfVz63EAM/8aBqhA3yaz01PisDHy7BkR+OSuXXZJdz5zgZmDovkt2eP9lBiERER99DuciIiIiIiR+GFpbvIzK/ktetnMCQy8JBzA3y9mZQYzpKMAvLKq7nhhTSig/149LLJ+Hjp+14REelf9JNNRERERKSLrLW8vWYP05MiOG5YZKtjZidHsSm3jGufW0VpVR1PXDGViEDfHk4qIiLifioyiYiIiIh00fqcUjLzK7lgUnybY2YPj8TaxrF/v3giY+JCejChiIhIz9FyORERERGRLnpnzR58vR2cOT62zTETB4cxLCqQ+anxnD2h7XEiIiJ9nYpMIiIiIiJdUNfg5IN1uZw6OobQAJ82x/l4Ofjy5yf2XDAREREP0XI5EREREZEu+Hp7PoWVtZzfzlI5ERGRY4mKTCIiIiIiXfD2mj2ED/Dh+BEDPR1FRESkV1CRSURERESkk3YWVPL55v18b2Icvt76lVpERARUZBIRERER6ZRv0vOZ/8hiBvh6ceWsoZ6OIyIi0muoyCQiIiIi4gJrLc8s2cmPnllJbGgA7/94DskDgzwdS0REpNfQ7nIiIiIiIh2oqW/gD+9u4rVV2Zw2JoYHFqQS5KdfpUVERFrST0YRERERkXbkl9dw04tprNpdzK0nDef2U0bgcBhPxxIREel1VGQSEREREWnDptxSrntuFUUHann4+5P43sQ4T0cSERHptVRkEhERERFpxX/X7+Xnb6wjbIAPb944i3HxoZ6OJCIi0qupyCQiIiIicpiPN+zllpdXM2VIOI9fPoWBwX6ejiQiItLrqcgkIiIiInKYbzIKCA3w4eXrjsPP28vTcURERPoEh6cDiIiIiIj0NvtKq4kPC1CBSUREpBNUZBIREREROUxuSRVxYf6ejiEiItKnqMgkIiIiInKYvaXVxIYGeDqGiIhIn6Iik4iIiIhICwdq6ymtqmNQqGYyiYiIdIaKTCIiIiIiLewtrQbQcjkREZFOUpFJRERERKSFvSWNRSYtlxMREemcThWZjDEOY0yIu8KIiIiIiHhabmkVAHEqMomIiHRKh0UmY8zLxpgQY0wgsBnYZoz5hfujiYiIiIj0vH1Ny+ViQv08nERERKRvcWUm0xhrbRlwHvARkAhc4dZUIiIiIiIesre0iqggX/y8vTwdRUREpE9xpcjkY4zxobHI9J61tg6w7o0lIiIiIuIZuSXV6sckIiLSBa4Umf4N7AICga+NMUOAMneGEhERERHxlL2lVcSGamc5ERGRzuqwyGSt/ae1Nt5ae5ZttBuY1wPZRERERER63N7SahWZREREusC7owHGGD/gQmDoYePvdlMmERERERGPqKipp7y6ntgwLZcTERHprA6LTMB7QCmQBtS4N46IiIiIiOfsLakC0EwmERGRLnClyJRgrT3D7UlERERERDwst7QagDjNZBIREek0Vxp/f2uMGe/2JCIiIiIiHravtHEm06AQzWQSERHpLFdmMs0BfmSM2UnjcjkDWGvtBLcmExERERHpYbkl1RgDg7RcTkREpNNcKTKd6fYUIiIiIiK9wN7SKgYG+eHj5cqEfxEREWmp3SKTMcYB/NdaO66H8oiIiIiIeMze0mrtLCciItJF7X5FY611AuuMMYk9lEdERERExGP2llYTq35MIiIiXeLKcrlYYJMxZgVQefCgtfZct6USEREREelh1lr2llQxNyXK01FERET6JFeKTH9yewoREREREQ8rq66nsraBuFAtlxMREemKDotM1tpFPRFERERERMST9pVWAxAbpuVyIiIiXdFhkckYUw7Yppe+gA9Qaa0NcWcwEREREZGetKuwsTNEbKiKTCIiIl3hykym4JavjTHnAdPdlkhERERExAM+3bSPYH9vxsWHejqKiIhIn9Tu7nKtsda+C5zkhiwiIiIiIh5RXdfA/zbt54yxg/Dz9vJ0HBERkT7JleVyF7R46QCm8t3yORERERGRPm/htjwqauo5NzXO01FERET6LFd2l/tei+f1wC5gvlvSiIiIiIh4wPvrcokK8mXmsEhPRxEREemzXCkyPWWtXdLygDFmNpDnnkgiIiIiIj2nvLqOL7bksWDaYLy9Ot1NQkRERJq48lP0YRePiYiIiIj0OZ9v2U9NvZNzJ2qpnIiIyNFocyaTMWYmMAsYaIy5o8WpEEDdEEVERESkX3h/bS7xYQFMTgz3dBQREZE+rb2ZTL5AEI2FqOAWjzLgIvdHExERERFxr8KKGr5JL+CcibE4HMbTcURERPq0NmcyWWsXAYuMMc9aa3cbYwKttZU9mE1ERERExK3eWbOHeqflwskJno4iIiLS57nSkynOGLMZ2AJgjJlojHnMvbFERERERNzLWsurK7OZnBjGiJhgT8cRERHp81wpMv0DOB0oBLDWrgOOd2coERERERF3W51VTEZeBZdOS/R0FBERkX7BpT1arbXZhx1qcEMWEREREZEe8+qKbAJ9vTh7Qqyno4iIiPQLbfZkaiHbGDMLsMYYX+A2mpbOiYiIiIj0ReXVdXy4fi/zU+MI9HPlV2IRERHpiCszmW4EbgHigRwgtem1iIiIiEif9MG6vVTVNbBg2mBPRxEREek3OvzaxlpbAFzW8pgxJtBtiURERERE3Oz1VdmMjAkmdXCYp6OIiIj0G+3OZDLGxBtjpjYtk8MYE22M+TOQ3iPpRERERES6WW5JFWuzS5g/KQ5jjKfjiIiI9BttFpmMMT8F1gIPA8uMMVfS2IspAJjSM/FERERERLrX51v2A3DamBgPJxEREelf2lsudz0w0lpbZIxJBDKA4621y3ommoiIiIhI9/ts836SogJJHhjk6SgiIiL9SnvL5aqttUUA1tosYLsKTCIiIiLiKQ1Oy7rsEh75Mp3/LN55xPmCihrqGpzt3qOsuo5lmYWcOiZGS+VERES6WXszmRKMMf9s8Tq65Wtr7W3uiyUiIiIi8p2Ne0q58ukVFFbWNh8bGjmAk0c3LnnbnFvG+Y8tITrEj5+ePILzJsXj5TiyiLRwWz51DZZTtVRORESk27VXZPrFYa/T3BlERERERKQtLy3PorqugYcuTeW4pEiufHoFv31nI9OTIvB2OLj1ldWEBPgQ4u/Dz95Yx2MLM7jj1JGcOW4QjhbFps827ycy0JfJieEe/DQiIiL9U5tFJmvtcz0ZRERERESkNdZaFm7LY27KQOanxgPwl4smcMFjS7jv461YILOgkhevOY6ZwyL5ZNM+HvhsO7e8vJoxsSH8/PQRzBsZTV2DZeHWPM4YN6jVWU4iIiJydNqbySQiIiIi4nFb9pazt7Sa20+Jbj6WOjiMq2cn8VRTb6YbThjG7OFRAJw1PpbTxw7ivbV7+Mfn6Vz97ComJ4Yxb2Q05TX1WionIiLiJioyiYiIiEiv9tW2PABOHDXwkON3nDaCL7fmERLgw89OHXnIOS+H4YLJCXxvYhxvrMrh4S/Tuf+z7fj7OJibcuh9REREpHu0W2QyxngBt1lrH+yhPCIiIiIih/hyax7j40OJDvY/5PgAX28+vG0OPl4OfLxa3zTZx8vBD45L5ILJ8by2MpsBvl4E+Hr1RGwREZFjTus/jZtYaxuA+T2URURERESOIe+u2cM5D3/D04t3UlZd1+qYospa1mQVM29UdKvnB/h6t1lgasnfx4srZw3l4qmDjyqziIiItK3jn8iwxBjziDFmrjFm8sGH25OJiIiISL/23NJdbN9Xwd0fbmbGn7/gd+9uIH1/+SFjFm3Pw2nhpDaKTCIiItJ7uNKTaVbTn3e3OGaBk7o/joiIiIgcC/aXVbMmq4SfnzaCE0ZE89zSXby+KocXl2Uxe3gkV84cysmjY/hyaz5RQb5MiA/1dGQRERHpQIdFJmvtvJ4IIiIiIiLHjv9t3g/A6WMHkRITzN8vnsidZ47i1ZXZvLRsN9e/kEZ8WAAlB2o5Y1wsDofxcGIRERHpSIfL5YwxocaYB4wxq5oe9xtj9FWSiIiIiHTZ/zbtY1hUIMOjg5qPRQb5ccu84Xz9y3k8fvlkBkcEUFnbwDkTYj2YVERERFzlynK5p4GNwCVNr68AngEucFcoERERkd6upr6B7KIDDI8O9nSUPqe0qo6lOwq5Zm4Sxhw5Q8nby8EZ42I5Y1ws5dV1BPv7eCCliIiIdJYrRaZka+2FLV7/yRiz1l2BRERERHozay0fbdjHfZ9sIae4ii/uOIFhA4M6vlCaLdyWR73TcvrYQR2OVYFJRESk73Bld7kqY8ycgy+MMbOBKvdFEhEREemdqusa+P6Ty7jl5dV4GYO1sHJXkadj9TmfbtpHdLAfqQlhno4iIiIi3ciVmUw3As+36MNUDFzpvkgiIiIi7uN0Wt5Zs4cnvs4k2N+bsXEhjIkLYWxcKCkxQfh5e7V57aLt+SzLLOLOM0dxzZwkpv3f56TtLmbBtMQe/AR9W3VdAwu35XP+pHg18xYREelnXCkylVlrJxpjQgCstWXGmCQ35xIRERHpduuyS/j9extZn1PKmNgQjIE303KoXNoAgLfDMDw6qLnodOa4QcSFBTRfv3BbHkF+3lw9JwlvLweTEsNZnVXiqY/TJy3dUciB2gZOc2GpnIiIiPQtrhSZ3gImW2vLWhx7E5jinkgiIiLSF3y0YS8vLtvNvy6bQuiA3t83x+m0XPPcKrwc8OCCicyf2DiTxum0ZBUdYFNuGZv3lrIpt4xv0gt4e/UeXlq+my/uOAFjDNZaFm7LZ/bwSHy8GjsOTBkSzpdb8yg5UEvYAF8Pf8K+YUlGAb7eDo5LivB0FBEREelmbRaZjDGjgLFAqDGm5U5yIYC/u4OJiIhI75VfXsOv31pPWXU9f3h/Iw9dOsnTkTq0ZV8ZBRU13H/xRM6flNB83OEwDI0KZGhUIGdPiG0+/uqKLH799gZW7S5m2tAItu+vYG9pNbednNI8ZlJiY0+hNdklzBsZ3XMfpg9btrOQyYlh+Pu0vSxRRERE+qb2Gn+PBM4BwoDvtXhMBq5zfzQRERHpre75cDPVdU4umZrAe2tzeX9dLgC7Cyv54dMruO/jrTQ4rYdTHurbjEIAZg+Pcmn89ybGMcDXizdWZQONS+UAThw5sHnMxIQwvByG1buLuzlt/1R6oI5NuWXMHOba34GIiIj0LW3OZLLWvge8Z4yZaa1d2oOZREREpBf7alse76/L5fZTRnDLvGS276/gd+9sIL+8hgf+t416p+Xr7fnsLqzkwQWpvWbGypIdBSQPDGRQqGsTsgP9vDl7fCz/Xb+Xu84dy8Jt+YyMCSY2NOCQMaMGBbM6q/8VmarrGjhQ20BEYPctA1y+sxBrYcYwLZUTERHpj9qbyXTQjcaY5v1ljTHhxpin3ZhJREREeqmKmnp+985GhkcHceOJw/D2cvDgglTqGiz3fLiZsfGhfPnzE/nd2aP5eOM+Ln9qOcWVtZ6OTW29k+WZRS7PYjro4qmDqaxt4M20HFbtLjpkFtNBU4aEszarpNfN3DoaB2rrWfDEMuY/uhhru+9zLcssws/bQWpiWMeDRUREpM9xpcg0wVrbvG2KtbYY6P2NF0RERKRbVdc1cO1zK9lXVs29F4zHz7txhlJSVCCPXT6Ze+aP5ZXrZhAfFsC1c4fxyA8msT6nlAsf/5bsogMezb42u4SqugZmJXeuyDRtaDhDIgfw10+2UddgOaGVItPkxHAqaxvYtq+8u+J2WXVdw1Hfo77ByW2vrGFddgnZRVVkFlR2Q7JGSzMLmTo0vPn/OyIiItK/uFJkchhjwg++MMZE4NqudCIiItJP1NQ3cMMLaSzfWcQDl0xk2tBDlzvNGxnNFTOH4uUwzcfOmRDHC9dMp6C8hvMf+5YNOaU9HbvZkowCHAZmDovs1HXGGC6anEBFTT1Bft5MHXLkMq8pQxp/TUrz8JK5lbuKSL37fzzw2fYu38Nay10fbOLzLXlcOycJgG93FHZLvuLKWrbsLWNGUuf+DkRERKTvcKXIdD/wrTHmHmPMPcC3wF/dG0tERER6ytIdhdz6yhoO1Na3er6uaWbLou353HfBeOanxrt87+OGRfLWTbPw83aw4Imlzc2ze9qSjALGx4cSOsCn09deOCUBY2D28Eh8vY/81SkhPICoID/WeLD5d1FlLbe+vAanE/75RTovLd/dpft8umkfLy7L4sYTkvnt2aOJC/Vn6Y6Cbsm4fGcRADOTVWQSERHprzosMllrnwcuAvYDecAF1toX3B1MRERE3G9nQSU3vLCKD9bl8trK7CPONzgtP3t9HZ9u2s9d3xvDgmmJnX6PlJhg3r55FkMjA7nmuVW83sr7tGVHfgV//3QbWYVdX25XWVPP2uySTvdjOiguLIB/LEjlZ6eNbPW8MYYpQ8JYllnYLcvVOsvptPzs9bUUVdby+o0zmTdyIL9/dyOfbd7f6Xut2lWMn7eDX5w+EmMMM5OjWLqjEGc39JtalllIgI8XExLUj0lERKS/cmUmE9baTcDrwHtAhTGm879hioiISK9SXl3Hdc+vwsthGB0bwlPf7KSuwdl83um0/ObtDby/LpdfnTGKH81O6vJ7xYT48/qNM5mVHMkv31rPPz7f3mFD6TVZxVz0r2955KsMTrp/IXe+vZ49JVWdfu8VO4uod9ouF5kA5qfGMyImuM3zl05PJLe0mv/775Yuv0dXPflNJl9ty+e3Z48mdXAYj142mfHxodz6yupO/++VnldB8sCg5mWPs5IjKT5Qx9Zu6De1dEdjP6bWZoOJiIhI/9DhT3ljzLnGmHRgJ7AI2AV87OZcIiIi4kZOp+X219ays6CSRy+bzM9OHcGekir+u34v0Nib508fbOK1VdncdtJwbjox+ajfM8jPm6d/NI2LpiTwj8/T+dVb6w8parW0aHs+P3hyOcH+Prx540wunzGEt9L2cNoDi/how16X3/NAbT2vrszC19vR3DvJHeaNjOb644fxwrLdfLAu123vc7gNOaX87dNtnDF2ED+cOQSAAb7ePPKDydTUO3ljleuzxgDS95czIiao+fXBpW3fHuWSuaLKWrbtL2dGJ3tiiYiISN/iyldJ9wAzgO3W2iTgZGCJW1OJiIiIWz36VQafb8nj92ePZlZyFCeNiiYlOojHF+3AWst9n2zluaW7uW5uErefOqLb3tfHy8HfLprAbSen8PqqHK59bhWVNYf2glqdVcw1z64kKSqQN2+aydShEdx17li+/PkJjBgUzM0vrebej7ZQ30aBChqLZO+vy+Wkvy/i0037uXZOEv4+7t3R7Benj2TKkHB+/dZ6MvMr3PpeAFW1DfzktTVEBflx34XjMea7puuDIwYwZ3gUb6zKcXmpW3l1Hbml1aS0mLEVFxZAUlQgS4+y+ffqpn5VhzeMFxERkf7FlSJTnbW2kMZd5hzW2q+AVDfnEhERETf5dkcBD36+nfmpcVw5aygADofh+uOHsXVfOde/kMa/F2Vy+YxEfnPW6EOKF93BGMMdp47gvgvGszijgMueWt5caDpQW88dr60lJsSfV66bQXSwf/N1CeEDeO36mVwxYwj//jqTK/6zgoKKmlbf47GFO7jtlTVEBvny1k0z+eUZo7r1M7TGx8vBw9+fhK+3g5tfWu32/kz3fryFzPxK7r9kImEDfI84f/HUwewpqXJ5d7iMvMbCWEp00CHHZyZHsnxnUbtFvY6kZRXj7TBMSAjt8j1ERESk93OlyFRijAkCvgZeMsY8BLS+/YyIiIj0annl1dz2ylqSogL58/mHzn6ZnxpPTIgfn23ez4WTE7j73HHdXmBq6dLpiTz6g8ls2FPKDS+kUVPfwJ8/2sLuogP8/eKJre4E5+vt4J7zxnH/xRNZnVXM9x5ezNrskkPG7Cyo5KEv0jlz3CDe//EcpgzpudkzcWEBPLAgla37yvnTB5vc9j5fbc3j+aW7uWZOUpu9pk4bE0NogA+vubhkLn1/Y5Hp8N5Ts5IjqaipZ8Oe0i7nXb27mLFxIW6fTSYiIiKe5UqRaT5QBdwOfALsAL7nzlAiIiLS/Spr6rn15TVU1NTx2GVTCPTzPuS8r7eDey8Yz4/nDeevF03A4XBfgemgM8YN4i8XTmBxRgEL/r2MF5dlcc3spA63ub9wSgJv3TQLL4fhkseX8vLyLKy1WGv5/bsb8fNy8KdzxzY3sO5J80ZGc/OJybyyIpt31uR0+/2X7ijklpdXM2pQML84vfUd7wD8fbw4LzWOTzfto+RAbYf33b6/HD9vB4MjBhxy/GAfJVdnRB2ursHJupwSJruxJ5aIiIj0Dm0WmYwxMwCstZXW2gZrbb219jlr7T+bls+JiIhIH5FbUsVFjy9l5a4i/nLhBEYOan2ntJNGxfDz00f2aHHmoikJ/P6cMazNLmFETBA/b6dw0tK4+FA+vHUOM5Mj+c07G/jVW+t5Y1UOizMK+MUZI4kO8e/4Jm5yx6kjmJ4UwW/e3khG3tHvzHbQou35/OiZFSSEB/D8NdM7nBl0ybTB1NY7eW9tx83ID99Z7qCoID8mJITyzJJdXdrdb8veMqrrnExOVJFJRESkv2tvJtNjB58YY5b2QBYRERFxg3XZJcx/dAnZRQd4+kfTmJ8a7+lIR7hmThJP/nAqz1zVceGkpbABvjz9o2ncdtJwXl+Vwy/fWs/EhFAuO26IG9N2zLupP9MAXy9ufmk1B2qPvtPA/zbt47rnVpE8MIhXr595SL+qtoyNC2VsXAgvLNtNRU37GQ7fWa6l+y+eSE1dA9c8LSA+wgAAIABJREFUu7LD+xzuYNNvd+7uJyIiIr1De0Wmll9jee6rQBEREemy/67fyyX/Xoqft4O3bprFiSOjPR2pTaeOiSE+LKDT13k5DHecNpInfziViYPDuO/CCR5ZJne4mBB/Hrp0Eul5FfzhvaPrz/TBulxufmk1Y+JCeOW6GUQEHtnouy0/OTmFnQWVXPrEUvLKq1sd09rOci2lxATz6GWTSc+r4LZX1tDg4o51AGlZJQwK8SeuC3+3IiIi0re0V2RyGGPCjTGRLZ5HHHz0VEARERE50tZ9ZRRVtt1nx1rLI1+mc8vLqxkXH8q7t8xuc4lcf3HqmBjeu2U2o2NDPB2l2ZyUKG49KYU303J43cUG3Id7My2Hn7y6hsmJ4bxwzfRWG6K357Sxg3jqh1PZkVfJhf/6lsz8iiPGtLWzXEvHjxjIXeeO5cutefzj8+0uv//q3cWaxSQiInKMaK/IFAqkAauAEGB10+uDx0RERMQD6hqcXPz4Um5+KQ1rj5xRUlPfwB2vr+Pv/9vOealxvHTtcUQF+XkgqUDjTKJZyZH84b2NbNvXuf5MLy7bzc/fWMes5CievXoawf6dKzAdNG9UNK9eP4MDNQ1c+K9vWZ1VfMj5tnaWO9wVM4Zw0ZQEHv0qg7TdRR2+777SavaUVKnpt4iIyDGizSKTtXaotXaYtTaplcewngwpIiIi31mbXUJ5dT3LMov4cmveIecKK2q47MnlvLNmD3ecOoIHF6Rq23gP83IYHrp0EsH+Ptz0UhqVLvY0+s/infzu3Y2cNCqap66cygBf744vasfEwWG8ddMsQgJ8+MGTy/hs8/7mc+l5re8s15o/fm8M8eEB3P7aug77Mx0sZk1ODDuq7CIiItI3tDeTSUREpMccqK3nr59sZVdBpaej9HqL0wtwGBgcEcC9H2+lvsEJNDZuPu+xJWzYU8ojP5jEbSenYIznexMJDAz245+XTmJXQSW/eWdDqzPQWnr0qwzu+XAzZ44bxOOXT+m2QuHQqEDeumkWI2KCueGFVby0fDcA2/dXMDz6yJ3lWhPs78ODl6SSU3yAuz9ov9fU6t3F+Ho7GBsX2i35RUREpHdTkUlERHqFF5bu5rGFO/jBk8vI7cI26ceSxRkFjE8I47dnjSYjr4LXV+WwaHs+Fzz2LVW1Tl67YSbnTIjzdEw5zMzkSG4/ZQTvrc3llRVt92fakFPK3z7dxrkT43j4+5Pw9e7eX9eigvx45boZnDBiIL99ZyP3/28b6fvL2+3HdLipQyO4+cTGHf0+2bi3zXFpWcVMTAjt9s8gIiIivZN+4ouIiMdV1TbwxNeZjIsPoby6nsv/s5yCihpPxzpqJQdqufrZlezpxqJZeXUda7NLmDs8itPHDmLqkHDu/WgLVz+7kvjwAN778WxSB2tpUm91y7zhjQ20P9jUZjH18UU7CPb35v/OH4e3l3t+VQv08+bJH05lwdTBPPxlRrs7y7XlJ6ekMD4+lDvf3kBe2ZG71u0rrWZddgkzk6O6K7aIiIj0ci795mKMmWOMuarp+UBjTJJ7Y4mIyLHkpeW7Kays5U/njuXpq6aRW1LFlU+voKy6ztPRjsqKnY09k95fm9tt91yeWUSD0zJ7eBTGGO48azSVtfWcOGIgb940i3htE9+rORyGP58/DqfT8sTXmUec31lQyUcb93LFjCFdbvLtKm8vB/ddOJ6fnJwC0Okd4Hy8HDy4IJWqugZ+8eb6I5YAvrNmD04LF0yK77bMIiIi0rt1WGQyxvwR+BVwZ9MhH+BFd4YSEZFjR3VdA//+OpNZyZFMGRLBtKERPH75FLbvL+eaZ1dSVdvg6YhdtrOpv9TijPxuu+fijAICfLyYPKRxttKUIeEsvfNknvzhVIL8jq4xtPSMhPABnDcpnldXZh0xY++JrzPx8XJw1eye+T7PGMPtp45g3R9PY8awyE5fPzw6iN+ePYZF2/N5Ydnu5uPWWt5My2bqkHCGRgV2Z2QRERHpxVyZyXQ+cC5QCWCtzQU6N59aRESkDa+uyCK/vIbbmmZTAJw4MpoHF6SyancxN76YRm2984jrKmvq+WTjXn7xxjpO+vtCzn9sCbe8vJrHFmY0N8L2tMz8xiLTyl3FVNd1T7FscUYB05Mi8PP+rhF0TIg/DhcaNkvvcdOJydTUO3l68c7mY3ll1byVlsPFUxIYGOzXo3lCA7o+a+ry4xKZN3Ig//ffLWTklQOwLqeUHfmVXDQlobsiioiISB/gSpGp1jbOf7YAxhh9HSUiIt2irLqOxxbuYHpSxBGzKM6ZEMe9549n0fZ8bn99LQ3O75bi7MivYO5fv+LGF1fz6aZ9JEcHEejrzebcMv76yTZue3UNdb2g0LSzoBJfbwe19U5W7Cw66vvtLa0iI6+COcPV46avSx4YxFnjY3lh6W5Kq+qob3Dy8JcZ1DudXH/8ME/H6xRjDH+5aAKBft789LW11NY7eTMtGz9vB2dNiPV0PBEREelBrsyrf90Y828gzBhzHXA18KR7Y4mIyLHg3o+2UFBRwxM/nNrq+UunJ1JWXcefP9pKsJ83914wnpIDdVzz7EocBl6+9jimJ0Uc0hz5qW8y+X//3YLTuYZ/umFnrs7ILKjk9LGD+HTjPpZkFHD8iIFHdb8lGYUAzFaRqV+45cTh/Hf9Xm56MY0d+RXsL6vhoikJDInse9/nRQf7c98F47n+hTT++slWPli3lzPGDSLEzX2lREREpHfpsMhkrf27MeZUoAwYCfzBWvuZ25OJiEi/tji9gFdWZHPD8cPa3Q3t+uOTKauq55GvMgjy82Zjbim5JdW8cv1xTBkSccT4a+cOw2EMd3+4mTvf3sD9l0x058doU1l1HQUVNYyNCyG/vJpv0guamxt21ZKMAqKCfBk1SKvW+4MxcSGcNiaGz7bs54QRA7lnfiInjYr2dKwuO23sIC6dNpinmpYAXjhZS+VERESONR0WmYwxtwNvqLAkIiLdpaKmnl+9tZ5hAwO5/dQRHY7/2WkjKKuua/7H6wOXTGy1wHTQ1XOS2FlQyasrs7h7/lgCPdAQe2dTP6ZhUYE0OAfyt0+3UVBRQ1RQ13vtpO0uZuqQCPVf6kceunQS5TV1RAf7ezpKt/j9OWNYmllIbb1TM+5ERESOQa6sIQgBPjXGfGOMucUYE+PuUCIi0r/9/dNt5JZW8beLJuDv49XheGMMd31vLDecMIzfnzOGC1yYIXHa2BjqGmy39ELqisyCCgCGDQxs7qG0JKOgy/crqKghq+hA865y0j8E+Hr1mwITQKCfN69dP5OXr5uBl4qhIiIix5wOi0zW2j9Za8cCtwBxwCJjzOduTyYiIv1SUWUtL6/I4pIpg9udjXQ4h8Nw55mjuWaOa1u7Txsaga+3g2/Su17YORo78ytxGEiMCGRcfCihAT4sPoosa7NKAJiUGN5dEUXcYlCoP0lRfa+vlIiIiBy9zqwfyAP2AYVA320YICIiHvXKiixq651cM9e1YlFX+ft4MX1oBIsz8t36Pm3JLKhkcMSA5sbjs5IjWZJRgLUWYzo/w2NtdgleDsO4uNDujioiIiIi0i06nMlkjLnJGLMQ+AKIAq6z1k5wdzAREel/6hucvLhsN7OHRzIixv3Nq+ekRLF9fwX7y6rd/l6Hy8yvPGQ2x6zkSHJLq8kt7VqWNdnFjI4NJsC34+WFIiIiIiKe4EpPpiHAT621Y621f7TWbnb15saYM4wx24wxGcaYX7dy3s8Y81rT+eXGmKGHnU80xlQYY37u6nuKiEjv9b/N+9lbWs2PZrl3FtNBc1MaeyEdzTK1rrDWsrOgkmFRQc3HRg4KASB9f3mn79fgtKzLLmXSYC2VExEREZHeq80ikzEmpOnpX4EsY0xEy0dHNzbGeAGPAmcCY4DvG2PGHDbsGqDYWjsceBD4y2HnHwQ+du2jiIhIb/fskl0khAf02DbtoweFEBnoy+KjaLjdFfvKqqmqayBp4HczmVKiGwtOGXkVnb7fjvwKKmrqmZSopt8iIiIi0nu115PpZeAcIA2wQMsGEhYY1sG9pwMZ1tpMAGPMq8B8oOVMqPnAXU3P3wQeMcYYa601xpwHZAKVrn0UERHpzdZkFbNiVxG/PWt0j+065XAYZg+PYvFR9ELqip35jT+6klsslwsP9CUqyJf0/Z0vMq3JKgYgdbCKTCIiIiLSe7VZZLLWntP0Z1fXNMQD2S1e5wDHtTXGWltvjCkFIo0xVcCvgFMBLZUTEemDDtTWs21fOauzSvh4w17SsooJ9vfmkqmDezTHnJQo3l+Xy7b95YwaFNLxBd0gs6CxyNRyJhPA8Ogg0vM6v1xuTVYJoQE+2rFLRERERHq1DneXM8Z8Ya09uaNjrV3ayjHr4pg/AQ9aayva+9bZGHM9cD1AYmJiB3FERMQdnE7LnpIqNu8tY+vecrbuK2PrvnJ2FVZim/6rP2pQMLefMoLzJ8UTOsCnR/O17MvUY0Wm/EoCfLwYFOJ/yPGU6GDeXbun07Oq1mSVMCkxrMdmYomIiIiIdEWbRSZjjD8wAIgyxoTzXUEoBIhz4d45QMuvqxOA3DbG5BhjvIFQoIjGGU8XGWP+CoQBTmNMtbX2kZYXW2ufAJ4AmDp16uEFLBERcbMnv87koS/SqaipB8AYGBIxgFGDQjgvNZ5RscGMiQ1hcMQAj2WMDQ0geWAg36QXcO3cjlZ6d4+dBRUkRQUeURRKiQmivLqevPIaYg4rQLWloqae7XnlnDU+1h1RRURERES6TXszmW4AfkpjQSmN74pMZTQ29O7ISiDFGJME7AEuBX5w2Jj3gSuBpcBFwJfWWgvMPTjAGHMXUHF4gUlERDzvzbQcBoX6c/XsJEbHBjMiJphAvw4nyfa4uSkDeXVlFjX1Dfh5e7n9/TILKhkXH3rE8eFNzb/T91e4XGRan12CtZCqpt8iIiIi0su1ubuctfahpn5MP7fWDrPWJjU9JrpS8LHW1gM/Bj4FtgCvW2s3GWPuNsac2zTsPzT2YMoA7gB+fdSfSEREesSB2nrSm2bY/OC4RCYlhvfKAhPAnOFRVNc5Sdtd3KXrN+4p5Yr/LGd1VsfXV9U2kF104JCm3welRAcDdKov08HMqQkqMomIiIhI79bhvwastQ8bY8YBYwD/Fsefd+Haj4CPDjv2hxbPq4GLO7jHXR29j4iI9LyNe8pwWpjQyoyd3mZGciTeDsM36QXMSo7q1LVvpeXwm3c2UFPvJDO/ko9+MpfQgLb7Sq3LKcHZxsyjqCBfwgb4kJ7n+g5z36QXMD4+tMd7WYmIiIiIdFabM5kOMsb8EXi46TEP+CtwbrsXiYhIv7c+pwSACYN7f5EpyM+bSYlhLE4v6HBsdV0DyzILeWHZbm59ZQ0/e2MdkxLD+M+VU9lXVs0f3tvY7vUHZx5NTgw/4pwxhpToIDL2u1ZkKq2qIy2rmBNGDHRpvIiIiIiIJ7myruEiYCKwxlp7lTEmBnjKvbFERKS3W59TSmyoP9HBrvUW8rQ5wwfyjy+2U1xZS3igb5vjbn5pNV9uzQMg0NeLG44fxi9OH4m3l4OfnJzCA59tZ97IaM6bFN/q9at2FZESHUTYgNbfY3h0MB9v3OvSDnPfZhTQ4LScMFJFJhERERHp/TqcyQRUWWudQL0xJgTIA3pmex4REem11ueUMCGh989iOmhOShTWwpIdbc9mysyv4MuteVw1eyhL7zyJjX86nTvPGo23V+OPy5tPTGbqkHB+/+5GsosOHHG902lZnVXClCFHzmI6KCU6iJIDdRRW1naYedH2fIL9vZk0WP2YRERERKT3c6XItMoYEwY8SeMuc6uBFW5NJSIivVrpgTp2FR5gQh9qRj0xIZRgf+92l8y9tjIbL4fhphOSiQ0NOGKmkbeXgwcXpAJw+2trqW9wHnJ+R34FpVV17RaZRsQ0Nf/uYMmctZaF2/KZmxLVXOQSEREREenNOvyt1Vp7s7W2xFr7OHAqcKW19ir3RxMRkd5q/Z7GfkwT+1CRydvLwazkSL5JL8Bae8T52nonb6blcMroaKJD2l4CODhiAPecN45Vu4t5bOGOQ86taurH1O5MppggADI62GFu+/4K9pVVqx+TiIiIiPQZbRaZjDGTD38AEYB303MRETlGrc8pBWB8H9hZrqU5KQPZU1LFrsIjl7p9tnk/hZW1XDo9scP7nDcpnvmpcTz0RTqrs4qbj6ftLiYi0JekqMA2r40O9iPY35vtHcxkWrS9sS/U8SoyiYiIiEgf0V7j7/vbOWeBk7o5i4iI9BHrc0oYGjmA0AE+no7SKXOHRwGwOD3/iELQqyuziA8L4PgU14o6d88fx6pdxfz01bV89JO5BPl5k7a7mMmJ4e029D64w9yKnUUs2p7PsKhA4sIC8HIces2i7fmMGhRMbGhAJz+liIiIiIhntFlkstbO68kgIiLSd6zPKWXa0AhPx+i0IZEDSAgP4Jv0Aq6YObT5eFbhAb5JL+D2U0YcUexpS2iADw8uSOXSJ5Zy1/ub+PWZo9hZUMmCaYM7vHbGsEgeW7iDK59ubHHo6+VgSOQAkqICSRoYyNDIQFbuLOaq2UPbv5GIiIiISC/S3kwmAIwxP2ztuLX2+e6PIyIivV1eeTV7S6v71M5yBxljmJsSxYfr9lLf4GxuqP1mWjYOA5dMS+jU/aYnRXDLvOE8/GUGDc7GPk9T2+nHdNAvTh/Jj2YPZVfBAXYWVJBZUMnO/Ep2FlSycFs+tU0NxU8aFd3JTygiIiIi4jkdFpmAaS2e+wMn07jDnIpMIiLHoPXZjf2YJg7uO02/W5ozfCCvrMhmXU4pU4aEY63lw/V7mZkc2aWlabednMLX6QW8s2YPvl4OxrnQp8oYQ3SwP9HB/kxPOnRGWIPTkltSRcmBOsb3wUKeiIiIiBy7XNld7tYWj+uASYCv+6OJiEhvtGRHAT5ehrFxIZ6O0iWzkiMxBhanFwCwZW85mQWVnD0+rkv38/Fy8NCCVAb4ejE+IRR/H6+jyuflMAyOGKACk4iIiIj0Oa7MZDrcASClu4OIiEjvV1vv5L21uZw6JoYBvl35EeJ54YG+jI8P5Zv0fH5ySgr/3ZCLl8Nw+tiYLt9zaFQgr14/g4CjLDCJiIiIiPRlrvRk+oDG3eSgcebTGOB1d4YSEZHe6cut+ymqrOXiKR03t+7N5gyP4t9fZ1JeXcdHG/Yxc1gkkUF+R3XPCQl9c/mgiIiIiEh3ceVr6L+3eF4P7LbW5rgpj4iI9GJvrMohJsSPuSlRno5yVOakRPHYwh08vXgXOwsquf74YZ6OJCIiIiLS53VYZLLWLgIwxoQcHG+MibDWFrk5m4iI9CJ55dUs3J7P9ccPa96Vra+aMiScAB8vHluY0bRUbpCnI4mIiIiI9Hkd/ivBGHO9MWY/sB5YBaQ1/SkiIv1YdV0DFzy2hHs+3Ex1XQPvrN5Dg/P/s3ff0VGVWx/Hv5OeQHoICQmd0Am9SlVABRQpIhYUFMXe7rXrq171XnvvqKgoKIIooqLSO0rvvQRISCGNNJLMnPePhyLSUiYZkvw+a806cNrsiSFm9uy9H4ur20e7OrRS8/Zwp3ODEI4WOujWMJSQalrPQkRERESktIrSLvcQ0MKyrJSyDkZERC4cC7YnszoundVx6SzYnszRQjvt6wbToEZ1V4fmFN0bhTF/WzKDYiNdHYqIiIiISKVQlH6HXZgV5UREpAr5dUMCQX6eTBjdkSN5BexPza0UVUzHDWkbxU1d6zIwtparQxERERERqRSKUsn0GLDUZrOtAI4e32lZ1r1lFpWIiLjU0UI7s7ckMaBVBH2ahvP7/b34ffMhrmob5erQnCa0ujfPDm7p6jBERKSqyc8BT1+w2VwdiYiI0xWlkukjYC6wHDOP6fhDREQqmOQjRxnw1iJemrWVjJyCs563aHsKWUcLubyVaSUL9PPk6g618azgA79FRERcKi8T3mgBc/7j6khERMpEUSqZCi3LerDMIxERkTK3eGcymxMy2ZyQyaQVcdzRuyE3da2Hr5f7Kef9sjGBAB8PLmoY5qJIRUREKqFN30NuKix9G1pfCzUauzoiERGnKspH0vOOrTAXabPZQo4/yjwyERFxujVx6fh5uTPznu60qxPEi79upfer85i0Io4CuwOA/EIHf2xOpF/zCLw8VLkkIiLiNKsnQnB98KwGsx4By3J1ROUjfT+k7XV1FCJSDopSyXTdse1jf9tnAQ2cH46IiJSlNXHptI4OomVUIBPGdGLF7sO8/Ns2Hp++gfGLdvOv/o3x83LnSF4hA1pFuDpcERGRyiNpCxxcCf1fADd3mPUobPsFmg50dWRl5+gRWPQaLHsPvP3h7pXgp3oFkcrsvEkmy7Lql0cgIiJStnLz7WxJyGRcr5OfEXRuEMrU27syZ0sSr/y2jbsnrcHH0w1/bw+6x6hVTkRExGnWfAVuHtB6JPgEwqovTKIpZTvkpIKnH3S9C3wCXB2pcxxYCd9cD1mHoPlg2DITZj8NV77j6shEpAydN8lks9luPNN+y7K+dH44IiJSVjYczKDQYdG2dvAp+202G32b16RP03B+XHuQt+fs4OKmNfH2cD/LnURERKRILMusIleYD+u+gSaXQ7VjH+IMeAW+vBJmPwPu3mDPhw1TYPhnUKutS8MuNcuCXx8BmxuMnQvR7eG3J2DZu9DmBqjTuWj3yU4x22r64EukoihKu1zHv/3ZB7gEWA0oySQiUoGsiUsDoE2doDMed3ezMbRdNEPbRZdnWCIiIpVPVhJMGwsHV0HsNRBcD3JSoO3fPr+v3wMe3m0STJ6+ELfMXPNJP+j/HHS+3SSoKqJdc01r4KA3TYIJoPdjsGk6zHwAxi0Ad89z38NeAJ9dCnkZcMvvEKJpLSIVwXknulqWdc/fHrcCbQGvsg9NREScaU1cOnVC/Air7u3qUERERCqv/X/CRz3NtuHFpk3uj6fAvxY0uuTUc32DwcvPJJPqdoPbF0OjvqaN7pvrTBtdRWNZsOAlCIiGNted3O9dHS57EZI2wY93QcrOc99n1edweCcczYKJQyEruUzDFhHnKMmyQTlAjLMDERGR4ssrsLPt0JETVUpnY1kWq+PSaHuWKiYREREpJcuCP8fDhAHg4Q1j/4BrJsKDm6Hfc2YWkdt5WtH9QuDaySYZs+MP+LAH7FtWPvE7y56FsH8FdL/ffB3+rtkV0PVu2Pg9vNsevhoOO2eDw3HqeXmZMP9FqNcDbpoBRw7B18PNIHERuaAVZSbTT5jV5MAkpZoDU8oyKBEROcmyLBIzj7I7OYtdKdnsTs5id3I2u1OyOJiWi+PYT+hpd3Sjfd3gM94jPiOPpCNHaVfnzMdFRESkFPJzTBvY+m8g5lIY+pGpUgIzT+iie4t+L5sNutwBtTvD1Jvh84HQ5zHo/uD5k1QXggUvg38ktB11+jGbDS59AbrdC6smwF+fwlfDIDQGOo+D1teaiqel75j2wn7PQlR7uPpzU9n16aVw9QSo0aTcX5aIFI3Nsqxzn2Cz9frbXwuBfZZlHSjTqEqgQ4cO1sqVK10dhoiIU6zal8aXy/ayKzmLPcnZZOfbTxzz9XSnQY1qNKhRnQZh1agX5sfj329kSLso/juk1RnvN3N9PHdPWsOMuy8iNlrVTCIiIk6Tuhu+HQWJm6DP49Dj3+BWkoaRM8jLhJn3w8ZpUL8XDPsEqoc7595l4eBqGN/HVGJ1ueP85xfmw+YfYPkHEL8avANMomnNRDMkffhnJ8/d8QdMH2cSepe/BO1urLgzq0QqGJvNtsqyrA5FOfeslUw2m60RUNOyrAX/2N/DZrN5W5a1q5RxiojIP1iWxVfL9/HsT5sJ8PWkRa0AOnQIMUmlsOo0qFGNiAAf3NxO/aVq4fYUZq6L5/8GNcfH8/RPOdfEpePt4UbTiEqyLLKIiMiFYNss+P42k+y4firE9HXu/X0CYNin0KA3/PIwjL8Erp8C4c2c+zzOsmuu2bYaUbTzPbwgdoR57P8LVnwIKz8FbHDxU6eeG9MP7lhqvt4/3Wta8VqPdGr4IlJ652qXexN4/Az7c48du6JMIhIRqaIK7A4e+34DU1cd4OKm4bxxTRsCfc+z8soxQ9tFMX3NQeZuTWJAq8jTjq+OS6NVVCBeHk76ZFVERKSqW/wmzH4aImLN7KXgemXzPDabqdqp2RImj4RP+5v2sX8OEb8Q7F0M4c2hWmjxr63d0Twyn4fcNAipf/o5/hEw6gf45GKY+wK0GHL63CcRcalzvduoZ1nW+n/utCxrJVCvzCISEamiZqyNZ+qqA9xzcSM+ubFDkRNMAN0ahlEzwJvvV5/ezZybb2fTwUwN/RYREXGWgjyY+xw0vgxu+b3sEkx/F9UObp0LQXXg66shcfOZz3M4YMJA+Oxy2DDVtKSVB3uBWVGvXvfS3ScgEmo2P/txNzdT5ZQRZ1agK4n0/WZIu5xf2j54q41phRQpgnMlmXzOcczX2YGIiFR1P62PJyrIlwf7NT6tHe583N1sXNU2ivnbkknJOnrKsT/3ppJvd9CtUZgzwxUREam6kjaDo9DMD/Isx7dGgdFw44+mumn9t2c+Z99i80jeAtNugTeaw5z/QHpc2cYWvxYKsqHuRWX7PAANLzYrzy18BfKzi3/9/Bfhl39DxkHnx1bZrPsG0vaYIe0iRXCuJNNfNpvt1n/utNlstwCryi4kEZGqJy07n8U7UhjUOhJbCYdYDm0bTaHD4qd18afsX7wjGS93NzrXD3FGqCIiIpKwzmwjY8v/uauFmRlNm76HMy3itHoieAfCA5vghmkQ3REWvwFvtYZJI2HHbFPtZFmw/Tf4pC/89kTp49q32GzLI8lkOzazKTvZDA0vjvwcM2wc4PAO58d2obEXnv59cjTLJI3yMs59rWXBhmMLy2/+oWQJvarKXgCLXjPD+6u6QSdFAAAgAElEQVSYcyWZ7gfG2Gy2+Tab7bVjjwXAWOC+8glPRKRqmLXpEIUOiytia5X4Hk0i/GkZFcC0f7TMLdqRQvu6wfh5nWsMn4iIiBTZofUmkRN8hrlB5aHFEFOZFP+PFqbcNNj8I8ReDV7VoFFfuHYy3LcOuj8AB1fC18PgnbbwySUwaQQcWAmbppc+pr2LIawJVK9R+nsVRZ3Opl1xyVuw6ouitwVu+wXys8yfUyp5kqkgF97tAHOfP3X/yk/h5wfhvS4m0Xg2CWvh8E6IvcZ8zbbMLNt4K4vMePh8kKkg3Pyjq6Mpd2dNMlmWlWhZVjfgWWDvscezlmV1tSzrUPmEJyJSNfy0Lp76YdVoUat0q79d3b42Gw9msv5AOgBJR/LYeugI3WPUKiciIuI0CeshopWpqHGFpgPBzRM2fn/q/g1TwX4U2o46dX9QHbjk/+CBzWa1uoAoyE2HQW9A36ch8yBkJZc8HnshxC0v/Tym4rrsf2ZA+E/3wtttYPmHplLpXNZNhsDa4OUPKdvLJ05XWfmZaXVbP+XUaqbtv5k5Yr5BJtE47VbIST39+g1TzffZZS9CUF1YN6nsYrUXlt29y5rDAdkpkLjJfM0+7AGHNph/a+1Gnf/6Sua8ywxZljXPsqx3jj3mlkdQIiJVSdKRPJbvPswVsSVvlTtuSLsofD3d+Wr5PgCW7EwBoGdMOX2qKCIiUtnZC82bSVe0yh3nG2zmEm36wbzBPW71lyb5VavNma/z8IJWw2HML3DvauhwM0R1MMeOtwCWxKF1ptKlXjm0yv1dSAO4bQFcP80k0mY9Am/FwqLXz9ymdOQQ7JprKnPCGlXuSqb8bNMm6VXdDEk/tMHsz0k1CcGWw83XrtejpvXyvU6mou14Msphh43TIKYf+IVA65Gwe0HZzLFK3QOvNoKFrzr/3s6Ul2G+t366z7SeftwHXm8Oz9eAVxrCB93MHLRqNeC2+ebfWhWktaxFRFzs1w2HcFgwqHXJW+WOC/Dx5Kq2tZixLp6MnAIW7Ugh2M+z1BVSIiIiVVJehmk1+uHOk8mcwzugMBciXJhkAmg5FDIPmBY4MEmiQ+uh7Y3Fu8/xZFnCmpLHsneJ2dYt50omMNVkMX3h5lkw5lfz32XOs/BmS5j7wqkVOhu+A8thEiZhjSt3kunPj83MqqHjweYGW382+3fOAcsOTS43Scc+j5lkU0AUfDcavr3BJOP2LYEjCdDqanNd65GAdfaB86Wx+A3T6jn3OVMJdCEqyIXJ15rvrS0zIWO/SfY26A3d7oHLXoKrP4cxs0yCqUZjl4brShrQISLiYjPXx9Okpj+Na/o75X43dKnL5D/3M3X1ARbvSOGiRmHFXq1ORESkSivINa1GC1+F3GNJirajoG5X0yoHENnadfEBNBkA7t7mTX9uunmj7u5t5jEVh08ghDQsXSXT3sUQGgP+NUt+D2eo2w1GfQ8HV5uhywtfhmXvQfMroekgWDsZotpDWIyJd/23puLHq5pr43a2vEwzq6pRP2g6AGp3MUmmPo/B9lngFwa12p08P6IljJ0Dy96Fef81VU0hDU0VVOPLzDkhDaBOV9Nu2P0B57WKZhyAtZPMv6/U3SahG1gbancyiaeM/eac9P0n/5yVCG2uL79WNHshTL0Z9i2F4Z9By2Hl87wVlJJMIiIuNG9rEn/tTeOhS5s47Z4tagXStk4Q787dQVpOAT00j0lERKRojmaZ5NLSdyA7yVQp9HwIvr7aJCTqdjXVQh4+phLGlXwCzGDvvz4xD58g6PuMqa4orsjWZgB4SeSmQdwyU1l1oYhqByO/hqQtsPRd2PqTSY4AXP6K2YbFmO3hna5PGDrbig/Nf5c+j5u/Nx0Ivz8Bh3fBzj9Mws3tH01N7h7Q/X5zbMbd5r9p7Ejw8jt5Tqvh8PO/TAWYsyp1lr4DWNDrYZPU+uQS+HKwqb4q+Mdqdh4+EBhtjs24GwrzoNOt5lh2imkJbNDbubPSHA7THrftF/O9owTTeSnJJCLiIruSs7h38hpa1Arg5oucuzrNqC51eXCK+USyu+YxiYiInF3aPjOnZ9dc2D0fjmZC/V7Q8zOo38Oc03SQmVdz+Uum4ie8uXlT7mo9/21Wc2t8GTS8xLQ/lUStNmYuT06qmb9TVLnpMHGIebPf5vqSPXdZCm8GV70H9jdN+9fB1dD2BnPseJIpZUflSjLZC+DP8RDT3yTbwFQz/f4E/PF/pgW08aVnvz6sEYz+xSRVanc69Vi9Y/8e9i8vfpKpMP/078+sJFj1uUlmBdUx+66faqryvP1NRVNgtHkE1QG/UJNAKsyHKTfCL/82lWjZySY5XJBjEq3dHyhebGfjcMDM+2DtV9DrEeh8m3PuW8ldAD8ZRUSqnozcAm79YiVeHm58fGMHfL3cnXr/Aa0ieW7mZoL9vIgK8nXqvUVERCq0o1mmvWvXHJNYOrzT7A+IguaDod1NULvjqdfEXgMbpsCO300lU4sh5R/3mUS1O5lIKI3jSZaEtWageFHkZcJXw+DQRrjmq9MTEhcSd09T4dKg98l9IQ0BW8WZy7RyAnh4m4TMP6uQ/m7br6YKr8MtJ/eFNIDwFrB1plkt7nz/jd3coNmg0/eHxphKubgV0K6Is78cdphxL2yZYRJIdTqfPLbsPbDnn5oUCm0Ig9899z09vGDElybRNPtpsLlD7AgzfH72MxAQXfy20TPF/ePdZkW9Hv+G3o+V7n5ViJJMIiLlLDffzl1fryYuNYdJt3YpkySQj6c771zbDg93zWISEREBIGmrqXyIWw6OAvD0g3rdoeNY86Y7rPHZ22wa9DYrRi16zVSCVKbKFzj5euKLmGQ6mmVaCBPWwtVfQJPLyja+suDpA8F1IWW7qyM5vy0zYeb95s9/fQIDXj17cnHV5yZh2qjvqfubDoSkTeZ73ruEc0Dd3KB2Z1PJVBT2QvjhDpOg9Q2Gr4fDTTMgorX5t7T0bbPKXVij4sdyPNG09mvz7zOkPhQehYlDzXP614T6PYt/3+Nm3m8STL0fh96PlPw+VZBWlxMRKUcZuQWM+nQFS3el8OKwWDrVL0ZJejF1jwmjS4PQMru/iIhIhWFZ8PODZmZL1zvhxhnwyF64/jvocgfUaHLuOS7uHubNcPyxFdgiKlmSyTcYgusVbfh3fjZMGgEH/oJhn5654qWiCI0xqwWW1vbfIXVP8a/LSjIVM+eSvh9+vAsi28BVH5jB1+MvNtVB2YdPPTdtr6nOazvq9HbOZleYbdOBxY/z72p3NtV/2SnnPs9eCNNvMwmmi5+C2xebuWETh8DEq2De8+bf1KA3Sh6Lhxd0GGMSTGAqvUZ+baqhJo2ELT+V7L4J62H1l9D1biWYSkBJJhGRcpKUmcc1Hy1j/YEM3r2uHcPbR7s6JBERkaphz0Izk6fP49DvP9Cgl3lDWhyxI8zW5g41mzs/RleLbG0qk84lPwcmjzRDoYd+DC2uKp/YykpYY0jZaWbvgBkUnnGgePfIy4RvrjUDsYsqNx1+eQheawK/PX728+yFMO0Wk4ga/hm0uQ7uXgld74I1X8E77Uxl0/FE1eqJJll6fO7U30XGwi2zof3oYr2809TpYrb7V5wj7gIT98Zp0PdZMzssMNpUMXn4mGrCQW+a7yHv6qWL5598g+DGH808rm9vgAUvmyRzcSx+A7z8zdB/KTYlmUREykHc4RyGf7iMuNQcPhvdkQGtIl0dkoiISNVgWWZZdv9aZt5SSdVqaypfajQFz0o47zCyjamEyU078/GCPPjmOtizCK760Kw0VtGFxUBhLmQehMx4GH8JvNcF1n1b9HvsXQyOQjPj6/Cuc5/rcBxLDrU3yaGwxmZId/K2M58//38mmXPFm6Y6B8yqgpe+AHcsgYhWJrn1cW/Yt9Tcu1E/CKp95vvV7mjmU5VGrbZmrlPcWVrmCvNh6hjY/AP0f96sWHdcSH0YtxDuWmEqkJy5Ctzf+UfA6J/N/Kp5L8B3o00FXlGk7DRD/juNNQkrKTYlmUREytiWhEyGfbiUzLwCJt3ahe4xYa4OSUREpHL7e+XC7nlmhkzPf5k5PCVls8GIL2DIB6WP70JUq43ZJqw//VjhUVMVsnueGcrc+pryja2snFhhbrsZGO0oNBUw02+DabeaKqXz2T3PVOe4eZgVzs4mYR18dqlpfQttCLctMIkQr+rw+5Onn79rnplb1HbUmRN64c3gpp9g+ATTujbhcsg6VPpKpfPx9DXfK2eqZCrMNwmdLT/Bpf+Dbvecfk718JPtbWXJ0weGfAj9noPNP5qvffr+81+35A1T5djlzrKPsZJSkklEpAyt3JvKNR8tw91m47txXWlTW5+IiIiIlKn8HHi9ObzbEWY/ax4B0ebNemnVbFH5hn4fF3ksybTwFdg527RgOeyQtg+m3AQ7/4Ar3jpzK1ZFFdbYbNd+Deu/hW53w82zoM8TptXrw+6w/69z32PXPDNMu+kgU0lUkHvq8dy0k9VGqbvNXKUxs0z7WrUw00q243fYOefkNVlJMH2cie/yl87+3DYbtBwKd/8F3R80McT0L9GXolhqdzbzyQryTu4rPGpWe9v2M1z+ipl95mo2G1x0L1w3xXwfj+9z9gosMEmodd+Yisfq4eUXZyWjJJOISBmZtzWJGz5dQVh1b6be0ZWYmiVcyUNERESKbs8COBJvqkuWvGXmDPX8d/FnMFU1fiHQ50kzHP2rYfByfXi+JrwVC9t/NSualXWVTHmrVgO8A01CyT/SJGrc3KHXwzDmV8AyFTALXznzgO70/WZweMOLodOtkJdu7gWmNW71RNMat/Iz6Hgr3LPKzFVy+9vb8M7jIKiuqWZKWG9mQk2/3axiePUE8Kp2/tfhXR36Pm2GXv9z4HdZqNMF7PknZ3gV5JlKt+2/wsDXoPNtZR9DcTTuD2PngHcAfD7I/Hc5k4WvmO2ZKrCkyMrhO1BEpOr5ce1B/jVlHU0j/fl8TCfCqusXWxERkXKx7RfzZnLsHMjPgkProV4pljKvSno9ZCo/tv9mqmv8Qk1rU2RrM4unsrHZTMvcwZVmQPXfh1DX6WxWRJv5AMx9HnbNh6EfmQHWx+2eZ7YN+pj2tRpNzYwl7wAzTylpM9TuAgNfNfOTzsTD28wumjIKPupxcv+gN0zl3IWodmezjVtukrm/PmJaUge9aWYtXYhqNIZb58B3Y2DG3ZC4yXzdjyfltv0Kq7+ALnedfaaVFInNKu6k9QtUhw4drJUrV7o6DBERth06wmVvLaRTvRA+uakD/j6lHLAoIiIiReNwmBW76nU3VSAi57PoNTi4GkZMPLXC6DjLMi1Uv/zbzF0a/C40u8Ic+26MGbj9r60mYfXneHMemCHxvR+FlsOKNuA6Yf2xweup4BsMza4su8HYzvB2W9MKmJsGviGmre/4CowXMnsh/PEULH/fJAevnmBaHD+4CAKjTHJaVY+nsdlsqyzL6lCUc1XJJCLiZLO3JGJZ8O517ZRgEhERKU/xqyE7CZoMcHUkUlH0+Ne5j9ts0OZaqN0Jpt0C346CEV+a+Ue755sZSMeTQa2vNbOK6veElsOL17oWGWseFUWjfrD6S/P1u+g+8Al0dURF4+4Bl/0PwpubKrXxF5u2ycKjZoi6EkylpiSTiIiTLdqRTPPIAGr4639SIiIi5Wrrz2Bzh5i+ro5EKpvQhmZO0xdXwrSx0O8/puqo4cUnz/GuDle977oYy1P/580cqKLMjLoQtRtlWiW/vcEMZB/8/snVBqVUNPhbRMSJso8WsmpfGj0ah7k6FBERkapn269Qt5tpNxJxNk9fuO5bM7Nn1iNmX4PerozIdTy8Km6C6bg6XeC2BaZVss11ro6m0lCSSUTEiVbsOUyB3aJnTA1XhyIiIlK1pO6G5C1qlZOy5RcCN0yDauEQ2Qb8a7o6IimNwChofoHPv6pg1C4nIuJEC7en4O3hRvu6+gRVRESkXG2bZbZNLnNtHFL5BdeDO5eDo8DVkYhccJRkEhFxokU7kuncIBQfT3dXhyIiIlK1bP8VajSDkAaujkSqgmqhro5A5IKkdjkRESc5mJ7LruRsesZoHpOIiEi5KsiFuOXQ6BJXRyIiUqUpySQi4iSLdyQD0EPzmERERMrX/hVgz4f6vVwdiYhIlaYkk4iIkyzckUK4vzeNa1Z3dSgiIiJVy55FYHOHul1dHYmISJWmJJOIiBNkHy1k8Y4UesTUwKbVKURERMrX3kUQ1Q68/V0diYhIlaYkk4iIE7z+x3Yycgu4tlNtV4ciIiJStRzNgoOroF4PV0ciIlLlKckkIlJKa/enM2HJHm7oUocO9UJcHY6IiEjVErccHIVQX0kmERFXU5JJRKQUCuwOHp22nhr+3jx8WVNXhyMiIlL17F0Ibp5Qu4urIxERqfI8XB2AiEhFNn7RbrYeOsLHo9oT4OPp6nBERESqnj0LIbojePm5OhIRkSpPlUwiIqXwzZ/76RETRv8WEa4ORUREpOrJy4CEdVC/p6sjERERlGQSESmx1Ox84lJzuKhRmKtDERERqZr2LQXLoXlMIiIXCCWZRKTc/Pu7dQx+dzHr9qe7OhSnWH/AvI7Y6EAXRyIiIlIFZSXD0nfAw8e0y4mIiMspySQi5WJ/ag7TVh9gY3wmQ95fwvMzN5OTX+jqsEpl3f4MbDZoFaUkk4iISLnaORs+6AYHVsKAV8DD29URiYgISjKJSDmZ9GccbjYbv97Xg5Gd6vDJ4j30f2MhC7cnuzq0Elt/IJ2GNarjr4HfIiIi5aMgD2Y9Bl8NA79QuG0etLvR1VGJiMgxWl1ORMrc0UI73/61n0uahtO4pj//HdKKwa1r8dj3G7jxsz8Z2i6KpwY2J7ia12nX5uQXcufXq9mRmEXbOkG0rxvMNR1r4+fl2h9flmWx7kA6vRqHuzQOERGRKiNpC0wbC4kbodNt0O8/4Onr6qhERORvVMkkIk43b2sSt09cxe7kLABmbTxEanY+o7rWPXFO5wah/HJfD+65uBEz1sbT9/UF/Lj2IJZlnTinwO7gzq9Xs3B7Ms0iA1i1L41nf9rMszM2l/tr+qeD6bmkZOXTurZa5URERMqUZcGf4+Hj3pCVCNd9Z1rklGASEbngqJJJRJwm+2ghz/+8hcl/xgHw595UJozuyMRl+6gfVo2LGp66CpuPpzv/6t+EgbGRPDJtA/d9s5apqw5wZetadGsUxqu/bWP+tmReHNqKkZ3qAPDkDxv49q/9PNCvMRGBPkWKK6/Azsz1CfRtFk6Q3+nVUiWx/kAGAK2jg5xyPxERETmDrGSYcTdsnwWN+sFV70N1VRGLiFyolGQSEafIyC3gqveWsPdwNuN6NmBIuyjGfrGSaz5eRl6BgycHNsPNzXbGa5tGBPD9Hd34Yule3p+/i0U71p849tClTU4kmADG9WzI5D/38+ni3TwxsPl541qyM4Unf9jInpRsrm4fzStXty79iwXW7U/H091G00h/p9xPRERE/iFuOUy5EXLT4bKXoPM4sJ35dwkREbkwKMkkIk7x0YJd7EnJZuItnegRUwOA7+/oxk0T/iLucDbD20ef83p3Nxs3d6/PmIvqsS3xCIt3pODt4cYNXeqecl7tED8GxUYyaUUcd/VpdMbKpEMZeSzcnszvmxOZvSWReqF+XNI0nO/XHOSei2OoE+pX6te77kA6zSMD8PZwL/W9RERE5G8sC/76BGY9CkF14IbvIaKlq6MSEZEiUJJJREotKTOPz5bsYXCbWicSTADhAT5Mv7Mb6TkFRW5Ts9lsNI0IoGlEwFnPub1XQ35cG8/EZfu455IY8grs/LU3lYXbk1m4PYVtiUfM8/t7c+/FjbizTyMycgvo8fI83p23g5eHn17N9Oi09fy5J5U7+zTiqja18HA/+8g6u8Niw4EMhrY7d+JMRERESuC3J2D5exBzKQz9GHzVmi4iUlEoySQipfb23B0U2i0e7Nf4tGM+nu5EBDq32qdZZAB9mtTgsyV7WLkvjRV7DpNX4MDL3Y2O9YMZ2q4pPRvXoGmEP7ZjZfU+nu5c16kOXy3fxz0Xx1A75GQ1U05+IdPXHMTdzca/v1vH+/N2cl/fGAbF1sL9DC1+u5OzyM6307q2fukVERFxqk3TTYKp461w+cvgpnWKREQqEv3UFpFS2ZuSzTd/7ufaTnWoG1qt3J737otjyMwrZH9qDiM71mHC6I6sfbofX4/twrheDWkWGXAiwXTc7b0a4maz8d68nafsX7AtmaOFDj65qQMf3tAeLw837vtmLZe9uZBfNiTgcFinnL92fzoAraO1spyIiIjTpMfBjPsgqgNc9j8lmEREKiBVMolIiViWxeaETP73y1Y83d245+JG5fr87esGs+nZS/HxLHqVVESgD9d2qs3Xx+Y5Ha9mmrXpEMF+nnSqF4KHuxv9m9fkl40JvPHHdu78ejXNIgN4oG8MPRvXYNKKON6fv5NgP08a1KheVi9PRETK2s45EBAF4U1dHYkA2Ath2liwHDDsE3D3dHVEIiJSAkoyiUix7Eg8wk/r4pm5PoHdKdm4u9l47PKmhAf4lHssxUkwHXd7b7M63fvzd/K/obHkFzqYuzWJy1tGnJjD5OZmY1BsLS5vGcmMdQd5a/YObpu4Ch9PN/IKHHRtEMrjA5qdsZVOREQqgMTNMGkE1GwJt83XimUXgrnPwf4VMPQTCKnv6mhERKSElGQSkfPak5LNzGOJpW2JR7DZoEv9UMb2aMBlLSMIqVa0od4XgshAX67pWJvJf5pqpl3J2RzJK+TSFhGnnevuZmNI22iuiK3F92sOsnRnCiM61KZbozAXRC4iIk7hcMDMB8BRCAlrTWKjThdXR1W1rZ0ES96E9mMg9mpXRyMiIqWgJJOIYFkWt365ishAH5676uQSwftTc7h70mrWHcgAoEPdYJ69sgWXt4og3L/8K5ec5Y7eDfn2r/28N28XANW83LnoHIkjD3c3RnSozYgOtcsrRBERKStrvoT9y2HAq6Z6Zvn7SjK50r6lMONeqN8TBrzi6mhERKSUlGQSEZbsPMzsLYkAdG4QwqDYWhTYHdwzeQ27U7J5cmAzBrSKpFaQr4sjdY5aQb6M6BjNt3/tp5q3B72bhpeo9U5ERCqYrGT442mo2x06joWM/bD0HTNwOqiOq6OretL3wzfXQ3BdGPGl5jCJiFQCWrJBRHhv3k7C/b1pHR3IUz9sJOlIHm/N3sHa/en8b2grxvZoUGkSTMfd0dsMKk/PKThjq5yIiFQyBXkwfRzkZ8OgN8wcpo63Ajb4c7w5Jz8Htv8GayfDio9g4zSwrHPe9pxm3APT7yjdPSqzJW9CfhZcNwV8g10djYiIOIEqmUSquFX70li2+zBPDmxG7ybhDHx7ETd//heb4jMZ0SGaQbG1XB1imYgK8mVkxzpMW32APk1quDocEREpS/nZMPla2LMQrngLajQ2+4NqQ/MrYfUXYHMz29y0U6/dNB0Gvw8+AcV7TnsBbJgKBTlQszl0u8c5r6WyyD4Ma76G2BEQ2tDV0YiIiJOokkmkivtg/k6C/Dy5tlMdGoVX56FLm7DxYCb1Q6vx9BUtXB1emXpqUHNmP9gLfx+V54uIVFp5GTBxKOxdBEM+gvY3nXq88x3mnKVvQ73uMGo63LsGHtoF/Z+Hrb/AJ5dA8rbiPe+h9SbBFBBtWvT2LXXea7rQpMfBR71MRZjDXrRrVn4KhbnQ9e6yjU1ERMqVKplEqrAtCZnM3pLEg/0aU83b/Di4+aL6OCyLS5rVPLGvsvLycKt0bYAiIvI3OakwcQgkboThE6DFVaefU6czXD8NwmLMbKC/63YPRLaB70bD+Ivhqveh+eCiPXfcCrMd9b2povpuDIxbAP6VsEV752yzUl/CWlj3jRngXautaUk8k4I8044Y0x/Cm5VvrCIiUqZUySRSRdkdFs//vJlqXu7c1LXeif1ubjZu69mQhjWquy44ERGR0jqSCJ8PhKQtMHLSmRNMx8X0PT3BdFz9HjBuIdRoClNuhN+fAnvh+Z9//3IIrAM1mpih1nkZ8HEf2L2gZK/nQha/FnyCYMjHkLYXxveBF+vC54Pg9ydN22DKTnA4zPnrJkNOiloIRUQqocpdpiAiZ/X2nB0s2XmYF4e2ItBP7WIiIlKJZByEL6+EzHi4fgo06F26+wVGwZhfYNajpq0uYa2pjKoWZo7vnA3bZkH/58DT1wz6jlthElQAES3h5lkw7Rb4cjBcdC/0eRI8vEoX14UiYR1EtobW10BMP9gywySeEtbBio/BftSc5+UPkbGQutucX6+Ha+MWERGnU5JJpApasD2Zt+fuYFi7aK7pWNvV4YiIiDhP6h6TYMpNN/OV6nRxzn09vM2qdFEdYOYD8FFPuPJtU6WzbrI5J7I1tBsF6fsg6xDU7nzy+lptTEXUb4/DkrdMRdOwT0ybXkVWmA9Jm6HLHebvfiHQfjS0P3bcXmCqyRLWHWupW2dmVfV69OztdCIiUmEpySRSieQV2Plu5X62J2bhZgObzYa7mw03G7jZbLgd+/OkFXE0qenP81e1xKZf8EREpLJI3m4STIV5cOOPENXO+c/R9nqo2QKmjIKvhoGbB/R8CLb8ZIZZtxt1ch7TPxNcXtXM6naN+sKMe0yi6rIXod2NFTfhkrwF7PkmwXYm7p6meikyFhhVrqGJiEj5U5JJpBIosDv45s843p23k8TMowQda39zOCwcFjgsC7vDwrLAblmE+3vz3vXt8PVyd3HkIiIiTmIvMK1oDjuM/tkkgspKrTZw2wJYfmwQeEQrqBYOvz4EB1dD3DLwDoDw5me+vtkVENUept8OP90LO/+AK942VUBgqoPO1EqXlwE+gWX3ukoiYZ3ZRrZxbRwiInJBUJJJpBJ4d+5O3pqzgw51g3njmjZ0axjm6pBERETK1+GdcCQerlKepLoAACAASURBVPqwbBNMx/mFwMVPnvx762tg9tOmmungaojuCG7n+DAnoBaM+gGWvQNznoMDF0GbayFuOez/E2p3MgPLfYPMjKc5/zFtdmP/MAmqC0X8WpNQC67v6khEROQCoNXlRCqBZbsP0zo6kO9u76oEk4iIVE2Jm8w2oqVrnt8nEFpdbWY0JW0p2iwoNze46D4YO9u00i16HfKzTEve/j/hiysgO8Ws0Lb4dbDssGl62b+W4jg+9NtNbytERESVTCIVnt1hselgBsPbR2u+koiIVF2Jm8DmDmGNXRdDx1tg9Rfmz38f+n0+tdrAXStMgul4O1zTQfDtDfBOO9Mm12kcpGyHbb9C/+edH3tJ2AshcSN0HOvqSERE5AKhjxxEKrg9KVlk59tpFR3k6lBERERcJ2mzSTB5eLsuhsjWZvU5mztEdyjetW7up85biukH108Fmxt0uxcufwmaDjRtgcnbnRt3SaVsM0PWzzb0W0REqhwlmUQquPUHMgCIjb7ABoGKiIiUp8TNUPMsg7bL08BX4cq3TftbadXvAQ/thv7PmdXnmlxu9m/7+dzXWZZp2XM4Sh/DuWjot4iI/IOSTCIV3PoDGfh6utOwRnVXhyIiIuIaeRmQEXf21dzKU6220PYG593v77OOAqNN1dC2X898rmXB9t9g/MXwfhdY/t6px9P2weYfzXnOEL8WPKtBaEPn3E9ERCo8JZlEKrgNBzNoGRWAu5vmMYmISBWVtMVsy2NVOVdrMsAMBc9KOrnPskziaXwfmDQCclKgRjNY+g4U5J08Z+oYmHIj/HAnFB4t2vNZFhTkQmYCJG2FuBVmay88NvQ79tyr6ImISJWiwd8iFVih3cGm+Ayu61TX1aGIiIi4zvGV5apKkmn+/2D7LGg7Crb9AgteMgmf4Hpw5bvQeiTsWwJfDoZ1k6DDzbD5Bzi4ChpebPYd3gl9HoPMeEiPM6vY5WVAXrrZ5qaf/Ls9//Q43L3AUQidbiv3L4GIiFy4lGQSqcB2JmeRV+DQPCYREanakjaDdwAE1nZ1JGUvopV5nX+ON49D6yG4Pgx+H2JHgLunOa9+L6jVDpa8Ba2vhTn/Me2E10+FLTNg+h0wcYg51+YGvsFm8LhPkNkGRh/7+9/2+QaBd6CplErcBKm7TUJLRETkGCWZRCqw40O/WynJJCIiVVniZghvZoZjV3Y2m1llbsWHENIArvoQWl0N7h6nn9fjQfj2Bpg80iSErp9qWttaDDGzo1L3QHBdk7Q6npwSEREpBSWZRCqwDQcyqO7tQf1QJ6xgIyIiUhFZFiRtghZDXR1J+en9KDTqBw16n55c+rsmAyGsCeyeD/V6QKO+J48F1zMPERERJ9Lgb5EKbP2xod9uGvotIiJVVeZBMzuoKsxjOs43GGL6njvBBGZlut6PgJsn9PtP1aj0EhERl1Ilk0gFlV/oYEtCJqO71XN1KCIiIq6TuNlsq1KSqThaDoOY/uDt7+pIRESkClAlk0gFtT3xCPmFDlpFaR6TiIhUYUnHVpYLb+baOC5kSjCJiEg5UZJJpIL6c08qAG1qB7k4EhERERdK3AwBUaaFTERERFxKSSaRCmrO1kRiwqtTO8TP1aGIiIiUL4cDds2Fb66HjdMgqp2rIxKRCsSy27EfOeLqMEQqJSWZRCqgjNwCVuxOpW/zmq4ORUREpPwtfh0mDoG4ZdDtHhj4hqsjEpEKoCAhgeR332Nnv37s6NWbgoSEU48nJVGYluai6EQqByWZRCqgBduTKXRY9G0W7upQREREyt+eBVCzFTy4Bfo9C9VruDoiEbnAZc6axc5+/Ul591286tbFKigg5aOPThwvTEtjz+Cr2NmzFwcfepiclSuxLMuFEYtUTEoyiVRAc7YkElrNiza1NX9CRESqoOTtENEKPLxdHYmIVABZS5Zw8KGH8Y2NpeHsP6g7YQLBVw8nfdr35B84CEDy629gz8wk4MoryJo3j303jGL3FVeQOvEr7JmZLn4FlZNltyuRVwkpySRSwRTYHczbmkSfpuG4u9lcHY6IlCPLsnDk57s6DBHXysuArENQo7GrIxGRCiB3/XoO3HMv3g0aUPvDD/CKjgYg9LbbsNlsHP7oQ3LXriX9u+8IufFGar3wAjELFxD5/HO4+fqR+MIL7OjZi/jHnyB3/XolRUrJkZtLxk8zOXDvfWzr2ImdPXsR/+STZP72O3nbtlOYkoJVWOjqMKUUPFwdgIgUz8q9aWTmFdK3meYxiVQllmVx4K67yZo7F5uvL+6BgYSNu43ga691dWgi5St5u9mGNXFtHCJywcvbto39t96GR2gotcd/jHtAwIljnhERBI0YQdrkyeSsXIVHzZqE3XUXAG5+fgQNH07Q8OHkbtpE+rdTyJg5k4zvv8endSyRzz2HT2MluovL/C5zF9lLl+FeI4zAQYOwZ2ZyZNZvZEydduI8m7c3gYMHEzJ6NN4N6rswYikJJZlEKpjZWxLx8nCjR0yYq0MRkXKUNW8eWXPnEjBoEB41apC7Zg2Hnn8B7yZN8WvX1tXhiZSflGNJphpKMonI2eVt307c6DHYfH2p89mneIafPss09NZbSf/uO/L37CHqzTdwr17ttHN8W7TA9z/PEv7wQ2T+9BPJ777H3mHDqXH//QSPuoHChAQKEg7hG9sKNz+t+nwu2YsXk710GTUeeIDQW8diczONVVZBAbkbN1KYmEjh4cPkbdlCxg8/kD5lCtX79CH05jH4duiAzaYujorAVlnK/Tp06GCtXLnS1WGIlCnLsuj96nzqh1Xj8zGdXB2OiJQTR14euwddgc3HmwbTp2Pz9MSelcWeIUOx7IU0mD4d98BAV4cpUj7++D9Y/gE8ngDu+rxURE53dMcO9t00GpuHB3UnfolX3bpnPTf166/J372Hmk8+UaQkRmFqKgn/939kzZ4DNhscez/t0zqWehMnYvPyctrrqEwsh4M9w4bjyMyk4a+/nPfrVHj4MGmTJpM2aRL2tDR8WrYk9OYx+Pfvj81DP/vLm81mW2VZVoeinKuZTCIVyK7kLPYdzlGrnEgVc/izzyg4cICIJ5/E5ukJgHv16kS9/hqFSckkPPV/mhEhVUfydghtpASTiJzR0V272Dd6DDZ3d+p88fk5E0wAIddfT8RTTxa5SsYjJITod94h6vXXCB13G5EvvEDNxx4lb916El9+xRkvoVLK/PVXjm7ZQo377i1SIs4jNJQa99xNo7lziHjmaRxHjnDwwX+xq/+lpH7xBY68vHKIWkpCSSaRCuSPzUkAXNLs9HJfEbnw5G3bxs6LLyF306ZiX2tZFgWJiWQtXMjhj8fjf9llVOvS5ZRzfFu1IvyBBzjy++/E3XgTmb//rmGZUvmlbIMwzUIRkdMd3b2HfaNHg81GnS8+x7t+2czzsdlsBAwYQPj99xM0bCghN91EyE03kfbVV2T8/LPTn89x9CiZf/xB3tatTr93ebAKCkh+6228GzcmYODAYl3r5utL8MiRNPj1F6LfexePWpEk/u9FDj39TNkEK6Wmj4BEypBlWcRn5OHt4UZY9dIvszxnSyItowKIDPR1QnQiUpYsy+LQs/+hID6e9G++wfe554p2XWEhqZ9/TsqHH+HIygLAzd+fmg8/dMbzQ8aMBnc30r6cyMF778MjIoLgkSMJGnE1HiEhzno5ANjT08letoy87dsJveUW3KtXd+r9Rc6rIA/S9kLL4a6OREQuMEf37CHuppvAYVH3yy/wbtCgXJ8//N//Inf9ehKe+j+OzJoFgM3LG++YGHxaNMenWTM8woo3UzVv2zbSv5tKxk8/4cjIAHd3wm6/nbDbx52obL7QOXJzSfzv/yiIiyP6ww+wubuX6D42Nzf8L7kE/0suIfHlV0idMIGQMaPxadrUyRFLaWkmk4iTFNgd7ErOYnN8pnkkmEd6TgFRQb7Mf6g3nu4lLx48nHWUDi/M5t6LY3ignz7BFbnQZcyYQfzDj+AREYEjK4uYxYtw8/E55zV5W7aQ8MST5G3eTPXevanWswfe9erh3awZHsHB57zWstvJmj+f1K++ImfZcmyengQMGEDwDTfg26pliV6DVVhI7oYNZC9aTNaSxeRt2AgOBwAhN91EzcceLdF9RUoscRN80A2GfQqtlGgSESN/3z72jboRq7CQul98jndMjEviKEhMJP7RR7GnHAbAkZ1NQXz8ieMe4eH4NGuGT4vmeDdrhk+z5njWijw5ANtup+DAAbJXrCB96jTy1q/H5umJf79+BF41mMyffybjxxn4xMZS66UXy6xSy1ly1qwh4dHHyN+3j5DRowl/5GGnDO+2Z2Sws/+l+LZqRZ1PxjshUjmf4sxkUiWTSAnkFdjZcDDjlITStsQj5BeaN1/eHm40jfDn8paR+Hm58+niPfy+KZGBsZElfs5525KxLOjXXPOYRC509qwsEl95BZ/YWMLvv4+4m2/hyJw5BJ6lRNxx9Cgp773P4U8/xT04mKg338T/0v7F+kXM5u5+4hO+o7t2kfb116T/8CMZP/6IT+tYQm64Af9LL8XtPHMQ7OnpHJk9m6xFi8letgxHZia4ueHTqiVhd9xBte4XkT51Kqlff03QiKvxbtiwWF8bkVJJ3ma2apcTkWPy4+LYd9NorIIC6nzuugQTgGfNmtSdMOGUffbMTPK2bCVv82bytmzm6JYtZC1adOJDG9zd8QgNxS3An4IDB7GOzRryjmlEzccfI+CKK0580FS9Z0+q9+lDwtPPsGfoMGo+8jBB11xzwa265sjPJ+Wddzn86ad4RNSkzucTTmv5Lw33wEDCxo0j6eWXyV62jGpdu55y/MjcuWQvW45v69b4dWiPZ0SE055bzk+VTCLFlJlXwPAPlrI90bSxBPt50qJWIC1qBdC8VgDNIwOoH1YNj2NVS3aHRe9X5xEZ6MuUcV3Pdetzun3iKtbuT2fZYxdfcP8jEZFTJb70Mqmff069Kd/i06IFOy/pi3dMI+p8/PFp5+asXEnCk0+Rv3cvgUOGUPORh3EPCnJKHPYjR8j44UfSvv6a/L17cQ8NJWjE1fi2isXNzxe3atXxadL4xADOzD/+4NAzz2I/fBiPmjWp1v0iqnfvTrWuXU+JqfDwYXZddjm+rVtTe/zH+pkk5Wf+i+bxRAJ4qnVcpKorSExi77UjsbJzqPPF5xWmdcqRm8vR7dvJ27KVgkMJFCYnY8/IwCsqGu+YRvg0N5VOZ/v/a0FiIgmPP0H2kiVU69WTWs8/j0eNGuX8Ks4sb+tW4h95lKPbthE4bCg1H30Ud39/pz+P4+hRdl1+OR5BwdSb+h02NzcsyyLlgw9IefsdcHcHux0Az+ho/Dp0wK9jB/zat8ezbl397lJMqmQSKSOFdgd3T1rD7uRsXru6NRc1CqNmgPc5f0i5u9kY1aUu//1lK1sPZdI0IqDYz5tXYGfhjmSGtI3SD0SRC1xBfDypEycSNHwYvq1aARB45ZUcHj+egqQkPMPN4H57VhZJr71G+uRv8IyKovann1D9ooucGou7vz8ho24g+PrryF66jLSvvuLwhx+dWG4ZwK1aNapddBHYbBz57Te8mzej9gfv49Oq1Vl/3niEhhJ2150kvfgSWfPn49+nj1PjFjmr5G0QVEcJJhEBIGPGjxTGJ1Bv2tQKk2ACM8zat3VrfFu3LtH1njVrUnv8x6R9PYmkV19l95WDiXzuP/j37evkSIvOKizk8CefkPze+7gHBRL9wftl+vuBm7c34ffdR/wjj7Kr/6VU79ULe1oqmb/8SuDgK4l45hmO7t5N7sqV5KxcSdaCBWT88AMA7jXCTNLp2MM7JuZEy6KUniqZRIrhuZmb+XTxHl4c2oqRneoU+br0nHw6/3cOw9pH898hrYr9vPO3JTF6wl9MGNORPk20spzIhezQf/9L2qTJNPr9Nzxr1QLMaje7Bwwg/OGHCb7+Oo7MmkXSG29SmJhIyI2jqHHffbj5+ZVLfAWJSRQmJeHIzcGemkb20qVkzZ9PYWoqYXfcTthttxVpmKiVn8/uwVdhOew0+Omn87bhiTjFB90hIBKu/87VkYjIBeDAffeTt3kzjf743dWhuMzRXbuIf+hh8jZvJmjECCKeebrcEyZH9+wh/pFHyVu/noABl1PzqafOO0vSGSzLImP6Dxz57TeyV6zAysujxv33ETpu3GkflFmWRf7u3eT8ZZJOOStXUnjoEACedepQf8q3Tqskr4xUySRSBmasi+fTxXsY3a1esRJMAEF+XgxuU4vpqw/yyGVNCfQt3moQs7ck4uflTtcGocW6TkTKV2FaGunfTSVw4IATCSYA7wb18Wkdy+FPP+XwRx9hz8jAOyaG6DffwLdNm3KN0bNmOJ41TyarAy67FMuysPLzcfMu+iqYNi8vaj7+GPtvvY20iRMJveWWsghX5CSHHQ7vgAa9XB2JiJTCkbnz8AgLxTc2ttT3ytu4EZ/Y4n+AW5l4N2xIvW8mk/TmW6R+9hleDeoTOnp0uT2/ZVkcuPMu7KmpRL3+GgEDBpTbc9tsNoKGDiFo6BAceXnYMzJP+R3nn+d6N2yId8OGBI+8BsuyKDgYT/aSJRx69lmS332PiCefKLfYKzMlmUSK6Ic1B6kX6seTA5uV6Pobu9ZjysoDfLdyP2N7nLqkan6hg/j0XA6k5XIgLecf21wSj+TRr1lNfDxLtuSniJxbYWoqR7dtO21wZHGlTZqElZtLyBkSLiHXXUf8E0/i37cvwdeMwK9z5wumNNtms2ErRoLpuOo9elC9d29S3v+AwCuvvGDmQUgllR4HhXlQo4mrIxGREnLk5HDw/vuxLIta//0vgVcMKvG9CtPSKDh4kODrrnVihBWTzcuL8If+Tf6+fSS/9jrVOnfGp1nJ3rMUV/6ePeTv2UPEM0+Xa4Lpn9x8fM67iu/f2Ww2vKKj8LpmBHlbNpM2eTLBI6/Bu1GjMoyyalCSSaQIHA6LVfvSuLxlxImB3sXVMiqQbg1DeWv2Di5tEUHtENMa88fmRO6ZvJq8AseJc91sEBnoS3SwL91jwogK8mVouyinvBYROd3hjz4i9YsviX73nTPOM7Dy8zn82We4+fnhVa8eXvXr41mrFjb3k4lfR04OaRO/onrv3vg0Pn3lq8DBgwkYNOiUayqDmo8+wq4rriTpjTep9d8XXB2OVGYp281WK8uJVFg5f/2FlZ+PZ+3axD/0EAWHEggdO/asMwDjn3wSCgqp9dKLpx3L27QZAJ8WLco05orCZrMR+fxz7LlyMAf/9W/qT5uKm2/Zz6/LmjcfMCvfVVQ17r2XzJ9/IfHFl7SgiRMoySRSBLtTssjILaBd3dL1Fr80LJYBby3ivm/WMGVcV7YeOsK9k9fQKLw6N3WtR3SwH9HBvkQE+uBZwmSWiBRfzpq1AMQ/9jj1GzfGq86pLbGHJ3xO8ptvnbLP5umJZ906eNevj1e9+tjT07CnpxN669izPk9lSzABeNWrR8iNo0j99DOCrx15Yti5iNMlbjRbVTKJlIuCxCQ8goNOrEDqDFmLFmPz8aH+99M49PQzJL/2OgXx8UQ8+eRp/4/M27aNjKnTAAgcNpRqnTqdenyj+Zng07y50+Kr6DyCg4l88X/sv2Us8Q8/QsTT/4dHWNhp5+WuX0/8Qw/j26YN1fv0oVr37rhXr1ai58xasADvxo1PGRNQ0XiEhJxY0CR74f+zd99xVZV/AMc/5y7g3sveyHBb4ihTEffIVZqr0twjNctViqa5d5llWlqZWm5Nq1+aae7SXLlSMQcgouy9LnDhnt8f10gUFQQE8Xm/Xr2sc57znO+FhHu+93m+39/RtxDbsotCPMUKQgGcCk0A4IUiJpm8HLTM6VaL0zcSmbUjkKFr/sJeq2bVwAa8Vt8L/yqOeDloRYJJEB4jU2YmGZcumZd4KxTcHDsWU2Zm7vmsmzeJXb4c67YvUu3IYXzWrcV9zmzs+/dD41ORzKBg4r79lsTvt2JV/wW0L7xQiq+mdDiNGIHSyYmoBR+WdihCeRZxDuwrglXJF5MVhKddZnAw11q25HJDP0IHDCRmyVLSTpzAlJVVpHnT/vgDrV9DlNbWeHy8EIchg0ncuImbo0ZjMhjyjI376isUOh0qV1eiP17E3Q2rMi5eQOPjg9Km8J2byzN9kyY4j3uPlAMHuNauPTFLlmJKT88zJmHTZoyRkaQcPMitsWO56u/PjSFvEr9+Pcbw8ALfKyc5mfRTp8pFUsahd280FSsSPvkDUv84XOT55OxsohYuJDMkpBiie7KIJ1lBKIBToQnYa9VUdnq0DP+dujxXge7PV2DN0VCSDEa+GdAAF+uC7x8WBKF4ZVwMBKMRm5c64rFgPpmBlwgPmEBOYiKyLBM1ew4oFLhOnozK0RFt/frYvfoqrgEBeH3xOVV2/sIzZ05TZfcuvL78srRfTqlQ6vU4DR+O4dQp0k+fLu1wSlzasWPIRmNph/H0CT8L7o+3UL4gPElkWUbOySmWuTLOnwdZxqZ9e0wpKcR++SU3+g/gil8jbrw5lLiVKzFcvIhsMj18stuybtwgKzQUfdNmAEgKBa4BAbhOmULqgQOEDhxIdnw8YO7KmvzrLux798Z59Ggy/v6blN2788xnuHgRy1q1iuX1ljdOQ4dSZcd29M2bE7tsGZGzZueeM2VlkbJ3LzYdO1L9yGF81q7Bvm9fjLduETV7DtdatyG4S1eiP/sMY3T0A++TduQI5OSgb9WyhF9RyZM0GjyXLkFpb0fY0KFEzpl7T+KzMFL27Sd+5SqygoKKMcong0gyCUIBnApN4AUf+2Lbnzuziy+d6rizvO8L1PQQn74IQmkynDsHgFXduli3aoVLwHhS9u0j6KWXiZo3n9RDh3AeNQq1u/t955BUKvOnqXr94wq7zLHr0R2lrS1xK1eVdigFlhkccs+nuw9j+PtvbgwcROIPP5ZQVEK+0uMhMRQ8RJJJEPKTERhIyCtdCO3dBzk7u8jzZV4LArUa9zmzqfTDNqofO4rnF59j16MHxsgIohd+zPUer3LVvzE3R48hYdOmh/48TT1sXh2ib9Y0z3GHvn2osOQzMv+5zPVeb5AVGkrcihVIFhY4DByAbdcuWFSrRvSnn+Ym+LPj4sgOjxD1mB5AU7Einos/xb5vX5J++SU3YZR2+Aim5GRsXuqIpFKhbdAA14kTqLLrVyrv3IlLQABKa2vivvqa0D59MUZE3PceqQcPobS1xapu3cf1skqURbVqVNq6Ffv+/UhYt46rzVsQPmkyqUeOFCqhChC/dg1qT0/0rVqVULRll0gyCcJDJKRlERSTVuR6THeytlTzee96tKguOjEJQmkznDuHukKF3M5ojkOGUGnbVtReniSsXYtFjRo49OtbylGWfQqtFvs+fUjdt4/M4ODSDuehsm7eIrhzZ661ak3MkqW5n54/TOrBQwCkHTtakuEJd4swJ4PFSiZByEvOySH26xWE9OyFMSoKw7lzxK9dV+R5M69dw6KiD5JaDYDSxgbrNm1wm/IBVXbsoOrvh/BY+BH6Nm0wXDhP5IyZ3Hpv3D1b2u58ME/74zBqLy/UPj733M+mbVu8v12NKTmZ673eIGn7duxefw2VoyOSUonL+HEYQ2+QsGEDABkXLwJgWUskmR7GoW8fyM4mcfMWAJJ37kRpa5tvR12LypVwHDIYn3VrqbhpIzkJCYT2H5BvoknOySH199/RNWtWrmpOKiwtcZs8GZ8N67Fu04aU334jbMib3Bg0+IEJtzsZLl7E8Ncp7Pv0KVdfm4ISSSZBeIjTN27XY/IWNSAEoTwynDt3zydwls88Q8UNG6jw6Sd4fr4USSX6ZBSEfd8+SBYWxK0q+6uZUg8ehJwcLH19iV22jGutWhM5axZZYWEPvu72J/HpJ07e8zBVlhijoomYPqPAb4jLvNwkU/n4tFwQikvsF8uI+eQTrNu0ocruXehbtiRmyRKMt2498Do5J+eBK54yr11D84BW7moXF2w7d8Zj3lyq7tuH66T3ST14kIQ1a8zzZ2cTMX0GV5s2I+3YcUxZWaQdP46+WdP77gzQPv88Phs3oNDrkSQJx8GDc8/pmjdH17wZ0Z8uJuv6dQwXLoAkiaLfBaCpWBF98+YkbNpETnIyqfv3Y92+fW4C8X6s6tTBe+U35kTTgIFkx8XlOZ9x/jw5CQnoW7YswehLj7ZePTwWzKfan0dwmzEdw/nzBHfpStKOXx66WjBhzVoUWi12r/Z4TNGWLSLJJAgPcSo0AZVCoo6nXWmHIghCMTNGRZEdEYHVc/c+uEpKJTYdO6Lx8iqFyJ5MKgcH7Hp0J/l/P2OMenAdh9KWevAgmkqV8F61ksq/7MCm08skfL+VoPYduPXeexguXLznmuz4eDLOn0ft401OXBxZ166VQuQFk/zLLyRu3sz1nr3IuHy5tMMpuoizYOcNWofSjiRfck4O6WfOkJOaWtqhCE+RnNRU4tetw7rti1T49BNU9va4TZ0CQOTsOeYaTbJMdkwMqUeOEPftt4RP/oCQV1/j8gv1Cer4Ur41nEwGA8abN7Gocv8k050kScK+f3/0bdoQ9fEi0k6c4OY7I0ncvBkUCm4MHUrU/PnI6enobtdjuh+LSpWotG0rlf73E2o3tzz3cJ89G8nCgvCJ75Nx7m80lSo91dvUC8O+Xz9y4uIIf38SpvR0bF7qWKDrrOrWxfubFRhv3SLu6xV5zqXsPwAKBfqmTUoi5DJDYWGBfa9eVP5hm7kw+Pjx5tpkw4cTt3IVhgsX8/w9yo6JIXnnTmy7dUNpbV2KkZcekWQShIc4FZqAr4cNVpqnb6mjIJR3hrP/1WMSiofDwIHIOTnELF1SZlf6mNLSSD9+PPfTV4sqVfCYO5eqe/fiOGQwqb//wfVXXyV04CAMf/+de13akSMgy7iMHWv+7+MnSiP8AskIDERpdSEw6AAAIABJREFUZweSRGjvPqQdfcK395Xxot+J328l9I3eXGnoR0j3HsSt/ra0QxKeAombt2BKTsZx2LDc1UHqChVwHjWK1IMHCe31BlcbN+Fqs+aEDXmT6AUfknroEAprPTp/f4xhYbl1Ce+UGRwMsozFA1Yy3U2SJDzmzkHl7MSN/gNI/eMP3GbMoMrOX9DWq0fixk2gVqPza/jQuZQ2NlhUrnzPcbWrK27TpmI4d47UQ4fEVrlC0DVpjKZKFVL370fp5IS2QYMCX2v13HPYdnqZhC1byE4w7/DIjo8nYcMG9C1bmn/XPAU0FStScf06KixejM0rnTGG3SR64UKuv/oqV/wbE/b2O8R/9x0xy5YhG43Y9+1T2iGXGpFkEoS7GLJymPTD32w6cYOUDCPnbiYWaz0mQRDKDsO5c0gaDZbPPlvaoZQbGm9vHIcMJmnrNuK++aa0w8lX2tGjyEbjPUv81a4uuIwbR9WDB3AJCCArKIiwYcPJSUoCIPX3P1Da22Pdvj1qDw/Sjx8vhegLJiMwEKt69ai4eRNqDw/C3hrx5K5oMiRCQkiZ3iqXvHsXak9PnEaMQDYaif74Y3JS00o7LKEcM2VlEf/dd2gbNcKqdu085xz690PfogWyLKNv0xrXyZPw/nY11Y4cNncTW70aj48+BJWK1AMH7pn7325YFtUKnmQCUNrZUWHRIiyqVcPz86XY9+qJ0sYG7xVfY9+nDw59+6LQFa1Ts+3LL+euwrESRb8LTJKk3PqSNh06FLpOkOOwYcgZGcTf3g4Z+/nnmAwGXMa9V+yxlmWSWo1Nh/a4T59OlZ2/UPXQITwWLsSmfTsyr10jav4CEjduQteiORaVKpV2uKVGFJkQhLv8fO4WG0+EsfFEGLN3BJJhNFHfp2wuzxcEoWgM585hWbMmkkZT2qGUK87vvosxIpKYRZ+gcnbGrmvX0g4pj5SDB1FYW6Ot93y+55V6PY5DBqNr0piQ7j2I+eILXN9/n7TDh9E1bYqkUKD18yN1/35kkwlJUbY+szOlp5MVEoLNSy+hdnPDe/Uqgrt149bYd6m09fsiP+QVmSxD7FVwrl6w8f/WYyqjneVyEhNJP3ESx8GDcR41Eu0L9bgxeAiGs2fL/TYSofQkb99OdnQ07vPm3XNOUqnw+urLB16vtLZG17ABKfsP4DJuXJ5zmVevgVqNxtu70HFpn3+eytt/zhuPRpO7ja84uE2bhmRlhXW7dsU259PAtksXMi5efKRmJhZVqmDdti0J69Zj3bIlCZu3YN/zdSyqVCmBSJ8calcXbDt3wrZzJwCMERGknz6N9oUXSjmy0lW23hUJQhmw8UQYVV30bBzaiCZVnXCzscSvskgyCUJ5IxuNZFy4ILbKlQBJocBj3lx0jf2JmDKV1D/+KO2QcskmE6mHDqFr2uShRU8tn3kGu9deI2HDRpK3bzcXOG1uriei9WtITlISmVeuPI6wCyXjn8sgy7kFcVVOTlRY+DFZoaFEzJxZ+tsYr+yGLxrAzb8KNj636Hf+ScHSlnK7iLx1u7bA7e23SiXpf50s3cCEYiNnZ5OTklKwsUYjGYGBJGzaRPikyebaSHfUa5FlmeRdu+8polyoeEwm4r5ZiUXNZ9E1afzI8+hbtSYrKIis69fzHL+7s1xZo7Szw2PuXNTu7qUdyhNFYWWF++zZaPLp7lcQjsOGYUpJ4caQN1FYWeE0cmQxR/jkU7u7Y/vyy3nqiT2NRJJJEO7wT2QyZ8MS6dXAC/8qjnzdvz7HJrfBSW9R2qEJglCMTOnpxK9Zi5yZmW/Rb6HoJI2GCkuWYFG9GjfHjMVw/vwDx8smE6GDBhVL6+0HybgYSE5MLNYF7IbjPGY0CisrIqZMBUlC17QpADo/P4AyuWUuIzAQAMua/20D1TXyw2nkOyT/vJ2kH34ordDMom7/v3Dxx4KNjzgLtl6gcyy5mIogZc9eVG5uWNaqBYBCp8PS15f0vwqYRCuE7JiYYp9TuD9TVhYJmzYT1L4D11q3wRidt6GBLMsYb90i+ddfiVrwIdd79+Fyg4aEdO9B5IyZpOzfT8L69UR/vCj3mrivV3Br7FhiPlvySDHJskzMp4vJCgnBcciQ+3ZqKwh9q1YApBw4mOd4ZlDQAzvLCU8nq1q+6Jo1w5SaiuPwYagcxIfwQv5Ekkl4KsiyzJkbCaRnPbjd5KYTYWiUCrrX83xMkQmC8DhlXb9O1Pz5XG3RkuiFC7GsXTs3aSAUP6Vej/dXX6FycCBs+FtkhYbed6zh3DnSjx4j6sMPc5MkJSH14EFzsqh58wKNVzk44PT228hGI5a1a6OyN9foU7u7o/b2LpPFvzMCA1E6OKBydc1z3Gn4cLT+jYicNZuMy6W4Aisu2Pxn4M/mrXMPE362zNZjMqWnk3b4MNYvvpjnYV9bvz4Z5/7GlJlZbPdK3rmTq82ak3as7CU2y6OMy1cIatuOyBkzUDo4YMrIIOazz3LP56Smcr3Hq1xr8yK33n2PhA0bwGTCvufreCz6mCp791D92FHse/cmfvVqEn/6iaTt24n59FNQq0k7fLjQqwplk4moufOIW7ECu9dfx6ZjwTqE3Y/GswIWNWqQun9/7jGTwYAxLKzAneWEp4tLwHjsXnsNh/79SzsUoQwTSSbhqXDgcjTdlv1Jvdl7GL72L348c5MkgzHPmAxjDj+euUU7X1ccdKI+iyCUF3JODin7D3DjzaEEdehI/PoN6Js3x2fDeipu2fzUtpd9XFTOznit+BpkmRtvDiU7NjbfcSm7diOp1Sjt7Qif+H6xPpwDmDIzSfrf/0jcuhWr557LTRYVhEOf3mj9G2H32qt5juv8GpJ+8mS+LcBLU8alS+ZaY3etcJCUSiosXIjCxppb776LKa2UClPHB4GkgKQbEH7mwWOTbprHl9HOcql/HEbOzMT6xRfzHNfWr2/eNnVHd8KiMGVm5q6GiVu9qljmFO7PlJlJ+PjxyKYcvFevouKWzTj07UvSDz9iuHgRWZaJnDaNjMuXcZk4kYpbt1Lj1F9U3LQR10mTsH35ZTSenkiShOuk99H6+RE5dRrhkz9A6+eHa0AAxvBwskJCChSPnJ2N4exZwgMmkLBuHQ6DBuE2c0ax1IPTt25F+unTuV3DskJCCt1ZTnh6WFavjvvsWSgsxC4P4f5Ekkl4KpwNS0IhwWsveHE2LJF3N5/jhdl76L/qBBuO3yAmJZPdFyNJMhjp1aDwRQ4FQSh7clJSiPvmG4Latefm22+TeeUKTqNHUe3Afios+hhtvXpF2mYgFJxFpUp4ffUl2bGx5m5td3XdkmWZ5N9+Q9ekCR5z55J59SoxSx5tK0l+krbv4Fqr1oRPfB+FTofLe+8W6npJo8Fn9WrsX3stz3Fd02aYUlJI3LKl2GIF89cjaccvGCMiCn2tKSuLzKtXc+sx3c1cn2khWSEhRM6aVTr1meKC4JmXQVLCpZ/zH5OdCYc/hS/8QKmB6mWzwG/K3r0o7ezQ1s9b5FX7Qj2QpGLbMpewbh3G8HB0TZqQduh3c4t5ocTELP6MzKtX8Zg3D52/P5Ik4TTiLZR2dkQv+JDETZtI3vkrzmPH4DhoIFa1fO9bv0hSq6mw+FNU7u5YVKqE59Il6Fubt6mlHT583xiywsJI2LSJm6NGc8W/Mdd7vUHyzp04jR6Fy4SAYvv9Zd26NeTkkHa7dl7mtWtA4TvLCYIg/Et0lxOeCoHhyVRy0jG7ay1mvuLL2ZuJ7L4Yya4LkUz+8Twf/HQevUaFl4MVjauUzZoPgiAUzq3x40k79DvaBg1wCRiPdZs2ZbaI6dPAqk4dPBd/Stjb73Br9Gi8vlye29Uv4++/yY6IwHrMaPTNm2PXsyfxq1Zj+/LL902WFFRWWBgRU6diUb26ObnYqFHxPZy1fRFdkyZEffgRWr9GWFQunnbFKXv3Ej5+PCpXV7xXrSxU957MK1chOztPPaa76Ro1wumdd4j9/HO0Df2w69E9z/kS7ZiXkQTpsVChPmSmmLfMtZkO/35PZBmu7ILdkyE+GGq8BO3mgGPZ62Bkyswk9eBBrNu2RVLlfUuttLXFonp10k/+BSPMx9JPnkTt7Y36rm2MD5OdkEDsl1+ha9Ecj3nzuNaqNfFr1uA+Y0YxvRLhTmnHjhP/7bfYvdELfbNmuceVNjY4jx5F5MxZpJ86ha55MxyHDCnQnCp7eyr/7yckpRJJo0FpY4PGx4fUw4dztx2ZV93uJ+3IEdKO/IkxLMx8rYc7Nh3ao2vcGG2jRoVahVkQlr6+qJydSdi4CesXXyxSZzlBEAQQK5mEp8SliGRqetgCoFBI1PO2Z1LHZzk4viW7xjZjTJtqVHbRM7p1NRQKsbJBEJ50GVeukHbod5xGjcRn7RpsOnQQCaYyQN+iBe6zZ5P255+EfzAF2WQCIHnXblCrzZ+oA85jx4Ask3rkSJHuJ8sykTNmIimVeC75LHdFQnGRFArc581DYWFB+IQJyEbjwy96CFNWFtELP0bt442ck0No334YLlws8PUZgeaxD0vOOY14C22jRkTOnk3m1au5x2OWfs61Vq1zt84Uu7gg85+OVeDZV8xb4aJv1+CKuQzrusPGXqBQQ99t8MbGMplgkrOzCR8/HlNKCravvJLvGG39+qSfPYtsNJK0fTuh/foT1PElYr/6GlNWVoHvFbt8Oaa0NFzHj0fl6IhN504k/fS/kvsePcWy4+MJnzQJjbc3rgEB95y3e+01LKpXR+XsjMeHHxYqGauwsspNrAPomjUj/cTJ3K3B0Z98wq1Ro0n+eTsW1arhOmUKlX/dSdV9+3CfPRubjh2LPcEE5p9jzuPew3DuHKEDBpJ++nSZ7iwnCELZJ5JMQrmXlG7kVqKBmu4295yTJIln3GwY+2J1/vdOE16r71UKEQqCUNzi16xBsrTEvnfv0g5FuItd9244jx1L8vbtRC9ahCzLpOzeja6xP0ob889plb09ak9PMi4WrQB48vbtpB05gvN775ZYO2G1qwtus2aRceEC0YsXF3n7WcLadRhv3MBtylQqrl+HwsqKGwMGkH7yZIGuz7h0CYW1NWqvB/8+M9dn+giFXs/Nse9iSk8nbuUqYr/4guyoKNLvKmj+b0KwyOJvb/NyqALPdAIkOLMOdk2C5Y3h5iloPx9GHIGqLz5wquKSk5hIysGDmAyGAo2XTSYipkwlZc9eXCdPQtfIL99x2gb1kdPTiV+/nojJH2BV/wX0TRoT8+mnBHfqTMr+Aw/9/yUnMZGEjZuw69Edi2rVAHDoPwA5I4PELd8X7oUKD2QyGAh7awQ58fF4fLwQhVZ7zxhJpcJn/Toq//y/Iid8dE2bIGdkkP7XX2QGBxP/3Rpsu3Sh+vFjeC37Aoe+fbCoVOmxbOu269oVz8+Xknn1KoZTp0RnOUEQikQkmYRy71JkMgDPuovivoLwNMiOiyP55+3Ydu1SIp/6CkXnOHyYuePSylVEfDAFY3g4Nu075Blj6etLxsWCr+C5W3ZCAlHzF2BVty72vXoVNeQHsmnfDttXexC/chU3BgzMrWlSWNnx8cQuX46uRXP0zZqi8fHBZ8N6VK6u3HhzKKmHDj10jozAQCyfeaZAD6YqZ2cqLPyIrOBgQvv2I3rhQqzbt0eh05F2/FiesTcGDiKkZ0+M4eGP9Npy/buSyaESWLuCtz8cWwbHlsPzfWH0afB/G5QPXkUhZ2cXOCl0P4k//sT13n240rgJN98aQcySpQ+9RpZlouYvIOmnn3AaNfKBHZa0L5jrNEUv+BC1hweeS5fiuXQpXiu/QVKrufn224QNG05m8P2LP6cdPwFGI7bd/tvSaFmjOrrG/sStXEnk7Dmk7NtHTkpKIV65cDc5J4db4wPIOH+eCos+xqp27fuOVVpbo7S1LfI9dQ0bIqnVpB0+QtS8+SgsLc21lkppBZF169b4fLsalZsb+iZNSiUGQRDKB5FkEsq9wHBzkqmmx70rmQRBKH8SNm5CzsrCof+A0g5FuA9JknD9YDLW7dqR9MMPoFJhfbsQ7r8sa9bEGBZGTlJS7rG0EyeI/uTT+3aou1Ps0qXkpKTgNnsWklJZ7K/hbu4zZ+I2YwYZly8T3LUb0YsWYUpPL9C1JoOBtKNHiZj8ASaDAdcJE3LPqd3c8Fm3FosqVQh7ZyRJv/yS7xyyLJMVFkbmP5cLVcdK5++P09tvkxEYiK5ZMyos/Mi8zevof0mmrLAw0k+cIOPc34T0eJW0Y8cLPP894oOQrSuQnXz7a9P0XfO2ueGHoPNnoHPKMzz99BnCRo4kMygoz/GI6dMJfqXLI3f2S9y2jYhJkzClpuL01nD0LVqQsHkzOYmJD7wu9vMvSFi7FocB/XF6++0HjlU5O6OpXBmlrS1eX32Zm/TWN2lC5Z9+xOX9iRjOnCG4SxeiFi4kJzX1njnSjh1FodViVbtWnuOukydjVbcuiT/8wM13RnLFrxEhPXsSvXgxacdPFGo73tNOlmWi5s4jdd8+XD/44J5OgSVFodVi9cILJG7ZQtrhwziPGonKsXTrglo99xxVD+zHtkePUo1DEIQnm0gyCeVeYEQyTnoLXKwtSzsUQRBKmCkzk4QNG9C3aFFsRZiFkiEplXgs/Ah9q1bYdu2C0s4uz3lLX1/AvP3rX7Gff0Hc118T1K49McuW3TeJk3XjBglbvsfutVexrF695F7EHSSlEvtePamy8xdsO3cmbsU3BHXqRMrevQ/cEhX75ZdcbujHjUGDSf39d5xGjLin0LfKwQHv775F+9xzhI8PIGHzf93sclLTiJw3j2tt2hDUth1yZiZWd3U6exint0fg9dWXeC5dgqTRoG3UiKzr1zFGRgKQsmcvAF4rVqB0cODGkCGk7N1bqHvkigsiLcmNq02bmZNV1dtBz7XgXjfPMJPBQNT8+YT26UPq3n1EzpiZ+3XMuHyZpB9+xBgWhuH06