Module 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROBLEM STATEMENT \n",
    "# 4.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>14 Day sma</th>\n",
       "      <th>30 Day STD</th>\n",
       "      <th>Upper Band</th>\n",
       "      <th>Lower Band</th>\n",
       "      <th>Mid Band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Jun-2017</td>\n",
       "      <td>2521.60</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>2547.4</td>\n",
       "      <td>2486.0</td>\n",
       "      <td>2518.6</td>\n",
       "      <td>2507.00</td>\n",
       "      <td>2513.28</td>\n",
       "      <td>1574017</td>\n",
       "      <td>3.955950e+09</td>\n",
       "      <td>83931</td>\n",
       "      <td>599286</td>\n",
       "      <td>38.07</td>\n",
       "      <td>2572.610714</td>\n",
       "      <td>71.824613</td>\n",
       "      <td>2716.259941</td>\n",
       "      <td>2428.961488</td>\n",
       "      <td>2572.610714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>12-Jun-2017</td>\n",
       "      <td>2507.00</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>2512.9</td>\n",
       "      <td>2470.4</td>\n",
       "      <td>2484.9</td>\n",
       "      <td>2487.20</td>\n",
       "      <td>2487.48</td>\n",
       "      <td>813253</td>\n",
       "      <td>2.022948e+09</td>\n",
       "      <td>44849</td>\n",
       "      <td>306655</td>\n",
       "      <td>37.71</td>\n",
       "      <td>2570.146429</td>\n",
       "      <td>60.249637</td>\n",
       "      <td>2690.645703</td>\n",
       "      <td>2449.647154</td>\n",
       "      <td>2570.146429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-Jun-2017</td>\n",
       "      <td>2487.20</td>\n",
       "      <td>2465.0</td>\n",
       "      <td>2481.1</td>\n",
       "      <td>2447.0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>2451.55</td>\n",
       "      <td>2464.89</td>\n",
       "      <td>587707</td>\n",
       "      <td>1.448635e+09</td>\n",
       "      <td>52410</td>\n",
       "      <td>268108</td>\n",
       "      <td>45.62</td>\n",
       "      <td>2562.460714</td>\n",
       "      <td>58.090647</td>\n",
       "      <td>2678.642008</td>\n",
       "      <td>2446.279421</td>\n",
       "      <td>2562.460714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>14-Jun-2017</td>\n",
       "      <td>2451.55</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>2476.0</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>2465.4</td>\n",
       "      <td>2470.20</td>\n",
       "      <td>2469.41</td>\n",
       "      <td>744571</td>\n",
       "      <td>1.838651e+09</td>\n",
       "      <td>42987</td>\n",
       "      <td>456486</td>\n",
       "      <td>61.31</td>\n",
       "      <td>2551.432143</td>\n",
       "      <td>56.909942</td>\n",
       "      <td>2665.252026</td>\n",
       "      <td>2437.612259</td>\n",
       "      <td>2551.432143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-Jun-2017</td>\n",
       "      <td>2470.20</td>\n",
       "      <td>2469.0</td>\n",
       "      <td>2469.7</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>2411.20</td>\n",
       "      <td>2427.94</td>\n",
       "      <td>1498599</td>\n",
       "      <td>3.638509e+09</td>\n",
       "      <td>65157</td>\n",
       "      <td>1140716</td>\n",
       "      <td>76.12</td>\n",
       "      <td>2539.500000</td>\n",
       "      <td>64.820537</td>\n",
       "      <td>2669.141074</td>\n",
       "      <td>2409.858926</td>\n",
       "      <td>2539.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "19    TCS     EQ  09-Jun-2017     2521.60      2520.0      2547.4     2486.0   \n",
       "20    TCS     EQ  12-Jun-2017     2507.00      2497.0      2512.9     2470.4   \n",
       "21    TCS     EQ  13-Jun-2017     2487.20      2465.0      2481.1     2447.0   \n",
       "22    TCS     EQ  14-Jun-2017     2451.55      2455.0      2476.0     2455.0   \n",
       "23    TCS     EQ  15-Jun-2017     2470.20      2469.0      2469.7     2405.0   \n",
       "\n",
       "    Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "19      2518.6      2507.00        2513.28                1574017   \n",
       "20      2484.9      2487.20        2487.48                 813253   \n",
       "21      2450.0      2451.55        2464.89                 587707   \n",
       "22      2465.4      2470.20        2469.41                 744571   \n",
       "23      2410.0      2411.20        2427.94                1498599   \n",
       "\n",
       "        Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \\\n",
       "19  3.955950e+09          83931           599286                   38.07   \n",
       "20  2.022948e+09          44849           306655                   37.71   \n",
       "21  1.448635e+09          52410           268108                   45.62   \n",
       "22  1.838651e+09          42987           456486                   61.31   \n",
       "23  3.638509e+09          65157          1140716                   76.12   \n",
       "\n",
       "     14 Day sma  30 Day STD   Upper Band   Lower Band     Mid Band  \n",
       "19  2572.610714   71.824613  2716.259941  2428.961488  2572.610714  \n",
       "20  2570.146429   60.249637  2690.645703  2449.647154  2570.146429  \n",
       "21  2562.460714   58.090647  2678.642008  2446.279421  2562.460714  \n",
       "22  2551.432143   56.909942  2665.252026  2437.612259  2551.432143  \n",
       "23  2539.500000   64.820537  2669.141074  2409.858926  2539.500000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv('TCS.csv')\n",
    "df['14 Day sma'] = df['Close Price'].rolling(window=14).mean()\n",
    "df['30 Day STD'] = df['Close Price'].rolling(window=20).std()\n",
    "df['Upper Band'] = df['14 Day sma'] + (df['30 Day STD'] * 2)\n",
    "df['Lower Band'] = df['14 Day sma'] - (df['30 Day STD'] * 2)\n",
    "df = df.dropna()\n",
    "df['Mid Band'] = (df['Upper Band'] + df['Lower Band']) / 2\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>14 Day sma</th>\n",
       "      <th>30 Day STD</th>\n",
       "      <th>Upper Band</th>\n",
       "      <th>Lower Band</th>\n",
       "      <th>Mid Band</th>\n",
       "      <th>call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Jun-2017</td>\n",
       "      <td>2521.60</td>\n",
       "      <td>2520.00</td>\n",
       "      <td>2547.40</td>\n",
       "      <td>2486.00</td>\n",
       "      <td>2518.60</td>\n",
       "      <td>2507.00</td>\n",
       "      <td>2513.28</td>\n",
       "      <td>...</td>\n",
       "      <td>3.955950e+09</td>\n",
       "      <td>83931</td>\n",
       "      <td>599286</td>\n",
       "      <td>38.07</td>\n",
       "      <td>2572.610714</td>\n",
       "      <td>71.824613</td>\n",
       "      <td>2716.259941</td>\n",
       "      <td>2428.961488</td>\n",
       "      <td>2572.610714</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>12-Jun-2017</td>\n",
       "      <td>2507.00</td>\n",
       "      <td>2497.00</td>\n",
       "      <td>2512.90</td>\n",
       "      <td>2470.40</td>\n",
       "      <td>2484.90</td>\n",
       "      <td>2487.20</td>\n",
       "      <td>2487.48</td>\n",
       "      <td>...</td>\n",
       "      <td>2.022948e+09</td>\n",
       "      <td>44849</td>\n",
       "      <td>306655</td>\n",
       "      <td>37.71</td>\n",
       "      <td>2570.146429</td>\n",
       "      <td>60.249637</td>\n",
       "      <td>2690.645703</td>\n",
       "      <td>2449.647154</td>\n",
       "      <td>2570.146429</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-Jun-2017</td>\n",
       "      <td>2487.20</td>\n",
       "      <td>2465.00</td>\n",
       "      <td>2481.10</td>\n",
       "      <td>2447.00</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>2451.55</td>\n",
       "      <td>2464.89</td>\n",
       "      <td>...</td>\n",
       "      <td>1.448635e+09</td>\n",
       "      <td>52410</td>\n",
       "      <td>268108</td>\n",
       "      <td>45.62</td>\n",
       "      <td>2562.460714</td>\n",
       "      <td>58.090647</td>\n",
       "      <td>2678.642008</td>\n",
       "      <td>2446.279421</td>\n",
       "      <td>2562.460714</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>14-Jun-2017</td>\n",
       "      <td>2451.55</td>\n",
       "      <td>2455.00</td>\n",
       "      <td>2476.00</td>\n",
       "      <td>2455.00</td>\n",
       "      <td>2465.40</td>\n",
       "      <td>2470.20</td>\n",
       "      <td>2469.41</td>\n",
       "      <td>...</td>\n",
       "      <td>1.838651e+09</td>\n",
       "      <td>42987</td>\n",
       "      <td>456486</td>\n",
       "      <td>61.31</td>\n",
       "      <td>2551.432143</td>\n",
       "      <td>56.909942</td>\n",
       "      <td>2665.252026</td>\n",
       "      <td>2437.612259</td>\n",
       "      <td>2551.432143</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-Jun-2017</td>\n",
