07_Stock_Screener.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SimFin Tutorial 07 - Stock Screener\n",
    "\n",
    "[Original repository on GitHub](https://github.com/simfin/simfin-tutorials)\n",
    "\n",
    "This tutorial was originally written by [Hvass Labs](https://github.com/Hvass-Labs)\n",
    "\n",
    "----\n",
    "\n",
    "\"I am King Arthur and these are my knights of the round table. Go and tell your master that we have been charged by God with a quest for the Holy Grail and he can join us. Well I can ask him, but I don't think he'll be very keen, because he has already got one, you see.\"\n",
    "&ndash; [Monty Python's Holy Grail](https://www.youtube.com/watch?v=M9DCAFUerzs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "A stock-screener is a very common tool used to search for stocks that meet certain criteria, e.g. low valuation ratios, high sales-growth, etc. This tutorial shows how to make a basic stock-screener using the signals calculated in the previous tutorial.\n",
    "\n",
    "We will also show how to back-test a stock-screener to see if it would have been able to discover profitable investments in the past. And we will show how to optimize the screener criteria to maximize the average returns.\n",
    "\n",
    "It is assumed you are already familiar with the previous tutorials on the basics of SimFin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/magnus/anaconda3/envs/simfin/lib/python3.6/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import differential_evolution\n",
    "from datetime import datetime, timedelta\n",
    "import os\n",
    "\n",
    "# Import the main functionality from the SimFin Python API.\n",
    "import simfin as sf\n",
    "\n",
    "# Import names used for easy access to SimFin's data-columns.\n",
    "from simfin.names import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Version of the SimFin Python API.\n",
    "sf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boolean whether this is being run under pytest. We will\n",
    "# use this to make certain parts of the tutorial run faster.\n",
    "running_pytest = ('PYTEST_CURRENT_TEST' in os.environ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimFin data-directory.\n",
    "sf.set_data_dir('~/simfin_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimFin load API key or use free data.\n",
    "sf.load_api_key(path='~/simfin_api_key.txt', default_key='free')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seaborn set plotting style.\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Hub\n",
    "\n",
    "In these examples, we will use stock-data for USA. It is very easy to load and process the data using the `sf.StockHub` class. We instruct it to refresh the financial data every 30 days, and the share-price data must be refreshed daily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "hub = sf.StockHub(market='us',\n",
    "                  refresh_days=30,\n",
    "                  refresh_days_shareprices=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data-hub not only makes the syntax very simple, but it also takes care of downloading the required datasets from the SimFin server and loading them into Pandas DataFrames. Furthermore, the data-hub's slow processing functions use a disk-cache to save the results for the next time the functions are called, and the cache automatically gets refreshed when new datasets are downloaded from the SimFin server. Lastly, the data-hub's functions are also RAM-cached, so the second time you call them, they return the results nearly instantly. Altogether, the data-hub makes it much easier and faster to work with the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Signals\n",
    "\n",
    "First we calculate financial signals for the stocks, such as the Current Ratio, Debt Ratio, Net Profit Margin, Return on Assets, etc. These are calculated using data from the financial reports: Income Statements, Balance Sheets and Cash-Flow Statements, which are automatically downloaded and loaded by the data-hub.\n",
    "\n",
    "Note that we set `variant='latest'` because we are only interested in the most recent signals, and we are not interested in the signals from several years ago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-ttm\" on disk (3 days old).\n",
      "- Loading from disk ... Done!\n",
      "Dataset \"us-balance-ttm\" on disk (3 days old).\n",
      "- Loading from disk ... Done!\n",
      "Dataset \"us-shareprices-latest\" on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "Cache-file 'fin_signals-899db0b4.pickle' on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 606 ms, sys: 44 ms, total: 650 ms\n",
      "Wall time: 646 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_fin_signals = hub.fin_signals(variant='latest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Current Ratio</th>\n",
       "      <th>Debt Ratio</th>\n",
       "      <th>Gross Profit Margin</th>\n",
       "      <th>Net Profit Margin</th>\n",
       "      <th>Return on Assets</th>\n",
       "      <th>Return on Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.546234</td>\n",
       "      <td>1.533173</td>\n",
       "      <td>0.254655</td>\n",
       "      <td>0.543289</td>\n",
       "      <td>0.207438</td>\n",
       "      <td>0.113309</td>\n",
       "      <td>0.225569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.773074</td>\n",
       "      <td>1.483910</td>\n",
       "      <td>0.123108</td>\n",
       "      <td>0.204391</td>\n",
       "      <td>-0.068689</td>\n",
       "      <td>-0.053102</td>\n",
       "      <td>-0.121529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAC</th>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>0.724773</td>\n",
       "      <td>2.117862</td>\n",
       "      <td>0.500277</td>\n",
       "      <td>0.402427</td>\n",
       "      <td>-0.077948</td>\n",
       "      <td>-0.056495</td>\n",
       "      <td>-0.144093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.742019</td>\n",
       "      <td>0.535107</td>\n",
       "      <td>0.412472</td>\n",
       "      <td>0.267971</td>\n",
       "      <td>0.035049</td>\n",
       "      <td>0.026007</td>\n",
       "      <td>9.943750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAOI</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.413807</td>\n",
       "      <td>2.959300</td>\n",
       "      <td>0.258352</td>\n",
       "      <td>0.226747</td>\n",
       "      <td>-0.195713</td>\n",
       "      <td>-0.080987</td>\n",
       "      <td>-0.129639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Asset Turnover  Current Ratio  Debt Ratio  \\\n",
       "Ticker Date                                                    \n",
       "A      2020-02-26        0.546234       1.533173    0.254655   \n",
       "AA     2020-02-26        0.773074       1.483910    0.123108   \n",
       "AAC    2019-11-04        0.724773       2.117862    0.500277   \n",
       "AAL    2020-02-26        0.742019       0.535107    0.412472   \n",
       "AAOI   2020-02-26        0.413807       2.959300    0.258352   \n",
       "\n",
       "                   Gross Profit Margin  Net Profit Margin  Return on Assets  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26             0.543289           0.207438          0.113309   \n",
       "AA     2020-02-26             0.204391          -0.068689         -0.053102   \n",
       "AAC    2019-11-04             0.402427          -0.077948         -0.056495   \n",
       "AAL    2020-02-26             0.267971           0.035049          0.026007   \n",
       "AAOI   2020-02-26             0.226747          -0.195713         -0.080987   \n",
       "\n",
       "                   Return on Equity  \n",
       "Ticker Date                          \n",
       "A      2020-02-26          0.225569  \n",
       "AA     2020-02-26         -0.121529  \n",
       "AAC    2019-11-04         -0.144093  \n",
       "AAL    2020-02-26          9.943750  \n",
       "AAOI   2020-02-26         -0.129639  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fin_signals.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then pass the argument `func=sf.avg_ttm_2y` to `hub.fin_signals`, so as to calculate 2-year averages of the financial signals. We then get another DataFrame with the 2-year average Current Ratio, Net Profit Margin, Return on Assets, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache-file 'fin_signals-4a256f6a.pickle' on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 3.07 ms, sys: 0 ns, total: 3.07 ms\n",
      "Wall time: 2.62 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_fin_signals_2y = hub.fin_signals(variant='latest',\n",
    "                                    func=sf.avg_ttm_2y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Current Ratio</th>\n",
       "      <th>Debt Ratio</th>\n",
       "      <th>Gross Profit Margin</th>\n",
       "      <th>Net Profit Margin</th>\n",
       "      <th>Return on Assets</th>\n",
       "      <th>Return on Equity</th>\n",
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       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
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       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.560788</td>\n",
       "      <td>2.409627</td>\n",
       "      <td>0.232643</td>\n",
       "      <td>0.545047</td>\n",
       "      <td>0.135872</td>\n",
       "      <td>0.075154</td>\n",
       "      <td>0.147350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.795342</td>\n",
       "      <td>1.439858</td>\n",
       "      <td>0.117903</td>\n",
       "      <td>0.227904</td>\n",
       "      <td>-0.034798</td>\n",
       "      <td>-0.026921</td>\n",
       "      <td>-0.061588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.790911</td>\n",
       "      <td>0.558048</td>\n",
       "      <td>0.441507</td>\n",
       "      <td>0.270854</td>\n",
       "      <td>0.032806</td>\n",
       "      <td>0.025837</td>\n",
       "      <td>3.782614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAOI</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.507580</td>\n",
       "      <td>2.780494</td>\n",
       "      <td>0.203145</td>\n",
       "      <td>0.302432</td>\n",
       "      <td>-0.076878</td>\n",
       "      <td>-0.027878</td>\n",
       "      <td>-0.046729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.966834</td>\n",
       "      <td>1.462086</td>\n",
       "      <td>0.141038</td>\n",
       "      <td>0.438166</td>\n",
       "      <td>0.052073</td>\n",
       "      <td>0.050930</td>\n",
       "      <td>0.140132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Asset Turnover  Current Ratio  Debt Ratio  \\\n",
       "Ticker Date                                                    \n",
       "A      2020-02-26        0.560788       2.409627    0.232643   \n",
       "AA     2020-02-26        0.795342       1.439858    0.117903   \n",
       "AAL    2020-02-26        0.790911       0.558048    0.441507   \n",
       "AAOI   2020-02-26        0.507580       2.780494    0.203145   \n",
       "AAP    2020-02-26        0.966834       1.462086    0.141038   \n",
       "\n",
       "                   Gross Profit Margin  Net Profit Margin  Return on Assets  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26             0.545047           0.135872          0.075154   \n",
       "AA     2020-02-26             0.227904          -0.034798         -0.026921   \n",
       "AAL    2020-02-26             0.270854           0.032806          0.025837   \n",
       "AAOI   2020-02-26             0.302432          -0.076878         -0.027878   \n",
       "AAP    2020-02-26             0.438166           0.052073          0.050930   \n",
       "\n",
       "                   Return on Equity  \n",
       "Ticker Date                          \n",
       "A      2020-02-26          0.147350  \n",
       "AA     2020-02-26         -0.061588  \n",
       "AAL    2020-02-26          3.782614  \n",
       "AAOI   2020-02-26         -0.046729  \n",
       "AAP    2020-02-26          0.140132  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fin_signals_2y.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Growth Signals\n",
    "\n",
    "Now we calculate growth signals for the stocks, such as Earnings Growth, FCF Growth, Sales Growth, etc. These are also calculated using data from the financial reports: Income Statements, Balance Sheets and Cash-Flow Statements, which are automatically downloaded and loaded by the data-hub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-quarterly\" on disk (3 days old).\n",
      "- Loading from disk ... Done!\n",
      "Dataset \"us-cashflow-ttm\" on disk (3 days old).\n",
      "- Loading from disk ... Done!\n",
      "Dataset \"us-cashflow-quarterly\" on disk (3 days old).\n",
      "- Loading from disk ... Done!\n",
      "Cache-file 'growth_signals-899db0b4.pickle' on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 896 ms, sys: 55.8 ms, total: 952 ms\n",
      "Wall time: 956 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_growth_signals = hub.growth_signals(variant='latest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Earnings Growth</th>\n",
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       "      <th>Ticker</th>\n",
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       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>2.389241</td>\n",
       "      <td>0.015707</td>\n",
       "      <td>-0.005128</td>\n",
       "      <td>-0.049396</td>\n",
       "      <td>0.456410</td>\n",
       "      <td>-0.157270</td>\n",
       "      <td>0.050672</td>\n",
       "      <td>0.072998</td>\n",
       "      <td>0.056414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>63.916667</td>\n",
       "      <td>-0.450249</td>\n",
       "      <td>4.390244</td>\n",
       "      <td>-18.687500</td>\n",
       "      <td>-13.428571</td>\n",
       "      <td>-0.577670</td>\n",
       "      <td>-0.142976</td>\n",
       "      <td>-0.053117</td>\n",
       "      <td>-0.242773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.177646</td>\n",
       "      <td>-0.358006</td>\n",
       "      <td>0.246334</td>\n",
       "      <td>12.931507</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>-1.323651</td>\n",
       "      <td>0.026921</td>\n",
       "      <td>-0.004097</td>\n",
       "      <td>0.030452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAN</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>-0.358158</td>\n",
       "      <td>-0.066800</td>\n",
       "      <td>-0.089639</td>\n",
       "      <td>-0.082894</td>\n",
       "      <td>0.564822</td>\n",
       "      <td>0.146337</td>\n",
       "      <td>0.058293</td>\n",
       "      <td>-0.004476</td>\n",
       "      <td>0.011266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAOI</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>-4.228538</td>\n",
       "      <td>-0.227521</td>\n",
       "      <td>1.352626</td>\n",
       "      <td>1.814266</td>\n",
       "      <td>-0.134604</td>\n",
       "      <td>-0.575232</td>\n",
       "      <td>-0.307879</td>\n",
       "      <td>0.061574</td>\n",
       "      <td>-0.182705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Earnings Growth  Earnings Growth QOQ  Earnings Growth YOY  \\\n",
       "Ticker Date                                                                    \n",
       "A      2020-02-26         2.389241             0.015707            -0.005128   \n",
       "AA     2020-02-26        63.916667            -0.450249             4.390244   \n",
       "AAL    2020-02-26         0.177646            -0.358006             0.246334   \n",
       "AAN    2020-02-26        -0.358158            -0.066800            -0.089639   \n",
       "AAOI   2020-02-26        -4.228538            -0.227521             1.352626   \n",
       "\n",
       "                   FCF Growth  FCF Growth QOQ  FCF Growth YOY  Sales Growth  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26   -0.049396        0.456410       -0.157270      0.050672   \n",
       "AA     2020-02-26  -18.687500      -13.428571       -0.577670     -0.142976   \n",
       "AAL    2020-02-26   12.931507       -2.500000       -1.323651      0.026921   \n",
       "AAN    2020-02-26   -0.082894        0.564822        0.146337      0.058293   \n",
       "AAOI   2020-02-26    1.814266       -0.134604       -0.575232     -0.307879   \n",
       "\n",
       "                   Sales Growth QOQ  Sales Growth YOY  \n",
       "Ticker Date                                            \n",
       "A      2020-02-26          0.072998          0.056414  \n",
       "AA     2020-02-26         -0.053117         -0.242773  \n",
       "AAL    2020-02-26         -0.004097          0.030452  \n",
       "AAN    2020-02-26         -0.004476          0.011266  \n",
       "AAOI   2020-02-26          0.061574         -0.182705  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_growth_signals.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then pass the argument `func=sf.avg_ttm_2y` to `hub.growth_signals` so as to calculate 2-year averages of the growth signals. We then get another DataFrame with the 2-year average Earnings Growth, FCF Growth, Sales Growth, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache-file 'growth_signals-4a256f6a.pickle' on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 2.93 ms, sys: 0 ns, total: 2.93 ms\n",
      "Wall time: 2.43 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_growth_signals_2y = hub.growth_signals(variant='latest',\n",
    "                                          func=sf.avg_ttm_2y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Earnings Growth</th>\n",
       "      <th>Earnings Growth QOQ</th>\n",
       "      <th>Earnings Growth YOY</th>\n",
       "      <th>FCF Growth</th>\n",
       "      <th>FCF Growth QOQ</th>\n",
       "      <th>FCF Growth YOY</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>Sales Growth QOQ</th>\n",
       "      <th>Sales Growth YOY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.925614</td>\n",
       "      <td>-0.079011</td>\n",
       "      <td>0.048283</td>\n",
       "      <td>0.114152</td>\n",
       "      <td>0.755644</td>\n",
       "      <td>0.153974</td>\n",
       "      <td>0.074754</td>\n",
       "      <td>0.074321</td>\n",
       "      <td>0.072362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>31.437500</td>\n",
       "      <td>-0.998458</td>\n",
       "      <td>1.513706</td>\n",
       "      <td>-9.870153</td>\n",
       "      <td>-7.410476</td>\n",
       "      <td>-0.429950</td>\n",
       "      <td>0.029193</td>\n",
       "      <td>-0.052962</td>\n",
       "      <td>-0.049524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>-0.064767</td>\n",
       "      <td>-0.377766</td>\n",
       "      <td>-0.103596</td>\n",
       "      <td>5.776634</td>\n",
       "      <td>-2.404891</td>\n",
       "      <td>-0.661826</td>\n",
       "      <td>0.047378</td>\n",
       "      <td>-0.005656</td>\n",
       "      <td>0.046528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAN</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.462982</td>\n",
       "      <td>0.034378</td>\n",
       "      <td>0.317814</td>\n",
       "      <td>0.450612</td>\n",
       "      <td>0.468240</td>\n",
       "      <td>0.311686</td>\n",
       "      <td>0.093859</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>0.073692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAOI</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>-2.540670</td>\n",
       "      <td>-0.846023</td>\n",
       "      <td>0.080003</td>\n",
       "      <td>0.061254</td>\n",
       "      <td>-8.583626</td>\n",
       "      <td>-0.252245</td>\n",
       "      <td>-0.280525</td>\n",
       "      <td>-0.148189</td>\n",
       "      <td>-0.274146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Earnings Growth  Earnings Growth QOQ  Earnings Growth YOY  \\\n",
       "Ticker Date                                                                    \n",
       "A      2020-02-26         0.925614            -0.079011             0.048283   \n",
       "AA     2020-02-26        31.437500            -0.998458             1.513706   \n",
       "AAL    2020-02-26        -0.064767            -0.377766            -0.103596   \n",
       "AAN    2020-02-26         0.462982             0.034378             0.317814   \n",
       "AAOI   2020-02-26        -2.540670            -0.846023             0.080003   \n",
       "\n",
       "                   FCF Growth  FCF Growth QOQ  FCF Growth YOY  Sales Growth  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26    0.114152        0.755644        0.153974      0.074754   \n",
       "AA     2020-02-26   -9.870153       -7.410476       -0.429950      0.029193   \n",
       "AAL    2020-02-26    5.776634       -2.404891       -0.661826      0.047378   \n",
       "AAN    2020-02-26    0.450612        0.468240        0.311686      0.093859   \n",
       "AAOI   2020-02-26    0.061254       -8.583626       -0.252245     -0.280525   \n",
       "\n",
       "                   Sales Growth QOQ  Sales Growth YOY  \n",
       "Ticker Date                                            \n",
       "A      2020-02-26          0.074321          0.072362  \n",
       "AA     2020-02-26         -0.052962         -0.049524  \n",
       "AAL    2020-02-26         -0.005656          0.046528  \n",
       "AAN    2020-02-26          0.011348          0.073692  \n",
       "AAOI   2020-02-26         -0.148189         -0.274146  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_growth_signals_2y.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valuation Signals\n",
    "\n",
    "Now we calculate valuation signals for the stocks, such as P/E, P/Sales, etc. These are calculated from the share-prices and data from the financial reports. Because the data-hub has already loaded the required datasets in the function-calls above, the data is merely reused here, and the data-hub can proceed directly to computing the signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache-file 'val_signals-9541e2e6.pickle' on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 2.91 ms, sys: 0 ns, total: 2.91 ms\n",