UKHkHr4CBHTpqNr2pRK27biPHo0zuPey93adt/Qv/2W2C++wLZ7d1wmTizQarEKn36Kz8YNaCpWzHNcUqtxHDiQKrt+xbZzZ+JXriKoY8d7vq7pR4+hbdDgntUtFlWr4r3ia2ocP4bP2jU4vTUcSVIQt+IbbgwYwJWGftwYOoysmzcL/oV5SsWvWkXChg04DB6MQ98+j/Xe+qZNMKWloalSpcxs75YkSXReFQShSESSSSj3LkUki61ygvAUMIaHE/7+++TEx+MwaGBphyMUgMLCAq/ly/CYM+eec5a+5tU4/26Zy0lNI/30aWw6d0bXpAmxS5YS1L4DCd9/f89qlpglS5FUKpxGjCj5F3EXlaMjHvPn4bN+HUqdnpsjR3HzrRFk3e4UdSfD2bPEfLYEfZMmeH31JdVPHMd55Dv5zqu0tsbrmxXomjcjcvp0YleswHD+AiE9upOwbj1WtWrjOm0qlX/ZgU3btoWKWVIq0bdogcLS/GHMvzWG0o+bVyyl7N2LxbPPom/WlIqbN2NRozqRc+c92na1+CDSY61AlolasCDflUjpJ08S3KUr8d+twf6NXrgEBJB+8iQpe/YA5gLJCmtrJEtLkn/dVajbG86f59aYMVhUrUqFxZ/mJm8sq1dH36oVCWvX5Zu8TNy2jegFH2Ldrh3us2YWuOCzZY3qWFSufN/zKicnPObNpeKWzagcnbgVMCF3RZMxMpKs69fR+je67/WSRoO2QQOcR4+m4qaNVD9+DM9ly7B77TXS/viD5J2/FijOp1XSjl+IXvgxNi91xGX8uMd+f+u2bVE6OuI2daootC0IQrkhkkxCuZaVbeJqVKrYKicI5ZicnU3UwoUEdehI6r79OL41HK1f/oV4hSeHyt4etYdHbpIp/fgxyM7G7tVX8Vy6BJ8N61FXqEDk1GmEdO1K6qFDyLJMxqVLJO/YgUP//qhdXEotfu0LL1Dph224TJxoTpp06mxefXV7G5MpM5PwD6agcnPD4+OF6Fu0QKnXP3BOhaUlXkuXYvPSS8Qs+oTrPXsiZ2bh8923eC75DIfeve9ZBfUoLGrUQGlnR9rRY2THxmI4cwbrF9sAoNTrcJs8meyICOK//Tb/CcJOQPiZe4+nx4MhgYxoI5JGQ+Y//5D044+5p01paUTOnkNov/4gy3h/9x1u06bhMKA/FtWqEf3RQlKPHCHt0O84Dn0TfcuWJP/2W4G2zGVcvsLNd9/l+us9UVhb4/XVl/d8vR2HDSUnMZHE7/MW1E7etZuIqdPQNWmCx8cLkVSqh96vsKzq1MF91kxkg4HkHTsASLu9ZVHn71/geZR6PdatW+H2wWTUXl5FqmtW3qWdOEHEpElo69fHff78QnWKKy4aHx+qHzl83+LxgiCUPdmm7NIOocwr/t+SglCGBMWkkpVjyreznCAI5UPSjh3Er1yFbZdXcB4zBrWHR2mHJBQTS9+auR3mUv/4A4VWi/b55wDQ1quHz8YNpPy2h+hPFhE2/C20jRohG40obG1xfHNIaYYO3N4SNWggNi91JGrBAmKXLCX5fz/jOm0q6SdOkhUUhNeKrx+aXMozp0aDx8KPULm5kRMXh+uk9+/ZaljkuBUKtH5+pB0/TsrefSDLWL/43+oobf36WLdrR+zXK7Dt3gO16+1kXtRF2DsTru4Gmwrw7kW4c9tNfDCyDBlhCdh06kRWcDDRn32GdYeOZFw4T8SUqRhv3cK+Xz9c3h2b291LUqlweX8iYUPe5ObIUahcXHDo25fUQ7+TsmsX6Sf/uu9DekZgILHLl5OyZy8KnQ7HYcNwGDgg36YA2uefR9ugAXGrVqNt1AiL6tVJO3yEWwEBWNWti+fSJSjuaEFf3Cxr18bimWdI2LwFu549ST92FKW9fW5XuULP5+tLxoULxRxl+ZB57Ro3R45C7eWF5+dLUVhYlHZIgiA8AXaF7GLKkSno1Dp8bHzu+cfb2htLlSjRIpJMQrl2KeJ20W+RZBKEcitp2w+ofbxxX7BA1JEoZyx9fUnZs5eclBTS/jiM1t8f6Y6HfEmSsGnfDutWLUnYvIXYL74gJzERl/HjUNqUnZ/7aldXPD/9lNQerxI1ezZhQ94EScK2Wzf0zZoVej5JqcR1QkAJRPofXSM/UnbvJn7NGtTe3lhUz5vocAkYT+qBA8QsXozH+2/DgXlwdgNY2ECVNhC0DxJCwOGOrWJxQWSnK8hJSsXStyb2PV/nes9ehPbpQ+bly+ZueuvW5nZlu5O+SRP0rVqReuAATpPeR2Flhb5FcyStluRff70nyWQ4f57YZctJPXAAhbU1Tu+8g0P/fg/tCuY0aiQ3Bg8hpEtX1J6eZMfFYVGlCl5ffZlvS/viJEkS9j1fJ3LmLDIuXCDt6DG0jfweeYWNpW9NUnbtIjsh4anstJmydy9Wzz2HyilvMXljVDQ3hg1DstDg9fXXxZ6kFQThyXI+5jynok4xwHfAA99HhiSFMP3P6VS2rUxNx5qEJody5NYRfrr2U55x7jp3OlTqwPA6w9GpdSUdfpkkkkxCuRYYnoyFSkElp6fzL7gglCWGc+dIO3ECxzffLLZkUFZoKOknT+L87rsiwVQO/Vv8O/mXnRhv3cJx6NB8x0kaDQ79+mLbtQtphw8/tu5QhaVv2gTt9p+JX7mS9JMncX1/YmmHdF9aP3MdoKzgYByGDL7n75fGywv73q8Tv2Y99hmrsXLIBv93oNk4SI2GZX5w/QjRq3/C0tcXmw7tIT6IjETzihErX1+s6tbF5pXOJG/fgcPAgTiPGY3Cyuq+MbnNnEGKvz923bsDoLCywrplS1J++w23qVOQVCrSz5whdvly0n7/A4WtLc5jRmPfty9K64LVZtQ1bEi1A/tJOXCAlH37UDk747l0yWNLWtp06kTURwuJ/vAjsqOj0TUq+Fa5u1nVMnekywgMfOpa0hvDw7k5chT61q3xWvZF7vGc1DTC3nqLnMQkfNauQeNZoRSjFAShtH1/5XvmHZ9Htimbhu4NqemYf3dWQ7aBcYfGYaG0YEnrJbjp3HLPpRnTCE0O5UbyDa4nX+dS3CVWX1jNL0G/MK7+ODpW6vjUvUcVSSahXAuMSOYZN2tUSlF+TBBKU05KCjdHjyE7KgqLKlXvaVf/qBJ//BEUCmy7dimW+YSyxbKm+c1e3IoVAOiaNn3geKW1NTYdO5Z4XEWh0GjMBclLoSh5YWgqVUTl4kJ2dPS9STtjBpz4GifjCpI0FkT/44P3hs1I9j7m81b2oHUi+8IB4lYcxeKZZ8xJprggMtIdQCFhUaMGAO5z5uA8ekyBHvbVLi449O+X55h1xw4k79xJ3KrVpB87Stqf5i1mzuPew/6N3ij1hf+QSeXsjP3rr2P/+uuFvraolNbW2LzUkaRtPwCge0DR74f59+9PxsWylWTKSU0l9vMvsHv99RLrApr6x2Hzn/v3k376NNp69QCInDmTzCtX8Fq+DKvbSWxBEJ4+WTlZzDs+j21Xt+Hn7sfJyJMcCDtw3yTTghMLuJpwleUvLs+TYALQqXXUdKyZ59pzMeeYe2wuE/+YSKoxlddrPP7fJ6WpRJ+8JUnqIEnSZUmSrkmS9H4+5y0kSdp8+/xxSZIq3j7eVpKkU5Iknb/9Z+uSjFMon2RZvt1ZruxsmRCEp1X0x4vIjolB5eJC9KJFyNlFL5oo5+SQ9ONP6Jo1Re3qWgxRCmWNytERlZsbxlu30FSuLFYdPEaSJKFv0QJ1hQpY1a3734kcI3zdEvZMRVm5Ac5vDyc9JImUk5fvvBh8GpN61NydLvOff8i6ccO8kilJi0WVKrkrlhQaTZG+r/pmzVBotcR88gkZV67iMmECVfftxWno0EdKMJUF/ya31B4eqL28Hnkepa2tufj3HXWZTGlpZN28WeQYH5UpK4ubI0cR/+23hE+YUKCi7Y8i7fAfqFxdUTo7Eb3oE2RZJnnXLpK3b8dpxAj0zZuXyH0FQSixKV0KAAAgAElEQVT7otOjGbR7ENuubuPN2m/y1Ytf8Zzzcxy4cSDf8UGJQfxw9QcG1RpE0woP/rDrX3Wd67Lx5Y3MajyLTpU7FWf4T4QSSzJJkqQEvgA6AjWBNyRJujs1OARIkGW5KvAp8OHt47FAZ1mWawMDgLUlFadQft1MMJCQbsRXdJYTnlKyLJd2CIC5g0/i5s04DBiA65QPyAoKIvGHH4o+75EjZEdFYde9RzFEKZRV/26Z0zcr2Bs7ofi4Tp5Exa3f560JFHsVYi5B+/nQdyt2Q8ZgUa0q0Qs/zu2cB4BPE9KC0lDozHWMUvbsgbhgDFHZuStsioPC0hK32bNwnTqFqnt+w3HwoBKvnVTSLOvUQevfCJvOnYu8xcKylm+eDnORc+YS/EoXsmNj84xL3LqVzKCgIt3rYeScHMIDJpB+7Bi2XbqQceEC8WuL/y2+bDSS9udR9M2b4/zOOxhOnSJx61YiZ8zEslYtnIYPK/Z7CoLwZDgTfYaeO3pyNeEqi1osYky9MSgVSlp5teJywmXCU8Pvuea30N+QkOj3bL98Zrw/pUJJt2rd0Kqf7N9Jj6IkVzI1BK7Jshwsy3IWsAm4ez9DF+C72/++FWgjSZIky/IZWZb//Q5fBCwlSRJtH4RCORocB4BfZcdSjkQQHj85J4cbgwdza3zJFgd+GFNGBhFTp6L28sJ59Cis27bF6rnniF36Oab09CLNnbjtB5T29li3alk8wQplkmXNZwHQNS18gWyhaBRWVvcWjI65ZP6zkvn7IalUuEx8H2NYGAlr1+UOkz0bkRphgXWD6ljUfJaUX3dgTEwhJyUzN3FYXGxffhmHPn0eWM/pSSJJEj6rV+Py7tgiz2Xl64vx1i2yExLIjo0leccO5PR04lZ8kzsm9dAhIqZMJWzoMHKSk4t8z/zIskzU3Hmk7N6Ny4QJuC+Yj75lS2I+W0LWzVvFeq/0M2cwpaWha94Mux49UPt4Ezl1GiaDAY+PPkRSq4v1foIglH2yLLP5n80M3jUYnVrHhpc20K5iu9zzLb1aAnAg7N7VTHtC9/C8y/M4a50fV7hPvJJMMlUAwu7475u3j+U7RpblbCAJuDsj0AM4I8tyZgnFKZRTR4PicNJrqOZS8NbQglBexK1cRfrRYyT/+ivZ8fGlEoNsMhE+aRLG0Bu4z56FwsoKSZJwmRBAdkwMscuXP/Jqq+yEBFL278f2lc55uo0J5Y/tK69g37s3Or+GpR2KABB9CSQlOP7XbU7ftAm6Fs2JXb489+eNITIbk1GBvkI2Nm3bYrjwD6mR5uLblr7Ft5JJeLB/E3oZFwNJ2LIF2WhE6+dHwqZNGKOikbOyiJo337wtNTqaiGnTS2QVbOzy5SRs2IDDkME4Dh6EJEm4TZ+GJElEzphRrPdM++MwqFToGjVCUqtxefddAFzGj8eicuWHXC0IQnmTmZPJ9D+nM+f4HBpXaMyGlzdQ1b5qnjEVbStSybYSB8MO5jl+Pek6VxOu0tan7WOM+MlXkkmm/Nb33v0b5IFjJEnyxbyFbni+N5CkYZIk/SVJ0l8xMTGPHKhQ/siyzNGgOPwqOz511fwFIePSJWKWLsWybh3IySFl9+5SiSN60SJSft2FS0AAukb/Fa/V1quHzSudiVvxDeHjxpGTklLouVN27wajEduuXYszZKEM0nh54TZtqkgmlhXRl8CxCqgt8xx2nTABk8FAzJIlwO3CyxLoLK5gXctcMy32qgtIEpbPPPPYw35a/bs10XDuLIkbN6Fr1gz3ObORc3KIW7GC+LVryQoNxX3WTJxHjyZl1y4Sv/++WGNI2LyF2CVLse3SBZdx43KPq93dcX73XdIOHyZx69Ziu1/q4cNon3sut6OgTYcOVN2/D4d+fYvtHoIgPBki0yIZ+OtAfrz2I8PrDGdp66XYaPIvpdLKqxV/Rf5FctZ/Kzr33tgLwIs+ZbNrbVlVkkmmm8Cd1Qo9gbs3OeaOkSRJBdgC8bf/2xP4Eegvy3K+m8RlWf5aluX6sizXd3YWy9eE/1yPSycyOQN/sVVOeMqYMjMJnzARlZ0dXl9+iaZqFZJ2/PLY44hfv574lauw790bh8GD7jnvMX8+zmPHkrz7N0K6diPz2rVCzZ/083Y0VatgIR5WBeHxig4El2fvOWxRpQr2vXqRuOV7Mq5cIfX339FW80CZfh3NmblobGWy49PQVK6MQvdkFuR+EiltbVF7exP/3RqyY2Jw6NcXjZcXdt26kbh5M7HLlqNv0QJ98+Y4vjkEXWN/oubNL/TP5PtJ3rOHyJkz0bVojvuc2XnrewH2fXqj9W9kvmdwSJHvZ4yOJvPSJXTN8m6vVXt4FHluQRCeLH9F/kXPHT0JSQ5hcavFjHx+JArp/umPVl6tyJazOXLrSO6xPaF7qONU556OcsKDlWSS6SRQTZKkSpIkaYBewM93jfkZc2FvgFeB/bIsy5Ik2QG/AJNkWT6CIBTS0SBzPabGVUSSSXh6yNnZRE6bRubVq7jPnYPK3h7bTp0wnDqF8Vbx1rx4kJT9+4maOw99q1a4fjA539WEklKJ01vD8Vm3lpyUFGK//KrA82fdvInh9GlsO78iVioKwuOUlQ7xIeCS/3Y3p5HvoNDrifhgivlBv4W5g5cUfw3rVuaH/uIs+i0UjFUtX0zJyWh8fNA1NRfQd3prODLmbm+uk8wNoCWFAvcFC1Botdx69z1MGRlFum/aiROEjxuPVe3aeH76ab61kCSFAo8FC1BoNIQHBCDfWTwekLOyuN63L8FdunJr3Hhily8nec8eMoND8u1SmnbkTwD0zUUNN0F4Euy/sZ/jEccLdU1Megwr/l5BYFzgfcf8GvIrQ38bio3Ghg0vbaCNd5uHzlvbqTYOlg7sDN5JjimHmyk3CYwLFFvlHoGqpCaWZTlbkqSRwG5ACaySZfmiJEmzgL9kWf4ZWAmslSTpGuYVTL1uXz4SqApMlSRp6u1j7WRZji6peIXy5c+gWFxtLKjkJD4tFZ4OpqwswseNJ2XPHpzHjM5tz2zz8svELP6MpJ07cRo6tMTjMJw/z633xmHp60uFRR8jKZUPHK99/nn0zZuTduwYsiwXKGmUvGMHALadXi6WmAVBKKDYy4Cc70omAJW9PU5vjyB6gblZsP7l1+GnleBQGetGo4j76TBWtYq36LfwcJa+viTv/BX7Pn1yVxKpK1TAfcYMJLUKTcWKuWPVLi54fLiAsKHDiPrwQ9ynT3+ke2b88w83334HtZcXnl8uf2DHP7WrK+5z53Bz5ChilizBZfz43HMJm7dg+OsU2gYNMJw5Q/Iv/63MldRqNJUqYVG1CipXN+TsbNKPH0fp7CRWuQrCEyDblM3E3yeSkZNB58qdCWgQgL2l/X3HhySF8N3F7/g56GeMJiMb/tnAlk5b7inInZiRyJxjc/B18mX5i8ux1lgXKB6lQkm3qt1YeWElfXf2pYZDDUBslXsUJZZkApBleSew865j0+749wzgtXyumwPMKcnYhPJLlmWOBcfTtKqoxySUf6bMTDL/+YeYJUtJO3IE18mTcOjfP/e8xssLq7p1Sd7xS4knmbLCwgh7awQqJye8li8rcBtxnb8/yTt2kHn1KpbVqz9wrCzLJP28HW39+qgr3N1LQhCEEhV9u7PcfVYyATj07k3ixk2YsrKwqPEM9P8f6F2xsvHAc/kydH5+jylY4V82HTqQGRyMbffueY7b9eie73h9s2Y4DBpE/OrV6Pz9sWnXLt9x92O8dYsbQ4ei0Ovx/mbFvR0K82H94ovY9exJ3Dcr0TZsiL55c3JSU4ldtgytnx/e365GkiRMaWlkBgeTeS2IrKBrZF69huHv82THHURSqZBUKnMyTbz/E4QiuRx/mVlHZ2FjYcPr1V+nmWczVIriTR1cT7pORk4Gfm5+/BryK0fCjzDNf9o9q47ORp9l9YXVHAg7gEapoXu17jT3bM74Q+MZd2gcK9uvRK34b6Xk52c/J82YxnT/6QVOMP1rTL0xVLOvxsKTC7kQd4FnHZ7F09qzWF7v06REk0yCUBquRacSm5qJv9gqJ5QzcnY2mUHBZFw4j+Hv82ScP0/G1atgNIJSifvcOdj16HHPdTadOhE1dy4ZV648NIlz33tnZZGybx/6li3zbROenZBA2LDhkJ2N19dfo3JyKvDcOn9zUfD0o0cfGl/GxUCygoNxGDjggeMEQSgB0YGgtAD7SvcdImk0eH2zApPBYH7Q93g+95x1q1aPI0rhLuoKFfCYO7dQ17i8O5b0kyeJmDIVq1q1ClzTSJZlwqdMQTZk4LNpI2p39wLf03XS+xjOnSM8YAKVfthG4rZt5CQk4DJ+XG7SSKHTYVW7Nla1axfq9QjC06Kgq8If5LfrvzHlyBR0ah0RaRGMPjAaN50bS1sv5RmH4lslGBhv3u42yW8SOXIOUw5PYeyBsfSs0ZNx9cdxPOI4qy+s5nT0aWw0NgyrM4w3nnkDRyvzM94M/xlM/GMin/z1CRMbTgTgn/h/+P7K97zxzBtUs69233vfjyRJvFz5ZZp5NmPNxTW84PpCsb3ep4lIMgnlztHgf+sxFfwhVxDKoqwbN3KTSYYLF8gIDEQ2GABQ6PVY1qqF48CBWNauhVXd51C7uuQ7j81LHYlasICkH3/CcuKER4ol+rPPiF+5CosaNfBcugSNt3fuOVNmJjffGYnx1i28V6/CovL9H0Dzo/bwQOPjQ9qfR3EY8ODkUdKPPyKp1di0b/9Ir0MQhCKIvgTO1UH54LePGi+vB54Xyj5Jo6HCoo8J6dadW+MD8FnzHZLq4Y8NyTt3kn70GK7TpmJRtepDx99JYWmJ52eLCenxKmGjRpEVch3rjh1EQkkQCmjJ6SV8e/FbajvVpr5bfbpU6YK3zX/v1wzZBmYfnc2gWoPyTcCYZBNfnP2Cr//+mjrOdVjccjF2lnb8HvY7807MY/yh8WzutBmdunjKkVyKu4SVyoqKNhVRKpSsf2k9i08vZk3gGn4O+hlDtgF3nTsTG0yke7XuaNV5V8i/VPklzseeZ92ldQQlBtG1alc2X96MrcaWEXVHFCk2G40NI58fWaQ5nmYiySSUO39ei6OCnRVeDgXbqiMIZVHsl18Rs3gxAJKFBZbPPovdq69iVbsWlrVqo6noc0+XnvtROTpi0749iVu24DTiLZQ2+bduvZ/006eJX7UaXZMmZFy4QEiPV3GbPh1dk8YobW0Jn/g+htOnqfDpJ2hfeLRPfLSN/Un+38/IRmO+xWGzExKIXrCApP/9jE3nzihtbR/pPoIgFEH0P+DTuLSjEB4TjY8PbjNnEB4wgdhly3AePfqB43NSUohasABLX1/se/Z85Hu6z5vLrdFjQKXCZcyYR5pHEJ42u67vYsX5FTRwa4DBaGDl+ZXsCNrBrz1+ze2otjd0L9uDtxOSFMK6l9ahVPxXNzM1K5VJhydxMOwg3ap2Y0qjKWiUGgDa+LTB1sKWIb8NYe6xucxrNq9YYg6MC6SGfY3cONRKNQENAmjk3oifrv1EK+9WtK/YPs9WuLu9V/89bCxs+PHqj0z8w7yaabr/dGwtxPvE0iSSTEK5IssyJ67H06pG/is6BOFJkBkcQswXX6Bv3RrnUSOxqFo138RLYTgOfZPknTtJ2LARp7eGF/g6U3o64e9PQl2hAhU++4ycxERujR5N+O3CrAq9HlNqKi4BAdh07PjI8en8/UncuAnD+fNo69XLPS7LMim7dhE5ew45yck4vT0Cx7feeuT7CILwiDKSIPnmfYt+C+WTbefOpB35k9jlX6L1a4TOr+F9x8YsXUpObBxey5Y9tOnDg9i0a0f21ClISmWeouSCIOTvcvxlph2ZxnPOz/HVi1+hVqrZFbKLgN8DOB5xHH8PfwB2BO9Ao9BwIe4C265u4/UarwMQmhzK6P2jCU0OZVLDSbzxzBv3bLmr71aft+q8xbJzy2jk0YhXqrxSpJhNsolL8ZfoWrXrPeeaeTajmWfBOkSqFWpG1B3B8DrDORl5kmuJ1+hWtVuRYhOKTiSZhHLlelw68WlZNKj48CKTglAWybJM5KxZKCwtcZ81s1C1jR7E8tln0TVrRvyaNTgMHIDC0vKB400GA1k3wohfvRpjWBje332LUq9Dqdfhs2kj6cePmwuvBgdhUa0a9v36FSk+XcOGIEmk/Xk0N8lkjIomctYsUvftw7JWLbxXr8KyRo0i3UcQhEcU/Y/5zwcU/RbKJ7epUzCcPUt4QACV/vdTvoW8DecvkLBuPXa9ehbL9jaHPn2KPIcgPA2SMpMYc2AM1mprPmn5CWql+UPJVt6tsNHY8OO1H/H38CcmPYZjEccYUmsI52LO8dnpz2jj3YZ/4v8h4PcAlJKSr9t+TUP3+yeSh9UZxonIE8w5Noca9jVyu689itDkUAzZBmo6Fs/vFIWkwM/dDz930VyiLBBJJqFcORWaAMALPiLJJDyZknfsIP3YMdymTyu2BNO/nIYNJbRffxK3bcvzBt6UlkZGYCCGv89jOH+ejL//xhgennveYdAgcxLoNoVGg75ZM/TNCvYpU0Eo7eyw9PUl7ehRnN55m8StW4n+aCGy0YjLhAk49O9XoHoggiCUkGhzgVaxkunpo9DpqPDJIq737EXE5A/wXPZFnlUOpvR0wgMCULm44DJ2bClGKghPlxxTDhN+n0B0ejSrO6zGWeuce85CacFLlV7ih6s/kJSZxM6QnZhkE52qdKJT5U70+LkHw/cM52riVaraVeWzVp89tIuaUqHkw+Yf8saONxi1fxQbXt6Ak9WjvVcNjDP/TnnWQfxOKY/EO3ahXDkVmoCNpYoqzvrSDkUQCi0nOZmoBR9iWacOdq+/XuzzW9Wvj9VzzxG/chWSSo3h/N9k/H2ezGvXwGQCQO3pidVzdbF7/TU03t6ofXywrPl4Vi7o/P2JW72aGwMGkn7iBNqGDXGfPQuNj89jub8gCA8QfQk0erAVRb2fRpY1a+ISMJ6oefNJWLceh359c89FffgRWaGheK9eLerlCcJjtOTMEv4M/5MZ/jOo61z3nvPdqnVj0+VN7ArZxY7gHfg6+lLZtjIA/X37s+rCKtr6tGVOkzn3FNW+HxetC0vaLGHgrwMZe2AsK9uvxEJpUejYL8VdwkJpQRW7KoW+Vij7RJJJKFdOhyZQz8cehaJorTsFoTQk/bydnLg4vL5cXqR6FvcjSRKOw4dxc8TbRE6fbl49VKc21m3bYlWnNpa1a6NycCj2+xaUrrE/cStWkBEYiNusmdi99lqR2/AKglAMUiIh9Ag4PwMFbDgglD/2/fqR9udRoj/6CLW7G5bPPoshMJDEzZtxGDIYXSOxTUUQHpdd13ex6sIqXqv+Gj2q98h3zLMOz1Ldvjorzq8gKj2K9xu+n3tu1POjaOHZguddni/0ey1fR1/mNp3LuEPjmHNsDrObzC50/JfiL1HdvjoqhUhHlEfiuyqUG0kGI1eiU+hUx720QxGER5K8YwcWNWqUaLtmfcuW+Kxbi8rVFbWnZ5lK4mj9/HBfMB+dvz9qV9fSDkcQhNQYODgPzqwHkxFeWljaEQmlSJIk3OfPI6Rbd26OHJV73KLmsziLLnCCUGxkWSbNmIZek//OjCsJV3ILfU9qOOm+80iSRLeq3fjw5IcoJSUdKnbIPadSqKjnWu++1z5Mu4rtGBQ3iNUXVjOg5gCq2lct8LWyLHMp7hIdKz16wxihbBMfRwnlxpkbCciyqMcklH2y0Uj8d9+RdvRo7rGssDAMZ89i0+nlEr23JElo69dH4+VVphJMAJJCgV3XriLBJAhlxW9T4PRaqNsLRp2CBm+WdkRCKVPZ21P5fz/htfIb3GbNxHnsWLyWLUOh0ZR2aIJQLmT9n737Dm+q/AI4/k3SvUs33YMyWqCUTdl7CKgMFdmCskFFcYMiCvITGYqKA0HZQzayd9kFyiylLV3QSfdMk/v74zKsFCgdpMD7eZ4+Kcm97z23JjH35Lzn1RTywaEPCFoZxOQDk7mYcrHY4xkFGUzcOxEzfbNijb4fpIdXD/SUerSo3gIbY5sKjXW433CM9YxZfHHxY+0XlxVHljqrwpp+C1WPqGQSnhkh0WkoFVDf1UrXoQjCAxVGRxP/3vvkh4aiqlYN7507UJmZkbl1GwCW3bvrOEJBEITbYoKhVg/oNV/XkQhViMrSErOgIF2HIQjPnKzCLN7e9zbHE47Tyb0TR+KPsOP6Dho7Nmao31BaVG/BlINTSMhNYHGX4o2+H8TayJqFHRbial7x/fSsjKzoU6MPK6+sZFzAOJzMSjeb5NKt202/bUTT72eVqGQSnhmnY9Ko7WSBqaHInQpVjyRJpK9dS+RLL1MYHY3dxAlobt0iddEvSJJExpbNGDdqiL6zs65DFQRBgKxESI8Bl8a6jkQQBOGZl1GQwdB/hnI68TRftfyKOW3nsKvvLiY3mkxMZgxj94yl45qOHLlxhI+bfkyAfUCpx25evfkjV44rq8F1BgOw9NLSR24rSRI3s29yMO4geko9aljVqJSYBN0TV+PCM6FIo+VsTDp9GlbOG6gglEdRWhoJn00la9cuTJo2pfqsmeg7OlIQFcWtJUswDgig8FoEjtOm6jpUQRCeR5c3w5F50Hsh2PnK98Wfkm9FkkkQBKHSzQ2ZS0R6BD90+IEgZ7lS0MzAjCF+QxhQewD/RP3D8svLecHrBfr69tVxtPc4mTnR3as768LX8Va9t7A0tCStII3YrFhiMmOIy4ojJiuG2KxYojKiyCzMBKChQ8NHTvUTnl4iySQ8E8ISs8gp1Ih+TEKVkxMczI0PPqQoLQ379yZTbdgwFLdXZ7KfNImsHTuJf/dd0NPDvEsXHUcrCMJzpSALtn8AZ/+S/312GXT6XP497iQo9cGpnu7iEwRBeA6cTTrL2qtrGVxn8N0E07/pK/Xp6d2Tnt49dRDdow3zG8amiE3039KfrMIsstXZdx9ToMDB1AFXc1c6uXeiVrVad3+EZ5dIMgnPhJDoNAAC3USSSagatIWFJM/5jlt//IGBtzeeP/2IUZ3iDQ71q1en2pAhpC5ahFmbNuhZi+evIAhPSPRR+PstyIiFVpMh5ihc/edekin2JDjWBX1j3cYpCILwDFNr1Xxx7AscTBwYGzBW1+GUiY+1DyPqjuDyrcu4mbvhau5699bZ3BlDlaGuQxSeMJFkEp4JJ6+nYW9uiIu1+DAs6F5eaCg3P/2MgrAwrAe8hv1776E0Lvm5afPmSPLOnqXakMFPOEpBEJ5LRYWw/2s4Mhes3GDYdnBrBkcXwo4P4VYUWLrCjRBoMEjX0QqCIDzT/rr0F+Fp4cxtNxcTfRNdh1NmEwMn6joEoQoRSSbhqSdJEsERKQT52Fa5JdmF50vRrVskf/cd6WvXobK1weXHhZi3a/fQfVRmZrgvXfKEIhQE4bmWdAXWj4SEUDmB1PVrMDSXH6vZVU4yXd0B7i1AnSv6MQmCIFSC9Px0dkbvZGvkVkKSQmjr0pYObh10HZYgVBiRZBKeelcSskjJLqSlj62uQxGeY1JREdf79kOdlES1oUOxHTsGlZmZrsMSBEEArRZOLILdU8HAFF5ZBrVfAOQvavbF7mPjtY1MsatB9avb4XYz1hNGBoSe/5Xh/sNRKsSCxIIgCGWVV5TH/tj9bIvcxuEbhynSFuFl6cX4BuN5rdZrug5PECqUSDIJT73D4SkAtKwhkkyC7hSEh6O+cQOnGTOw6vOyrsMRBEG45+j3sOtTqNEFen8PZvYAnEs+x5xTcwhJCgGgtmMtRl3cBwZmYGLL/GtrOJd8jtisWKY2nyoSTYIgCI9BkiSO3jjKlsgt7InZQ25RLvYm9gysPZAeXj2oaV1TzMIQnkkiySQ89Q5dS8HbzhQnS9GPSdCdvHOhAJg0bqTjSARBEP7j+iGwqwUDVoFCQXRmNPNC5rErehc2RjZ82uxT1oWv47A6n1FaNVzZQrpvZ0KTQ/Gw8GB9+HpUChWfNvtUXBAJgiCU0oZrG/gs+DPMDczp5tmNHl49CLQPRKVU6To0QahUIskkPNXy1RpORKXyamM3XYciPOfyQkNRWVuj7+qq61AEQRCKS7oMbs24VZDGT+d+Yk3YGvRV+oypP4YhfkMw0TchOS+ZRaGLyDCxxjI3jWBre6SkK8xoOYO9MXv57cJv2JnYMbr+aF2fjSAIQpUnSRJLLy2lVrVaLOu+DAOVga5DEoQnRiSZhKdaSEwa+Wqt6MckVKqCiAgyNm8m7+w59F2cMfTxwax1Gwy9PO9ukxd6DuN69cS3/IIgVC35GeRnxrFUv5Df13cnvyifPjX6MDpgNLbG9/7f2dK5JT+d+4mj7oF0vbyHQ+RhbWiNv60/dW3rEpEewcorKxlZdyR6SvHxURAqmlbSkpqXiq2xWMjmWXAq8RTX0q/xRYsvRIJJeO6ITwnCU+1weAoqpYJm3ja6DkV4xqgTE8ncspWMLVsouHwZlEqMatUie08YGWvXkfLDQmocPIDS2BhNVhaFEZFYdO+u67AFQRCKS7rCzGrWrEs7QzvXdkxqOAkvS6/7NvO38cfS0JJDlrZ0dm3CkYxwgpxb3u3D9GKNF9kft5/jN48T5Bz0pM9CEJ55K6+s5OsTX+Nm7kZb17a0dW1LA/sGIqn7lFp5ZSUWBhZ09eyq61AE4YkT71rCU+3wtRQauFphZiieykL5aTIzydq5k4zNW8g9cQIkCaN69XD46EMsunVDz84OgOyDB4l98y2yDxzEomsX8s+fB0nCuF59HZ+BIAhCcQnxx9lobsorHt35pM2sB26nUqpo4dSCIwknON9rHmnbBtLSueXdx1s5t8Jc35xtUdtEkkkQKsHf1/7G1dwVV3NXVlxZwdJLSzE3MKelc0vaubYjyDkICwMLXYcpPMC04Gm4mrsy3H84SblJ7InZw6A6gyV5vpYAACAASURBVDDWEz1jheePuDIXnlppOYWcj89gYocaug5FeAYkL/ie1EWLkNRqDNzdsR07FssXemDg4XHftqZBQahsbcncvh2Lrl3IC5WbfhvXq/uEoxYE4VlVqCkkqzCLfE0+BUUF5GnyyC8q/ruHpQd+Nn4PHefP2F1IwLDA8Y88ZkuXlmy/vp1fQ39FgYKg6veSSQYqAzp5dOKfqH/4tNmnGOkZlfcUBUG4LTwtnCu3rvBBkw94vfbr5KhzOHrjKPtj93Mw7iDbo7ajp9CjoUNDPm3+Ke4W7roOWfiXyPRI1oWvA+Bi6kWqm1ZHK2npX7O/jiMTBN0QSSbhqbX+TDySBK1qiH5MQvlkHzpMyg8/YN6pEzZvjsTI3/+h/RAUKhUWnTuTvm4dmuwc8s6FYuDlhcpCfMMoCEL5ZRVm0W19NzIKMh65bQ+vHrwd+DYOpg73PZaen86a3Gi6ScY4m7s8cqwW1VsAsD9uP/Xs6mFlZFXs8e6e3Vkfvp79cfvp6iGmgAhCRdkauRWVQnX3dWWqb0pH9450dO+IRqvhfMp59sfuZ8WVFfxw5ge+afNNsf0lSRJ9nHRoT8weAEbUHcHvF35HK2lp7dIaV3OxGIzwfBJJJuGpk5mvZtrGi6w/E09Dd2vqu1g9eidBeABNejo3P/4YAx9vqv9vNkpDw1LtZ9G9G2nLl5O9bx95oaGYtWpVyZEKgvC8OBJ/hIyCDN6s9yYuZi4Y6RlhpDKSb2//bqgyZEvkFpZcXMLeyH94waY+zfxfp4ljk7vJoRVXVpCnkBhm6V+q49oa21LHpg6XUi/Ryvn+97RGDo2wM7ZjW+S2hyaZCjQFKFBUqWa3kiQRlhbGruhdOJk60de3r65DEp4zHx/+GBsjG95p9E6x+7WSlq1RW2lRvQU2xvf3GFUpVQTYBxBgH0CBpoCVYStJzUu9u+3qsNUsvbSUHzv+KJIaOrI3Zi91besyMXAiDR0aMvvkbEbWHanrsARBZ0SSSXiqHI1IZfKacyRk5jOhQw3Gt/dBT6XUdVjCUyxh+pcU3bqF508/ljrBBGAcGIiegwOpv/+OJjUV4/r1KjFKQRCqkkupl6hhXQN9pX6ljL8/bj/WhtaMqT8GlVL1wO0mBE7gJas6LNj+Jtu0p1lzIAQFCmpVq0Uzp2b8Hb6ONrl5+NZsWupjt3Ru+cAkk0qpoptnN5ZfWU58djwZBRnEZMUQlxVHbFYsMZkxxGbFkpSbhJGeEW1c2tDFowttXNqgr6qcv9XDSJLEhZQL7IrZxa7ru4jLjgPAWM+YXt69qlQSTHi2haeFsyliE6b6poxrMK7Yc+904mkSchJ4O/DtR47Tz7cff13+i40RGxnuP5yMggzmhswlqzCLMbvH8Ff3v7A0tKzMUxH+IyEngQupF5gYOBGQ30P/3c9OEJ5HIskkPBXy1Rr+tyOMXw9H4WlryrrRLQhwFRVMQvlk7dlD5tat2E2aiFGdOo+1r0KpxKJrF24tWQqAUT2RZBKE50FEegSvbHmFLh5dmNVq1kOTQGVRpC3iUNwh2rq2LdXYrld28s2tLIrScrng1Yzjft04dvMYf17+kyJtESPSM8C+dqmPP6j2IFzMXKhjU/J7Ynev7iy9tJSu64pXMtkY2eBq7koTxya4mruSnJfM7ujd/HP9H/rU6MO0FtNKHUN5aCUt55LPsSt6F7ujd3Mz5yZ6Cj2aOjXljbpvoKfU49MjnxKSFEIzp2ZPJCZBWHZ5GQA56hxOJJwoloTYErkFEz0T2rm1e+Q4XlZeNHJoxJqwNQz1G8riC4vJLszmo6YfMfvkbCbum8iiTotEAvUJ2huzF4AObh10HIkgVB0iySRUeRfiM3hn9VmuJmYzqJk7H3avhYmBeOoK5Ze9/wAqS0tsRowo0/4W3bpxa8lSFEZGGPn6VnB0giBURSFJIQDsuL4DcwNzPmv2WYX2QjmTdIbMwkzaurZ99MbqfAhdBbVeQM+xLgF7Picg8E3e6voWuepckoPn4h41Cxwe3hz836yMrHipxksPfLxOtTpMaTwFtVZ9dyUsF3MXTPVN79v2o6Yf8eWxL9kYsZGxAWOxM7ErdRxlUaAp4LWtrxGeFo6+Up+g6kGMDRhLW9e2d6s7ctW5fH70c4Ljg0WSSXgi0vLT2BK5hV7evdgdvZu9MXvvJpkKNAXsvL6Tju4dS70KWT/ffkw5NIXNEZtZdnkZ3b2681qt17A0sGTKoSl8cOgDZraaKRJNT8je2L14Wnriaemp61AEocoQV+pClaXRSvx0IIK5u69ibWLAkuFNaONbuR9QhedLQVQkBj4+KPTK9lZoVL8++s7O6Ds5odB/8lNBBEF48s4nn8fS0JJ+vv349fyvWBpYMqnhpAob/0DsAfSV+nebcD/UlS2Qnw6Bg8CjFYSuhm3vgWdrTAzNcE+PB1M7MK24BTIUCgUD6wws1bZ6Sj2G+Q9jffh6Vl9dzdiAsRUWR0n2xe4jPC2cyY0m06dGH8wMzO7bxkTfhED7QI7cOMI7vFPCKIJQsdZeXUuBpoDh/sPJL8pnX+w+Pmn2CUqFko3XNpKtzqaXd69Sj9fRvSPWJ6yZFjwN4O7rqrtXd1LzU/nm5DekF6Qzt91cLAzEgiSVKaMgg1MJpxjmP0zXoQhClSKa2QhVUr5aw5DfTzB7Rxid6ziyY1JrkWASKlxh1HUMPD3KvL9CocD1l0U4ff1V+QLJvAFaTfnGEAThiQhNDqWebT0mNJhAP99+/HbhN47eOFph4x+IO0Bjx8YlVgbd58yfYOkGnm1BpQ8950JmPGwaD5IESZcfa6pcZXC3cKeNSxtWh62mQFNQqcfacG0DjqaODKw9sMQE0x0tqrfgatpVknOTKy2WzMJM4rLiuJx6mazCrEo7jlC1qTVqVl5ZSXOn5nhbedPerT0peSmEJoeSX5TPz+d+JsAugCaOTUo9poHKgBd9XqRIKqKvb99izb4H1RnEzFYzOZN0hsHbBhObFVuqMWedmMXc03Mf+/yedwfiDqCRNGKqnCD8h0gyCVVOYZGWMctCOBKRwsyX6/L9gAZYm4qSX6FiadLT0aSmYujpVa5xDL28MHAtx2oueekwPxBO/FKuOARBqHxZhVlEZkRSz64eCoWCKU2m4GDiwIIzC5AkqdzjR2VEcT3zeummyqVFQ+QBaPA6KG9/nHNrBh2nwcX1sH8mJF0B+9JPlassA+sM5Fb+LbZFbqu0YyTkJBAcH0wv716P7GUV5BwEQPCN4AqPI+xWGEO2DyFoRRDd1nej/5b+9NrQi7NJZ8s8ZkU8t4QnS61Vcz75PN+FfEdSXtLd6r9WLq3QU+ixN3Yvq8JWkZSXxITACY895fb12q/T3bM7o+qPuu+xHl49+LnjzyTlJtF7Q29mn5xNRkHGA8cKSQzhr8t/8duF3ziffP7xTvQJCL4RzOH4w7oOo5jYrFh+OvcT80PmY29i/8AedoLwvBJJJqFK0Wgl3ll9lr1XkvjyRX9ebeJWob0uBOGOgqgoAAy8dDyHPvkKFOXB5c26jUMQhEe6kHIBCYl6dnKjf0OVIaPrj+Z8ynn2x+4v9/gHYg8A0MalzcM3lCQ4sUj+PeD14o8FTYT6A+DATFDn6LySCaCJYxNqWNfgr8t/VVrCZHPEZiQkXvR+8ZHb+lr7YmNkw5EbRyrs+LnqXGaemEn/Lf2JzIhkbMBYpgdNZ1arWRipjBi+Yzjrw9c/9rhX067SaW0ndkfvrrBYhYqXUZDBwbiDzA+Zz7B/htFieQsGbBvAn5f+pJlTs7s9mCwMLGjs2Jid13fy2/nfaObUjMaOjR/7eA6mDsxqPQsbY5sSH2/i1IT1vdfTw6sHf176k27ru/H7hd/JL8ovtp0kSXx7+lvsje2pZlSN/536X5VLas46MYv3D75PjjpH16GQnJvM5AOT6b6+Oz+c/QFXc1e+bvk1SoW4pBaEfxM9mYQqZd6ecLaE3uSDbrV4vam7rsMRnmGFkXKSydCrfJVM5ZYcJt/GHJWrmozFqomCUFWFJocC4G/rf/e+Xj69WHxxMfPPzKeNa5syXWzkFeWxL2Yfq6+uxtfal+pm1R+8cX4mbH0Xzq8G/75g9Z9KSoVCnjZ3KxJijz1W0+/KolAoGFR7EJ8Ff8bJhJM0cSr91KB/S8pNYt3VdQysMxBzA/O790uSxIZrG2jo0BBXi0dXlioVSlpUb8Gh+ENotJoHVj5lFmZyLukcHhYeVDer/sDtknKTGLdnHFduXaF/zf6MbzC+2DLyQc5BvHfgPaYGT2V9+Ho6unWkg1uHR8aakpfCuD3jSMxNZO3VtXR07/jIcxOerF/P/8rWyK1EpEcgIaFSqKhVrRZ9ffsSYB9AA/sG2JvYF9unvVt7ZhyfAcCEBhMqLTZHU0emB01nUJ1BzD09l+9Of8eKKysYFzCOF7xeQKVUsTtmN6HJoXzR4gvUWjXTj01nb8xeOrhXjelfWYVZRGVEISGx9upahvgN0UkcWknLmrA1zA2ZS6GmkLfqvUWfGn1wMnPSSTyCUNWJJJNQZWTkqfn9cBTd6zoyqo23rsMRnnaaIjj2g3xB5t0eXBqD3r1pl4XXo1Do66Pv7KzDIIGUq/KtpIHIfeD34FWdBEHQrdCUULwsvYo109VX6jM2YCzvH3yf7VHb6eHV47HGXH55OfNC5pFblIuTqdPDLzrjTsO64ZAeA+0+hlbvlrydniG8tkKeNlc98LHiqSzdPLsx++RsNlzbUOYk0y+hv7AybCX/XP+H7zt8f7cXzZmkM8RkxTCy3shSj9XCuQWbIzdz+dblYknDf/v48Md3K9QMVYZ4WnriZemFt5U33pbeeFt5k6/JZ/ze8WQUZPB9h+9p7dL6vnEsDS1Z2HEhyy4vY2vkVr49/S3fnv4WX2tfOrp1pL1be3ytfYtVbucX5TNx70TSC9Jp7dKa4BvBZBVmFUuuCbpVqClkwZkF+Fj5MDZgLA3sG+Bv64+JvslD92vn2o4Zx2fQ1rUtde3qVnqcvta+LOy4kJMJJ5lzag6fHPmEJZeWMLHBROaFzMPHyode3r2QkFh+eTlzTs+htUtr9FW6X9DkTvWolaEVSy8tZUCtAU88rrBbYXxx9AtCU0Jp6tSUz5p9hpuF2xONQRCeNiLJJFQZy45Hk11QxJi2ProORXja5aUhrR5K3skj6JtK6Bv/DwzM4dVl4CVPQymIjELf3a3MK8tVmOQwuWdKZjyE7xJJJkH4L60WcpLA3FGnYUiSxPnk87RxvX8qWxePLvx6/ldmHJvBofhDNHdqTjOnZjiYOjx0zJDEEGadnEUTxya8We9NGjo0LLkSSquF4Hmw90swd4Jh2+X+Sw9jUg0aj3icU6xURnpGdPbozLaobXyi/uTuhfiu6F2sD1/POw3foYZ1jQfuX6gpZFvUNura1iUmK4YBWwcwKXAS+Zp8dl7fibGeMZ3dO5c6nuZOzQE4En+kxCTTvph97I/dz1C/oXhZehGRHkFERgRnks6wLap4byl7Y3uWdF1CbZsHT03UU+oxxG8IQ/yGEJcVx96YveyJ2cOP535k4bmFuJi50NG9Ix4WHlzPvM7JhJNcSr3Ed+2+w8bIhoNxBzkUd4juXt1LfY5C5YrOjEYraXnD/43H+u/iYOrAjx1/pHa1JzuVtbFjY5b3WM6O6B3MOz2PcXvHAfBDhx/uVum90+gdxu4Zy/rw9bxS65UnGl9JzqfIPaI+bvox7x18jy2RW3ipxpP5nJSrzuWncz+x9NJSLA0t+brV1/Tw7CHaeAhCKYgkk1Al5Ks1/H74Oq1q2OLvbPnoHQThQVLCyf2uH8mHsshNssOsbWtch/aEHR/Cns/Bcw8oFBRGRmLoUwUSmslh4NoE7GtB+E75YlIp5vYLwl2XN8K6kTD+NFjrbhp1XFYcaQVp1LW9v/JAqVAyq9UsFp1fxNEbR9kauRUAL0svmldvTnOn5jRybFRsxbiMggymHJqCs5kz37X97sGroWUlwN9vQeR+qNMbes4DY+vKOMVK19O7J+vC17EnZg89vXui1qiZdWIWibmJHL95nDEBYxjqNxQ95f0fT/fF7iOzMJOxAWNxMXdh3J5xTDs6DQADpQFD/IY8soLk32yMbahdrTbBN4J5q/5bxR6701/Jx8qHCYET0FcWr5zIUecQlRFFRHoESblJ9PTuiaNp6ZOgLuYuDPYbzGC/waTkpbAvdh97Yvbw1+W/KNIWYaA0wN3SnanNp9LBrQNaSYuNkQ17YvaIJFMVEpUhT7v3tHz83o53ejQ9aQqFgq4eXeng2oE1V9dwK/8WrZxb3X28lXMr6tjUYV34uiqTZPKw8KCLRxd+u/Abiy8uprdP70rvgXQw7iBfHf+K+Ox4+tTow9sN3y42BVYQhIcTSSahSlgXEkdKdgGj2wToOhThKZa/eynJM6eTHaeHytoWI383ck+cQvJegCIoEba+A9HBSM5NKIyNxbxz6b/1rhSFOZARA4GDwcoNLqyDm2fBuWpMbxGEKiHhPGjVELEXGg3TWRihKXI/pvp29Ut83Mfah29af4NW0hKeFs7RG0c5evMoa6+uZdnlZegp9KhnV49m1ZvR3Kk5Sy8tJSU3hT+7//ngBNPVnbBhFBTmQs/58nvFU/wtegP7BjibObMlcgs9vXuyKWITibmJfNXyK/bF7mNeyDz2xuzly6Av8bIq3i9vw7UNOJg40MypGSqlitU9VxOTGYOdiR3WhtZlqi4Icg5i8YXF901D++X8L9zIucEfXf+4L8EEYKpvir+t/wOn2T0OW2Nb+vn2o59vP7IKs0gvSKe6afH+T0qFknZu7dgWuY0CTQGGKsNyH1cov8iMSAA8LD10G0gZ6Kv0GVB7wH33KxQKenr1ZNbJWUSkR+Btpbv2FXeqR1tUb4FCoWCY3zCmHJrCvth9dHCrvJ5RXx77klVhq/C09GRxl8U0cmxUaccShGeV+Lpc0DmNVuKXg5HUc7GkuXfJq2QIwsMUxsQQP+xFosZ9RW6SPnajhuKzZx+2o95Cm5tL3rlzEDAATGzhyDwKY2OhqAjDil5Z7sYZyH/wMsH3SQmXb+18wacDoJCrmQRBuCftunwbuV+XURCaHIqxnvEjL7qUCiU1q9VkqP9Qfu70M0deO8IvnX9hiN8Q8jX5/Hj2RwZtH8Su6F1MDJxYcqJCkmDnJ7C8nzw97q0D0HDIU51gAvlv08OrB8duHiMhJ4Ffz/+Kv40/L3i9wLdtvmV269nEZsXSb3M//rjwBxqtBpAbawffCKaXd6+7yRdjPWNqVqtJNaNqZZ6+ElQ9CI2k4cTNE3fvi86M5o+Lf9DbuzcNHRqW/6Qfg7mBOa7mriU2GO/g1oHcolyO3zz+RGMSHiwqI4rqptUx1jPWdSgVqqtnV1QKFVsit+g0jps5N0nNT73bt6qzR2c8LDyYfXI2uercSjnmxdSLrApbRX/f/qztuVYkmAShjESSSdC5XZcSuJ6ay+g23mKes/BIuadPk33oMIVx8agTE0mYNo2Irl3JOnEZmyBHfHbvwnbSFJQmJpg0bQoqFdlHjoC+MTR9C8J3UHjmEAAGnhWYZMqIg186wIFvSr/PnZXlbGuCqS24NBJJJkH4r1vylBSiDsjTSXUkNDkUf1v/EqdyPYyhypBmTs2Y1HASq15YxYFXDjC7zWw+aPIBg/0Gl7zTtT0QvAACh8CIPWBXswLOoGro6dUTraTl3f3vEpcdx5v13kShUMjTeDy78nfvv2np3JJvT3/LkH+GcD3jOpsjNqOVtPT26V2hsdS3r4+pvimHbxy+e9+qsFUATGo4qUKPVV5NHJtgqm/K3pi9ug5FuC0qI6pMU+WqOltjW5pXb87WyK1oJfk9N78on7NJZ5EkqdzjS5LEssvL2HF9x0PHu1M9Ws+2HiD3Nfsi6AtuZN9gzuk55Y6jJL+d/w1zfXPebvg2BiqDR+8gCEKJRJJJ0LlVJ2NxtDCis59um7oKVV/aylVEvz6Q2JEjiejYkWtt2pK2ehVWXtl4T++D/S97UdnfWy1OZW6Ocf365Bw+It/ReATom1BwaDVQjiRT3GkIXVP8vpA/5RXiwneVfpyUMFCooNrtaSE1OkN8CGQnly2u0ijMgd3T7l24C0JFiTwAxxdBXnrFjpt2Xe5BlJcGCaEP37YwBy5tgtsVMBXl2M1jXEi9QBPHsq2K9m/WRtZ09ejK67VfL7mviCTBwW/AwgW6/w/0jcp9zKrEw9KDurZ1CU0Jxdfal7aubYs9bmtsy9x2c5nZaiZRGVH03dyXpZeW0sC+Ae4WFduTS1+pTxPHJgTHByNJEoWaQjZHbKa9a3tsjW0r9FjlZaAyoLVza/bF7rtb4SXojlbScj3z+jOZZAI5GXwz5yanE0+j1qqZtG8Sg7YPYtrRaag16jKPK0kS3576lpknZjL5wGRG7hx5d9rhf11IvoCB0gBfa9+79zWwb8DAOgNZFbaqwqv6IjMi2R29m1drvfrgKcyCIJSKSDIJOpWYmc+Bq8n0aeiMSimqmIQHy9y5k4QvvsCsTRvcli7B8ZP3sWusxLtnOk5fz0H/5a9KbJhtGtSC/IsXKUpLk1dbajCIwquX0LO1QWVehqWgz6+FxV1h/Qh5ehyApgjO/AlKPTlxlBFXurGSw8DGG/Ruf1tWuycgyWNVlnMr4fB38OdLlZvMEp4vN0Nh+Suw/T2YUwe2Tq6YRGZ+BuTdgvq3e4c8bMpczHH4qSWsHgRnl5f/2LdlFmbyyeFP8LDwYIjfkAob94GiDkLscWg56d57wzOmp3dPAEbWG1liBbNCoaCHVw829N5Ac6fm3Mq/RZ8afSollqDqQdzIucH1zOvsi91HekE6L9d4uVKOVV7t3dtzK/8WmyM36zqU515iTiJ5RXnPbJKpnVs7TPRM2ByxmWnB0zhy4whtXduyPnw9I3aOIDUv9bHHlCSJ+Wfms+TSEl6t+SqfNP2ES7cu0WdTH+aennvfFLjzKeepbVMbfVXxvmjjG4zH3cKdqcFTK3Ta3O/nf8dQZcjAOgMrbExBeF6JJJOgU3+fiUcrQZ9AF12HIlRhOUePcmPyexjXrYvz3O8wbdIE65oabL3jMBi9Fur2feC+Zi1bgiSRe/SofEfdfhRmKDFweszVmSRJTs6sewOcG4KJjVwRBHBtN2TGQ+v3b/97T+nGTLkKtve+ocO+Nni1gxOLoKjw8eK7IyMOgr+XYz04G26eK/74mT/B0lVesWpZXyjILttxhOdXRjysGSY/7wFyUmHl63K10aC/oU4vCFkCPzSR+wqVp7LpTj8mt6ZgV7vkJFNRgfxaXNxVTvhaOEPoqrIf8z9mnZhFSl4KX7X86sn0Xjk4G8wcocGgyj+WjvT17csPHX6gi3uXh25nZ2LH/Pbz2dh7I728e1VKLC2cWwAQfCOY9eHrcTJ1oplTs0o5Vnl1dOtII4dGzDg2g6tpV3UdznPtTvWNl6XXI7Z8OhnrGdPRvSMbrm1gU8QmxtQfw4L2C/im9TdcTL3Ia1tfI+xW2GONufzKcn49/yt9avThw6Yf8kqtV9j84mZ6ePbgtwu/0Xtjb3ZH70aSJNRaNZdSL5W4mqexnjFftJCnzc04PqNCzvdm9k22Rm6lj28fqhlVq5AxBeF5JpJMgs5IksSaU7E0crfGy06UpQr3y798mbjx44kZNhx9V1dcfvoRpfHti7zwXWDtAe4tHjqGkb8/SgsLuS8TINn6UpClh4HN/SsGPZCmCLa+K1/I+veBQRug1WT5gjdiL5xeDKb20PJtMK8u3/coRYWQGnF/r5UW4yDrprzSXFns/RJ2fizHuvdLWPrivWbkCefl6qvm46D/EvnfqweVPaElPJ92fAgX18NffeTk0pohkJ0Ir/4F3u3hpZ/QTjgDdfvLCc/5DeDEL1CWKRZ3kkzWnuDVFmKOgTr/3uMJ5+GX9nJSNeB1GH0EGg6D64cgPaZcpylJEpsjNrMpYhMj642823y2UsUck2MPmvDMTZP7N32lPq1dWpeqD6NCocDLyqvSeja6mrviZu7GhmsbOHrjKC/6vFhi4+2qQE+px+w2szEzMOPd/e+So87RdUjPragMuVLzWa1kAnjR50UkJPrU6MOo+qMA6ObZjSVdl6CRNAzaPojd0btLPd768PUE2AXwWfPP7k4VtjG24cuWX7Kk6xIsDCx4e//bjN4zmn0x+8jX5JeYZAIIdAjkrfpvsSliExuvbSz3uS48txCAIXWeQLWqIDwHHq97pSBUoDOx6UQk5zDz5WfzWyBBpsnMRNJo0LN+eOWQpNGgjo0l/8oVckNCyAs5Q/6FCyjNzbEdM4ZqQ4egsrCQN1bny/1fAgc9crUlhUqFafPm5Bw+giRJaHKL0BYqMTQtZWKlMAfWDoer/0DQJOgwVZ6W1/gNOPYjbHsfbkXIj+kZyBfZV7bIPWEedqFyK1Lu4WT7nySTdwewryM3/a3/6uOtJiVJcoLL7yV48UdIuiQ3Iz80Bzp9LveNUhlCvf7y1MFe82HjWPnnpZ9LnG4oCMVEHSTzyia2NniRXla1MT08D9S50HuhXOEHqLVq+u8fj4WhBV8OWo3rofmwbbJcodf5S7n3WGmf13eTTO5ykun4jxB3AtxaQPA82Pe1/FwesBp8b1fF1OsP+76Uq5lav/fYpyhJEicTTrLw3EJOJ56mrm1d3qz35mOP81jU+XB1OxyYLa+C2XBo5R5PKKZF9RasDFuJAgUv+ryo63AeytbYlm9af8OInSMYs3sMvta+qLVq1Fo1hZpC+UdbiFqrRoGCEXVH0NSpqa7DfuZEZkRiYWDxTFe9NHZszMbeG3G3cC+W5PWz9WNlj5VM2jeJt/e/zZiAMYyqN+qhieDswmzC08IZXX90ib3oAh0CWfXCKlZcWcEPZ3/gSLz8xeDDkvuj6o3idOJpZhyfgb+t/yNXTDwNEQAAIABJREFU/nyQNVfXsOHaBt7wfwMnM6cyjSEIQnEiySTozNrTcRjpK+lRT7yhP2vyw8JImDqNgqgotBkZoFJh0aUL1YYNw6hWTQpjYii4FkFBxDUKr0VQEBFBYVQUUqGc+FEYGWFcty52b7+N9Wuv3ksu3RF9GIry5IvVUjBtGUTWjh2kLFhA/qXLABgYpDx6x6xEWN5fbjbc41u5cfgdeobQ/hP4+/bFZ8Pb3375tIezf8kNvF0bP3jslNtl5v+tZFIooPntxE/kPjlpVVpJl+SKEp+O8mp6zg3lRNWxH6HBQPmiu/YL8kU5yPdlJcDe6WDuCJ2nl/5YwvNHU0TijimMdnYlPD2EPcb6LBx9BIPUSKjR8e5mmyM2cy39GkYqI/oc+4T3m79Hn6ajUez6VH49ebWFzjPA0f/Rx7wVBcbVwMgSPILkRvkhf8KeLyDupJxQ7THn3nMa5ISUe5Dcf6zV5MdK1J64eeJucsnO2I4PmnxAX9++6Csfo/LxcZ1eArs+lSsOzavDC3PAwLTyjifcJ8g5iJVhK2levTnVzarrOpxHauzYmCmNp/D9me+5ln4NA5UBBkoDDFQG6Kv07/6ekJPAqN2jmB40nRe8XtB12M+UqIwovCwrr8KuqvCyKvmLYDsTO37v+jufB3/OwrMLuZZ2jelB0zHRNylx+9CUUCQk6tvXf+Cx9JR6DKoziC4eXZhzeg4peSm4mD24nYZKqWJmq5n029yP8XvH09GtI05mTlQ3rY6TmRNOpk6YGzy892ZIYghfHf+KIOcgxjcY/9BtBUEoPZFkEnQiX61h87kbdPN3wtyoEj+8C0+cJj2duLHj0BbkY9mjO/ourhQlJZG+di2Z27aBnh4UFd3dXt/ZGUMfH0xbBmHoYI5h0WWMnK1QGBhBdTf4b4IJ4OpO0DMCj5alismsZUtQqUhZ+CN6Dg5YBDpjYnhJXg79QdU7yWFyz6KcFHh1BdTsev82dfvJlRXmTvLUPZB7KqGAiD0PTzIl3+6nYVuj5HH3fCFPAfJoBapSvkbuTNPzanfvvvafwMW/5Ubf+en393lp9a6caAqeLyeamo8t3bGE507k0TmM0k8nw8CEIbVeY8mlJXwQ+j2zW8/mTs2eWqtmUegi/G38mdN2Dp8e+ZTPj33Bfpc2TBu2BduLm+DATLlBd4OB8vPT/CEri6Zdh2q3p6MYmoNLYzi/GoysoM9vD+7HVv9V2DReTva6NCz+2J0ls/91cXgy4SQLzy7kVOKpYsklQ5Vhmf5WpXbznDwV16UxtHkPPNs8vAJSqBRNHJsQYBfAcP/hug6l1AbUHsCA2gMeuk1mYSaT9k3iw0MfkpCTwDC/YVV2KuDTJiojitYurXUdhk4ZqgyZ0XIGNaxr8N3p74jNimVB+wU4mDrct+255HMoUFDPtt4jx7U3sWdmq5mlisHexJ7/tfkf049N56/Lf6HWFp+Wba5vXizhZKgyxEjPCCOVEYZ6hqy8shIXMxe+af2NeG0IQgUSSSZBJ/aHJZGVX8TLgc6P3lh4akgaDfGT36MoMRH3v/7EuP69b6xsx40lY/3fFCUnY+jjjYG3D4ZenihNTOSLvlO/wY5PQFMAcUrQFoG+KXwYVzwRJEkQvgM8W8vVOqWg7+SE14a/UZqbo+/oCKd+hy1vQ2YcWLndv8P1I7ByAKgMYOhWcA4seWClEobvAP71TaZJNajeQE74tP3gwUElXwFLt5IrFvQMocUEubfSjy2g03R5KtCjvjGN2Ad2tcDyX68rSxe5B9Oh/8nn6tmm+D4KBXSbBTlJsOMjMHN4aCN14fl0NvYg48KXoqcy4I9uf1Lbtg72JvbMPjWbz49+zmfNP0NPqceWiC3EZ8fzUdOPcDJzYlHnRSy/vJy5IXN5eeurTG0+lQ4TzsDB/8Hxn+HCermXWfOxYFDCN+Bp14u//lqMgzBvaP8pWDykCrZOb9j2HpxbUTzJlJ/BwmWdiLKwo2atl3A0dWTd1XVPPrkEoM6D9W/Kiwi8uqx4NZbwRJnom/Bn90pc1VNHLAws+KnjT3x8+GPmhczj7/C/GeI3hF7evTDSe3Z7flW2jIIMUvNTn9mm349DoVAwzH8Y3lbevHfgPSbum8iSbkvuew89l3QOH2sfzAwqvgdrY8fGbHpxE1pJy638W9zIvsGNnBskZCdwI+cGN7NvcjPnJtfSr1GgKaCgqIB8TT5qrRpbY1vmtZ+HhUEJX2gKglBmIskk6MT2CwlYm+jT3MtG16EIFSj5++/JOXwYx88/L5ZgAlCZmVFtcAmrJWUlwMZxcG2XPDWs90L54jFkqVyJkBYFNv+aZ58aIV94Nh/3WLEZ1vhXxZBdrdsBh92fZDq/FjaMliuTXl9zr0LpQfRKuBj16QCHvpWXUc/PhNxUeRn23FTIvSX/pIY/vBKr+Vj5vHd+AitekZNqnWeA0wO+BVTnQ/Ttpsf/1XKSXM3U5M2SK7eUKnhpkbxK2N+j5Ite73b3byc8lw7EHmDy3gnYa4r4qf08XG3rADDYbzCZhZn8HPozkRmRfNXyK34O/Rk/Gz9aObcCQKlQMrDOQJpXb86Hhz5k0v5J9PbuzQftPsSs0XDYPVXun3ToW3BrJk+lazQcjCzkhvsZseD/r+Xka/eUfx7FyBJq9YDza+TElLWHvMrk36P4RZWLQXY0/4TMA3jyyaU79nwhJ5sHrhMJJqHSGKgMmNV6Fp3cO7H4wmKmH5vO6rDVrOm55pmf6lVZnoem34+rtUtrvmr1FZP2TWLmiZlMbT717mNaScu55HN08+xWqTEoFUpsjW2xNbalnt2jK6Y0Wg2AqGAShEogurwKT1xBkYY9l5PoVMcBPZV4Cj4rsvbuJfXHn7Ds8zJW/fuVbqdLm2Bhc3k1pW6zYeD6e9UJDrf7tSReKL5P+E75tkansgf77yTTHZIEh+fCujfkqSvDdzw6wfQgvl1B0srJqn+myEuSX1gPCRegKB+sXMG/r9wv5kEUCqjZDcYck/82CRfg59awYQxk3rh/+5ij8tgl9XAyNIcJIdBs1IOPp28kV1PY+sKqgfLqdslXxcpzz7n14euZuG8C3gX5LHXoiKtXh2KPj2swjm9af0NEegS9N/QmPjue0fVH33fx6m3lzbLuyxhZdySbIzfTZ1MfTqnT4JW/5NdawyFywnn3VPjnQ3mnzDi5otG6jBdyQZMASW5+H3sSgudzNnovRQoFc5KSCW44jRU9VrDt5W28Xvv1J5dg0mrk1faOLYTGI+UeaoJQiZQKJZ09OrO8x3ImBk4kLC2M6MxoXYf11LqTZBKVTMV1cOvAiLojWHt1LX+H/333/oj0CLLV2QTYB+gwuvuplCqRYBKESiIqmYQn7nB4CtkFRXTzFw2/nxWF169z4/0pGPn54fjZZ4/+djQ/E/75AM4uA6cAePkXsPMtvo19bVAo5eRKnd737g/fKa/IVtYEEMhVAya2chXBHQdmwf6vwb+PvDJbSRVKpeXSCMYclyuETGzkqoqyfpBR6UPTN+UVsw59C8d/kquSWoyXp9QZ3i49j9gLSn25OXJZGVvJVRW/d5ZX1AO50XLHafKS6sJzQ5IkFoUu4vuz3xOEEXPS0jEZ+EWJ23bz7Ia/rT+fHP7k7tL0JdFX6TMhcAKtXVrz0eGPGL5jOEP9hjKuwTgM3JrJG20cCxc3Qo///WtlOY+ynYRTPXhjNyzvB0teAI2aUz6BqDQpNFCaYXJpE/7+S8o2dlnFnpBX2rt5Tp662qnkv6kgVAaFQkF7t/bMC5lHSFIIHpYeug7pqRSVEYW+Uv+paBL/pI0LGMeFlAt8eexL/Gz98LX25WzyWQAC7KpWkkkQhMojykiEJ277hQTMDfVo4SOmyj0LtLm5xI2fgEJPD5fp76PU5Dx8h+hg+DFI7pXS+j0Ysfv+BBPI/ZZsfCDx4r37CnPlKWHlqWK6w67WvUqmogK5qqBmD3j51/IlmO6wryU39TapVjGNfI2t5NXfxp2UK6UOzIIFDeVphVqNvBKdW7Pyr0pl4SQnyN7YLU+hq9FZXvkqZGn5z0F4Kmi0GmYcn8H3Z7+np00DFkRdxaTtxw+d0uVq7sqSbkv4pfMvj0wyB9gHsLbnWvr69mXxxcW8tvU1wm7dfi3W7QeFWXB1h7yyHJQvoWznCyP2gnMjsK/NSSt7/Gz9MPF7Ga7+Iye8n4SsRPh7NPzWCbKToe/vMHhjyX2oBKESeVp4Us2oGqcTT+s6lKdWREYE7hbuogqmBCqlilmtZ2Gqb8oXR79AK2k5m3SWakbVcDV31XV4giA8IaKSSXii1Botuy4l0rGOA4Z64n/OT7uCiAgSZ82iICIC15lT0F/TRZ4mZuEsT7tS6d9eyUmSb7VqiDokXzQO3wGuTR5+AAd/iD9179+xx0BTWHz1tLKyqyn3X5IkuQooPwMaDXvwanNVhbUH9FsMzcbIjbo3jYdjP0LSJejwWcUcw8BEXhnPtbG8RPyKV2HzRLkqq1aPijmGUGUtPLeQVWGrGFZ7MJOCl6K0ryP3SSqF0vZ4MdE34bPmn9HWtS2fHfmM17a+xvgG4xlc63VUZg6oQ1cTaWmHp8oAA4vSVQuoNWqOJxzncuplenn3urfCkakNDN1CbmEOF1a1YojfELAPghOL4PJmaPB6qcYvE41aPs7+mXKj75Zvy9NkDSu++a0glIZCoaCBfYPnLsmk0WoqJCl0J2nSwa3Dozd+TlUzqsbkxpP5+PDHrL26lnPJ56hvV1/0ABOE50gVv5oSnjXHIlPJyFPT1f8hS1YLVZokSWQfOkzMyDeJ7PECuceO4/DhB5ilLJeTEB0/B/cWctImOwlyU+RG1/npciVSkzdh1OFHJ5gAHPwgPUYeC+QElVJPrtgpL7taUJAB2Yly/yHjanLT4aeFa2N4Yyf0XQyFt6vHfCqgwuu/9Ayg/1J5xbw1w+TG6xVAkiS0krZCxhIqjiRJbIvcRpBzEO/kgzI9BrrOBFXlfCfV2qU1f/f+mzYubZhzeg7Dd43kfVdP2uSfo2/yXtq6OjLt2HRCEkOQJOm+/fOL8tkbs5ePDn1Em9VtGL17NPPPzKf/lv6cuHni3oYKBWdTQymSimjs2Fie0mrtCedXV8p5ARB5AH5qKSeDXZvI/dU6ThMJJkHnGjo0JD47noScBF2H8kQciT9C0+VNuZh68dEbP0J4WjiZhZk0cmxUAZE9u3p69aSJYxO+O/0d0ZnRVa4fkyAIlUtUMglP1PYLCZgYqGjja6frUITHpM3PJ2PTJm4tXUrhtQhUdrbYTZyA1SuvoHfzAKw5Cj3nQcOhFXdQx7rybeIlcL/dILx6YMVcpN2ZonfjDFzZJvc8UumXf9wnSaGQV96q1QNSwsHRv3KOY2gmJ7Pm1YOw7fJqXeX07oF3KdAU8EOHHyogQKGiRGdGE5cdxxCv3rDtc3klN682lXpMayNr5rSdw+bIzXx9/Guuo6BDTi6B+QWcsPdgW9Q21oWvo4F9A0bVH0WAXQAH4w+yO3o3B+MOkleUh4WBBe1d29PJvRP2JvZMOTSFkbtGMjFwIsP95SqsUwmnUClU8sWOQiG/5g/Ohsyb9xYcqAgZcbDjY7i0Aazc4dUVchN/8S2+UEUEOgQCEJIYQnev7o+9f2hyKJaGlrhbuFd0aBXuVv4tPj78MQWaAnZd34WfjV+5xjuVKFdXN3IQSaaHUSgUfNLsE/ps6gOIfkyC8LwRSSbhiUnKymfb+Zu0q2mPkb6YKve0UCclkbZiBekrV6FJS8Owdm2cZn6NRffuKA0MQJ0PSz6Tp7Y1GFSxB3e4/WEw8YKcQIkPgZaTKmbsOyvMHZkH6hy54ffTSs+w8hJMd1i7yz2yog6UO8mUXZjNvth9FGmLuJR6iTo2dSooSB2RJIg7BaEr5d4/Aa/pOqKy0RRxOP4wAC2vHZZ7fXX+8okcWqFQ0Mu7F909u6OQQPVDY8iO5KXaLcjt/AWbIjbx6/lfeWvXW6gUKjSShmpG1XjB6wU6uneksWNj9JX3ksQreqxgavBUvjv9HQ4mDvTw6sGpxFP42fhhqn+7b1nd/nDgG7lPUrPREDhYXomxrIoKIHiB3KBf0kLbj+SG+frG5fzrCELFqmldExM9E0KSHj/JdDDuIBP2TsDOxI71vdZjblD610x8djwH4w5yKO4QF1Mv8nrt1xlZd2SlTaOSJIlpwdPILMzE3cKdw/GHmdSwfJ8hTieeprppddH0uxQ8LT0ZEzCGPy/9+fT/f14QhMcikkzCE1FQpGHUn6cpUGsZ195H1+E80xJnzqIwKgr7D6Zg6FnGpb9vywkOJnbUaCS1GrP27ak2ZDAmjRvf+0CYkwrB8yAjBl7cXDENrv/NwhmMrOQkU8wxkDTg0apixjZzkFd9izkKZo7yFD/h4TxbQ+hquc9MOaq+jtw4QpG2CKVCyZ+X/uTrVl9XYJBPUFYCnFspr5KYclW+78I6uY+VvlG5hk7MSWR9+HqG+A3BRP8JNIe+uAHWDuOQqwce+sa4XNws9w4qT9PtMtBT3v5YUref3Nze2gMTfRNerfUqL9d4mQ3XNhCXFUdrl9Y0sG/wwB4rpvqmzGw1k+TcZKYfm04N6xqcTznP4DqD721k6yOvpnjoW3lK2/5Zcl+2pqPKVtm0aYKcaKzdEzrPkBOzglAF6Sn1ytSX6XTiad7Z/w7uFu5cz7zOt6e+ZVqLaQ/cXq1Vczbp7N3EUkSGPN3a1dwVL0svFpxZQEpeCh80+QClomI7eGglLWuvrmVf7D7ea/Qeaq2auSFzScpNwt7EvkxjSpLE6cTTBFUvxyquz5kRdUcw1G/ovfd2QRCeC+IVL1Q6SZL4dMMFQmLS+WFAILWdLHQd0jNLW1BA2sqVSPn55Bw9is2ot7AZMUKuOHpM6hs3iH/nXQzc3XCZPx8DCy0knIe9X8q3Cech64a8ce1ecgKioikU8pS5hAtgaAFKfXBtWnFj29aEuBNyUkCsEvNonm3g1O9yRZlb2f877I/dj5WhFd09u7M6bDWTAifda9JcVUgSXNoIh+eAhQu0eR+qB0BRIVzdDmeWwbXdcuLTtRn0WiD39Vr1utxMul6/Mh86PT+dt3a9RURGBI6mjrxU46UKPLEHuLyZPEMLTimLeCU9AyzdoNU7lX/cB6n/KhxdKPdOus1AZUD/mv1LPYSeUo+ZrWbSZ3MfRu4cSZH2dj+mf/PpIP/EnYbg+fLP0R/kqXQtxoN97dIdLDpYTjC1fAc6Ti11jIKgK4EOgSw4s4D0/HSsjKweuf3l1MuM2zOO6mbV+aPrHyy+uJjFFxbT2b0zLZyLf0lzKuEUy68s5+iNo2Srs9FT6tHIoRF9fPvQyrkVHpYeaCUtc07NYcmlJaTnp/N1q6/L3ZhbK2n55uQ37I/dT2JuIkXaIpo5NWNgnYGEp4UzN2QuR+KPPPI9VZKkEqurojKiuJV/S/RjekwiwSQIzx/xqhcq3R/B11l9Ko7x7X3oUa8C+148Bm1ODtmHDmHevj2KMiRcnha5J08h5efjNONLco4cIWX+AjK3bMXpi88xaVT6D0XawkLiJk5CUqtxbleAwYqWUJgtP6hQySuzebaSE0AO/uBeid/qOfhByFJ5ZTqXxhW75Lfd7SRT3b4VN+azzLM1oJCnzJUxyVSkLeJg3EHaurZlUJ1BrAxbyYorK8o9haFC3TwHWyfLzw1bX4g+DIu2ylV0SZcgNxXMnSBoIgS8LlfEAGi1cuVPyJLiSaaMeLB0LtWhc9W5jNkzhtisWKwMrdgTs6fECyJJkhiwdQCRGZFUM6pGNeNqOJg43P1xNHXEwVT+3c7ErthUsvtotRC5n5OejSnMD6Nl78Xg2AgMTB/jj1bBqnnBh7Hl7mPkZObEtObTePfAu6gUKhrYNyh5Q5eG0H8J3IqCYwvhzF9yhZpPJ2g9ufhiA1qtXAHpHChPhdNqYNv7cjKy9XvlilcQnpSGDg0BOJN0hnZuD1+xNTozmlG7R2FmYMaiTouwNrJmbMBY9sfuZ+rRqXzf/nscTBzIUmfx3env2BW9i2pG1eji0YVWLq1o5tTs3jTV25QKJZMbT8bC0IIFZxbQwb0DXTy6lPl8JEli9snZLLu8jHau7ejq0RUnUye6e3VHqVDia+2LvbE9h+MPPzDJlFeUxx8X/2DpxaWMb/B/9u4zOqrqa8D4MzOZ9B5SISSEGkIJNfQqvYgFG4iigiIqiuAfVF4VERVRwYYgWAAVVEAR6b333kMIhPQC6W0yc98PF4IYSjKZFMj+rcWK3HvuOXswgczOPvu8zBPBT9xwX/oxCSFE8UiSSZSpHeeSmfLPKe4L9ua1++pVSAx5584R/coY8s+fx75tG2p88QU6p1L03ajEsrZtRWNtjXPfvrg+9BAugwYR/+57XBz6JC4PP4T3uHHoXO/8E8uEqVPJPXaM6mMHYxM7U+215N9aTSp5Bpd6K1CJeDcCQ7b6xr/z/yw7d5NH1dPqqrew7Lz3Knt39XPg/Ba1sscMhxIPkZ6fTlf/rtRwqkH3mt357exvjGwysny2hd1JahTMvx901mp1UugQNcG6Z7aaeAjsCM2GQu1uRavftFq1r8+GyZB8Tk0+HVwAy1+CIX9A3duf/pdvzGfMpjGcTDnJ510+Z2/8Xn478xtZhqwib9BOpJzgeMpxutTogr3enpScFM6lnmN7zHZyCnJuGKtBg4edBz72auLJ086TTEMm0RnRZORn8ErQILplJ7PVwQG7AjtaVm+rvv6KZqE+LT0De/J08tOk5KQU+XMswr0W9P0EukyE/fNgzxz4vhc0fQJ6TIYrkbDqDfXAgGr14IFv1cq+hGMw+EfLJsGFKEONqjVCr9VzMPHgbZNM8VnxjFw7EoA5Pebg46CeDmyjs+H99u8zbNUwHv77+g9q7KzsGB06mqdCnsLO6s79yJ5t9CxLw5ey6PSiUiWZfjzxIwtPLWRo8FDeaPVGkUokjUZD++rtWR+1ngJTwQ3VNYqisDJyJZ8f+JyE7AS87b35ZP8nNPVsSki1643C98fvx8vOC38nf7PjFEKIqkCSTKLMRKVkM/qXgwRVc+DzR5ui1Zb/yTrpq1YR+9bbaO3s8Bj1AinfzeXiE0PwnzMbvW/FVFWVpcyt27Bv3RqtnfqNnWOnTgSt+Jvkb74h5Ycfydy4Ce+JE3Du3/+WjTZT5s0jddFiPJ57FucaCZDkoJ4aV1Hbybz/dRKMpfoxXVOro/pLFF9QZzXhkp9t1hvqzZc2o9fqaeenbq8Y1nAY6y6u46+Iv3i8QQU3zC7Ig9+GqZUpz60Cj9rqdVsXNalWnMRa6FDYNFWtZmo4CP65uuXs+NLbJpmMJiMTtk1gd9xuPujwAV1rdsXR2pGFpxayPWZ7kTdfay6swUqjY0qDp3Cpfv2n6oqikGnIJD4rnoTsBBKyEtSPV//7YvpF9sbvxVHvSHVHtbpq7KHP+MDBnu05sYT5hGFdGRJMFvZ6y9dL9oC9u1qV1GY0bJsOO75Qt08astQqtu7/B/vmwdweYGWr/t3UcFDZBC9EGbDR2RDqFcrmS5sZ22LsTb8nuJJ7hefXPU9afhrf9/qeWi439nls6tmUZQOXcfbKWZJzksk15jIgaECJtj/rtDoeqf8Inx/4nPAr4dR1q1vsZ9Py0jiYcJCdsTtZdGYRvQN7M77V+Ft+f9OhegeWnVvG0aSjhSfsHU06ysf7PuZo0lGC3YP5uNPH1HGtw8N/P8z4reP5rf9vOFo7FvZjauHdoswalQshxL1CkkyiTGTmFfDc/H0oCsx9qiVOtuV/NLwhIYGY18dh16QJ1WfOQO/tjUPr1kS//AoXHn0M/zmzsW3QoNzjKiv50dHkR0bi9vhjN1zX2tvjNW4czv37E/d/7xA7/g1Sly7F45lncWjfDo32erPN1GV/kvjJdJz79sFz7Fj1J/i+TSu2X5FXMGi0aj+mGq3uPF6UrVpd1BO0onapvWxKQFEUNl3aRJhvWGHVUlPPpgS7B/P72d95rP5jpf/mPfGUWj3U5gVwrVmyZ1dPUCtUHv35eoKppJy8oV5vdavV8SVqU3nvhmofJ2MB6Ir+s6soCu/vfp91F9cxvuV4BtYeCEBzr+a42bixIWrDDUkmRVFYe34lbbMycdk+Ax5dWHhPo9HgZO2Ek7VTsd6sZRmyePmXrkzwqgbZ8Qxv/Jx5r/teZW2vJpSaPApbP1G3Q7Z/FWwcoeWzasPwU39Dn2kWq7wSorwMrD2QSTsmcSDhQJE+Q1mGLF5c/yIxmTHMum/WLU8HC3INIsg1qFRxPFjnQb45/A2Lzyzm7TZv33JcWl4a+xP2sz9+P/sT9nPm8hkUFKy11vSp1Ycp7afctoF4G7826DQ6tsdsx8/Rj5kHZ7Li/Aqq2VVjcrvJ3F/n/sLnp3WaxvDVw3lv13u82+5dUnJSSMxJlH5MQghRDJJkEhZnMimMXXyYc4mZ/PRMawI8KqavR/befWAy4fN/k9B7qz9Vc2jbloCff+bSyJFcHDKU6jNn4tjh3jglJGvbNgAcOty8Mse2QQMCf/2FK4sWkfz1N1waMQK9vz9OPXpg5eGOYigg6csvcWjXDr+PPkKjXG303XJ4eb6MovR26hY9R8/y3aZ3lzqQcIDtMdsZGjwUDzsPyy8Q0FbdYhi5tcRJpsi0SC5lXOLpkKcLr2k0Gh6u9zDv736fEyknaFStkfmxmUzw5yg1UbR/HrR7Bdq+CNZOaqL0ZkkAkxEubIeji9XEUPsxENzf/BgAWgyH0yvUCpdn16pb8M6uhos71Eqw/5h5cCZLwpcwovEIhoVcPf3MaEAXe5gu/l1Yd3EdBqMB/dUT/Y4nHyc2J5EXs7IhY5fapNzMBIeDRs830Zd4vVYwu4ypdKwhlX035VkfHpp74zU7Vxj0jbqtUg4OEHehXoG9mLZ3Gn+E/3E5lCKBAAAgAElEQVRD8iTPmMeYjWM4dfkUM7rOKNow38JcbV3pHdib5RHLGdN8DE7W11saXMm9wk8nfmJbzDbOXlFP8bTR2RDqGcqo0FG09G5JE88m2Ohs7riOs7UzTT2bsiR8CQtPLcRoMjKi8Qiebfxska20zbya8WLoi3x56EvWXlxLNbtqgPRjEkKI4pAkk7C4GRvCWXsygUn9G9KxrmeFxZG9fz9aR0ds6te/4bpt/XoELl7Epedf4NILL+D73nu4PvRgBUVpOZlbt6GvUQPrWoG3HKPR6XAfMgTXwYPJWLeO1EWLubxgARgMANg2aUL1L75Qm6MnnICCHPANLZ8XcDuP/QxWd/4GsqqLzYzllY2vkJ6fzq+nf2VE4xEMbTi0WN98F5u1g1pRFrmlxI9uvLQRgE41bjyJsE+tPnyy7xOWhC8pXZLp6CI1wXTfe2qCdOs09RcAGrXPkM4adPrrH/MzIecK6B3U5FC3/zN//Wtqd1W3zdXvrVYCetQFKzs18fSfJNOPx39k3vF5DK43mJebvaxeTDwFy56HuCN07zyGZYZM9sbvpX11NSG+9txyrBSFriYbyE6G5HDwNLPnXcx+bA3ZfBH6KimBbcw+2rtKkwSTuEvZWdnRL6gfS8OXMqHVBFxtXSkwFfC/rf9jT/wepnaYShf/LuUSy+PBj/NXxF8sj1jOkOAhZBuyWXRmEXOPziWrIIvWPq15udnLtPRuSaNqjcze1tutZjem759Oz4CevNbiNWo41bjl2BGNR9DEswkHEw5yJOkIDd0bFtkyKIQQoihJMgmLWnUsji82hPNQ8xo80z6wVHNd/uUX8sLDcerSBfs2bdDalOyNcvb+/di1aI5GV/QNgN7Hh4CfFxLzyhji3noLQ2ws1V4afdfuszfl55O1Zw+ug+4v1mvQWlvj0q8fLv36oSgKSnY2xrQ0rLy9r/95xR5WP/rd4jSm8uQu39TdicFoYPyW8RgVI990V7cdzDg4g3Op5/iw44eWXaxuD7W59Y/91cqfOvcVq5JmdeRqmlRrUtg49honayd6BvZk5fmVjG85vvgNwE0mdV2NBvIyYf17ahP3dq+oTbjDXlBPhjMWgDH/6i/Djf+t1arx1+1luabNWh0M+vr6763toXY34s/+w/LqdehQoyPB7sH8ee5PPj3wKb0Ce/FW2Ftq9eDOr2DjFLBxAs9g2uxbiF31amyI2kD76u2vbpX7m3Y5uTj3nwm/PQlRO81PMp3fDBotulqd8LK786EAQoh7y+D6g1l0ZhHLI5bzZMMnmbxrMhuiNjCh9QQG1B5QbnGEeITQpFoTZhyYwVeHviLToJ5o26lGJ8a2GEttVzO3MP/H0OCh3BdwX2FPutvRaDS08W1DG982dxwrhBDiOkkyCYs5FZfO2N+OEOrvygcPNCp1wiZl1rcUJCWR+usiNHZ2OLRrh1O3rjh27oxVtWq3fbYgJYX8iAhcBt1/yzE6R0f8Z39L3P+9Q/LXX2OIjcWxU0cyN28m+9Bh/D6cin2LynvqmCk7m4z169HY2WFMSUHJzsahY8m3umg0GjQODmgd/rOtMe4wWDuCRx0LRSzK0ucHP+do8lE+7fwpHWt0pGONjozbMo4DCQcsv1jbl9WtYLu+hp8fVqvd+k4H/1tvqYhIjeDMlTNMaD3hpvcfrvcwyyOWs+bCmlseL32DgjyY0RgcvdVEV/wxyIxX+xNd6zPm3+q2MZWr4P58nbKLPw9/xZeHv6KmU02iM6Np59eODzt8iO7KBfjzRbi0Gxr0h/4zICMOmzmd6aANYu3FtTTzaoavrQexBVm86BAIwQPAwRMu7oQWT5sX1/nN4Ndc3folhKhy6rnVo4lnE/4I/4PE7ESWnVvGC01fYEjwkHKP5fWWr7PozCLcbd2pZleNUM9Qi/dA0ml1xUowCSGEMJ8kmYRFXM7KZ8T8/TjbWTHnyRbY6ku3faDgyhUKkpLwfPVVbENCyNy0kYxNm8ncsAE0GuyaNMGxa1ecevbEJqholUv2AfWNtX3L239zotHr8Z36Afrq1Un+6ivSli1D5+KCYjSS/M0sas6be9vnK4qSn0/0Sy+RtXNX4TWNtTUOrVtbbpHYQ1ebft+6iaaoHH478xsLTi7giQZP0DOwZ+H1uq51WXNhDdmG7OJXBxWHlTW0HQ2tRsCx32Hj+zDvPmg+DLq/Cw5Fe0GtjFyJVqO95RHVoZ6h1HKpxdLwpcVLMiWeYm9BKnXT8nFb8qx6rfEjlSep9B+pAW1ZdciB/nY1aRE6nFWRq/B39uezjtPRH/gR1v2f2tz+gdlqk2mNRu1D1vJZRhydT3idUN7c/iYaULfKtX1dHRPQDi7uutPyN5ebDtH7ocOrlnypQoi7zOB6g5m0YxKRaZE83uBxXmz6YoXE0dy7eeGpb0IIIe5ekmQSpWYwmnjx5wMkZuTx2/Nt8XIufXPmvPBwAGxDQnDs2AHHjh3wnjSJvNOnydi0icyNm0iaMYOkL7/E7+OPcenf74bns/fvR2Nri11IyB3X0mg0eL40God27UAxYRcaSsp3c0maMYPcs2exrWfmNpQyophMxL75Flk7d+Hz7rvYNm5EQUIiVu5uRauRzGUsuNr0+1nLzCfKhKIofHvkW7458g0dq3csckz7te0FkemRhHjc+WuhxKysmZp/kXU1fHhKH8ojR37F/uRyuO8daP5UYa8aRVFYeX4lYT5hhc1T/0uj0fBQ3YeYvn86f0f8Td9afdHdptfNvnP/8KyvN7Ud/fmx3tO4RmyB7pMs/xot5M+YLeRpNQxPjKWeT3servcwpEXD4iFqNVHtbjDwK3D5z0/Yu75J8ImlLL8QyR5y+N3emgB7b5zr9lbv12wHJ/+C1Evg6l+yoCK3gGKEoK4WeY1CiLtTr8BefHP4G1r7tGZC6wl3besAIYQQlYOUKIhSm7LiJLvPX+ajBxsT6m+ZLRfXkkw2/0rwaDQabIOD8XzxRWr98Tt1tmzGvlkzYt94g9Q//7zh+ez9+7ELDVUbWBeTffNm2LdogUanw/XRR9DY2nJ5/nyLvB5LSvxkOukrVuA5dixujz2KXUgITt26YhdqRoNuRVGrGRTlxutJp6EgF/wqQdNvcVNGk5EP9nzAN0e+YWDtgczsNrNII9QgF/VY6fOp58ssjq3RW8kuyOHTjBP0ql2PuT41yfxnLMztDjFqReGx5GNEZ0bTN6jvbee6v/b91HapzZvb3+T+v+5nydkl5Bvzi4wzmAxMjVpBNaOJ6JxERl1cRla/aeDkc5NZK55JMbH4zGKaO/hTL/k8fNYAvmmr/rq0D/p9BkOXFk0wAdi7Q9/paJx9adP4ST7tOYdXhm663gMroK36McqMaqaza8DGGWpKvxEhqjI7KztWPriSKR2moNXIWwMhhBClI/+SiFJJSM/lp10XGdY2gAeb3/qEjpLKOxuO1sUFK69bn06n9/bGf85s7MNaEzfxTVL/+AMAY0YGeadO33Gr3O1Yubnh8sAg0pf/TUFystnzWFrW7t1c/uEH3IYMwWPEc6WbLC0a5vWAj/xhijd83hg2fagmnOIqUdNvUUSeMY/xW8ez+Mxinmn0DFPaT0Gv1RcZ5+/sj5XGivNp5iWZFEVh7rG5hF8Jv+n9K7lXiMmM4fmmz7OgzwIaeTVlpiaVXrXrMqsggbR598Hfr7LyzO9Ya6zofmI9/P1q0aTmVa62riwZuIRPO3+Kg96Bd3e9S+8lvfnh+A9k5mcWjvvl1C+cM2YyCQ+md57OqcuneGXjKzdNSFUGO2J2EJ0ZzWMtXoYXtqun3zl4QkB7GLUdWj17+8bpjR6E57dC7w+h7n2g/1e1qHcjNVF0cWfJglIUCF+nnoSnK/q5I4SoWqy0srlBCCGEZci/KKJUoq9kA9C1vmWPvc4LD8embp07lmxr7e3xnzWL6JdfIe7tSSgGA1a+vqAopUoyAbgPG0bqr4u48usiPF9+qVRzWYKiKCR+/jlWPj54vTG+dOXsEZtgybNQkA+d/weGHEg4Dls+Uu9np4C1E7hb5jQXYTkZ+RmM2TSGffH7GN9yPMNCht1yrF6rx9/Z3+xKppjMGGYenMmi04tY1H9Rka1uJ1NOAtDIoxGhXqHMum8WJ5JP8O3Rb/nGuJn5joE8fn4Zqx0d6JSXh1Pkz6CYIOx58Aq+6Zo6rY6egT3pEdCDPfF7mHdsHp8d+Izvjn7HYw0eo2dgT2YdmUXHXANdq7dC49+F99q9x9s73uaf8/8Ur59TOVt0ZhEeth7cV/M+NaHj09hyfZC0OvAPK3mSKe6I2ii97s17ZAkhhBBCCGEOSTKJUolPywPAx6X0fZiuURSFvPBwXAb0L9Z4ra0tNb7+ipgxrxL/3mRs6tYBvR67pk1KFYdNrVo4du3KlV9/xWPkCLQ2NqWar7QyN20i98hRfN6ffPtYks7AufWQm3b9V07qjb9PjwHP+upJXNXqqs+ZTLD8JTXRZO2onhgmTb8rlaTsJEatH0VEagQfdvyQ/kF3/hoJcgkiIjXCrPVOpJxQ181JYsymMfzQ64cbtuQdTz4OQLDH9YRRSLUQvuz2JWcun2H20dnMVdajoNA3+HloMBi+CIUzK2+ZZLrm30dHn0g+wbzj85h7bC7fHfsOvdaKCUlJaFqqX+MDaw9k7rG5/BXxV6VLMsVnxbMtehsjm4xEX1YVQwHt4Nw6yEoGh9ufvFkofJ36sW6PsolJCCGEEEJUSfIOUpRKfHouAD4WaPZ9TUF8PKaMDGzq1i32M1pra2rMnIFTjx7khZ/DrlEjtHZ2pY7F/cmhGC9fJmP9+lLPVRqKyUTSjJlYBwTgOmjQrQdmX4Yf+sCaN2HLNDjyK1zcAenRasWDey0I6gJdJsJzG64nmEBNKA34AoIHQn6m9GOqZC6mX+TJVU8SlRHF192/LlaCCdQk06WMSxiMhhKveSLlBHqtno86fsTRpKO8t+s9lH9tdTuecpxA50CcrJ2KPFvfvT6fdfmMpQOXMrH1RLq2Gat+/vk1h9Mrb71oTmqR7XQh1UL4rMtnLB+0nMfqP8akmv2pWVAAvmqSSaPRMLD2QA4kHCA6I7rEr7Msnbl8BgWFjjU6lt0iAe3Uj3+OglMroCDvzs+Er1H/XzhatgpVCCGEEEJUbVLJJEolIT0XaystrvaW+wl9YdPvEiSZADTW1lT/7FOSvv4au6ZNLRKLfZs2WPn5krbsT1z69bvzA2UkfeUq8s6exe/T6Wj0t/mz3jBZfZM+YiP4Nit5JZLOCh6aB9s+hUYPlS5oYTEnkk/w4oYXURSF73t9T6NqjYr9bC2XWhgVI1EZUYWnzRXXyeST1HerT59afYhMi2TWkVn0qdWHDtU7FMYV5ht22znquNWhjlud6xca9IWNUyAjAZy8bxyceAq+7QghD8DAL0B/Y6I40CWQt9q8pX6ea3Tgeb0aakDtAXx56Ev+jvibUaGjSvQ6y1JsViwA1R1v0tTbUmq0hvavwqEFEL4WbFyg4QBo9DDU6lR4yl+hrGSI3g9dJpRdTEIIIYQQokqSSiZRKvFpufg421r0uFtzk0wAGr0er1dfxamrZY7k1mi1uA4aRNbOnRgSEiwyZ0koikL66tUkfPQRNvXr49ynz60HRx+AAz9C2AtQvYX5W92srKHrRPCsd+exosztjN3JM2uewVZny/w+80uUYAIIcr16wlwJm3+bFBMnU04SUi0EgBGNR+Bi48Lyc8sBSMhKICknqcTxUP/qCXNnVxW9d/Q3MBXAsd/Uiry0mJvPEX8MPBvc0ADbx8GHMN8w/or4C5NiKllMZSguMw5rrTXutu5lt4hWCz3eg9fPwJAlaiLvxF+wYBB82gBWvqGeYnetQuzcekCBuj3LLiYhhBBCCFElSZJJlMq1JJMl5Z09i5W3NzoXF4vOay6XQYPAZCLtz7/Kdd3cU6eIenIYMa++hpWHB34ff4TmVokjkxH+eU09wl2qE+4ZqyJXMXrDaGo41WBB3wUEugSWeI5azrUAStyX6VLGJTIMGYR4qEkmvU5P78DebLy0kcz8zMJ+TdfuF5tXQ3ANgDP/STIpCpxYpp529tivkBwOc7pA1J6ic8QfU5tn/8fA2gOJyYzhYMLBksVUhmIyY/B19C2fY8F1evX0uQe+hfHh8Mh8qBmmJp/n3Qczm8L69+DIInDwUvuuCSGEEEIIYUGSZBKlEp+ea9Gm3wC54eFmVTGVFeuaNbFr2YK0Zctu6EdTVgpSUoib9H9EPvgQeRER+Lz3HrWWLsG2QYObP6Aoav+luCPQayrYOpd5jKLsrbu4jje2vkFTz6b80PsHvOzN651jr7fHz8GvxJVMJ5LVJFJDj4aF1/oH9SfPmMe6i+s4nnwcnUZHA/dbfF7eikajVjOd3wz5Wdevxx2BK5HqVrkGfeG59WDtAD/1h4MLro/LTIKMuMJ+TP/WvWZ3HPQOLDu3jKj0KLZGb+VUyqmSxWdhcVlx+Dr4lv/CejtoeL/a3H98OAyaBR51YMdMOL9JrWKSxv5CCCGEEMLC5DtMYTZFUSySZDKmpXF5/nxMeXkoRiP55yKwqVe5tmq5PvAA+RcukHP4cJmtoeTnk/L9D0T06k3qsmW4DxtG7TWrcXv0ETQ63c0fMhbAynHqiXCNH1HfoIu7nqIofH3oa+q61WV2j9k4W5cucVjLtRaRaZEleuZEyglsdDY39HFq6tmUmk41WXF+BSdSTlDHtQ62VmZ8/TfoCwW5ELHxXwsuBa0VNLja0NwrWO0tFtBOPfVw1QT18z3+qHr/JpVM9np7egb0ZHnEcvot68foDaMZvmY4idmJJY/RQmIzY/Fz9Kuw9QGwdYHQJ+DJpeqWukGzoOubFRuTEEIIIYS4J0mSSZgtNdtAfoEJ71Jul7vyyy8kTP2QmFdfIy8iAiU/v1JVMgE49eqNxs6OtKXLSjVP/NSpxE+diinv+ulPiqKQsWkT5wcMJHHaNOxaNCdo+V94T5yAzvk2yQVDLiweCvvmQrtX4IHZapWIuOvtiN1BRFoEw0OGY6OzKfV8QS5BXEi7gEkxsS9+H50Xd+bjvR+TmZ95y2dOpJyggXsDrLTXz4fQaDT0D+rPvvh9HEo8VPJ+TNfUbKsmPo79rlbiXdsqF9QV7P/Vu8jeXe0x1OZF2DMLFj4IkVvUezdJMgG80PQFXmj6ApPbTebLbl9SYCpg2r5p5sVZSnnGPFJyU/BzqOAk0785eqoJJ5cybEQuhBBCCCGqLEkyCbPFp+cClLonU/a+/WgdHMjctInol18GzGv6XZZ0jg449+pF2t9/k3v6tFlzGBISubJgIVfmL+DCY4+Tf/EiWbv3cHHok0SPehG0Wvxnf0vN2bOxCQq684QnlqrNk3t/DD3fl60v95CfTvyEl50XvQN7W2S+IJcgco257IrdxaubXgXg51M/M/DPgayOXF1kG6jRZORUyqmb9lvqH9QfBYWcgpzCpuAlptNDy2fh5F/qSXMxByE16uaVeDor6P0h3P81RO1St3u51AQ7t5tO7efox+jQ0TxQ9wG6+HfhucbPsebCGnbG7Cx2eFujt7IpapN5r+1f4jLjCmMSQgghhBCiKpB3pcJs8WlXk0wu5ldaKAYD2YcP4zJoEF7jXsdwMQo0GmxqFyPJUs48x76GzsWF6BdHU3D5comfz1i3DhQFrwn/wxAbS0T/AUQ9/TSGS5fwnvQ2Qcv/wrFz5+JPGHsIrB2h9cgSxyIqrzOXz7A7bjePBz+OXqe3yJxBLurX06ubXkWn0bGw70J+7vsz1eyqMX7reJ5f9zwX0i4Ujr+YfpHsguybJpH8nf0J9VQbRjfyMLOSCaDbJGj+FGybDkueAa1e3UZ3K82GwtP/gKM31OpU7GWeafQMAc4BfLDnA/KMeXccH58Vz7gt4yxS/RSbGQtQMT2ZhBBCCCGEqACSZKrisnbtInv/frOeLaxkcrEze/3cU6dQsrOxb9USj+eew3PsWFwGDkRrZ/6cZUXv5UWNr76kIDmZmDGvohgMJXo+Y/VqbOrWwePppwlaugTnHvfhPXECtdeuwX3IEDT6EiYU4o6oW4akgumeMv/kfOys7Bhcb7DF5ryWZDIpJr7o9gX+Tv409mzMr/1+5c2wNzmWfIwHlz/IV4e+Ircg944nxz0d8jRNPJtQx62O+UFptdB/BjQfBlcuQJ3ut6xOKuTfGl47Cf0/L/Yy1jpr3gp7i6iMKL4/9v0dx3+892NyCnKIzowmLS+t2OvcTGyWmmSSSiYhhBBCCFFVWN15iLhXKYpCzLjxGFNT8Z38Hq4PPVSi5+PTctFowMvJ/Eqm7H37ALBv0QKAaiNHmD1XebBr3BjfD6YQO/4NLj3/Au7Dh+PQru2tG3NfZUhMJPvAAaqNHg2Avnp1qn/2mfmBmIwQf1yt7hD3jKTsJFZGruSReo/gYuNisXldbV15tP6jtPVrS6jX9WPrdVodjzd4nB4BPZi+fzqzj87mn/P/4Ofoh52VHYHOgTedr3tAd7oHdC99YFot9J8J3o0hqJhVfLqS/7PV1q8tfQL7MPfYXPoF9aOmc82bjtsavZX1Uetp59eOnbE7OZlykrZ+bUu83jWxmbHoNDqzTwYUQgghhBDibiMlEFWYIToaY0oKOldX4t56m+RZs4r0ZrmdhPRcPBxs0OvM/zTK3rcf68BArDw9zZ6jvLkMGID3xAnknjzJpREjONejB0lffY0hLu6Wz1zbKufcu5dlgkiJAEMW+Da1zHyiUlhwcgEmxcTQhpZPHr7d5m2617x5YqiaXTU+6vgR83rOQ6/Tszd+L8Huwei0t0+eWoRWC2EjwbN+mS4zvtV4rHXWfLDng5v+PZdbkMuHez6klkstpnaYClBY0WWuuKw4vO29b2ieLoQQQgghxL1MkkxVWM7hwwD4fzsL54EDSJr5BfGTJ6MYjcV6Pj49t3T9mIxGsg8cwL5VK7PnqCjuTz1Fna1bqP75Z9gEBpL81Vec69adqJEjSV+3rshWuozVa7CuXRubOqXYXvRv145xlyTTPSMtL43FZxbTp1Yf/J38KySG1r6tWTJgCW+FvcUrzV+pkBjKiqe9Jy81e4mdsTtZc3FNkfsLTy0kOjOat8PexsPOg5pONTmZcrJUa8ZmxuLrKP2YhBBCCCFE1SE/Xq3Ccg4dRmNvj21wMH4ffYTey4uUufMwJqfgN/0TtDa3TyDFp+VSw83e7PXzwsMxZWRg36ql2XNUJK21Nc59+uDcpw/50dGkLllC2pKlxLz8CjoPD1wfGITrww+jdXAge/9+qo0aZbnF4w6DzqbMqz9E+fnl1C9kF2TzbKNnKzQOvU7PYw0eq9AYyspj9R/jr3N/MW3vNDr4dcDR2hGAfGM+P5/6mfZ+7Wnt2xpQ+1EdSTpSqvVis2Jp5X33JdGFEEIIIYQwl1QyVWE5hw9j17gxGisrNFotXuPG4T1xAhnr1nHp2ecwpqff9vnSVjJl71Mbjtu3vDuTTP9mXaMGXmPGUGfjBmrM+ga70FBSfviRiN59uPD4E6AoOFlqqxyoTb+9G6pHwYu7XpYhi4WnFtLVvyt13epWdDj3LJ1Wx6Q2k0jOSebrw18XXl99YTXJOckMazis8FpItRBis2K5nFvykyQBDCYDidmJUskkhBBCCCGqFEkyVVGmnBxyz5zBLjT0huvuTz2F36fTyT5yhItDhmKIj7/p87kGI6nZBnycbc2OIXvfPvR+fuj97p2TlzRWVjh17Yr/119RZ+NGPF97DTQa7Jo2xaauhZIHigJxR2Wr3D3kj7N/kJ6fznONn6voUO55jT0bM7jeYH45/QunUk6hKAoLTi6gjmudG5p8N/RoCGD2lrnE7ERMionqjtUtErcQQgghhBB3A0kyVVG5x4+D0YhdaNFEhUu/ftScMxtDbCwXHn+CvHPnioxJSM8FwNvMJJOiKGTv339X9mMqLr23F9WeH0mddWsJXLwIjUZjmYlToyA3FXyaWGY+UaHyjfn8dOInwnzDaOIp/0/LwyvNX8HVxpUpu6ewJ34Ppy+fZmjw0Bu+RoPdg9Gg4USyec2/YzNjAfB1kEomIYQQQghRdUiSqYrKvtr0267pzathHNq2JWDBfBSDgQtDhpJ98NAN9+PT1CSTj0vJk0yKyUTyrFkYL1/GvvW9m2QqM3FX+8T4ht5+nLgrrIpcRVJOEs+EPFPRoVQZLjYujGs5jqPJR/nf1v/hZuNGv6B+N4xxtHYk0CXQ7BPm4rLU0yb9HO+dSk0hhBBCCCHuRJJMVVTO4SPoA2pi5e5+yzG2DRsSuOhXrFxdiRo+nIyNGwvvxV+tZPItYZLJmJZG9KgXSf7iS5z79cO5f3/zXkBVFn8UNDq1J5O4qymKwsJTC6ntUvuGrVqi7PUP6k8rn1Zczr3MI/Ufwdaq6N9lIR4hZieZYjJjAPBx8ClVnEIIIYQQQtxNJMlUBSmKQs6RI9iH3rkSxrpGDQJ+/QWbevWIfullUpcsBa5XMhV3u5xiMpG2fDnnBz1A5s6deE96u1gn2ImbiDuiniqnt6voSEQp7U/Yr27VajjUctspRbFoNBreafsO99W8jyeCn7jpmBCPEBKzE0nKTirx/HGZcXjaeWKjk7/jhBBCCCFE1WFV0QGI8meIicGYnFyk6fetWLm7E/DTj0S/9DJx776LbaMQ4tMLcLDW4WR759PNsnbuJGH6dPJOnsK2YUNqzPj8ltv0RDHEHYHa3So6invC0aSj/Hr6Vxz1jrjbutPWry2hXuW3DXHhyYW42rjSP0gq+ipCgHMAn3f9/Jb3Q6qFAGrz7872nUs0d2xWrJwsJ4QQQgghqhypZKqCcg5d7cdUzCQTgNbeHr9Pp6NzdiZ24kSSrmTifYetcrlnzhA1YiRRzzyLKTUNv08+IfCP3yXBVBrpcZCZIE2/LSA1N5Uxm8aw6dImVkau5Jsj30uogqwAACAASURBVDB8zXC2XNpSLutfyrjEpkubGFxv8E23aomKV9+tPlqNtvD0v5KIy4zDz0H6MQkhhBBCiKpFkkxVUM6hQ2js7LCpW7dEz1m5ueHzzv+Rd/IU9Tcuw+cWW+UM8fHETnyTyEEPkHPkCF5vvEHQqpW4DOiPRnuPfMrt+AL2flf+60ZeTYAEti//te8hiqIwefdkUvNS+bH3j+x4fAfbH9tOPbd6vLb5NXbE7Cjz9X868RM6jY5H6z9apmsJ89nr7Xmh6QtsjdnKwGUDWXF+BSbFdMfnknOSic2KlabfQgghhBCiyrlH3vGLksjavRv7li3RWJV8t6Rzz5449+1Ll70raJiTcMM9U24uiZ99TkSv3qSvWIH78OHUWbsGj2eG31u9l/IyYdNUWP8u5JasuqHUIjaCfTXwbly+695jVpxfwbqL63gp9CUauDcA1BPH5vSYQ5BLEGM2jWF33O4yWfvslbOMWDuCxWcWM6D2ALwdvMtkHWEZo5qOYlG/Rfg5+jFx20T6Lu3LrCOziM2Mvel4RVF4d+e7aNFyf+37yzlaIYQQQgghKpYkmaoYQ2ws+efP49C+ndlz2I+bQLrenr6LP8eQoCaaFIOBmNfGkjJnDk49exK0ahXeb4xH5+pqqdArj7OroSAH8jPh8C/lt67JBBGboHZXuFcqwipAQlYCU/dMpblXc54OefqGey42LnzX8zv8nfx5ecPL7IvfZ7F1r+ReYcruKQz+ezCnr5zmzbA3+b+2/2ex+UXZCfYIZkGfBUzrNA1/J39mHZ5F7yW9eW7tc6w4v4LcgtzCscvOLWNL9BZebfEqQa5BFRi1EEIIIYQQ5U/eqVYxmTvUbUCOHTqYPcfpHC3vtnkGm+wMop55loKUFOLefpvMTZvweef/qP7JNKxrVLdUyJXPiWXg6APVW8LeOWrypzwknoCsRAjqWj7r3aN+Pv0zOQU5TGk/BZ1WV+S+m60bc3vOxc/Rj9EbRnMo8VCp1jOYDCw8uZB+y/rxx9k/eKz+Y/zzwD883uBxrLRy9sLdQqfV0adWH77r+R2rH1rNqNBRRGdEM3HbRLr+1pX3dr3HxqiNfLz3Y1r7tGZI8JCKDlkIIYQQQohyJ0mmKiZr+w6svL2xrl3b7DlOxqUT7uaP2+dfYIiOJqJvP9L+Wo7nmFdwe/xxC0ZbCeWmQ/g6CBkEYS/A5Qh1C1t5uLZObUkymSvPmMey8GV09e+Kv7P/Lcd52Hkwt+dcvO29GbV+FEeTjt5x7mxDNm9tf4svD31JXGYcANuit/HQ8of4eN/HNK7WmCUDlzAxbCIuNi4We02i/Pk5+jGq6ShWPriS73t9T1f/rqyIWMGYTWPQarRMaT8FrUb+eRVCCCGEEFWP/Bi9ClGMRrJ27cKpx31oNBqz5zkZm46nkw1+XdrjNHMGMS+/gvvTT+PxwgsWjLaSOrMKjHkQ8iD4NYO1b8He2VD3vrJfO2IjeAaDszQTNteaC2tIzUvl0QZ3brbtae/J3J5zGb5mOC+se4Hven1HiEfITcfmGfMYs2kMe+P3oigKc4/NJcgliHOp5whwDuCrbl/RqUanUn3dicpHq9HSyqcVrXxa8WbYm6y7uI6azjXxdfSt6NCEEEIIIYSoEPKj1iok9/hxTOnpOLYv3clkp+LSaejrDIBTly7U27sH7wn/qxpvoE8sA+fqUKMVWFlDi+FqZVNKRNmum58NF3dB7W5lu85dYPWF1aw8v5Lzaecxmowlenbx6cUEOgcS5hNWrPHeDt7M6zkPZxtnRq4dyZnLZ4qMKTAV8MaWN9gdt5vJ7Saz6qFVPNPoGfRaPeNajmPZwGV09u9cNb4+qjBHa0ceqPsALbxbVHQoQgghhBBCVBipZKpCMrdvB40G+7ZtzZ4jv8BEeGIGnet7Fl7T2tlZIrzKLycVzq2H1iOvN95uORy2TYd9c6H3h2W3dtROtYKqiieZYjJjGL9lfOHv7azsaO7VnDDfMMJ8w2jg3uCW25ROpJzgaPJRJrSeUKKEj6+jb2FF04i1I/j7gb9v2O42bd80Nl7ayITWE7i/jnqa2JjmYxjTfIyZr1IIIYQQQggh7k6SZKpCsnbsxLZRI6zc3Mye41xiJgajUljJVKWc/BNMBmj04PVrTj7QcBAcWghd3wIbx7JZO2IT6KwhwPxTAe8FWy5tAeCrbl+RmpfK8eTj7I3fy2cHPgPU0+Fa+7SmjW8bwnzDqOlUszChtPj0Yuys7BhYe2CJ163hVINPO3/KkJVD2Bi1kQfqPgCofZiWhS9jUJ1B0uhZCCGEEEIIUeVJkqmKMGZkkHPkCB4jnivVPCfj0gFo6FeFkkyKAnu/gzVvgndjqP6f7TBhz8PxP+DoImhVuj/fm8pNg9P/QM22YG1v+fnvIluitxDoHEhn/84AhZVDidmJ7Inbo/6K38O6i+sA8HHwwc3GjSt5V0jMTuTBug/iZO1k1tqNqzXGz8GPDVEbCpNMO2N3kmvMpX9Qfwu8OiGEEEIIIYS4u0mSqYrI2rULjEYcO3Qo1TwnY9Ox0+sI9HCwUGSVnCEXlr8Mx36Der3hgW/hv1utarQC31A1EdXy2aL3SyMzERY+CGmXoM/Hlpv3LpRlyGJf/D6eaPBEkXte9l4MqD2AAbUHoCgKURlR7Inbw974veQW5FLXrS4eth6lqjbSaDR0q9mNxWcWk2XIwkHvwPqo9bjauEofHiGEEEIIIYRAkkxVRsa69ehcXLBr2rRU85yMS6OBrxM6bRVpYnzkVzXB1OVN6DT+ei+mf9No1GqmP0dB5BYI6mKZtS9HwoIHIDMBnlgMdcrhBLtKbFfsLgwmQ2EV061oNBoCnAMIcA7gkfqPWDSG7jW7s/DUQrbFbKObfze2XNpCj4AeWGnlr1IhhBBCCCGEkNPlqgBTXh6ZGzfi1LMHGr3e7HkUReFkbDrBVakfU+RWcPKFzm/cPMF0TciDYO8Be+ZYZt344/B9L8hNhaf+rvIJJoDNlzbjZO1EqFdohcXQzKsZ7rbubLy4kT1xe8g0ZHJfgPy/EUIIIYQQQgiQSqYqIWv7dkxZWTj16l2qeWJSc0jPLag6Tb8VBS7ugMCOd94Cp7eFFk/D9s/hykVwCzB/3Ys74ZfH1Cbiw5aDVwPz57pHmBQT22K20cGvA3qt+YnS0tJpdXT178rqC6ux0lrhoHegjW+bCotHCCGEEEIIISoTqWS6B5ny81EUpfD36atWo3N1xSGsdanmPRlbxZp+p0SoW9UC2xdvfMtnAQ3sm2v+mqdXqlvkHL3gmTWSYLrqWPIxLudevuNWufLQvWZ3sgxZrDi/gk41OmGts67okIQQQgghhBCiUpAk0z0m9+xZznXqTPw77wJgys1Vt8r1KN1WOVBPltNooIGPeadzVWrZl+GngXBwwfVrF7erHwOK2SzdpToE94eD8yE/u+QxHFoIi4eCV0M1weTqX/I57lFbLm1Bp9HRoXrpGtdbQphvGA56BxQU7qspW+WEEEIIIYQQ4hpJMt1DDHFxXBoxElNWFqm//caV339Xt8plZ+Pcp3Rb5bLzC1h5LI46no7YW99juywNOfDr42rT7s0fgrFAvX5hBzh4QbW6xZ+r9fNqH6Vjv5Ushu0z4K/RUKuT2oPJwaNkz9/D1lxYw6Izi2ju3RwXG5eKDgdrnTWda3TGzsquUiS9hBBCCCGEEKKykCTTPcKYns6lkWqCKXDxIhzatSPh/Skkz/kOnZsb9q3N3yqnKArjfz9KeGImb/YLtmDUlYCxAP54Bi7tgWZDIT0Gzq37Vz+m9nfux/RvAe3Au5HaAPxfWxZv6+xaWP+O2jz8icVqLyZBRn4GE7dNZNyWcQQ4BfBu23crOqRCb7R6gx97/4i93r6iQxFCCCGEEEKISkOSTPcAU34+0aNfIu/CRWp89SW2DRvi9+l0dB4e5B49qm6VszK/+ujrTef451gc/+vdgK71vSwYeQVTFPjnNTizEvp+Av1ngKMP7P8erlxQE04BxezHdI1GA61HQuIJNUlVHHu+VU+we3AOWNmU+GXcq2YenMmqyFWMajqK+X3nU9O5ZkWHVMjDzoOGHg0rOgwhhBBCCCGEqFQkyXSXU0wmYv/3P7L37cPvww9xaKOedGXl5kaNmTOwrlUL10ceMXv+dScTmL72LINC/Xi+U5Clwi57uemwdTrMaAyn/r75mM0fqv2TOo6D1iNAp4fmwyB8HRxZpI4JNGM7VOPBYOsKe2bfeWxKBERsgBbD1fVFod1xu+lYvSMvhr5YoSfKCSGEEEIIIYQoHkky3eUSP55GxqrVeI0fj0v/fjfcs2vShNqrVmLXKMSsucMTMnht8WEaV3fho4eaoCnJtrGKdHCBmlza+D7kpMKat6Ag/8Yx++bBlo+h2ZPQ7e3r15sPU6uRtn0K9h7gacbpbtb26jyn/4G06NuP3TcPtFbQ4qmSr3MPS85J5mL6RZp7N6/oUIQQQgghhBBCFJMkme5iKT/8yOWffsLtySdxf2b4bccqikJ8Wm6x507Nzue5+fux1euYM6wFtnpdacMtHyYjrJ4A7kEwYhM8/AOkXoSDP10fc3I5/PM61OujbpH7d/LM1R/q9gSTQe2vZG5irdVzgKImkW4lPxsOL4TggeDkY94696hDiYcAaObVrIIjEUIIIYQQQghRXJJkukul/fMPiR9/jFOvXnhP+N8dq4zmbY+k47SNxKTm3HHuAqOJl389RGxqDrOfbI6vi52lwi57yeGQn6luf6veHOp0h5rtYOsnalLnwnZY8hzUaAUPfw+6m/SqavmM+jGwo/lxuAWoSayDP4HhFsm9Y79Dbpoaq7jBwYSD2OhsCPEwrwpPCCGEEEIIIUT5kyTTXShr9x7iJkzErmUL/KZ9jEZ3+yqjXIORb7dEYDAqbDydeMf5P1p1mm3hyUwZ1IgWAe6WCrt8xKoVMPhd3Wal0UD3SZCZAKvegF+fALdA9RQ361ucDFa3Jzw0T91KVxphIyE7BY4vKXpPUWDfd+DVEGq2Ld0696CDiQdpXK0xeulTJYQQQgghhBB3DUky3WXyzkcS/dJL6ANq4v/112ht7nwa2aK9USRn5uNgrWPTHZJMSw5EM3d7JE+3C+TRViU7zUtRFC6mXyzRMxYXexD0DlCt7vVrAe2gTg84tACsHWDoErC/TfJMo4HGD986CVVctTqrPZ32zlaTSv+2by7EH4M2L5q/Je8elWXI4vTl09KPSQghhBBCCCHuMpJkuosoJhNxkyaBTkfNOXPQubjc8Zn8AhOzt56nVaAbg1v6szMimVyD8aZjD0VdYeKyY7QN8uCtfsEljm9j1Eb6L+vPX+f+KvGzFhNzEPxCQfuf6q6eUyCoq5pgcvUvn1g0Gmg9EuKOwKW916/HH1ObkdftCaFDyieWu8iRpCOYFBPNvSTJJIQQQgghhBB3E0ky3UVSlywh58ABvN8Yj97Pr1jPLDsUTVxaLqO71qFrAy9yDSZ2nU8pMi4hPZfnFxzA29mGb4Y0R68r+afG9tjtAEzZPYXwK+Elfr7UCvLVBI7fTZpFezWAYX+Cd8PyjanJo2Djop50F38M8jLh9+Fg5waDZoFWvgT/61DiIbQaLU09m1Z0KEIIIYQQQgghSkDe4d4lCpKTSfxkOvYtW+Ly4IPFe8ZoYtbmCBpXd6FzPU/Carljpy+6ZS7XYOT5BQfIzCvgu2EtcXOwNivGvXF7CfUMxUHvwNjNY8k2ZJs1j9kST4IxT234XVnYOELn8XBxB3zbAT5rCCnn4ME54FCtoqOrlA4mHKS+W30crR0rOhQhhBBCCCGEECUgSaa7RMLH0zDl5OAz+b07niR3zZ7Iy1xIyeb5zkFoNBps9Tra1/Fg4+lElKs9ghRF4a1lxzl8KZXPHmlKAx9ns+KLy4wjKiOKnoE9mdZpGlEZUby7693CdcrFf5t+VxbtXoZx4TBgJtQMg14fQFDnio6qUjKYDBxNOkozr5tUowkhhBBCCCGEqNQkyXQXMMTEkP7333g88ww2QUHFfm5reBJ6nYau9b0Kr3Vt4EX0lRwikjJRFIVvNkew5GA0Y7rXpXcjX7Nj3Buv9hxq7dOa1r6tGR06mlWRq/j97O9mz1lisQfVbWhugeW3ZnE5VIMWT8OQ36Ht6IqOplyl5KRwKuVUscaeSjlFrjFXmn4LIYQQQgghxF3IqqIDEHeWd+4cAI6dO5XouW1nk2le0w0Hm+v/m68lnH7Zc4mIpEy2nE2iX2NfxnSve6tpimVv/F7cbNyo66bO81zj5ziYeJCP9n5ESLUQQjxCSjV/scQcUvsxyWltlcbRpKOM2TSGtLw0tj22DQe9wy3HGk1Gvjr0FdZaa1p4tyjHKIUQQgghhBBCWIJUMt0F8iIjAbCuVavYzyRl5HEyLp1O9TxvuO7nakcDHye+3xHJvguXeW9gCF8+3gyt1vzEjKIo7I3fS0uflmg16qeUVqPlww4f4m7rzuubXyc9P93s+YslP1vtyVTZtspVIefTztN7SW9Grh3Jb2d+Y2n4UoavHk5eQR4Gk4EDCQdu+/w3R75hV9wuJoZNpJqd9KsSQgghhBBCiLuNJJnuAvmRF9C5uGDl5lbsZ3acSwagU13PIvee7xxEv8a+rHm1E0+1CyxVggngUsYl4rPiCfMJu+G6m60b0ztPJyErgUnbJ92xP9Ppy6d5Z+c7fLD7g5IHEX8MFGPlavpdhVzJvcJLG14i25BNbFYs7+9+n3d2vkNjz8YsvX8p1lpr9sTtueXzW6O3MufoHAbVGcRDdR8qx8iFEEIIIYQQQliKbJe7C+RHRpaoiglg69kk3Oz1hPgVbeT9QLMaPNCshqXCY0+8mjxo7du6yL1Qr1Bea/Ean+z/hPkn5/NUyFNFxsRlxjFh2wQOJh4svPZSs5dwsXEpfhCVtel3FWAwGnht82skZCXwfe/vaVKtCeGp4ZxPO093/+7odXpCvUIL+3b916WMS0zYNoFg92DeCnur2I3thRBCCCGEEEJULlLJdBfIv3AB68DAYo9XFIWt4cl0qOtZ6iql4tgbtxcvOy8CnQNvev/Jhk/SvWZ3ZhyYweHEw0Xuzz46mxMpJxjXchwzu84EuOm4IvIyYN88WPwkbJoKTr7gbH7zcmGeKXumcCDhAJPbT6apZ1M0Gg313OrRO7A3ep0egDDfME5fPs2V3Cs3PJtbkMvYzWMB+LTLp9ha2ZZ7/EIIIYQQQgghLEOSTJWcMTOLgsTEElUynY7PIDkzj451y76vzbV+TK19W9+yAkWj0TC5/WR8HHwYt2XcDYmG9Px0VkaupH9Qf54KeYp2fu2w0lrdUNV0UykR8F13+GcsxByE4AHw0FxLvjRRDKsvrGZp+FKea/wc/YL63XJcax+1yu3f1UyKovDBng84ffk0H3X8CH8n/zKPVwghhBBCCCFE2ZEkUyWXf+ECANa1Aov9zLbwJODm/Zgs7dTlU1zOvVyYRLgVZ2tnPuvyGVdyr/DervcKry8/t5ycghwerf8oALZWtoR4hHAo8dCtJzu/Gb7rBllJ8OSf8NpxGPQ1BHawxEuqdDLyMxi7eSx/R/xd0aHcICErgfd3vU/jao15MfTF245tVK0RDnqHG/oyLQ1fyp/n/uT5Js/TqUbJTk4UQgghhBBCCFH5SJKpksu/erKczb8qmYwmhZx8I2nZBhIzcom+kk1EUian4tI5cimVtScSqOftiI9L2W89WndxHTqNji7+Xe44NtgjmNHNRrMhagPrLq7DpJhYfGYxTT2bEuwRXDiuuVdzjicfJ8+Yd+MEigJ7ZsOCB8HZD0ZshNpd4R7u4ZOen87ItSNZd3EdXx76EqPJWNEhAWBSTEzaMQmDycDUDlPRa/W3HW+ltaKFd4vCSqYTKSeYumcq7fzaMarpqPIIWQghhBBCCCFEGZPG35VcfmQkaLWc1rqwfu0ZtoYncyw6FdPtD2rj+U5BZR6boiisvbCW1j6tcbMt3sl3wxoOY3Xkaj7Y/QFGxciF9AtM7TD1hjHNvJrxw4kfOJ58nBbeLdSLBfmw8nU4OB/q94MHZ4ONk6VfUqWSlpfGyHUjCb8SzuB6g/n97O/sjN1JxxodKzo0fj39K7vidjGpzSQCXQKL9UyYTxhbo7dy5vIZxm4ai4edBx91/AidVle2wQohhBBCCCGEKBeSZKrk8i9Ekl/Nm/u/24dOqyHU35XnO9fG2VaPtZUWaystNjotNnot1rqrv7fS0SKgeEmf0jh75SxRGVE83ejpYj9jpbVicvvJPLbiMSZum4ibjRs9A3veMKaZVzMADiUeUpNMmUnw25MQtQs6joOub4H2xiK8ffH72HRpE/nGfApMBfQK7EVbv7alfo0VJTU3lZHrRhKRGsGMrjNo69uWDVEb+OPsHxWeZIpIjeDzA5/TsXpHBtcbXOznwnzDABi5biQZ+RnM7zO/2MlJIYT4//buPMzKuv7/+PMzGzDDsDPIIqsICIos4tbXFBQJMjRzyTJFMbO+avWzMssyyy6XMnet1AC/uFRYqYACipgLKogoKMYy7CC7sg0wM5/fH+dIgAPCnBnOmfH5uK77Omfu+3Pf9/s+vC9lXtz350iSJCnzGTJluA0fzOPd0JBjOzThT9/qS8N6+34s6WCasGgCWSGL/of2P6D9ujbpyrAew3jw3Qc5q/NZ1Mmus9v2RnUb0alhJ6Z/OJ3hzfrB4xfA5jXwtYehx9mfOt7T85/m+leuJycrh/ycfHaU7+Dp+U/z54F/pneL3ildYzqsL1nPZRMuo/ijYu7sfydfaJ2Ya2roYUMZNXsUq7espnl+9c+3VZEdZTv46b9/Sn5OPjeeeONeJ3uvSOfGnWlcpzHrStZx/XHX06NZj2qsVJIkSZJ0sDknUwZbum4zOxYuYkOzljzwzT4ZFTB98qjcMS2OoWm9pge8/3d6foere1/NsO7DKtzeq0UvZq6cRtnDp0Msh0vGVxgwjZw9kutevo6+Lfry4rkv8tL5LzH+q+NpVb8VV75wJQs+WnDAtaXTupJ1XDrhUhZ+vJC7+9+9M2ACOLvz2ZTFMv45759pq+/+mffz/rr3+eXxv6RZvQP79sKskMXFPS7mkh6XHNAdUJIkSZKkmsGQKUN9tGUH19w3kTpl2znt9GNoXJCX7pJ2M2/DPBZ+vJDT2p1Wqf3rZNdh+JHDaVS30ac3bt9C72Wz2VhWwrwWXeCyydAq8Qjdh5s/ZNyCcfz6tV9z5j/P5HfTfsfAdgO579T7qJ9XH0jcCXX/qfeTk5XDdyd9l7Vb11b6Oqva0o1LWV+yvsJta7eu5dLnLmXJx0u4u//dnND6hN22t2vQjn6H9GPM3DGUx/KDUe5uZqyawUOzHuLMw85kQLsBlTrGJT0u4Qd9fnBAd0BJkiRJkmoGH5fLQJu2lXLxiDfIWbgIgDZHdfuMPQ6+CYsmEAiVDhsqFGNi3qWnv0+vDfPg0NZM6HUm73/4GtNmTmP6h9NZumkpAAW5BRxddDTndDmH87uc/6nJo9sUtuG+AffxjXHfYPT7o7mq91VVV2clxBh54oMnuPXNW2lctzEPnPoAnRt33rl9zdY1DH9uOMs2LeOeAffsnL9oT2d3Ppuf/PsnTF0xlRNanVDhmOqwecdmrvv3dbQsaMm1/a49aOeVJEmSJNUchkwZpmRHGZeNnMY7Sz9iZJc8eBnyOnRId1lA4tG0f8z9B5tLN7N261r6tOhzwI9MVah0G8x6El6/H1bMhPotaH3+3yia8Vv+NOshABrVaUTvot58vevX6XNIH7o07kJO1r7bt3uz7vRu0ZvJSyanNWRau3Utt7xxC+MXjuf4lsczf8N8Lnr2Iu465S46N+7Mq8tf5f6Z97Ny80ruO/U+jjnkmL0ea0C7ARROLeSZ+c9USci0Zusaij8qpldRr31+nre+eSvLNy/nL6f/hYLcgpTPK0mSJEmqfQyZMkiMkasem8HU4rXcfm5PDhs7gw35+eQUFaW7NJ4tfpbfTfsdPZv3pHuz7hTkFjC009DUDrpxJUx7OLFsXg3NusCQ26Hn+YS8An6Tn8/ijxfTp0UfOjbqSFY48Kc7+x/an1vevIXFHy+mbYO2qdX7GT7a9hHzN8xn3oZ5zN8wn/kb5jN3w1zWlawjK2Rxde+ruaTHJazcvJLLJ17OZRMvI8ZIWSyjad2m3DfgPvoe0nef56iTXYfT2p/Gs8XPsrV0K/Vy6qVU8w2v3sCUpVNoUrcJp7c/ncEdBtOzec/dHmd7fvHzPDn3SYYfObxGTqQuSZIkSTo4Qowx3TVUib59+8Zp06alu4yUTZi9ktWbtvGNY9ux+NLhlK5fR8cnn0xrTR+s+4ALx19I1yZdeWjgQ+RmpzgB+bLpMPUBmP0PKC+Fw0+HYy+HjqdAFc/Vs2zTMgaNGcQ1fa/hou4XVdlxV25eySvLXmHehnk7Q6XVW1fv3J6fk0+nRp3o1KgThzU6jH6H9KNb0/8+9rihZAN3z7ibhnUaclKbkziy2ZGfeuRvb95Y8QaXTriU2066jUEdBlX6GtZsXcOpfzuVUw49hayQxZSlU9hWto3W9VszsP1ACnIKWFuylvHF42lZ0JLRg0en/mcvSZIkSapRQgjTY4z7viMiyTuZMszA7ocAibuati1YQH6vXmmtZ0PJBq6efDWFuYXcfvLtqYUMi16DSTfAkqmQVwjHDId+l0HTTlVW755a129Nl8ZdeGHxC1UWMq3asorznjmPdSXrqJdTjw4NO3B8q+M5rNFhO0OlQwoO2eedV43qNuL646+v1Pn7tOhDUX4RYxeMrTBk2l62nUD4zD+rsQvGUhbLuKr3VXRo2IFN2zcxeclkxhaPZdTsUZTFMgrzCmlTvw03/8/NBkySJEmSpH0yZMpQ2+fNo3TFCupdNjxtNZSWl/Ljl37Mqi2rGDFoROXnX1ozNxEuzXkG8HwB1wAAFiJJREFUClvCoFvg6AugboMqrXdv+rftzwMzH2Dt1rU0rdc0pWPtKN/BNVOuYWvpVkYPHk2PZj0q9RhfKrKzsvlS+y8x+v3RbCjZsNs39G3esZlvjf9WYiLxI4dzbpdzqZNd51PHiDHyr/n/4qhmR9GhYWLOr/p59Tmj0xmc0ekMtuzYQk5WDnnZmfWthpIkSZKkzHVwfzvWfvv4uQkQAg1OOy1tNdz11l28tuI1fn7czzmq+VEHfoBNq2Hs/4N7j4UFU6D/z+HKt+C47xy0gAkSIVMk8tLSl1I+1h3T72DGqhnccPwNHNX8qIMeMH1iSMchlMZSJiyasHNdeSznZy//jHkb5tG2sC23vnkrg8cM5vE5j7O9bPtu+89ZN4e56+fylU5fqfD4+bn5BkySJEmSpANiyJShNk6YQL0+vclp3jwt5x9fPJ6/zP4L53U5j692/uqB7bx9C0y5De46GqaPgL6XwFUz4KQfQV5+tdS7L10ad6FVQSteWPxCpY9RUlrCw7MeZtR7ozi/y/kM7ji4Cis8cF2bdKVjw46MXTB257oHZj7A84uf55q+1/DI4Ed4aOBDtC5szU2v38SX//FlxvxnDDvKdwDw1PynyM3KTWlOJ0mSJEmSduXjchloW3Ex2/7zH1pc99O0nP+DdR/wi1d+Qe+i3vzkmJ/s/44xwoz/g8k3wcYV0O0MGHADNDus2mrdHyEETml7Cn/74G98vP1jGuTt/11U28q28ej7jzJy9kjWlqzlxNYn8qNjflSN1e6fEAJDOg7h7hl3M2jMIJrWa8o7q99haKehfLPbNwHo17IfIw8ZyavLX+WeGfdww2s38OC7D3J5z8sZVzyOkw89mYZ1Gqb5SiRJkiRJtYUhUwbaOGEiAIVpeFTuk4m+G9RpwO9P/v2BTfb84s0w5WZo0w/OGQFtj6u2Og/UkA5DeGzOY1w0/iLu7n83bQrbfOY+H237iKteuIq3Vr3FcS2P47ajbqNvi76EKv4GvMo6r8t5lJSWsGLzClZvWc1XOn2FXxz/i93qCyFwYusTOaHVCUxZOoV7376X619JTDi+t0flJEmSJEmqjBBjTHcNVaJv375x2rRp6S6jShR/9WxCbi7tn3g89YNt3QD1Gn32OBITfV8x6QqmfzidEYNGHNg8TPOeh/87G3p+Hc68DzIkiNnVq8tf5Zop15Adsrn95Ns55pBj9jp22aZlXDHpCpZuXMpNX7iJL3X40kGstPqUx3JeWPwC7655lyt7XUlOljmzJEmSJGnvQgjTY4x992esczJlmO1LllDy3nsUDhyY+sFWvgu3doTZ/9yv4Xe9dRdTV0zl+uOuP7CA6aNl8ORlUNQNhvw+IwMmgBNancBjQx6jcd3GfHvCt3lizhMVjlu1ZRUXjruQNVvX8MfT/lhrAiaArJDFqe1O5Qd9fmDAJEmSJEmqUoZMGWbno3KnV0HINPNxiGWJx9jKy/c5dNeJvs/qfNb+n6O8DP5+CZRug3NHpWVi7wPRrkE7Rg8ezfGtjuc3r/+G30z9zc7JsAFijPzqtV+xcftGRgwasc+7nSRJkiRJ0n8ZMmWYTVOmUPeII8hr89lzBu1TeRnMGgP5TVmz9gN+Nu5iBvx1AHPWzfnU0EpP9A3w/lOwZCoMvg2adU6t5oOkMK+Qu/vfzbAew3jigye4fOLlrC9ZD8AzC57hpaUvcWWvKzm88eFprlSSJEmSpJrDkCnDHDrsKFqf3S71Ay16lbhxBY/1+RpfaduGcWtmsKN8B9+b9D1Wbl65c1hKE33HCC//AZoeBkedl3rNB1F2VjY/7PNDfvuF3zJz1Uy+PvbrvLr8VW5+42aObn403+j2jXSXKEmSJElSjWLIlGGyNi0hr3g0bF2f2oFm/Z2/NG7Kb5eOp0dhe55ctpwHuwxjS+kWrph0BSs2reDv//k7w54bxuotq7nj5DtoVq/ZgZ1jwYuwYiaccBVkZadWb5qc0ekMRgwawfay7Vw+8XK2lW3jxhNvJLuGXo8kSZIkSeliyJRp+lwMpSXwzt8qf4zS7Uyd+zR3Nirg9Pan88ehY+hQ0IrDpz3CHSf/gYUfL2TgmIH86rVfEWPkti/expHNjzzw87xyB9Q/BHqeX/laM8CRzY/k8S8/zhfbfJHrjr2ODg07pLskSZIkSZJqnGr9eqkQwiDgTiAbeDDGePMe2+sAo4A+wFrgvBjjwuS2nwKXAmXAVTHG56qz1ozR6mhoeTRMHwH9LqvUN7WteO/v/LhRXTrUa8GNJ9xIyMmDk34ET13JsR9M5ndf/B1vrnyTIR2G0KNZD8K+zlFeBivehvmTE3cuZefCsd+BguaJn0/9FeTUqezVZoyi/CLuGXBPusuQJEmSJKnGqraQKYSQDdwLnAYsBd4MITwVY3xvl2GXAutjjIeFEM4HbgHOCyEcAZwPdAdaAZNCCIfHGMuqq96M0udieOb7sHQaHLr/3262estqnl/8PI++/Qd2hCz+cOr95Ocmv+2t14Ww+HWYcjMDGt7DgH7X7v1A64phQTJUWjAFSjYk1rc4Eraug0fPhZy6UKch9L2k0pcpSZIkSZJqj+q8k6kfMC/GuAAghPA4MBTYNWQaCtyQfP934J6QuK1mKPB4jHEbUBxCmJc83mvVWG/mOPJr8NzPEnczfUbItHLzSiYtmsTERROZsWoGkUiHHaX8vskxdGiyy7e9hQBn3AEbl8PTV0NuPejUH+o1ToRIxS8l71aaDOsXJvYpbAVdh0DHU6DjF6F+EZTtgFlPwpt/hu5fhboNqu1jkCRJkiRJNUd1hkytgSW7/LwUOHZvY2KMpSGEj4CmyfVT99i39Z4nCCF8G/g2QNu2baus8LSrU5gImt75Kwz6LdRtuNvmZZuWMWnRJCYsmsA7q98BoHPjzlzR8SwGTh1Bp7rN4fTbP33c7Fw4ZySMGAxjLk2eqyFs3wixHPLqQ/v/geO+mwiWmnX+9ON62bnQ87zEIkmSJEmSlFSdIVNFE/3E/RyzP/sSY/wT8CeAvn37fmp7jdbnYnhrJLz7NzhmOADvrX2P+2fez4tLXgSgW5NuXN37ak5teyrtt2+HvwyC3AbwrX9BwV6+Ka5uAxj2bOLOpfXFiUfj8pskQqU2fRMhkiRJkiRJ0gGqzpBpKXDoLj+3AZbvZczSEEIO0BBYt5/71m6tekHj9lD8Ett7f4tr/30tExdNpDCvkCt6XsEZnc7g0MLkR7R+EYwaCiEbvvVPaPipm752V6c+dB1c7ZcgSZIkSZI+P6ozZHoT6BxC6AAsIzGR9wV7jHkKuIjEXEtfA16IMcYQwlPAoyGE20lM/N0ZeKMaa808IUBRd1g1h7ELxjJx0USGHzmcS3pcQmFe4X/HbfwQHjkTdmyGi8dB007pq1mSJEmSJH1uVVvIlJxj6X+B54Bs4OEY4+wQwo3AtBjjU8BDwCPJib3XkQiiSI77K4lJwkuB731uvlluV0VdiXOfY9TskRze+HCu6nUVYdc5krauh0fOgo0rE4/IHdIjfbVKkiRJkqTPteq8k4kY4zhg3B7rfrHL+xLgnL3sexNwU3XWl/Gad+O1vBzmfTSfX5/4690Dpm2bYPQ5sHYuXPAEHNovfXVKkiRJkqTPvWoNmZSioq480rCQpjkFDO6wyxxKO0rg8Qtg2XQ4dxR06p++GiVJkiRJkoCsdBeg3b2x4g2mrZxGeSxnfk42L+fX4+sFHcnLzksMKCuFMZdC8RQYei90OyO9BUuSJEmSJOGdTBnn/pn3M+3DaRTlF9GkbhPqRDi3JP53wKwxMOcZGHQzHL3nPOqSJEmSJEnpYciUYe4dcC8vLnmR8cXjeXn5y5yX05TGa+b9d8C8SVDQHPpdnr4iJUmSJEmS9mDIlGHyc/MZ3HEwgzsOpqS0hNwXb4H5d0LpNsjKhQWToePJkOWTjpIkSZIkKXMYMmWwujl1oUV3iGWwdh6Ul8Hm1U70LUmSJEmSMo4hU6Zr3jXxuup9+HhZ4n3HU9JXjyRJkiRJUgUMmTJd08MgZMHqObD0TWjeDRq0THdVkiRJkiRJu3Fin0yXWxeadITlb8Oi13xUTpIkSZIkZSRDppqgeVeY/zyUbYNOPionSZIkSZIyjyFTTVDUDWI5ZOdBuxPSXY0kSZIkSdKnGDLVBJ9M/t32OMgrSG8tkiRJkiRJFTBkqgmKjki8+q1ykiRJkiQpQ/ntcjVBUTf46p+hy5fSXYkkSZIkSVKFDJlqghDgqHPTXYUkSZIkSdJe+bicJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllhkySJEmSJElKmSGTJEmSJEmSUmbIJEmSJEmSpJQZMkmSJEmSJCllIcaY7hqqRAhhNbAo3XXsoRmwJt1FSBWwN1UT2KeqCexT1TT2rGoC+1Q1weepT9vFGJvvz8BaEzJlohDCtBhj33TXIe3J3lRNYJ+qJrBPVdPYs6oJ7FPVBPZpxXxcTpIkSZIkSSkzZJIkSZIkSVLKDJmq15/SXYC0F/amagL7VDWBfaqaxp5VTWCfqiawTyvgnEySJEmSJElKmXcySZIkSZIkKWWGTLsIIRwaQpgcQng/hDA7hHB1cn2TEMLEEMLc5Gvj5PoQQrgrhDAvhPBOCKH3HsdrEEJYFkK4Zx/n/Gly/w9CCKfvsv7hEMKqEMKs6rpe1RyZ0pshhLohhDdCCDOTdfyqOq9bNUum9Gly/cIQwrshhLdDCNOq65pV82RKn4YQuiT785Pl4xDC96vz2lUzZUrPJtdfHUKYlazDftVOB7tPQwhNk+fbtOeYEMJNIYQlIYRN1XW9qpmqsk9DCGW7/D/8qX2c86LkceeGEC7aZX3t7dMYo0tyAVoCvZPvC4H/AEcAtwLXJtdfC9ySfD8YGA8E4Djg9T2OdyfwKHDPXs53BDATqAN0AOYD2cltJwG9gVnp/lxc0r9kSm8mj1c/OSYXeB04Lt2fj0tmLJnSp8ltC4Fm6f5MXDJvyaQ+3WVMNrASaJfuz8cl85ZM6VmgBzALyAdygElA53R/Pi6ZsaShTwuALwDf2XNM8ngtgU3p/lxcMmupyj7dn/4CmgALkq+Nk+8bJ7fV2j71TqZdxBhXxBjfSr7fCLwPtAaGAiOTw0YCZybfDwVGxYSpQKMQQkuAEEIfoAUwYR+nHAo8HmPcFmMsBuYB/ZLnfwlYV5XXp5orU3ozebxP0vbc5OLEbgIyp0+r+LJUy2Ronw4A5scYF6V8gap1MqhnuwFTY4xbYoylwBTgrCq8VNVgB7tPY4ybY4wvAyUVbJsaY1xRNVem2qQq+3Q/nQ5MjDGuizGuByYCg5Lnr7V9asi0FyGE9kAvEndqtPikAZKvRclhrYElu+y2FGgdQsgCfg/86DNOU+H+qdau2i3dvRlCyA4hvA2sIvEfzddTuR7VTunuUxLh54QQwvQQwrcrfyWqzTKgTz9xPvDYgV+BPm/S3LOzgJOSjynlk/gX/kNTuR7VTgepT6WUpNKnyfd1QwjTQghTQwhnUrHP5e/7OekuIBOFEOoDY4Dvxxg/DiHsdWgF6yLwXWBcjHHJPvbd1/5ShTKhN2OMZcDRIYRGwD9CCD1ijM4dpp0yoU+BE2OMy0MIRcDEEMKc5B2iEpAxfUoIIQ/4CvDT/albn1/p7tkY4/shhFtI/Ev8JhKP1JXub/36fDiIfSpVWhX0KUDb5N81OwIvhBDejTHOP4D9ay1Dpj2EEHJJNNzoGOOTydUfhhBaxhhXJG+PW5Vcv5Td/wWnDbAcOB74nxDCd4H6QF5yQq/XgV8mxw7fx/7Sp2Rab8YYN4QQXiRxy6chk4DM6dMY4yevq0II/yDxqIchk4DM6dOkLwFvxRg/rLILVK2TKT0bY3wIeChZ02+TYyXg4PZpjNEv9VClVFGf7vp3zQXJ34l6hRCaAX9Mjv1Fcv+T99j/xaq+powTM2BiqExZSCSNo4A79lh/G7tPBHZr8v0Qdp8I7I0Kjnkxe5+wrju7T6y4gF0mAwXa48TfLjFzehNoDjRKjqkH/Bv4cro/H5fMWDKoTwuAwuSYAuBVYFC6Px+XzFgypU932f44MCzdn4tL5i6Z1LNAUfK1LTCH5AS2Li4Hu0/3Zwy1cEJll9SWqupTEpN410m+bwbMBY6o4HxNgOLk+MbJ9032GFPr+tQ7mXZ3InAh8G5yzhmA64Cbgb+GEC4FFgPnJLeNI/E8+jxgCzDsQE4WY5wdQvgr8B6J242/FxOPIhFCeIxE6tkshLAU+GVM/OuRPp8yojeTyf7IEEI2iTnd/hpjfCa1S1Mtkil92oLEo5yQuGP30RjjsyldmWqTjOhTgOS8NqcBl6d0RartMqZngTEhhKbAjuT69ZW/LNUyB7VPAUIIC4EGJO52OhMYGGN8L4RwK3ABkJ/8PerBGOMNlb0w1SpV1afdgD+GEMpJ/E50c4zxvT1PFmNcF0L4NfBmctWNMcZ1ALW5T0MyPZMkSZIkSZIqzW+XkyRJkiRJUsoMmSRJkiRJkpQyQyZJkiRJkiSlzJBJkiRJkiRJKTNkkiRJkiRJUsoMmSRJkqpACKEshPB2CGF2CGFmCOGHIYR9/l0rhNA+hHDBwapRkiSpOhkySZIkVY2tMcajY4zdgdOAwcAvP2Of9oAhkyRJqhVCjDHdNUiSJNV4IYRNMcb6u/zcEXgTaAa0Ax4BCpKb/zfG+GoIYSrQDSgGRgJ3ATcDJwN1gHtjjH88aBchSZKUAkMmSZKkKrBnyJRctx7oCmwEymOMJSGEzsBjMca+IYSTgWtijF9Ojv82UBRj/E0IoQ7wCnBOjLH4oF6MJElSJeSkuwBJkqRaLCRfc4F7QghHA2XA4XsZPxA4KoTwteTPDYHOJO50kiRJymiGTJIkSdUg+bhcGbCKxNxMHwI9ScyJWbK33YArY4zPHZQiJUmSqpATf0uSJFWxEEJz4AHgnpiYm6AhsCLGWA5cCGQnh24ECnfZ9TngihBCbvI4h4cQCpAkSaoBvJNJkiSpatQLIbxN4tG4UhITfd+e3HYfMCaEcA4wGdicXP8OUBpCmAmMAO4k8Y1zb4UQArAaOPNgXYAkSVIqnPhbkiRJkiRJKfNxOUmSJEmSJKXMkEmSJEmSJEkpM2SSJEmSJElSygyZJEmSJEmSlDJDJkmSJEmSJKXMkEmSJEmSJEkpM2SSJEmSJElSygyZJEmSJEmSlLL/DzqonbgFM7HnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb523f7ba58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha_factors = [\"USFASTD_1DREVRSL\", \"USFASTD_EARNYILD\", \"USFASTD_VALUE\", \"USFASTD_SENTMT\"]\n",
    "\n",
    "facret_df = pd.DataFrame(index = my_dates)\n",
    "\n",
    "for dt in my_dates: \n",
    "    for alp in alpha_factors: \n",
    "        facret_df.at[dt, alp] = facret[dt.strftime('%Y%m%d')][alp]\n",
    "\n",
    "for column in facret_df.columns:\n",
    "        plt.plot(facret_df[column].cumsum(), label=column)\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cumulative Factor Returns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Previous Portfolio Holdings \n",
    "\n",
    "In order to optimize our portfolio we will use the previous day's holdings to estimate the trade size and transaction costs. In order to keep track of the holdings from the previous day we will include a column to hold the portfolio holdings of the previous day. **These holdings of all our assets will be initialized to zero when the backtest first starts**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_nas(df): \n",
    "    numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()\n",
    "    \n",
    "    for numeric_column in numeric_columns: \n",
    "        df[numeric_column] = np.nan_to_num(df[numeric_column])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "previous_holdings = pd.DataFrame(data = {\"Barrid\" : [\"USA02P1\"], \"h.opt.previous\" : np.array(0)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Barrid</th>\n",
       "      <th>h.opt.previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA02P1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Barrid  h.opt.previous\n",
       "0  USA02P1               0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_holdings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "previous_holdings = pd.DataFrame(data = {\"Barrid\" : [\"USA02P1\"], \"h.opt.previous\" : np.array(0)})\n",
    "df = frames[my_dates[0].strftime('%Y%m%d')]\n",
    "\n",
    "df = df.merge(previous_holdings, how = 'left', on = 'Barrid')\n",
    "df = clean_nas(df)\n",
    "df.loc[df['SpecRisk'] == 0]['SpecRisk'] = median(df['SpecRisk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Barrid</th>\n",
       "      <th>USFASTD_1DREVRSL</th>\n",
       "      <th>USFASTD_AERODEF</th>\n",
       "      <th>USFASTD_AIRLINES</th>\n",
       "      <th>USFASTD_ALUMSTEL</th>\n",
       "      <th>USFASTD_APPAREL</th>\n",
       "      <th>USFASTD_AUTO</th>\n",
       "      <th>USFASTD_BANKS</th>\n",
       "      <th>USFASTD_BETA</th>\n",
       "      <th>USFASTD_BEVTOB</th>\n",
       "      <th>...</th>\n",
       "      <th>IssuerMarketCap</th>\n",
       "      <th>Yield</th>\n",
       "      <th>TotalRisk</th>\n",
       "      <th>SpecRisk</th>\n",
       "      <th>HistBeta</th>\n",
       "      <th>PredBeta</th>\n",
       "      <th>DataDate</th>\n",
       "      <th>DlyReturn</th>\n",
       "      <th>DlyReturnDate</th>\n",
       "      <th>h.opt.previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12430</th>\n",
       "      <td>USAZZL1</td>\n",
       "      <td>1.895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.430560e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.542009</td>\n",
       "      <td>53.503883</td>\n",
       "      <td>0.855973</td>\n",
       "      <td>1.148298</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>20040106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12431</th>\n",
       "      <td>USAZZP1</td>\n",
       "      <td>2.410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.614480e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.293774</td>\n",
       "      <td>76.269788</td>\n",
       "      <td>0.495782</td>\n",
       "      <td>0.872594</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12432</th>\n",
       "      <td>USAZZR1</td>\n",
       "      <td>2.120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.280000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.228029</td>\n",
       "      <td>34.504433</td>\n",
       "      <td>0.464922</td>\n",
       "      <td>0.853555</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20040106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12433</th>\n",
       "      <td>USAZZX1</td>\n",
       "      <td>-1.522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.031860e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>126.289675</td>\n",
       "      <td>123.366131</td>\n",
       "      <td>1.116599</td>\n",
       "      <td>1.320642</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>20040106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12434</th>\n",
       "      <td>USAZZY1</td>\n",
       "      <td>-2.405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.306788e+08</td>\n",
       "      <td>0.620877</td>\n",
       "      <td>32.573024</td>\n",
       "      <td>22.297109</td>\n",
       "      <td>0.813423</td>\n",
       "      <td>0.911993</td>\n",
       "      <td>20040102</td>\n",
       "      <td>0.010721</td>\n",
       "      <td>20040106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Barrid  USFASTD_1DREVRSL  USFASTD_AERODEF  USFASTD_AIRLINES  \\\n",
       "12430  USAZZL1             1.895              0.0               0.0   \n",
       "12431  USAZZP1             2.410              0.0               0.0   \n",
       "12432  USAZZR1             2.120              0.0               0.0   \n",
       "12433  USAZZX1            -1.522              0.0               0.0   \n",
       "12434  USAZZY1            -2.405              0.0               0.0   \n",
       "\n",
       "       USFASTD_ALUMSTEL  USFASTD_APPAREL  USFASTD_AUTO  USFASTD_BANKS  \\\n",
       "12430               0.0              0.0           0.0            0.0   \n",
       "12431               0.0              0.0           0.0            0.0   \n",
       "12432               0.0              0.0           0.0            0.0   \n",
       "12433               0.0              0.0           0.0            0.0   \n",
       "12434               0.0              0.0           0.0            0.0   \n",
       "\n",
       "       USFASTD_BETA  USFASTD_BEVTOB  ...  IssuerMarketCap     Yield  \\\n",
       "12430        -0.318             0.0  ...     3.430560e+07  0.000000   \n",
       "12431        -1.100             1.0  ...     2.614480e+06  0.000000   \n",
       "12432        -1.167             0.0  ...     8.280000e+04  0.000000   \n",
       "12433         0.247             0.0  ...     2.031860e+06  0.000000   \n",
       "12434        -0.411             0.0  ...     8.306788e+08  0.620877   \n",
       "\n",
       "        TotalRisk    SpecRisk  HistBeta  PredBeta  DataDate  DlyReturn  \\\n",