       "      <td>2470.20</td>\n",
       "      <td>2469.00</td>\n",
       "      <td>2469.70</td>\n",
       "      <td>2405.00</td>\n",
       "      <td>2410.00</td>\n",
       "      <td>2411.20</td>\n",
       "      <td>2427.94</td>\n",
       "      <td>...</td>\n",
       "      <td>3.638509e+09</td>\n",
       "      <td>65157</td>\n",
       "      <td>1140716</td>\n",
       "      <td>76.12</td>\n",
       "      <td>2539.500000</td>\n",
       "      <td>64.820537</td>\n",
       "      <td>2669.141074</td>\n",
       "      <td>2409.858926</td>\n",
       "      <td>2539.500000</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-May-2019</td>\n",
       "      <td>2157.85</td>\n",
       "      <td>2160.05</td>\n",
       "      <td>2184.35</td>\n",
       "      <td>2138.80</td>\n",
       "      <td>2154.00</td>\n",
       "      <td>2151.95</td>\n",
       "      <td>2163.53</td>\n",
       "      <td>...</td>\n",
       "      <td>4.763441e+09</td>\n",
       "      <td>86096</td>\n",
       "      <td>975603</td>\n",
       "      <td>44.31</td>\n",
       "      <td>2161.478571</td>\n",
       "      <td>74.401631</td>\n",
       "      <td>2310.281834</td>\n",
       "      <td>2012.675309</td>\n",
       "      <td>2161.478571</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-May-2019</td>\n",
       "      <td>2151.95</td>\n",
       "      <td>2151.00</td>\n",
       "      <td>2168.00</td>\n",
       "      <td>2130.40</td>\n",
       "      <td>2156.00</td>\n",
       "      <td>2152.85</td>\n",
       "      <td>2151.85</td>\n",
       "      <td>...</td>\n",
       "      <td>3.926902e+09</td>\n",
       "      <td>77713</td>\n",
       "      <td>781526</td>\n",
       "      <td>42.83</td>\n",
       "      <td>2171.360714</td>\n",
       "      <td>69.643943</td>\n",
       "      <td>2310.648600</td>\n",
       "      <td>2032.072829</td>\n",
       "      <td>2171.360714</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-May-2019</td>\n",
       "      <td>2152.85</td>\n",
       "      <td>2145.00</td>\n",
       "      <td>2184.10</td>\n",
       "      <td>2136.10</td>\n",
       "      <td>2171.20</td>\n",
       "      <td>2172.55</td>\n",
       "      <td>2166.98</td>\n",
       "      <td>...</td>\n",
       "      <td>4.906405e+09</td>\n",
       "      <td>106297</td>\n",
       "      <td>1251459</td>\n",
       "      <td>55.27</td>\n",
       "      <td>2175.592857</td>\n",
       "      <td>67.073082</td>\n",
       "      <td>2309.739022</td>\n",
       "      <td>2041.446692</td>\n",
       "      <td>2175.592857</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>10-May-2019</td>\n",
       "      <td>2172.55</td>\n",
       "      <td>2175.00</td>\n",
       "      <td>2192.00</td>\n",
       "      <td>2125.85</td>\n",
       "      <td>2130.25</td>\n",
       "      <td>2135.80</td>\n",
       "      <td>2161.83</td>\n",
       "      <td>...</td>\n",
       "      <td>4.409349e+09</td>\n",
       "      <td>85446</td>\n",
       "      <td>873181</td>\n",
       "      <td>42.81</td>\n",
       "      <td>2175.878571</td>\n",
       "      <td>65.064738</td>\n",
       "      <td>2306.008047</td>\n",
       "      <td>2045.749096</td>\n",
       "      <td>2175.878571</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-May-2019</td>\n",
       "      <td>2135.80</td>\n",
       "      <td>2133.00</td>\n",
       "      <td>2167.00</td>\n",
       "      <td>2121.65</td>\n",
       "      <td>2134.00</td>\n",
       "      <td>2128.75</td>\n",
       "      <td>2143.87</td>\n",
       "      <td>...</td>\n",
       "      <td>3.648325e+09</td>\n",
       "      <td>74014</td>\n",
       "      <td>677242</td>\n",
       "      <td>39.80</td>\n",
       "      <td>2174.357143</td>\n",
       "      <td>64.017856</td>\n",
       "      <td>2302.392855</td>\n",
       "      <td>2046.321431</td>\n",
       "      <td>2174.357143</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>479 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "19     TCS     EQ  09-Jun-2017     2521.60     2520.00     2547.40    2486.00   \n",
       "20     TCS     EQ  12-Jun-2017     2507.00     2497.00     2512.90    2470.40   \n",
       "21     TCS     EQ  13-Jun-2017     2487.20     2465.00     2481.10    2447.00   \n",
       "22     TCS     EQ  14-Jun-2017     2451.55     2455.00     2476.00    2455.00   \n",
       "23     TCS     EQ  15-Jun-2017     2470.20     2469.00     2469.70    2405.00   \n",
       "..     ...    ...          ...         ...         ...         ...        ...   \n",
       "493    TCS     EQ  07-May-2019     2157.85     2160.05     2184.35    2138.80   \n",
       "494    TCS     EQ  08-May-2019     2151.95     2151.00     2168.00    2130.40   \n",
       "495    TCS     EQ  09-May-2019     2152.85     2145.00     2184.10    2136.10   \n",
       "496    TCS     EQ  10-May-2019     2172.55     2175.00     2192.00    2125.85   \n",
       "497    TCS     EQ  13-May-2019     2135.80     2133.00     2167.00    2121.65   \n",
       "\n",
       "     Last Price  Close Price  Average Price  ...      Turnover  No. of Trades  \\\n",
       "19      2518.60      2507.00        2513.28  ...  3.955950e+09          83931   \n",
       "20      2484.90      2487.20        2487.48  ...  2.022948e+09          44849   \n",
       "21      2450.00      2451.55        2464.89  ...  1.448635e+09          52410   \n",
       "22      2465.40      2470.20        2469.41  ...  1.838651e+09          42987   \n",
       "23      2410.00      2411.20        2427.94  ...  3.638509e+09          65157   \n",
       "..          ...          ...            ...  ...           ...            ...   \n",
       "493     2154.00      2151.95        2163.53  ...  4.763441e+09          86096   \n",
       "494     2156.00      2152.85        2151.85  ...  3.926902e+09          77713   \n",
       "495     2171.20      2172.55        2166.98  ...  4.906405e+09         106297   \n",
       "496     2130.25      2135.80        2161.83  ...  4.409349e+09          85446   \n",
       "497     2134.00      2128.75        2143.87  ...  3.648325e+09          74014   \n",
       "\n",
       "     Deliverable Qty  % Dly Qt to Traded Qty   14 Day sma  30 Day STD  \\\n",
       "19            599286                   38.07  2572.610714   71.824613   \n",
       "20            306655                   37.71  2570.146429   60.249637   \n",
       "21            268108                   45.62  2562.460714   58.090647   \n",
       "22            456486                   61.31  2551.432143   56.909942   \n",
       "23           1140716                   76.12  2539.500000   64.820537   \n",
       "..               ...                     ...          ...         ...   \n",
       "493           975603                   44.31  2161.478571   74.401631   \n",
       "494           781526                   42.83  2171.360714   69.643943   \n",
       "495          1251459                   55.27  2175.592857   67.073082   \n",
       "496           873181                   42.81  2175.878571   65.064738   \n",
       "497           677242                   39.80  2174.357143   64.017856   \n",
       "\n",
       "      Upper Band   Lower Band     Mid Band                      call  \n",
       "19   2716.259941  2428.961488  2572.610714  Hold Buy/Liquidate Short  \n",
       "20   2690.645703  2449.647154  2570.146429  Hold Buy/Liquidate Short  \n",
       "21   2678.642008  2446.279421  2562.460714  Hold Buy/Liquidate Short  \n",
       "22   2665.252026  2437.612259  2551.432143  Hold Buy/Liquidate Short  \n",
       "23   2669.141074  2409.858926  2539.500000  Hold Buy/Liquidate Short  \n",
       "..           ...          ...          ...                       ...  \n",
       "493  2310.281834  2012.675309  2161.478571  Hold Buy/Liquidate Short  \n",
       "494  2310.648600  2032.072829  2171.360714  Hold Buy/Liquidate Short  \n",
       "495  2309.739022  2041.446692  2175.592857  Hold Buy/Liquidate Short  \n",
       "496  2306.008047  2045.749096  2175.878571  Hold Buy/Liquidate Short  \n",
       "497  2302.392855  2046.321431  2174.357143  Hold Buy/Liquidate Short  \n",
       "\n",
       "[479 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_buy(df):\n",
    "    if df['Close Price'] < df['Lower Band']:\n",
    "        return \"BUY\"\n",
    "    elif df['Close Price'] > df['Lower Band'] and df['Close Price'] < df['Mid Band']:\n",
    "        return \"Hold Buy/Liquidate Short\"\n",
    "    elif df['Close Price'] > df['Mid Band'] and df['Close Price'] < df['Upper Band']:\n",
    "        return \"Hold Short/Liquidate Buy\"\n",
    "    elif df['Close Price'] > df['Upper Band']:\n",
    "        return \"Short\"\n",
    "    \n",
    "df = df.assign(call = df.apply(select_buy, axis=1))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training classifier\n",