      "Wall time: 2.52 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_val_signals = hub.val_signals(variant='latest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Dividend Yield</th>\n",
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       "      <th>FCF Yield</th>\n",
       "      <th>Market-Cap</th>\n",
       "      <th>P/Book</th>\n",
       "      <th>P/E</th>\n",
       "      <th>P/FCF</th>\n",
       "      <th>P/NCAV</th>\n",
       "      <th>P/NetNet</th>\n",
       "      <th>P/Sales</th>\n",
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       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.008293</td>\n",
       "      <td>0.043118</td>\n",
       "      <td>0.034865</td>\n",
       "      <td>2.483898e+10</td>\n",
       "      <td>5.231462</td>\n",
       "      <td>23.192325</td>\n",
       "      <td>28.682425</td>\n",
       "      <td>-16.395366</td>\n",
       "      <td>-10.870451</td>\n",
       "      <td>4.810959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.017995</td>\n",
       "      <td>0.158181</td>\n",
       "      <td>0.101113</td>\n",
       "      <td>1.005810e+10</td>\n",
       "      <td>62.863096</td>\n",
       "      <td>6.321870</td>\n",
       "      <td>9.889966</td>\n",
       "      <td>-0.195519</td>\n",
       "      <td>-0.187793</td>\n",
       "      <td>0.221578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAN</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.003197</td>\n",
       "      <td>0.070038</td>\n",
       "      <td>0.090158</td>\n",
       "      <td>2.859474e+09</td>\n",
       "      <td>1.531051</td>\n",
       "      <td>14.277954</td>\n",
       "      <td>11.091661</td>\n",
       "      <td>10.942467</td>\n",
       "      <td>-6.545622</td>\n",
       "      <td>0.726264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.045864</td>\n",
       "      <td>0.060826</td>\n",
       "      <td>9.690244e+09</td>\n",
       "      <td>2.804749</td>\n",
       "      <td>21.803756</td>\n",
       "      <td>16.440415</td>\n",
       "      <td>-5.309106</td>\n",
       "      <td>-2.231962</td>\n",
       "      <td>0.998844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.010537</td>\n",
       "      <td>0.043021</td>\n",
       "      <td>0.047839</td>\n",
       "      <td>1.337191e+12</td>\n",
       "      <td>14.935512</td>\n",
       "      <td>23.244586</td>\n",
       "      <td>20.903413</td>\n",
       "      <td>-15.220262</td>\n",
       "      <td>-10.600094</td>\n",
       "      <td>4.995429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dividend Yield  Earnings Yield  FCF Yield    Market-Cap  \\\n",
       "Ticker Date                                                                  \n",
       "A      2020-02-26        0.008293        0.043118   0.034865  2.483898e+10   \n",
       "AAL    2020-02-26        0.017995        0.158181   0.101113  1.005810e+10   \n",
       "AAN    2020-02-26        0.003197        0.070038   0.090158  2.859474e+09   \n",
       "AAP    2020-02-26        0.001773        0.045864   0.060826  9.690244e+09   \n",
       "AAPL   2020-02-26        0.010537        0.043021   0.047839  1.337191e+12   \n",
       "\n",
       "                      P/Book        P/E      P/FCF     P/NCAV   P/NetNet  \\\n",
       "Ticker Date                                                                \n",
       "A      2020-02-26   5.231462  23.192325  28.682425 -16.395366 -10.870451   \n",
       "AAL    2020-02-26  62.863096   6.321870   9.889966  -0.195519  -0.187793   \n",
       "AAN    2020-02-26   1.531051  14.277954  11.091661  10.942467  -6.545622   \n",
       "AAP    2020-02-26   2.804749  21.803756  16.440415  -5.309106  -2.231962   \n",
       "AAPL   2020-02-26  14.935512  23.244586  20.903413 -15.220262 -10.600094   \n",
       "\n",
       "                    P/Sales  \n",
       "Ticker Date                  \n",
       "A      2020-02-26  4.810959  \n",
       "AAL    2020-02-26  0.221578  \n",
       "AAN    2020-02-26  0.726264  \n",
       "AAP    2020-02-26  0.998844  \n",
       "AAPL   2020-02-26  4.995429  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val_signals.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then pass the argument `func=sf.avg_ttm_2y` to `hub.val_signals` so as to calculate the valuation signals using 2-year averages of the financial data. We then get another DataFrame with e.g. P/E and P/Sales ratios calculated from the 2-year average Earnings and Sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache-file 'val_signals-2eee516b.pickle' on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 3.68 ms, sys: 0 ns, total: 3.68 ms\n",
      "Wall time: 2.72 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_val_signals_2y = hub.val_signals(variant='latest',\n",
    "                                    func=sf.avg_ttm_2y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.007991</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.035770</td>\n",
       "      <td>2.483898e+10</td>\n",
       "      <td>5.330825</td>\n",
       "      <td>35.816842</td>\n",
       "      <td>27.956083</td>\n",
       "      <td>-30.346952</td>\n",
       "      <td>-15.989044</td>\n",
       "      <td>4.929836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.018343</td>\n",
       "      <td>0.146250</td>\n",
       "      <td>0.054185</td>\n",
       "      <td>1.005810e+10</td>\n",
       "      <td>-49.304389</td>\n",
       "      <td>6.837590</td>\n",
       "      <td>18.455221</td>\n",
       "      <td>-0.211420</td>\n",
       "      <td>-0.202189</td>\n",
       "      <td>0.224521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAN</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.089579</td>\n",
       "      <td>0.094232</td>\n",
       "      <td>2.859474e+09</td>\n",
       "      <td>1.574964</td>\n",
       "      <td>11.163303</td>\n",
       "      <td>10.612067</td>\n",
       "      <td>7.546560</td>\n",
       "      <td>-9.358860</td>\n",
       "      <td>0.746833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.051564</td>\n",
       "      <td>0.069308</td>\n",
       "      <td>9.690244e+09</td>\n",
       "      <td>2.724053</td>\n",
       "      <td>19.393364</td>\n",
       "      <td>14.428400</td>\n",
       "      <td>-17.402736</td>\n",
       "      <td>-3.194522</td>\n",
       "      <td>1.008669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.010481</td>\n",
       "      <td>0.043733</td>\n",
       "      <td>0.047092</td>\n",
       "      <td>1.337191e+12</td>\n",
       "      <td>12.893375</td>\n",
       "      <td>22.866179</td>\n",
       "      <td>21.234865</td>\n",
       "      <td>-13.183716</td>\n",
       "      <td>-9.568547</td>\n",
       "      <td>5.052726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dividend Yield  Earnings Yield  FCF Yield    Market-Cap  \\\n",
       "Ticker Date                                                                  \n",
       "A      2020-02-26        0.007991        0.027920   0.035770  2.483898e+10   \n",
       "AAL    2020-02-26        0.018343        0.146250   0.054185  1.005810e+10   \n",
       "AAN    2020-02-26        0.002700        0.089579   0.094232  2.859474e+09   \n",
       "AAP    2020-02-26        0.001807        0.051564   0.069308  9.690244e+09   \n",
       "AAPL   2020-02-26        0.010481        0.043733   0.047092  1.337191e+12   \n",
       "\n",
       "                      P/Book        P/E      P/FCF     P/NCAV   P/NetNet  \\\n",
       "Ticker Date                                                                \n",
       "A      2020-02-26   5.330825  35.816842  27.956083 -30.346952 -15.989044   \n",
       "AAL    2020-02-26 -49.304389   6.837590  18.455221  -0.211420  -0.202189   \n",
       "AAN    2020-02-26   1.574964  11.163303  10.612067   7.546560  -9.358860   \n",
       "AAP    2020-02-26   2.724053  19.393364  14.428400 -17.402736  -3.194522   \n",
       "AAPL   2020-02-26  12.893375  22.866179  21.234865 -13.183716  -9.568547   \n",
       "\n",
       "                    P/Sales  \n",
       "Ticker Date                  \n",
       "A      2020-02-26  4.929836  \n",
       "AAL    2020-02-26  0.224521  \n",
       "AAN    2020-02-26  0.746833  \n",
       "AAP    2020-02-26  1.008669  \n",
       "AAPL   2020-02-26  5.052726  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val_signals_2y.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Signals\n",
    "\n",
    "We now combine all the basic signals into a single DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Current Ratio</th>\n",
       "      <th>Debt Ratio</th>\n",
       "      <th>Gross Profit Margin</th>\n",
       "      <th>Net Profit Margin</th>\n",
       "      <th>Return on Assets</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <th>Earnings Growth</th>\n",
       "      <th>Earnings Growth QOQ</th>\n",
       "      <th>Earnings Growth YOY</th>\n",
       "      <th>...</th>\n",
       "      <th>Dividend Yield</th>\n",
       "      <th>Earnings Yield</th>\n",
       "      <th>FCF Yield</th>\n",
       "      <th>Market-Cap</th>\n",
       "      <th>P/Book</th>\n",
       "      <th>P/E</th>\n",
       "      <th>P/FCF</th>\n",
       "      <th>P/NCAV</th>\n",
       "      <th>P/NetNet</th>\n",
       "      <th>P/Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.546234</td>\n",
       "      <td>1.533173</td>\n",
       "      <td>0.254655</td>\n",
       "      <td>0.543289</td>\n",
       "      <td>0.207438</td>\n",
       "      <td>0.113309</td>\n",
       "      <td>0.225569</td>\n",
       "      <td>2.389241</td>\n",
       "      <td>0.015707</td>\n",
       "      <td>-0.005128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008293</td>\n",
       "      <td>0.043118</td>\n",
       "      <td>0.034865</td>\n",
       "      <td>2.483898e+10</td>\n",
       "      <td>5.231462</td>\n",
       "      <td>23.192325</td>\n",
       "      <td>28.682425</td>\n",
       "      <td>-16.395366</td>\n",
       "      <td>-10.870451</td>\n",
       "      <td>4.810959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.773074</td>\n",
       "      <td>1.483910</td>\n",
       "      <td>0.123108</td>\n",
       "      <td>0.204391</td>\n",
       "      <td>-0.068689</td>\n",
       "      <td>-0.053102</td>\n",
       "      <td>-0.121529</td>\n",
       "      <td>63.916667</td>\n",
       "      <td>-0.450249</td>\n",
       "      <td>4.390244</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.294240</td>\n",
       "      <td>0.213787</td>\n",
       "      <td>2.647500e+09</td>\n",
       "      <td>0.413027</td>\n",
       "      <td>-3.398588</td>\n",
       "      <td>4.677562</td>\n",
       "      <td>-0.573424</td>\n",
       "      <td>-0.430663</td>\n",
       "      <td>0.233445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAC</th>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>0.724773</td>\n",
       "      <td>2.117862</td>\n",
       "      <td>0.500277</td>\n",
       "      <td>0.402427</td>\n",
       "      <td>-0.077948</td>\n",
       "      <td>-0.056495</td>\n",
       "      <td>-0.144093</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.970076</td>\n",
       "      <td>-2.029010</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.166485</td>\n",
       "      <td>-3.940417</td>\n",
       "      <td>1.034256e+07</td>\n",
       "      <td>0.066510</td>\n",
       "      <td>-0.461577</td>\n",
       "      <td>-0.253780</td>\n",
       "      <td>-0.075938</td>\n",
       "      <td>-0.062823</td>\n",
       "      <td>0.035979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.742019</td>\n",
       "      <td>0.535107</td>\n",
       "      <td>0.412472</td>\n",
       "      <td>0.267971</td>\n",
       "      <td>0.035049</td>\n",
       "      <td>0.026007</td>\n",
       "      <td>9.943750</td>\n",
       "      <td>0.177646</td>\n",
       "      <td>-0.358006</td>\n",
       "      <td>0.246334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017995</td>\n",
       "      <td>0.158181</td>\n",
       "      <td>0.101113</td>\n",
       "      <td>1.005810e+10</td>\n",
       "      <td>62.863096</td>\n",
       "      <td>6.321870</td>\n",
       "      <td>9.889966</td>\n",
       "      <td>-0.195519</td>\n",
       "      <td>-0.187793</td>\n",
       "      <td>0.221578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAME</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Asset Turnover  Current Ratio  Debt Ratio  \\\n",
       "Ticker Date                                                    \n",
       "A      2020-02-26        0.546234       1.533173    0.254655   \n",
       "AA     2020-02-26        0.773074       1.483910    0.123108   \n",
       "AAC    2019-11-04        0.724773       2.117862    0.500277   \n",
       "AAL    2020-02-26        0.742019       0.535107    0.412472   \n",
       "AAME   2020-02-26             NaN            NaN         NaN   \n",
       "\n",
       "                   Gross Profit Margin  Net Profit Margin  Return on Assets  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26             0.543289           0.207438          0.113309   \n",
       "AA     2020-02-26             0.204391          -0.068689         -0.053102   \n",
       "AAC    2019-11-04             0.402427          -0.077948         -0.056495   \n",
       "AAL    2020-02-26             0.267971           0.035049          0.026007   \n",
       "AAME   2020-02-26                  NaN                NaN               NaN   \n",
       "\n",
       "                   Return on Equity  Earnings Growth  Earnings Growth QOQ  \\\n",
       "Ticker Date                                                                 \n",
       "A      2020-02-26          0.225569         2.389241             0.015707   \n",
       "AA     2020-02-26         -0.121529        63.916667            -0.450249   \n",
       "AAC    2019-11-04         -0.144093              NaN            -0.970076   \n",
       "AAL    2020-02-26          9.943750         0.177646            -0.358006   \n",
       "AAME   2020-02-26               NaN              NaN                  NaN   \n",
       "\n",
       "                   Earnings Growth YOY  ...  Dividend Yield  Earnings Yield  \\\n",
       "Ticker Date                             ...                                   \n",
       "A      2020-02-26            -0.005128  ...        0.008293        0.043118   \n",
       "AA     2020-02-26             4.390244  ...             NaN       -0.294240   \n",
       "AAC    2019-11-04            -2.029010  ...             NaN       -2.166485   \n",
       "AAL    2020-02-26             0.246334  ...        0.017995        0.158181   \n",
       "AAME   2020-02-26                  NaN  ...             NaN             NaN   \n",
       "\n",
       "                   FCF Yield    Market-Cap     P/Book        P/E      P/FCF  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26   0.034865  2.483898e+10   5.231462  23.192325  28.682425   \n",
       "AA     2020-02-26   0.213787  2.647500e+09   0.413027  -3.398588   4.677562   \n",
       "AAC    2019-11-04  -3.940417  1.034256e+07   0.066510  -0.461577  -0.253780   \n",
       "AAL    2020-02-26   0.101113  1.005810e+10  62.863096   6.321870   9.889966   \n",
       "AAME   2020-02-26        NaN           NaN        NaN        NaN        NaN   \n",
       "\n",
       "                      P/NCAV   P/NetNet   P/Sales  \n",
       "Ticker Date                                        \n",
       "A      2020-02-26 -16.395366 -10.870451  4.810959  \n",
       "AA     2020-02-26  -0.573424  -0.430663  0.233445  \n",
       "AAC    2019-11-04  -0.075938  -0.062823  0.035979  \n",
       "AAL    2020-02-26  -0.195519  -0.187793  0.221578  \n",
       "AAME   2020-02-26        NaN        NaN       NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the DataFrames.\n",
    "dfs = [df_fin_signals, df_growth_signals, df_val_signals]\n",
    "df_signals = pd.concat(dfs, axis=1)\n",
    "\n",
    "# Show the result.\n",
    "df_signals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we combine all the signals for multi-year averages into another DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Asset Turnover</th>\n",
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       "      <th>Debt Ratio</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.560788</td>\n",
       "      <td>2.409627</td>\n",
       "      <td>0.232643</td>\n",
       "      <td>0.545047</td>\n",
       "      <td>0.135872</td>\n",
       "      <td>0.075154</td>\n",
       "      <td>0.147350</td>\n",
       "      <td>0.925614</td>\n",
       "      <td>-0.079011</td>\n",
       "      <td>0.048283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007991</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.035770</td>\n",
       "      <td>2.483898e+10</td>\n",
       "      <td>5.330825</td>\n",
       "      <td>35.816842</td>\n",
       "      <td>27.956083</td>\n",
       "      <td>-30.346952</td>\n",
       "      <td>-15.989044</td>\n",
       "      <td>4.929836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.795342</td>\n",
       "      <td>1.439858</td>\n",
       "      <td>0.117903</td>\n",
       "      <td>0.227904</td>\n",
       "      <td>-0.034798</td>\n",
       "      <td>-0.026921</td>\n",
       "      <td>-0.061588</td>\n",
       "      <td>31.437500</td>\n",
       "      <td>-0.998458</td>\n",
       "      <td>1.513706</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.149386</td>\n",
       "      <td>0.100850</td>\n",
       "      <td>2.647500e+09</td>\n",
       "      <td>0.386637</td>\n",
       "      <td>-6.694058</td>\n",
       "      <td>9.915730</td>\n",
       "      <td>-0.567889</td>\n",
       "      <td>-0.425994</td>\n",
       "      <td>0.215472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAC</th>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.034256e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.790911</td>\n",
       "      <td>0.558048</td>\n",
       "      <td>0.441507</td>\n",
       "      <td>0.270854</td>\n",
       "      <td>0.032806</td>\n",
       "      <td>0.025837</td>\n",
       "      <td>3.782614</td>\n",
       "      <td>-0.064767</td>\n",
       "      <td>-0.377766</td>\n",
       "      <td>-0.103596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018343</td>\n",
       "      <td>0.146250</td>\n",
       "      <td>0.054185</td>\n",
       "      <td>1.005810e+10</td>\n",
       "      <td>-49.304389</td>\n",
       "      <td>6.837590</td>\n",
       "      <td>18.455221</td>\n",
       "      <td>-0.211420</td>\n",
       "      <td>-0.202189</td>\n",
       "      <td>0.224521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAME</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Asset Turnover  Current Ratio  Debt Ratio  \\\n",
       "Ticker Date                                                    \n",
       "A      2020-02-26        0.560788       2.409627    0.232643   \n",
       "AA     2020-02-26        0.795342       1.439858    0.117903   \n",
       "AAC    2019-11-04             NaN            NaN         NaN   \n",
       "AAL    2020-02-26        0.790911       0.558048    0.441507   \n",
       "AAME   2020-02-26             NaN            NaN         NaN   \n",
       "\n",
       "                   Gross Profit Margin  Net Profit Margin  Return on Assets  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26             0.545047           0.135872          0.075154   \n",
       "AA     2020-02-26             0.227904          -0.034798         -0.026921   \n",
       "AAC    2019-11-04                  NaN                NaN               NaN   \n",
       "AAL    2020-02-26             0.270854           0.032806          0.025837   \n",
       "AAME   2020-02-26                  NaN                NaN               NaN   \n",
       "\n",
       "                   Return on Equity  Earnings Growth  Earnings Growth QOQ  \\\n",
       "Ticker Date                                                                 \n",
       "A      2020-02-26          0.147350         0.925614            -0.079011   \n",
       "AA     2020-02-26         -0.061588        31.437500            -0.998458   \n",
       "AAC    2019-11-04               NaN              NaN                  NaN   \n",
       "AAL    2020-02-26          3.782614        -0.064767            -0.377766   \n",
       "AAME   2020-02-26               NaN              NaN                  NaN   \n",
       "\n",
       "                   Earnings Growth YOY  ...  Dividend Yield  Earnings Yield  \\\n",
       "Ticker Date                             ...                                   \n",
       "A      2020-02-26             0.048283  ...        0.007991        0.027920   \n",
       "AA     2020-02-26             1.513706  ...             NaN       -0.149386   \n",
       "AAC    2019-11-04                  NaN  ...             NaN             NaN   \n",
       "AAL    2020-02-26            -0.103596  ...        0.018343        0.146250   \n",
       "AAME   2020-02-26                  NaN  ...             NaN             NaN   \n",
       "\n",
       "                   FCF Yield    Market-Cap     P/Book        P/E      P/FCF  \\\n",
       "Ticker Date                                                                   \n",
       "A      2020-02-26   0.035770  2.483898e+10   5.330825  35.816842  27.956083   \n",
       "AA     2020-02-26   0.100850  2.647500e+09   0.386637  -6.694058   9.915730   \n",
       "AAC    2019-11-04        NaN  1.034256e+07        NaN        NaN        NaN   \n",
       "AAL    2020-02-26   0.054185  1.005810e+10 -49.304389   6.837590  18.455221   \n",
       "AAME   2020-02-26        NaN           NaN        NaN        NaN        NaN   \n",
       "\n",
       "                      P/NCAV   P/NetNet   P/Sales  \n",
       "Ticker Date                                        \n",
       "A      2020-02-26 -30.346952 -15.989044  4.929836  \n",
       "AA     2020-02-26  -0.567889  -0.425994  0.215472  \n",
       "AAC    2019-11-04        NaN        NaN       NaN  \n",
       "AAL    2020-02-26  -0.211420  -0.202189  0.224521  \n",
       "AAME   2020-02-26        NaN        NaN       NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the DataFrames.\n",
    "dfs = [df_fin_signals_2y, df_growth_signals_2y, df_val_signals_2y]\n",
    "df_signals_2y = pd.concat(dfs, axis=1)\n",
    "\n",
    "# Show the result.\n",