       "12430   58.542009   53.503883  0.855973  1.148298  20040102   0.068966   \n",
       "12431   79.293774   76.269788  0.495782  0.872594  20040102   0.000000   \n",
       "12432   40.228029   34.504433  0.464922  0.853555  20040102   0.000000   \n",
       "12433  126.289675  123.366131  1.116599  1.320642  20040102   0.058824   \n",
       "12434   32.573024   22.297109  0.813423  0.911993  20040102   0.010721   \n",
       "\n",
       "       DlyReturnDate  h.opt.previous  \n",
       "12430       20040106             0.0  \n",
       "12431       20040106             0.0  \n",
       "12432       20040106             0.0  \n",
       "12433       20040106             0.0  \n",
       "12434       20040106             0.0  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Universe Based on Filters (TODO)\n",
    "\n",
    "In the cell below, implement the function `get_universe` that creates a stock universe by selecting only those companies that have a market capitalization of at least 1 billion dollars **OR** that are in the previous day's holdings, **even if** on the current day, the company no longer meets the 1 billion dollar criteria.\n",
    "\n",
    "When creating the universe, make sure you use the `.copy()` attribute to create a copy of the data. Also, it is very important to make sure that we are not looking at returns when forming the portfolio! to make this impossible, make sure to drop the column containing the daily return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_universe(df):\n",
    "    \"\"\"\n",
    "    Create a stock universe based on filters\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : DataFrame\n",
    "        All stocks\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    universe : DataFrame\n",
    "        Selected stocks based on filters\n",
    "    \"\"\"\n",
    "    \n",
    "    market_cap_condition = df['IssuerMarketCap'] > 1e9\n",
    "    previous_holding_condition = abs(df['h.opt.previous']) > 0\n",
    "    universe = df.loc[market_cap_condition | previous_holding_condition].copy(deep=True)\n",
    "    return universe.drop(columns='DlyReturn')\n",
    "\n",
    "universe = get_universe(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = str(int(universe['DataDate'][1])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20040102'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factors\n",
    "\n",
    "We will now extract both the risk factors and alpha factors. We begin by first getting all the factors using the `factors_from_names` function defined previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_factors = factors_from_names(list(universe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['USFASTD_1DREVRSL', 'USFASTD_EARNYILD', 'USFASTD_VALUE', 'USFASTD_SENTMT']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create the function `setdiff` to just select the factors that we have not defined as alpha factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setdiff(temp1, temp2): \n",
    "    s = set(temp2)\n",
    "    temp3 = [x for x in temp1 if x not in s]\n",
    "    return temp3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_factors = setdiff(all_factors, alpha_factors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also save the column that contains the previous holdings in a separate variable because we are going to use it later when we perform our portfolio optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "h0 = universe['h.opt.previous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.0\n",
       "1        0.0\n",
       "2        0.0\n",
       "3        0.0\n",
       "4        0.0\n",
       "5        0.0\n",
       "6        0.0\n",
       "7        0.0\n",
       "8        0.0\n",
       "9        0.0\n",
       "10       0.0\n",
       "11       0.0\n",
       "12       0.0\n",
       "13       0.0\n",
       "14       0.0\n",
       "15       0.0\n",
       "16       0.0\n",
       "17       0.0\n",
       "18       0.0\n",
       "19       0.0\n",
       "20       0.0\n",
       "21       0.0\n",
       "22       0.0\n",
       "23       0.0\n",
       "24       0.0\n",
       "25       0.0\n",
       "26       0.0\n",
       "27       0.0\n",
       "28       0.0\n",
       "29       0.0\n",
       "        ... \n",
       "12083    0.0\n",
       "12106    0.0\n",
       "12129    0.0\n",
       "12138    0.0\n",
       "12152    0.0\n",
       "12160    0.0\n",
       "12167    0.0\n",
       "12188    0.0\n",
       "12198    0.0\n",
       "12229    0.0\n",
       "12250    0.0\n",
       "12251    0.0\n",
       "12270    0.0\n",
       "12276    0.0\n",
       "12277    0.0\n",
       "12292    0.0\n",
       "12294    0.0\n",
       "12296    0.0\n",
       "12297    0.0\n",
       "12300    0.0\n",
       "12303    0.0\n",
       "12306    0.0\n",
       "12310    0.0\n",
       "12328    0.0\n",
       "12347    0.0\n",
       "12350    0.0\n",
       "12358    0.0\n",
       "12380    0.0\n",
       "12391    0.0\n",
       "12422    0.0\n",
       "Name: h.opt.previous, Length: 2265, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix of Risk Factor Exposures\n",
    "\n",
    "Our dataframe contains several columns that we'll use as risk factors exposures.  Extract these and put them into a matrix.\n",
    "\n",
    "The data, such as industry category, are already one-hot encoded, but if this were not the case, then using `patsy.dmatrices` would help, as this function extracts categories and performs the one-hot encoding.  We'll practice using this package, as you may find it useful with future data sets.  You could also store the factors in a dataframe if you prefer.\n",
    "\n",
    "#### How to use patsy.dmatrices\n",
    "\n",
    "`patsy.dmatrices` takes in a formula and the dataframe.  The formula tells the function which columns to take.  The formula will look something like this:  \n",
    "`SpecRisk ~ 0 + USFASTD_AERODEF + USFASTD_AIRLINES + ...`  \n",
    "where the variable to the left of the ~ is the \"dependent variable\" and the others to the right are the independent variables (as if we were preparing data to be fit to a model).\n",
    "\n",
    "This just means that the `pasty.dmatrices` function will return two matrix variables, one that contains the single column for the dependent variable `outcome`, and the independent variable columns are stored in a matrix `predictors`.\n",
    "\n",
    "The `predictors` matrix will contain the matrix of risk factors, which is what we want.  We don't actually need the `outcome` matrix; it's just created because that's the way patsy.dmatrices works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "formula = get_formula(risk_factors, \"SpecRisk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SpecRisk ~ 0 + USFASTD_AERODEF + USFASTD_AIRLINES + USFASTD_ALUMSTEL + USFASTD_APPAREL + USFASTD_AUTO + USFASTD_BANKS + USFASTD_BETA + USFASTD_BEVTOB + USFASTD_BIOLIFE + USFASTD_BLDGPROD + USFASTD_CHEM + USFASTD_CNSTENG + USFASTD_CNSTMACH + USFASTD_CNSTMATL + USFASTD_COMMEQP + USFASTD_COMPELEC + USFASTD_COMSVCS + USFASTD_CONGLOM + USFASTD_CONTAINR + USFASTD_DISTRIB + USFASTD_DIVFIN + USFASTD_DIVYILD + USFASTD_DWNRISK + USFASTD_EARNQLTY + USFASTD_ELECEQP + USFASTD_ELECUTIL + USFASTD_FOODPROD + USFASTD_FOODRET + USFASTD_GASUTIL + USFASTD_GROWTH + USFASTD_HLTHEQP + USFASTD_HLTHSVCS + USFASTD_HOMEBLDG + USFASTD_HOUSEDUR + USFASTD_INDMACH + USFASTD_INDMOM + USFASTD_INSURNCE + USFASTD_INTERNET + USFASTD_LEISPROD + USFASTD_LEISSVCS + USFASTD_LEVERAGE + USFASTD_LIFEINS + USFASTD_LIQUIDTY + USFASTD_LTREVRSL + USFASTD_MEDIA + USFASTD_MGDHLTH + USFASTD_MGMTQLTY + USFASTD_MIDCAP + USFASTD_MOMENTUM + USFASTD_MULTUTIL + USFASTD_OILGSCON + USFASTD_OILGSDRL + USFASTD_OILGSEQP + USFASTD_OILGSEXP + USFASTD_PAPER + USFASTD_PHARMA + USFASTD_PRECMTLS + USFASTD_PROFIT + USFASTD_PROSPECT + USFASTD_PSNLPROD + USFASTD_REALEST + USFASTD_RESTAUR + USFASTD_RESVOL + USFASTD_ROADRAIL + USFASTD_SEASON + USFASTD_SEMICOND + USFASTD_SEMIEQP + USFASTD_SIZE + USFASTD_SOFTWARE + USFASTD_SPLTYRET + USFASTD_SPTYCHEM + USFASTD_SPTYSTOR + USFASTD_STREVRSL + USFASTD_TELECOM + USFASTD_TRADECO + USFASTD_TRANSPRT + USFASTD_WIRELESS'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_matrix(formula, data): \n",
    "    outcome, predictors = patsy.dmatrices(formula, data)\n",
    "    return predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = model_matrix(formula, universe)\n",
    "BT = B.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2265, 77)\n",
       "  Columns:\n",
       "    ['USFASTD_AERODEF',\n",
       "     'USFASTD_AIRLINES',\n",
       "     'USFASTD_ALUMSTEL',\n",
       "     'USFASTD_APPAREL',\n",
       "     'USFASTD_AUTO',\n",
       "     'USFASTD_BANKS',\n",
       "     'USFASTD_BETA',\n",
       "     'USFASTD_BEVTOB',\n",
       "     'USFASTD_BIOLIFE',\n",
       "     'USFASTD_BLDGPROD',\n",
       "     'USFASTD_CHEM',\n",
       "     'USFASTD_CNSTENG',\n",
       "     'USFASTD_CNSTMACH',\n",
       "     'USFASTD_CNSTMATL',\n",
       "     'USFASTD_COMMEQP',\n",
       "     'USFASTD_COMPELEC',\n",
       "     'USFASTD_COMSVCS',\n",
       "     'USFASTD_CONGLOM',\n",
       "     'USFASTD_CONTAINR',\n",
       "     'USFASTD_DISTRIB',\n",
       "     'USFASTD_DIVFIN',\n",
       "     'USFASTD_DIVYILD',\n",
       "     'USFASTD_DWNRISK',\n",
       "     'USFASTD_EARNQLTY',\n",
       "     'USFASTD_ELECEQP',\n",
       "     'USFASTD_ELECUTIL',\n",
       "     'USFASTD_FOODPROD',\n",
       "     'USFASTD_FOODRET',\n",
       "     'USFASTD_GASUTIL',\n",
       "     'USFASTD_GROWTH',\n",
       "     'USFASTD_HLTHEQP',\n",
       "     'USFASTD_HLTHSVCS',\n",
       "     'USFASTD_HOMEBLDG',\n",
       "     'USFASTD_HOUSEDUR',\n",
       "     'USFASTD_INDMACH',\n",
       "     'USFASTD_INDMOM',\n",
       "     'USFASTD_INSURNCE',\n",
       "     'USFASTD_INTERNET',\n",
       "     'USFASTD_LEISPROD',\n",
       "     'USFASTD_LEISSVCS',\n",
       "     'USFASTD_LEVERAGE',\n",
       "     'USFASTD_LIFEINS',\n",
       "     'USFASTD_LIQUIDTY',\n",
       "     'USFASTD_LTREVRSL',\n",
       "     'USFASTD_MEDIA',\n",
       "     'USFASTD_MGDHLTH',\n",
       "     'USFASTD_MGMTQLTY',\n",
       "     'USFASTD_MIDCAP',\n",
       "     'USFASTD_MOMENTUM',\n",
       "     'USFASTD_MULTUTIL',\n",
       "     'USFASTD_OILGSCON',\n",
       "     'USFASTD_OILGSDRL',\n",
       "     'USFASTD_OILGSEQP',\n",
       "     'USFASTD_OILGSEXP',\n",
       "     'USFASTD_PAPER',\n",
       "     'USFASTD_PHARMA',\n",
       "     'USFASTD_PRECMTLS',\n",
       "     'USFASTD_PROFIT',\n",
       "     'USFASTD_PROSPECT',\n",
       "     'USFASTD_PSNLPROD',\n",
       "     'USFASTD_REALEST',\n",
       "     'USFASTD_RESTAUR',\n",
       "     'USFASTD_RESVOL',\n",
       "     'USFASTD_ROADRAIL',\n",
       "     'USFASTD_SEASON',\n",
       "     'USFASTD_SEMICOND',\n",
       "     'USFASTD_SEMIEQP',\n",
       "     'USFASTD_SIZE',\n",
       "     'USFASTD_SOFTWARE',\n",
       "     'USFASTD_SPLTYRET',\n",
       "     'USFASTD_SPTYCHEM',\n",
       "     'USFASTD_SPTYSTOR',\n",
       "     'USFASTD_STREVRSL',\n",
       "     'USFASTD_TELECOM',\n",
       "     'USFASTD_TRADECO',\n",
       "     'USFASTD_TRANSPRT',\n",
       "     'USFASTD_WIRELESS']\n",
       "  Terms:\n",
       "    'USFASTD_AERODEF' (column 0)\n",
       "    'USFASTD_AIRLINES' (column 1)\n",
       "    'USFASTD_ALUMSTEL' (column 2)\n",
       "    'USFASTD_APPAREL' (column 3)\n",
       "    'USFASTD_AUTO' (column 4)\n",
       "    'USFASTD_BANKS' (column 5)\n",
       "    'USFASTD_BETA' (column 6)\n",
       "    'USFASTD_BEVTOB' (column 7)\n",
       "    'USFASTD_BIOLIFE' (column 8)\n",
       "    'USFASTD_BLDGPROD' (column 9)\n",
       "    'USFASTD_CHEM' (column 10)\n",
       "    'USFASTD_CNSTENG' (column 11)\n",
       "    'USFASTD_CNSTMACH' (column 12)\n",
       "    'USFASTD_CNSTMATL' (column 13)\n",
       "    'USFASTD_COMMEQP' (column 14)\n",
       "    'USFASTD_COMPELEC' (column 15)\n",
       "    'USFASTD_COMSVCS' (column 16)\n",
       "    'USFASTD_CONGLOM' (column 17)\n",
       "    'USFASTD_CONTAINR' (column 18)\n",
       "    'USFASTD_DISTRIB' (column 19)\n",
       "    'USFASTD_DIVFIN' (column 20)\n",
       "    'USFASTD_DIVYILD' (column 21)\n",
       "    'USFASTD_DWNRISK' (column 22)\n",
       "    'USFASTD_EARNQLTY' (column 23)\n",
       "    'USFASTD_ELECEQP' (column 24)\n",
       "    'USFASTD_ELECUTIL' (column 25)\n",
       "    'USFASTD_FOODPROD' (column 26)\n",
       "    'USFASTD_FOODRET' (column 27)\n",
       "    'USFASTD_GASUTIL' (column 28)\n",
       "    'USFASTD_GROWTH' (column 29)\n",
       "    'USFASTD_HLTHEQP' (column 30)\n",
       "    'USFASTD_HLTHSVCS' (column 31)\n",
       "    'USFASTD_HOMEBLDG' (column 32)\n",
       "    'USFASTD_HOUSEDUR' (column 33)\n",
       "    'USFASTD_INDMACH' (column 34)\n",
       "    'USFASTD_INDMOM' (column 35)\n",
       "    'USFASTD_INSURNCE' (column 36)\n",
       "    'USFASTD_INTERNET' (column 37)\n",
       "    'USFASTD_LEISPROD' (column 38)\n",
       "    'USFASTD_LEISSVCS' (column 39)\n",
       "    'USFASTD_LEVERAGE' (column 40)\n",
       "    'USFASTD_LIFEINS' (column 41)\n",
       "    'USFASTD_LIQUIDTY' (column 42)\n",
       "    'USFASTD_LTREVRSL' (column 43)\n",
       "    'USFASTD_MEDIA' (column 44)\n",
       "    'USFASTD_MGDHLTH' (column 45)\n",
       "    'USFASTD_MGMTQLTY' (column 46)\n",
       "    'USFASTD_MIDCAP' (column 47)\n",
       "    'USFASTD_MOMENTUM' (column 48)\n",
       "    'USFASTD_MULTUTIL' (column 49)\n",
       "    'USFASTD_OILGSCON' (column 50)\n",
       "    'USFASTD_OILGSDRL' (column 51)\n",
       "    'USFASTD_OILGSEQP' (column 52)\n",
       "    'USFASTD_OILGSEXP' (column 53)\n",
       "    'USFASTD_PAPER' (column 54)\n",
       "    'USFASTD_PHARMA' (column 55)\n",
       "    'USFASTD_PRECMTLS' (column 56)\n",
       "    'USFASTD_PROFIT' (column 57)\n",
       "    'USFASTD_PROSPECT' (column 58)\n",
       "    'USFASTD_PSNLPROD' (column 59)\n",
       "    'USFASTD_REALEST' (column 60)\n",
       "    'USFASTD_RESTAUR' (column 61)\n",
       "    'USFASTD_RESVOL' (column 62)\n",
       "    'USFASTD_ROADRAIL' (column 63)\n",
       "    'USFASTD_SEASON' (column 64)\n",
       "    'USFASTD_SEMICOND' (column 65)\n",
       "    'USFASTD_SEMIEQP' (column 66)\n",
       "    'USFASTD_SIZE' (column 67)\n",
       "    'USFASTD_SOFTWARE' (column 68)\n",
       "    'USFASTD_SPLTYRET' (column 69)\n",
       "    'USFASTD_SPTYCHEM' (column 70)\n",
       "    'USFASTD_SPTYSTOR' (column 71)\n",
       "    'USFASTD_STREVRSL' (column 72)\n",
       "    'USFASTD_TELECOM' (column 73)\n",
       "    'USFASTD_TRADECO' (column 74)\n",
       "    'USFASTD_TRANSPRT' (column 75)\n",
       "    'USFASTD_WIRELESS' (column 76)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2265, 77)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Specific Variance\n",
    "\n",
    "Notice that the specific risk data is in percent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     9.014505\n",
       "1    11.726327\n",
       "Name: SpecRisk, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "universe['SpecRisk'][0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, in order to get the specific variance for each stock in the universe we first need to multiply these values by `0.01`  and then square them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "specVar = (0.01 * universe['SpecRisk']) ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor covariance matrix (TODO)\n",
    "\n",
    "Note that we already have factor covariances from Barra data, which is stored in the variable `covariance`.  `covariance` is a dictionary, where the key is each day's date, and the value is a dataframe containing the factor covariances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Factor1</th>\n",
       "      <th>Factor2</th>\n",
       "      <th>VarCovar</th>\n",
       "      <th>DataDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>1.958869</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_BETA</td>\n",
       "      <td>1.602458</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_DIVYILD</td>\n",
       "      <td>-0.012642</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_DWNRISK</td>\n",
       "      <td>-0.064387</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USFASTD_1DREVRSL</td>\n",
       "      <td>USFASTD_EARNQLTY</td>\n",
       "      <td>0.046573</td>\n",
       "      <td>20040102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Factor1           Factor2  VarCovar  DataDate\n",
       "0  USFASTD_1DREVRSL  USFASTD_1DREVRSL  1.958869  20040102\n",
       "1  USFASTD_1DREVRSL      USFASTD_BETA  1.602458  20040102\n",
       "2  USFASTD_1DREVRSL   USFASTD_DIVYILD -0.012642  20040102\n",
       "3  USFASTD_1DREVRSL   USFASTD_DWNRISK -0.064387  20040102\n",
       "4  USFASTD_1DREVRSL  USFASTD_EARNQLTY  0.046573  20040102"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariance['20040102'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, implement the function `diagonal_factor_cov` to create the factor covariance matrix. Note that the covariances are given in **percentage units squared**.  Therefore you must re-scale them appropriately so that they're in decimals squared. Use the given `colnames` function to get the column names from `B`. \n",
    "\n",
    "When creating factor covariance matrix, you can store the factor variances and covariances, or just store the factor variances.  Try both, and see if you notice any differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colnames(B):\n",
    "    if type(B) == patsy.design_info.DesignMatrix: \n",
    "        return B.design_info.column_names\n",
    "    if type(B) == pandas.core.frame.DataFrame: \n",
    "        return B.columns.tolist()\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['20040102', '20040105', '20040106', '20040107', '20040108', '20040109', '20040112', '20040113', '20040114', '20040115', '20040116', '20040120', '20040121', '20040122', '20040123', '20040126', '20040127', '20040128', '20040129', '20040130', '20040202', '20040203', '20040204', '20040205', '20040206', '20040209', '20040210', '20040211', '20040212', '20040213', '20040217', '20040218', '20040219', '20040220', '20040223', '20040224', '20040225', '20040226', '20040227', '20040301', '20040302', '20040303', '20040304', '20040305', '20040308', '20040309', '20040310', '20040311', '20040312', '20040315', '20040316', '20040317', '20040318', '20040319', '20040322', '20040323', '20040324', '20040325', '20040326', '20040329', '20040330', '20040331', '20040401', '20040402', '20040405', '20040406', '20040407', '20040408', '20040412', '20040413', '20040414', '20040415', '20040416', '20040419', '20040420', '20040421', '20040422', '20040423', '20040426', '20040427', '20040428', '20040429', '20040430', '20040503', '20040504', '20040505', '20040506', '20040507', '20040510', '20040511', '20040512', '20040513', '20040514', '20040517', '20040518', '20040519', '20040520', '20040521', '20040524', '20040525', '20040526', '20040527', '20040528', '20040601', '20040602', '20040603', '20040604', '20040607', '20040608', '20040609', '20040610', '20040614', '20040615', '20040616', '20040617', '20040618', '20040621', '20040622', '20040623', '20040624', '20040625', '20040628', '20040629', '20040630', '20040701', '20040702', '20040706', '20040707', '20040708', '20040709', '20040712', '20040713', '20040714', '20040715', '20040716', '20040719', '20040720', '20040721', '20040722', '20040723', '20040726', '20040727', '20040728', '20040729', '20040730', '20040802', '20040803', '20040804', '20040805', '20040806', '20040809', '20040810', '20040811', '20040812', '20040813', '20040816', '20040817', '20040818', '20040819', '20040820', '20040823', '20040824', '20040825', '20040826', '20040827', '20040830', '20040831', '20040901', '20040902', '20040903', '20040907', '20040908', '20040909', '20040910', '20040913', '20040914', '20040915', '20040916', '20040917', '20040920', '20040921', '20040922', '20040923', '20040924', '20040927', '20040928', '20040929', '20040930', '20041001', '20041004', '20041005', '20041006', '20041007', '20041008', '20041011', '20041012', '20041013', '20041014', '20041015', '20041018', '20041019', '20041020', '20041021', '20041022', '20041025', '20041026', '20041027', '20041028', '20041029', '20041101', '20041102', '20041103', '20041104', '20041105', '20041108', '20041109', '20041110', '20041111', '20041112', '20041115', '20041116', '20041117', '20041118', '20041119', '20041122', '20041123', '20041124', '20041126', '20041129', '20041130', '20041201', '20041202', '20041203', '20041206', '20041207', '20041208', '20041209', '20041210', '20041213', '20041214', '20041215', '20041216', '20041217', '20041220', '20041221', '20041222', '20041223', '20041227', '20041228', '20041229', '20041230', '20041231'])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariance.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diagonal_factor_cov(date, B):\n",
    "    \"\"\"\n",
    "    Create the factor covariance matrix\n",
    "    Just use the factor variances and set the off diagonal covariances to zero.\n",
    "    \n",
    "    Optimizer tries to reduce the portfolio's exposure to risk factors, then\n",
    "    it's less important to know exactly how factor one varies with factor two.\n",
    "    I believe this approach make factor model more conservative about information\n",
    "    in covariance matrix.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    date : string\n",
    "           date. For example 20040102\n",
    "        \n",
    "    B : patsy.design_info.DesignMatrix OR pandas.core.frame.DataFrame\n",
    "        Matrix of Risk Factors\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Fm : Numpy ndarray\n",
    "        factor covariance matrix\n",
    "    \"\"\"\n",
    "    \n",
    "    cv_matrix = covariance[date]\n",
    "    \n",
    "    num_of_factors = B.shape[1]\n",
    "    factor_matrix = np.zeros((num_of_factors, num_of_factors), dtype=float)\n",
    "    for i in range(num_of_factors):\n",
    "        factor = colnames(B)[i]\n",
    "        location = (cv_matrix.Factor1==factor) & (cv_matrix.Factor2==factor)\n",
    "        cv = cv_matrix.loc[location, \"VarCovar\"].iloc[0]\n",
    "        factor_matrix[i, i] = (0.01**2) * cv\n",
    "    \n",
    "    return factor_matrix\n",
    "\n",
    "Fvar = diagonal_factor_cov(date, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 77)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fvar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00845121, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.04305446, 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.02307853, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.00521378, 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.00726435,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.02185539]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fvar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transaction Costs\n",
    "\n",
    "To get the transaction cost, or slippage, we have to multiply the price change due to market impact by **the amount of dollars traded**:\n",
    "\n",
    "$$\n",
    "\\mbox{tcost_{i,t}} = \\% \\Delta \\mbox{price}_{i,t} \\times \\mbox{trade}_{i,t}\n",
    "$$\n",
    "\n",
    "In summation notation it looks like this:  \n",
    "$$\n",
    "\\mbox{tcost}_{i,t} = \\sum_i^{N} \\lambda_{i,t} (h_{i,t} - h_{i,t-1})^2\n",
    "$$  \n",
    "where\n",
    "$$\n",
    "\\lambda_{i,t} = \\frac{1}{10\\times \\mbox{ADV}_{i,t}}\n",
    "$$\n",
    "\n",
    "Note that since we're dividing by ADV, we'll want to handle cases when ADV is missing or zero.  In those instances, we can set ADV to a small positive number, such as 10,000, which, **in practice assumes that the stock is illiquid**. In the code below if there is no volume information we assume the asset is illiquid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lambda(universe, composite_volume_column = 'ADTCA_30'):\n",