    "from sklearn import preprocessing\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "train_X = df[['Upper Band','Lower Band','Mid Band','Close Price']]\n",
    "transformed_label = le.fit_transform(df[['call']])\n",
    "train_Y = transformed_label.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9582"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LogisticRegression Model\n",
    "import sklearn as sk\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "LR = LogisticRegression(random_state=0, solver='lbfgs', multi_class='ovr').fit(train_X, train_Y.ravel())\n",
    "LR.predict(train_X)\n",
    "print('Logistic Regression')\n",
    "round(LR.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Score\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5908"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machine\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import sklearn as sk\n",
    "from sklearn import svm\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "SVM = svm.LinearSVC()\n",
    "SVM.fit(train_X,train_Y)\n",
    "SVM.predict(train_X)\n",
    "print('SVM Score')\n",
    "round(SVM.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6555"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RandomForest classifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import sklearn as sk\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)\n",
    "RF.fit(train_X,train_Y)\n",
    "RF.predict(train_X)\n",
    "print('Random Forest')\n",
    "round(RF.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5365"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neural Network Classifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "MLP = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5,2), random_state=1)\n",
    "MLP.fit(train_X,train_Y)\n",
    "MLP.predict(train_X)\n",
    "print('Neural Network')\n",
    "round(MLP.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROBLEM STATEMENT\n",
    "# 4.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-May-2017</td>\n",
       "      <td>274.95</td>\n",
       "      <td>275.90</td>\n",
       "      <td>278.90</td>\n",
       "      <td>275.50</td>\n",
       "      <td>278.50</td>\n",
       "      <td>277.95</td>\n",
       "      <td>277.78</td>\n",
       "      <td>5462855</td>\n",
       "      <td>1.517477e+09</td>\n",
       "      <td>52501</td>\n",
       "      <td>4128324</td>\n",
       "      <td>75.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>16-May-2017</td>\n",
       "      <td>277.95</td>\n",
       "      <td>278.50</td>\n",
       "      <td>284.30</td>\n",
       "      <td>278.00</td>\n",
       "      <td>283.00</td>\n",
       "      <td>283.45</td>\n",
       "      <td>280.93</td>\n",
       "      <td>11204308</td>\n",
       "      <td>3.147583e+09</td>\n",
       "      <td>98347</td>\n",
       "      <td>8307567</td>\n",
       "      <td>74.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>17-May-2017</td>\n",
       "      <td>283.45</td>\n",
       "      <td>284.10</td>\n",
       "      <td>284.40</td>\n",
       "      <td>279.25</td>\n",
       "      <td>281.50</td>\n",
       "      <td>281.65</td>\n",
       "      <td>281.56</td>\n",
       "      <td>8297700</td>\n",
       "      <td>2.336261e+09</td>\n",
       "      <td>99107</td>\n",
       "      <td>5924804</td>\n",
       "      <td>71.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-May-2017</td>\n",
       "      <td>281.65</td>\n",
       "      <td>278.00</td>\n",
       "      <td>281.05</td>\n",
       "      <td>277.05</td>\n",
       "      <td>277.65</td>\n",
       "      <td>277.90</td>\n",
       "      <td>278.49</td>\n",
       "      <td>7924261</td>\n",
       "      <td>2.206822e+09</td>\n",
       "      <td>47830</td>\n",
       "      <td>5434449</td>\n",
       "      <td>68.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>19-May-2017</td>\n",
       "      <td>277.90</td>\n",
       "      <td>282.25</td>\n",
       "      <td>295.65</td>\n",
       "      <td>281.95</td>\n",
       "      <td>286.40</td>\n",
       "      <td>286.20</td>\n",
       "      <td>290.08</td>\n",
       "      <td>35724128</td>\n",
       "      <td>1.036268e+10</td>\n",
       "      <td>248579</td>\n",
       "      <td>19137631</td>\n",
       "      <td>53.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "0    ITC     EQ  15-May-2017      274.95      275.90      278.90     275.50   \n",
       "1    ITC     EQ  16-May-2017      277.95      278.50      284.30     278.00   \n",
       "2    ITC     EQ  17-May-2017      283.45      284.10      284.40     279.25   \n",
       "3    ITC     EQ  18-May-2017      281.65      278.00      281.05     277.05   \n",
       "4    ITC     EQ  19-May-2017      277.90      282.25      295.65     281.95   \n",
       "\n",
       "   Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "0      278.50       277.95         277.78                5462855   \n",
       "1      283.00       283.45         280.93               11204308   \n",
       "2      281.50       281.65         281.56                8297700   \n",
       "3      277.65       277.90         278.49                7924261   \n",
       "4      286.40       286.20         290.08               35724128   \n",
       "\n",
       "       Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "0  1.517477e+09          52501          4128324                   75.57  \n",
       "1  3.147583e+09          98347          8307567                   74.15  \n",
       "2  2.336261e+09          99107          5924804                   71.40  \n",
       "3  2.206822e+09          47830          5434449                   68.58  \n",
       "4  1.036268e+10         248579         19137631                   53.57  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('ITC.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>% change in op_cl</th>\n",
       "      <th>% change in lw_hi</th>\n",
       "      <th>% chng 5dymean</th>\n",
       "      <th>% chng 5dystd</th>\n",
       "      <th>Action</th>\n",
       "      <th>% chng 5dymean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>05-Jun-2017</td>\n",
       "      <td>319.20</td>\n",
       "      <td>315.00</td>\n",
       "      <td>318.25</td>\n",
       "      <td>313.30</td>\n",
       "      <td>317.5</td>\n",
       "      <td>317.45</td>\n",
       "      <td>316.83</td>\n",
       "      <td>...</td>\n",
       "      <td>3.004339e+09</td>\n",
       "      <td>69774</td>\n",
       "      <td>6130621</td>\n",
       "      <td>64.65</td>\n",
       "      <td>0.771775</td>\n",
       "      <td>-1.579955</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>06-Jun-2017</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.50</td>\n",
       "      <td>319.00</td>\n",
       "      <td>309.30</td>\n",
       "      <td>311.3</td>\n",
       "      <td>311.05</td>\n",
       "      <td>311.28</td>\n",
       "      <td>...</td>\n",
       "      <td>3.063364e+09</td>\n",
       "      <td>87515</td>\n",
       "      <td>6116482</td>\n",
       "      <td>62.15</td>\n",
       "      <td>-2.073622</td>\n",
       "      <td>-3.136114</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-Jun-2017</td>\n",
       "      <td>311.05</td>\n",
       "      <td>311.30</td>\n",
       "      <td>314.75</td>\n",
       "      <td>310.35</td>\n",
       "      <td>311.7</td>\n",
       "      <td>312.25</td>\n",
       "      <td>313.15</td>\n",
       "      <td>...</td>\n",
       "      <td>3.611978e+09</td>\n",
       "      <td>91062</td>\n",
       "      <td>8921486</td>\n",
       "      <td>77.35</td>\n",
       "      <td>0.304243</td>\n",
       "      <td>-1.417754</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-Jun-2017</td>\n",
       "      <td>312.25</td>\n",
       "      <td>313.00</td>\n",
       "      <td>313.25</td>\n",
       "      <td>309.40</td>\n",
       "      <td>311.2</td>\n",
       "      <td>311.60</td>\n",
       "      <td>310.82</td>\n",
       "      <td>...</td>\n",
       "      <td>2.418657e+09</td>\n",
       "      <td>111593</td>\n",
       "      <td>5953664</td>\n",
       "      <td>76.51</td>\n",
       "      <td>-0.449294</td>\n",
       "      <td>-1.244344</td>\n",
       "      <td>-0.001591</td>\n",
       "      <td>-0.001591</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.001591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Jun-2017</td>\n",
       "      <td>311.60</td>\n",
       "      <td>310.75</td>\n",
       "      <td>312.60</td>\n",
       "      <td>304.70</td>\n",
       "      <td>305.7</td>\n",
       "      <td>306.00</td>\n",
       "      <td>307.40</td>\n",
       "      <td>...</td>\n",
       "      <td>3.094129e+09</td>\n",
       "      <td>103882</td>\n",
       "      <td>5925048</td>\n",
       "      <td>58.87</td>\n",