    "df_signals_2y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Screener for Net-Net Stocks\n",
    "\n",
    "This is an old investment strategy used by Ben Graham who was the teacher of Warren Buffett, who also used the strategy when he started investing. The idea is to buy stocks that are cheaper than a conservative estimate of their liquidation value. In normal market conditions, few companies have stocks that trade at such low prices, and there may be very good reasons why the stocks are so cheap. But during market panics, it is sometimes possible to buy decent stocks at such low prices.\n",
    "\n",
    "The Net-Net formula is:\n",
    "\n",
    "$$\n",
    "    NetNet = Cash\\ \\&\\ Equiv + 0.75 \\cdot Receivables \\\\\n",
    "    + 0.5 \\cdot Inventories - Total\\ Liabilities\n",
    "$$\n",
    "\n",
    "This means P/NetNet ratios between 0 and 1 indicate the stocks are trading at a discount to their estimated liquidation values. The lower the P/NetNet ratio, the cheaper the stock is.\n",
    "\n",
    "We create the stock-screener for Net-Net stocks, by making a boolean mask for the rows in the DataFrame with signals that meet the criteria: P/NetNet > 0 and P/NetNet < 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (df_signals[P_NETNET] > 0) & (df_signals[P_NETNET] < 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows that satisfy the screener-condition have a value of `True` and rows that do not meet the condition have a value of `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker  Date      \n",
       "A       2020-02-26    False\n",
       "AA      2020-02-26    False\n",
       "AAC     2019-11-04    False\n",
       "AAL     2020-02-26    False\n",
       "AAME    2020-02-26    False\n",
       "Name: P/NetNet, dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use the boolean mask to select matching rows in the signal DataFrame, and show the P/NetNet signal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker  Date      \n",
       "AKER    2020-02-26    0.699461\n",
       "ALRN    2020-02-26    0.932777\n",
       "AMDA    2020-02-26    0.080273\n",
       "ASCMA   2019-09-11    0.368022\n",
       "CFI     2020-02-24    0.286719\n",
       "CGA     2020-02-26    0.057990\n",
       "CLBS    2020-02-26    0.755120\n",
       "CYCC    2020-02-26    0.949721\n",
       "CYIG    2020-02-26    0.093946\n",
       "GTXI    2020-02-26    0.431361\n",
       "HTGM    2020-02-26    0.865076\n",
       "KKR     2020-02-26    0.645634\n",
       "MN      2020-02-26    0.579902\n",
       "NHTC    2020-02-26    0.712768\n",
       "NLNK    2020-02-26    0.661926\n",
       "NSPR    2020-02-26    0.260560\n",
       "OMED    2020-02-03    0.800006\n",
       "RKDA    2020-02-26    0.672958\n",
       "RWLK    2020-02-26    0.425248\n",
       "SOHU    2020-02-26    0.529475\n",
       "SPRT    2020-02-26    0.234347\n",
       "SRRA    2020-02-26    0.445441\n",
       "WSTL    2020-02-26    0.697244\n",
       "Name: P/NetNet, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_signals.loc[mask, P_NETNET]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that some of the dates are not recent, so we can remove all rows with dates that are older than e.g. 30 days, by creating another boolean mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker\n",
       "A        True\n",
       "AA       True\n",
       "AAC     False\n",
       "AAL      True\n",
       "AAME     True\n",
       "Name: Date, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Oldest date that is allowed for a row.\n",
    "date_limit = datetime.now() - timedelta(days=30)\n",
    "\n",
    "# Load the latest share-prices from the data-hub.\n",
    "df_prices_latest = hub.load_shareprices(variant='latest')\n",
    "\n",
    "# Boolean mask for the tickers that satisfy this condition.\n",
    "mask_date_limit = (df_prices_latest.reset_index(DATE)[DATE] > date_limit)\n",
    "\n",
    "# Show the result.\n",
    "mask_date_limit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then combine the screener-mask and the date-mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask &= mask_date_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can show the recent stock-prices that are trading at Net-Net discounts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker  Date      \n",
       "AKER    2020-02-26    0.699461\n",
       "ALRN    2020-02-26    0.932777\n",
       "AMDA    2020-02-26    0.080273\n",
       "CFI     2020-02-24    0.286719\n",
       "CGA     2020-02-26    0.057990\n",
       "CLBS    2020-02-26    0.755120\n",
       "CYCC    2020-02-26    0.949721\n",
       "CYIG    2020-02-26    0.093946\n",
       "GTXI    2020-02-26    0.431361\n",
       "HTGM    2020-02-26    0.865076\n",
       "KKR     2020-02-26    0.645634\n",
       "MN      2020-02-26    0.579902\n",
       "NHTC    2020-02-26    0.712768\n",
       "NLNK    2020-02-26    0.661926\n",
       "NSPR    2020-02-26    0.260560\n",
       "OMED    2020-02-03    0.800006\n",
       "RKDA    2020-02-26    0.672958\n",
       "RWLK    2020-02-26    0.425248\n",
       "SOHU    2020-02-26    0.529475\n",
       "SPRT    2020-02-26    0.234347\n",
       "SRRA    2020-02-26    0.445441\n",
       "WSTL    2020-02-26    0.697244\n",
       "Name: P/NetNet, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_signals.loc[mask, P_NETNET]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Screener for Many Criteria\n",
    "\n",
    "It is very easy to combine many criteria in the stock-screener. Let use start with the condition that the Market Capitalization must be more then USD 1 billion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (df_signals[MARKET_CAP] > 1e9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then let us add criteria for the Current Ratio and Debt Ratio calculated from the latest financial reports, as well as the quarterly sales-growth year-over-year.\n",
    "\n",
    "We combine all these criteria simply by generating the corresponding boolean masks, and doing the logical-and with the previous mask, thereby accumulating multiple criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask &= (df_signals[CURRENT_RATIO] > 2)\n",
    "mask &= (df_signals[DEBT_RATIO] < 0.5)\n",
    "mask &= (df_signals[SALES_GROWTH_YOY] > 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create screener-criteria using the 2-year average signals, e.g. the P/E and P/FCF ratios which use 2-year average Earnings and FCF. We can combine screener-criteria from `df_signals` and `df_signals_2y` because their indices are compatible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask &= (df_signals_2y[PE] < 20)\n",
    "mask &= (df_signals_2y[PFCF] < 20)\n",
    "mask &= (df_signals_2y[ROA] > 0.03)\n",
    "mask &= (df_signals_2y[ROE] > 0.15)\n",
    "mask &= (df_signals_2y[NET_PROFIT_MARGIN] > 0.0)\n",
    "mask &= (df_signals_2y[SALES_GROWTH] > 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can ensure that we only get the stocks with recent share-prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask &= mask_date_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the stocks and signals matching all these criteria:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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></th>\n",
       "      <th>Asset Turnover</th>\n",
       "      <th>Current Ratio</th>\n",
       "      <th>Debt Ratio</th>\n",
       "      <th>Gross Profit Margin</th>\n",
       "      <th>Net Profit Margin</th>\n",
       "      <th>Return on Assets</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <th>Earnings Growth</th>\n",
       "      <th>Earnings Growth QOQ</th>\n",
       "      <th>Earnings Growth YOY</th>\n",
       "      <th>...</th>\n",
       "      <th>Dividend Yield</th>\n",
       "      <th>Earnings Yield</th>\n",
       "      <th>FCF Yield</th>\n",
       "      <th>Market-Cap</th>\n",
       "      <th>P/Book</th>\n",
       "      <th>P/E</th>\n",
       "      <th>P/FCF</th>\n",
       "      <th>P/NCAV</th>\n",
       "      <th>P/NetNet</th>\n",
       "      <th>P/Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CORT</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.777670</td>\n",
       "      <td>8.251327</td>\n",
       "      <td>0.297553</td>\n",
       "      <td>0.980215</td>\n",
       "      <td>0.304139</td>\n",
       "      <td>0.236520</td>\n",
       "      <td>0.259612</td>\n",
       "      <td>-0.427886</td>\n",
       "      <td>0.304865</td>\n",
       "      <td>0.484195</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.052041</td>\n",
       "      <td>0.064815</td>\n",
       "      <td>1.668077e+09</td>\n",
       "      <td>4.988627</td>\n",
       "      <td>19.215709</td>\n",
       "      <td>15.428588</td>\n",
       "      <td>7.108364</td>\n",
       "      <td>7.633042</td>\n",
       "      <td>5.844249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXEL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.536422</td>\n",
       "      <td>6.891039</td>\n",
       "      <td>0.013915</td>\n",
       "      <td>0.968741</td>\n",
       "      <td>0.639578</td>\n",
       "      <td>0.343084</td>\n",
       "      <td>0.381837</td>\n",
       "      <td>0.661894</td>\n",
       "      <td>0.232914</td>\n",
       "      <td>-0.230419</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104183</td>\n",
       "      <td>0.079122</td>\n",
       "      <td>5.877734e+09</td>\n",
       "      <td>3.665069</td>\n",
       "      <td>9.598526</td>\n",
       "      <td>12.638739</td>\n",
       "      <td>7.431102</td>\n",
       "      <td>8.119609</td>\n",
       "      <td>6.139010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAZZ</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.372021</td>\n",
       "      <td>3.894478</td>\n",
       "      <td>0.290197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.296062</td>\n",
       "      <td>0.110141</td>\n",
       "      <td>0.199368</td>\n",
       "      <td>0.171227</td>\n",
       "      <td>-0.609482</td>\n",
       "      <td>-0.315037</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089513</td>\n",
       "      <td>0.107708</td>\n",
       "      <td>6.801773e+09</td>\n",
       "      <td>2.227258</td>\n",
       "      <td>11.171601</td>\n",
       "      <td>9.284404</td>\n",
       "      <td>-7.134108</td>\n",
       "      <td>-5.823587</td>\n",
       "      <td>3.307486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRGO</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>0.672270</td>\n",
       "      <td>3.183860</td>\n",
       "      <td>0.360024</td>\n",
       "      <td>0.364800</td>\n",
       "      <td>0.120889</td>\n",
       "      <td>0.081270</td>\n",
       "      <td>0.181262</td>\n",
       "      <td>0.025325</td>\n",
       "      <td>-0.059288</td>\n",
       "      <td>0.054924</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005924</td>\n",
       "      <td>0.078005</td>\n",
       "      <td>0.084086</td>\n",
       "      <td>5.739382e+09</td>\n",
       "      <td>2.323731</td>\n",
       "      <td>12.819705</td>\n",
       "      <td>11.892627</td>\n",
       "      <td>-8.544562</td>\n",
       "      <td>-4.300290</td>\n",
       "      <td>1.549760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Asset Turnover  Current Ratio  Debt Ratio  \\\n",
       "Ticker Date                                                    \n",
       "CORT   2020-02-26        0.777670       8.251327    0.297553   \n",
       "EXEL   2020-02-26        0.536422       6.891039    0.013915   \n",
       "JAZZ   2020-02-26        0.372021       3.894478    0.290197   \n",
       "PRGO   2020-02-26        0.672270       3.183860    0.360024   \n",
       "\n",
       "                   Gross Profit Margin  Net Profit Margin  Return on Assets  \\\n",
       "Ticker Date                                                                   \n",
       "CORT   2020-02-26             0.980215           0.304139          0.236520   \n",
       "EXEL   2020-02-26             0.968741           0.639578          0.343084   \n",
       "JAZZ   2020-02-26             1.000000           0.296062          0.110141   \n",
       "PRGO   2020-02-26             0.364800           0.120889          0.081270   \n",
       "\n",
       "                   Return on Equity  Earnings Growth  Earnings Growth QOQ  \\\n",
       "Ticker Date                                                                 \n",
       "CORT   2020-02-26          0.259612        -0.427886             0.304865   \n",
       "EXEL   2020-02-26          0.381837         0.661894             0.232914   \n",
       "JAZZ   2020-02-26          0.199368         0.171227            -0.609482   \n",
       "PRGO   2020-02-26          0.181262         0.025325            -0.059288   \n",
       "\n",
       "                   Earnings Growth YOY  ...  Dividend Yield  Earnings Yield  \\\n",
       "Ticker Date                             ...                                   \n",
       "CORT   2020-02-26             0.484195  ...             NaN        0.052041   \n",
       "EXEL   2020-02-26            -0.230419  ...             NaN        0.104183   \n",
       "JAZZ   2020-02-26            -0.315037  ...             NaN        0.089513   \n",
       "PRGO   2020-02-26             0.054924  ...        0.005924        0.078005   \n",
       "\n",
       "                   FCF Yield    Market-Cap    P/Book        P/E      P/FCF  \\\n",
       "Ticker Date                                                                  \n",
       "CORT   2020-02-26   0.064815  1.668077e+09  4.988627  19.215709  15.428588   \n",
       "EXEL   2020-02-26   0.079122  5.877734e+09  3.665069   9.598526  12.638739   \n",
       "JAZZ   2020-02-26   0.107708  6.801773e+09  2.227258  11.171601   9.284404   \n",
       "PRGO   2020-02-26   0.084086  5.739382e+09  2.323731  12.819705  11.892627   \n",
       "\n",
       "                     P/NCAV  P/NetNet   P/Sales  \n",
       "Ticker Date                                      \n",
       "CORT   2020-02-26  7.108364  7.633042  5.844249  \n",
       "EXEL   2020-02-26  7.431102  8.119609  6.139010  \n",
       "JAZZ   2020-02-26 -7.134108 -5.823587  3.307486  \n",
       "PRGO   2020-02-26 -8.544562 -4.300290  1.549760  \n",
       "\n",
       "[4 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_signals[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also show a sub-set of all the signals and sort e.g. by the P/FCF ratios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>P/FCF</th>\n",
       "      <th>P/E</th>\n",
       "      <th>Return on Assets</th>\n",
       "      <th>Return on Equity</th>\n",
       "      <th>Current Ratio</th>\n",
       "      <th>Debt Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JAZZ</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>9.284404</td>\n",
       "      <td>11.171601</td>\n",
       "      <td>0.110141</td>\n",
       "      <td>0.199368</td>\n",
       "      <td>3.894478</td>\n",
       "      <td>0.290197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRGO</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>11.892627</td>\n",
       "      <td>12.819705</td>\n",
       "      <td>0.081270</td>\n",
       "      <td>0.181262</td>\n",
       "      <td>3.183860</td>\n",
       "      <td>0.360024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXEL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>12.638739</td>\n",
       "      <td>9.598526</td>\n",
       "      <td>0.343084</td>\n",
       "      <td>0.381837</td>\n",
       "      <td>6.891039</td>\n",
       "      <td>0.013915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CORT</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>15.428588</td>\n",
       "      <td>19.215709</td>\n",
       "      <td>0.236520</td>\n",
       "      <td>0.259612</td>\n",
       "      <td>8.251327</td>\n",
       "      <td>0.297553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       P/FCF        P/E  Return on Assets  Return on Equity  \\\n",
       "Ticker Date                                                                   \n",
       "JAZZ   2020-02-26   9.284404  11.171601          0.110141          0.199368   \n",
       "PRGO   2020-02-26  11.892627  12.819705          0.081270          0.181262   \n",
       "EXEL   2020-02-26  12.638739   9.598526          0.343084          0.381837   \n",
       "CORT   2020-02-26  15.428588  19.215709          0.236520          0.259612   \n",
       "\n",
       "                   Current Ratio  Debt Ratio  \n",
       "Ticker Date                                   \n",
       "JAZZ   2020-02-26       3.894478    0.290197  \n",
       "PRGO   2020-02-26       3.183860    0.360024  \n",
       "EXEL   2020-02-26       6.891039    0.013915  \n",
       "CORT   2020-02-26       8.251327    0.297553  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [PFCF, PE, ROA, ROE, CURRENT_RATIO, DEBT_RATIO]\n",
    "df_signals.loc[mask, columns].sort_values(by=PFCF, ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling NaN Signal-Values\n",
    "\n",
    "The signals are calculated from various financial data using mathematical formulas. If any data-item in the formula is NaN (Not-a-Number) then the result of the entire formula is also NaN, and then the screener-condition automatically evaluates to False, so the company is excluded from the results of the stock-screener.\n",
    "\n",
    "For example, the Debt Ratio (`DEBT_RATIO`) is calculated from Short Term Debt (`ST_DEBT`), Long Term Debt (`LT_DEBT`) and Total Assets (`TOTAL_ASSETS`). If just one of these numbers is NaN, then the resulting Debt Ratio is also NaN and the screener-condition for this signal will always evaluate to False, so the company is excluded from the screener's results.\n",
    "\n",
    "You might think that a solution would simply be to use `fillna(0)` on all the data-items before calculating the signals. This may work for some formulas and for some uses of the signals, but it is not a generally valid solution, as it may severely distort the signals.\n",
    "\n",
    "Consider for example the ticker AMZN, where all data for Short Term Debt is missing in all the reports, while the Long Term Debt is only missing in some reports. If you look at the data, it seems most likely that this is a data-error, and the Long Term Debt should actually be several billions of dollars. If we were to replace these missing values with zeros, then we would get very misleading Debt Ratios.\n",
    "\n",
    "In this example, AMZN had actually not reported these numbers in some of their quarterly reports. That is why the values are missing in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Short Term Debt</th>\n",
       "      <th>Long Term Debt</th>\n",
       "      <th>Total Assets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.310000e+08</td>\n",
       "      <td>12042000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.320000e+08</td>\n",
       "      <td>12397000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.640000e+08</td>\n",
       "      <td>14162000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18797000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16882000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17941000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19054000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.550000e+08</td>\n",
       "      <td>25278000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20339000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21022000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22834000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.084000e+09</td>\n",
       "      <td>32555000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.040000e+09</td>\n",
       "      <td>28377000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.042000e+09</td>\n",
       "      <td>29623000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.043000e+09</td>\n",
       "      <td>31861000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.191000e+09</td>\n",
       "      <td>40159000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Short Term Debt  Long Term Debt  Total Assets\n",
       "Report Date                                               \n",
       "2010-03-31               NaN    1.310000e+08   12042000000\n",
       "2010-06-30               NaN    1.320000e+08   12397000000\n",
       "2010-09-30               NaN    1.640000e+08   14162000000\n",
       "2010-12-31               NaN             NaN   18797000000\n",
       "2011-03-31               NaN             NaN   16882000000\n",
       "2011-06-30               NaN             NaN   17941000000\n",
       "2011-09-30               NaN             NaN   19054000000\n",
       "2011-12-31               NaN    2.550000e+08   25278000000\n",
       "2012-03-31               NaN             NaN   20339000000\n",
       "2012-06-30               NaN             NaN   21022000000\n",
       "2012-09-30               NaN             NaN   22834000000\n",
       "2012-12-31               NaN    3.084000e+09   32555000000\n",
       "2013-03-31               NaN    3.040000e+09   28377000000\n",
       "2013-06-30               NaN    3.042000e+09   29623000000\n",
       "2013-09-30               NaN    3.043000e+09   31861000000\n",
       "2013-12-31               NaN    3.191000e+09   40159000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the TTM Balance Sheets from the data-hub.\n",
    "df_balance_ttm = hub.load_balance(variant='ttm')\n",
    "\n",
    "# Show the relevant data.\n",
    "columns = [ST_DEBT, LT_DEBT, TOTAL_ASSETS]\n",
    "df_balance_ttm.loc['AMZN', columns]['2010':'2013']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple solution is to ignore signals that are NaN. For example, we could have the following criteria:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start the screener with a market-cap condition.\n",
    "mask = (df_signals[MARKET_CAP] > 1e9)\n",
    "\n",
    "# Ensure share-prices are recent.\n",
    "mask &= mask_date_limit\n",
    "\n",
    "# Screener criteria where NaN signals are ignored.\n",
    "mask &= ((df_signals[CURRENT_RATIO] > 2) | (df_signals[CURRENT_RATIO].isnull()))\n",
    "mask &= ((df_signals[DEBT_RATIO] < 0.5) | (df_signals[DEBT_RATIO].isnull()))\n",
    "mask &= ((df_signals[PE] < 20) | (df_signals[PE].isnull()))\n",
    "mask &= ((df_signals[PFCF] < 20) | (df_signals[PFCF].isnull()))\n",
    "mask &= ((df_signals[ROA] > 0.03) | (df_signals[ROA].isnull()))\n",
    "mask &= ((df_signals[ROE] > 0.15) | (df_signals[ROE].isnull()))\n",
    "mask &= ((df_signals[NET_PROFIT_MARGIN] > 0.0) | (df_signals[NET_PROFIT_MARGIN].isnull()))\n",
    "mask &= ((df_signals[SALES_GROWTH] > 0.1) | (df_signals[SALES_GROWTH].isnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows the stocks whose signals match these criteria. You can see that some of the signals are NaN, but the stocks are still included in the results, because the screener just ignores NaN values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>P/FCF</th>\n",
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       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HFC</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>9.029383</td>\n",
       "      <td>4.336958</td>\n",
       "      <td>0.128502</td>\n",
       "      <td>0.225054</td>\n",
       "      <td>2.503984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALXN</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>10.955085</td>\n",
       "      <td>8.861849</td>\n",
       "      <td>0.137039</td>\n",
       "      <td>0.213302</td>\n",
       "      <td>4.250523</td>\n",
       "      <td>0.142591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXEL</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>12.638739</td>\n",
       "      <td>9.598526</td>\n",
       "      <td>0.343084</td>\n",
       "      <td>0.381837</td>\n",
       "      <td>6.891039</td>\n",
       "      <td>0.013915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAZZ</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>9.284404</td>\n",
       "      <td>11.171601</td>\n",
       "      <td>0.110141</td>\n",
       "      <td>0.199368</td>\n",
       "      <td>3.894478</td>\n",
       "      <td>0.290197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FII</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>10.113870</td>\n",
       "      <td>12.676403</td>\n",
       "      <td>0.143542</td>\n",
       "      <td>0.218931</td>\n",
       "      <td>2.165258</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRGO</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>11.892627</td>\n",
       "      <td>12.819705</td>\n",
       "      <td>0.081270</td>\n",
       "      <td>0.181262</td>\n",
       "      <td>3.183860</td>\n",
       "      <td>0.360024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNTX</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>15.469407</td>\n",
       "      <td>16.677074</td>\n",
       "      <td>0.199055</td>\n",
       "      <td>0.224538</td>\n",
       "      <td>5.416747</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMS</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>14.874471</td>\n",
       "      <td>17.904471</td>\n",
       "      <td>0.137950</td>\n",
       "      <td>0.192937</td>\n",
       "      <td>2.167333</td>\n",
       "      <td>0.002316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CORT</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>15.428588</td>\n",
       "      <td>19.215709</td>\n",
       "      <td>0.236520</td>\n",
       "      <td>0.259612</td>\n",
       "      <td>8.251327</td>\n",
       "      <td>0.297553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       P/FCF        P/E  Return on Assets  Return on Equity  \\\n",
       "Ticker Date                                                                   \n",
       "HFC    2020-02-26   9.029383   4.336958          0.128502          0.225054   \n",
       "ALXN   2020-02-26  10.955085   8.861849          0.137039          0.213302   \n",
       "EXEL   2020-02-26  12.638739   9.598526          0.343084          0.381837   \n",
       "JAZZ   2020-02-26   9.284404  11.171601          0.110141          0.199368   \n",
       "FII    2020-02-26  10.113870  12.676403          0.143542          0.218931   \n",
       "PRGO   2020-02-26  11.892627  12.819705          0.081270          0.181262   \n",
       "GNTX   2020-02-26  15.469407  16.677074          0.199055          0.224538   \n",
       "MMS    2020-02-26  14.874471  17.904471          0.137950          0.192937   \n",
       "CORT   2020-02-26  15.428588  19.215709          0.236520          0.259612   \n",
       "\n",
       "                   Current Ratio  Debt Ratio  \n",
       "Ticker Date                                   \n",
       "HFC    2020-02-26       2.503984         NaN  \n",
       "ALXN   2020-02-26       4.250523    0.142591  \n",
       "EXEL   2020-02-26       6.891039    0.013915  \n",
       "JAZZ   2020-02-26       3.894478    0.290197  \n",
       "FII    2020-02-26       2.165258         NaN  \n",
       "PRGO   2020-02-26       3.183860    0.360024  \n",
       "GNTX   2020-02-26       5.416747         NaN  \n",
       "MMS    2020-02-26       2.167333    0.002316  \n",
       "CORT   2020-02-26       8.251327    0.297553  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [PFCF, PE, ROA, ROE, CURRENT_RATIO, DEBT_RATIO]\n",
    "df_signals.loc[mask, columns].sort_values(by=PE, ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back-Testing\n",
    "\n",
    "We will now test if the stock-screener would have been able to find profitable investments in the past. You can use this method to test which screener-criteria are important and which ones are not, so as to gradually refine your stock-screener.\n",
    "\n",
    "First we need to calculate the signals for all the historical daily data-points. We will screen for P/E ratios, Sales Growth, Return on Assets, etc., and these signals are highly susceptible to short-term variations, so we will use their 3-year averages instead.\n",
    "\n",
    "Then we calculate the 1-3 year average annualized log-returns for all the stocks, which is the investment horizon we will consider when testing if the screener's criteria could discover profitable investments in the past. Note that we are calculating the average log-returns for all 1-3 year investment periods, because the log-returns are easier to calculate using Pandas and numpy.\n",
    "\n",
    "Calculating the signals and returns takes a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-shareprices-daily\" on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "Cache-file 'fin_signals-92e21ef8.pickle' on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "Cache-file 'growth_signals-92e21ef8.pickle' on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "Cache-file 'val_signals-c16ad832.pickle' on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "Cache-file 'mean_log_change-90769289.pickle' on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 42.2 s, sys: 9.51 s, total: 51.7 s\n",
      "Wall time: 27.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Calculate the signals for all daily data-points.\n",
    "# We pass the arg func=sf.avg_ttm_3y to get the 3-year averages.\n",
    "df_fin_signals = hub.fin_signals(variant='daily', func=sf.avg_ttm_3y)\n",
    "df_growth_signals = hub.growth_signals(variant='daily', func=sf.avg_ttm_3y)\n",
    "df_val_signals = hub.val_signals(variant='daily', func=sf.avg_ttm_3y)\n",
    "\n",
    "# Combine all the signals into a single DataFrame.\n",
    "dfs = [df_fin_signals, df_growth_signals, df_val_signals]\n",
    "df_signals = pd.concat(dfs, axis=1)\n",
    "\n",
    "# Remove outliers in the signals by clipping / limiting them.\n",
    "df_signals = sf.winsorize(df=df_signals, clip=True)\n",
    "\n",
    "# Name of the data-column for the stock-returns.\n",
    "TOTAL_RETURN_1_3Y = 'Total Return 1-3 Years'\n",
    "\n",
    "# Calculate the mean log-returns for all 1-3 year periods.\n",
    "df_returns_1_3y = \\\n",
    "    hub.mean_log_returns(name=TOTAL_RETURN_1_3Y,\n",
    "                         future=True, annualized=True,\n",
    "                         min_years=1, max_years=3)\n",
    "\n",
    "# Combine the signals and mean log-returns into a single DataFrame.\n",
    "dfs = [df_signals, df_returns_1_3y]\n",
    "df_sig_rets = pd.concat(dfs, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a stock-screener similar to how we did it above. We will use nearly the same criteria as above with only minor changes. The result is a Pandas Series with boolean values for each stock and each trading-day, telling us whether or not the screener's criteria were met for that stock on that particular day. We can then use this boolean mask to select only the rows where the screener's criteria were met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start the screener with a market-cap condition.\n",
    "mask = (df_sig_rets[MARKET_CAP] > 1e9)\n",
    "\n",
    "# Screener criteria where NaN signals are ignored.\n",
    "mask &= ((df_sig_rets[CURRENT_RATIO] > 2) | (df_sig_rets[CURRENT_RATIO].isnull()))\n",
    "mask &= ((df_sig_rets[DEBT_RATIO] < 0.5) | (df_sig_rets[DEBT_RATIO].isnull()))\n",
    "mask &= (((df_sig_rets[PE] > 0) & (df_sig_rets[PE] < 20)) | (df_sig_rets[PE].isnull()))\n",
    "mask &= (((df_sig_rets[PFCF] > 0) & (df_sig_rets[PFCF] < 20)) | (df_sig_rets[PFCF].isnull()))\n",
    "mask &= ((df_sig_rets[ROA] > 0.03) | (df_sig_rets[ROA].isnull()))\n",
    "mask &= ((df_sig_rets[ROE] > 0.15) | (df_sig_rets[ROE].isnull()))\n",
    "mask &= ((df_sig_rets[NET_PROFIT_MARGIN] > 0.0) | (df_sig_rets[NET_PROFIT_MARGIN].isnull()))\n",
    "mask &= ((df_sig_rets[SALES_GROWTH] > 0.0) | (df_sig_rets[SALES_GROWTH].isnull()))\n",
    "    \n",
    "# Select the rows that satisfy the screener criteria.\n",
    "df_match = df_sig_rets.loc[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table below shows some basic statistics for the historical stock-returns when the screener's criteria were met. Because of outliers in the data, it is probably best to ignore the means and instead consider the medians."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Screener</th>\n",
       "      <th>No Screener</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>472547.000</td>\n",
       "      <td>3985606.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.118</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.242</td>\n",
       "      <td>0.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.985</td>\n",
       "      <td>-5.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.146</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.245</td>\n",
       "      <td>0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.113</td>\n",
       "      <td>4.669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Screener  No Screener\n",
       "count  472547.000  3985606.000\n",
       "mean        0.118        0.050\n",
       "std         0.242        0.343\n",
       "min        -4.985       -5.529\n",
       "25%         0.034       -0.085\n",
       "50%         0.146        0.091\n",
       "75%         0.245        0.224\n",
       "max         1.113        4.669"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine basic statistics for the stock-returns and show them.\n",
    "data = \\\n",
    "{\n",
    "    'Screener': df_match[TOTAL_RETURN_1_3Y].describe(),\n",
    "    'No Screener': df_sig_rets[TOTAL_RETURN_1_3Y].describe()\n",
    "}\n",
    "pd.DataFrame(data).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the screener was used, the median annualized return was several percentage-points higher than without the screener. These are log-returns but in this range of values, they are nearly the same as the non-log returns.\n",
    "\n",
    "The standard deviation was significantly lower when using the screener, even though the returns are significantly higher. This is contrary to what academic finance theory claims possible, because they believe that the standard deviation measures investment risk, and risk must be inversely proportional to returns. But the standard deviation is an absurd measure of investment risk as explained [here](https://www.youtube.com/watch?v=wr8NzThfpAE) and [here](https://www.youtube.com/watch?v=DzTlH6ipx98).\n",
    "\n",
    "Also note that there are millions of rows in the original dataset, but there is only a fraction of those rows that meet the screener's criteria.\n",
    "\n",
    "Let us now make plots of the signals versus stock-returns. First we need a helper-function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter(df, x, y, hue=None, num_samples=5000):\n",
    "    \"\"\"\n",
    "    Make a scatter-plot using a random sub-sample of the data.\n",
    "    \n",
    "    :param df:\n",
    "        Pandas DataFrame with columns named `x`, `y` and `hue`.\n",
    "\n",
    "    :param x:\n",
    "        String with column-name for the x-axis.\n",
    "\n",
    "    :param y:\n",
    "        String with column-name for the y-axis.\n",
    "\n",
    "    :param hue:\n",
    "        Either None or string with column-name for the hue.\n",
    "\n",
    "    :param num_samples:\n",
    "        Int with number of random samples for the scatter-plot.\n",
    "\n",
    "    :return:\n",
    "        matplotlib Axes object\n",
    "    \"\"\"\n",
    "\n",
    "    # Select the relevant columns from the DataFrame.\n",
    "    if hue is None:\n",
    "        df = df[[x, y]]\n",
    "    else:\n",
    "        df = df[[x, y, hue]]\n",
    "        \n",
    "    # Remove empty rows.\n",
    "    df = df.dropna()\n",
    "\n",
    "    # Only plot a random sample of the data-points?\n",
    "    if num_samples is not None and len(df) > num_samples:\n",
    "        idx = np.random.randint(len(df), size=num_samples)\n",
    "        df = df.iloc[idx]\n",
    "        \n",
    "    # Ensure the plotting area is a square.\n",
    "    plt.figure(figsize=(5,5))\n",
    "\n",
    "    # Make the scatter-plot.\n",
    "    ax = sns.scatterplot(x=x, y=y, hue=hue, s=20,\n",
    "                         data=df.reset_index())\n",
    "\n",
    "    # Move legend for the hue.\n",
    "    if hue is not None:\n",
    "        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try and plot the P/E ratio on the x-axis and the average stock-returns for all 1-3 year periods on the y-axis. The hue is the Return on Equity. This is just a random blob of points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(df=df_sig_rets, x=PE, y=TOTAL_RETURN_1_3Y, hue=ROE);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that these scatter-plots only use a sample of 5000 randomly selected points, because there is actually more than 1.5 million data-points, which would make the plotting very slow (or even crash the program), and even if it worked, it would make the plot very messy to look at. Because of the random sampling, the plots may change slightly if you run the function again, but the plots should generally be representative of the entire data-set.\n",
    "\n",
    "Now let us try and restrict the plot to only show the data-points where the stock-screener's criteria were met. This is still a blob of points, but perhaps not quite as random as before. The points are also shifted upwards because they tend to have more positive stock-returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(df=df_match, x=PE, y=TOTAL_RETURN_1_3Y, hue=ROE);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot it for only some of the tickers, then we can see a characteristic downward-trending slope, which is the topic of [this research](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmlHaWuVxIA0pKL1yjryR0Z). It turns out there is a mathematical reason why we are observing this downwards slope in the data, as explained [here](https://www.youtube.com/watch?v=OFCHKYgGJRA). The slope always has the same shape, it is just shifted up or down depending on the company's future sales and earnings growth, and its future stock-valuation ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gHAV63CLkjxFqChBq2EY0UH2sQzppSJJEusNDhsODKitYFnTx5jFx23fEzMT2DItqyggZca6a8yIjZ/ydT7YvIa1Vzv+48uGl+6sO6PjBKC0t5bvvviMYDJKSkkJJSQlLliz50RYNwE033XRUWjUi0QjCERIL+YgFa4mGo2xbp/P5uzrfzpSIBA22ffsi0cDh+4AR9vLaXXRJy6dP5t5K8Ofml/Je2XxqI00AvLjuC8yj3KpRk7MP6PjB6NmzJ++99x5FRUUAFBYWsnz5cioqKigoKPjB+QMGDMDv97Nu3brDFsOPEYlGEI6AaKCaVZNuZN2MPxKPwoLPYwSbLHZuhqpdKXiyisGy8FeuEK2bw0yRZDIdHnpmtOHVAdfzUI8LubR9f1q59+77kulI5mh3nqUNuhVJdexzTFIdpA269ZCuq+t6c1n/0tJSduzY0dxNpqoq6enpdOnSBVn+8Y/7m266aZ+6k0eCGKMRhCOgat0nqI4U2p56B0gSkgzW7nFozS6T0rIXi9+5AFMP40ptR/HI57C7D9832186RVbIdnrJdnopSYeIHmNQTickYFuglovbnYIa0uEoFhXYMw7jm/0cur8KNTmbtEG3HvL4zKZNm5r39UpPT2f9+vX73P/fVVX69u1L3759m2+fccYZP3jM4SYSjSAcAQ5PLtlFo1G0JEK+pQwYXsC6pU7SsyVy29ho3P4tpp6YlhuqL8OIh9FjQVQt6RhHfnJyqBrpksyI/FJkScapaqzdsZaMjMO3u+XP4ek87LAM/O/xzjvv8Oabb/KHP/zhsF3zSBCJRhD2ozEaojEeYleonvaebBwOx/9+EJDaqj9h/w4kJNZ/dgfJuT3p2GEwzuQMVLUXKS16IikalhHDkZyHrCZhxILosRCKoiIpmkg6h5mmqGjKyfWRd8kll3DJJZcc6zD+p5PrVReEw2x7sI5r57yEiUVBcg7/6HvFz3qczenF5vQSaarE7smhcecCGncuoFWfm/Ftj+Bt2ZMev/qQxuqdaO42rFjopnPPKJLpIxZrItSwldRWp2IZcWTVgeYSxTiFE5dINIKwH/OrN2CS2A12o78S0zLZEahDliQcika6w73fxzs8ORSP/Ce7lr2NI7kFTm8bJFmhcvV/8BtXs2mVl0jQwrJMOvdyIduCBGrXEm2qQI8FMGNBLMvEMg3s7syj8SsLwmEnZp0Jwn4MzumETU5sUHZuXikRI8bvF73LHxa9R1M8TG24Cd009nsNZ3JL2g64m4wOQ1kz7S5kRSNYt4mMFhLhgIVlgTtFAitKPOTDk9WZjPZnEaxZz/IPr2LFR1dTueY/hBrL0WOhY7q6XRAOhmjRCMJ+ZNg9vHv6bVSGGslxpnDfkg9Y17gLgL8sn8gfuo1me7CWLqn5aIpKQzSIbpm4VTsOVWu+jizLIEm06HoR9TsWkJzTFZd9B2dd1BK/zyS3jZ1dS56gau1E7J4WdD3vZdxZXWhz6m+oXPkBvq1zySocydZvnkZW7bQsuRx7UtZPhS0IxxXRohGE/chweki3e2iVlI6m2FCkxFsm05HMNR0H41A1Wial0RQLUxP2s7huC//ZspCZu1YR2r1Xyh72pAzySq8mp3gsmR1H4ExJxZtpkF8QQzJ3UbV2IgDRpl34dy0jEMghLF9Ep/M+ouDMR9n01Z+pXP0fdi1/m63znyYeaTzqr4dw/JoxYwaFhYVs3rwZgPLycgoLC3nqqaeaz/H5fBQXF/PQQw81H5s0aRIjR45kxIgRjBo1ij/+8Y/4/X4Axo0bx/nnn9987sqVKxk3btwBxyYSzS+UGY5QmJWLWdeAGQgd63COa26bg9ykVJJUjT/3vJC+mR34R99xPL/2C0bO+DvPrp5O2IwRiEd4Ye0X1MeCuGwOmmKJulb1kRBb/D5W+6qow4ZfScLhycaelInmSEZCQlbtKNre8R57SjvmTonx7YwYsydFseQWWLvHigCigSoMPfyDWAFioXoi/p1EmiqI/0JL5v8STZ48mZ49ezJ16tTmY/n5+cyaNav59vTp0+nQYW/FhNmzZ/PGG2/w6quvMmXKFD7++GNKS0upra1tPsfn8/H1118fUmwi0fwCmXEDa+tOrC07oaEJQmGRbH6GZM1Fsmrn1k5nsz1Qy9qGnQBM37mC6rCfmGXw7ClXcl6rXuQ4UpAkmWA8wqYmH6+s+5ZdIT9Xf/0Bt8//lJ3Bva0RzZWGrNgpPvcZ8nteS/HI56mrzSYcSCSW+moTPW7Rpt9t2D25pLY6lbYDfguS7QcxxiNN1G7+nEVvjWLx2+cRqFqBKXaePG5Ub5jGd/9vBHOf78V3/28E1RsOT+XmYDDIkiVL+Mtf/sKUKVOajzscDtq3b8/KlSsBmDZtGsOG7V3H8+KLLzJ+/HiysxOLhRVF4YILLqBdu3bN51x77bWHXA9NJJpfICkcxvIHMTdvJ/bPd4g9+hpWeRWWsf9BbQHqK+vIdHjIdnmbj2mySobdw9LaLfxm4ZtMKV9K3DIIxOJYSNRHQgxvVcQjy77CFw1T1uTjhTULCMVje6/hSsORkk9Gh3NISi/AnSyxZ8lHYQ8ZSQItuQ2FIyZiy3scgwJM84cz3ox4gPIlrwNgmTo1m74gFqol0lRBJFCFZVk/eIxwdFRvmMamWQ8TDSS2CYgGKtk06+HDkmy++OILBg4cSNu2bfF6vaxevbr5vuHDhzN16lQqKyuRZZmsrL1je5s2baK4uHi/1y4pKUHTNBYsWHDQ8YlE8wtkaTbkFlkY2yqQuxehnNYbS9exAj/eFfNLZwT9mL4qjI0ryE9JIlW2086dyd97X8roVr34e59LCRkxHl81hU3+Kt4t+4bqcCN2FT7e+i0xK0DHlDQe6DGEVwaN4cl+IzgnryM2ad+3n+ZMw+HJwTLj1G98lqEXNnDOr5ooKjUI187H0J1MfTPKdzN1pr0VxTBshEP7zkCTZBtJGR0BkFUXrXrfwPoZf2DR22Oo3zaPWNhHJFCNERN/66Nt24LnMP9rmwBTj7BtwXOHfO0pU6YwYsQIIJFYJk+e3HzfwIEDmTdvHpMnT2b48OE/eY3169czevRozjzzzH263+DQ964Rs85+gWSHHTPFjXblaKzyKqS2LUGRwTQxwlEUp/1Yh3hckYKNxJ66C/Q4UloOtpv+TBIy/bMLKU1vQ0CPYFj7fuA7VY1nVk9nZkXim+UNhUNo4UrDsCxeXvsdLZOS6eTNRotaWDaDZC1RcUDRXFhA697X4K9agTujkMby+bizOhNsaMLQEzPZTBMCjSaeFAl/1KB1fqIyrz0pgw6n/R91ZV/iSmtP485FNFWtotPQvxOsW8/W+U/RsmQcerQJzZkKih3NLioQHA0/Va37UKt419fXs2DBAjZu3IgkSRiGgSRJXHrppUBin5ri4mImTJjA5MmT+eqrr5of26FDB1avXk2/fv0oLCxk0qRJPPTQQ0Qi+ybEU045haeffprly5cfVIyiRfNLFY0Te/x1zJ1V0BTEmL8M/b3psKUcMyi+7X6fuXV9IsnktkUZNAqiEYwv3kVtaiA5bpKrJeOQNe7uMoL2nmwuatuP1kkZfFu7ufkaaxt20SO9Df9at4iyJh9tPKnsCDYwt66MkB6nJhykJhygKtRErW4gudJJzR9AU9VaNn/9F1QtCVWpx5uZeMt6vBLJqTLbNhisnB8n0CgRChqYhoU9KZMWXS/GndkFuzsLb15fwg1b2f7dy9RsmMqqT24GU2fHolewYk3Ew/6j9loGQ9WEwnVH7fmOJz9VNPVQi6l+9tlnnHfeeXz11Vd8+eWXfP311+Tl5VFVtTeBXXPNNdx9992kpqbu89gbb7yRxx57bJ/qzf+dZPa4+eabefXVVw8qRtGi+YWyKmvAMJCLO2DVNWDM+Aa5W0ewaxCNQ5ITKxaHWBzsGpLtl/uvIrftjNSqI7YRVxH/5F8Yi7/CNvJajO9mInfvj/nNFFJDTZx59sW08WSiySpV4UZuKBzCE6umYldsXNC2Dy+sm8G9pYN4a8NyzmzZgU+2rWXmzk24VI1n+48iauikai7uX/QZd3QdQMukZFJb9aPnpR8jyTYqlz9Jn0HXI9vScHiSqd5psGxOYqC/fLPBsHFOyjbrtOqoojkkVM2B09uGliVXUL99TvPvo0caMOJBKla9T7hhG+0G3YNlxtGS0o/o61jfWMbcRY9hU10M6DUed9LR3XjsWGvd71Y2zXp4n+4zWXXQut+hbRMwZcoUrr/++n2OnX322bz44ovNtwsKCn50P5rBgwfj8/m4/vrrMQyD5ORkCgoKGDBgwI+em5Z2cKWQfrmfHr9wUl4OJLuxTAOiMeTuHVH6dSf+1qdISU5sV52HUV6FOXMhSp8uKD2LkVw/r6DkicKMx5GCjVh6DDQHcvJPvIm86dguu5v4m49h7Uy0UuLT30QdNAaCfowZ76L0PZu0uhoKUjOIYhHUo7T3ZDPxzN/iiwZ4e9NcZlasxqXYubXLQF7dMBMsiSdOGcY9Cz+nNhLkT4u/4Ln+53Fn1wE8s3o+d3YdQH5SCs7ddc7aD/4DAV8ZUUVhe9BEC+ytPKzHwTIt6mvj5HVQYPduK3Z3FnZ3Fs6UfOq3zSPi30nrPr+mfvt8ILE5myzbaKpZjVSrkpTeAUlWD7q2WiBYyZbyWaR7C0hP3fvB5g/s4vPZv6PevwUARdEY2Ov3OBwpB/U8J6KsjonZXtsWPEc0UIXdnU3rfrc2Hz9Y/70NAMAVV1zBFVf8eF2+888/f5+1MWPGjGHMmDE/69offfTRQcUoEs0vVYob7Y7LQDchIw05L4fYyx9AMIzVGECfOgf13MGYkoT+8Uzkjm2wNBVZPYn+ZXyVRJ+/F8JB5JJBqOdeiZz8w2/1UjiEJSuwuxQNgCQrSLmt8TdWIt/6F5KaAsSe+x1JlkXKRbcTKCqlMtxIQyzIfYs/YGfIB8ApWQU8umISC2o2AVAfC/LCgJGARLbLw3ubl3ND+1O5tE0JiiTz5Iq5FKSkM7hFO1DsvFZfw5m5rZm5o4YbC7PZttagodak31AJK7qeFOs9arb0Ip55CikeL7IFqS4V1ZFM5xHPIkkSSBJ1ZV+haB46nvEQO5a8RtWajwFoWTKOlLw+eDI7Y3N6f/Ba7E8oXMvHn19NMJToshkx5Hns9r0znHRz7yw7w4himHFWrn83kZS8BdjtyQf0fCeirI7DDjmxnIjEGM0vlCzL1MajYLehT5uDZdeQHHsnAUgOO5ZuoI46DeXCs0GSoCl4DCM+/IzFX0I48TuZy2aDrv/gHMvvIz7jPUzdwDbuHuQepyEXdEe94BbCksHffROxuVLQZ32UGKG3LIzvZuIyLXIdyUzdvpSHelxA/+xCzs3vQUFKLg2xvWuW6mNB5lWv56o5z3NTp160T05Dt0UpzsgiEIuwuLacR5d/zXfV5QTiUd7f8g1/WfEOAWs92/VdDB6jMfo6Jzktm1jz6XVUr5/Mtq8fxBUuY2tjnPqoQVVTnICvjHjYx+opt7PozXOJ+HdSevG7qHY3/l1LkWSV1v1uI739mbhS22FYHPD6G8OMNycZgOraldhsibU+LkcGZ/Z/BG9yW7LSizmlx29ZvOpl5i16jE++uJ5q35qD+RMKJwiRaH7B6urqkJPdqGefirlyI7Zrz0fu3B6ldxeUIX2wKmpAs0FTiNgzb6FPmY3lP3lWmsuti5p/llKz9mmx7GFW70QdMhZryZfoU15HPX0sysV3oq9fytz6xdzX7kbskg3b6BtQx/4aKac1tlHXwuQJtJoznXEtuvOPVVMZnted2zqfjVOx8/vuo0m3e8hweLi109l8sXMVTfEIc6rW0jU9i8pwAwtrNpKs2Hml34Vc17E3HVLScSoa7TxZbAvUMrd6PZnOZJI8CqZsYplxTGNvyRt/QwUvLK9nfX0MTZWJNVViGTFa976JnOILqVj5HkY8iB4L0OmcxykY8ifioTpWfHglKz66CivmJ+LfdUCvp01x0qH1OQDYtRQ6tDmneWBZlmXcrhzOHfI8Zw34O5otiTUb/9P82KralQf0XMKJ5STqBxEOlqTZkLsWQJIDZdRpiYNxHXPpWojGUEednvim/tk8zD5dkVUFyUgJmhYAACAASURBVOU8pjEfDnKrjtiuexCrchtyl1OQvRmYwUYI+LEaapBzWiPltMZc8hXGjHcBiG1chnbHE6jdBzAEC2njCmL/fhwsC2XwGGzjxhN/4xGs6nIA0k2DZ4ZeTgxId3go81ez0V/B33r9Cq/dxSvrvmRF/XYAOqXkokhw47xXCekxHIqN9067k7FtSlhWv5O4afB03yuJmnGSbHbS7IkFmx63SrDJRVbxr6he/R5JmYVouX3ZvC7K8poo/XOdOHO6snn2I4Tqt5J/1j/o2PNOmiJ1VM95AJszjTan3M6GL/6Y+B2DNVSs/pAW3S4hGqwBQHOlI0n7/17qcHjp32s8fUpuQZE1nI40dukb0I0YdfUbWLD0KZJc2ZxSegcSEgWth7Fx2zTsWgoFbc45An9h4XghEo2AlOxGdlsYW3cgp3oBC33615grNgAQf/NTbBcNBctCctiJvTsd7cKzkTwn9voLyZOK4kmFjiXNx6zKHcRfvi/xu7Zsh+3aB7Aa9tZ9ItQEwSbMNd8hd+iG8e0M2L3a3lg0E+WUYViB7xW7bPThkTUkLdEt6bbZ+XDrd9xfMoYVvh1cVzSEbmmtKE1vQ5LNTmMszMM9LuL/lrxPSI+xtrGKVdUGHbwePti8k9ySVFq4nMjIzQU+AfxBHavwakpKr6IiZPLECpNR7V1cVJiCGqvCt2MB9dvmkj/0eZ5b7+Cr8kqK0jQeOfMptm2cgCWBzZVBPJT4Xd0ZhTRVr8a/awmu1Haktu5PNFBJqG4TaW0HI6GALKE5UzHiISzLQtWScDq8ONl3bCcc8TH1q1uJxhLTqF2OdPqV3smpvX5H75Jfoyp2nA6xsdvJTHSdCQBIsoQkKxjfrcRYuyUxrXmPWByrrgGlpAgrFkdOTcYM//hc+xOduW5xc+KwdpZBPIbSfwRSZktQVNThV2JWbEMu7IFkmqjnXY/c9VQA5PZdQZKwXXAL2J1IqVnIwy7Hz94urSxnCo/3uQynqtHWk8E/V39OzNSpjwU5/4t/cOXsF5i0fTG3dh5KjtNLttPLp1u30yMjnWf7nEprORVNt/P7b6dRGw7QFIsQ1mPomoXqUFlc7yTF4+XWXlDLl3xZtZCQ5iGmuAGJeFIrvipPDMqv88VYVtXApvIv+Wz+/9H1vFdoUTKOgjMeIqVFDxTFBlhUb5hCoGoljuQ8KtdOZPu3LxEJ7GTDF/cTDVSxYeafWP/574n4d2KaJtFAFeHGHUSaKklOTgzwG8beiQC6EQUsnA4vye6WuJwZP9laisejBIKVNPi3EQzVHP4/uHBUiBaN0ExKS0EpLiD+2TxsIwcTbwxALIY65kyMrxaiXtgSKScDWdexNu1ITCBI8RzrsA8rpdupGHM/BUNH7tANa+dm9Glvoo64GqlFG8yy1ch57dE/fglzw1JQbWg3/xWrx2BQNeIfPIv/8luJjH8Up+zgvW0TyQhkck7LM0jREh+62c4UaiNNZDtSuKPLOSSpdp5ZM715J8/Zleu4u+sIOnra8NdF6xiSn4PbtDPj31EiIYvigTIP9xiBhUVNOEjUNMhwONjoqyPFnkvMCnPDvFcI6lE+27kCr+aib4s+5PW4Bk3TsMlRClI1slwq+clQEQ+gqg5iMqR3vwrkJOLEMPQIFSvfA2Bd1Sp6XT6JrMKReDILsdm9uDM6Ulc2i4wOZ2DEI2xf/C/ye17Dyo+uJRaqJb3d6bQ99TfIWgpnD3qceYseJcmVTY/ia5B/ZDzsx/iD25n42TXE9SDtW53FKT1+i1vsw7OPTp060bFjRyzLQlEU7rvvPnr06EF5eTk33XRTczma999/n3feeYfXX3+dRx55hG+//RaPJ/H+dTqdvPvuu0csRpFohGZyshszEkMtLcKywDZuJFZjE8aKDSinloBhYK4tw9q2C6WkiPg3y1BPLUVO3v92xieUzJZo45/HaqhFSk4j9uzvIB4FVcVcMQ8rFoHWhYkkA6DHMZbNQTltDLFn76Xput9x9Te3sStcSVt3a17q/XfuWfpnhmSdCtre6bupmgtfNIgmq9gkhdNyOjNlxzIAeme2A0si3e7mzm6dyXY5KN8aJxKy6HyaxEpHBX+Zu40emelcVdSe9XXlQCrFadn4wjIWJsHv7YVTFW5ghbKT7Pbn0tau8u7QXALVEv4qi2wJunS8lOzMYhYue4bCNufhxkb1ttlktT+TzI7DqdkwFSwD04hRu3kGEX85LbpeTFanMehRPza7G3/VCtr0vZXq9VOI7e5+qyv7ipalV6FJKjkZ3Th3yIvIskqS66e3pA5HfBhGDFnRcDnSWF82mbiemBm4efsMTul5F/7ALjZv+4ys9C6kprRDVR1othO7G/dQOBwOJk2aBMCcOXN48skneeutt/Y5Z+LEibz55pu88cYbpKQk1i6NHz+ec845OmNjItEI+8rwJqoASAASuByofbpipXiwyivR30kU2zOWrkW75zpQZEx/EMmhIWk/LFt/opEdLnC4MO1OiEZAj6P0HYq5Yl5iPIbdlQJy22BVbE3cbl2EPvVNbBfdyhazll3hRDmPLYFt1DSW82yne9GCcUy9DjklsU5HkRUynYnEs6R2Cx6bk3dPv53aSBPtPVls8ldhWCYZjnTmVlQxODMfSQJPnsk/56wFYEcgRM+sVF5Z9y3lwUb+0nsovdLbUhmWuaFwCP/aMIs27kyG5ZVw49xXqYw08smZ45GbFBZOTiSiTStg2BVX8NnccfgD5fQquJS1HydWmVev/g8lF/4bf+VyWpaMQ1GdtOz6K2RbYiKIEWtEc6ZRVbUSrWVffKZBcoueQKIbVrV7kCQZE4to7SY82V2wzDjxWBDV5vxBd1k44mPGnHtpClUx6syXaPBvpWvhJeRl92HqrNtI8eRjmQafzLgOlzOD/NxT+Xrhn3HYU+nd/WZcjvSf3VI6VnZsnsrqRf8kHKzEmZRDca9byG//04UuD1QgEGjurtxj6tSpvPLKK7z++usHvbL/UIlEI+xDlmVI3d2vvmUn+lufJtbQZKWhdG6fOEmSsF08DHPVBixfI8opJRj+JpTsjJOmVI3s8mAYJtodT2IFGtGn710hrU9/G9s1/4e5aRWSNx1z3WLMjUtRz7qYPM1AkzViZgyX4iTdkYa2biX6xJeQWrTFds39yMmJelMxI0bYiNDanc7tC/4fvmiAX3c6m7mV63h3yzcAXNLuVLokdyVsi3LKRTYU+39VawbaJ6fxx9IhJNlshM0QOU43o1r1YVSrXpiYPLjkP1RGGhnaohsSDhp9e68Rj4IvZNK5690sWXzf7hL2CZapY8SDtOp9E/FQHU3Va1j3+T0AtCy5gqT0AqwWGWxy5SL7q0m1u8n25NL7ymmYehTZ5sSIR4n4y1FkhWDdBnYte5vcrhci25y4vG2Rv7fddTBUw67qRVw6ejILlz3Lpq3TUFUn5531GqPPeg13UjbBUA2BUCXDTn+Gz2ffTWNTYsaeJMn0LbkNWVaxa8dnC3vH5qksnfswhpEY3wwHK1g692GAQ0o2kUiE0aNHE41Gqamp4Y033mi+b+fOnfz5z39m4sSJZGbu25J87LHHmisyd+jQgSeeeOKgY/hfTo5PBeGIsHZWYdXvLrgY15EvHIrWMhvsGmZlLfp/Et/wzZUbsd14EUSjcJIkGgDFkwKeFIzkNNSzLyX+r4fA1FF6n4FRvQs5K4/423+HhlrUi2/H0uykYfHewFdZWbWE7t5ivLIbY/YksNkJnHspWyJlqLpGC1cuk7ZPYUHtIn7V5nye7HspMcNCAt7aNLc5hkW1ZVzabgCqZAMXgMx9vbrxUdl2emelk+G0Ma6gB+MXTsUXDXNabjt+130wET1O3IyTbHfQJ7MDbtXBr4tGsbAiwMA2btwpEoFGi/wihc2BGIUZrYnG/Ni9rXFnFROoXo03/xT0SAMbZ95Hm1PupKF8YXNcTVUrsLnSQVawKxpvbJrN0rqt9M5ox+Ndzqb8u5ewObzkdvsV66bcgalHyO48Bm9+X2q2fEXLrpcSjzRgd+8db9E0N7KcaBVv2prYo0XXw6zbPIl+pXfQFKygum4VPYqvBSAW37umKxbzU99YxvZd8+hWdAmxWAgLA7uWjMt5ZGu4/VyrF/2zOcnsYRgRVi/65yElmu93nS1dupR77rmneVwmLS2NlJQUpk2bxlVXXbXP40TXmXBckDu3RzVM5Pxs8LjRJ32ZmPKs2bDdeCFShhertgGrrgEsC0uWd1fYOrkodgdGdiu03z0HkTDGgukYH72IfMujaNc9uPskJVGk9Iv3yOt1Bvk5pycqBQT9xBpqCA//Ff9omMbUdTMB+F3x7Wxq2sJS3wqW+VYy9YwPcKvJmJhc1K4fjyxPfHBc3PYU0h1JaIpKQzTGS6vWcUVhB0oyUrHJsDPkZ4WvEl80UXF7VkUZt3Xpjy8S4Y0Ni/h9j9PItGfRK93LrqCFRzNZEdzG4IvaUxcyWN8Qwx/XaWloXDj8fWRZpWjYP4jG40iKTMiCzmNex+7OoX7Tng26JLIKRxIL+9Ask85Jbu4qHMiLG1UubNmFdVN/Q7g+UdPMsiwyC4ZRtfZjGnYsIK3zaMrq5uOOVuOQnMiqHVtzvTOJ4ac9jSTJeJPb0ODfCkBuVill278gxZOPrkfo3PECwmEfA3v/gXmLHsVuT6FPya2YlkG7VmdQXbuKVG8HZFQi0XpMS8emurBrx3biSjhYeUDHD0ZpaSn19fX4fImSR06nk1dffZVLLrmEtLQ0Ro0addie60CIRCP8NJuKJYFV24DkcTevqyEWx1yxEaldPlZtA+rQ/pjrtiB36YAZCiOfBIs5/5viTgaSMf31yG2KkLLykJK9mNU70d9/BqVjKSgq5sLPieUXYqTnEpFt2JMzSb7lUZqcEvOX7O1+m1M1n2JvJwD6ZPTEH/fzzpbXKfB0old6Lyae+VtkSSLZ5kRTVOoiTURNnWs6t0VBJmpaKBKkaHbae/Z+Y0+3u1BlmfZJ6fytx7m8t2MJz69ZgEu18c9TO2CaDhZVpfHe6mpu6O6lJM/Oep/FIwv93N3XTZ7DQVwyWNhUzvPrZtDancF9JedjBWpJLRhJWrshIMnEJQduGcq2T2P+kiewqUncNvgZYpaGf3ddN4BYsAq7OxeAjIKhbNw+g3C4jgb/Vlpk9cKIhZoTjcOeQijsoilQwblDnmfbrnl4Pa1Idufhr99EPOKnfauzkVCx21OobyxjUN/7cdhT2FW1hNysEj6ZcR26ESHJmcWIIf+ksWkHlmXhTW5NLB5Elm0kHaMWjjMph3Cw4kePHy6bN2/GMAy8Xi/hcOLLR1paGq+++ipXXHEFqampDBw48LA9388lEo3w0wwTpWU2+ooNqO3yUE4twSyvxNpeidwhH6lFJvTugrF+K8ZHM7C1zMLaXgklhSfXTLTvWb+zkk6lg7FME6uxDuOL96CxDmQ5sebmwtuQUjLQPnoB2nejvHUX5PRcNCnG8JZn8e8tibIr57UaQUWoilxnNncX38bV835NQA8Ck3my11/o6i3GgQtVUqiLNHHjvH+xNVBDsTePp/uNI0m2EdINVEmlICWD5wecx4q6KgbntqU+EsZULRo2m2yIJ2aAhfQ4M3au5oK2PblrVmI/mNtmVjG2wIOmSDw8MJMNvhhZskLcFuKBpf/BsEx2heqZtG0Rl7Y7lbqITEPUyYPza2iKNfH2OU6+Xf5PAOJ6kLIN/6ZXz3vJPuNBNsz4I6ojhdZ9b0GSbWQUjkCXLFYte4bS4muYu+gxVmvvc2b/Rwg1bsE0YkjRIMmONOyeFiiqRnHBWKIRP3qoFrfqwYgFiISqmfHNHxl91gTycvuxo+IbNpR9gm7EsGtJ6Lu7poLhaiRJQpJU0lJaM2Pe76n1raWg9TD69bgTWbbhdB7dL0TFvW7ZZ4wGQFEcFPe65ZCuu2eMBhItyEcffRRF2XdSRH5+Pi+88ALXX389zz2X2NHz+2M0AB988AGapnEkiEQj/DRVxgxHkEs7QSgCbhfKaX2QW2RhBcMQCBN7/h2wAFVBUhTiE2di8yRhFrVBdp5c2wp8nyTL4ElF6T4AfcsajNXfov36EVAUjEdvwjJN7Ku+Iee6h9DTMkiyNC5rexnn5Y/EoWisa9xA1Ixxe9GNaLJtd5JJ2BHcSVFKAQvqvqNbajFRXWJboBa36uDBHmPZFqjDqWpk2pNRJIWIodPGnUbrpDQCepSpO9ayye/jxg79uFztyTdV2wnqMdIdGpoM2S6FqpABQK8cO0XpCtuamsj1OIjGLBSbhCrJzbuGOhQbliEjK0HiUoi7eifx0Pwm1vt0vCltqfUlZsFlZpYS04PUx+soGvMKDruX+SteoLZ+DWcPfBy75qak8xXM/vZhmnZ/s1+65v/Rt/haLFQkdwrxsI/6bXPw5HTDnpRJpG4jqz79NZapk91pDJmdRmJTXcxf8jhnnPowrVqcSm5WKbKsYZoxNJsbw4iRkdYJVXGQ4smnvOrb5hg3bptG985XsGjlS3Qrugxdz0FVj87/6Z5xmMM962zt2rU/ejwvL2+fLZ2LioqYMyexL1H37t0P6TkPlEg0wk+S3UlYmWlIkSix1z6G3QU1bVedh+WwIyUnYbv+Qsz1W1BKijAqEiu3zZo65FQPRroX5QQvU7M/kqqilAxCbtcFy9AhGk6M1Zh7Z3WpoSao2obN6cKnukBKwS1rPLv+ZXYEdwLwXJ+/c1nbi3h7y/u0TspnSO4gLptzA/WxBu7pcD1jM8/gm1NvJmp38OSmr5m0fTEA93Qdycj8HviCjTy/5hs6ebMYlNuWD8pWYgG3+j7m/QFX8u6QS9F1C5flwB6L8/wgL5O3BGmV6qIwXeHKmXOpj8bwahqvnzGASFzmqX5X8NK6mbT1ZHFOyxJqYk3c+e0EdobqGZXfk7+ffhq7mizOGfw0azd/jMuZiTejO/O/+T+q61YB0LnDWLp3vozVmyaxrmwi6fkjyE1ujaIkyvFIkkJJwYWs/vTXhOu34s4qpvCsv7L8P+PwtupPwRl/omL1B4BEcm4pkcYdaA4v7qRcehRehhFtwm5PaS5fE47UM3bY24CEP1COJMlIyHiScpv/HoqsYWGytXwW23fN49LRn+I+SokGEsnmcE5nPlGIRCPsl5TkBN1oTjIA5s4qpHZ5WBW16NNmI2WkEn97MrbLzsXMyUApaoexahNytwJMyzppu9EAJJcbyeXGMvTEgk5ZQe4/AnPhDKS2nUlqVYD+zF3EJJkOtz3OpQtX8ue+pbzS92XqYnXct+x+QnqYS9pexKVtL6A6UsNGfxn1sQaG5ZzO0IYU9NduBsA27Aru7DuU6zuexfNrp+O22fHrEZbX7aQpHuXZ1fNJczh5sNdZPLBoBrppYtkM7KaD0C6Jr6fFkCQ4fYTJFXVfI3UcyspAiPpoojxMQyzGzmCIvy1eQZ/sDG4qHEl+kpd/rw3hi5j8pce1vLh+Iue37c0LaydhWiZdMkfjtCWT0eI0Nvk2EQjtHdhuClVgKElMI48zMjrS2ZXGqnVvMrjvfSxa+RLJ7nz0pirC9VsBCFSvRo/66TnqdQw9TDweIrX1QHKKLyBQtQpJ0ZBVBwO738HGz+4hHm6g8KyHSW7RE92MkpjsLfHBlIt2j9NkMvrsCeh6lNP7/YnKmuUUdRjNsjWJ6b+mGcc0D2wrBOHgiEQj7JfsdmEaRmKtzDfLwJOEUtwB0pIxN+3AqqjFqthddNLlQL10OEgyuB1gmCBLmLF4Yn1NJAY2Belk2jxtN0lRkdoWo798P8rg81DveQGrroL4s3dDKABSYkbezmCIJTV19M7KoCKo8XyfF3GqBjEzBijMqfqGwTn9yXVm09tdhPOLOYnCNKqK2qUv8qZl2KNh/tDxDBpsCs+u+YwtTdVcWXAaqXYnO4N+huZ1JMfp5sqOPVnXWEU3rQ0r5sexrEQZt+WLNQadNRTJcpDntvBqGg2xRIsmz+0iahh8WLYdkNFjKpPLEt16ZQ02xvcbwfNrJ/NtzWZynV6+q9zKkPxRGERp6c4lt/fv+WLuvdhUF32738YLmxcyq2ItX+xaxadDbieuB1mx9i06tR+LLCvIDi9IClgGsurA5kiFx29GTclAvuE+Iqn5VC17l5pNnwEQ8m0mObeEcMM2ANbP+CPdLnwTS3UQbCqnKVLzvXGaGnQ9jDe5DXbNQ36LgYBJOOLDprroUngxsnTiLzI+EZx873jhsJNTPDBsAMoZfcEwsWwKst2O3C4PuUcnzLJylFO6Y1bVIUkShmGgdCnA3LgDybLAm4wRi2EtW49c1BYpKxXZcfKN38jeDGw3PASxCMgyUkpmorUTCaGMvIbl/iCF3mTy3ElcOTOxVqZdspunBvQhy5VGQ6SRi9qcx3e1S3m699/wSi6kQgtr+wbU0TdgLJmFMfODxJMV9aRp2CVMK0+Urbl/yXu80v8mLGQUSeJvfYZTEfDTQcvBqcl4M2QCDYlxmdRMmV1VLtYt0hkwSuPl0/uzuTFIntuFYcg8PqAPTy1fQ+c0L7O37e0GbIiaeDU3umnQKimdf3S7lq3z7axcCz0GJ2PXdJbs+oYRpz+HaepIksLF2flc0borE8oWE5MU1NbnY4R9qO400uweNm+ZRqdRzxGqWkNa64FI332dSKyNteiLviRc3JZA3YbmGAK160hvN6T5turwEtfDyGac6kUTaNHnejSbm1g8gMfdEpstiWi0kfVln+BOyiEztTOdCy5E6+RkV/USwlGfqJ12FIhEI/wsstv1g2OSzY167mAwTIzZi9AnfAwWaPdcS/xfHyc2ToPEmpvcTOSitugzFyDlZUOfbsgpJ1+XmuzNaP7ZjEWxXfcnMA2iNgcfrynjjLxcVtbVN59T5g9gWBZGJEJ1tIb/W/Ywz/V+CsNSKAttJK+0D0mtOiOnZcLEl/Y+UflmkmQFZfegvWVBsmZnRV0NbXLbkqEkkxVNp7HGxGwp0amnjcwWMqpNIiNXJthk0X+EnUVfxGg3ROXd1XF+Xar8f/bOM0Cq8mzD16nTt/fKwrLL0ll6k95FRcQSNGiCJRhbop8tlhj0S9RoNJpgNJZILImKhKIoolRBlLosbWm7C9tn69TTvh8Di3yiCIKC2evX7pkzZ945M3Oe8z7v89w3t66sItEpcV33LrSLlugaK7O3QaNZM7m9bxxYMrd1uYjqpnp2LFWprYgEL8MI02ekQnHJWxSXRCrrBhX+mpIDS6it38ENE/7NwoNbmbNjKQAjUgu4JiWTnJzxiIJMdHQ2DlMlvOq91rdoJaVhU2PIKvw5O5fdjyCIZPW5DndCJ9oP/R/E7MmYgoIhCwTLl9JwYBWm5mPKiDlognlYFVrio9X3UN8U6esZ2u9eNK2FtRufAgQKci8+U1+FNr5Em01AG9+NKDdWix9j5QYOiw9j+QLHOHGae8oRTJPwX97A3Lob471VmDv2YRzHOvnHhKjaMDavJPyHG2hpaqAgNpoxGamMykjFJkV+ekNSkxAFMMNBcq0Y5nS8i5aGAzxf8heyXRkorlg+dybyalUz0nkXgRS5NzSHTcHljOeFwTfzp/4/44n+00kUZCYmpOHQw4REP2Z0AHf7EGElyJr3g6R3kCjdpfP+a0H2btORZIGuA1S0cnhsaAKflPloCJnsrte4Y3kdAgrvljRx/6AEXhqfSpJD4j+7mkk3YukZk4MWtlrfqx62EASFnMxRAHjc6SQndqe+cS+mqeML1LChbl/r/lu8ZcTGFbDGe4j3qvcRlhxU+vag/uJhpKEXIF9xG2r7HrjXrMbtyKDHZW/Q+4p3cBXtgvf/hZBzMXeuqufq9w+xvjKA6ozMSpoObaT47WtwWDKbXpmEoQdagwxATV0x6SkDaJ81mvHDnkBVfnw3O2cjP2igWbFiBePGjWPMmDH87W9/+8rjL730EhMnTmTy5MnMmDGDgwcP/gCjbOObsPxBhGgP6q1Xod56FdKk8xBioxDTk0GSkEb0QxrQPRKDvuRxY9U3IgRC6KHw1x77x4CYHtGH87zzZ86LdlIbChNvs/Hc8IH8ZdgAprTPiizGGxraa38kurGZdiGFe7Ouwa4ZeJ6+h54JsYzMSkNPbYfyP39BvvM5WroNYdIHq7j248/4YH8j3dzp2KoPYnzwOhR/hisQYvbGZUxe8jJ3fbGYAZeAr8miqswkIVWkoK/Ezp1+dm8Lo9pEmg5BfqzK4FSF3xRK/KqHDW/Q4F87W7jhw0ruWF6NWxG5LjEaV1UjrhY/g8bbcEcLRMUJ9B2lYpcMhubMYPqIV5k27HnkQIgO6SMQRRm36uKKnIGtZm2X5Qzkg0M7WFNTRidPJz6vNJGiuhOMcmOOvRQhvxChZBtq4QjEqHRcpKKGopGHTIceQ3hzRxMlDRo+zeKDfU1IUWl0nvoSqd1/Qo9pc5FkO70ufQMh6KN314hIqCI76Zw7BW/Dbgb0vBWbGv2jKQbo1asXAPv27ePaa69lzJgxTJgwgVtuuYXa2sga6ueff84ll1zC+PHjGT9+PG+++Wbr8//85z+Tn5/PgQMHWre9/PLL5Ofns3Xrd7fZ/sFSZ4Zh8NBDD/HSSy+RnJzMJZdcwsiRI8nNzW3dp6CggLfffhuHw8Frr73GY489xp/+9KcfashtHAdB1zGWrsVYswkkMaJ5pkjIU0eDLGOs+Bzt9cXIowci/2oG+jOvISTHR8qhN+1E6pqLIQpIyo9zUVbM6IBy7W8xD+0l3a7gUG3sa27h5pWfIRCZBD46qA8djGbkQRMxls/H3FsEig3PDQ/T0n8cmw/tZkFVKTM6FtJNUTAP7OA5Iwr9sEHbB+UV3J+fSfilhyHog09BueY31IX8AGz1VlIa9tIpLpWUbJEOfWCrVsY72hb6ZGaRKOcTnagwQBYpFEs5uO5pHHE5xKbehioJhA0L3bSwerinJQAAIABJREFUIyBJdYTn3xtxDr3sdkaP74Ngl1CkJoSQjt2VBP4WtNefxlVXweBJV9N7/EzW73iRXj1u4t/Driek+fE4Evjpijn8oc8vuGlpI4YF8fZmnhubjCAGiLY0lG4DKKteT5Y3mdCcpQjn50B+LFJ6FvGhyHsflKpwXV41iz5+ELstjglD/kCo6RAHNrwc6cdJ7kq3cY/SIWMEqj2GcN0+UhwZSHqIWHsS2o8k0ACEQiGuv/567rrrLkaOjKxjrV27Fq/Xi2VZ3H777Tz77LN06dIFr9fLzJkzSU5OZvjw4QDk5eWxaNEiZs2aBcD7779/zPX4u/CDzWi2bNlCdnY2mZmZqKrKpEmT+Oijj47ZZ8CAAa3duz179qSy8vRpArVxmrDA+HRz5G/DxFi7BUIaZigcSY8t/xxrTxnaC28j2hTUu2ZGfG5afFg7IykNwRf4ymFF8ceR1RUcbqSOPVCGTUGKSUAURDLdLpyyjAW4ZJkUpwPBE4MQHR8JMgBaCGPXBrQ+I7l7ywquSO9AN9NAn/soYpOX81KOKvH2jIsBQ48EmcNYtYeIVSO/HQFItLt4o2w9ZmEFSoLGPRsWs76mnL/uWkO9vZ41tfvxBWrY/f6t+Op2Ubt7CeH97zEl10NOtMLvBiXiVkX0z/8DDbVgmRhvPIrd34T0UQvC/m1oz9+PvvAlUBSsshJoqsd48ykc2OjX/ReoAqxfcxcfLbmcPXveZlanUezyhjEsSHXJjGnnQhQEbBg0WNEsrTDJSOpPi0+j5Y4R1CTX8vk7F1O08BdMbu/kjRFu7i0UWbP2DhqbywiGGmio2Iilh6g/EGlMbK4qoqVqGy57Avs+eojihb9k67yf46veTrC54nv3sdm9bzFz501izj97M3feJHbvW3zajr1gwQJ69uzZGmQgcg3Ny8vjn//8J1OmTKFLly5ARJbmjjvuOCaTNHr06NZrcFlZGR6P57TZCvxgv+aqqipSUo5q/CQnJ1NVVfW1+7/11lucd95538fQ2jgZBBCy01r/FdulgSIjJsTCl9dgTANa/KDrmKWVmFV1yBeOiNTbWsce0vIHaJ+UglnXgOn/cVlGxzvsxNhU/jlmKH8Y2JtXxwwlWpH5/c4DGFHxCImHz6UgIOZ2JyxA78QMumg65vJ3sUp3oy9+mR571vOPEQP5Y/eOPNwxBRGQBkSUeIW4FMROvbmhc38ubteFh/uNZXHZF0zO6cSCiq34jfAxpzwkhplXvoWgcezdvQBckxfFk3HxpD2rEwhbBGKPNj/iiQVDQO4dRn/9CazaCsyiTzF3fIHYZ2TkvSgqIiJO0Y1ddDO5/x+4YvjLuHSTQo+HIWnRnJ/j4oWRAoNt8wk1rkc2w9z2cRVbq1poNGWebBC4ekU9C5vzSRs2m1BzFW5fNUmv/w65Ym9rA6hphpEUJ7IjFkE8mqyxx0R8bBrLP2vdVl/2KfaoNATh+0vq7N63mOXrZtPirwAsWvwVLF83+7QFm927d7cGkv9PSUnJVx7r2rUrJSUlrf+73W5SU1PZtWsXCxcuZOLE09dY+oOlzizL+so2QTi+9u/8+fMpKir6imvc8QiFQl8ryfBDEQwGz7oxwekZl9vtJn3GBZi7SxFcDoRoN2FdRw1riD3yEXfuxzxUjTy8L5ZhIkgSWCZSl1zMcBhRkMCyCDc1sefgQaLdbpIVB8KeMvRNOxC7dcTs1pHdlYdO07v+bpyuz9LlclEQn4AgCBR5G1hYVsmelgCPzriP6KoDyImpICskNTfyVPfzInpq7ujW59tXLyC3cx+yWw7B1mLoOwaxxxCkoZNBlNDeegbl0htoNqp5eXcx9WEf4zO689veowkZJjM79WXevm30SkjDKasUeat499ABrp7wJKVrn8YRm0Nih0mwWEdcqIEAzU0G3qx+dJhgotaVIwy9CO1tO+rUQETrzThcCq3akfILIa8nQnIWlmrD2rMVwenBfHE2tnCQLlOuRxTbYwbg9k4e/rNqJjXeYgDGD3uSPw/vyYHSBRRXj+bDssjNxtxdYcaN7E5MRj/MTz/AqjyA8tF/mHjRbFZtn4PDFos7Pg9DD5E38Y/U7/0ET0Yf/LqPaEc8SfmTqN65EEGUSekyFRPw+SqoLwVNO/MptHWbnm3t8TmCbgRZt+lZOuacWbUAy7KOe339/9smTpzIokWLWLVqFa+88grvvPPOaXn9HyzQpKSkHJMKq6qqIinpq/Xsa9asYc6cOcydO/dbCb7ZbDYKCgpO61i/K9u3bz/rxgSnd1xiny6YoTCiTcUOWM0+LN1AvmQMVrUXnHaMD9diFu1CzM1GzM1C8DaDTcXYvR+pfQZ57TsgGiZWYzPa24e9bvaUoXbIPC3jDAYMsATszlOfyJ+Jz7KrINAlLoZt3gaeP1DNLzt3w1VRgtXSBE1erLJdSIMmQa/hYBpYjbXoE69kOw0sd+1h2KiRpDWH8ZTtxkpMQ0xtR+iqO4kVLDbU7UcVJZ4eMIO9zdXsajpEYXwHhqW0p2NUAjZJQhBgVucBDE5px+amWkKFv2JQSi4NdR7iOwnoCzWwwFMt8J4gs8A2kG7dZIbbROwj9mLaM1Gu+BX6ivmIqe0QUrPR5vwGTAN56k0InYcjZnZDf/fPkR4jQPTkoL1oYGw2EbNERv3iEf614hJMS6e6tog8dxbbd84lv++o1vMkAA5XDMkDb0bctB4TsA7sxPWftxh42UMIkkDRO9eSd+HTfLz1aeJj8mgseY3U5H7EZc0kq9+NZBRegyipBC0FvxFAlR3k5qZ/68/+u/Bl1YRvs/1kyc3NZf369V/7WFFREaNGHT2fRUVFdOjQ4Zj9Ro4cyWOPPUbXrl1xu09fRd4PFmi6devG/v37KSsrIzk5mUWLFn3F4a24uJj777+fF154gfj4s8O8qI2vR7QdvREQPC7QjYj4ZowHIaxhbo003pm7D2BV1aG9/SGCx4k0bRyCKEYKCjJTEOKiwW6DYMRu2NJ0zEM1CB5n5LingK/JZN92g/pqk4I+CrFJIpJ0drjnJDrs/H5AISYgCyLuYCPYnOBrRlv4EgDmts9Q7/gL1rApWIJFneDnmk9uRLcMXi55jXkj5hIt9sDcUwSxSbhFCbeuMX/ATCpFk6pgE6/tWU2SPYqCmAxi7CqNuohdVEhzxvDJoT3cs/59puZ0I8udgtDkIE4SIR5sD9oJl1lEZ8pcVeNGyQQzWE/d4t/gLd9A5jVvIWXno1xxG8gq4cdmRVKlgLl3C1L2UMxaCyElG/Zvj6wluTIxNkdmQGapia0iBo87lVC4mfycC/DoafTvfDNBbQe/7NWLXd4wdxWIKCEvlixDz6GIAR9Ul9M8dBoPfWEwIcMg1e6i5eAG0pIHULRzLk57Ap06XMTcEh9v7PCT4ZF5amQS962q5sqCWHonfX+XQLcz5XDa7KvbTweTJ0/mb3/7G5988knrAv+KFStITk5m+vTpXHrppYwdO5aCggLq6+t5/PHHufHGY5Wj7XY7t99+O+3atTstYzrCDxZoZFnm/vvvZ+bMmRiGwdSpU+nYsSNPPfUUXbt2ZdSoUTz66KP4/X5uueUWAFJTU5kzZ84PNeQ2viWWLxAxRFMkxJgokCUQAhFL6MMpUyHKjfLLyxEsoNlP+Pk3ocWPASizLke542q0F95BapeG4HFhHajAKPEhF3ZGOE7z6DcRaDGpLDXYvCqSHjm412DyNQ5cUWdHoAFI/JKHj2U6sGxhLF/T0R10DXQNWZJoUSQqWirRrcjFXLd0aoK1xL/5d/BWoqTejfHx25j7tiENGE98v9Fcvf51mrQALtnGzPyRXL3saVr0IDmeRB7ufQX/2hspYX1iy0rmjbkKV61K8H4/qCA8aWfFWoP2Tov2HonQg0EEm4ukX/6BFv9n7Ao6SREkYg7uRMjMQyochbF2EcgqUu9JmOUCVlBF7n0pYsZFCAkmSCooQTicsVJTHEwq+CuSqSA85ySwzU/768ewO2YR52cIOFNFjHfmEN7xObhjUGc9gj7kQl7f6eOjrWHKm8Pc0t1BfdhPXEJ3nOog8nMvoyYo4iOeN3ZEUq/lzTrLy3wUxKvct7qOty/M4PvqpOnf80aWr5t9TPpMluz07/ndbAJ0XUdVVex2O3PmzOGRRx7hkUceQZZl8vPzuffee0lISOCxxx7jN7/5DT6fD8uymDFjxjGFA0eYNGnSdxrP8fhBlQGGDRvGsGHDjtl2JKhApI67jXMLS9PRN25HykzB3LwTK9qN2LkDKArKtVMxNmxH6paHUVKK1CUX48NPkXp3jhQKHDlGaQViYizqzy/GEkX0dZsQu+YjtU+P+MD4Aoiuk/ASESDgO7omaOjHCCyfdQguD0gSYpf+CBtXYJWXIA2K5PAF1U6U3Uk7K5v8qI7sbNpNQXQ+GY4U1EtmYZkmNNRgbo5I3BgfvI6t59BWyf90Zxzb6sto0SMXu33NNVhfKg1wyDKCKGAeMMACaaCMpIn0GCyjBAXCc4LQFHmG8badmsuH8FJJC3f1daAnx9Ky9U1ihkxBHXA+ll3GFCxQNWS3HbPUQHvHApeAeuk+7LcnoH+hIBVoCHYdp9+NPlfG2BIJoKF/BMmbfT6m0YCghTF3fB4ZZEsD5ubV2PuOZFJuNDm2WjrExxBnNdBu8nNY9dW4tm/C2LODlnHXIUsiLkXAp0XeZ4ZHYW+DxtcsCZ8xjqzDrNv0LC3+StzOFPr3vPE7r8+UlJSQmZkJQIcOHfj73/9+3P369u3L22+/fdzHbrrppuNuf/XVV4+7/WRpk6Bp4/QS1hCiPWjzlkZM0ABpQgBpcCGWaSGkJKB/ugmrqg6xYzaYJpa3MeLOWVQCHhdi9zyMz4uxKmsRu+Qi9++BuWM/piQipCVhNbdAejLit0yjOVwiOQUy5SUG9TUmXfsr2Oxnz2zmeAh2J4LdiXLlHZHZjCAiuDwItkiAXVW9lj/1eQTN0rCFDWLLDhL++28R4lORJ1z1pQOJiAj8sd+VPLp1ARmuWHrGt0MRJTTTINEehUtSubHzAA75m7mwXWdEoClXw/OkA6EZhBaLJEXE9IDuEbDqDgemKIFstYE/9HOBGAZXClrdHg7MnUb65XNoXP0GiDLuHhehNOegvyFju95B6NUAQmwc2puPI7ijMXdKmMnjsEwnQmI6EAk0YrwI/kYkpw6KGimGaGmMvK20dpi+RhwqDCp6G4o/Q5w0nUZ/Cc1b38WeUUj80EvJaNxPHZU8MyqPhXtaKIhXaRel8GpTmN8PSSBK/X4LbzvmTDytC/+vv/46r776Kvfcc89pO+aZQLCOV/71JcLhcOsifHl5Ofv372fgwIFfcXA7WzgbF97PxjHBmRmXZRhYNfWE//RqqxKA2K0jYmEBYmYq4Wdfh/ompNEDEFITEVLiEUxAFMCmgihiajpCQxNWtRcxOw2cdmj2oS9dC6KAfMEIEAUEuw3hJMzVfE0mCJFrlmo7tQvM2fJZVgWq+WPRM/y+480Yqxdheaswt64BQLniV5h1lVh7tiIOmojpieXdkI9uSWk0h8MUeSsZnZlLaUst7TyJhHSL8pYGZFGgICqeRj1MtOXC9h6wOHLRV6arSP1kLAM4ZGGFLKzUWix7ADDRmyqwJeWjBxup++iPuPNGEqrdi6PbxMjsTLIjizFYbzhRpqhYdhORRgj4wGajfN4vEUSF9MmvoC+3sFoslEkqVksJgicKbeMKlF7nYW5ehZCSjeVrROjYnSbBwoGK0NKAYBco+/vU1nOUMv53qLEd2LLybgou+DsaNkQLkC3CpkhTbQ052Vnf+pwf77M/W74PZwPfdC6kBx988MFvevK0adOYPHkyDQ0NXHbZZZSVlbF27VpGjx59Jsb6namtrSUxMfHEO36PnI1jgjMzLkEUsYj42Jg794OqIE8ejtXkR0yOQxxciDykEDE1IVIOrZuE57yJsfxzzPIqxIRYRNNA++sbmMV7MYr3IA3ogb7yC8zNu7Aqa7FqvJF0nG4iOGzfemyqTUC1CUjyqc9mzpbP0q246B7XhbAk4EhpjyArmNvWRR40dcQu/bE69SGUkUul1UhqbCLRqoeaoJ+eCenUBwOkuWL5vOYQn1Ts4c/bPmVJ+W5MU2dIXCKq5kL4lw5HekD9FmJ7CXO7gfZOGDQLqZuE7i+j4s1ZqDnjCFtJiM4U3PmjsEJebLlDaKz4gh1L7qR65wLicgai9o7BqAwhukOYggF2JZKic8bQvHU+vvIVRF0wDrG7QkvlKtSULliWBqsXoW9ZjTRwIoI7GiEpA80MIfq8IBgI7hhETBq/eKP1HEUVXobkScKd3oPSdX/mwIoHkUUT2RPPoapVJMTm4HR4vvU5P95nf7Z8H84GvulcnDB1ZpomTqeTefPmMX36dK6//vpWf+o22jgeotsJvQpQ83PAF8ASBcQYN1ZZJWa1F6lHPpbHhWBZmF8UR2yiAWtvOcLk4Zi79h9t4mxsgbCG1CUXecxAtH8twQqFI2k2y0Lo2xXB+eOzHPg2JDsi7QCWEsbK64l621NY1eVgd6L98zHMqddzU9nTbG4o4o2hc7lx9buUtjQgCSKvDJ/GIxs/ZnRGRzbVHa2E2lBfw2X1B3HFRmP1AflwL6EwQEKIEtDmRrTpjGoDqadKWN5L1OBfsWNvV/buEBGEECMvUkmI7Y8m11O+4WXAwtSDlG14icS8iUQld6Tyvf/FkVGIK284kisBZ94YsnIGAtBcvIDGDf8i9aLH0QMVVL51C2nnP4q49B3MDR8jjpgKsoLw19lIVQcgJhH1Fw9jIZI87kGadizGmdUPJSodEfDuW0n9gZWIsg1XWg8UezTJ8V0QpRCmaSCKZ2d25sfECfMHlmVRXFzMggULWkvmzLN5JbWNswOHDewqxEZFAklYQ3txHsbC5YSf/idCINS6ZsORCUaMB0s3EDu1j6TLADE3K6Iw4LSjvfIflEvHoUwejvHZFvT5y7CafRibdmD5vypj89+CoKjsOlQN7igsSUJfMhex9wj2JjjY2hBpgtRMi9KWBgAMy2RD7UHibA6KvFVMzj6a7piW0QFFtuGXNbQxAtoDEsJsFfLFyOxG+PLrSjiy+qCk9GTfzsilxLKgZLuBWWdDRMEZ2751f1dCHn7vXsK1e4juORU1tQu1Hz1O5Vu3YGl+JHsykiMaT8E4Mq58CTWxAy3Fi9GbDnFowe2YYy/C6NOfmo//QHPxAoQRkyMHbqjBqj6I4POjrP+chNhBOPfVInhrMSQRmydSPtxl2lx2137G4k9uorRiNZruIxDynsFPpo0jnHBGc+edd/L4448zZMgQ8vPzKSsra1UKbaONr0NUFAy7haDroBuYlbVHH2z2QUMTQkwURnkVyg2XYTU0I7ZLwyythJw01BuvAH8QS9PBMDG/KI7I24R1sNmQJ54X6cuxLMySUoTYKITsk6hE+5FhWRaiJxY69kTMyCWsyBRXf4KJiV2yE6266BSTyI6GGmySTGFCOh2jE/jfTZ/QLS6Zd8ZciWCEkUPNaK4EXt75OfE2J5OyOmE3W6CpDjMuHvUuD/q/TcR8CewiopqAarORlAlVpZEb0IxMCSGqgZr3HqD9eTfiPbgOxZWAzZPKzg/vJeXC5/HV76Vu/zISB/4UrWQNLUXvEd39KixFRHIlIoiRiObOH03D+rkYwUYkdxxlz1+MZYTx7VyKMmE2cko2Vs1BhNhUUGXEhDSMdR8hFfRBSMrAFAzMuCy6XPwSvnA9W3e+DsDnW+aQkzkSQw/9YJ/ZfxPfGGgMw2DdunW8+OKLrdsyMzN56KGHzvjA2jj3kewqoGLmpCPFRUfEN5t9iD3yweUAXUfsnof+7yXI5w/H3HUAAkG0tz9EuW4agiJHemgamyMOnqGIFIr2j/lYjc3IF4/BPFiFmJOBVd8EX9Jc+y4YmkkwELk7l+RI1dq5gmizg82OHZiUMYbzUgYhCSKK5eCx/hOpC/mJtzn5rKqMReU7mZHXG900sEsyliSzK+Dnk51fMP9AZCY0Jj6B6gX/g+bdj6A6ybjyFaTrHAhuN1aphNjgQVgWZvCFKt56A4dTwlFqEqzYQrD0c6renIU9vSdx4+6htmwNSXmTMMwwOz64E4Ca3UvoNeUlzOoy9J1+tPhtqLE5rbkWQXGSdd18LMDSQljGUVsJ3Qhgu/h6rJhYDElBe8aO+tNLUM+7CKtBwar0IPpMUqP7Ynk0QqFdx54rQUSWz/2bk4KCAvLy8tB1HUmSmDJlCjNmzEAUxdbr93PPPUdtbS333nsvFRUV6LpOeno6zz//PBCxF3jkkUfYv38/siyTl5fHfffdR0JCxMhv9uzZLFmyhOXLl5+S4O03BhpJktiwYcMpvPU22jiKGOXGEEXUW6+CsBaRmHnjfaQB3RHbZ6JcMREME33tZuRxQyCsYaz6AnlEP7R/vY+1txxkCfWWKzHWF2FV1QGgv/ke6k3TMQ9VI6alYTX7Tlk54Ms01lsseztIQaFCcpaEZZk43edOsDmCS3HhOqxOXBf0c/2Kd1BEmYf7jKVzfDJLD5Xwyq4vuK5TP6oDLVQHfKS5ovGG/AjAyLQOxApQ590PgBX249//Kc3bFhM36DpsOYXobykYy3RYqZP8qASShWUICHGRoG9pAYLlG7BCPhI6jMQyDHx1u1vHKNs8IIrY3f2wvEHqlj1O4rjfIAgilfN+jeH3Ys/sQ9L5s0G2Ed3vpzR98QZqcifs7frR3FhK+fKn8SR1Ie2mK7AORqFvAsEJ+towZnGkak79hYIzPUTvTjM4UP0ZndpPRhRVJOHct6ew2+3Mnz8fgLq6On7961/T3NzMzTfffMx+Tz/9NIMGDWLGjBkA7NixA/hme4GEhARM02Tp0qWkpqayfv16+vfvf9JjPGHqrGvXrtx8881MmDABp/NoR/b/b7Rso41vQnI7McNhMAz0/3yCcvEotHVbEVMSwFDBriJ27oC5dRfqrVdGRBptaiTIAOgGxtbdkTWdIzgd4HEh2GyEn/s3UreOyBeNjAh3niKGbrL9c42B42zs266z/QuNdgUy3QYo2BznXrA5QrzdyZyhF7OodAd2WUYRJe7uOQIDk2UHSyiur2Jmp36sqTrAz/L7Mjm7gL1NXgKijByVit5UgSCp2FK6UP/pi4SqdhCuL8UzYTJCnANjo472T1B/IiLkiQhaOilT/kzgwBrcXc9HcMWhVe4E1YEzth3R6f1IyZuEw5GAqIUh0cCIbiEmehaC4iCwfy2G3wuCSPywm7C0SENvdO8rcHcai6UHsUSR4vduwzJ1Gg+uxxnXgai0QiRHNKKmYL5stL5/Y6NFqGUVmYTITppIVOp5NIcacIknpzLxXdlYupglRc/S4K8kxpnCuK430ivr9PXVxMfH87vf/Y5LLrnkK02Y1dXVDB48uPX/Tp06AV9vL3CEdevW0bFjx1bBzTMSaKqqqpBlmQ8//LB1myAIbYGmjZNGVFVMKYg0qCfa0k9Rxgwm/Ld/gz+ImJ+DfNm4VgVgExBNCyE1AauiNiKb3z4TIS0BaWQ/rNoG5PFDIqXUGUnIV56PGBuFVVWH5bCD24F4CmZqkiySlCGia3BgR+RCtXODTsfuCrZzPMuS4vRwSU5X5u0v5i/FnwJwXad+5ETF4VFs+PUwhQnprKrcj1NRmbN9HR944nh86p+J91WjOONo2vQWanw7XHkjCZSuJ9iwCS1+H66bxiKICoYoYekgG9HY/P1wdBuAviWEv912FJtC5T9/juROJnfqkwRKPqFq9QMARPf7KaGoWGr2fkT+sHuxJecDAgnj7iV4cDN1Hz8BQMKYu3B0GIJpGliWgWUetaLQAl5KPvkd7QbchCRHIQ+ORV+pgwjycAl9Rzn+PStxth+M1HEUbl04JhV3ptlYuph3vpiNdliCpsFfwTtfzAY4rcEmMzMT0zSpq6s7Zvv06dO57bbbmDt3LoMGDeLiiy8mOTn5G+0FABYuXMikSZMYPXo0TzzxBJqmoZzkb+uEgeaJJ544qQO20cY3YbqdiJ3aYVbVYezc11rabO7cB00+hLhoBLeTmvJykmLiUK6bhlVWiRAfA047otuFMPE8LE3DOlCB9rd/g2mh/vpqtH8uxNp/CFQF9darMD2uk5OqOUxGe5mWpmP7mI9UwB4x4jtXadZCrKjY2/r/orId3N9rFIXxaQQMnZAe4rzUHD4oj6S39jZ7mbZ6Ma+PvIIoSWR/j58Q0DVUBBzR6dQu/QMpFz6K9+MnsUyd+OG3ILlT0OdrGBsNrAYLDHA9nosvsIW4MXcRLt+EZPPg2728dRz+khXET3qIfWufQtN8yJKN9KteQZDtVM3/n9b9mra8iy25AMmdiGmGyRn8a8o3voI7sRPupM7sX/sskuLCksIIF4exT3KBDZqbtxA38jbiht2MqNjBEvDv/ZSYwmnf27lfUvRsa5A5gmYEWVL07GkNNHB8G5ahQ4eydOlSVq5cyYoVK5gyZQoLFy78xuOEw2GWL1/O3XffjdvtpkePHqxevbq1AvnbcsJAEw6HmT9/PiUlJYRCRys0TtDn2UYbx0VWZAyPK+JPU1VHa3Ij2o0VDIM/gOB24m1uJjkjI1JNG31sU50gihDS0JasAfPwDyocjgQZiKzxbNqBmJmC2S79pIONwy0SDhkUDlM4tN8kK09CkiLWB6nldeg1LUjdOkYWqBX5rFXJOB4u2caY9I4U1UdMBsel55HjiaFZD/Oz5W/RGA7SLzGD+wpH8X75TvY01vGT3J6U+hrYXl/N33dG9MbGpHXgtowMonpMoX7N3/DvXQ2AGWwiYey9iHkZ6Esjsw0hQwTRpGjxLcRkDsDuySDasnB3Gou3eicA7k5jMHQ/giijuhKRJRuWZSIIEvas3mje/Yj2KGL6X43kSSJUUUTjF28Q1ecKelz8EvWlqyl+71cUTHwCy9KQMAtAAAAgAElEQVTZ8s7P6DD8HgRBRDLd2GJSKC/6N6ldL0F2xGKFfRjZo5Ds375h87vS8DV2AF+3/VQpKytDkiTi4+PZs2fPMY/FxMQwefJkJk+ezPXXX8/69eu/0V5g5cqVtLS0cMEFFwAQCASw2+2nP9DcddddJCcn8+GHH3LdddexcOFC8vLyTupF2mjjy0iKArEKJhbqLVdh7i9HbJeONm8pyk8mYTa1IMvf/NW0VBkxMwVj3+E1HFFESIjFqq2PpNnapaMvWo5yzZRIhdtJUlthUnPIJCFFpKrUIK2dhM+0IeR2Qqwux6r2on/8GWJ6MmbfLojR398F67sQa3cwKiOX/slZ6KZBjOpAkWRWl5fQeNgr5rOacjTT4N5eI4lS7eiGQaMWPKaxc5O3EqvLACR7NGb4qCCqGfajNx5C7RCDchvQ5ELqLhGWqhAlGw1lawFIyBiLu/MEHFl9sCwDyZVAbdkauk74E4R8VH/yvySf/zCSM4a4Qdfiyh2OGpdN/ZoX8O34kJh+V2H46qiedzupl80htt0wYrOHEGg8SFPFZpI6TaK5cjOHtkTKmVO7XU56j5/g9+5BUpzY3EnUldWQ9O2saE4LMc4UGo5jExBzmmwCALxeLw888ADTp0//iqnZp59+Ss+ePXE4HLS0tFBaWkpqairDhg37WnuBRYsWMXv2bM4//3wA/H4/o0aNIhAInNTs/oSrm3v27OHOO+/E5XJx+eWX8+KLL34lSrbRxinh8US8arLTMIpKUKaOxdiwHf3fH9A+/qsmeF9GtNuRR/ZDnn4+8qXjID4aZdZlyFdNRr3xCsxdB5DGDgbl1HRj03IktLDFrs0aXQcorHkvxPy/h1izXETKTCP8lzcwN+9EX7wCc9cBrOC504+R5HDTPiqOBLuLO9YtZru3mu7xqa29mAl2F4oo4VFs+LQQkiiQandxcU7X1n0mZXVidc1BlNzhJI69ByW+PUpcNonj76Np23toLTuo3HAt3sYHMUUvsj2ablOeJzFvAjmDb0eNjscMHu6ZUexgGtib6gnvXI5WvZvAgfWYRmRGJLsTAQHvqr/SXPQffLs+oubD3xPTdzoAgmynbs8yBFEl3FJJVEoP4nOGY2hHA2DjwfWYhkGwuYo9K/9AqKWa7PQ4TOPMO2seYVzXG1GkY1UsFMnOuK7fzSYgGAxy4YUXMmnSJK6++moGDx7ML3/5y6/st23bNqZOncrkyZO5/PLLmTZtGt27d2+1F3j11VcZO3YsEydOZN68ebjdblatWnXM7MXpdNK7d28+/vjjkxrjCX+FRxZ9PB4P+/btIz4+noMHD57Ui7TRxvEQZRGi3ZgCiB0y0Zetw9wcSaVgmojTxiHEfP1MQYhyI/fu3Pq/ZVqI2WmYJWVIQ3qhz1+GuXYz8pTRiAkxJzU2h0tkyCQ7pmHR0mhRXR4pUqguNzENs1UwFA67iRomZ7ce9FeJszt5cuD5lPsaSbA7eWX4pexpqqNTbBK7GmtZVLqDVZX7uTqvN2MzOlIYn8r8cT8lbJp8UVPO7zct55PkTK7L60uHqU8jChZGoIm4ATOonPdr9KYKJGcsiBaiBXZ3JjmDf03NrsXsW/cE2f1/gaKmIsgJCIaGu9MYQhXbqFn6B9ydJ6CbMgpgGBaCrGAEGlrHbgabEG0eUi75M0ZzBfbGOkx/HXHp/QhVFBHY/gEp+SNR7XGUbXyJtB5XYuoBtEAdiR3HYepB9FAjYQHsnuTv5XwfWYc53VVn3+T82b9//9YqsZkzZzJz5szj7vd19gKfffbZV7Y988wzJz3GEwaaKVOm0NjYyE033cSMGTMIhUJfcWVro43vguByQFIcVs2X5EDCGmZVLdI3BJqvHEcUsCQRITsV/Z2lmMWRmbfmX4By1QUREU/bie3Aj2BzCICArpmtnm2CAIakIk06D2PJaoSUBIyundFNlXOxTCDO7iTOHinxjVbsCAj8fPlbPDnwfJYd2sOj/SewsnI/s1a9y7jMPK5IzcRR+hnn557HiLFXsr2lgV+uXcAFmflcmZZNlCseURRxdRoHpkZ07yuQHLFoTVU0F/0H/66PiB74cxKH3weijOqIobJ4Hge3vE7X8c9gyygk7Scv0RKIxRSiCPhM9m3X6VjQnvgRt1FZX4alBUgc9xuUhA4Ey76gesE9CJKKs/0QDMOg6t07APDt/JDMn79NbM5wJMVB0X+uRwvUA9B54lOUb3qV3GF3fa/nu1fWxNO+8H8ucMJAM316ZHo6cOBAVqxYccYH1MZ/H4IkQWwUyk8mob26ACQReeJQTF/wxE/+f4jRHkyr+ZgZB2ENTBNzxz7EvOyTshYAkFUYeYmN0p0GWfkSJSUm7Xv1JJjTGb8P1r8rMWA8WKKJXfdFSrRFEfEkguTZgE2W0S0Tv67hDQXokxBZwFhwIHLH/OrujYyPT0Ja9kd8uz8hcfxv6BMVwxvDphHlr6H2/QcJqw4SJzyAv/eVeEN+LMVNbNgHlknjuogtde3iB0ic8lfUhEwMJURChzG01O6ics/HuFIvpK4qgbQcGdVusWG5Rpe+KkvettO9Xzqplz6HIJggyphhH+HqSLe/La0boYoilNjMY96TEWykYsc8UgouaA0yAM3V2xBFCVH+7xRk/b454RqN1+vlt7/9LbNmzQIiazbvvvvuGR9YG/9dCIIASfEoP78Y+fxhGHUNSDmntlIrxniQp46O+N0kxqFcNgGzshbtlflYDc0nfTyHSyQlS6bfGBs2p0VKhkjYUlk8T+KTDyR8zRa6ZvHp4hBBXSE8+zm0uQswG1tOafw/JBmuaG4o6M9/DhRzd88RJNiPVVpQJBnLMgnX7MbSwzTU7KXM38TCxibEcfehN1dTv/pvNAZa0C2LZYf20CTZEaRj+y4EmxtZUWhY+3ca1rxAdq9rSO08jrhkifxeKp4YEcsUiU0UObBLp7HGYvV7BjXeBPbsSaRoUzSWPR13wTgkVwJYBoIogSDg6jgCRBlX3ijkqFTa9b0eSXGRlB9Z0FZdicRmDSS+/UjEH4EEzbnACWc0d999NxMmTGjVO8vKyuLWW2/loosuOuODa+O/C1GWICEWKy6aQwcPknkK1WKtxEWj/PxiAMzaevQX5wFgNfkg9dT9Q8J6Cw5bFLJs0Wekyt4inZR2EuGgRWWpiSnbEYlYHlh19RD9fTnSnx6ibXZ+0rEXF2oFbPFuplN0F27rNoTlFfuYmJGLvH81hqnjHjCDYGUx2x0p3L5mAQBpzij+MuYe2P4eSXYbsaZGj8QkdCOEbo8iacqfaN7wGrbs87DFpRKqKsKZMwhBkvHtWoY7fxTilwKSahdI7yBTVxFZH8vOl6kuNyleH5mt1lWaDB3jIWnyw5EyZncSTRvexJU3krjhNyPIdlq2L8G74hnsaT3IGH07WX2uA8vACvtRGmoQtCDnZM7zHOOEgaa2tpaLLrqIl19+GYgUB5yKqFob3z+aEVlbkM+xz0sQRVpavttsQFQUiIvGamrB/GwrCCB2yEJM/+ZqthPhra+koCAWgHadLJIzVXZt1Fm7RCOtnYSoH+5UFwSEqHMryBzBLsnYJTfd4jqxrvYz+ibmMzKtPboeJDZuBE3teuEVDOK3r2RL9FHjuUP+JnAlEDvkBjTvfio/+F/k6FRixv6G1w/uZnh6AakTH0VAAd1LsHwjDeteAUEkadJDWAhojYdAkNHEeExDQFYgPlWk/1gVSYay3UdlZbyVJiENJNWOpHowgs1E9bsSI9iEZeg0rH8BZ/YA4gbfgHflMwRLVuDMGcjBV3/aegxnZm+UqNNXXtzG8TlhoHE4HDQ1NbXWZG/btu0YzbM2zk6q/SHmbC1FlUSu7ZpJvP3bL4L/mBCi3ChTRsMFIyK9Nu7T991VbRKGbtGpj0L7rjJOt4CtvgpjcCFC93x0m4Nz+awn2OOZlDGWoB7Gp4coCx/it7tfJi+qI1fnXIrWVMHE/HHMKyuhWQsxMTMPmzOacNhP9bzbkTuOQO/7U4qaGxie3oF71y/h6UHnk+iwoTeLNG2JCEFimTQXLUJyxFDx1s1E972KqMKfEAorGCgoNju53RRM0yImXqSy1EAPw/grQrRUrCLYVE5Kl4uRoxIpWnAj+cPvo+K167D0EM2b3ib9ypfJ6rIISw8hyHaUxI5oNbuR3Imo8Tk/7En+L+GEgeaOO+7guuuuo7y8nBkzZnDw4EGeeuqp72NsbZwiDSGN+9buYkttZD2iJaxze2F7YuznvlLtqSB8lxTcNx1XFHC4QQuaKJE6XFZuiEG2D6HpU4tOvUXaf72E1DmDXVaxyyoxtm48HvU7msNhgqaGMugaHJbBP4ZfjDfcjFuxEbACuEQJJSaT5l5X8LPV72FYJu09cdzUdRDBkI+AZaKKMvaMXvh3R/oxHO360Vy0EDUxF2fuSLw1IhvWqDjdFv1GW4iSiSAIuGJg0k/tiBLUlbzL/tURiSzvvk/oesFf8HtLQNewvuQzozUcpHbZE4QObcGVN4rUqU9hBhsRbFGR9Z1znCM2AZZlIUkS9913H4WFhZSXl3PDDTewcOFC1q1bx6xZs8jMzCQQCJCQkMDMmTMZMWLE9zLGrw00Pp8Pl8tFjx49+Mc//sGuXbuwLIu8vDxstm/v097G949hWjSEjlZdeUMadcHwf22gOZMIgoCmCWz9NEy3gSqFwyLrNWFvgDiqMbaGEXPST+tM6odCEiSiVA81wTq2eSsZmJLJhrqNFFd+wKjUYfxt9yvc3Ol6qgiSMvlhPq4ox7Ai6yt7m73E2RzI1dshpRMhZyyJY+4kUDAWyeZBiW9P+dpXcHUYgiEnsHyeDT1sUl8NxevD5KUsx57Wg7AQjaKKaP5D+OtKWscWbD6EZZkkdBhDwFeFK38Mvp0foiblYUvKJ1QVqZzz7fqIuGE3IbmTKCmtpFP0uZ82+7JNwMqVK3niiSeYO3fuV/br06cPzz33HBDpvbnxxhux2+0MHDjwjI/xa5P3F110Ee+//z4AqqrStWtXunXr1hZkzgFibDJ398klWpVJdKj8vEsmxd6Tr7Zq49vR3GDSdYDKR28FWfBigLoKjcTKbfD3N9Femkd43jKMwLmjHHAist0xDEppj4hAYWwv4tQ47t/0CD/LvZKZn97ClBU/5aH9LzMooyPqYTXSgpgkYi0dfdWzSAKEDQPZnYgrbzS2tB6IzhjSfvI8clwWovr/PIUsk8DeNVS8cS2qEKC5QUFyZJPR8ypke6QRN7vfDQiiSs7gX+FIyCV22I1kXvcuKVOfjtgNHFYAkKPTI38LwnGFJ880i8s/ZNJHl9J74XAmfXQpi8s/PPGTToKWlhaioqJOuF9BQQGzZs06bkA6E3ztjObFF1/k4Ycf5q233uKBBx4gMzPz63Zt4yxDEkXaeRw8OrgT9SGN13Ye5Fe92p/4iW2cEjGJIqU7DZrrIxcuw69j7d4fedBpRxnaC2PZOkzTQD6vD8I5oov2dciiRII9Cn/IT6XhZWzaaMamjaRBa6QiEBGIfO/QR1zebhr/HjKZBgsSJBGpaAHxU5/E0jXcNGMqMqKkgi0y25MS2qNEp6KHNUZMUfj8YwOnxyKvwEvdv5djhnwEmg1Wvxei/1iV6MQsek57DcsMA0LEL8cZi+qMx9DD6EEvLbXFuOM7kn7tO2g1e1A8SQiq6yvl1t8Hi8s/ZPbWxwgakZuOikAVs7c+BsDEjDGnfNwjEjShUIiamhpeeeWVb/W8Ll26HFcN4EzwtYEmMzOTOXPmsGzZMi6//HJ69OhxTLXZqcgQtPH9EWNXQIBEXeXe+FwMy2Jvox+XIhGlyDiUc0dx+GxHVQVS00yGjtSprZOprhHJG9Ibc3cp0pBC9OXrMTdHGgutKi/KZeNPixPoD43T5iQenaAZREDCKdmJUaNpCDfilBxEKS6MZc9g27saLaMnsRMfouaDhzFDzQgIJIy9BzUu65hjiooDVXEQq2gMu1BFNH3UvHUrZqiFmKG3UrpXoaXRYu2SMKOm2TGNRGTVwuYQkOXI9UkQJYxwE7uW/Y6svjPZ8s41GEaIgvGPo7iTQHEgqd9/KvPZnc+3BpkjBI0Qz+58/jsFmi+nzjZu3Midd955Qvl/OL6VwJniG4sB9u/fz9y5c+nRowfTp09vK2s+x4ixKcTYFGr8IR5ct5sNNU3YJZEXRncj093WPHDa8Pmwb9xM8qYdJHfNh2GFWEoayj3XI1gG2j8WtO5q1Tdiafo5p4v2dUTboogmkqrRDZ3Xhj7PrsY95Hiy+VPxX7ii9wSyR92MLorUW2GU825gSc0q4pRo+lpBEgCtuQpME8EWKQeX7R4UVUFRIRhwknTJHLBMqitVtr4XmYkoKjR6TZbPC9F/rEJypowkH7XcNrQAMem9Kfv8BcL+WgBKPv4d7QbeQqDhAEn5k7Dbv99lgMpA9UltPxV69epFfX09Xq/3hPsWFxfToUOH0/ba38TXBponn3ySJUuWcOedd35vlQltnBnCpsWGmiYAgobJkgO1TMxObCtTP100+TCWRPxYqFqD2rU9+voirL7dMatrkccORJu7ECwL+YIR6Cs+Rx7eFzHmxLn0cwlZkkn5P/bOO77K8vz/72eflZzsRQYkhISEEXYQkC1DKeBCrVqtq65vW61UrcVRrbX9Wsevrta2jq9K1SrULS5A2RshYZMB2TtnP+P3x4FAiggICQbzfr14kfOcZ1xnPdd9X9d1fy57IjbRjs/wcVv+LWBZvL3vY57d/k+i1Sj+NvIJhqaM5qWdr1Fj+bhMi6FuyTPEjr6Bhq+exWiuInrMjQiqE0lxYLNHYtliCQUtYlMhPSdIKAAFYxSWvR8gLVtCs4t88bYfu0tg1DQNu0tEUhxImgvFHtNmn+KIJeitpWTlX4hI7EdCTOemA5LsCVT4qr5x+6li165dGIZBVFQUPp/vqPsVFxfz9NNP89BDD52ya38bR3U0uq6zYMECbLZuLaCujiIKZETYKWnxIQAD4iJ4e3cVV+amnG7Tzgz+e3oiCEi9UsEIISbFoX+6IqxSIMsggrlkLYwZQihkoihnXpQgSoskikgag014dC/PbX8BgIZgI6/ueZPz0y5lWsrlJNplvDXbiBp+Oc3r/0Xz2vkA+PdvJuWS5/DV7MCWMhDZHomqCagaFJ6jEfRblO/Saay1GDxWYcl//FgW9MyVaWmyQDSxuxKIy5xIXK/xKLZIjJCP5H4XUfzxXQBYpk7IVw+dWHV2c8517XI0ADZJ4+ac607qvAdzNBAOhz3yyCPf2IxvzZo1zJo1C5/PR2xsLPfcc0+nVJzBtziaO+64o1MM6KbjSXBoPDk2j9VVjaQ4bXxWXkeCQ+Olon38OLcHCY7uSsKTIsKJdO7ZmBuKEftnY+6rRn/jI8QBfZBnjEM+92yshmYEyyL44jsIPRJBFPHVBcBuoZsykiKgus6s8nOHbMer+9qN5DNdmfy1aB3LqkqYldGX23pmIZjh9gIHMf3NYFmIso26T/+IM+ts7L0KkWyRyIqArISlaSKiRRwRAqIUdkBlOw22b/CTkSOTN1xBEOLwey1Sh92IgEF10TuIkkLKwB+jB1twRPbs1PfjYB7mqW1/o9JXTZI9gZtzrjup/AwcvU1AampqW65mxIgRrF279qSuczJ8t65Q3XQ5EhwaZyVH8+X+evrFRhAyTZ7dXMKPczuxxeAZihjpghEDkAb1xdi4Hf3NjwAwd5WBL0DorUVIsyci2DSUi6ciJMdRtE0mK9nLnhKNos06cckCQ8aJ2JxnTpGGKqqoIZnnCh/nzZKFpDpS6GHP44mq8PvzZVUpN/YpQA004h56GYHKLegt1cRP+Q2WKFLxxi1g6rRu/YAeV/4fUtJhoUYLtqwM0XeozIQLbfg9Fnu2huV/tq3XycyXWfFxgIZqC1mRmf6TCBLyZhHXezKmqSMrDsqrmsk8sTZFJ8301Mkn7Vi6It2O5gdEtE2lX2wkv1u1g6IGD38Zm4ciCjT6Q0SoElJ3scd3RnQ5MJpbEWMiOajGJQ3rh7GhGMFhR4xwYWkqyAqWP0RuH4GA4KZ4URBZFeiVpxAICJimiSPizPkcavbX0LtPb67LvpKgobOqprLtualpfVhaW4nP3EW+M5Xe5/8ZybJo2rgw3DDN1Nv2NVprgL5tj1WbQN4whW3rdYZNFLHM9vFLSRZoqQ9XVekhqNhjkNo7Akeku22fQKCmg151N//Ntzqag5ULMTExNDY2snbtWnr27NlplQrdnHqSHRoPj8oFy2Jnk5enNpVyVnIUvd0O0iO7iwNOCpuKkJaE+sufgCyFBT237EQ+92yCf30D9foLCT33OlZ1PURHwjU/Zvz54TLnT173422xyBsmkzNYaaueOhNQJAVFUvDqteRHx/HCuIsJGDq7muv4T0kRvxwwCN3y8VT5AobHFpCdkoMZ9OHKP4/WLe9hSx2Eltyv/TlVgeSeEvEpYtuy86ETVMp36vTsKyNK4cZ1eouFJEN0gkhthUl69qH3tXvxeedxVEfzxhtv8PTTT2NZFjfeeCNvvPEGmZmZbNy4kRtuuIHzzz+/M+3s5hRhUyRsikRJs487vizGAt7fW83LUwbS6A91y9ScBJKqYjosEAXw+hFSEqCkguBfXgXDxGpsCTsZgIZmrOo66uw2GqstvC3h0ffW1Tp9+kv4fRaKKiJJZ0ohNMTb4wiEgtT4a7h9+XuIgsAfC6eTaItg1hcXY5NsiIJARvoMKkJeEsfdQszYWxBFOTzD+S8EQUDRwu+P4oZeeRYxiQqVpQaLF4SYdImNugoTl1tg07IQg8eqeFtNKksMQkGLtOyenfwO/HA5qqN56aWXePfddwkEAkycOJGPPvqIhIQEGhoauOqqq7odTRenIRDk4HItC2gO6EgIGJZFrL0raw6fXkSbBjYNoiKxGpvDLQp8AXDYwooAmgqBIMgSQlwUtIIr6pAz0ewgtLbib1XwKDZiEsU25fQzAU1RsUkyvx8+FdOy+GLfLub0HsgfBz9Coj2K1XVrqA80Mr/kLfLcOUzrMYlYzd3uHLpuEQpYhIKg2sBmD89SZEWkrkJn01fhkNuuzSEy8xX279UpGK0gqxbb1upsXRN+fv8ek+GTVZxnUKjy+8pRHY0syzidTpxOJ2lpaSQkhGu9o6Ojz6gv/g+VVJed4YluVlU1UZgUhd8webF4H+f1jO92NKcIISoS9eZLsVo8EOHElEXU23+CsbcCMzmJ2mYbTrdIc73J0AkqLQ0mfQpkArqGz2vhdguUFOtExUtoNgvTFDBNkBULexcuGjAti/9Z9h+yI2O5feDZ/Gzp2/gNnUeGT6Vf5FmYBIhWYnm86BlcspPBMQPo4UgJh99CARqCAcpaGukVEUNTuUpShoyiioiiQK88mdgkkYDfQlFh9Wd+hk/U2PhVkP4jNeqqzTY7GqpNLPNbDO3mlHFURxNWpQ2hKArPPPNM2/ZgMIhhGEc7rJsuQpxd5a6hWdT6Q+xq8jJvxXaSHBp2ueur2X6fEKIiEKLC2mYigMuJGBeNETJJjgdPs4UoQXS0SEZuWPlZVkQio2HtFwEkSaCuyiS9j8znb/kYMVnD5hDwew1sDgG7s+uNxlNdUdzWfwxOWeHvxaup8oWb3D24/nN+mjOU3637lEcKJ7Kufi0toRZsosYru99gbNIoBCK4/LN/ETQNxidnclefKaz5LEREtEBmvozDJRLfQ8KyLPxeixGTbPi9Js31UFOhkzdUoaY8gGlC3jAZSep8Yc1TycEIE4SbVIqiSExMDB6PB9M0eeutt4iKiqKpqYnZs2fz8ssvY1kWEydO5J577uGKK64A4IEHHqBfv34dFqk66rf0ySefbPu7R49DJbANDQ3MnTu3Q4zppnMxPS1UeQL8ce0ufLrB5bk9SHF2J0g7A0kRUVQRm11AUSEUNAn4LPZsNXj/ZR/LPwwwdLzG0OAy+hkbUQ0fOQUKlSUGn7zu5/2X/JTtNAj4TULBrjUsd6s2ftSzL4WJ6USphxaER2t2/IaOBayprmRk/HDOSZlES0hiao8ZiIiYeLiu7yDskswteaOoDbWQXBCkqd6gsvTQAFgQwk7YGSkSGSMxdqaGqQtExgqcd7WdGVfbcUSIVJR0rffuv4mOjmbhwoUsXLiQSy65hKuuuoqFCxfyySefcOmll/Loo48C8OijjzJnzpy2e3lsbCwvvfQSwWCwU+w86owmNTX1iG1Llizh7LPPJjExsUON6qZzaKqrZVhmb96cPhgApyxhkyUaAyFskohN7rrhma6CzSkSlwwIEPLD1yvCcvbV5Sblu3R6BgOY763BblkkZA9h+YeHbgwlxTqOCIGYBJEeKb2wLKvLhLUdsoomyfy8/+hwwzQ9xI+zB/HrlR8gCSJT0/oSq/WntNXH/25cTIzm4LeDx3PNsqu5MON8/rdwOs8Vr+DjfTtwKSovjbsYrCANfgVN17AsAUUB1SaiqAKKKpA9QMTbavLOCz70A+2azrm0c5VPPijbwFNFi6jyNZJoj+LmvpOZllbQIdc6mEt/4YUXWLt2Lffcc0/bczExMQwePJgFCxZw8cUXd8j1D+eE5t2PPfZYR9nRzWkiUlNIdtpIdoYrfj4ureG2JUW8WLyPpsOap3XTcdgcIja7iCCBKB2+XQgXEgDU1OFwCaT1PrRDapZMValB8ZoQkqDibbEw9K4TCpIEkQS7i5vyRnJx5gBMy+LBYefw2sRLiVZtNAZNfrPqQ0paG1lft59/bFvLuMQxvL73TVKckXy8bwcAP+tbSFFjDe+VFdGqB1nasIv9Rj1NzQZNdSZ+/6GZjmYXmHKZjdzBMqNnyETGdF7o8YOyDTy0cQGVvkYsoNLXyEMbF/BB2YYOuZ6iKMydO5eHH36Yu+++G1Vtn3u97rrr+Mc//tEpqZATWrB5OhoFddN5NAd1Hlq9C3Vg4ZoAACAASURBVICihlaGJbgZnOA+xlHdnCpkGcafr7FtvU5ckkhcvIW5eUdY4mbiSBRNoN8Imcx8GcsEWYWafYRLfC348FUf48+3EZPYtWaibs2GW0vCEwqiiBKtoQCKIGEhoBzmeVVJJmAZ9I3KQTeDpDgiiNYc2GWF36wOqw0sLCnid0MnI4UUNi3R8TSHGDpBJS7ZRNXC5eJRcRJDxkvs3bsXzdaz017nU0WL8BvtB29+I8RTRYs6bFazZMkS4uPj2bFjB6NGjWr3XFpaGgMGDOCdd945ytGnjhNyNPfee29H2dHN94D/HkYY3QOLTsXmEIlLEXDHiUgi7Nvmp8edPyMYhM8/lmht9DF5jg2HC/w+2LQsRNmO8Gi0X6FCnwKZ7Rt0RpzTNUuinUp4xB0jhRcOC4LF42fN4NFNS4izObkiewCbGnSGxA7k4a8f46nRcylpaWRT/SG1gVq/h1jNQdVahcoD+ZclCwOcc6mNmn0heuaG1Z4hLBzcmVT5Gk9o+8lSVFTEsmXLeP3117nsssuYPn16W/XwQW644QZ+/vOfM3To0A6x4SAnNG8cNGgQACtWrOgQY7o5vUSqEj8fmEGay8aFvRPJjur6zbm6GrIsYHeIqDaR5GwbQdXJwvki9VUWwQDs+lpHsYVzDrUVhxLZ1eUGiiaQlCHi81js36OzdXUQT3PXTXa7VBu9I2J4cOgU7hhwNj2cMUxLnYRLdjI1ZRJPFP2ZXhFuzk3PJfZAl86ZGXns97YgioccrSBA0A9rPgtSVW5S7/eypHwP/igHnlDnJMMBEu3fLKx2tO0ng2VZ3Hfffdx9992kpKRwzTXX8MgjjxyxX1ZWFllZWXzxxRen3IbD+U5aZ3feeWeHG9ZN5xOhKszKSmJyRjw2ScSpdEvhnU5sDhFPS3hle2tTeHYZlyIhyyKSYpA7WGb9khCCADmDFFxRAg6XQHW5ydJ3wrmd7Rt0Jl9i67KLEiVJIsHharfNoTgYlTCCAdH5SIJBnOLmxfEXETJNZEHEpweJHaIQbNVRHBYZww2aQvVMutGOSgjRa6Ofmo6mCVT6GshSYjvltdzcdzIPbVzQLnxmkxRu7nvqRTZff/11kpOT28Jll112GW+//TarVq0iJaV9e5Abb7yRWbNmnXIbDueod5JbbrnlG7dblkVjY8dM9bo5/dgOVJ518/3AGSEy6WIbpdt03HEi0fEiPo/JqkUheuZJzPipHVECw/AT4bbhbbGorTiU3PU0W2fkokSX4sSlHJpx+3ytbKjbT4zmIM7m5Iv67Ywa14sW08/Fn76OzwiRGxXPbf3HkKRF0lAH+m6Fnvmdl4M8mIfpqKqzW2+9te3vOXPmMGfOnLbHkiTx1ltvtT0+vNVzbm4uxcXFp8SGo3FUR7Ny5Ur+8Ic/HNGF0bIs1q9f36FGddNNN4dwRor0HXaoYmjLqiDluwzKdxkIAsy6zk5ZZQm9tByK1oTIHqiya7NOMAC98qS2SjZfq0lDjUlEtIjdGe7rcqbwftk2/rltDQAxmp2Xx88hxm7nk13b8R2YQRQ31mCXFQKEqHI2kJMYj2h1rpDstLSCDkv8f585qqMZMGAATqeTwsLCI55LT08/JRdfsmQJDz30EKZpctFFF3H99de3ez4YDDJ37ly2bNlCVFQUjz322Deu7+mmmx8ShwttCiJtHT4lBWRVZMemIFMvt2OZ4W0OV3gW9PF8Pz6PxegZGoIgIkrWGaMSPSI+jRe2rcECIhQNy4KQaZAblYAkiBiWSaozPHu5/LP55EUncsfAsURp9tNr+A+Eozqa559//qiVK/Pnzz/pCxuGwQMPPMA///lPEhMTufDCC5kwYQK9e/du2+eNN94gMjKSRYsW8d577/G///u/PP744yd97W666cr07CvRWCvTWGcyaLSKZjugYKyI5A6SqSwz2L9HJy1bbnMkeghamywmz7GxfWOIkmKD6HiRcbPDShA2J4hduB9Rr8gY/m/CJextaWBgbDIJdhe6aVLhaea1iZdS0tJAH3ccty77DwHTYH3dfjbXV9AjNfLYJ+/mpDnqN6ujyyM3bdpERkYGaWlpqKrKueeey6efftpun88++4zZs2cDMGXKFJYvX969lqebHzw2h8iQ8SrjZ9uITxWR5EO/VbtLpFdfhZxBarvZiqyEe7JoNoGS4nAOp6HGpHyXwVfv+2nt4mnXaM1OtjuOyanZJNjDxQNB0yAnKoGmoJ+Py7fj1UPs9xxqGZ3kiCBkdes2dganrayoqqqKpKRDAo6JiYls2rTpiH2Sk5OBsJp0REQEDQ0NxMTEdKqt3bRHN0x8holdEpGlrjsK7soo6okNBO1OkfHna5gGSDIYB5aQOCMFWptg69oQwycqXXpW89/YJZlKbzNRmp1F+3YiiSIPj5jK+6XbyI9OIDMihki1cyVofqicNkfzTTOT/55FHc8+/00gEKCoqOjkjDvF+P3+751N8N3scielsKrexydltUxIjaUwzkVTRfn3wrbOoKvbFRuTyKSL3OzeqhOXLNJQbeJtsXDHCNTV1VFbW3ta7Oookt2RqIqN3wwaz8flOwjoOrN75rG9Mfw6v4+f5ZnIaXM0SUlJVFYeWtFbVVV1xKrVpKQkKioqSEpKQtd1WlpaiIr69sVNmqbRt2/fb92nsykqKvre2QTfza49TV4eWbsFgLXVzbx8zkB698lBOcUzmzPpPesMTsQu07SIilcJ+i22rgkxbKJKam8Jhyue+Pj402ZXR3KeI5fChHSqfK2UtjZyTlofJH/ohGz7Pjqlo7UJACguLiY3NxfDMEhNTeWPf/wjkZHhnNSOHTv43e9+R1VVFZZlMXPmTG666SYACgsL+fjjj3G73VRXVzNmzBheeeWVNvWAwsJCPvjgA6Kjj+x6ejSOeXfYsGED1157LdOnT2fKlCmcc845TJky5YTejG+if//+7N27l7KyMoLBIO+99x4TJkxot8+ECRN4++23Afjoo48oLCzsktIaZxL+wwT4Eh0qkihQ5Q3g6Rbg/N7T7DXZXKKzYY+BN2ThiBAZOl6jT4FyxlSfHQ1JlEh0RJDrjmNCShZJjghqqqpOt1knzdHaBCxcuBCHw8HChQt59913cbvdvPLKK0B4lnnjjTdy/fXX89FHH7Fw4ULWr1/Pq6++iiAIDBw4kA0bwkKf69evJy8vr21Jy+7du4mOjj4hJwPHMaO56667uOOOO8jPz0eSTt1CPlmWmTdvHtdeey2GYXDBBReQnZ3NE088Qb9+/Zg4cSIXXnghd9xxB5MnT8btdnerR3cwIcOgzNPCorIShiUk0ScqGpfSXvE13q4xOzORLfUtzBuRzWPr9tAaMrhzaCaZioR8BsX4zyT8QZMvvg7x6aZwcmZwlsT5I1QiHD+sz0uVFU5n/9gPy7bx9JYVVPlaSLRHcFN+IVPTcjr8ugUFBWzbtg2Ad955h8GDBzN69GgA7HY78+bN44orruDHP/4xgwcPZt26dYwdO5b169dz1VVX8fHHHwNhx3NQiuxEOKajcblcR8w0ThVjx45l7Nix7bb9/Oc/b/tb07R2Ddi66VgagwGu/vxD/IbBP7Z9zf9NmE52VPufZZxd5Zr8NAzL4okNe1ld3QTAXcu28dS4fJKc3cnV7yOBEFQ0mOSnSXgCFnurTEJnoGLA95kPy7bx+/Wf4z9QiVHpa+H36z8H6FBnYxgGy5cv58ILLwRg586d5Ofnt9snPT0dr9dLa2srgwYN4qmnngLC1cG33norL774IhB2NIMHDz5hG445nCksLOTRRx9l06ZNFBcXt/3r5szDp+vtQmOlrc3fuF+sXUUWBA4PYgrA7mYf1d5AxxrZzXfCYYM5wzWGOUVm95H51Xk2HMrptuqHxdNbVrQ5mYP4DZ2nt3SMSLHf72fmzJmMGDGCpqamNt2zYzXIGzBgAEVFRXi9XnRdx+l0kpaWRklJScfNaNauXdvufwhXfh2M93Vz5hChqoxKSuGryv1kuCIZEBtPtc+LAEQoKjb50Nclxq5y68CeNId0PCGDmwZk8NfNpdxXmH36XkA3RyXohc/f8ONtCVdyjpiskpQhEbAMNFu3tl1nUOVrOaHtJ4vNZmPhwoW0tLRwww038Morr3DllVeSnZ3N6tWr2+1bVlaGw+HA5QqvQUpPT+ff//43eXl5QDj0tnjxYurq6sjMzDxhW77V0RiGwZVXXsnUqVNP+MTdnF5qfV6WVe2n2udlRkYWiY5jS/5HazbmDRlJwDCQRZG9LU3c+uVnRCoqT46aQGakG/mwPF2iU+MXBb1YVdXIUxtLcCkSkiDQ4A/iUuRTXonWzXfHMGlzMhBerFmz36D/SBUjZGIpFi0+gY17dUK6xdn5CpE/sPxNR5Noj6DyG5xKoj2iQ68bERHBPffcw0033cSll17KjBkzePbZZ1m2bBlnnXUWfr+fBx98kGuvvbbtmMGDB/Piiy+2iSsXFBRwxx13UFBQ8J0Ksr71myRJEi+99NIJn7Sb04thmvynZBcPrVvJ34o2c8eKxVR5Pcd1bJRmI9HhREDg0Y1ridVsPD9uCtV+L8WN9dT6vO33V2VGJUdzy8AMfjuiN89uLuXWxVtZXtmIT+9edX0qCfqb8Hlq8HvrMPQT66PS5LPIGRIeV2p2yMiVqSw1MQyo2W8S9EBxeYgP14VYtFHnpc8DtPq/mwqHP2jR7DVp8VlHtA/+IXNTfiE2qf3Y3ibJ3JR/pJ7kqSYvL4/c3Fzee+89bDYbTz/9NM888wxTpkxhxowZ9O/fn8svv7xt/8GDB1NWVtYWJsvPz6eysvI7hc3gOEJno0eP5oUXXmD69OntlJwPTrG6+f4RMAx2NB3SFNnb/M25lm9DlUSyIt38NLc/1T4vn5SXsqqmgnN69OTKnHxibOGkf4xdJQZIi7CzYFclH5TUAHD3smLemD4YTRIRu0vST5pgoJnGumKa6rdRX72JrH4/JiomD1nRjut4u0OAZJEfXWPH12qxfnGQ5AwJX4tJKGhRvDrEwLM08tMVvAGLt5YHMU0LOLHPzh802bTXYM0uHV2HS8ekfYdXe2ZyMOHfUVVnh7cJAI5Q2X/22Wfb/s7JyeHll18+6rmmTZvGtGnT2h6rqsrXX3/9nW07pqP517/+BcA///nPtm2CIHQ3Pvse41AUruyTx4qq/Xh1naty8/lsXyljklNxyjLRtmMr1roUlV8OGIrf0GkMBvigbA8Ar+0q5ke9stocTXva35SaAjpYkOzqrkQ7WSxTR9e9fL36CQaN+g0BXz3V+5cRE98fmyPumMdHuwSUVImQZeGMFBg1XaO12WTX1zqZeQquwSp//yxAWa1JcrTAjVNtaBK0+kx0E2QRXPZjh9ICIXA7RXolSmQnS+yo0ImKMPEGQJPBpnZuOK7GX8u/S95BEiRmpU8n3nbs96ojmZqW0ynlzN83juloFi9e3Bl2dHOKyYyM5LVJ51Ht87KyuoInNq9ja0MdV+f2Oy5HAxBjs1Hl8RCrtXcUiihS6fXglGUi1EMj6rE9YthY28SuJi+X5/SgJaTjVEQa/UGibN0hlJNBECQM3U+PXpMJ+BtY/9WDACRnjGfQqN8e83hJFHA7Dw0EAj4Tl1sktbfEnuIgPQarlNWG650rGiwq60zS40R0EwQsAjo01xk4bQIum9CuVcHhBA149kM/pgWfSiHuvMDO/joTp03E47OQJRNVgShnxzucOn89v9/8KIurlgFQ4inlV/m3EqV2XrOzbsIc09G8884737h9xowZp9yYbk4dmqSgiDrzVn9FhdeDiMDEHulIgkiV10PsN85IjkSVRCJUjV/2H8KSynJ+lJHFV5X7eHbLJn5VMJSzk3u0CRNG2xR+kpvKfk8AWYQ/r99DVqSDmwdmIAZCRGpnfj2t31uDYeho9mhk+dTN5FSbm7jkoeghLxWln7dtr6tchx7yEuU+sb4qml3Eog7VvpN+o3LR0ZDEcNGAKIDbKWBa8O6aIBMGKKzdpZMQKYIA2SkSMa5vdjT1LSbmgdROyABf0CLCLtLis/jnZwEaWi36pkpcMkYl0i4gih0XVg2aIUo9+9oel3jKCBgnltvq5tRwTEezZs2atr+DwSArVqwgPz+/29F0AaI1G4+OHMejm9ZwXnomO5sb+fXKpThlmefOPue4ErXRNjs2SeactAzOScvg032lPLZpHQC/X7eSYVNmEnnYaVyKRHqEjUs+XI9hwd5mH9nRTiakxmKYFtH2M3dm4/NUs3H5H/B5qug/4jYiY/qiqqeug6MgaCSmjkKzx1K1bwVYJj1zzsfXWoVgBoGex30uv6+e5R//D411ReSPfpyQOpJfnGtjS6lBTrKEKoI3BEN7KzzzQYBmX9h7XDFWpaHVJOYokjXJ0SIJboHqJou0OJEIm4BhwZ4qk4bW8DmKyg1afBYCIIkWDq1jHE6U4ubW3OuZu/ZeJEHklpzriFa/XSuxm47hmI7m/vvvb/e4qamJO++8s8MM6ubUIQoCWe4o5g0ZCcCDH4UXhnl0nZd3bOWWnn2O6zx2RcGuKDQF/DQFDi3IdKtHJqJjbArVvmC4BNKyyIx0MDzRjU830Y4SbjkTME2THZtfoqL0CzL7zgFE9GATWCaqdmoKZ2z2SLytPpwRPZg4+3UEBGTFwaI3LyCr32UkpY44fnuNII11YZHIpsqv8McOp8ljMSRTQpFgbYlOfKSI0ya2ORmAikaTvqkKTR6TqiaTSLtIhB2ctrDjiXSI3HqujZABqgwBnwdZc9Ij9tBnn5ko4tDCjxdtDBHU4ZwChehTrLdmV2wMjS3gPxNeDdumRKBKZ/6s+vvICas3O51O9u7d2wGmdNNRRKkaDQE/mZHutmq0bHc09bKIGvATpR1fiOdfu7Yzs2cmg+MTccgykaqGTW6/2E8URaI1hT+OyuXNnRXc0C+DeSu2U+cP8pthvXGrKpp8pq7PsEjNnEZkTDZLP7gOSVQ5a8r/Izp+ANIpusE5XIlAIoYepHzPItYtvZfMvnPomT0TT8s+FDUCVTt210hJUolLHkZtxWr2717A2IKfEbLc+IIWG8sMcnpI7K8z8fgNxvWT+eJrnUiHQEFPmT+85ScjQWDOKI3XvgwweaBKgmDi1A45m4OU7y0ns3cO8W6R/znXhjdoYlNEPt8cIj9NIqTDsmKd/fUmF49ScdkE3Kcwf+NSXLiU7grZ080xHc3NN9/ctkDHNE127tzJxIkTO9ywbk4dNlkmWXbx2Fnj+ahsLw5ZwSnLzN9ZzI35Bcd9ntyoGEDgjd3bEYCC2AQm9DiyfFWTJYYluMmJcvKXTSWUtPgAuH/lDt6cPhhNPvPCZ6Iokt3/JzQ37Awn6i0Tw/BTtO45hoz9HQ5nIj5vDXrIiyCIyIoTm/27N/CTZJWk1LOYcvG7NDfsYtG/ZxEVm8vwCX9i356PcbhScMf0OWpFmmaPYdi4hwn4apEVFzu3PE9m3zk01O+mp1OhdN18CkY/RMhwkRVnMaKnjGoX+OdnAWJcAtP7qZRt1flRnkqd12JdnUl+Ojg1AU1pP3PVFBFNAUkw8OsSD73hw7LgyyKdX8ywsWK7TqPHoqLBorhc59yhCqIIdhXUM3ZQcmqpra3l4YcfZsOGDbjdbhRF4dprryUyMpKbbrqJ1NTUtn1//etfc9ZZZ/HMM8/w7rvvIooioijywAMPMHDgwA6x75iO5vBFPJIkkZKS0s7obroO8XYHI5OSeWTdKq7pO4BEh5MnNq/j2r796RnhPuZ6l4K4eBoCAfpFxwLw+f5SJFFgbHIqcfb2uQhZEonSFOLsh0byUZqCJ6RjWBYJjuNb/9GVsDsTsDBxuTOQZI3CSY9jmTpYFj5PLSU7FlC0/jnyBt9ISsYELFNHs8cgit+tLZRmjyYU9LBr66tYlsGg0fNY8eltNNWFtQgHjZpHz5xZRz3eZo/BZo/B561BljSaG3aw5as72p43ApVEuFIJShaWESKIk331JrdNs7HsbT8BH0CIyZfa+HRLiLW7dC4fqxLUQRZF7Fr775NpCVQ3mhzsZ2hZ0OKzcNlg5nCV9Xt0PAcWiZbXmsRHithUs1uh4BhYlsXNN9/MrFmzePTRRwHYt28fn332GZGRkQwdOpTnnnuu3THr16/niy++4O2330ZVVerr6wmFOq7VxzG/4cuXL+e2225rt+3Pf/7zEdu66Rq4FY3CxBS2NdbzwrYDDcxqqnhpwrRjytREqho+XSc/Jo4bliwCYF1tNQNi45EE4YiyaVEUuLRPD0KmRbU3yJw+ydy7cgeNAZ3nJvQ7I52Nw5nEkDH3YRoh1i69j7qqdciKi3EzXqK2ch19+l+Fofv55K0LDmx/Ec0eh6p9NxkSSdaITx5O9b7lyLKdprptbc/VVa0jKe1sJFlDUY/+2dod8WTmXYplGURG96a5YSfxycORFSe67qV43dM0N+4ib/DNzJ2Zj2xywMmEqak0MExo8lpYFizeYiCLBmfntw8Xup0iFtA7SWRnpUlWkkhKjMiN02ws2hBi016DO2bZeOZDP1WNFqIAP59hw7RMJMnCqYodWqXWGXxYuodntmygyucl0e7gxvwCpqb3OqlzrlixAkVRuPTSS9u29ejRgyuuuIKVK1d+4zE1NTVER0e3FQQdbJbWURxzqLB06dIjtnUv1uy6xNkdXJyVQ43/kJRMSyiIRz++0YxTVlDFQ3mZbHcUIuDVdXzBAC2h9uWj0TaFmwdkMKdPMr9btZPiBg+V3gCe0JksTyNhWgZ1VeHqPD3USkXZEtJ7n0dCygi2b3oBgPiUYQT8jRSvf46q8uUEAycuriiKMhl9ZlI46f9hWgY9cy8IWyDZ6JV7IeuXPYSnufSY5wmHxwVGTn6SSRe8zaAx96JqUZTv/oiSHQtoqNnM8kW3YrP2IOh7SekVvnXYnQIxPSTqWkxmDFORRIuBGRK9kyUCoSMlaKKcIleO1/jNhXbOH6ny6AIfa3YYTBqoMHe2DVGEqsbwrMa0oKjMoKHVorzGYk+1SbO36/Y2+LB0Dw+vX0mlz4sFVPq8PLx+JR+W7jmp8+7YsaNN/PKbWLNmDTNnzmz7V1payqhRo6ioqGDKlCncd999rFq16qRsOBZHndHMnz+f+fPns3fvXmbPnt223ePxHNHLoJuuhUtVubR3X1ZXV1HhbeXmfoNoDByfvL9LVUmw2zm/VzZ7W5r4ef8h3LdmObpl8tDw0SiAJAg45EOjWVkUibEp1PvDTijFqeFUzlzFYLszDm+rjsOVjLe1AhCISxyEZRnIiguXuyctjbvoM+Bqlr5/HaYRYNfWVxkz/e+otihULQqb/fg7GGq2KLyhJJIiUskdeA3Z+ZcjiCLluz+msvRzElOGExV39JbFfm8di9+7Gj3kJTVzKrkF17F/76eotigs69CAwLJMTCPEys9uYti4vzF0fCaiLBLA4rrJNvbVGZiWzF8X+ekRIzJjmEJQzaDRY+J2CG25XrdTxDAtvAG4faadkAENrSZ//TjA1RM10uNFSmtMFAkKesl8sinEmp1hef0ZQxXG5MtoStcLpz2zZUO7NhwQ7lj7zJYNJz2rOZz777+ftWvXoigKc+fO/cbQGcBbb73FmjVrWLlyJb/85S+5/fbbOf/880+ZHYdzVEczbdo0Ro4cyaOPPsqvfvWrtu1Op5PY2NgOMaabzsMuy/yhcAxNgQA1fh/prvaVSrruJxRowTD8RySuI0ST6/vk4DHhofWr2N7UAMC9q5fx8IgxmIZ5xDcrVlN4ZcogqrwBkp0aEYpEWYsPb8gg0akRdYYt5nS4khgz/e/UVq7DHdMbUdII+OrR7DGMnPQY5Xs+QhQVTOOQg29u2EnJjncYOPLOsFOS7d8a8jqcYDCIIErYnYkEfPWs+uIuaitWo6iRJKaN/tZjPS3liILMyEmPs2/vIkp3voszogdrF/+WMdP/RnLGeFoadpFTcB3luz8gFGzF792GrPgJtbZSW7WO9KzpxPVMZd1uHX/QYupghcff8RPUIdIucOt5NuyK1dbRUxIFIuwC2CFkmKiySN80iZXbQ1w9UaPVZyEI0Owz2Vlx6Oa8tdxgWB+Zrvh1qfovQdpjbT9esrOz2zpgAtx7773U19e3NTo7GpIkMWLECEaMGEGfPn1YsGBBhzmaow4L3G43GRkZPPnkk9TX17Nu3TrS09ORJIn9+/d3iDHddB7Rmo0I3STVFcGoxBQEAer9Phr8PpqDQbytlVSWLWHV53eya8sr+L11AHg91VSVLaF42TxC+z/n0p7pAKQ6Xfxq4FCCpoHH0KnxefEGD4XR7IpMklNjYHwkcXaVryoaufiD9Vz1ySb+snEvzUH9G+3syjhcSSSljmJP8dssefcqKkq/wDJ1JNlOevZMFC2S5PRxAERG98Yd0wdPUwmaLYri9c+x+os7aW7cjWWeWJjxYEXZ5AsXMvH8N3A4k751f7szkbyht7B26b3s/Pr/+HrVn2msKyYh9SzWLr2PnAHXUDjpcaJi+5KWdS5jZ7yEYQSwLJPli27FMkOEgq0IRj19UgyinAIVDSYHP9Jmn0Wjx2q3HudwFEkkyglzRqtcPEoj2imQGAUum0CCW2RY70OjlpE5cpdt2JZo/+bFu0fbfrwUFhYSCAR49dVX27b5/f5vPWb37t3tlqkUFRWRkpJyUnZ8G8csBnj66adZt24dZWVlzJo1i0AgwO23385rr73WYUZ10zlUV1bSJyeHbU0NzFv9FU5ZYd6QkaiiSJSosHXd0wwf/wf8vjoC/jpAQA95WLvkt4BFZdlSxv/oVW7rN5CChBR+u+orSlqb6RXh5oFhZ1Gu6+S6o7Er7e8MAcPkowMqzwCfl9fzs/4ZnfviOwnV5ian4Kf0GfATJElDEEU2rXyUsp3vEhWXx/Dxj9B/xK9oadrD6i/uJm/ILezZ9iZ7t/2b1MwphIKtvslUIAAAIABJREFU7Cv5jJj4fqi2eGT52BVqoWArhu7HNHUUUUEQjx6mNE2dyrKlJPYYiaelvG27p7kcVXPTM2c2jbVFBPz1yIqdpPRx7N32FgFfeODRs8/5OCNSWfLeTxFEicKJj/HT8YMRZQmHBt4AxEYISCK0HsXRQLg83H3Y/VZTJDQF9lYbpMeL3HlBuNBEEi2CJnRFX3NjfgEPr1/ZLnxmk6QTWmLwTQiCwFNPPcXDDz/M888/T0xMDHa7vS0SdTBH02bHjTeSmprKgw8+SHNzM5IkkZGRwQMPPHBSdnwbx/zWfvTRRyxYsKAtT5OYmEhra2uHGdRN59IYDHD3yqVUHOhX8+zWjUxJ60mOzaD/8F9StO5Z6qrCcuMjJ/8FmyMGOHTDaG0uZWp8H4oDAUoOtH7e09JEayhEUUMdMZpGmhzZrnRak0TOyYjjy4pwyG1sjxjUM7hJmt0RD0DA34ih+6koDQvVNtZupWT7u2Tlz8Ed04ezz32egL+Bpm3bULUoMvtezNL3rz1QBh3LuBkvI7u+fXZiWSY1FatY+ekdgEXOwGvI7n81ylGkcExTp3rfcnyeSvIG38yWNU+iapHkDLwaWYkAy8A0dfy+Oryt+6mrXEdW/qUE/M2oqhN3TDaiqBIZnUVzw062b/oH2f1NohOHcM9FGo0egYAOq7frTB1y4u5BUyDKKfDYf3wEQyAIcNeFdkzTxKbSpZrrHczDnOqqM4CEhAQee+yxb3zu8O7IhzN//vyTvu7xckxHo6oqgnAokefz+Y5xRDddCQHQDuuaaZMk7LKM3RGNbhtDekRvhA1/oXbfMvbv/YS+Q26iV+5FlO36gISU4Siqi8qST0nPnoMqigRNE02UcMgyQxOSEBCo9nqJtdlQDlxHFAQKk6KZP3UQHt0gxakRqX63tSRdBZ+nmr3bF5CWOZUR4x8BQWBP0Rtk9DmP9V/ejyBI9B9xO5HRvckZeB16yEtT3fbwOhwg4KtD14/929N1H3u3vQ1YxCYWkJQ2hj3FbxAd34+IqMwjigxk2Ube0FtY+v719Owzm0kX/BtRVKjev4rUXlNoadzF0g+uwzSCpGf/iL6DbgBAwOTzhZcSDDRhdyYyfPwjLH73KiKiMomIyqSxbjOlO94lOWMciXEFTB0cjfs7rIdxO0Sqm0wCB4oiLQtqmkyK9xlIIozvp3yrmvT3janpvU5p4r+rcMxf9+TJk7n//vtpaWnh3//+N2+++SYXXHBBZ9jW4bT462j0VlLdvIeshGFEORJPt0mdTozNzp8Kx/KnjatxyQpzeucSMgxW19QStExEFPoOvZOg91ekZZ9HKNBMzsCfktJzAk1121j9+d2Mmvo0sqzw93FTWV61n4Gx8TxfvJlf9B/CE5vXsru5iT+fNZ5UpwtJDN9sIlSZiDPcuRzENHV2F71BXNIgPnnrQixLJzZxEEPO/h1rl9xDXdUGAAwjwODR83C4Ehkw4g503Yu6/lmCgSYiY7JRlGPH8mXJRkrGeKrKvyJ/6K0sX/RzgoEmAEZPe47Ksn0kp52Ndnhxh7snE2a9hmno+DyVVJYuoVffi5BkG3u3vXXAycwkd+A1GEYAWZRpadzddl6fpwrLshgw4g5c7gwEwWLZR7dgWQZlu95jwqz5uGO+WwGRQxOIdgqkxYmU1ZrER4aLCL7cqmNaoBswaaAMCLjs4QrHbr5/HPOXfv3117N48WIURaG4uJif/exnjB07tjNs63AqGrfzjy9vBiDOlcF1Zz+L+wfobNIjIvlF/yF8VLaXfxRtZm7BMEws/rRhDbIo8JvBhQw652lMPYCnaSeGESA2YQAgMm7m/6FqkaiaRtA0COo6T2xax5T0DEzLYlV1JX7D4IVtX3Nrv8HE2Gw0BkLopoUsCmdctdk3I+CMSKZq33JiEwvoN/wXiKKCICqEQodabOshL6YZXiei2aORgnbGz5qPHmxFVl3Yncf+bgqiRErPSUTF5iHKapszAGio+ZqK0sXUV28mI3smLnc6mi0KQRDBsqjet5ym+u3EJQ9F1dyIokhC6lnUVq4jZ8DVfPHulQT9jST2GMXAs+4KL+gMedBsMahaJPv2fkJz/U5GT3uuXVm031uLO+b4BFy/CbdT5NpJGiEjHLR98TN/WyuCZq/F1jKD2maLs3IVJNHE8Q0yON2cXo5rSDl27Ng252JZFu+//z7Tp0/vUMM6g53VhxYp1baWYFhnXuXT8RJ7YFX/