    "    universe.loc[np.isnan(universe[composite_volume_column]), composite_volume_column] = 1.0e4\n",
    "    universe.loc[universe[composite_volume_column] == 0, composite_volume_column] = 1.0e4 \n",
    "\n",
    "    adv = universe[composite_volume_column]\n",
    "    \n",
    "    return 0.1 / adv\n",
    "\n",
    "Lambda = get_lambda(universe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alpha Combination (TODO)\n",
    "\n",
    "In the code below create a matrix of alpha factors and return it from the function `get_B_alpha`. Create this matrix in the same way you created the matrix of risk factors, i.e. using the `get_formula` and `model_matrix` functions we have defined above. Feel free to go back and look at the previous code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_B_alpha(alpha_factors, universe):\n",
    "    return model_matrix(get_formula(alpha_factors, \"SpecRisk\"), data=universe)\n",
    "\n",
    "B_alpha = get_B_alpha(alpha_factors, universe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2265, 4)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_alpha.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2265, 4)\n",
       "  USFASTD_1DREVRSL  USFASTD_EARNYILD  USFASTD_VALUE  USFASTD_SENTMT\n",
       "            -0.032            -0.298          0.644          -0.922\n",
       "            -0.059            -1.072         -0.646          -0.991\n",
       "             0.457            -0.060         -0.668          -0.092\n",
       "            -0.128            -0.681          0.028          -0.772\n",
       "             0.374             0.392         -0.735           0.489\n",
       "            -0.254             0.185          0.188           0.033\n",
       "            -0.317             0.748          2.049           0.628\n",
       "             0.157            -0.438         -0.449           0.631\n",
       "            -0.550            -0.691         -0.645           0.225\n",
       "            -0.169             0.038         -1.002           0.053\n",
       "            -1.019            -0.946         -0.588           1.371\n",
       "             0.334            -0.197         -0.465          -0.287\n",
       "             0.490             0.036         -0.370          -0.685\n",
       "            -0.169            -0.206         -1.332           0.628\n",
       "            -0.642             0.213         -0.208          -0.082\n",
       "            -0.609            -0.491         -0.429          -0.775\n",
       "            -0.098            -0.491         -0.437          -0.775\n",
       "            -0.230             0.367         -0.050           0.168\n",
       "            -0.175            -0.126         -0.483           0.110\n",
       "            -0.116             0.514          0.233          -0.348\n",
       "             0.701            -0.611         -0.782          -0.342\n",
       "            -0.249            -0.308         -0.934           0.030\n",
       "             0.735            -1.912          0.090           2.681\n",
       "            -0.007            -0.492         -1.112           0.238\n",
       "            -0.133            -0.393         -0.676          -0.205\n",
       "             0.639            -0.869         -0.347           2.628\n",
       "            -0.339             0.799          0.451           0.715\n",
       "            -2.745             0.799          0.456           0.715\n",
       "            -0.560             1.409         -0.473           0.339\n",
       "             0.166            -0.030         -0.361          -0.455\n",
       "  [2235 rows omitted]\n",
       "  Terms:\n",
       "    'USFASTD_1DREVRSL' (column 0)\n",
       "    'USFASTD_EARNYILD' (column 1)\n",
       "    'USFASTD_VALUE' (column 2)\n",
       "    'USFASTD_SENTMT' (column 3)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have the matrix containing the alpha factors we will combine them by adding its rows. By doing this we will collapse the `B_alpha` matrix into a single alpha vector. We'll multiply by `1e-4` so that the expression of expected portfolio return, $\\alpha^T \\mathbf{h}$, is in dollar units. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_alpha_vec(B_alpha):\n",
    "    \"\"\"\n",
    "    Create an alpha vector\n",
    "    \n",
    "    Expected return should be in dollar units, but alpha factor exposures\n",
    "    represent the % change expected for each stock. Based on the ranges of\n",
    "    values in the factor expousre data, which are mostly between -5 and +5 and\n",
    "    centered at zero, I made an assumption that a factor exposure of 1 maps to\n",
    "    1 basis point of daily return on that stock.\n",
    "    \n",
    "    Parameters\n",
    "    ----------        \n",
    "    B_alpha : patsy.design_info.DesignMatrix \n",
    "        Matrix of Alpha Factors\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    alpha_vec : patsy.design_info.DesignMatrix \n",
    "        alpha vecrtor\n",
    "    \"\"\"\n",
    "    \n",
    "    scale = 1e-4\n",
    "    row_sums = np.sum(B_alpha, axis=1)\n",
    "    \n",
    "    return scale * row_sums\n",
    "\n",
    "alpha_vec = get_alpha_vec(B_alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2265"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(alpha_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.080e-05, -2.768e-04, -3.630e-05, ...,  1.900e-06,  1.679e-04,\n",
       "        3.930e-05])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optional Challenge\n",
    "\n",
    "You can also try to a more sophisticated method of alpha combination, by choosing the holding for each alpha based on the same metric of its performance, such as the factor returns, or sharpe ratio.  To make this more realistic, you can calculate a rolling average of the sharpe ratio, which is updated for each day.  Remember to only use data that occurs prior to the date of each optimization, and not data that occurs in the future.  Also, since factor returns and sharpe ratios may be negative, consider using a `max` function to give the holdings a lower bound of zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective function (TODO)\n",
    "\n",
    "The objective function is given by:\n",
    "\n",
    "$$\n",
    "f(\\mathbf{h}) = \\frac{1}{2}\\kappa \\mathbf{h}_t^T\\mathbf{Q}^T\\mathbf{Q}\\mathbf{h}_t + \\frac{1}{2} \\kappa \\mathbf{h}_t^T \\mathbf{S} \\mathbf{h}_t - \\mathbf{\\alpha}^T \\mathbf{h}_t + (\\mathbf{h}_{t} - \\mathbf{h}_{t-1})^T \\mathbf{\\Lambda} (\\mathbf{h}_{t} - \\mathbf{h}_{t-1})\n",
    "$$\n",
    "\n",
    "Where the terms correspond to: factor risk + idiosyncratic risk - expected portfolio return + transaction costs, respectively. We should also note that $\\textbf{Q}^T\\textbf{Q}$ is defined to be the same as $\\textbf{BFB}^T$.  Review the lessons if you need a refresher of how we get $\\textbf{Q}$.\n",
    "\n",
    "Our objective is to minimize this objective function. To do this, we will use Scipy's optimization function:\n",
    "\n",
    "`scipy.optimize.fmin_l_bfgs_b(func, initial_guess, func_gradient)`\n",
    "\n",
    "where:\n",
    "\n",
    "* **func** : is the function we want to minimize\n",
    "\n",
    "* **initial_guess** : is out initial guess\n",
    "\n",
    "* **func_gradient** : is the gradient of the function we want to minimize\n",
    "\n",
    "So, in order to use the `scipy.optimize.fmin_l_bfgs_b` function we first need to define its parameters.\n",
    "\n",
    "In the code below implement the function `obj_func(h)` that corresponds to the objective function above that we want to minimize. We will set the risk aversion to be `1.0e-6`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_aversion = 1.0e-6\n",
    "\n",
    "def get_obj_func(h0, risk_aversion, Q, specVar, alpha_vec, Lambda): \n",
    "    def obj_func(h):\n",
    "        # factor risk\n",
    "        f = 0.5 * risk_aversion * np.sum( np.matmul(Q, h)**2)\n",
    "        \n",
    "        # idiosyncratic risk\n",
    "        f += 0.5 * risk_aversion * np.dot(h**2, specVar)\n",
    "        \n",
    "        # expected portfolio return\n",
    "        f -= np.dot(h, alpha_vec)\n",
    "        \n",
    "        # transaction cost\n",
    "        f += np.dot((h-h0)**2, Lambda)\n",
    "        return f\n",
    "    \n",
    "    return obj_func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient (TODO)\n",
    "\n",
    "Now that we can generate the objective function using `get_obj_func`, we can now create a similar function with its gradient. The reason we're interested in calculating the gradient is so that we can tell the optimizer in which direction, and how much, it should shift the portfolio holdings in order to improve the objective function (minimize variance, minimize transaction cost, and maximize expected portfolio return).\n",
    "\n",
    "Before we implement the function we first need to know what the gradient looks like. The gradient, or derivative of the objective function, with respect to the portfolio holdings h, is given by:  \n",
    "\n",
    "$$\n",
    "f'(\\mathbf{h}) = \\frac{1}{2}\\kappa (2\\mathbf{Q}^T\\mathbf{Qh}) + \\frac{1}{2}\\kappa (2\\mathbf{Sh}) - \\mathbf{\\alpha} + 2(\\mathbf{h}_{t} - \\mathbf{h}_{t-1}) \\mathbf{\\Lambda}\n",
    "$$\n",
    "\n",
    "In the code below, implement the function `grad(h)` that corresponds to the function of the gradient given above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2265,)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specVar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2265,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grad_func(h0, risk_aversion, Q, QT, specVar, alpha_vec, Lambda):\n",
    "    def grad_func(h):\n",
    "        g = risk_aversion * (np.matmul(QT, np.matmul(Q,h)) + \\\n",
    "                             (specVar* h)\n",
    "                            ) \\\n",
    "            - alpha_vec \\\n",
    "            + 2*(h-h0) * Lambda\n",
    "        \n",
    "        return (np.asarray(g))\n",
    "    \n",
    "    return grad_func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimize (TODO)\n",
    "\n",
    "Now that we can generate the objective function using `get_obj_func`, and its corresponding gradient using `get_grad_func` we are ready to minimize the objective function using Scipy's optimization function. For this, we will use out initial holdings as our `initial_guess` parameter.\n",
    "\n",
    "In the cell below, implement the function `get_h_star` that optimizes the objective function. Use the objective function (`obj_func`) and gradient function (`grad_func`) provided within `get_h_star` to optimize the objective function using the `scipy.optimize.fmin_l_bfgs_b` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00845121, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.04305446, 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.02307853, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.00521378, 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.00726435,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.02185539]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_aversion = 1.0e-6\n",
    "\n",
    "Q = np.matmul(scipy.linalg.sqrtm(Fvar), BT)\n",
    "QT = Q.transpose()\n",
    "\n",
    "def get_h_star(risk_aversion, Q, QT, specVar, alpha_vec, h0, Lambda):\n",
    "    \"\"\"\n",
    "    Optimize the objective function\n",
    "\n",
    "    Parameters\n",
    "    ----------        \n",
    "    risk_aversion : int or float \n",
    "        Trader's risk aversion\n",
    "        \n",
    "    Q : patsy.design_info.DesignMatrix \n",
    "        Q Matrix\n",
    "        \n",
    "    QT : patsy.design_info.DesignMatrix \n",
    "        Transpose of the Q Matrix\n",
    "        \n",
    "    specVar: Pandas Series \n",
    "        Specific Variance\n",
    "        \n",
    "    alpha_vec: patsy.design_info.DesignMatrix \n",
    "        alpha vector\n",
    "        \n",
    "    h0 : Pandas Series  \n",
    "        initial holdings\n",
    "        \n",
    "    Lambda : Pandas Series  \n",
    "        Lambda\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    optimizer_result[0]: Numpy ndarray \n",
    "        optimized holdings\n",
    "    \"\"\"\n",
    "    obj_func = get_obj_func(h0, risk_aversion, Q, specVar, alpha_vec, Lambda)\n",
    "    grad_func = get_grad_func(h0, risk_aversion, Q, QT, specVar, alpha_vec, Lambda)\n",
    "    \n",
    "    optimizer_result = scipy.optimize.fmin_l_bfgs_b(obj_func,\n",
    "                                                    h0,\n",
    "                                                    fprime=grad_func\n",
    "                                                   )\n",
    "    \n",
    "    return optimizer_result[0]\n",
    "\n",
    "h_star = get_h_star(risk_aversion, Q, QT, specVar, alpha_vec, h0, Lambda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we have optimized our objective function we can now use, `h_star` to create our optimal portfolio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_portfolio = pd.DataFrame(data = {\"Barrid\" : universe['Barrid'], \"h.opt\" : h_star})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Barrid</th>\n",
       "      <th>h.opt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA0001</td>\n",
       "      <td>-4.103059</td>\n",
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       "      <th>1</th>\n",
       "      <td>USA0011</td>\n",
       "      <td>-22.200676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA0031</td>\n",
       "      <td>1.519784</td>\n",
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       "      <th>3</th>\n",
       "      <td>USA0062</td>\n",
       "      <td>-5.749995</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USA00E2</td>\n",
       "      <td>6.810527</td>\n",
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       "      <th>5</th>\n",
       "      <td>USA00F1</td>\n",
       "      <td>-5.726061</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USA00G2</td>\n",
       "      <td>7.589228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USA00H1</td>\n",
       "      <td>4.809374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USA00I1</td>\n",
       "      <td>-9.642367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USA00J1</td>\n",
       "      <td>-3.183782</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>USA00K1</td>\n",
       "      <td>-7.864453</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USA00P1</td>\n",
       "      <td>1.483460</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>USA00R1</td>\n",
       "      <td>-3.249811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USA00S1</td>\n",
       "      <td>0.690273</td>\n",
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       "      <th>14</th>\n",
       "      <td>USA00V1</td>\n",
       "      <td>-4.741222</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USA0131</td>\n",
       "      <td>-11.272261</td>\n",
       "    </tr>\n",
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       "      <th>16</th>\n",
       "      <td>USA0161</td>\n",
       "      <td>-7.186826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USA01I1</td>\n",
       "      <td>2.181586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>USA01J2</td>\n",
       "      <td>-6.903525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>USA01L1</td>\n",
       "      <td>-1.096186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>USA01P1</td>\n",
       "      <td>-0.097555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USA01Q1</td>\n",
       "      <td>-4.284195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>USA0202</td>\n",
       "      <td>13.541043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>USA0231</td>\n",
       "      <td>-4.554782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>USA0281</td>\n",
       "      <td>-5.341278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>USA0291</td>\n",
       "      <td>14.614120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>USA02A1</td>\n",
       "      <td>2.234342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>USA02B1</td>\n",
       "      <td>-8.424432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>USA02H1</td>\n",
       "      <td>0.465728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USA02P1</td>\n",
       "      <td>-4.872854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12083</th>\n",
       "      <td>USAZGN1</td>\n",
       "      <td>-1601.158503</td>\n",
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       "    <tr>\n",
       "      <th>12106</th>\n",
       "      <td>USAZHT1</td>\n",
       "      <td>90.648179</td>\n",
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       "    <tr>\n",
       "      <th>12129</th>\n",
       "      <td>USAZJ11</td>\n",
       "      <td>42.231280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12138</th>\n",
       "      <td>USAZJH1</td>\n",
       "      <td>-1586.998243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12152</th>\n",
       "      <td>USAZK61</td>\n",
       "      <td>738.306872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12160</th>\n",
       "      <td>USAZKM1</td>\n",
       "      <td>4546.795659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12167</th>\n",
       "      <td>USAZKZ1</td>\n",
       "      <td>3361.043829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12188</th>\n",
       "      <td>USAZM61</td>\n",
       "      <td>1138.285888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12198</th>\n",
       "      <td>USAZMU1</td>\n",
       "      <td>-30.553497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12229</th>\n",
       "      <td>USAZOE1</td>\n",
       "      <td>-8467.260237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12250</th>\n",
       "      <td>USAZPE1</td>\n",
       "      <td>-3718.480450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12251</th>\n",
       "      <td>USAZPF1</td>\n",
       "      <td>744.244415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12270</th>\n",
       "      <td>USAZQD1</td>\n",
       "      <td>-7310.184072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12276</th>\n",
       "      <td>USAZQK1</td>\n",
       "      <td>1394.555671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12277</th>\n",
       "      <td>USAZQN1</td>\n",
       "      <td>2226.564959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12292</th>\n",
       "      <td>USAZRB1</td>\n",
       "      <td>2360.422418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12294</th>\n",
       "      <td>USAZRD1</td>\n",
       "      <td>110.473285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12296</th>\n",
       "      <td>USAZRG1</td>\n",
       "      <td>-2094.575011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12297</th>\n",
       "      <td>USAZRK1</td>\n",
       "      <td>-2502.803003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12300</th>\n",
       "      <td>USAZRU1</td>\n",
       "      <td>-436.814318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12303</th>\n",
       "      <td>USAZRY1</td>\n",
       "      <td>-411.688528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12306</th>\n",
       "      <td>USAZS21</td>\n",
       "      <td>347.856011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12310</th>\n",
       "      <td>USAZS81</td>\n",
       "      <td>2721.606498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12328</th>\n",
       "      <td>USAZTI1</td>\n",
       "      <td>-13.198416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>USAZUA1</td>\n",
       "      <td>453.988981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>USAZUH1</td>\n",
       "      <td>1081.268618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358</th>\n",
       "      <td>USAZV51</td>\n",
       "      <td>5397.217340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>USAZWU1</td>\n",
       "      <td>765.842154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12391</th>\n",
       "      <td>USAZXI1</td>\n",
       "      <td>513.539864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12422</th>\n",
       "      <td>USAZZ81</td>\n",
       "      <td>528.777989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2265 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Barrid        h.opt\n",
       "0      USA0001    -4.103059\n",
       "1      USA0011   -22.200676\n",
       "2      USA0031     1.519784\n",
       "3      USA0062    -5.749995\n",
       "4      USA00E2     6.810527\n",
       "5      USA00F1    -5.726061\n",
       "6      USA00G2     7.589228\n",
       "7      USA00H1     4.809374\n",
       "8      USA00I1    -9.642367\n",
       "9      USA00J1    -3.183782\n",
       "10     USA00K1    -7.864453\n",
       "11     USA00P1     1.483460\n",
       "12     USA00R1    -3.249811\n",
       "13     USA00S1     0.690273\n",
       "14     USA00V1    -4.741222\n",
       "15     USA0131   -11.272261\n",
       "16     USA0161    -7.186826\n",
       "17     USA01I1     2.181586\n",
       "18     USA01J2    -6.903525\n",
       "19     USA01L1    -1.096186\n",
       "20     USA01P1    -0.097555\n",
       "21     USA01Q1    -4.284195\n",
       "22     USA0202    13.541043\n",
       "23     USA0231    -4.554782\n",
       "24     USA0281    -5.341278\n",
       "25     USA0291    14.614120\n",
       "26     USA02A1     2.234342\n",
       "27     USA02B1    -8.424432\n",
       "28     USA02H1     0.465728\n",
       "29     USA02P1    -4.872854\n",
       "...        ...          ...\n",
       "12083  USAZGN1 -1601.158503\n",
       "12106  USAZHT1    90.648179\n",
       "12129  USAZJ11    42.231280\n",
       "12138  USAZJH1 -1586.998243\n",
       "12152  USAZK61   738.306872\n",
       "12160  USAZKM1  4546.795659\n",
       "12167  USAZKZ1  3361.043829\n",
       "12188  USAZM61  1138.285888\n",
       "12198  USAZMU1   -30.553497\n",
       "12229  USAZOE1 -8467.260237\n",
       "12250  USAZPE1 -3718.480450\n",
       "12251  USAZPF1   744.244415\n",
       "12270  USAZQD1 -7310.184072\n",
       "12276  USAZQK1  1394.555671\n",
       "12277  USAZQN1  2226.564959\n",
       "12292  USAZRB1  2360.422418\n",
       "12294  USAZRD1   110.473285\n",
       "12296  USAZRG1 -2094.575011\n",
       "12297  USAZRK1 -2502.803003\n",
       "12300  USAZRU1  -436.814318\n",
       "12303  USAZRY1  -411.688528\n",
       "12306  USAZS21   347.856011\n",
       "12310  USAZS81  2721.606498\n",
       "12328  USAZTI1   -13.198416\n",
       "12347  USAZUA1   453.988981\n",
       "12350  USAZUH1  1081.268618\n",
       "12358  USAZV51  5397.217340\n",
       "12380  USAZWU1   765.842154\n",
       "12391  USAZXI1   513.539864\n",
       "12422  USAZZ81   528.777989\n",
       "\n",
       "[2265 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Exposures (TODO)\n",
    "\n",
    "We can also use `h_star` to calculate our portfolio's risk and alpha exposures.\n",
    "\n",
    "In the cells below implement the functions `get_risk_exposures` and `get_portfolio_alpha_exposure` that calculate the portfolio's risk and alpha exposures, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_risk_exposures(B, BT, h_star):\n",
    "    \"\"\"\n",
    "    Calculate portfolio's Risk Exposure\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    B : patsy.design_info.DesignMatrix \n",
    "        Matrix of Risk Factors\n",
    "        \n",
    "    BT : patsy.design_info.DesignMatrix \n",
    "        Transpose of Matrix of Risk Factors\n",
    "        \n",
    "    h_star: Numpy ndarray \n",
    "        optimized holdings\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    risk_exposures : Pandas Series\n",
    "        Risk Exposures\n",