       "      <td>-1.552288</td>\n",
       "      <td>-2.592714</td>\n",
       "      <td>-0.008368</td>\n",
       "      <td>-0.008368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.008368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "15    ITC     EQ  05-Jun-2017      319.20      315.00      318.25     313.30   \n",
       "16    ITC     EQ  06-Jun-2017      317.45      317.50      319.00     309.30   \n",
       "17    ITC     EQ  07-Jun-2017      311.05      311.30      314.75     310.35   \n",
       "18    ITC     EQ  08-Jun-2017      312.25      313.00      313.25     309.40   \n",
       "19    ITC     EQ  09-Jun-2017      311.60      310.75      312.60     304.70   \n",
       "\n",
       "    Last Price  Close Price  Average Price  ...      Turnover  No. of Trades  \\\n",
       "15       317.5       317.45         316.83  ...  3.004339e+09          69774   \n",
       "16       311.3       311.05         311.28  ...  3.063364e+09          87515   \n",
       "17       311.7       312.25         313.15  ...  3.611978e+09          91062   \n",
       "18       311.2       311.60         310.82  ...  2.418657e+09         111593   \n",
       "19       305.7       306.00         307.40  ...  3.094129e+09         103882   \n",
       "\n",
       "    Deliverable Qty  % Dly Qt to Traded Qty  % change in op_cl  \\\n",
       "15          6130621                   64.65           0.771775   \n",
       "16          6116482                   62.15          -2.073622   \n",
       "17          8921486                   77.35           0.304243   \n",
       "18          5953664                   76.51          -0.449294   \n",
       "19          5925048                   58.87          -1.552288   \n",
       "\n",
       "    % change in lw_hi  % chng 5dymean   % chng 5dystd  Action  % chng 5dymean  \n",
       "15          -1.579955         0.001064       0.001064     1.0        0.001064  \n",
       "16          -3.136114        -0.000182      -0.000182    -1.0       -0.000182  \n",
       "17          -1.417754         0.000365       0.000365     1.0        0.000365  \n",
       "18          -1.244344        -0.001591      -0.001591     1.0       -0.001591  \n",
       "19          -2.592714        -0.008368      -0.008368     1.0       -0.008368  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['% change in op_cl'] = ((df['Close Price'] - df['Open Price'])/(df['Close Price'])) * 100\n",
    "df['% change in lw_hi'] = ((df['Low Price'] - df['High Price'])/(df['Low Price'])) * 100\n",
    "\n",
    "df['% chng 5dymean'] = df['Close Price'].pct_change().dropna().rolling(5).mean()\n",
    "df['% chng 5dystd'] = df['Close Price'].pct_change().dropna().rolling(5).mean()\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>% change in op_cl</th>\n",
       "      <th>% change in lw_hi</th>\n",
       "      <th>% chng 5dymean</th>\n",
       "      <th>% chng 5dystd</th>\n",
       "      <th>Action</th>\n",
       "      <th>% chng 5dymean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>05-Jun-2017</td>\n",
       "      <td>319.20</td>\n",
       "      <td>315.00</td>\n",
       "      <td>318.25</td>\n",
       "      <td>313.30</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.45</td>\n",
       "      <td>316.83</td>\n",
       "      <td>...</td>\n",
       "      <td>3.004339e+09</td>\n",
       "      <td>69774</td>\n",
       "      <td>6130621</td>\n",
       "      <td>64.65</td>\n",
       "      <td>0.771775</td>\n",
       "      <td>-1.579955</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>06-Jun-2017</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.50</td>\n",
       "      <td>319.00</td>\n",
       "      <td>309.30</td>\n",
       "      <td>311.30</td>\n",
       "      <td>311.05</td>\n",
       "      <td>311.28</td>\n",
       "      <td>...</td>\n",
       "      <td>3.063364e+09</td>\n",
       "      <td>87515</td>\n",
       "      <td>6116482</td>\n",
       "      <td>62.15</td>\n",
       "      <td>-2.073622</td>\n",
       "      <td>-3.136114</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-Jun-2017</td>\n",
       "      <td>311.05</td>\n",
       "      <td>311.30</td>\n",
       "      <td>314.75</td>\n",
       "      <td>310.35</td>\n",
       "      <td>311.70</td>\n",
       "      <td>312.25</td>\n",
       "      <td>313.15</td>\n",
       "      <td>...</td>\n",
       "      <td>3.611978e+09</td>\n",
       "      <td>91062</td>\n",
       "      <td>8921486</td>\n",
       "      <td>77.35</td>\n",
       "      <td>0.304243</td>\n",
       "      <td>-1.417754</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-Jun-2017</td>\n",
       "      <td>312.25</td>\n",
       "      <td>313.00</td>\n",
       "      <td>313.25</td>\n",
       "      <td>309.40</td>\n",
       "      <td>311.20</td>\n",
       "      <td>311.60</td>\n",
       "      <td>310.82</td>\n",
       "      <td>...</td>\n",
       "      <td>2.418657e+09</td>\n",
       "      <td>111593</td>\n",
       "      <td>5953664</td>\n",
       "      <td>76.51</td>\n",
       "      <td>-0.449294</td>\n",
       "      <td>-1.244344</td>\n",
       "      <td>-0.001591</td>\n",
       "      <td>-0.001591</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.001591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Jun-2017</td>\n",
       "      <td>311.60</td>\n",
       "      <td>310.75</td>\n",
       "      <td>312.60</td>\n",
       "      <td>304.70</td>\n",
       "      <td>305.70</td>\n",
       "      <td>306.00</td>\n",
       "      <td>307.40</td>\n",
       "      <td>...</td>\n",
       "      <td>3.094129e+09</td>\n",
       "      <td>103882</td>\n",
       "      <td>5925048</td>\n",
       "      <td>58.87</td>\n",
       "      <td>-1.552288</td>\n",
       "      <td>-2.592714</td>\n",
       "      <td>-0.008368</td>\n",
       "      <td>-0.008368</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.008368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-Jul-2018</td>\n",
       "      <td>269.85</td>\n",
       "      <td>271.10</td>\n",
       "      <td>271.30</td>\n",
       "      <td>267.60</td>\n",
       "      <td>268.15</td>\n",
       "      <td>268.55</td>\n",
       "      <td>269.28</td>\n",
       "      <td>...</td>\n",
       "      <td>1.898492e+09</td>\n",
       "      <td>51220</td>\n",
       "      <td>4655530</td>\n",
       "      <td>66.03</td>\n",
       "      <td>-0.949544</td>\n",
       "      <td>-1.382661</td>\n",
       "      <td>-0.005219</td>\n",
       "      <td>-0.005219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.005219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>19-Jul-2018</td>\n",
       "      <td>268.55</td>\n",
       "      <td>268.90</td>\n",
       "      <td>274.60</td>\n",
       "      <td>266.25</td>\n",
       "      <td>273.25</td>\n",
       "      <td>273.30</td>\n",
       "      <td>270.99</td>\n",
       "      <td>...</td>\n",
       "      <td>2.427206e+09</td>\n",
       "      <td>78638</td>\n",
       "      <td>5238088</td>\n",
       "      <td>58.48</td>\n",
       "      <td>1.609952</td>\n",
       "      <td>-3.136150</td>\n",
       "      <td>-0.002733</td>\n",
       "      <td>-0.002733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.002733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>20-Jul-2018</td>\n",
       "      <td>273.30</td>\n",
       "      <td>273.25</td>\n",
       "      <td>274.80</td>\n",
       "      <td>270.65</td>\n",
       "      <td>274.35</td>\n",
       "      <td>274.00</td>\n",
       "      <td>273.32</td>\n",
       "      <td>...</td>\n",
       "      <td>1.796403e+09</td>\n",
       "      <td>53813</td>\n",
       "      <td>3190994</td>\n",
       "      <td>48.55</td>\n",
       "      <td>0.273723</td>\n",
       "      <td>-1.533346</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-Jul-2018</td>\n",
       "      <td>274.00</td>\n",
       "      <td>279.50</td>\n",
       "      <td>286.65</td>\n",
       "      <td>277.15</td>\n",
       "      <td>283.95</td>\n",
       "      <td>284.15</td>\n",
       "      <td>283.57</td>\n",
       "      <td>...</td>\n",
       "      <td>7.388650e+09</td>\n",
       "      <td>147326</td>\n",
       "      <td>14378063</td>\n",
       "      <td>55.18</td>\n",
       "      <td>1.636460</td>\n",
       "      <td>-3.427747</td>\n",
       "      <td>0.009206</td>\n",
       "      <td>0.009206</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>ITC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>24-Jul-2018</td>\n",
       "      <td>284.15</td>\n",
       "      <td>284.50</td>\n",
       "      <td>287.00</td>\n",
       "      <td>283.55</td>\n",
       "      <td>285.05</td>\n",
       "      <td>285.65</td>\n",
       "      <td>285.37</td>\n",
       "      <td>...</td>\n",
       "      <td>3.456265e+09</td>\n",
       "      <td>116799</td>\n",
       "      <td>7696166</td>\n",
       "      <td>63.54</td>\n",
       "      <td>0.402591</td>\n",
       "      <td>-1.216717</td>\n",
       "      <td>0.011551</td>\n",
       "      <td>0.011551</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.011551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>285 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "15     ITC     EQ  05-Jun-2017      319.20      315.00      318.25     313.30   \n",
       "16     ITC     EQ  06-Jun-2017      317.45      317.50      319.00     309.30   \n",
       "17     ITC     EQ  07-Jun-2017      311.05      311.30      314.75     310.35   \n",
       "18     ITC     EQ  08-Jun-2017      312.25      313.00      313.25     309.40   \n",
       "19     ITC     EQ  09-Jun-2017      311.60      310.75      312.60     304.70   \n",
       "..     ...    ...          ...         ...         ...         ...        ...   \n",
       "295    ITC     EQ  18-Jul-2018      269.85      271.10      271.30     267.60   \n",
       "296    ITC     EQ  19-Jul-2018      268.55      268.90      274.60     266.25   \n",
       "297    ITC     EQ  20-Jul-2018      273.30      273.25      274.80     270.65   \n",
       "298    ITC     EQ  23-Jul-2018      274.00      279.50      286.65     277.15   \n",
       "299    ITC     EQ  24-Jul-2018      284.15      284.50      287.00     283.55   \n",
       "\n",
       "     Last Price  Close Price  Average Price  ...      Turnover  No. of Trades  \\\n",
       "15       317.50       317.45         316.83  ...  3.004339e+09          69774   \n",
       "16       311.30       311.05         311.28  ...  3.063364e+09          87515   \n",
       "17       311.70       312.25         313.15  ...  3.611978e+09          91062   \n",
       "18       311.20       311.60         310.82  ...  2.418657e+09         111593   \n",
       "19       305.70       306.00         307.40  ...  3.094129e+09         103882   \n",
       "..          ...          ...            ...  ...           ...            ...   \n",
       "295      268.15       268.55         269.28  ...  1.898492e+09          51220   \n",
       "296      273.25       273.30         270.99  ...  2.427206e+09          78638   \n",
       "297      274.35       274.00         273.32  ...  1.796403e+09          53813   \n",
       "298      283.95       284.15         283.57  ...  7.388650e+09         147326   \n",
       "299      285.05       285.65         285.37  ...  3.456265e+09         116799   \n",
       "\n",
       "     Deliverable Qty  % Dly Qt to Traded Qty  % change in op_cl  \\\n",
       "15           6130621                   64.65           0.771775   \n",
       "16           6116482                   62.15          -2.073622   \n",
       "17           8921486                   77.35           0.304243   \n",
       "18           5953664                   76.51          -0.449294   \n",
       "19           5925048                   58.87          -1.552288   \n",
       "..               ...                     ...                ...   \n",
       "295          4655530                   66.03          -0.949544   \n",
       "296          5238088                   58.48           1.609952   \n",
       "297          3190994                   48.55           0.273723   \n",
       "298         14378063                   55.18           1.636460   \n",
       "299          7696166                   63.54           0.402591   \n",
       "\n",
       "     % change in lw_hi  % chng 5dymean   % chng 5dystd  Action  % chng 5dymean  \n",
       "15           -1.579955         0.001064       0.001064    -1.0        0.001064  \n",
       "16           -3.136114        -0.000182      -0.000182     1.0       -0.000182  \n",
       "17           -1.417754         0.000365       0.000365     1.0        0.000365  \n",
       "18           -1.244344        -0.001591      -0.001591     1.0       -0.001591  \n",
       "19           -2.592714        -0.008368      -0.008368    -1.0       -0.008368  \n",
       "..                 ...              ...            ...     ...             ...  \n",
       "295          -1.382661        -0.005219      -0.005219     1.0       -0.005219  \n",
       "296          -3.136150        -0.002733      -0.002733     1.0       -0.002733  \n",
       "297          -1.533346         0.002685       0.002685     1.0        0.002685  \n",
       "298          -3.427747         0.009206       0.009206     1.0        0.009206  \n",
       "299          -1.216717         0.011551       0.011551    -1.0        0.011551  \n",
       "\n",
       "[285 rows x 21 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "val = []\n",
    "\n",
    "for value in df['Close Price'].iteritems():\n",
    "    arr.append(value[1])\n",
    "    \n",
    "for i in range(0,300):\n",
    "    if arr[i+1] > arr[i]:\n",
    "        val.append(1)\n",
    "    else:\n",
    "        val.append(-1)\n",
    "\n",
    "df['Action'] = pd.DataFrame(val)\n",
    "df = df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.593"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "train_X = df[['% change in op_cl','% change in lw_hi','% chng 5dymean','% chng 5dystd']]\n",
    "train_Y = df[['Action']]\n",
    "\n",
    "# transformed_label = le.fit_transform(df[['call']])\n",
    "# train_Y = transformed_label.reshape(-1,1)\n",
    "\n",
    "import sklearn as sk\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)\n",
    "RF.fit(train_X,train_Y)\n",
    "RF.predict(train_X)\n",
    "print(\"Random forest\")\n",
    "round(RF.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cd93329550>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABH4AAAI/CAYAAAABYR7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3hb5fn/8feRvPfe27FjO8vZiwxCGGGXQllllJZCS1ugC2jpF0qhhbZ00F8pq6yyR4CEQIAEsndixyPxtuO9lzwka5zfH7KdON62PHO/rosrRDo6epzYis6t5/7ciqqqCCGEEEIIIYQQQojpRzPRCxBCCCGEEEIIIYQQY0MKP0IIIYQQQgghhBDTlBR+hBBCCCGEEEIIIaYpKfwIIYQQQgghhBBCTFNS+BFCCCGEEEIIIYSYpqTwI4QQQgghhBBCCDFN2Y3nk/n5+alRUVHj+ZRCCCGEEEIIIYQQ09rRo0drVVX17+u+cS38REVFceTIkfF8SiGEEEIIIYQQQohpTVGUU/3dJ61eQgghhBBCCCGEENOUFH6EEEIIIYQQQgghpikp/AghhBBCCCGEEEJMU1L4EUIIIYQQQgghhJimpPAjhBBCCCGEEEIIMU1J4UcIIYQQQgghhBBimpLCjxBCCCGEEEIIIcQ0JYUfIYQQQgghhBBCiGlKCj9CCCGEEEIIIYQQ05QUfoQQQgghhBBCCCGmKSn8CCGEEEIIIYQQQkxTUvgRQgghhBBCCCGEmKak8COEEEIIIYQQQggxTUnhRwghhBBCCCGEEGKaksKPEEIIIYQQQgghxDQlhR8hhBBCCCGEEEKIaUoKP0IIIYQQQgghhBDTlBR+hBBCCCGEEEIIIaYpKfwIIYQQQgghhBBCTFNS+BFCCCGEEEIIIYSYpqTwI4QQQgghhBBCCDFNSeFHCCGEEEIIIYQQYpqSwo8QQgghhBBCCDFFvLK3kKe/zJ7oZYgpRAo/QgghhBBCCCHEFKCqKs/vLOCVvUWYLepEL0dMEVL4EUIIIYQQQgghpoDsKh2VzXpaDCbyqlsmejliipDCjxBCCCGEEEIIMQXsyK7p/v/UkoYRn8diUbn/3VRe3FVgi2VNqGd35LHxWOlEL2NSk8KPEEIIIYQQQggxBezIrmZmoDuezvakljSO+DxvHirmo5Qy3jtSYsPVjb/KJj1Pf5nDC9OggDWWpPAjhBBCCCGEEEJMci0GE0eKGlib4M+8cC9SikdW+Cmpb+NPn53EwU5DbnULTW3GXsc8seUEt718CFXtnSO0M6eGJ7acGNFz29q7h0swW1Syq3R9fh3CSgo/QgghhBBCCCHEJLc3rxaTRWVtfADJ4V7kVOloNZiGdQ6LReWBD9PQKAp//NYcAI6d1TKmqiofpZSzM6eGb7Kre9ynN5p56MM0XtxdiN5oHt0XNEoms4V3Dhfj5+aAqsKx4pG3vk13UvgRQgghhBBCCCEmuR3ZNbg6aFkY6c38cC8sKqSVNvU4Rqc3Dhj6/NahYvbl1/HbyxLZMDsIrUbh2KmeBZO86hZqWwwoCjz9ZQ6WM6aH/W//Kcqb9ACUNrTb8Ksbvh3ZNVQ06fntZYloNQpHTtVP6HomMyn8CCGEEEIIIUakw2Th5T2FPLU1q/u/z9MrJnpZQkw7qqqyK6eGlTP8cLDTkBzuBdAr5+f3m09w+b92U9/a0escpQ3WFq/zZvhxw+JwXB3tSAx25+hZhZ99+XUA3HdBPJnlzXyRWQlAs97Iv3fkEeThBEBJQ5vNv87hePPgKQI9HLl8bgizQzw4XCQ7fvojhR8hhBBCCCHEiLyyt5DHPj3BS7sL+O/uQp7fmc8v3j/eY4eAEGL08qpbKGtsZ+3MAAC8XR2I8nXpMdmrqc3I5uPl6I3WFqizPbM9F5NF5clvz0FRFAAWRniTWtKIyWzpPm5ffi1h3s78ZN0MYv1d+dtXOZgtKi/sLKCxzcgfr5kNTOyOn5L6Nnbk1HD9onDstRoWRflwvKSRDpNl8Aefg6TwI4QQQgghhBi26mY9z2zPZX1iALlPXErOExt48pq5tHWYOVU/sTsBhJhuduZYx7ivmenffVtyZ8BzVwDzx6llGEwWIn1deGP/qR7FnIqmdj5KKeP6xeGEebt0374wyoe2DjNZlToAzBaVAwX1rIj1RatR+PmFM8mtbuHlPYX8d08hl88NZm18AA52Gkon8Of8ncPFKMD1SyIAWBTpjcFkIaO8aeAHnqOk8COEEEIIIYQYtqe2ZmM0qzx8WVL3bUkhHgCcrGieqGUJMS3tyK4hLsCNUC/n7tuSw72o1hmoaNKjqipvHypmTqgnD21IpLxJz7aTVd3H/nd3IRYV7lwV0+O8CyO9AbrbvU5WNNPUbmRFrB8AG2YHkRjswROfncRotvDLi2ai0SiEeTuPaauXNatI1+d9RrOFdw+Xsi4hoPvPY2GU9es4UiQ5P32Rwo8QQgghhBBiWFKKG/jwWCl3nBdNlJ9r9+0zAtzQahROlEvhRwhbaTWYOFRYz9ozdvsAzI+wFjtSSxpJLWkkq1LHjUsiWJ9oLYi8uq8IgIbWDt46VMyV80II93HpcY4QTyeCPJy6Cz/7O/N9lsf6AqDRKPziwngAblgS3v3zHubtQkn92LV6Pbsjn0v+sZtDhb0LOe8cLqG2xcDNSyO7bwtwdyLS14UjkvPTJyn8CCGEEEIIIYbMYlF5dPMJAtwd+cm6GT3uc7LXMsPfTXb8CGFDu3Jq6DBbuvN9uiQGe+BgpyG1pJF3DpXg4qDlyuQQ7LQablkeyYGCerIqm3l9/ynaOszctSam17kVRWFhpHd34Wdffi2x/q4EdgY4A1yQGMBz313AgxsSu28L93amdAx3/ORWtWCyqPz4zWNUdk4RA9iXV8vvN2WycoYvq+N7FsIWRfpw5FRDd+ubOE0KP0IIIYQQQoge3j5UzIGCuj7v+/BYKcdLGnlwQwJujna97k8MdueEFH6EsJkPj5US6OHIshjfHrc72GmYFeLB7txaNh0v58p5Id0/k9cvCsfRTsPzOwt4dV8hFyQEkBDk0ef5F0R6U9bYTmlDG4cK67vbvLooisIls4N7/LyH+7jQ0GakxWCy8VdrdaqulYQgd9o6TPzozaMYTGbyqnXc9cZRov1cefbmhWg1So/HLI7ypr61g4La1jFZ01Q2pMKPoihFiqKkK4qSqijKkc7bfBRF+UpRlNzOX73HdqlCCCGEEEKIsbY1o4KHNqZz68uHuts+uuzPr+N3n2SwMNKbq5ND+3x8YrAHFU16Gtt6j5MWQgxPtU7PN9k1fGt+WK9CB1hzfk5WNNNuNHNDZ9AxWKd+XZ0cykcpZTS0Gfnx+bH9PkdXzs+re4to7TCzIta332O7hHlbs3VKxiDg2WJRKa5vY1WcH3+9bh4pxY088EEat79yGEc7La98bzGezva9HreoM+fnqLR79TKcHT/nq6qarKrqos7fPwhsV1U1Dtje+XshhBBCCDFBzBaV3Kq+wzDF1GG2qPz83VSOFY//xUtlk54HN6YzO9SDSB8XfvDaYY6XNAJwsKCOO149TLi3C8/fshBNHxehcDrgWXb9CDF6n6SUY7aoXLswrM/7u3J+EoLcmRfm2eO+21ZEAbAkyoeFkT79PkdSsAeOdhrePGgdAX/2zqK+hHdOBhuLke5VOj0Gk4UIX1cunRPM3Wti+Ti1nNoWA/+9bVGPqWRnivV3w9vFnsMS8NxL772ZQ3cVsLbz/18DdgAPjHI9QgghhBBihD5NK+e+d1P56v41zAhwm+jliBHKrtSxMaUMi6qyIGL8NtVbLCq/fP84BqOFZ26Yj6ujHdc+t4/bXjnEg5ck8NinJwj1duatO5fh5+bY73kSgzsLP+XNvVpGhBCn1egMdJgt+Ls54mDXe0+Gqqp8cLSU+RFe/b6mL4r0xk6jcOvyKBSlZzE2KcSDx6+ezeKo/os+YG0ZmxfmxaGiepKCPfB2dRh07V0h0WOx46eo1nrOKF/rc/zq4pkoCqyI9WVeuFe/j7PmFfl05xWJ04Za+FGBLxVFUYHnVVV9AQhUVbUCQFXVCkVRAgY8gxBCCCGEGFMnKppRVWs4pxR+pq6UEutFy86cGswWtc/2jrHw8t5C9uTV8qdr5hDjb/3+efP7y7j2uX08uDGdWH9X3rpzKf7u/Rd9APzcHAlwd+Rkhew+E6I/rQYT657egU5vzcjxdrFnZpA7z9wwn4DOYOWMsmayq3Q88a3Z/Z4nxMuZvQ+uI6Cfn8vvLovs8/azLYj05lBR/ZDavLrW6+KgHZOR7sX11oyeKF/rBDGtRuGBSxKG9NhFUd5sO1lFdbO++89RDL3Va6WqqguADcA9iqKsHuoTKIryQ0VRjiiKcqSmpmZEixRCCCGEEIPLr7a+WT5YINvcp7KUYmtrVUObkbTSxnF5zqzKZv68NZuLkgK5YXF49+0Rvi68+YOl3LgknLfvXEaA+9AupBKDPQZs9VJVldtfOcSfPjs56rULMRVtz6pGpzfx03UzuH99PJfOCSattInvvXqY1s7A5PePluBop+HyuSEDnivQw6nXbp/hWhpt3RW0Mm5ou/QURSHc22VMWr2K6tqw1yoEew6/cLMm3h9FgRteOEBGWZPN1zZVDanwo6pqeeev1cBHwBKgSlGUYIDOX6v7eewLqqouUlV1kb+/f1+HCCGEEEIIG8ivaQHgYGGdjLOdwlKKG1gY6Y1GgW+yx+eD049TylFRefLbc3tdQMYFuvOna+YO69PzpBAP8qp1dJgsfd5/5FQDO7JreH5XAe8dLhnV2oWYij49Xk6AuyP3rY/n3vVxPPGtOfz7pgVkVeq4561jtHWY+CS1nItnBfUZZGxra2f68/ady1gbP/Rr9nAf5zFp9TpV10qYtwt22uEPIU8M9uDNHyylrcPMt57dy0u7C7BY5N/DQf8kFUVxVRTFvev/gYuADGATcFvnYbcBn4zVIoUQQgghxMAMJjPF9W0EezpR29JBfo2Ms52KmtqM5Ne0si4hgPkR3uzM7vOzVZvLqdIR6++GzxCyPYYiMdgDo1klr7qlz/vfOVSCq4OW5TG+PPxJxrjtbBJiMtDpjezIqeHSOcE9WjnPTwjg8atnsyO7hmv/s5+mdmO/oc62pigKy2N9h7VzKKxzx4+tP2g4VddGpG/fAc5DsSLWj8/vXcX5MwN4fMtJHtyYZsPVTU1DKaEFAnsURTkOHAK2qKq6FXgSuFBRlFzgws7fCyGEEEKICVBc14bZonJ9Z5vOwcK6QR4hJqPUzgLI/HAv1sb7c7y0iRqdYcyfN7tSx8wgd5udLynYeq6TfbR7NbUb2ZJezpXJofz75gX4uznyozeOUd/a9/j3jLImbvnvQQprpZgppoevTlTRYbJwxbzgXvfduCSCn5w/gxMVzQR5OLFyxuQNSA/zdqbFYKKp3Wizc6qqyqm6tu58n5HydnXg+VsWcu3CMD5JLe939+G5YtDCj6qqBaqqzuv8b5aqqk903l6nquoFqqrGdf4qzeRCCCGEEBOkq81rXUIAgR6OkvMzRaUUN6AoMCfMk/MTrLNTduX0bPd662AxL+zKJ720CbMNWhh0eiNlje3EB9qu8BPt54aTvabPnJ9NqWXojRZuXBKOj6sDz968gBqdgXvfSaGlM9uky768Wm544QC7c2vZfLzcZusTYiJ9mlZBqJcz88P7ntr3i4vi+fmF8TxyRdK4hbuPxOnJXrbL+alr7aDFYCLCZ+Q7frooisK6hAAMJguZ5ed23s9oxrkLIYQQQohJoqulJtbfjaXRvhwosOb8jDbwU4yvlOJG4gPccXeyJynYA393R3bk1PDtznaPr7Oq+M1H6d3HezjZsSrOn79eNw9nB+2InjOnyvq9M9OGhR+tRmFmoHufO37eOVxCUrAHc0I9AZgX7sUfrp7FAx+ms+qpr7lrTSy3Lo9kR3YN972TSpSfCwaThcNFUswUU19Tm5HduTV8b2U0mn6KOoqi8LML4sZ5ZcMX5u0MQElDG3PCPG1yzlN1naPc/UZf+AHruHuAo6camB/Rd6HtXDD8tCQhhBBCCDHp5Ne0EuzphKujHUtjfKjWGSiqs33ophg7FotKakkj8yO8ANBoFNbE+7MrpwaT2UJ9awe//iCdhCB3dv/6fP55QzLnxfmxJb2iewT8SORUWceu27LVC6wBzycrmnvkf6SXNpFZ3swNS8J7FCWvXxzBx/esZG6YF09+nsXKJ7/mnreOMTfMk/fvWsHqOH+OnWrAZD632zXE1JNV2YzeaO7+/ReZlRjNKpfP7d3mNdV07fgpteFI91N11pbOyFG2enUJ8HAi3MeZo6dG/ho5HUjhRwghhBBiGsivaWFGgBsAS6N9AThYIDk/U0lhXStN7cbuwg/A+TMDaGo3klrSyO8+zqCpvYO/X59MuI8LVyWH8n+XzwLoN0R5KLIrdbg4aAn1ch7113CmxGAPGtqMVDbru297+3AxTvYarkoO7XV8crgXr92xhA9/tJy5YV5cPjeE/31/KZ4u9iyK8qa1w0xWpc6maxRiLOVW6bjkH7u5+t97u39GN6eVE+Hj0r3jbSrzcLLH09nepq1eRXVtaJTTu4lsYWGEN0dONZzT0y6l8COEEEIIMcWpqkp+dQux/tbCT6y/K35ujhwslNaYqSS1uDPY+Yx2hPPi/NBqFB7dnMmW9AruWx9PYrBH9/2BHo64O9l179oZiZwqHXGB7v22nYxUUuc6v8mqoa7FQKvBxKbUci6dEzzgeOqFkT68dscS/nXj/O72tcVRPgAckXYvMYVsz7JO5atq1nPFv/bw8p5C9uXXcfnc4GnThhvm7UyJDXf8FNe1EuzpjKPdyFpX+7IwyocanYHSBtsVqKYaKfwIIYQQYsRUVR2XiUNiYJXNelo7zMT6W7fGK4rC0mgfDnbm/IipIaWkAXdHO2Z0FvAAPJ3tWRjhTUZZM/MjvLhrdUyPxyiKQlyAG7lVI9/xk1OlY2ag2+AHDlNCsAcOWg2/+SidhY9vY86jX9BiMHHjkohhnyvEy5lQL2cOF53b7RpiatmZXUNCkDtb71vNvHBPHvv0BGaLyuVzQyZ6aTYT7u1CSb3tCj9FdW02y/fpsrCzmH7k1LlbOJbCjxBCCCFG7MXdBax4cjtljefup2iTQX61NRMhNuD0xfvSGB/Km/Tn9CecU01KcSPzwr167bzZMCcIN0c7nr5uHnba3m/f4wLcR9zqVdtioLalg5lBHoMfPExujnZ8du8q/nPzAh69Iom71sTyy4viu8NWh2tRlDeHi+qlmCmmhBaDiSOn6lkz059ADyfe/MEyfnXxTG5YHE5isG3ztCZSuI8zpQ3tNvu5PFXXarN8ny4zg9xxd7TjyDlcOJapXkIIIYQYkapmPf/YlovRrPJ1VjW3LIuc6CWds7pGuZ+5U6Qr52d/QV13AKeYvNo6TGRV6vjx2the992+IorrF4fj4tD3W/e4QDfePVJCXYsBXzfHYT1vd7CzDSd6nWlGgFt39tRoLYry4ZPUckrq24nwle9pMbnty6vFaFZZGx8AWCfd3XP+jAlele2F+1in7tW0GAhwd+p1f4vBhJ1Gwcl+8NatpnYjDW1GIm38b5ZWo5Ac4XVOBzzLjh8hhBBCjMiTn2dhsqj4uTmyM7t6opdzTsuvacHd0Q5/99MX/XEBbvi4OnCw4Nzd2j6VpJc2YbaoPYKduyiK0m/RByCus2gzkl0/OZ1hyfFBtm/1srXFUdadQmMx1r1Zb+T+d1Nt2rIizm07cmpwddCycIQ73KaK7pHuZwU8G0xmntuZz7I/bufWlw9htgy+I6i4cxKlrXf8ACyM9Ca7Skez3mjzc08FUvgRQgghxLAdPVXPRyll/HBVDBtmB7Evvw6DyTz4A8WYyKtuISbArUdYqEajMD/ci/SyxglcmRiqlBLr31Ny+PAvEuM6d9TkjKDwk12lw9vFHv9h7hSaCPEB7rg72Y1JTsfbB4v5KKWMj1PKbH5uMf1sPFbKJ6ll/bY3qarKzuwaVs7ww8Fuel9yh3v3HOne1mFia0YFF/5tF09+nkWsvyuHCut5eU9hr8d+nVXFxmOl3b8v6hzlbuuMH4BFkT6oqrWl9lw0vb8LhRBCCGFzZovKo5tOEOThxI/Pj2XtTH/aOszndO88gMWiUtbYTvkE5B3l17T0aPPqMivEg/yaVvRGKcpNdnvzaonxd8XH1WHYjw32dMLVQUveCCZ7ZVfqiA90nxIThjQahUWR3jYPeDaaLby6rwiAA4V1Nj23mDreO1zCve+kDJpV02ow8dDGdO59J5WfvZPa5w6S/JoWyhrbWTPTf6yWO2mEdRZ+HtmUyexHviDp/77g7jeO4Wyv5X/fX8LH96zkwqRA/vJlNrlnvEZtSavgB68d4efvHWdXTg0AxZ077iLGoD05OcILjcI52+4lGT9CCCGEGJb3j5SQXtbEP29IxsXBjuWxvjhoNezIrmblDL+JXt646jBZeHzLCQ4XNVBY24LeaEGjwHeXRfKLC2fi6dL/yGpb0emNVDUbiA3ovTU+KcQDs0Ulu1LHvPDeLURicmho7WBffl2viV1DpSgKMwLdyR3mjh9VVcmpauGaBaEjet6JsDjah2+ys6lv7RhRkawvn6VXUNGkZ2agO0dPNWAwmW06SlpMflmVzTz8cQYdZgvXLgxjVVz/BZsd2TUYTBauTg5hc1oFx0sa+deN83u8xu7IthYy1s4MGPO1TzRnBy0/XB1DWWM7Ae6O+Ls7Eu3ryoVJgd1h9H/81hwu+vtOfvH+cTb+aAU7smu4950UFkR409Ru5OfvHWfrfasoqm0lwN1xwNbWkXJztCMhyIOj5+hkL9nxI4QQQoghK65r48mtWSyO8ubKedZxtC4OdiyJ9ul+o3sueedwMa/vP4WfmwM3L43kiW/N5tblUbxx4BTrnt7B+0dKsAwh12A0Cmo6J3r1seMnKdgTgBMVzWO6BjE6X56oxGxRuXRO8IjPERfgNuzCT3mTnhaDifgxCnYeC4ujfADbfWqvqiov7i4g1t+V+y+MR2+0cLykySbnFlNDh8nC/e8ex8PZDl9XB17r3P3Vn62Zlfi6OvD0d5J5765lmC0q3/7Pvh5tgjtzaogLcCPUy3mMVz85/ObSRP590wIeuWIWP147gw1zgntMIPR3d+Txq+eQVtrEve+k8uM3jzErxINXvreYZ26cT7PeyC/fP05RXStRY5Dv02VRlDcpxY2YzJYxe47JSgo/QgghhBiSZr2RO147jKrCU9+e26M1ZO1Mf3KrW86pse4tBhP/3JbLshgfXr9jCb+7PImbl0by6JWz2PzT84j0deFXH6Tx07dTMI7hm8yuQN++JieF+zjj7mhHZrlcyA5HSX0bf/zsJA2tHePyfJ+lVxLh48KskJGPVI8PdKNGZ6Cxbehr7gp2nhk0dQo/c0I9cdBqOGKjgOdDhfVklDXz/fNiWBbjg6LAgQJp9zqXPLM9l5MVzfzpmrnctDSC7VnV3SHDZ9MbzXx9soqLZgWi1SgsjPThs5+tYnGUD/e9m8pr+4po6zBxsKCeNfHTv81rOC6bG8wV80LYkl5BjL8rr92xBHcnexKDPXj4skR2ZNdwuKiByDGc2Lcw0pu2DjNZlcNvi53qpPAjhBBCiEGZzBbuefMYRbWt/Oe7C4g5a3fJ2s4cgx3n0HSvF3cVUNfawUMbEnvlo8wK8eSDu1fwwCUJbEmv4L53Uses+JNf04KdRukzE0FRFBJDPDhRLjt+hmprRgWXPrObF3YV8Enq2Af9NrUZ2ZtXy4Y5QaPK2YkLsBZvhrPrJ7szbyM+YOoUfpzstcwN82TbySrSS5sGzWMZzEt7CvF2seeaBaF4uTiQGOQhhZ9zSEpxA8/uyOPahWFcmBTIzUsj0SoK/ztQ1Ofxe3Jrae0wc8ns07vzPF3seeV7i7koKZBHNmXy4zeP0WG2nBNtXsP1+FWzuX99PG/8YCleLqdbNW9ZFsn6xEAAovzGbsfPkmhrcfef23PPuV0/UvgRQgghxIBUVeXRzZnszq3liW/NZkVs7xyfWH/rlvZzpd2rWqfnxd0FXDYnuN/sHI1G4UdrY3n4ssTu4s9YvNHMr2kh0tcFe23fb+tmhXhwskI3pFG65zKDycwjn2Rw9xvHiPFzxd/d0eYhwn356mQVJovKpbNH3uYFp3d85VYNvfCTU6kjyMNpXLKobOmmpRGU1Ldzxf/bwwVP7+Qf23Jo7xh+gHlhbSvbTlZxy7JInOytmT7LYny7c37E9NbWYeIX7x8n2NOZ/7siCYAgTycunh3Eu4dLaOsw9XrM1sxK3J3sWB7j2+N2J3stz968gOsWhrEjuwZney2Lo6f3GPeR8HSx5971cfidNUVQURT+fO1c1icGdn+QNBaCPZ155PIkvjpRxa8/SBvzVuzJRMKdhRBCCDGgZ3fk88aBYu5aHcP1iyP6PEZRFNbM9OeTlDI6TJZpP772X9vz6DBZ+OXFMwc99gerrIG9j285CQo8c8N8tBrbTFAqqW8jo6x5wBahpGAP2o1miupa+8wBEtbi5o/eOMbXWdXcsTKaBzck8KsPjrMvvw5VVcd04tVn6RWEejkzN8xzVOcJ9XLG2V5LbvXQWxiyq3RTqs2ryzULwrggIZCtmRV8klrOP7bloqpw/4XxwzrPq3sLsddo+O7yyO7blsf68vLeQlKLG1l61sW9mD5UVeXhjzMorG3lze8vxcPpdPHz9hVRbEmr4OOUcm5aevrfPKPZwlcnqrgwMbDPf+PstBr+fO1covxcsdcqEhA+TD6uDrx026Ixf57bV0aj05t4+qsc3Jzs+P2Vs6bEVMPRmt7vyoQQQggxYqqq8qfPTvKXL7K5cl4ID1ySMODxa+P9ae0w2yx7Y7IqrG3l7UPF3Lgkgughbkn/waoYHtyQwJa0Ct48eGpUz2+2qHyaVs4t/z3I6r98Q3lTO5fMDur3+KTOolDmAO1eqqryTVY1rYben3CPxs6cGt44MLqvdzzsyavl66xqHtyQwP9dkYSDnYYl0T7U6AwU9ZP1YQvNeiO7c2vYMHt0bV5g3WEWF+jWnfk0mFaDidzqlpIVojwAACAASURBVClZ+AHrzoHrF0fw1p3LSAr2GHbYs8lsYXNaBRfPDiLA3an79iVRXTk/0/t17Fz37uESNh4r494L4lhx1jTKRZHeJAV78Nq+oh6thAcK6mhqNw74eqsoCvecP4Mfro4ds7WL0fvJuhnctTqG1/ef4i9fZE/0csaFFH6EEEII0YvJbOGBD9N4flcBtyyL5O/XJ6MZZJfKihl+2GsVduRM/XavvGodRbWtvW7vmjziYKfhZxfEDeucd62OYVWcH3/emk1lk37Ea3tmey4/eSuFgppW7r0gjj0PrOOaBWH9Hh8X4I69Vhkw5+f1/af43quHbfoG2GxRefjjdB7bfAKd3miz89qaxaLy1NYsQr2c+d7KqO7bl3ROjzpcOHYFgO0nqzCaVS6dO7o2ry4zAtzIqRrajp8/fHoCo9ky4EXsVJEc4cXxksZhtW0cLmqgvrWDS8/6+j1d7EkK9mB/Qa2tlykmiYyyJv5vUyar4vz46brer+OKonD7iiiyq3RsO3k6t25rRiUuDlpWS2jzlKcoCg9uSOCmpRG8f7SUuhbDRC9pzEnhRwghhBA9mC0q97x1jPeOlPKzC+J47KpZQ2pNcnO0IynEk4yyqT1Bqlqn55pn93HxP3bx9qHi7k9861s7uPnFgxwvaeSv183D391xkDP1pCgKj189G6PZwqObMke0NpPZwtuHilk7059dvz6f+9bHDzou2MFOQ1yAe7+TvY6eauDxLSew0yhsPFaK3mibbJNdOTWU1LfTYbZM6uynLekVZJQ18/ML43u0ZswIcMPH1YFDY7iDbUtaJcGeTiSH9Z0TNVxxAe5UNRtoah+40PZFZiXvHC7h7jWxLIiY+jkkyeFe6AwmCmqHnm/0RWYljnYa1vSRJ7I8xpdjxY02+1kQk0ez3sg9bx3Dx8WBf1yf3O+/bVcmhxDq5cydrx/hJ28dI7+mhS8yqzh/ZkB3HpSY2hRF4Q9XzWbzT87D1214/55PRVL4EUIIIUQPHxwt4YvMKn57aSI/vzB+WC0oYV7OVIxiN8tk8MgnmehNFuZHePHQxnTuezeVgpoWrn9+PzlVOl68dRGXzhnZDo1IX1d+dkEcWzMr+epE1bAfvyu3hmqdgRuXRAwrJ2hW52Svsycg1bYYuOfNYwR7OvPvmxfQrDexJa1i2Ovqy+v7i/B3d8TPzYEvMittck5bM5otPP1lNjMD3bl6fmiP+xRFYVGkN4dGueOnvcPcY6Kb3mgmtaSR/x04xa7cGi6ZHTTobrqhiusMeB6