zF5ZBEyDxzatY09L+Cb15OZ1XJfbH3vVZ+z5+kXyh96Cz1uNK6onjgP5B4BYu4OLsnKY0TMLURC5Z/WXbYnPeJuDooZa+kTF89CqnaysaqIwKYp5w7OJth3pbI4n6f19JehvxDR1ZMWJrNgRRYmktLFERPUmK28Oqz77NT5vNQNH3smICY+x7OMbEQWZgrPuPiCaGUZWbciqDY7DwRxO2PFH4vNUk5Q2hsqypdgcCSSljSUmYSCCICFJGgF/A3qoFUm2o+s+TDOIw5WIzR5DMNCEojqJjstn5OQnqSxbStAfFmSt2vcVlmUycfbrtDSV4IpMJxRoJj552IE2BjH06DWZfXs+IS55KBHRmSf9nka5REzTpMkLkwtUXl0SwKkJnFOgsGhjkKG9Ff7wbx+6CT8eqzKwp4wqdzub7wtH/TV7PB5ee+01qqqqmDBhAoWFhcyfP5/nn3+erKysM8LRDEqfxopdrxM0fOQmn40snnlij8dLlKbx09x+NAT8CEL7vI0qSeimTmPVWkZM+CNfr36chpqtZOVfRp8BV6HZovB5a2mo+Zrmhh2kZp3Lq6U1/GZwIX8v3kyi3cHMXr359fLFzC0Yw8qqsANbUdnIfo//CEdjWhZqfBJL99WTYFdJcmq4u8jMx+etZe2SeTTVbyO34DpSMiZic8RimiHsriS2rf9rmzz/msX3MPmCtxg15VnAwnaKZ9N2ZwIDCn9N38E3oShOairWsGHZQ8iKg9HT/sr6Lx+gvnojw8b+noa6YnZ+/RIAiamjGFh4J+W7PyY2cSD79nxKXNIhsUXNHhbqbKjZitdTQWRUJmsW343dlYKiOLGyphOXPJzM3ItpbSlj65q/0G/YL7E5Tm79nSiKRLvAaRO46wI7vqDFllKdpGiJxV+HOCg28c6qEDkpUrej+R5xVEczd+5c7HY7gwYN4tVXX+Xvf/87Ho+Hxx9/nP79+3emjR1GnDOdO6b9h5ARACyEE++acEZhk2ViBTtVXg/zhozksU1rkQSB2wcORcSCIbfhbymlvnozYLHz65cOiDYK1FWtZ/XnvwZg77a3uOzcFyn1B7m1XwGflpdy5afvcUv/wcTYFATCIRABcGsyDf4g0Ye1eq73h6gPmnxSVsvORi8/6duD0T1icMjffzWB/Xs/oWZ/uO/PphV/JDF1FN6W/ai2aIKBZpTDNM1kxYFphDAtnZId/yGr7yU4jlFVdqI4I1LwtlRgGAG2rH0SyzIIBVsoWvc0sYkF1FdvpLW5lMbarW3HNNXvQNe9rP7iTkZNfY60rCmYpsHY816koXYrialnUbTuWbLyLmbt0nm4o7MZNfVZgoFmVJsb0wyxp+hfNDfsBKBX7kUgiPi84ZJ2RXUhyyfWEfRwVFlAdQm4TQuHprBhj05SlMiOinDYMSlaQOrimmhnGke9s5aWlra1cb7kkksoLCzk888/x+U6c3o7hKwQuhHg1RVzafbXMnvQXaQJA3DZjl/+40xDPVB19ptVXzIkPglZFJAEgQ11tbyzdxfD4hMoPPsPbF3ya0RJDasHhPztxBx9nipC/jqSTAOXnM34HumMTUnDLssoosyTY/P4qLSWc9Lj2NPk5cOSWi7PTSHDZcehygjA1vpWPi6tBeD+VTt469whXcLRKIf1PhElFSyLin3L6J03B0GUyMq7DD3kwdtaSd6Qm9m55TXsznjqqzZSV7mO4RP+hOMEQ2XHwhGRjKdlHw5XSlv4yxmRRigY1lZraS4ld9D1fPXRBixTJ2fgNezaGl4n11S/jYSUYeHXprrRbDGU711EauZkSna8g6H7CAQaSHCOwO5MACAY8DBi4qNs2/A3FM1N7/wfEwy08sV/5pCcMZHcguuQZRuKFnlS7a5FUSDKKTA4U8a0IDlGxB+0GJYt47T9cBzNoEGDmD9/PnPnzgWgoqICl8tFREQE0dHROJ1OZs+ezaRJkwCYMmUKM2fO5KabbgLg1ltvZcaMGZxzzjkdZuNRHc3hMXJJkkhNTT2jnAyAJEh8WvQ3yhvCo7nXVt7NL6e8eZqtOv3E2R08NHw0+zytxNsdtOoh7lu9DBOL1TWV9B05msy+l5DYYyQ7t7yCKzKDjOwfsXf7WwT9jRScdQ+mEaBs1/skZ0wgJr4finrouzM0MYp+sS5WVTXx66/CDmp5ZQOvTClAkURskthuXY1w4F9XID5lONn9rqSpYQeZuRdhWgZRMdlAuJQ46G8id9CNNNZsZcfmlynf/T4jJz9J6c53CQWaweoY0UhnRA9GTPgTO7e8giw7Sc2cTGtLOcnp43FFpuL3NzDloncxjRANdVsp2b4AR0QPevSc1HYOVXOgag569plNVfmX1FSsIi3rXOKShrS7lqo5kWSF/KG/AEFAFGXWLP4lsQkFpKSP5bO3L8LConDin4lPGXHSTeEOthEY1feHW3GWk5PDwoULAbjzzjsZN24cU6dOBeD5559n3bp1TJo0iYaGBpxOJxs2bGg7dv369cybN69D7TuqoykuLmb48OFAuADA4/EwfPhwLMtCEIQOV/vsDCRJxXbYCFRTnAhHX7z8gyLO7uDTfaXcs+pLnhg9AfOwRZp+S6RnxgS2b/oHphkiZ8DVSIqL8T96jVCwGVlxsujNWViWzp7iN5k4+812jgbCvUn2NB1aF+LTTWp8QURBINGhMTY5ih2NCWxr8HBNfhoRStcIa2r2GLL6XUHAX4eq/H/2zjs+qir9/++5ZXoySSa9QRohQOgIoSiIgKACIq6rrooNbKvYV13LqqyoX8sWV+W3lt3VlV1dwa4oXRCQ3kINhCSE9DZ95t77+2MggBApSWje9+uV1ys55957njuZmc8953nO80QQDDQRGZ3T3B8MuZHQsDvSyMm/ji597kBVwil/+pz/BwxC+/mirPYkuvS+i9qqDcz/9DqSO15EetYYvv3fBEAjPrmAXoOfIMKRwYiJnyCIxqPOrkxmB/EpBTgTeiIIxuYNqYciikZEawyFhYXk5HTE7uhAZFQm29a/g6qGN8Vs/PFVBo9+E9Hyy8md+PWeMt7YuJUKj5cEq4XbuuVycXpKu47Zu3dvXnzxRSAsKkOHDmXRokVomkZpaSlms5m4uCP/h21Ji5/ejRs3tuvAZwKSIHF+p+vxBd00+ioZ3e1uDAYBl68Gu/mX8+ZviZ6x8fxpw2pWVlVwQ25XPtu9k16x8XSMcODSIuh6/jQCwVrcWggNP6IawmiKwu+tRjskgs/dVIbJEosgiM1JIq2yyOiOcSwsq6Wo0cM1ucmsqWpkeGr4da8tLWZqz2z8qoZdEpt3gKuqdkbXJBEECYvViaXZ8Z3U3OfzVGMxO6ko+4GVCx8FoGPniXTKn8Swse9RsvML9u1ZSNd+92CxxuH1VFGzbw0WWwJWezIWW+u/DCTZTISjI8Mv/y+yMYKNK17hQKaHyr0/ABqOmOxjXudEqoNKkoWufe6kvmYr9qiOzcEQEY5MRPGXE4Dz9Z4ypq/a0ByJuc/jZfqqDQDtKjbdunVj+/btBAIB1qxZQ79+/SgpKWHnzp1s3ryZ3r17t9vYB2hRaETxzF8PbxtERnW7izr3Xr7d/CbF1Wu4bdg7utAQLpL2/y4YiSsYZFhSGuM7ZjO/bA+T5n+FwWDgwwuH8tm6/2NH5XIsciR3DHuHwgWP0HfoNOJTBlJZtpSYuO44nJ1orNuOLSIZDAKyHHYEx1tN/HFgJ/a6/Swtr6PGG8AsCYRUlYjEZFQNovdHmwUVlaJGDx9uL6d3vIPBSdFEniWRaIqi4G7czfrl/0d61qWU7vqqua+saA65+ZOYO+tgpt3EtCE443vy48JHm9P/9BnyNOk5l7aJPZJsZcfG95BMEaRkjmTPjs8BjfiUgWha+0zpTZYYElILiHLmEhWTi6opdMgZi2w8uYJvZyNvbNx61DIBb2zc2q5CYzQayc7OZvPmzaxdu5ZbbrmFkpIS1qxZw+bNm+nVq1e7jX2As2M9oh2JtMRQ4yrlb/OvJyv+PC7Ov5sGTwV2YwyWk6x6eK4QYZRZV1uJXRQRBQPb6hv488Y1zf1Bxc+OynAFP2+wkS3l32MQRFQlRNe+vyWv12SM5mjWLvkj3fr9FiXkA+oxaBrS/txbiTYzgsHAFdmJGAUDVkliU42LNzfuIdVuZnK3dGItRuoDIabM24hfUflidxV/vqALOVFWLKKE6QyvK+/zVLDsu/twN5UgyVYSUgdTUboUgPjUAjAI0ByLFw4i0NCoqVzXfI3KvctIyRiJKLV+BiAbbXTqcSNe1z5kczQjrvgYv68Wqz0ZVW3ftWOTJYac/OvbdYwzlQrP0VMItdTelvTq1Ysff/wRt9uNw+GgZ8+evPfeexQWFvLrX/+63cf/xQsNgChI9EwbTc/00Xyx/mUsxkh+1e+ZX7zQ2GQjw1LScQUC1Pv9JFisxFusVHo9ZEREYhBkIi3xNHorAUiP7YE9YRCKJGGzpBPw1RAMuOjc81YKV7/B3uJ5GM1RDBnzFmbtYPniQ0s6V3j8TF20GZ+isqaqkWSbiUld0ggqKn7loKO8uNFLtElme30DQ5KjsZ/h6WyU/XnKyovnk5Y1hmFj3ycQaCLC0RGDQaTvBc9StPk/xCT0JMqZRyjoIT1rDHt2fI7BEK6i2RYicwCTOQqTOYpgwE19TTmBYCOhoJ/onymQptM6EqwW9h1FVBKsJx/qfbz06dOH6dOnN/vdc3NzWbduHTU1NeTk5Bzj7NZzZn86TxEmycbIbnfw+vwbafKFQ2rnbHyNiX2ewHgc+aXOZcJlmw3IokCd38dfBl8IhJMblnl83DDk7xRV/kjHmFwqGnfw+sqnSIjM4uYhryMJJsCDbIpgb/E8ILxrvmzXt3ToNB5VCe7PBn0QTdMIHvJU7Q2FxcUmi1yZk8hH2/eRHWUlN9rOxzv2YRQFuseGBetMFRuTKZp+w55n9eKnMJoiiXJ2xhaRgqIECfobwABJ6cNwJvRCEI3M/d+VIIoMvfRdcrpPQpIsiFL7vA9low1nQj6hoGd/FoMz8zU8F7itW+5hPhoIJ7G9rVtuu4/dq1cvSkpKmDIlnBRVkiScTidJSUkIpyA/XIvvqn79+jVnbD6Ucynq7ACKGkTTVKRDIn5k0URIDfLLcVW2TIQsE1IUooxmlleW09MZR2F9Hf/eXkh+TCyTu4wi4K/io5VPAVDRuJMdlSvo3WEMgkFEVQOYLbH4vGERj4nrxpY1bxId15W0zIuRDhFzmyTyhwE5vLa+mFSbmYnZ4Q2MDpPMdbkpXJQWS6UnwOPLtpIXbWdqz474VY0le+sYluY8M8sNCAK2yHQGjvobomRCliNRlCC1letYMf9hZKOdgSP+gt2RjqIEGTT6NXZsfJ+q8h9JSh+KyexoZ/PE5tmlTvtxwA/T1lFna9asOezv6dOnH3GM0+lk69ath7X961//atW4J0KLQrNs2bJTZsTpxmKMJBDycf2gV/ls7YtYjZEMyPoV3kAjVlP7fsjPBmRRJNJkhGCQbjGxeBSFP65ehka49szo1BQy7EZk0UxQCVf2i7ImUe+pBDRsxkjOv/Qd9hYvICqmE6oapLp8JYmpgwn4mwAJSQ5LeoRJpiAxilyHBbMsEWs5uKzmMElsrXfzzIrt/LpTEsPT4iisc5MXY2dwShSKpvIzRWNPKaGQn1CgCYMgoYR8bF79Orndb6SqbA12RwdM5hhWLnyMgK+OgK+ODStepsfARzEYDEQ5O9N7yFMIP1OwTOfs5OL0lHYPZz4TafFTKYriYT9NTU1UV1c3/5xLiIKEKIigaWTF9yM2oiPvfv9bVhV/RigUPN3mnRGYJRmTIBJQlP0bKMOz3RSbnWipkc/Wvsj1A1+hf+aVXFfwErIgs3nvPBZseRtPqAlbRAqZnSfidu1l7dJpDB79BmW7v2PNkqfxuPagHLKcYJUlmsr2HCYyAK6gQmaklb8N60a/hCgmfbuOXY0eVlc28Jd1xWytc1Pj9Z/Kl+WohIIeyovnMf/T37Bi/u8AjczOV7DkmztYtfgJFn4+CUXxYTzkIcZocrB9w7sUb/sEn7dWFxmdc4pjLsguWLCA5557jn379hEVFUVFRQUdO3bk66+/PhX2nTKsxhhUTeHHXbPpmjyU4V2mkBCZiS/UiF3SQ50BHCYTyaqdeWV7eLR3fz4s2sqYtEx2V/1AakwXzHIEg3KuxizZeef7u5BEI1f1exZFDdLgqcBhTSAp/XyinLlsWTOD0qJwmO8P3+1h8MVvIhsjWlzCqfUF+O2CzfymczIN/hAVXj+xFiO50XbuXxzel/HV7ireH9W6srhtQTDgYuWix0FT8Xkq2b11Fh1zr8Dr3rf/CI3qijX0H/4Sm1b+BdkYQYeccYiiCUGUUUI+fJ5qzNbY03ofOjptxTHXGV555RU++OADMjIyWLhwIW+99VZz5MK5hCzJGCUrtw19C8Eg8uOuj6loLMIX8pxu084o4ixWhiankRcdzUM9+6FoKl2Sh6OoIV6b9xv++t21NHorSYjMpqR2I5+snc7qPV/yj6X3UuupxCdYsdpTD6sWqQS9KCEf9dWbCIV8Rx230hOgqNHD86uKSIswMbpDPE6zzD73wRlMQNXwKir+0Okv9yAekglclMwYDAacCeGNcbLRTnRsFwTRTFxSP1IzRoYrY26fzdxZE/n2o3E01G4jFNSr2eqcGxxTaERRJCYmBlVV0TSNQYMGsXnz5mOddlZi0ASqXXtYvP09yhu28+na5wE9J81PiTaZqfT6uHPxXP69YwtG0cjCrf8AIKB4WbjtH/RICyfoEwUZTVPYW7+FQChIg9eFX1Xo1u9eomK7YI1Iod+w5yjbNYfNq/+G31d31DFjzDImUcCvqDyydBsxZonpgzozKCmaTlHhbAPDU504jBI7GrxUeNp3Cc3rrmTruncoKvwvXncljXW78HlrATCaoxk46i/EJZ1HRucriU/uj8ddSZ8LnmXUr75kxBWzMVvjqa1cR23VRqz2JOyRqezZFs5VpWkKu7d+rAuNzjnDMZfOIiIi8Hg89OnTh4ceegin03lKwuFOB7JkRlEO98m0107psxmjKNInLp6ZIy6lqLEBRdNIiMykrD68hJXk6AQadEocyIV5t/L+Dw8xpudTfF/lYl5ZIaOSExmSmMiA4S8T8DdSUvQ129e/TVrWpUii6ahjRptk/jWyB6sqG+gZF0mELGGSREKqyouDO6Pu/z8tLqtlxqYScqKsPNk/hzjL0a/XGrzuKlYtfpKqveHNqq6GPXTqfhMrFz1O3/OfASAyuhO9hjyJqgZprNuJIIgIBoHSoq8QRBOpmRcT8DWQknERGARUJYgzsReVZT8AEJPQA1fTniPCv3V0zkaOKTSvvfYaJpOJRx99lNmzZ+NyuXjjjTdOhW2nHFkykuDIoiDrKoqqVtIrfQyyYMblq8Vu1j/wh2ISJayiBBp8XFzJbwa+xNriL4iwxJIR2wdFDTGs802IBomRXe/A7hjAg/O/AWBVdQUznXFYzTImSwyiaKRLn7tIz7nssFr2hyKLAmkRFtIiwpvbGv1BvtlTzarKBq7MTiLBaqTSG+D/1uwKj1HZyOKyOiZkt219FwBVDdJUX9T8d2PdDlQ1SM+C37Ft3dtUla8gM+9XJHe8CEGMAk1DECS2rnuLpLTzkYw2Gmq34UzsSU35KhzR2Qiynd6Dn6Si9HuM5mh8nipKdnxJZFQmRlNkm9+DzrlDXl4enTp1IhQKIYoil19+OTfccAOCILB8+XLuuOMO0tLS8Hq9xMbGcssttzBs2DAAioqKePLJJ2lsbCQQCNC3b1+eeeaZNrfxmEIzY8YM7rvvPkRR5MorrwTg5Zdf5r777mtzY84EJMHI+Z1uoH/mFZhlOx5/PSbZhh1daH6Kw2Smd1wCXaOdGAwwIOtq1pd8xStzJmKULAzOuY5+HcdjNcaxx3u43ySgKBSueZtO+dfTKf9GQEE8gdokW+vdPLdyJ3nRdgQD1AeCJFhNdIm2sbnODUBkO2zgjIyMRDZG0LnnFNYunYYgynTqcROhgJuGuq3s3PxvANYu/WN4A6YgYzRGomoKGbkTWLHgEbyufXTtexeqksG6ZdPZufkDBo9+E0Ey4W7cy5a1fyfgr2fA8JdZ8/3T5Pd/AEGQ9OAAnaNiNpubSwTU1NRw//3309TUxN133w1A3759efPNNwEoLCzkzjvvxGw2U1BQwLRp07jhhhuaa9X8dK9NW3HMNbDFixcf0bZgwYL2sOWMwG6OwRNowOWrZWPZPN5f9jBiO6ZuP9uxSBJRZjMOkxmL0UpO4gAy4nqT5OhE97SLMBgMzN/yBklWG5dnZBFrtvDrzGwMDVvYveVDls99gIC/7oREBqDS40c0GHigdwZfF1cRUmFLnYuXz+/C1J4duKVrGj3j2mYm4PE3UtGwk0Vb/wnWJvxagKQOQxl55aeMuGIWZms8y+Y/wNH8eQu/uJEVCx9Bkmzs2fEFnqYyNE1h449/xmgM2+dqLEbTFEwmBxl5Exlw0SsMvvhNigr/w97ieXhce1nw2fW4GorxuMrb5J50Tg/fFFdx+eerGPjfpVz++Sq+Ka5q0+s7nU6eeeYZ3n///aMu++fl5XHHHXfw3nvvAVBZWUli4sFZf25u+2QpaPGRb+bMmcycOZPdu3dz+eWXN7e73W66devWLsacKURanDT5qvAH3dw05C82RgGKAAAgAElEQVREmtu3VsO5RIwthV/1eyacQUKMZF7ZHkzOiQgGA9dkd2FSTh6Ne5eyedGTAHg9FQQCTc3VGQ8QGWkjGHAjinK4UuVPOC8higGJUdT6glyUHsvLq4v4Xd8sZm7dS694B52irESZ2yavQ0Dx8Nd51xFUfAgGiftGfkRERPp++6tY/OVvCPjqEEUzWV2vpap8BRm5E9lbPI+Arw5PYynBYBPWiNTma5osMWhaeO9QWtYYhP1RarLRQcDfwKIvbkLTlP2JLkN43fuoqVzH7q2z6Tf0Oaz2tq3CqdP+fFNcxfSVO/Htz9m3z+Nn+sqdAIzq0HbfMWlpaaiqSk1NzVH7u3btyltvvQXApEmTuOGGG+jVqxeDBw9mwoQJREa2/VJti0IzevRoCgoKeOmll3jggQea2202G07nub2vJMIcS+ekwXROGny6TTkridhfYmFZxV6mr13Bs+cNptTlIt5q483N67gnK5sdpkiCQRc9Bz6Gz1uNIzqr+Xy/r5Foh5G9xXMxW+OJjMo6ohZLnNXEg30ykQwGvt1TzYN9MnllzS6m5HdgQVkNtf4gfeMjj9j0eTK4fDXNGQ9ULUS1q5jY/UJjtjgZetk/Kd+zEJPFSW6Pm8nqeg1qyM+WdW+RkjESDAZ2bv4PHTuNRVV8+4MHJqFqGkPG/B1JtmHeX79GNlqwR6Zz0RUf01S/C5MlhtWLnsQgSEQ4Mmis3YaqBlp9Tzqnnjc27GkWmQP4FJU3NuxpU6GBnw9iOrTviiuuYPDgwSxevJi5c+cyc+ZMPv30U4zGtk2+1aLQOBwOHA4Hf/7zn9m2bRurVq0Cwut9rRWa+vp67r33XsrKykhJSeHVV1/F4Tg81UthYSFPPfUULpcLQRC4/fbbGTNmTKvG1Tm1VHo9XNIhE5MgUlhfy9qaKq7t1IUfG+oZPPZ9BC2E27UPR3TmYeeFgk2sWvQkNRWrAehR8AiZeVcecf0Eq4mQojIgKQrRYOC6vFQeXbqFal84cvDpATmMSG/9BzjCEkdyVGf21m8h1p4ejqrbj8EgYItIIbvrNc1tJqLwuisxm6MxGAx0yJ2A113BvFlXkdf7NhLThiAbI6goXYo1IhnzTypMSrIFu5yG1Z6E111JTvcbsEWms2PT+8Qk9ERsITJP58ympZD7tg7FLykpQRRFnE4nO3fuPKJ/8+bNZGUdfLBLSEhg4sSJTJw4kUsvvZRt27a1+arVMb2l77//Pu+//36zs2jq1Klcc801XHvttSc96IwZMygoKGDy5MnMmDGDGTNm8OCDDx52jNls5vnnn6djx45UVFQ0K297TOt02odBiSmEVJVYsxlBMLDP46akqYluzjg8gMEQot6SwYLSCgYmSsSZTZglIwaDQF3VhubrVO9bSVr2pc0F0w5FEgWijBIBVcNplptFBqDBH6IpEMQoCJikk0/p4rDEc/3AlwkqfmTRhMP688tWAX8Dqxf/YX/FSpBNkWTkXcWwse9TW72J6LguGEQjyRkXIYrGFjMmC4KELSIZs3U0fl8deb1vQ5KsRywz6pwdJFhN7DuKqCRY2+7Boba2lieffJJrr732qEmRt2zZwt/+9jemTZsGwKJFiygoKECWZaqqqqivrychoe2XZY8pNDNnzuTDDz/EZgtvipsyZQq//vWvWyU0c+fObc4cOn78eK677rojhCYjI6P594SEBGJiYqitrdWF5izCabYwPLUDrmCQD3ZsZXllOS8WXMADPyykS3QMv8rqzKR5X6Oi8ffCDbw3fAwBRUM2iHTMvYKiwpkYBImMzleiKkH8ig+DQTgsRxhAlNmILxT2d/y6UxIzt5Vzd4+OJFhN/H1TCWM6xpNkNYYTg54kUdaww7SwsJCovIPOU7e/gX0N2/AFXXRw9sBujkFVQ3jcYad9r0GPo6pBNq/8czjkOX0YiuLDdAIhy6IoY9XF5azntvz0w3w0AGZR4Lb89FZd1+fzMW7cuObw5nHjxnHjjTc2969cuZLx48fj9XpxOp38/ve/p6CgAIAlS5Ywbdo0TKaw2D344IPExbW9T/q44j9lWT7q7ydLTU0N8fHhD058fDy1tbU/e/z69esJBoOkp7fuH6Jz6nEYTfhCITbWVdMvPpEviosYnJRCblQMG+uqUfdHajUGAzQFg2gaxFliye52PZldrkIUjRgMMu7G3VSULiUhdTBWu3LERkazJNIUCDEqPZZfZSdR4fVz+/xNAHy+q5L3RvVEI4ijDcs/K2qIVbs/5csNr5CTUECiIwefy41JMNOz4HdsXv0GGATW/RBO215a9A0Xjp+Jxdb2e3t0znwO+GHe2LCHCo+fBKuJ2/LTW+2fKSwsbLGvf//+zW6Po/HII4/wyCOPtGr846FFoQmFQkiSxNixY/nVr37FyJHhlCLfffcd48ePP+aFJ02adNQsz1OnTj0hAysrK3nwwQd5/vnnjysjgd/v/9kX/nTg8/nOOJvg1NlliYjgmuzOLKssJ9VmZ2BiCncu/o7XhlxEnNlClc9L95hY7LLEf3du5aqsXPbuKMdsNhMXZ8MoNrJkzt30LHgYr7sCVQ2iqkE8PgNVVQcja2RZJiY5DdEAuxoPpm/xhFSCikZNKEggEKS6tPik7+XQ1ywy2sr2ymWYpUgu7/0Yby26nRp3CVnx5/Hrvs9w3rD/Y8/OT5rPDQaaUJQA9XVl7KtoOmkbjmXXmcSZahecHttGdYhrc8f/2UCLQnPllVcya9Ysbr311mZV1DSNp556iu7dux/zwu+++26LfU6nk8rKSuLj46msrCQm5uibIV0uF1OmTGHq1Kn07Hl8WXlNJhN5eWdWOdrCwsIzziY4tXbFBPz0jkvAKslUeT34FIXpa1bwh36DiDKaMIsijX4/XaKdaGg4HA4yM8NBAg119SSkDCDgbyA6thubV/8VMNBr4GPk5nY6IqW+NxhiYFI0aXYzJS4fw1OdbK5toj4QYmBiFLEdM086Nc2hr5mqqQzSruZHcTa7q1dT4y4BYGflCryhJkySndSMURRvnYW7qZSsLteghHwYjRF0SIvGbI1rs3IA+nvsxDlR285UwTwbaFFoDg2B6969+3GJy/Fy4YUXMnv2bCZPnszs2bMZPnz4EccEAgHuvPNOxo0bx+jRo9tsbJ3TQ6TRhIABnxLCLstck92ZL/fsYtau7YxOz6DE1USyzcZnxTvJi4ohNiW5+VyjMZK4pPMwmiLZ+OPL1FSsBWD1kmfoM/gpJKP9sPICFlnCIku8NrQr9YEQG6qbeH5VEX5FZcDFPanfHzDQ2jxogkEgI7Y3SZGdcAfrMGBAQ8MoWfGHPPzpu6u5Y9g/GDx6BpoaIhBoxOuuZsk3d2AyRzPgoleQjZHtXkFTR+d006LQ1NbW8s4777R44qHOphNl8uTJTJ06lY8++oikpCT+9Kc/AbBhwwZmzpzJtGnT+Oqrr1i5ciX19fXMmjULCJcoPVOfjnSOjd1oxI4RzethfEY2/eIT2dZQx7RVy3h+wPmEVJUoowl3KMiMwvWMSutIl2gnUbZ4kjoMJRhoQlUPprLR1CCqGsLVsIuY+CMfhGRBYEVFPX9dF14qizbJ+EIqq6saWFnRwJP9c1rtszHJVkyyFbww+YK/s6t6FVnx/Ziz6XUUNch/f3yCW89/A0kLF45b8vXtBANNBHx1bF71GvEpBSSkDMBi0zdg6py7tCg0qqridrvbZdDo6Gj+8Y9/HNGen59Pfn4+AOPGjWPcuHHtMr7O6SXOYqXW5yU7Mopok5kLktKo8XnxKwp3dO3Jb+Z9SWMgwDclu/lw5FiiTGbMlhgE0UivQU+wenE4q0DXvvegqkHqa7biaiwlIbUAkzm6eRy7UWJkWiyqBsUNHq7unIw3qPDWphJkQSCotl1mboclHoclnkRHNl9v/AvbK8KhzbER6UiijKIqqCEV2RRJMBD2zxhNDmor11FTsY7u/e9HNtrazB4dnTOJFoUmLi6Ou+6661TaovMLIsYc3hMTb7WhaRqxZjN7XE3samygMRDe+a4BDQE/aYSXxYxGO5o1ju4DHsLvq0PTVHZvm0Vqxgh++PY+JPn3JHcY2jyGJBiIs5qYkJmAOxii2hfg9vmbCGkat+enYpXavtyFxRjBiC5TcFjiUTWV/plXoKqwu3oNq3Z9yuhh09m57m1M5mhSMy9mx6b36dzzFirLfiA6rut+v03bJwPV0TmdHJePRkenPTEYDNhkIyk2O0GLysCEZH6o2MuQpBQSLdbDjjWZHRhN3Qn4G9HUIHFJ/diw4lUuuOwdPE17qSj9gciYHCyHZDq2GSV8ikq81cSHY3qjohEhi1jl9vlCt5udXJh3S/Pf9Z5y/r3sYRQtRIWriEu6TSXT2YPvv76dvuc/zaIvbkQJ+ZBkOxdN+EjfkKlzwrz++ut8/vnnCIKAIAjExcWRm5vbvD+xrKyM66+/nlmzZnHnnXfy0EMPkZ+fT2lpKTfddBOPP/44Q4YMaTf7Wvyk/VzUmI5OWyMYDESZwhmcH+3dH0VVEQWB2J8IDYSFyWR2EAq48Xkq6dL7dvbs+IKta2cA0DF3Ap17TcFiPRhG6rS0be6mE8OAKMgoSogaVwl7G7aRZEmhe//7CfjqiYnvTk7+DRgMIsGgmyPzH+jotMyaNWtYsGABs2bNwmg0UltbSzAY5IYbbmDChAlkZWUxbdo07rnnnsM2vO/bt4+bb76Zhx9+uF1FBn6mTEBUVFS7Dqyj0xJxFit1e0qIO0Rkqrwe5pftoaixAU8wHDUmGW2kZo3GFpFK1d5lzcdW7l2Oz11FwN94ym0/GjY5ihuH/JWOsb3o0+EyzsucgAEDHlc51ohUuvS+i4qyZezeNgtPUyl+35lht07bM2eXiwmzSxj8791MmF3CnF2uVl+zqqqK6Ojo5kSYMTExJCQk8Mgjj/DUU0+xcOFC3G43Y8eObT6nurqam266ialTpx416retOTdrMuucU9T4vExZ9C2/W76Ya777nN1NB7+IZdmGIJvpkHMwcCQ9+zI8rr1sWvlX3K69p3UZWFWChAJNxJsSuL7vs1ySP5VISxz2iGQSUwchyhaa8LFRrcAdl0FAUwn4606bvTrtx5xdLqavqKHCo6ABFR6F6StqWi02gwYNory8nFGjRvHUU0+xYsUKAC644AKioqJ4+OGHefLJJw875+GHH+baa689ZVtHdKHROePxKwpl7vCHUQPW1lQe1m82R5OQOpjhEz5i6Nj3iInLZ8WC37F760esmPsgnqa9qErwKFduX1Q1RGN9ET5PJT98ew/zP7kWT8MePK59KEoAszUWvxbgraVTWV86h28K36AyWI0oW3Qf6TnIG+vq8CuH/1/9isYb61r3YGGz2fj44495+umniYmJ4d577+Xjjz8G4JprriE/P7958/MBCgoK+PTTT/F6vUe7ZJujC43OGY9FkujtDDvIrZLE4MSUI4+xxWG2xGIyx7Bz8weghRMXej2VeNz78Pvr8XnrT6ndPk815XsWsLNwJk31O0nNHIWrYRfffz2FNUufpbG+CE1TCIQ8zee4go2sKv2WqqZduticY1R6lBNqPxFEUaR///7cfffdPP7448yZMwegOTjgp9xyyy10796de+65h1AodER/W6MLjc4ZT7TJzLT+Q/jviMv474jLSLYdfb+J0RSJ1Z5I176/xWiOQhCNdOt3D153JUrIjxLy4PeeumUpg0HAgIjRGN75n5IxkjVLnsXdWEJ58Xz27voOX30JF3f7LaIgkxyVS1Z8P77b/CZ/X3wHTb4jcwXqnL3EW4+ebqil9uOlqKiI3bt3N/9dWFhIcnJyyyfs59FHH8Vut/PYY4+1+0ONHrCvc1YQYzYTYzYf17GRUZkMG/sefl8DrsY9xMTlU7R5JmW755KUPoRO3W8+omJne2CxxZOecwkBfwOqFkKSzBgMAtr+2ZYgypTt+II+fe+kR9ooat1l/HvZw/hDLlQtBOgzmnOJ23pEM31FzWHLZybRwG09on/mrGPj8Xh49tlnaWxsRBRFOnTowNNPP33M8wwGA9OnT+e2227jhRde4OGHH26VHT+HLjQ65xwGQcRqT0YULYiiGZ+nih2b3gOgqPC/pGZeDBiw2GJ//kJtgNWehGyMIK/3HXhc5RSM+DOFq18nMiqTqNgumCxxGEJBjKZIoqyJGCUbVmMUl/d8CNXvIihF4Ak1oaghrLIDs5494KxlZIYdCPtqKj0K8VaR23pEN7efLN26dWPmzJlH7evfvz/9+/c/rO1ALTAAo9HI22+/3arxjwddaHTOWUyWaBTFDxxep10y2sPFyVz7MFniEMW2yaDcErLRjtddiaYEkCQr/YY9j0HTUBQ/FaVLcTXspmvfu7Dh4PrznsPVuId9RXMotW4nvdtVfLH+FcobtnJR3mQ6xvbGYdU3dJ6tjMywt1pYzkZ0H43OOY3VnojRFEX3AQ/hTOhJr0FPYDRGsmnlq6xc+Hsa67ahBNq2ZvvRMBgEVi1+Cg2NsqKvmTv7KtYseZbkDsOITerXfFxTxXpWf3c/aRlj6Nj5cppcZZTUrqeqaTcldZtRtCD17nI83lpUJdDuduvotAX6jEbnnMdsdZKWfQkJqYOQ5Ug2r/kbZbvCUTk/zPktQ8f+C6sxqZ1tiGXwxa+jKH42rfwzADUVq2mo2UZK5sjm4xLSBtN9wCPIRjNLv7kTkyWGGwZMZ3nJFyREZPDiV+MQBYlJA18lJSoXQ8CF2XL0ek46OmcK+oxG5xeB0RiBxZaIQRBRlYMzGFUNzwoa63bidVe1qw0WWzyCICGIB9PhGM1R7N09j11b/ofPU43FGkdS+mCWz30Qd1MJtZXrKN38IT3TxjB3y9/RUAmpAeZu+TuBkAefGsDvb58s6zo6bYU+o9H5xSCKMqIok9frNtxNpfg9NfQc+Ci1VZv4cf5DJHW4kJ4Fv8Nsbb8gAdnkYNCo19ix8T2iYrsQGZ3N3FkTgbDopHQcDgiIopEDW0wlyUKkJY6EyCzqPeUAJEZkUl26lF3BKrp3GNPufiYdndagC43OLw6rPZG+5z+Dpql43FX8OP8hAMqL55F/3n3tOrYsW4lN7ENkdCdUVeGr/xxcNnM3hktBm61OBo76K+uXv4TZEktuz1tQQiGu6P17Vu/+BFkw0TEyh9Ktn6A4kyip3UCM3K1d7dbRaQ360pnOLxKrPQmzNZ6Q/2C2AHtkBwyGcAh0Y91OQiFfu41vNEWAFiI77yrAQIQjY3/YNQiCRERUNv2GPkePgoexWOOwRyZhMznpl3YJ9rpyijd+QIf8a1lfNo+OsT0JBPTAgF8y+/bt4/bbb2fkyJEMHz6cp59+mkAggNfr5f777+eyyy7j0ksv5eqrr24uaNmrVy8ASktLufTSS9vVPn1Go/OLRRRlouPzueDSd2lq2ENcUl9++Pa3NNbtxCBIjLhiNlLEsXdYnyxmayw5+ZPI6notBoPhsHLOgiAgydbDiqCJoogtIpmc7jeiotEUrGfyBTOwm51UqtvbzU6dMxtN07jrrru4+uqref3111EUhccff5wXXniBuLg4YmNjeemll4BwFgFZbl358pNBFxqdXzRmixOzxUlMfHfqa7bRWLcTAE0NEfDXI0omBEHGaIo8xpVOcnyr84g2VVVoqi+icM0bREZlkdX16sNKVFv3F0azk3DEuTpnNit3BPl8ZZA6l0a03cClfWX6Zrfui3/ZsmWYTCauuOIKIPxA8uijjzJs2DAuuugicnJymo/9aXLNU4UuNDo6+zFbnThiOtFQu43srr8h4Ktj1aLHsUWk0aPgd8gmxymxI+CrY/GXNxMMuCgvno/J4iSry1WnZGyd9mPljiAzFwcI7s+hWefSmLk4vOTZGrHZvn07Xbt2PazNbreTkpLC9ddfz0033cQ333zDgAEDuPzyy+nYseNJj3Wy6EKjo7Mfs8XJwFGvoQS9GASJbz8ah6oGaarfhSMmh5TMUSTEt8/M5lA0TSUYOBiybLY48bor0DQNo8mBJOs1OM9GPl8ZbBaZAwSVcHtrhEbTNAwGw1HbAb777juWLFnC0qVLmThxIv/5z3/Iyso66fFOBj0YQEfnEMwWJ7bIVAyCiMFwMGRY0zTKir7FoNajhNrX8S4b7fQe/ASSbCOr63WYLDHM+Wg8cz4cS+XeZSih9s9koNP21LmOniS1pfbjJScnh40bNx7W5nK5qKmpISMjA5vNxsiRI3nqqacYO3YsCxcubNV4J4MuNDo6R8FocjBw1F+JjutGauYoUjMvJiXjIrZv/Bdb17+N112Jx11FMND6Urw/RZKtpGSMZMQVs8jpdi2bVv4FVfGjaSHKixfi89YcNuPROTuIth856/i59uOloKAAr9fL7NmzAVAUhenTp3PttdeyadMmGhoaAAgEAuzYseO4Sgi0NbrQ6OgcBVE0EhPfg/MufJH0nLGIkoXvv5pC2a45bF07g9KibxBFI3XV29plfEm2YLbGYjBI2CPTAUjPvpTE9CEsn3s/a3/4I35vbbuMrdM+XNpXRv7JvlpZDLe3BoPBwGuvvcbXX3/NyJEj6d+/P4IgcPvtt1NSUsJvfvMbLrvsMi6//HK6devGqFGjWjXeyaD7aHR0WkAQRKy2BCTJQjDgIuBvaO7zuPehhPxEOFKpqy7EYo3DZHEeda28NSiKn7zet2F3dCAxbQjzP70WTQ3RULuVqJhcMrtcgyjqH+OzgQN+mLaOOgNISkrijTfeAGD16tXcf//9bNy4kfHjxzN+/PijnrNmzRoAUlNT+fzzz1ttw8+hv0N1dI6B0RRJKOSj+4AH2fjjn7BFJJPd5Wo8rlIqy34gvdN4QiEveGuPGq7cGkRRZvX3f6TnwAfw+w4vRR0KeggFmxDF1hXO0jl19M1uG2H5OXr37s38+fPbdYwTRRcaHZ3jwGqLJ7nDcJLSL0DTNPzeWrZv/Cf5593Hri0fYo9IwxaRCoDJEtNmMxuzNZY+Qx4nEGhkz/bP6D/sBTavfh27owNxyefRVLcLxREiMSGiTcbT0WkPdB+Njs5xYrHF0dCk0FS/k0Vf3kRO/iQWf3krOzb8k7VLp+FqLEZVQ+wrXYrfW9dm45os0bgbS4mMzsLuyCC76zVERmexdukfkU12vvnPaFYvuh+ft6bNxtTRaUv0GY2OzglQX99ETk4/Rl35OZqm4PMcLC3gaijGkA4WaxxedwWgYWqjWjEWWzy7t81GVUPEJfUjytmZ9OzL+OHbe0BTaazdTl3VRhwxnRElEyZzVJuMq6PTFugzGh2dE0SSzFhs8Uiyjexu1wNgsjjp2Hkim1b+ldWLn8TtKiMY9BDwN7XJmNaINLr3v5/YxN54PVVsWfs2BoOAu6mk+RiLNZ71y15k+4Z/4vXosxudMwd9RqOjc5IYTZHk9riJzLxfoYR8bF75N8r3zAUM4RmFwYDfV8ve3XOJTeyFyRqHLFtPaixZNiPLiShKNCZzDL2HPI7fV8/gi99k7+65JKQOwuOuJC17NPXVhfi9VUiiGdlka9ub1tE5CfQZjY5OKzCaIhElK6GQh8qyJQBkdJ6IJNlAVZg/+2rWLHmaeZ9cTdDf2Opd/aJowmyNRTbaaaovonj7p6TnjMVojkIQBFbMe5Bt69/m+6+m4PfXtnvVUJ3TT15eHuPGjWv+mTFjBgDXXXcdQ4cObU5FA3DHHXccVh4gNzeXf/3rX839Tz/9NB9//HGb26jPaHR0WonZEoUomRgxcRZKyI9BkNi27h3iU85DUcI1bZSQD697H6qqACqy0YHJfPJ504KqisOZy5olz5KYOghHdA61roqD/YEmfJ4qird/Tl7vKUiSGeMpSgqqc2oxm8188sknR+2LiIhg1apV9O3bl8bGRqqqDn/wcDqd/POf/+Sqq67CaDQe9RptgT6j0dFpA2TZgskSiyhbUEI+dm/7GIstoTnkOby730DZrm8o2fkVwUDTSftvGvxBPi2qYKvbzHnjPsEa1weDaCMxbTAWWxIAiWnnEwp66djpMpbPvZ/lcx/E49rXVrerc5LsKgwya4aH919yM2uGh12FwWOf1AouueQSvvzySwDmzJnDiBEjDuuPiYmhoKCgOX1Ne6ELjY5OGyEIIhZrHEZzND0HPsqmVa8xcORfOf+Sd+gx8FFWLX6C+upC0rMvoXDN3ygt+gaftxZVVVu8ZjDowd20l7rqwuaQ6eImL5kOKysrGtjjCjCnzE2tZiEQ8ND3gme5aML/SMsagy0ihdWLn6a+ejPV+1ayftkLhIKeU/Vy6PyEXYVBls8J4GkKL2V5mjSWzwm0Wmx8Pt9hS2cHhAXCedB+/PFHFEXhyy+/ZMyYMUecf+utt/L222+jKMoRfW2FvnSmo9PGmC3RpGWPIS65P67GEipKv6do8wcADBj+CqsWP0nnnrdSUfI965e9QOdeU7BFpCOKBxNhKUoANKiv3sz3X98GmkpC6mD6nP8HVE2k0h1gQFIUt8/fhKJpzNhUwgejerB56bNY7Ynk9b6NpvpdcOjG0TZOj6NzYqxdHEQJHd6mhMLtGXknny3g55bOBEGgT58+fPnll/h8PlJTU484Ji0tje7du/PZZ5+dtA3HQhcaHZ12QJIs2CKSEUSZCEcHElMHI0pGRNFIVExnqvYuZ8em9wGo2bea8y99F0GUMZljCPjq2bH5A9KzxiBJFpLSL6C8eD4Vpd+jqSEyIu0YRYGd9W6U/Y5ed1Chzh8ibchfcEgaKHVsXfcOvQY+xsaVf0aSLXTrN5VQKIB0kpFvOq3jwEzmeNvbiksuuYS77rqLu+66q8VjpkyZwj333EPfvn3bxQZ96UxHp50wGIT9STnNYBBQlRCCZKZDp7GHZQ4I+BvwuMpZufBxPE2llJcsIiN3AlvWvsmmVX8hr9dtpGePJS65PwZBxmGSSbRI9I6PJMEaduB2irLRGOHwL2wAAB3hSURBVAhx7Tfr2NoQRJKsRDk7s37FS6RkjCT/vAeoLFvO+mXP4WosaclknXbEGnH0GWVL7W1F3759mTx5MpdcckmLx2RlZZGVlcWCBQvaxQZ9RqPTLqiKBioIsr5cY7LEkJDSH4BQyI+mqXTqfiM1levwuivoWfAIVlsC2V2vQRBNpHS8kA3LX6Fs17cALJt7H4NHvY4kWxEEib3FCyjbNYec/Bv4f8O64FUNlLp8PLNiBwCL9tYwMDmbTt1vRFG8qEqI+potrPvhjwC4m8roP/z/QFORjVHIRn2GcyroOURm+ZzAYctnohRubw0HfDQHGDJkCA888EDz3waDgZtvvvmY17n99ttbzPTcWnSh0Wlzgi6N8oUBgg0aKSONmJyGNk+ff7YiSSaUoBXRKNP3gmcxmaMJBT3M+d940FQiorIYfPHrzWHREA6N1tBQNY2Ap4rlc+8DoLToG4Zf/l8UKZb/bS+n0htANBgY1SEOAKs9AY+rCjVUR9XeZeHxZRs9BjzM6sV/wNWwh2797iY+uQBjK0KtdY6PA36YtYuDeJo0rBEGeg6RW+WfASgsLDxq+6H7Yw6lpfIAnTt3ZsuWLa2ypSV0odFpUzRVY9/3AaqWhh/bjFFBkofLiCZdaA5gstjxeWqx2pKQjDaKt30KWjjyrKl+J0rIT5c+d+FxlRPw19N78BOIoglNDYAgkZh2PvtKFgEaSshH3e7/8ru+11HrDxFplLBLB4MKrPY4VGsMnXvdRl3VJhzOPPaVfE/V3uUArFz4e0Ze+SlGdKE5FWTktV5YzkZ0odFpUzQNQvudm9mTTKhB2PtdEGdvCWMMSCbxGFf4ZWC2Hky2mdRhKNs2vIOqBIiJ646i+Fj3wwt07/8gJnMU/kAT3381BbujA7k9biYz70oABEFClMwkpg3GU7mUaKODSHNHRIMROPhlJggi9shUhlzydwSDxO5tsw72iTKgPwTotC+60Oi0KYJoIPkiIxBAkGDHu34sSQKyQyHaKiGIGoKkf7Editkax4grZuH31WEyRbP029/SVF/Eoi9uZOTET1n02Q1oWghXYzGOmBy87gq693+A6oq1CKIJVVWoLl+JM6EnK+b9jZz8SThicjCaHEiy5eA4lnBRtvTsS3A3leJqKCav9x3Ih2QM8Pvq0bQQRmMUgl65U6eN0N9JOm2OKVog7TIjdRsUbOkCKSOM7P0ugGuXSvpYI0aHLjSHYrbE4Pc1YDILgIZsjMBkiaVbv6lgMGAQBLT9e+kEQUaULNTXbCE+uR9oAqoYIif/Ohprd5Df/342rHiZproi8nrfTlKHoViscYePZ42jW7+pKCHf/hxp4a8Br7uCFfN/h99bS+8hTxIdl48o/vKWeXTaHl1odNoFySzg6ASy3cDuj/0E6sLLaWXmIOljZUSTHll/KCazAwjPLM4b9nw4J5qm4XVXcN6wF9i67i3sjo4kpA1Gkmwsn3sffYf+EbM1nsaq7WiqSnRcV8qL51OzbzUA6354jpj47hgIi8uhyEY7stHe/Leqhti8+nVqK9cBsOy7e7lowv8QrbGn5P51zm10odFpN4wOAU2l+WkcQFM0aN/9aWc9Fls8EP7yFyQjgiCTlnkxJosTv7eO8upFeD2VGE0OVMWPxRaPz1OH21V22HKXKJpRQh5CQUtLQx2GKJmbfxdEU9velM4vmtMiNPX19dx7772UlZWRkpLCq6++isNx9MyyLpeL0aNHM2LECJ544olTbKlOa5HtkHmNieKP/UhWAymjjIjm8GxGUzU0Fd1n0wKCIGGxxu5fSosOhzlrGk31Ozn/krcxILJy8e/xuSvoPuBhird9SlrGCHK6XU9j/U6yu15LUeFHdO372+Maq3OPWwn6w1mfuxc8hMkc3dzvdVeEhU+Qm4VQ58wgLy+PTp06oSgKmZmZPP/881gsFnr16sWaNWsoLS1lzJgxZGRkEAwG6datG9OmTUOWT92y6GlZv5gxYwYFBQXMmTOHgoKC5voJR+PVV1/lvPPOO4XW6bQlgixgTTKQfYOZjKtMmKLDb7mgS6P06wC7/+fHX9tyUkmd8N4bqz0JyWjHYBBITL8AszWBbRveprr8R1yNe1g+734yOo1n+byH8Pvryeh8JYqi0Kn7DcddTtpsddJr0GP0H/5/2CMzMAjhCEGvu5Il39zFnA8vY9XiJ/G6K9vzdnVOkAO5zj7//HNkWWbmzJlHHJOens4nn3zCZ599xr59+/jqq69OqY2nRWjmzp3bvAN1/PjxfPfdd0c9buPGjdTU1DBo0KBTaZ5OGyNIAqYoAWNk+O2mKhrlCwJULglRt15h+z98BF262BwLizWOiKiORDg6YDRFIEoHl7cEQcZqT8KZ0ANNVbBY44hy5uCI6YQoHn+dkSZFotwv8+nuKnbUu2kKhKit3EBT/U4AqvYuJ+BvbPN7+6VQszbIhhc9rHrMzYYXPdSsbdsyAX379qW4uLjFflEU6d69OxUVFS0e0x6cFqGpqakhPj48/Y6Pj6e2tvaIY1RV5fnnn+ehhx461ebptDcqBA9JJBhy636bE8VgMJDd7TpSM8fgTOjFgBF/RZSsJKYPxWSJYfFXk9m08i8ET7AsgCuocPv8jbywqojvy2pwBUMIcYPI6X4LEPbdyEY7tb4AAUV/ODgRatYGKZ4dIFAffrMH6jWKZwfaTGxCoRCLFi2iU6dOLR7j9/tZt24dQ4YMaZMxj5d289FMmjSJ6urqI9qnTp16XOf/+9//5vzzzycpKemExvX7/S2mZDhd+Hy+M84mOH12ybJM4oXpeMpUQh6N9AkSFXXlNJUefFLWX7NjIwgCabmTMRg0quu8mCxedmz8Fz5PeGlL00K4XU2U72v5CfdQYmJi8Bpt1PmD3NwllS7OSG76bj02SeTFwZM4LzYXqz2ZVTUKLq2enCgrkkHAKkJDeRmhUOiIa55Jr9dPOdW27f02iPYTTdGC4XZnz5P3lxya66xv375MnDjxiGP27NnDuHHjKC4uZtSoUXTu3PmkxzsZ2k1o3n333Rb7nE4nlZWVxMfHU1lZSUzMkWvIa9asYdWqVXzwwQe43W6CwSBWq/WwZHFHw2QykZeX11rz25TCwsIzziY4/XZ1nhJ++4lmAzFyCpDS3He6bWuJM9Wu2rpCrLY4+l/4ImuWPIvRFEnXvndjtScQFZ1w/Bdy++kaY+eC1BjuX7yFen+Ien+I19YX81Dvwfg1jbrGetJsRiySSI0vSEgTiUpJJ856ZKTamfp6wYnb1lpROjCTOd724+Xn6tEc4ICPprKykuuuu465c+cyfPjwVo17IpyWqLMLL7yQ2bNnM3nyZGbPnn3UG37ppZeaf//444/ZuHHjMUVG5+xCjtD30rQlB2rdFIz4EwaDiMUWd+yTfkKCzcQfB+YSUjWiTBJV3gAAI9NjWVnVwP927KNPfCQDEqN5Y8MeviquQjTAcwM7E2WSkUX9f9oSxijDUUXFGHXqoi7j4+N54IEHmDFjxikVmtPyrpg8eTJLlixh5MiRLFmyhMmTJwOwYcMGHnvssdNhko7OOYEgyVjtiSclMgeIt5pItpt5bmBnhqc5GZcZT1dnBM+s2MHmWhf/2rKXXY0ezkt08IcBOVyYGss3e6oIqbqj7edIHiFj+MkKmUEOt59KLrroIrxeLytXrjxlY56WGU10dDT/+Mc/jmjPz88nPz//iPYJEyYwYcKEU2GazhlCZOTR91XpnDpS7GYe7p1FSFWp9h3uXAiqGj+U17GmqonJ3dLoHGWn1h+ksq6J1AgLgsGATdITqB7KAT/M3m+DBOo1jFEGkkfIrfLPwMG0/y21/7QcgMFg4NNPP23VmCeKnhlA54xCVTR8lSrBFTHUZAZx5EhIVn1D5+kiwhT+ijAI/P/27jwqqvNu4Pj3zsZMABXGAAqKUpcopkGjh8Q1kuMSF9QY7GuS86qv0STSWDWp1djXtr4xLm3Spp5TSrOc2pzWuCRI1fKqcQGJEUxeEjVaD+5gAuKwyeIszH3/oE4ZAcUwM+Dw+5zjOc5z7/X5zXOv87v3uc99LkvierHjXBGPPtiJB00G9l+xoALrv7jA1qcG89z/5vHW6IG89X8XOVtWzX8OiOTREHPbfoF2xhzX+sRyP5JEI9oVR7XK2dSbOO1g+aKOvv+l0OkHnjlMHVYndbWg0YM+UO4l3IsuAQamxYQzrkdXDFoNO84VuUakB2g11Drq+EGXQM6VV5N5tf5xhY1fXmDbhB+2XdCi3ZBEI9oV1QHOBr00tybjbC17jZPKs3UUZdoxRWiImmRwPUAqWsao02L8V3fYlN5hXKu1Ulh1k5cG9cThdKIAhgaDAbQKGDQartVYUQGdomA2tfzhUeE/JNGIdkVrVHgwXkdJjgNjuEKn/p7p53da4dLHNlDhZkkdQb3rCIuXRPN9dTUZSH44GpvTia3OyaavL/PakBgC9Vqe69+dU5YbzBsYBVotizNPc/lGLTP7RDB3QBRdJdl0OJJoRLuie6D+xWmhw1UCjAHogzx4f0bBNQOBIjmm1QINOgKBCqudsVFm3sg9x8DQQF7+YTRaRUGvKOy6dI3LN2oB+PhcEf/RtxvVNgeBBvnp6Uhkb4t2R/eAQsHlix590E9jhJgfBVCUZefBx3R0iZURUZ7SOUDPqMgQBod1wqDREPSvJFJaa6N70L9fPfCATotOo3DoqoVR3UPoHCBXNh2FnNeJDkFv0hDUSyHmuQA0BoWiQ3ZuljhxOmS+Lk8waLWEGg2uJANQp6qEBOh5bUhvpsWEs2HEQ9x0OHnz+Hmq7dLunjJ48GC3z5988glr1qwBYNOmTYwaNYpp06YxadIkt2HOAO+//z4TJ05kypQpJCYmsnPnTq/EKFc0okNw1DipPOdEG6BwcYsVgOtfOhiYbMLQpY2D81MaRSFQryW3qIIH9Fr+dvYqzz3UHUWpT0LCN+bOncv8+fO5dOkSTz/9NBMmTECv17NlyxaOHj3Kjh07CAoK4saNG83OpN9akmiE37NXOesHF4Qp1F7795l0XQ046+6woWgVs8kAqpNlg3tT7XDgVCH15BVWDu2DQVPfmVJutXOo0MK1GitJfboR6ucDBeyf2bBvt6FaVBSzgj7JgH6Eb75zr169MJlMVFZWYjabSU1N5S9/+QtBQfWv9A4ODmbGjBleqVsSjfBrqlPl2jEHRYfsGEKg7zwT13Md2MpVzI/q0HS8Z+d86tq/7rXddOi5XmvlR/260dVkoFOADqeqkn6hmM1nCtkw4iGOfFtG5wAdsaFBhBr1aDX+1bNv/8yG7QMr1E8fh2pR6z9Dq5JNw9mbASoqKkhISGi03jfffEN0dDRms5mqqiqqq6vp2bPn9673XkiiEX5NVcFWVn8VYyuDq/us9FtgRK0DjQ55lsZHjDoNUcEmooJNrjJ7nZNz5dU83z+S9AvFHCiwAPCjvt2Y/oNwogKN6Pxokk77dpsrybjY6stbk2hun7351iTEt/z5z39m27ZtFBYW8t5777nKFcV3M274z14UogkarUK3BAP6zgqKFoJjdBRn2/nm7dq2Dq3D02s1zBvYg8ggI2dKq1zlp0uryCkqp9zW+P029zPV0vR9qebKPWXu3Lns3buX3/72tyxfvhyr1UpQUBAmk4mCggKv1n2LJBrh9wJCFQa8bCR2mQlrqZOSzx08+JgOjV7mUGtr0cEmHg3rxLP9uwOgUSAxJpyzZVX48ITbJxRz01+ouXJPGz9+PIMGDSItLQ2on0X/V7/6FVVV9Um+qqqKrVu3eqVu6ToTfk9RFPTBCqpTJWKknrDH9WgNikzW2Q5oNQpdTQEkRJl5LCKEansd/1dSzvMPRRIS4F830PRJBrd7NAAY6st9JTk5mVdffZVZs2bx7LPPUlNTw8yZM9Hr9eh0OubNm+eVeiXRiA5D0dQnHNH+hBgNhAA1dgcRgeEE67U+vYfgC7fuw3h61Nntrwlo+FqVV155xW3ZoEGD2Lt3r+vzggULWLBgQavqbwlJNEKIduMBvX//JOlH+G44c3si92iEEEJ4lSQaIYQQXiWJRgghhFdJohFCCOFVkmiEEEJ4lX8P8RBCiA4gJSWF3bt3o9Fo0Gg0dOrUicrKSmpqaigtLSUqKgqAVatWsXbtWrdtCwoKmD17Nj/96U+9Fp8kGiGEuI/l5eVx+PBh0tLSMBgMlJaWYrfbCQ8PJycnhw8++IDU1FTX+g3nRTt79iwLFy5kzpw5Xo1REo0QQviIPceCI+1b1FIbSqgB3Yzu6OPNrfo3S0pKCAkJwWCofz4nNDS0RdtZrVZee+01Vq9eTVhYWKtiuBu5RyOEED5gz7Fg//Ayamn9HDRqqQ37h5ex51ha9e+OGDGC7777jgkTJvDLX/6S3NzcFm3361//miFDhvDkk0+2qv6WkEQjhBA+4Ej7Fmy3zdRsU+vLWyEwMND1+ubQ0FCWLl3KJ598csdtMjMzOXr0KCtWrGhV3S0lXWdCCOEDt65kWlp+L7RaLfHx8cTHx9OvXz927tzpmu/sdqWlpfziF7/gD3/4AyaTqcl1PM3vEo3VauXMmTNtHUYj7TEmaL9xQfuNTeK6N+01Lri32KxWa6vqUkINTSYVJbR1c59duHABjUZDr169gPrv1L1792bXf/3113n++ecZOHBgq+q9F36XaOLi4to6BCGEaEQ3ozv2Dy+7d58ZFHQzmk8KLVFTU8Mbb7xBZWUlWq2W6Oho1qxZ0+S6eXl5HDp0iG+//ZZdu3a5yocPH87PfvazVsVxJ4qqqt59vZsQQvipM2fOMGDAgBav741RZ+3FndrC765ohBCivdLHm/0msdwLGXUmhBDCqyTRCCGE8CpJNEIIIbxK7tF4UEJCAoGBgWg0GrRabaOHplRVZe3atWRmZmI0Glm/fj2xsbFejenChQssXbrU9bmgoIDFixczd+5cV1lOTg6LFi1yTbw3btw4fvzjH3slnpUrV3L48GHMZjO7d+8GoLy8nKVLl3L16lUiIyP53e9+R+fOnRttm5aWRkpKCgAvv/wyM2bM8GpcGzZs4NChQ+j1enr27Mm6devo1KlTo23vtt89HdemTZvYtm2ba6qRZcuWMWbMmEbbZmVlsXbtWpxOJ0lJSSxcuNCrcS1ZsoSLFy8CcOPGDYKDg93m1brFm+313XffsXz5cq5fv45Go2HWrFnMmTOnXRxjHZoqPGbs2LGqxWJpdvnhw4fV+fPnq06nU83Ly1OfeeYZH0anqg6HQx0+fLhaWFjoVn7s2DF14cKFPokhNzdXPXXqlDp58mRX2YYNG9TU1FRVVVU1NTVV3bhxY6PtysrK1ISEBLWsrEwtLy9XExIS1PLycq/GdeTIEdVut6uqqqobN25sMi5Vvft+93Rcv//979X33nvvjts5HA71ySefVK9cuaJarVZ16tSpan5+vlfjamjdunXqpk2bmlzmzfYqLi5WT506paqqqt64cUMdP368mp+f77Vj7PTp057/EvepO7WFdJ350IEDB5g+fTqKohAXF0dlZSXXrl3zWf2ff/45PXr0IDIy0md13m7YsGGNziRvtQvA9OnT+fTTTxttl52dzYgRI+jSpQudO3dmxIgRHDlyxKtxjRw5Ep2u/qI/Li6OoqIij9XXmrha4sSJE0RHR9OjRw8MBgOTJ0/mwIEDPolLVVUyMjKYMmWKx+prqbCwMFcvQVBQEDExMRQXF7eLY8xbBg8e3OyyxMREli1b5la2YsUKRo0ahc1W//BoaWkpCQkJruUXL15kwYIFjBs3jqeeeoqf/OQnXL9+vVUxSqLxsPnz5/P000+zdevWRsuKi4uJiIhwfY6IiKC4uNhnse3Zs6fZ//xfffUViYmJvPDCC+Tn5/ssJgCLxeKaPTYsLIzS0tJG69zeduHh4T5tu48//pjRo0c3u/xO+90b/vrXvzJ16lRWrlxJRUVFo+Vt2V5ffPEFZrPZ9aR6U3zRXoWFhZw5c4ZHHnnkvjjGPO38+fOoqsrx48epqalxW6bVatmxY0ejbaxWKy+++CKzZ89m//79ZGRkMHv27Cbb617IPRoP2rJlC+Hh4VgsFubNm0dMTAzDhg1zLVebeDZWURSfxGaz2Th48CCvvvpqo2WxsbEcPHiQwMBAMjMzSU5OZt++fT6Jq6Xasu1SUlLQarUkJiY2ufxu+93TZs+ezaJFi1AUhXfeeYf169ezbt06t3Xasr127959x6sZX7RXdXU1ixcv5vXXXycoKKhF2/iizRzHr+D4+zeoZbUoISZ0ibHohvX0aB237Nq1i8TERC5cuMDBgwfd9smcOXPYvHkzs2bNarRNXFyc2xXOY4891upY5IrGg8LDwwEwm82MGzeOEydOuC2PiIhw634pKiry+nsgbsnKyiI2NpauXbs2WhYUFERgYCAAY8aMweFwtPoM5l6YzWZXF+K1a9eafJ/G7W1XXFzsk7ZLS0vj8OHD/OY3v2n2R+du+93TunbtilarRaPRkJSUxMmTJxut01bt5XA42L9/P5MmTWp2HW+3l91uZ/HixUydOpXx48e76mrrY8xx/Ar2v+WhltUCoJbVYv9bHo7jVzxWR0MZGRlMmjSJyZMnuwZs3NKtWzeGDBnSaLBGfn6+VwYoSaLxkJqaGqqqqlx//+yzz+jbt6/bOgkJCezcuRNVVfnqq68IDg72WaLZs2cPkydPbnJZSUmJ62zuxIkTOJ1OQkJCfBIX/LtdAHbu3Nnk+zFGjhxJdnY2FRUVVFRUkJ2dzciRI70aV1ZWFu+++y4pKSnNznLbkv3uaQ3v63366adN1vfwww9z6dIlCgoKsNls7Nmzx+0s1VuOHj1KTEyMWxdUQ95uL1VVWbVqFTExMcybN89V3h6OMcffvwF7nXuhva6+3MNOnDhBSEgIkZGRPP7445w+fbpRF+tLL73E+++/3+SVnKdJ15mHWCwWkpOTAairq2PKlCmMHj2aLVu2APXdHWPGjCEzM5Nx48ZhMpl48803fRJbbW0tR48edZtor2Fce/fuZcuWLWi1WoxGI2+//bbXulmWLVtGbm4uZWVljB49mldeeYWFCxeyZMkSduzYQbdu3XjnnXcAOHnyJB999BFr166lS5cuLFq0iGeeeQaA5ORkunTp4tW4/vSnP2Gz2Vw/WI888ghr1qyhuLiYn//857z77rvN7ndvxpWbm8s///lPACIjI137tWFcOp2O1atX88ILL1BXV8fMmTM9+oPeVFxJSUn84x//aHRC48v2+vLLL0lPT6dfv35MmzbNFWt7OMZuXcm0tLw19uzZw8WLF10nF1VVVezbt4+kpCTXOtHR0QwYMICMjAxXWZ8+fTh+/LjH45FJNYUQ4nu6l0k1b/53RpNJRQkxYfyfp753DIMHDyYvL8/12el0MnbsWLZt2+bqpjx27BgpKSls3ryZFStW8MQTTzBx4kTy8/N58cUXATh48CA3b95k6tSprFq1iieeeAKov7IPDw+nf//+d4zjTm0hXWdCCOEDusRY0GvdC/Xa+vJWqK2tZfTo0a4/mzdvJjw83JVkoH44+vnz5xs9TtG3b1+399IYjUb++Mc/8uGHHzJ+/HgmTZpEWloaZnPrJgKVKxohhPie7vU1Ab4cdeZr8poAIYRoB3TDevpNYrkX0nUmhBDCqyTRCCGE8CrpOhN+b8CAAfTr14+6ujpiYmLYsGGD67mY1atXM23aNLZv305ubi7BwcEAmEwmPvroo7YMW9wnVFX12awL7dXdbvXLFY3we0ajkfT0dHbv3o1er3dLIF9//TVxcXEALF++nPT0dNLT0yXJiBYxGo1YLBafPPTYXqmqisViwWg0NruOXNGIDmXo0KGcPXsWqJ90sFevXmi12rtsJUTToqKiKCwspKSkpK1DaVNGo9H1PqumSKIRHYbD4SArK4tRo0YBuP0dYOPGja6XXvXp04e33nqrTeIU9w+9Xk/v3r3bOox2TxKN8Hs3b950TUcydOhQ1xQj2dnZbtMALV++nIkTJ7ZJjEL4M0k0wu/dukfTUG1tLZWVlW5PTwshvEMGA4gOKScnh/j4+LYOQ4gOQa5oRIeUlZXFhAkT3Moa3qMB2L59OwaDwdehCeF3ZK4z0SHNmDGDbdu2odfr2zoUIfyeJBohhBBeJfdohBBCeJUkGiGEEF4liUYIIYRXSaIRQgjhVZJohBBCeJUkGiGEEF4liUYIIYRX/T+pbKa7ks63pAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get all tickers that match the screener-criteria at some point.\n",
    "all_tickers = df_match[[PE, TOTAL_RETURN_1_3Y]].dropna().reset_index()[TICKER].unique()\n",
    "\n",
    "# Randomly select some of those tickers.\n",
    "tickers = np.random.choice(all_tickers, size=20, replace=False)\n",
    "\n",
    "# Make the scatter-plot for only those tickers.\n",
    "plot_scatter(df=df_match.loc[tickers].reset_index(),\n",
    "             x=PE, y=TOTAL_RETURN_1_3Y, hue=TICKER);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "\n",
    "We can also automatically optimize the parameters of the screener-criteria by using a heuristic optimizer such as [Differential Evolution](https://en.wikipedia.org/wiki/Differential_evolution) as implemented in the [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html) library. Because this optimizer does not require the mathematical gradient of the problem, it can often optimize very hard problems that cannot be made into simple mathematical formulas.\n",
    "\n",
    "We will use a simplified screener in this example. First we define the boundaries for the screener's parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "bounds = \\\n",
    "[\n",
    "    (1e6, 1e12),  # mcap_min\n",
    "    (0.0, 1.0),   # debt_ratio_max\n",
    "    (0.0, 30.0),  # pe_min\n",
    "    (0.0, 50.0),  # pe_max\n",
    "    (0.0, 1.0),   # roa_min\n",
    "    (0.0, 1.0),   # roe_min\n",
    "    (-0.2, 0.5),  # sales_growth_min\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a function for running the stock-screener, which returns the rows from `df_sig_rets` that match the screener-criteria with the given parameters. This is a simpler version of the stock-screeners we made above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def screener(x):\n",
    "    \"\"\"\n",
    "    Run the stock-screener with the given parameters x\n",
    "    and return only the rows that satisfy the screen-criteria.\n",
    "    \n",
    "    :param x: Array with parameters for the screener.\n",
    "    :return: Pandas DataFrame.\n",
    "    \"\"\"\n",
    "\n",
    "    # Unpack the screener-parameters.\n",
    "    mcap_min, debt_ratio_max, pe_min, pe_max, roa_min, \\\n",
    "        roe_min, sales_growth_min = x\n",
    "    \n",
    "    # Start the screener with a market-cap condition.\n",
    "    mask = (df_sig_rets[MARKET_CAP] > mcap_min)\n",
    "\n",
    "    # Screener criteria where NaN signals are ignored.\n",
    "    mask &= ((df_sig_rets[DEBT_RATIO] < debt_ratio_max) | (df_sig_rets[DEBT_RATIO].isnull()))\n",
    "    mask &= ((df_sig_rets[ROA] > roa_min) | (df_sig_rets[ROA].isnull()))\n",
    "    mask &= ((df_sig_rets[ROE] > roe_min) | (df_sig_rets[ROE].isnull()))\n",
    "    mask &= (((df_sig_rets[PE] > pe_min) & (df_sig_rets[PE] < pe_max)) | (df_sig_rets[PE].isnull()))\n",
    "    mask &= ((df_sig_rets[SALES_GROWTH] > sales_growth_min) | (df_sig_rets[SALES_GROWTH].isnull()))\n",
    "\n",
    "    # Select the rows that satisfy the screener criteria.\n",
    "    df_match = df_sig_rets.loc[mask]\n",
    "    \n",
    "    return df_match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define the objective function (aka. cost or fitness function) that must be *minimized*. This basically just wraps the screener-function above and measures the median returns for stocks that match the screener-criteria with the given parameters.\n",
    "\n",
    "The objective function also penalizes screener-parameters that result in too few matching stocks or too few data-points, so as to avoid over-fitting the screener-parameters to just a few stocks that happened to perform really well during this period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(x):\n",
    "    \"\"\"\n",
    "    Function whose return-value must be minimized by changing\n",
    "    the screener parameters x.\n",
    "    \n",
    "    :param x: Array with parameters for the screener.\n",
    "    :return: Float to be minimized.\n",
    "    \"\"\"\n",
    "\n",
    "    # Run the stock-screener and get the matching rows, which\n",
    "    # contain a Ticker and Date as well as the signals and\n",
    "    # future stock-returns.\n",
    "    df_match = screener(x)\n",
    "\n",
    "    # Did the stock-screener find any matching rows?\n",
    "    if len(df_match)>0:\n",
    "        # Get the stock-returns for the matching rows.\n",
    "        # Drop NaN-rows because we count the rows further below.\n",
    "        df_returns = df_match[TOTAL_RETURN_1_3Y].dropna()\n",
    "\n",
    "        # The optimization objective is the MEDIAN stock-return.\n",
    "        # We use this instead of the mean to avoid outliers.\n",
    "        # It is negated because the optimizer performs\n",
    "        # minimization, but we are actually interested in\n",
    "        # maximizing the median stock-return.\n",
    "        objective = -df_returns.median()\n",
    "    \n",
    "        # Now we will penalize the objective to try and find\n",
    "        # stock-screener parameters that result in certain\n",
    "        # characteristics for the matching stocks.\n",
    "        \n",
    "        # Initialize the penalty. This number will be lowered\n",
    "        # towards zero in the tests below, and finally multiplied\n",
    "        # with the objective value.\n",
    "        penalty = 1.0\n",
    "\n",
    "        # Gradually penalize when there are fewer data-points.\n",
    "        limit = 50000\n",
    "        if len(df_returns) < limit:\n",
    "            penalty *= len(df_returns) / limit\n",
    "\n",
    "        # Gradually penalize when there are fewer unique stocks.\n",
    "        num_tickers = len(df_returns.reset_index()[TICKER].unique())\n",
    "        limit = 100\n",
    "        if num_tickers < limit:\n",
    "            penalty *= num_tickers / limit\n",
    "\n",
    "        # Gradually penalize when there are fewer stocks per day\n",
    "        # on average.\n",
    "        num_per_day = df_returns.reset_index()[[DATE, TICKER]].groupby(DATE).count()\n",
    "        mean = num_per_day[TICKER].mean()\n",
    "        limit = 50\n",
    "        if mean < limit:\n",
    "            penalty *= mean / limit\n",
    "\n",
    "        # Gradually penalize when the P/E signal is missing / NaN.\n",
    "        penalty *= len(df_match[PE].dropna()) / len(df_match)\n",
    "\n",
    "        # Apply the penalty to the objective function.\n",
    "        objective *= penalty\n",
    "    else:\n",
    "        # The stock-screener did not find any matching rows,\n",
    "        # so use a very high value. A small random value is\n",
    "        # added to avoid premature stagnation of the optimizer,\n",
    "        # in case all trials in its populations have the exact\n",
    "        # same objective value.\n",
    "        objective = 1000.0 + np.random.rand()\n",
    "        \n",
    "    return objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need a small helper-function for printing the best-found screener-parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_parameters(x):\n",
    "    \"\"\"\n",
    "    Print the parameters for the screener-criteria.\n",
    "    \n",
    "    :param x: Array with parameters for the screener.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Unpack the parameters.\n",
    "    mcap_min, debt_ratio_max, pe_min, pe_max, roa_min, \\\n",
    "        roe_min, sales_growth_min = x\n",
    "\n",
    "    # Print the parameters.\n",
    "    print('Market-Cap Min:   {:.0}'.format(mcap_min))\n",
    "    print('Debt Ratio Max:   {:.2f}'.format(debt_ratio_max))\n",
    "    print('P/E Min:          {:.1f}'.format(pe_min))\n",
    "    print('P/E Max:          {:.1f}'.format(pe_max))\n",
    "    print('ROA Min:          {:.2f}'.format(roa_min))\n",
    "    print('ROE Min:          {:.2f}'.format(roe_min))\n",
    "    print('Sales Growth Min: {:.2f}'.format(sales_growth_min))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then run the optimization. This may take several minutes on a quad-core CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 1: f(x)= -0\n",
      "differential_evolution step 2: f(x)= -0.000296088\n",
      "differential_evolution step 3: f(x)= -0.00988036\n",
      "differential_evolution step 4: f(x)= -0.00988036\n",
      "differential_evolution step 5: f(x)= -0.00988036\n",
      "differential_evolution step 6: f(x)= -0.0800614\n",
      "differential_evolution step 7: f(x)= -0.0892057\n",
      "differential_evolution step 8: f(x)= -0.0892057\n",
      "differential_evolution step 9: f(x)= -0.0892057\n",
      "differential_evolution step 10: f(x)= -0.0951714\n",
      "differential_evolution step 11: f(x)= -0.0951714\n",
      "differential_evolution step 12: f(x)= -0.0953303\n",
      "differential_evolution step 13: f(x)= -0.0953303\n",
      "differential_evolution step 14: f(x)= -0.0953303\n",
      "differential_evolution step 15: f(x)= -0.096113\n",
      "differential_evolution step 16: f(x)= -0.0975919\n",
      "differential_evolution step 17: f(x)= -0.0975919\n",
      "differential_evolution step 18: f(x)= -0.0975919\n",
      "differential_evolution step 19: f(x)= -0.0975919\n",
      "differential_evolution step 20: f(x)= -0.0975919\n",
      "differential_evolution step 21: f(x)= -0.0975919\n",
      "differential_evolution step 22: f(x)= -0.0975919\n",
      "differential_evolution step 23: f(x)= -0.0975919\n",
      "differential_evolution step 24: f(x)= -0.0975919\n",
      "differential_evolution step 25: f(x)= -0.0975919\n",
      "differential_evolution step 26: f(x)= -0.0975919\n",
      "differential_evolution step 27: f(x)= -0.0975919\n",
      "differential_evolution step 28: f(x)= -0.0975919\n",
      "differential_evolution step 29: f(x)= -0.0975919\n",
      "differential_evolution step 30: f(x)= -0.0976299\n",
      "CPU times: user 39min 15s, sys: 47 s, total: 40min 2s\n",
      "Wall time: 10min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Only do a short run if this is run using pytest.\n",
    "# This is to save time during automated testing.\n",
    "maxiter = 2 if running_pytest else 30\n",
    "\n",
    "# Run the optimization.\n",
    "# Note the increased population-size which seemed to\n",
    "# perform better than the default choice of only 15.\n",
    "result = differential_evolution(func=objective, bounds=bounds,\n",
    "                                popsize=30, disp=True,\n",
    "                                maxiter=maxiter)\n",
    "\n",
    "# Get the best-found screener parameters.\n",
    "best_parameters = result.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the optimized screener-parameters that gave the best investment performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Market-Cap Min:   3e+10\n",
      "Debt Ratio Max:   0.51\n",
      "P/E Min:          10.9\n",
      "P/E Max:          49.3\n",
      "ROA Min:          0.03\n",
      "ROE Min:          0.00\n",
      "Sales Growth Min: -0.06\n"
     ]
    }
   ],
   "source": [
    "print_parameters(x=best_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then try and run the stock-screener with the optimized parameters, and make a scatter-plot of the matching P/E ratios versus the average 1-3 year stock-returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the screener with the optimized parameters,\n",
    "# and get the rows that satisfy the screener-criteria.\n",
    "df_match = screener(best_parameters)\n",
    "\n",
    "# Scatter-plot of the P/E versus stock-returns.\n",
    "plot_scatter(df=df_match, x=PE, y=TOTAL_RETURN_1_3Y,\n",
    "             hue=SALES_GROWTH);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also print the basic statistics for the average 1-3 year stock-returns from the screener with the optimized parameters. Note that these are actually worse than for the stock-screener and parameters we had made further above. The reason is probably that the optimized screener-parameters also try to ensure that the screener finds a diverse set of stocks, and that the P/E signals are actually present; while the stock-screener further above would also select stocks where the P/E signal was NaN (Not-a-Number)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    137012.000000\n",
       "mean          0.109838\n",
       "std           0.221997\n",
       "min          -4.985282\n",
       "25%           0.044153\n",
       "50%           0.129563\n",
       "75%           0.200254\n",
       "max           0.753124\n",
       "Name: Total Return 1-3 Years, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_match[TOTAL_RETURN_1_3Y].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also show how many stocks the screener found for every single day. We had programmed the `objective()` function so it would penalize the screener-parameters, if they resulted in there being an average of fewer than 50 stocks that matched the screener every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_match[TOTAL_RETURN_1_3Y].dropna().groupby(DATE).count().plot(grid=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For every day, we can show the average 1-3 year return for all the stocks matching the screener with the optimized parameters. We can see that the screener seems to work better in some periods than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_match[TOTAL_RETURN_1_3Y].groupby(DATE).mean().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Ideas\n",
    "\n",
    "The screener optimization shown above can be extended in many different ways. Here are a few ideas to get you started:\n",
    "\n",
    "- Split the data-set into training- and test-sets as shown in the other SimFin tutorials on Machine Learning. Does the screener work just as well on stock-data that it has not seen during the training?\n",
    "\n",
    "- Create an *ensemble* of stock-screeners by making different screeners that are specialized for different things, e.g. one screener could be specialied for high-growth companies, and another screener could be specialized for under-valued companies. You can also run the optimization several times for each screener, and then average all of the results when building your investment portfolio.\n",
    "\n",
    "- Use multi-objective optimization to find optimal trade-offs e.g. between average returns and probability of loss. You can modify this [implementation](https://github.com/Hvass-Labs/FinanceOps/blob/master/04_Multi-Objective_Portfolio_Optimization.ipynb).\n",
    "\n",
    "- Use Genetic Programming to optimize both the screener-parameters and the entire screener-formula, to find which signals should be used, how they should be used, and what the parameters should be.\n",
    "\n",
    "Please share your results with the rest of the community, that is what SimFin is made for!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "This is published under the\n",
    "[MIT License](https://github.com/simfin/simfin-tutorials/blob/master/LICENSE.txt)\n",
    "which allows very broad use for both academic and commercial purposes.\n",
    "\n",
    "You are very welcome to modify and use this source-code in your own project. Please keep a link to the [original repository](https://github.com/simfin/simfin-tutorials).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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