    "    \"\"\"\n",
    "    \n",
    "    risk_exposures = np.matmul(BT, h_star)\n",
    "    risk_exposures = pd.Series(risk_exposures, index=colnames(B))\n",
    "    return risk_exposures\n",
    "\n",
    "risk_exposures = get_risk_exposures(B, BT, h_star)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USFASTD_AERODEF      -4087.960700\n",
       "USFASTD_AIRLINES      -898.901696\n",
       "USFASTD_ALUMSTEL     -5780.182383\n",
       "USFASTD_APPAREL       3249.051054\n",
       "USFASTD_AUTO          3161.522332\n",
       "USFASTD_BANKS        17927.599520\n",
       "USFASTD_BETA         -5725.262518\n",
       "USFASTD_BEVTOB       -8724.149405\n",
       "USFASTD_BIOLIFE      -7264.438783\n",
       "USFASTD_BLDGPROD     -1746.867327\n",
       "USFASTD_CHEM        -12211.402958\n",
       "USFASTD_CNSTENG       1459.296665\n",
       "USFASTD_CNSTMACH       690.753209\n",
       "USFASTD_CNSTMATL      2683.034563\n",
       "USFASTD_COMMEQP      -1245.496824\n",
       "USFASTD_COMPELEC     -1977.722839\n",
       "USFASTD_COMSVCS     -21355.327228\n",
       "USFASTD_CONGLOM      -1951.528656\n",
       "USFASTD_CONTAINR     -5426.637581\n",
       "USFASTD_DISTRIB       5899.458023\n",
       "USFASTD_DIVFIN        8722.050855\n",
       "USFASTD_DIVYILD        431.535980\n",
       "USFASTD_DWNRISK      15458.508760\n",
       "USFASTD_EARNQLTY   -136944.849427\n",
       "USFASTD_ELECEQP      -8249.572252\n",
       "USFASTD_ELECUTIL      9495.316115\n",
       "USFASTD_FOODPROD     -4455.727234\n",
       "USFASTD_FOODRET       3178.102071\n",
       "USFASTD_GASUTIL       7201.731944\n",
       "USFASTD_GROWTH     -135439.917922\n",
       "                        ...      \n",
       "USFASTD_MIDCAP       53928.090729\n",
       "USFASTD_MOMENTUM    -48470.182105\n",
       "USFASTD_MULTUTIL     -2089.333372\n",
       "USFASTD_OILGSCON      5180.670507\n",
       "USFASTD_OILGSDRL     -1463.839963\n",
       "USFASTD_OILGSEQP     -1435.360057\n",
       "USFASTD_OILGSEXP      5533.866757\n",
       "USFASTD_PAPER         -321.833549\n",
       "USFASTD_PHARMA       -3692.584388\n",
       "USFASTD_PRECMTLS     -2774.347347\n",
       "USFASTD_PROFIT     -100637.596595\n",
       "USFASTD_PROSPECT     52070.291437\n",
       "USFASTD_PSNLPROD    -16145.769481\n",
       "USFASTD_REALEST     -14492.807366\n",
       "USFASTD_RESTAUR      -1076.405814\n",
       "USFASTD_RESVOL      -25917.736885\n",
       "USFASTD_ROADRAIL     11854.156526\n",
       "USFASTD_SEASON       55882.635279\n",
       "USFASTD_SEMICOND      -305.588866\n",
       "USFASTD_SEMIEQP        893.571886\n",
       "USFASTD_SIZE         -5489.217445\n",
       "USFASTD_SOFTWARE     -6466.150324\n",
       "USFASTD_SPLTYRET      2744.226370\n",
       "USFASTD_SPTYCHEM    -11073.968684\n",
       "USFASTD_SPTYSTOR     10776.613128\n",
       "USFASTD_STREVRSL     43350.516587\n",
       "USFASTD_TELECOM       1550.827643\n",
       "USFASTD_TRADECO      -5910.354531\n",
       "USFASTD_TRANSPRT     -1373.909611\n",
       "USFASTD_WIRELESS      4603.739514\n",
       "Length: 77, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2265, 4)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_alpha.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_portfolio_alpha_exposure(B_alpha, h_star):\n",
    "    \"\"\"\n",
    "    Calculate portfolio's Alpha Exposure\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    B_alpha : patsy.design_info.DesignMatrix \n",
    "        Matrix of Alpha Factors\n",
    "        \n",
    "    h_star: Numpy ndarray \n",
    "        optimized holdings\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    alpha_exposures : Pandas Series\n",
    "        Alpha Exposures\n",
    "    \"\"\"\n",
    "    \n",
    "    return pd.Series(np.matmul(B_alpha.transpose(), h_star), index = colnames(B_alpha))\n",
    "\n",
    "portfolio_alpha_exposure = get_portfolio_alpha_exposure(B_alpha, h_star)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USFASTD_1DREVRSL    2.259431e+06\n",
       "USFASTD_EARNYILD    1.753308e+06\n",
       "USFASTD_VALUE       2.255237e+06\n",
       "USFASTD_SENTMT      1.271437e+06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_alpha_exposure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transaction Costs (TODO)\n",
    "\n",
    "We can also use `h_star` to calculate our total transaction costs:\n",
    "$$\n",
    "\\mbox{tcost} = \\sum_i^{N} \\lambda_{i} (h_{i,t} - h_{i,t-1})^2\n",
    "$$\n",
    "\n",
    "In the cell below, implement the function `get_total_transaction_costs` that calculates the total transaction costs according to the equation above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total_transaction_costs(h0, h_star, Lambda):\n",
    "    \"\"\"\n",
    "    Calculate Total Transaction Costs\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    h0 : Pandas Series\n",
    "        initial holdings (before optimization)\n",
    "        \n",
    "    h_star: Numpy ndarray \n",
    "        optimized holdings\n",
    "        \n",
    "    Lambda : Pandas Series  \n",
    "        Lambda\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    total_transaction_costs : float\n",
    "        Total Transaction Costs\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "\n",
    "    return np.dot((h_star-h0)**2, Lambda)\n",
    "\n",
    "total_transaction_costs = get_total_transaction_costs(h0, h_star, Lambda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104.87520269773562"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_transaction_costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting It All Together\n",
    "\n",
    "We can now take all the above functions we created above and use them to create a single function, `form_optimal_portfolio` that returns the optimal portfolio, the risk and alpha exposures, and the total transactions costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def form_optimal_portfolio(df, previous, risk_aversion):\n",
    "    df = df.merge(previous, how = 'left', on = 'Barrid')\n",
    "    df = clean_nas(df)\n",
    "    df.loc[df['SpecRisk'] == 0]['SpecRisk'] = median(df['SpecRisk'])\n",
    "  \n",
    "    universe = get_universe(df)\n",
    "    date = str(int(universe['DataDate'][1]))\n",
    "  \n",
    "    all_factors = factors_from_names(list(universe))\n",
    "    risk_factors = setdiff(all_factors, alpha_factors)\n",
    "  \n",
    "    h0 = universe['h.opt.previous']\n",
    "  \n",
    "    B = model_matrix(get_formula(risk_factors, \"SpecRisk\"), universe)\n",
    "    BT = B.transpose()\n",
    "  \n",
    "    specVar = (0.01 * universe['SpecRisk']) ** 2\n",
    "    Fvar = diagonal_factor_cov(date, B)\n",
    "    \n",
    "    Lambda = get_lambda(universe)\n",
    "    B_alpha = get_B_alpha(alpha_factors, universe)\n",
    "    alpha_vec = get_alpha_vec(B_alpha)\n",
    "  \n",
    "    Q = np.matmul(scipy.linalg.sqrtm(Fvar), BT)\n",
    "    QT = Q.transpose()\n",
    "    \n",
    "    h_star = get_h_star(risk_aversion, Q, QT, specVar, alpha_vec, h0, Lambda)\n",
    "    opt_portfolio = pd.DataFrame(data = {\"Barrid\" : universe['Barrid'], \"h.opt\" : h_star})\n",
    "    \n",
    "    risk_exposures = get_risk_exposures(B, BT, h_star)\n",
    "    portfolio_alpha_exposure = get_portfolio_alpha_exposure(B_alpha, h_star)\n",
    "    total_transaction_costs = get_total_transaction_costs(h0, h_star, Lambda)\n",
    "  \n",
    "    return {\n",
    "        \"opt.portfolio\" : opt_portfolio, \n",
    "        \"risk.exposures\" : risk_exposures, \n",
    "        \"alpha.exposures\" : portfolio_alpha_exposure,\n",
    "        \"total.cost\" : total_transaction_costs}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build tradelist\n",
    "\n",
    "The trade list is the most recent optimal asset holdings minus the previous day's optimal holdings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_tradelist(prev_holdings, opt_result):\n",
    "    tmp = prev_holdings.merge(opt_result['opt.portfolio'], how='outer', on = 'Barrid')\n",
    "    tmp['h.opt.previous'] = np.nan_to_num(tmp['h.opt.previous'])\n",
    "    tmp['h.opt'] = np.nan_to_num(tmp['h.opt'])\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save optimal holdings as previous optimal holdings.\n",
    "\n",
    "As we walk through each day, we'll re-use the column for previous holdings by storing the \"current\" optimal holdings as the \"previous\" optimal holdings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_previous(result): \n",
    "    prev = result['opt.portfolio']\n",
    "    prev = prev.rename(index=str, columns={\"h.opt\": \"h.opt.previous\"}, copy=True, inplace=False)\n",
    "    return prev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the backtest\n",
    "\n",
    "Walk through each day, calculating the optimal portfolio holdings and trade list.  This may take some time, but should finish sooner if you've chosen all the optimizations you learned in the lessons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimizing Portfolio: 100%|██████████| 252/252 [20:51<00:00,  4.97s/day]\n"
     ]
    }
   ],
   "source": [
    "trades = {}\n",
    "port = {}\n",
    "\n",
    "for dt in tqdm(my_dates, desc='Optimizing Portfolio', unit='day'):\n",
    "    date = dt.strftime('%Y%m%d')\n",
    "\n",
    "    result = form_optimal_portfolio(frames[date], previous_holdings, risk_aversion)\n",
    "    trades[date] = build_tradelist(previous_holdings, result)\n",
    "    port[date] = result\n",
    "    previous_holdings = convert_to_previous(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "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>Barrid</th>\n",
       "      <th>h.opt.previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA0001</td>\n",
       "      <td>-67.097201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA0011</td>\n",
       "      <td>-2105.386491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA0031</td>\n",
       "      <td>-120.489835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USA0062</td>\n",
       "      <td>-386.141154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USA00E2</td>\n",
       "      <td>756.487371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USA00F1</td>\n",
       "      <td>-754.525196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USA00G2</td>\n",
       "      <td>924.917444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USA00H1</td>\n",
       "      <td>1271.014759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USA00I1</td>\n",
       "      <td>-278.337901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USA00J1</td>\n",
       "      <td>-428.284215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>USA00K1</td>\n",
       "      <td>172.080199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USA00P1</td>\n",
       "      <td>-444.067118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>USA00R1</td>\n",
       "      <td>-763.242440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USA00S1</td>\n",
       "      <td>-353.740398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>USA00V1</td>\n",
       "      <td>403.188162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>USA0131</td>\n",
       "      <td>-233.455107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>USA0161</td>\n",
       "      <td>-317.247067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>USA01I1</td>\n",
       "      <td>739.058489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>USA01J2</td>\n",
       "      <td>-429.757848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>USA01L1</td>\n",
       "      <td>315.719879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>USA01P1</td>\n",
       "      <td>-163.421866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USA01Q1</td>\n",
       "      <td>-481.254956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>USA0202</td>\n",
       "      <td>897.391349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>USA0231</td>\n",
       "      <td>-1082.015047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>USA0281</td>\n",
       "      <td>-851.035038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>USA0291</td>\n",
       "      <td>372.939960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>USA02A1</td>\n",
       "      <td>432.818069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>USA02B1</td>\n",
       "      <td>5.191192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>USA02H1</td>\n",
       "      <td>698.103310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USA02P1</td>\n",
       "      <td>-1307.456682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12209</th>\n",
       "      <td>USAZNZ1</td>\n",
       "      <td>2713.733785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12216</th>\n",
       "      <td>USAZOE1</td>\n",
       "      <td>-13719.974870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12223</th>\n",
       "      <td>USAZOO1</td>\n",
       "      <td>-873.206100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12232</th>\n",
       "      <td>USAZP71</td>\n",
       "      <td>3202.964272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12237</th>\n",
       "      <td>USAZPE1</td>\n",
       "      <td>-2644.505605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12238</th>\n",
       "      <td>USAZPF1</td>\n",
       "      <td>1320.834200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12256</th>\n",
       "      <td>USAZQD1</td>\n",
       "      <td>-1100.956726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12261</th>\n",
       "      <td>USAZQK1</td>\n",
       "      <td>3448.438193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12262</th>\n",
       "      <td>USAZQN1</td>\n",
       "      <td>-1458.653783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12264</th>\n",
       "      <td>USAZQR1</td>\n",
       "      <td>345.377049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12269</th>\n",
       "      <td>USAZR11</td>\n",
       "      <td>-95.396275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12276</th>\n",
       "      <td>USAZRB1</td>\n",
       "      <td>9876.055629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12278</th>\n",
       "      <td>USAZRD1</td>\n",
       "      <td>13137.502102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12280</th>\n",
       "      <td>USAZRG1</td>\n",
       "      <td>-3628.931214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12281</th>\n",
       "      <td>USAZRK1</td>\n",
       "      <td>4355.942332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>USAZRU1</td>\n",
       "      <td>-2153.537288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12287</th>\n",
       "      <td>USAZRY1</td>\n",
       "      <td>-3491.022836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12290</th>\n",
       "      <td>USAZS21</td>\n",
       "      <td>-2985.748959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12294</th>\n",
       "      <td>USAZS81</td>\n",
       "      <td>-1743.280012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12311</th>\n",
       "      <td>USAZTI1</td>\n",
       "      <td>-1005.730931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12323</th>\n",
       "      <td>USAZU11</td>\n",
       "      <td>-2120.449207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12328</th>\n",
       "      <td>USAZUA1</td>\n",
       "      <td>6516.285625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12331</th>\n",
       "      <td>USAZUH1</td>\n",
       "      <td>680.129010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12335</th>\n",
       "      <td>USAZUY1</td>\n",
       "      <td>3412.257159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12338</th>\n",
       "      <td>USAZV51</td>\n",
       "      <td>-4652.249410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12351</th>\n",
       "      <td>USAZWD1</td>\n",
       "      <td>1334.750829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12359</th>\n",
       "      <td>USAZWU1</td>\n",
       "      <td>3661.216203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12371</th>\n",
       "      <td>USAZXI1</td>\n",
       "      <td>-2524.660861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12403</th>\n",
       "      <td>USAZZ81</td>\n",
       "      <td>-3025.054328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12415</th>\n",
       "      <td>USAZZY1</td>\n",
       "      <td>-5044.659000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2662 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Barrid  h.opt.previous\n",
       "0      USA0001      -67.097201\n",
       "1      USA0011    -2105.386491\n",
       "2      USA0031     -120.489835\n",
       "3      USA0062     -386.141154\n",
       "4      USA00E2      756.487371\n",
       "5      USA00F1     -754.525196\n",
       "6      USA00G2      924.917444\n",
       "7      USA00H1     1271.014759\n",
       "8      USA00I1     -278.337901\n",
       "9      USA00J1     -428.284215\n",
       "10     USA00K1      172.080199\n",
       "11     USA00P1     -444.067118\n",
       "12     USA00R1     -763.242440\n",
       "13     USA00S1     -353.740398\n",
       "14     USA00V1      403.188162\n",
       "15     USA0131     -233.455107\n",
       "16     USA0161     -317.247067\n",
       "17     USA01I1      739.058489\n",
       "18     USA01J2     -429.757848\n",
       "19     USA01L1      315.719879\n",
       "20     USA01P1     -163.421866\n",
       "21     USA01Q1     -481.254956\n",
       "22     USA0202      897.391349\n",
       "23     USA0231    -1082.015047\n",
       "24     USA0281     -851.035038\n",
       "25     USA0291      372.939960\n",
       "26     USA02A1      432.818069\n",
       "27     USA02B1        5.191192\n",
       "28     USA02H1      698.103310\n",
       "29     USA02P1    -1307.456682\n",
       "...        ...             ...\n",
       "12209  USAZNZ1     2713.733785\n",
       "12216  USAZOE1   -13719.974870\n",
       "12223  USAZOO1     -873.206100\n",
       "12232  USAZP71     3202.964272\n",
       "12237  USAZPE1    -2644.505605\n",
       "12238  USAZPF1     1320.834200\n",
       "12256  USAZQD1    -1100.956726\n",
       "12261  USAZQK1     3448.438193\n",
       "12262  USAZQN1    -1458.653783\n",
       "12264  USAZQR1      345.377049\n",
       "12269  USAZR11      -95.396275\n",
       "12276  USAZRB1     9876.055629\n",
       "12278  USAZRD1    13137.502102\n",
       "12280  USAZRG1    -3628.931214\n",
       "12281  USAZRK1     4355.942332\n",
       "12284  USAZRU1    -2153.537288\n",
       "12287  USAZRY1    -3491.022836\n",
       "12290  USAZS21    -2985.748959\n",
       "12294  USAZS81    -1743.280012\n",
       "12311  USAZTI1    -1005.730931\n",
       "12323  USAZU11    -2120.449207\n",
       "12328  USAZUA1     6516.285625\n",
       "12331  USAZUH1      680.129010\n",
       "12335  USAZUY1     3412.257159\n",
       "12338  USAZV51    -4652.249410\n",
       "12351  USAZWD1     1334.750829\n",
       "12359  USAZWU1     3661.216203\n",
       "12371  USAZXI1    -2524.660861\n",
       "12403  USAZZ81    -3025.054328\n",
       "12415  USAZZY1    -5044.659000\n",
       "\n",
       "[2662 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_holdings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2004-01-06 00:00:00')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dates[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profit-and-Loss (PnL) attribution (TODO)\n",
    "\n",
    "Profit and Loss is the aggregate realized daily returns of the assets, weighted by the optimal portfolio holdings chosen, and summed up to get the portfolio's profit and loss.\n",
    "\n",
    "The PnL attributed to the alpha factors equals the factor returns times factor exposures for the alpha factors.  \n",
    "\n",
    "$$\n",
    "\\mbox{PnL}_{alpha}= f \\times b_{alpha}\n",
    "$$\n",
    "\n",
    "Similarly, the PnL attributed to the risk factors equals the factor returns times factor exposures of the risk factors.\n",
    "\n",
    "$$\n",
    "\\mbox{PnL}_{risk} = f \\times b_{risk}\n",
    "$$\n",
    "\n",
    "In the code below, in the function `build_pnl_attribution` calculate the PnL attributed to the alpha factors, the PnL attributed to the risk factors, and attribution to cost. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "## assumes v, w are pandas Series \n",
    "def partial_dot_product(v, w):\n",
    "    common = v.index.intersection(w.index)\n",
    "    return np.sum(v[common] * w[common])\n",
    "\n",
    "def build_pnl_attribution(): \n",
    "\n",
    "    df = pd.DataFrame(index = my_dates)\n",
    "    \n",
    "    for dt in my_dates:\n",
    "        date = dt.strftime('%Y%m%d')\n",
    "\n",
    "        p = port[date]\n",
    "        fr = facret[date]\n",
    "\n",
    "        mf = p['opt.portfolio'].merge(frames[date], how = 'left', on = \"Barrid\")\n",
    "        \n",
    "        mf['DlyReturn'] = wins(mf['DlyReturn'], -0.5, 0.5)\n",
    "        df.at[dt,\"daily.pnl\"] = np.sum(mf['h.opt'] * mf['DlyReturn'])\n",
    "        \n",
    "        # TODO: Implement\n",
    "    \n",
    "        df.at[dt,\"attribution.alpha.pnl\"] = partial_dot_product(fr, p['alpha.exposures'])\n",
    "        df.at[dt,\"attribution.risk.pnl\"] = partial_dot_product(fr, p['risk.exposures'])\n",
    "        df.at[dt,\"attribution.cost\"] = p['total.cost']\n",
    "        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb523f7b5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attr = build_pnl_attribution()\n",
    "\n",
    "for column in attr.columns:\n",
    "        plt.plot(attr[column].cumsum(), label=column)\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('PnL Attribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build portfolio characteristics (TODO)\n",
    "Calculate the sum of long positions, short positions, net positions, gross market value, and amount of dollars traded.\n",
    "\n",
    "In the code below, in the function `build_portfolio_characteristics` calculate the sum of long positions, short positions, net positions, gross market value, and amount of dollars traded.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_portfolio_characteristics(): \n",
    "    df = pd.DataFrame(index = my_dates)\n",
    "    \n",
    "    for dt in my_dates:\n",
    "        date = dt.strftime('%Y%m%d')\n",
    "  \n",
    "        #p = port[date]\n",
    "        tradelist = trades[date]\n",
    "        #h = p['opt.portfolio']['h.opt']\n",
    "        h = tradelist['h.opt']\n",
    "        \n",
    "        # TODO: Implement\n",
    "        long_positions = h[h>0].sum()\n",
    "        short_positions = h[h<0].sum()\n",
    "        \n",
    "        df.at[dt,\"long\"] = long_positions\n",
    "        df.at[dt,\"short\"] = short_positions\n",
    "        df.at[dt,\"net\"] = h.sum()\n",
    "        df.at[dt,\"gmv\"] = long_positions + np.abs(short_positions)\n",
    "        \n",
    "        \n",
    "        df.at[dt,\"traded\"] = np.sum(np.abs(\n",
    "            h-tradelist['h.opt.previous']\n",
    "        ))\n",
    "        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5127cef60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pchar = build_portfolio_characteristics()\n",
    "\n",
    "for column in pchar.columns:\n",
    "        plt.plot(pchar[column], label=column)\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Portfolio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optional\n",
    "Choose additional metrics to evaluate your portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission\n",
    "Now that you're done with the project, it's time to submit it. Click the submit button in the bottom right. One of our reviewers will give you feedback on your project with a pass or not passed grade."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}