o3atap+ehjenMCvHg/vXDy8SZrOZ3Bqynlgyt6GyxqGzNqGRNvD8uDr0jT5fF+NJhspBa0mjTdYqJZbao/PzdVMoa2vn3zfMHvNh3stfy+X2r+Om6GWw/Wc36v+2ktsXAxdNgh5w4TatRCPJ0GvzAaUDCnYUQQgjRrdVg4q9f5rAw0psfrIoe9uNDvJzYdrJqzANxx8rWjAo+z6jk15fM5O7VsfxnZz5Pf5nNJ6nluDpoefV7S1geO7rA1x+ujmFTajn/90kGy2N9cXMc+tux9w6X4ufmwLqE4Y0JTgrx4P2jpdToDAR4WN/kmswWfvpWCg1tHWz88QqSgj2I8XPlrUPFfHth/61jQ1Fc18aOnBp+ui6OGp2ezccrMJjMky7s9N3DJRTVtfHf2xb1WUhbEu3DlyeqqGrWE+gx/IuDT9PKuf/dVIxmFQc7Da4OWnR6E6bOlqRAD0duWtJ3YPpIxAda83ryqnUsjOy9k0dVVX79QRqtBhP/vCF52oSwx/q74eZoR2pJA9cO4Xs3rayJymY9v57ddzj74mgfNArsz69jmQQ8TxuPbznBtpPVPHbVLBZG+gx6vIeTPb+4aCa3LI/k/32dR1pp07Bfe4WYLKTwI4QQQohuz+/Mp0Zn4PlbFo6ocBPs6YzBZKGhzYiPq8MYrHDsNLUZ+d0nmcwK8eDOVTFoNNaQzkWR3jy7I5/71scx3wZtMfZaDX+8Zg7XPrePf32dy0MbEof0uLoWA9tOVnH7iqh+R7f3Jyn4dMBzV+Hn79ty2F9Qx1+uncusEOtEqRuXRPDEZyfJrhzdtKc3Dp5CoyjctCSCkxXNvH2ohH35dZw/c/JcNLV1mPjn9lwWRXr3ezG3JNp6cXiosJ4r5oUM6/x1LQZ+93EG8YHuXDIriJYOE60GE57O9swJ9WR2qCehXs42LZCGejvjZK/hYEE9Lg52ZJQ3kVvV0r3jqK3DzNFTDfz+ylnMCJiaoc590WgU5oZ5DnmHztaMSuw0ChckBvZ5v6ezPbNCPNlfUMf9tlyomDCv7i3klb1F3LEymluXRw3rsQHuTjx21eyxWZgQ40QKP0IIIYQAoKKpnRd2F3D53OAR536EeFmLCuWN7VOu8PPEZyeob+3gldsX9yisLI3xtflY54WR3qyJ9+frk9VDLvx8nFqOyaJy3aLwYT9fYudkrxMVzZyfEMDevFqe3ZHP9YvCe5zv2wvD+MsX2bx18BS/H+GFjt5o5r0jJVw8K5AgTye8Xe1xddDyZWc+xmSxKbWcGp2Bf9+0oN/iS1KwB64OWg4XDb/w8/iWk7QYTPz9+uTunThjTatRmBHgxsaUMjamlOGg1RAb4Iaz/env5++fF82tZ4wvny6Sw714YVcBeqN5wAwWVVXZmlHBihl+eDrb93vcmnh/nt2RR0l9G+E+LmOxZHGWGp0Bo9lCyCC5ZcO17UQVj316gguTAvntZUN7vRViupHCjxBCCCEA+OsXOVhUBh3bPpBgT+sb9vLGdmaHetpqaWPuQEEd7x0p5UdrY8dt3fPCvNiVU0Nbh6nPnJEzqarK+0dKmBfuNaKdOB5O9kT4uJBZ3kRdi4H7300lxs+VR65M6nGcj6sDG+YEsTGljAc3JOLsMPzWrE3Hy2lsM3LLsigAHO20rE0I4KsTVTx+9exhZRONpW0nqwjzdmZxVP9FTjuthgUjyPnZmVPDRyll/OyCuHEr+nT547fmkFWpY1aIB3EB7tOmnWswyeFemCwqGWVNLIrqv40nu0pHUV3boOO2b1keyfO78nlpd8GIi6BieH79wXH2F9Tx52vnceUwC619MZotvHekhMc/PcmsEE/+eUP/Yc5CTHfnxr8EQgghhBhQRlkTG1NK+d7KqFF9ut31Se1UCnhWVZWnv8wmyMOJe4c5on005oR6YlEZcMx6l/SyJrIqdVw3iuydWSEeZJQ18+sP0mhsM/KvGxf0WXC6aUkEOr2JzWnlQzrv8zvzuePVw/z1i2w+T6/gtX1FxAe6sSzm9MX3xbOCqG0xkFrSMOL121J7h5ndubWsTwwctNVqSZQP2VU6mtqGNpK+1WDiNxvTifV35Z7zBy4ujIW5YV58Z1E4s0I8z5miD1hHugODtnttzahEUeDCpL7bvLoEejhxdXIo7x4pob61w2brFH1TVZVjxY2YLSo/ezuFp7ZmYbaogz+wDxaLyqbj5Vz4t5389qMMZod68N/bFg1aYBdiOpPvfiGEEELwp89P4u3iwD3nzxjVeXxdHXDQaihvarfRysbegYJ6DhdZc0/Gc0zvnDDrzqK00t47FP534BSbU8tZFefH+QkBvHekBEc7zbDbjc6UFOzB5xmVFNe38egVSSR1tn+dbUm0D7H+rrx1sJjvDKGt7KU9hbR3mNmZU9N9ofaHq2b1KKisnemPvVbhy8yqIYWqjrW9ebUYTBYuSBy89WxxtA+qCkdO1febCXOmv32VQ1ljO+/fvXzShVlPZwHuToR6OQ+p8LM40gd/98HHN/9wdQzvHy3l9f1F3DdNJqBNVmWN7TS1G3n0iiRyqlv4z458MsubmR/uRU2LgepmAxE+Lvzm0gTsBsg4O1RYz2OfZpJR1kxCkDsv376I82cGTMlhA0LYkhR+hBBCiEluR3Y1dS0do5601J89ubXszavjkSuS8HDqP/NiKDSdo1HLG6fOjp9ntucS4O7I9YuHn50zGoEeTgS4O5JR1nsE9ev7iihrbOdQUT1Pf5UDwNXJIQNmkgymq9BzQUIAt62I6vc4RVG4ZoE166dGZxjwArm6WU+NzsAjVyRx45IIsit1lDa0c9GsngUSDyd7VsT68UVmJQ9uSJjwi7DtWVW4OdqxNHrw7KbkcC8ctBoOFVoLPxaLSrPeiKezfa+v41RdK6/uK+LGJREsHqDdSIyNeeEDBzwX1baSVanjd5cn9XvMmeIC3bkgIYDX9hVx1+rYEbU+iqHJ7Nz5OC/ci9tXRpMU7MHvN2eyK6cGX1cHvF0d2HayCq0GfntZ77+/ssZ2/vTZST5NqyDY04m/Xz+Pq+aFopHWLiEAKfwIIYQQk96Tn2dRUNPKqji/7olMtqKqKk9tzSLUy5mbltpmrHSIlxMVjZNrx09etY63D5VQ2azn8atm490ZPH24qJ79BXU8fFniuO726TIn1JP0swo/1To9udUtPLQhgWsWhLEju5rDRfXcuSpmVM91Xpwfv7wonpuXRg7e3tQ5zSq1pHHAlpiui7VZIZ442WuZF+7FvHCvPo+9aFYgv/0og9zqlnHLvVFVFb3R0uOC3WJR2XaymjXx/kNqhXKy1zI3zJM3DxbzcWoZtS0dmC0qv7wonp+s69ka+M/tudhpFO5fP34tg+K05HAvPkuvpLbFgJ9b74LlM1/n4qDVcOmcoCGf8641sXzn+f18cLSEW4Y5DUoMXWZ5MxoFEoKsBervLovk2wvCsNMq3WH7j3ySwYu7C5kV4snV80MB68/zy3sL+csX2SgK3Lc+Top0QvTh3Gn8FUIIIaagGp2BrEodHWYLr+4rsvn5P8+oJL2siZ9fGG+ztpQQT+dJk/GzNaOC7zy3n/V/28Xr+4v4KrOKa5/bR2lDG2Dd7ePn5sDNSydmytGcME/yalpoNZi6b9ufXwfAilg//N0duW5ROH++dh5xoyyWONpp+cm6uO6i14DrCvXETqOQUjxwJk/XbqX+2sbOdGFSIPZahQc+TKNhnDJTXt5bxNI/buv++wZrXlKNzsD6pKFPGPvBqmgWRXmzOs6fu9fEcN4MP57Znkdeta77mLzqFj5OKePW5ZE2L9CKoUkOtwZ1pxb33vWTUtzAxmNlfH9VdHcI/VAsjvJmfoQXL+4uxGS22GytoqcT5U3E+rv1KNg4O2h7TFh8+PIklkT78MCHaWSUNVHdrOe2Vw7x+JaTrIrzZ/sv1nLf+ngp+gjRByn8CCGEEJPYvvxaAGL9XXnjwKkeBYLRMpkt/PWLbOID3bo/PbWFYC8nKpv1Iw7mtJWsymbufuMY1To9D21IYP9DF/D695dQrTNwzbP7eOdQMbtza7lzVcyEXSjMCfVEVa1j1rscKKjDw8luSMWUseJkryUpxIOUPi6gz5RR3kSMnytujoNvIg9wd+JfNy4gs7yZ657fT/k47Ar7NK2cZr2J332cgapavx+3naxCo8Da+KEXfi6ZHcyr31vCX66bx68uTuDv1yfj7KDlwQ/TsXR+n/9zey5O9lruXjP+gc7Cak6oJ1qNwvHSnt+3FovKo5syCXB3HHaOmaIo3LU6luL6Nr48UWXL5YozZJQ1M2uQ1zx7rYZnb16Aj6sDP3jtCJf8czeHi+p54luzefHWhYTaeAy8ENOJFH6EEEKIcXKkqJ6cKt3gB55hT24tns72PPXtuTTrTbx3pMRm6/ngaCkFta386uIEm464DfFyxmxRqdZN7K6fj1LK0GoUPvzRCu5aE4ufmyPLYnx5/+7laBSFBzem4+1iz3eXTcxuH7BeqAKkl55u99qXX8fSGN8JHzs8P9yL46WNA+5yyChrZlbn1zAUl8wO4vU7llDVpOfb/9lH7jB/HoajrsVAakkjMX6ufJNdw+a0CgC2naxmUZTPkHY+9cff3ZGHL0vkyKkG3jpUTFZlM5uPl3P7iih8+2gxEuPD2UHLzED3Xjk/Hx4r5XhpEw9uSBhSkfJsFyYF4uqg5VBhva2WKs5Q12KgslnPrJDBX0v83Bx54ZZFNLZ3EOThxKc/PW9I7atCnOuk8COEEEKMg2a9kRtfPMBFf9/Fd57bz6bj5XSYBm4bUFWVvXm1rIj1ZVGUD4sivfnvnpG3G7x3uIQHPkjjsc0n+NuX2fx9Ww4LIrxYP4TJRsMR0tlGMZEBzxaLyubUclbH+fW6EE8I8mDjj1ewNNqHBzck4DqCC0FbCegMeO7K+SltaONUXRsrYgcPHR5r8yO8aeswk1PV0uf9Da0dlDW2M3uYO5OWxfjy7l3LMVlUbnrp4Ji1z+zMqUFV4envzGNumCePbc4ks7yJkxXNNvmev3ZhGCtn+PLU51n8ftMJ3B3t+OHq0eUwidGbF+5FSnEjX2RWojea0emNPLU1m+RwL65OHtnORq1GIcbfjfyavn8WxOiczgob2mvJnDBP9j6wjk9+spIZAeOTFybEVCeFHyGEEGIcHCyox2hWuXFJOJXNen72dgpr//INlQNk4RTUtlLepGflDD8A7lwdQ2lDO1szK4f9/JVNen77cTqfpVfw3pES/vVNHnUtHTx0aaLNPykN9rLmm1RM4Ej3Q0X1lDfp+21hC/Fy5t27lnP9YtsEWo/G3LDTAc9n5vtMtAUR1ryUlJK+c366LtZmD2PHT5ekEA8e2pBAjc5AQW3ryBc5gK+zqvFzc2RemBdPXjOXhjYj33vlMMCQxrIPRlEUnrh6Dh1mC/sL6rjjvGi8XEa+i0jYxvWLw3G003DX/46y+PFt3PjiAWpbDDx65axRTXiK8XeloGZsvlfPdV2vJcNpb/V1c+yR/yOEGJj8tAghhBDjYG9eLU72Gh69chY7frmWl29fRF1rBw+fkT3S12MAVsVZiwDrEwOJ9nPlhV0FmC0qedU6Nh4r5bP0ikGf/6XdBVhU+OzeVWT8/mLyn7iUE49dMiYjp0O8unb8TFzh55PUMlwctANOpJosZod6kt8Z8Lw/vw5fVwfiA90melmE+zjj6+rAsVN95/xklFuLVUP9lP5sicHWx508I9/IVoxmCztzaliX4I9Go5AU4sGdq2Ko1hmI8XMl1t82f75Rfq48fHkSMwPd+f6qaJucU4xOcrgXB39zAa/fsYRLZgdxqq6Nm5dGkNzPtLmhivV3o6yxnfYOs41WKrpkljcR6uUshVMhxpCMcxdCCCHGwd68WpZE+3ZPzlqXEMgvLornj59lsTmtgivnhfR6zJ7cWsK8nYnwcQGs7QbfPy+ahz/OYPYjX9BuPH0B8s0v1xLt59rncze2dfDWoWKunBdCeOe5NBoFhzHKkPFwssfN0W7CWr0MJjNb0iq4eFYQLg6T/61OV8BzZnkz+/LrWB7rOynyKhRFYX6EV787fjLKmgjzHvnFWqy/G/ZahZMVOq5KHs1Kezt6qgGd3sS6hNMtXfdeEMeevBoum9P7Z200blkWyXeXRkyKvzNhZafVsDren9Xx/vz52rk2+buJ8be+vhbUtgwpi0YM3YnywYOdhRCjIzt+hBBCiDFW1awnt7qF82b0zG25Y2U088I8eXRTJvVnjbc2mS3sz6/jvBl+PS5arl0YxjXzQ/nOojD+et083r97OQ5aDa/uLez3+V/bd4q2DvO4ThsK9nSasFavHdk1NOtNXJVs2wv8sdIV8Lz5eDmVzfpJ0ebVZX6ENwU1rTS29R6/nlnezOxRXAA72GmYEeA+Jjt+vsmqxl6rcF6cf/dtzg5aPv3pKn601vY/B1L0mbxs9XfTtUtM2r1sq9VgorCuVYppQowxKfwIIYQQY6yrZevsC3o7rYY/XzsPnd7I7zdn9rgvrawJncHEeXE9H+Nkr+Vv1yfz+6tmc+3CMBZH+XDFvBDeP1pKU7ux13O3dZh4dV8h6xMDmBk0fiGYIV7OE7bj55PUMvzcHDhvxuQpoAwkwMOJQA9H3j9qndi2fBIEO3eZH2Ftjzl7SpJOb6SwtnXUn9InBo9N4efrrGqWRPuMaIKTEH2J9nNFUZCAZxs7WdGMqsLsUNnxI8RYksKPEEIIMcb25tXh7WJPUnDvN7Yzg9y55/wZfJJazpa001k9e3JrUZShhfx+b2UUbR1m3jvce9T7O4dKaGgzjskuh4GEeE3Mjp9mvZFtJ6u5fG4IdlMo+HNOqCd6o4VgTyeifF0mejnd5oZ5oVHgWHHPws+JUQQ7nykp2INqnYG6FsOoznOmkvo2cqtbWJcw+fOdxNThZK8lxNNZdvzY2OmJXrLjR4ixNHXeEQkhhBBTUPdI9hl+/U6U+fHaGcwK8eCet47xy/ePU9diYE9eLbNCPPBxHTw/ZXaoJ0ujfXh1X1GP0dgdJgsv7S5gSZQPCyNtH+I8kGBPZ2pbOjCYxjcIdWtGJR0my5Rp8+rSVUCZLPk+Xdwc7YgPdCeluGfOT0bXxdooP6U/HfCsG9V5zvR1VjVAj3wfIWwhNsCNglrZ8WNLmeVN+Lo6EOjhONFLEWJak8KPEEIIMYbya1qpbNYP2HbkYKfh/buX86O1sXycUsa6p3dy7FRD9xj3objjvGjKGtv56kQVAHqjmUc2ZVDepB/33T5gzfgBBhxXb2utBhPP7cgnxs911BN8xtvcsM7CT8zkafPqsiDSm9SSRiyW09PnMsubCHB3JMDdaVTnTuhsP7Rlu9fXWdVE+7n2G3YuxEjF+FlHuvc3iVFY/WdH/pB/pjPKmkkK8ZhUBW8hpiMp/AghhBBjqCvfZ+UgLVsuDnY8cEkCn9+7isRgd0wWlQuG0aqyPjGQcB9nXt5bSHFdG9c+t4+3D5Vw15oY1s70H/wENhbaOdK9bAxGuudW6Siq7d1u8djmExTWtfLEt+ZMuYuI1XH+/OHq2VzRx3S3iTY/3Aud3tRjp0NmWfOo27wAfN0cCXB35GSlbQo/Or2R/QV1nD9TdvsI24sNcKOtw0xl88Tkl00FzXojT23N4rmd+YMe22GykFutkzYvIcaBJN4JIYQQY2hvXi3hPs5EDDG3JS7QnbfvXEZZYzth3kPPetFqFG5fEc0fPj3Bhn/uQqtRePHWRVyYNDE5J8GdhZ+KIQY8VzXrqW42MCds4AsAvdHMjS8eoNVg5h83JHPxrCAAtqRV8O6REu45P3ZShSMPlZ1Wwy3LIid6GX2aH+ENwM6cWmL93dAbrRdrF8+yzfdWYrCHzVq9Pkktp8Nk4Yp5wTY5nxBniu3cRZZf3Uqwp/MEr6Z/qqpOWPG7rMFa7N+VU4PZoqLtp8UZIKdKh9Gsyih3IcaB7PgRQgghxojJbGF/Qd2wp0spijKsok+X7ywKw9fVgRkBbmz52aoJK/rA6VavwQKejWYLz+/M5/y/7uCa/+ztczLZmTYdL6e2pYMAD0fufuMoz+3Mp7ShjQc3ppEc7sV96+Nt9jUIqxg/V/zcHPnDpydY+Pg2bnvlEBYVZtlgxw9YCz951To6TJbBDx6Aqqq8dbCYxGCPKdfqJ6aG2IDOke6TOOentsXA3Ee/7N5tOt66Cj8NbUbSy5oGPHZ3rnWNCyK9x3xdQpzrpPAjhBBCjJH0siZ0etOwsnpGw93Jnh2/WsvGH68k3GdiJ0M52WvxdXWgbIAdPwcL6rjsmd386fMs4gPdMZpVduXU9Hu8qqq8vKeQhCB3tt67mkvnBPPk51lc8a89qCo8c8N87KfQJK+pQqNR2PSTlTz17TmsSwigRmfA3cmOBRG2uVhLDLb+3Y92THZ6WRMnKpq5aWnElGv1E1NDgLsjrg5a8qsnb+Enr7oFncHElvSKwQ8eA2e29+7M7v/1HOCz9ArmhXt1twYLIcaOvDsSQgghxsg3nW96xzOw193JfsCt9eMpeICR7iX1bdz80kHaOsy8dOsiPvzRCnxcHbonMvVlf34dWZU67lgZjbODln/dMJ+frZtBU7uRJ741e8jtdGL4QrycuX5xBH+9bh7f/HItaY9chL+7babwJHVP9hpdzs9bB4txttdOuYluYupQFKVzstfkHene9Zq7J3didvyUNrThYKdhXpgnO3L6fz0vrmsjvayJS2cHjePqhDh3SeFHCCGEGAMtBhP/21/E2pn++Lqdm2Nqgz2d+8342ZxWjsmi8vady1ifFIhWo7B2pj/fZFdjtvQ9Mee/ewrxc3Pgys4Le41G4ecXzSTj9xdzVXLomH0dojdb7qiJ9nPFwU4zqsKPTm9k0/FyrpgXjIeTvc3WJsTZuiZ7TVYVnZMUi+vbKK5rG/fnL2tsJ9TLmbUzAzhe0khDa0efx32eYd2RdOkcyeMSYjxI4UcIIYQYA6/tK6KhzXhOZ86EejlT3s+On0+PVzA/wqtHS9q6hAAa24ykFDf0Or6wtpXtWdXcvDQSJ3ttj/tcHGRWxVRmp9UQH+hGVuXIA543HS+nrcPMTUsnZ0C2mD5i/d0oa2ynrcNkk/PZejR8RaOerk2feyYg56eswVr4WTPTH4sKu/tZw2fpFcwN85zwtmQhzhVS+BFCCCFsTKc38sKuAtYlBJzTIbPBnk7o9CZ0+p6Bzfk1LZyoaObyuT1bclbF+WOnUdjeR7vXK3sLcdBq+O4knXwlRicxyGNYO37yqnUYTGagZ6jzvEGmwgkxWjH+1oDnQhu0ez26KZMr/99emxZ/Kpr0xAe6E+zpxJ68gTN2zvbgh2m8uKtgVM/fteNnXpgXXi72feb8lDa0cby0SXb7CDGOpPAjhBBC2Nire4toajdy/zm82wfOGOne1LPd69PjFSgKXHbWm35PZ3sWR/nw9cmehZ+mNiPvHynlyuQQm+XKiMklMdiD2pYOqnX9h4F32XS8nPV/28Wix7fxi/eO89q+IjLLm7lpSbiEOosxF+PfOdJ9lO1eH6eU8eq+ItLLmkYdbH6miqZ2gj2dOG+GH3vz6vptnT1bXrWOdw6X8NTWLHKrRrb7Tm80U9vSQai3M1qNwqo4f3bm1GA5aw2fp1cCcOlsKfwIMV6k8COEEELYUFO7kRd3F7A+y291KAAAIABJREFUMZA55/jug7jO0cefpp2eLqOqKpvTylkc5UNQ58j3M12QGEB2lY6S+tPZFH/flkO70cwdK6PHftFiQiR2BzwPfMFZ22LgkU8ymBXiwUVJQXx5opJHN5+whjrPl5wnMfai/VxRFCgYRbEmr7qF33yU3h1svmOQ6VfDUdmkJ8jTmfPi/GhqN5JZPvBI9S7vHynFTqPg4qDld59kDLgLqVqn58K/7SS1pLHH7V0TvbqmdK2N96e2xcCJs3bzbUmvYHaohwTyCzGOpPAjhBBC2NArewtp1pu4b33cRC9lwiUGe3BVcgj/2ZFHXrX1gj67SkdedQtXzOt78tK6hAAAvsm27vrZdqKKV/cVccfKaJJCPMZn4WLcJQa7A4NP9npkUyatBjN/vz6Zp78zjyMPr+elWxfx39sXSaizGBdO9lpCvZxHvOOnvcPMPW8ew8ley8u3LyYuwI2dObYp/OiNZupaOwjxdGLlDD8Adg9hupfRbOHDY6WsSwjg15ckcKCgnk3Hy/s9/uOUMnKrW9iR3XN3ZllDZ+HH21r4WR3vD9Dj6ytrbCe1pFHavIQYZ1L4EUIIIWyk1WDiv3sKuXhWILNDz+3dPl1+d3kSLg52/GZjBhaLyubj5WgU2NDPCN8Yfzei/VzZfrKaqmY9v/rgOEnBHjywYeY4r1yMJy8XB8J9nDlS1DvYu8vWjAq2pFXwswtmEB9oLRQ52mlZnxTIili/8VqqEMT6uw15x09lk569ebXd//3mo3Syq3T8/fpkgjydWBPvz8GCepuERVc1W1slgzyd8HNzJDHYY0hj3b/Oqqa2pYPvLArnxiURzA3z5A+fnqT5rHy2Lh+lWItCWWft0Ova8RPWWfjxd3dkdqgHO7Kru3cQfZ7eOc1L2ryEGFcyBkMIIYSwkdSSRnR6EzcuiZjopUwafm6O/PbSRH79YRrvHinh07QKVs7ww2+AEffrEgL43/5T/PTtFPRGC/+6aT6Odtp+jxfTw5p4fzYeK8NgMvf6+25s6+DhjzOZFeLBXWtiJ2iFQljF+LtyoKCOZ3fk4e/miL+7I9F+roR7u6DpHKmVV93Cczvz+TilDNNZGTf3nB/Lms7dMGtm+vPSnkIOFNSxLiFwVOvqylML6Wy1Ou//s3ff0XHVd/7/X3dm1KvVJatLLnLvlWKKIaGXkIQSWhJS+W3KN9nsbrJJYFP3m2yShdRfElpCIEACGEIoNgRww93Ykq1qFavX0ajOzP3+IVmgyEWyR7ozo+fjHI6lO/eO3nCObfTS+/N+Fybq4a3H1DvgUUToqf8M/fOuGqXEhGnDnGTZbYb+67oFuvbBt/Xjl4/qW9fMH3VvSUOXiuu7FGq3qaRhdIdeXXuv7DZDabHvHeO9cHayHtxSroJ/f1FRoQ71e7yalx6r3KSoc/p3BTAxBD8AAPjIiXkHS7NmWFyJf7lpRaae2Vurbz13SP1urz674fTfuF8yN0W/fatSOyvb9MMbF6lgeIsOgtvFc1P02PZq7axs0/mzkke99r0XS9TRM6CH716pEDsN67DWhbOT9fTuWv3wpSOjrseEOVSUHqvIMLveONqsMMfQJsLL56fJPhwIRYTYtWDme8dWV+YmKCLErtePNPsg+BnquDkxP+28Wcn6zZuV2lnVpgtnJ8s0TVW39Sg1NlzhIUNBUFNXn7YcadY9F+TLMfx7a1FmvG5dna1HtlXpphWZmp/xXgfrX/bWyWEzdMvqbD28rUqufreiwoa+pazr6FVabPjI+0jSx8/LV2x4iJx9brkG3Orp95zyqC+AyUPwAwCAj+yr6VB+UpTiIpk18n6GYeg71y/UB3/ypkLshi6ff/JjXiesyE1QUnSY1hYk6qYVmVNUJay2Nj9JoQ6btpQ0jwp+OnsG9Ze9dbp5Vfaob0ABq2yYk6ID37pcvQMeNTv71eTsU1lTtw4d79Kh450qbezV5zYU6q71uUo8TXejNDQzaG1Bok/m/Jzo+EkfDn5W5SYo1G7Tq4cb1djZp0e2V+ndui4VpkTrwVuWaU5ajJ7eUyeP19RNy0f/WfuVy+bqbwcb9PW/vqunP71ONpshj9fUs3uP68LZyVpXkKiHtlbpSKNTy7KHfthR1947Mtj5hISoULr0AD9A8AMAgA+Ypql9NR06v5BZIydTkByt/7p+gVq6+xUfGXrae0MdNr325QsVHeZgPfc0EhFq19r8RL1+pEn/efW8kevPHziuAY9XH16RZWF1wFgRoXZlJ0YqOzFSK3ITzvp9NsxJ1uaSJlW2uJR3Dkeg6jv6FBcRoshQx0h9y3Nm6NHtxyRJc1Jj9OWNs/XwtmO65oG39O1r5uvPu2q0KjdB+f/UWRkXGaKvfXCuvvLUAf15d40+sjJb2yta1dDVp69fVTSyia+k/n3BT0evVuWd/X8HAJOH4AcAAB+o7+xTs7Nfi7PirS7Fb03kG/e4CLqmpqOL5iTrW88fVlWLa2QGyNN7ajUnNWbU8RggmJyY9/PGkSblJeWd9fvUd/aNdPuc8OkNBcpKiNCNyzK1Ki9BhmHoI6uy9MUn9ulrzxyUJH3mFMdvb1yWqSd31ej7fyvRZfPS9Je9dYoJc+jSolSFOWyKDnOMzPlxe7xq6Oob0/EDwD9wSBoAAB84Md9nCcEPcNYumpsiSdoyvCa6vLlbe6s7dOPymXR/IWjlJEYpLynqnI971Xf2jgl+LpydrB9+aLFW5yeO/B5KiQnXI3ev1pc3ztaqvARduejkG7ZsNkP3X7dAXX1u3bfpsF56t0EfXJim8BC7DMPQ3LQYFdcPBT8NXX3yeM2RVe4A/AvBDwAAPrC/pkOhdpvmpsdYXQoQsHISo5SfHKUtR4a+AX5mT61shnTdkpkWVwZMrgtnJ2tbRav6Bj2Sho4PD3q8E3qPhs4+pcWNL3ix2wzde8ksPfmptSNHw05mblqs7lyXq7/srVN3v1vXL31vFlBReqxK6p0yTVN17UODpen4AfwTR70AAPCBvTUdmpcRy9px4BxdNCdFj24/pu5+t57ZU6cLZicrJTb8zA8CAezC2cl6aGuVvvzn/Wp3DehwfZe8XlMvfeGCkfXsp9M36FGra0AZcb7/vfKFS2dp04HjshuGVr9vhs/c9Bg5t7tV19Gruo7h4IeOH8Av0fEDAMA5cnu8OljbyTEvwAcumpOiAbdXP3r5iOo7+3TjMja7IfityU/UjMgQvXq4Ua5+ty6fl6Y+t1c/eKlkXM83dg1t9EqbhOAnJjxEj318tX5750rZbO8duZyb9t6AZzp+AP9Gxw8AAOeotKlbvYMegh/AB1bmzVBkqF0Pba1STLhDG+elWl0SMOkiQu3a9m+XyGEz5LAP/Ww+OSZMD2wp0+1rc7Q85/Tbst5b5T45wcus1LHHmOemDV0rru9SXUevkqJDFR5C1yvgj+j4AQDgHDHYGfCdMIdd5xUmyTSlqxdn8I0kpo3wEPtI6CMNbdtKjQ3Tt58/LK/XPO2z9Z1DHTfp8VN3LDIqzKGcxEiVNDhV19FLtw/gxwh+AACYANM0Vd3aM+ra/poOxUeGKCcx0qKqgOBy6XCXz03LOeaF6SsqzKF//cBcHajt1DN7605773sdP1M7D2tuWoyKG7pU197LfB/AjxH8AAAwAbuOteuC/96iX7xePnJtX02HFmfGs24a8JEbl2Vq073naWn2DKtLASx13ZKZWpIVrx+8VKLufvcp76vv6FNcRMhpN3RNhqL0WFW1uFTb3qvMGfzwA/BXBD8AAEzAu3WdkqQfvFSiTQeOy9Xv1tFGpxZzzAvwGbvN0IKZcVaXAVjOZjP0zavnqdnZr///zYpT3lff2Tfl3T7S0IBnrykNeLwc9QL8GMEPAAATUNbUrZhwh1bmztCXntyvh7ZWyWtKSwl+AACTYGn2DK3MnaE3jjaf8p76zl5Lgp+i9PeGPhP8AP6L4AcAgAkoa+rWrJRo/epjK5QRF67//vsRSaLjBwAwaRZnxuvQ8S4Nerwnfb2hs09pk7TR63SyZkQqKnRoADszfgD/RfADAMAElDd3qzAlWglRofr9Xas0IzJEuYmRSogKtbo0AECQWpQVrwG3V0canGNe6xv0qNU1YEnHj81maM7wWneCH8B/Te30LwAAAlhHz4BaugdUmBItScpLitJTn1mn3gGPxZUBAILZksyhrtL9tR1j5l81dlmz0euExVnxqmnvVWx4iCVfH8CZ0fEDAMA4lTV1S9JI8CNJBcnRDKEFAEyqrIQIzYgM0f6ajjGvHe84EfxY03Hz5cvm6JnPrLPkawMYHzp+AAAYp/Lm4eAnOeYMdwIA4DuGYWhRZrwO1HaOea2hq1eSlB5vTcdPdJhD0WF8Wwn4Mzp+AAAYp7KmboU5bMwxAABMucWZcTra6FTPgHvU9fpOa496AfB/BD8AAIxTWVO38pOjZbcZVpcCAJhmFmfFy2tK79Z1jbpe39GnuIgQRYbSdQPg5Ah+AAAYp7LmbhUkR1ldBgBgGlo0POD5QO3oOT817T10+wA4LYIfAADGoW/Qo9r23lGDnQEAmCrJMWGaGR+hfe8b8NzQ2ae3Slu0riDJwsoA+DuCHwAAxqG8uVumKYIfAIBlFmXGjRrw/Oj2KnlMU3euy7WuKAB+j+AHAIBxONkqdwAAptKizHhVt/WozTWg3gGP/rijWhuLUpWdGGl1aQD8GBPAAAAYh/KmbtkMKS+JGT8AAGsszoqTNDTnp76zT+09g/r4eXkWVwXA3xH8AAAwDmXN3cpOiFSYw251KQCAaWrhzDgZhrSvpkMvHKjX/IxYrcpLsLosAH6Oo14AAIxDWVM3x7wAAJaKCQ9RQXK0HtterdKmbt29Pk+GYVhdFgA/R/ADAMAZuD1eVba4VEDwAwCw2KLMOLV09ys5JkxXLU63uhwAAYDgBwCAM6hu69Ggx1RhMsEPAMBaS7LiJUkfW5PD8WMA48KMHwAAzoCNXgAAf3H5/DTtre7Q7WtzrC4FQIAg+AEA4AzKmoeCH456AQCslhobrv/5yBKrywAQQMZ91MswDLthGHsNw9g0/HmCYRivGIZROvzrjMkrEwCAqeXqd8vt8UqSyptcSo0NU2x4iMVVAQAAABMzkY6ff5FULCl2+POvSXrNNM3vG4bxteHP/9XH9QEAMGWcfYN6bv9x/WlnjQ7WdUqSwhw2ub2m1uSzLhcAAACBZ1zBj2EYmZKulPQdSV8avnytpA3DHz8s6XUR/AAAApDXa+q/XijWn96pVs+AR3PTYvSljbMlSa4Bt1z9bl2xkM0pAAAACDzj7fj5iaSvSop537VU0zTrJck0zXrDMFJ8XRwAAFPhF2+U63dvV+q6JRm6Y12ulmTFyzAMq8sCAAAAztkZgx/DMK6S1GSa5m7DMDZM9AsYhnGPpHskKTs7e8IFAgAwmXZUtOpHLx/R1Ysz9D8fWULgAwAAgKAynuHO6yVdYxhGlaQ/SbrYMIzHJDUahpEuScO/Np3sYdM0f22a5grTNFckJyf7qGwAAM5dS3e/7n18r3ISo/S9GxYS+gAAACDonDH4MU3z30zTzDRNM1fSRyVtNk3zNknPSbpj+LY7JD07aVUCAOBjXq+pLz6xT529g3rwlmWKDpvIvgMAAAAgMIx7nftJfF/SRsMwSiVtHP4cAICA8Os3K/RmaYu+dc18zcuIPfMDAAAAQACa0I83TdN8XUPbu2SaZqukS3xfEgAAk6uyxaUfv3JUH5ifpo+uzLK6HAAAAGDSnEvHDwAAAcc0Tf37MwcV5rDpvmvnM9cHAAAAQY3gBwAwrfx5d622VbTq3z5YpJTYcKvLAQAAACYVwQ8AYNpodvbrOy8Ua1VuAke8AAAAMC0Q/AAApo37Nh1W74BH371hoWw2jngBAAAg+BH8AACmhfLmbj2//7g+s6FAhSnRVpcDAAAATAmCHwDAtLC1rEWSdMOymRZXAgAAAEwdgh8AwLSwraJVGXHhyk6ItLoUAAAAYMoQ/AAAgobb41VVi2vMda/X1PaKNq0pSGR9OwAAAKYVh9UFAABwLrxeU7uOtev5/cf14sF6tboG9MdPrta6gqSRe442OdXmGtDa/EQLKwUAAACmHsEPACCgffqx3Xr5cKPCQ2y6pChV/zjSrKd2144KfraVt0qS1hYQ/AAAAGB6IfgBAASsdteAXilu1M2rsvX1K4sUFebQV5/arxcO1Kv3Oo8iQu2ShoKfrIQIZc5gvg8AAACmF2b8AAAC1raKVpmm9KHlmYoKG/pZxnVLZ8o14NGrxY2Sho6C7ahs05o8un0AAAAw/RD8AAAC1ltlLYoOc2hxZtzItTV5iUqLDddf99ZJkg7Xd6mzd5BjXgAAAJiWCH4AAAHrrdIWrclPlMP+3l9nNpuha5dk6I2jzWpzDWh7BfN9AAAAMH0R/AAAAlJ1a4+q23p0XuHYQOfaJTPl9pp64WC9tpW3KjcxUulxERZUCQAAAFiL4AcAEJDeLm+RJJ03K3nMa0XpMZqTGqOnd9dqZ2Ub3T4AAACYtgh+AAAB6a2yFqXFhqsgOWrMa4Zh6NqlGdpX0yFnv1tr8gl+AAAAMD0R/AAAAo7Xa2prWYvWFybJMIyT3nPtkpkjH68l+AEAAMA05bC6AAAAJupwfZfaewZ13qxTBzoz4yO0riBRba4BpcSGT2F1AAAAgP8g+AEABJy3yobm+6wvSDrtfQ/cskyDHu9UlAQAAAD4JYIfAEDAebusRXNSY87YyZMQFTpFFQEAAAD+iRk/AICA0jfo0c7KNq0vPH23DwAAAACCHwBAgHmnqk39bu9p5/sAAAAAGELwAwAIGJ29g/rms4eUEBWq1XkEPwAAAMCZEPwAAAKC2+PVvY/vVXVbj35x6zJFhTGmDgAAADgT/q8ZABAQvve3Ev3jaLO+d8NCrc6n2wcAAAAYDzp+AAB+78l3avTbtyp157pc3bwq2+pyAAAAgIBB8AMA8Gut3f36xrPv6rzCJH39yiKrywEAAAACCsEPAMCvPb6zWv1ur751zTw57Py1BQAAAEwE/wcNAPBbgx6vHt1+TOfPSlJhSozV5QAAAAABh+AHAOC3Xnq3QY1d/bprfa7VpQAAAAABieAHAOC3HtpapZzESG2YnWJ1KQAAAEBAIvgBAFiutr1H1z7wlv6w49jItQO1Hdp9rF13rM2VzWZYWB0AAAAQuBxWFwAAmN48XlNfemK/9td2an9tp442OPWNq+bpoberFBVq14dWZFpdIgAAABCwCH4AAJb61T/KtbOqTT/80CKVNjr1mzcrdaTRqT3HOnTL6mzFhodYXSIAAAAQsAh+AACWOVjbqR+/fFRXLkzXTcszZRiGZqXE6D/+elCDHlO3r82xukQAAAAgoBH8AAAs0Tvg0b88sVdJ0WH6zvULZBhDc3w+vDJLs9NidKzVpfzkaIurBAAAAAIbwQ8AYBSv19T9LxxW36BX331fIONrP3r5iCqaXfrjJ1YrPjJ01GtLsuK1JCt+Ur4uAAAAMJ0Q/ABAgKtp61HmjAifBDSmaeq+TYf10NYqSdLCmXG6ZXX2Ob/vP+t3e/TkrhpdszhD6wqTfP7+AAAAAIawzh0AAthDb1fq/B9u0dN76nzyfv+7uUwPba3S3evzdF5hku7fdFiVLS6fvPf7/eNoi7r63Lp+2UyfvzcAAACA9xD8AECAen7/cX1702FJ0osH68/5/R7dVqUfv3JUNyybqa9fWaT/vmmRQuyGvvjEPrk93nN+//d7dl+dZkSG6Dy6fQAAAIBJRfADAAHozdJmfenJfVqZk6CbV2XrrbIW9Qy4z/r9Ht1+TP/53CFdWpSiH9y4SDabofS4CH3n+oXaV9OhB7eUq901oL/urdO9j+/Vv/xp71mHQa5+t14tbtQVC9MVYuevIQAAAGAyMeMHAALMwdpOffrR3SpIjtZv7lihQ3Wdenxntd4sbdHl89NG3Tvg9irEbpxy/o/Xa+q/Xz6iX7xerovnpuiBW5aNCmOuXpyhV4sb9dPXjuqnrx2V15TiI0PU0TOo2akx+txFhROu/9XiRvUNenXN4owJPwsAAABgYgh+ACDAfPfFYsWEh+jhu1cpLiJEK/MSFBvu0KuHG0cFP529g7rkR68rzGHX1YszdPXidM1Ljx0JgQbcXn31qf36677junlVtu6/dr4cJ+nAue/aBZKknIRIXVyUqkUz43Tvn/bqJ68e1cVzU1SUHjtyr2ma6u53KyY85JT1P7vvuNLjwrUyN8FX/0kAAAAAnALBDwAEkEGPV3tr2nXzqmylxoZLkkLsNl00N0WbS5rk8Zqy24aCnce2H1NL94DWFSTqN29W6JdvlCstNlzhIUPhjmvAo2Znv75y+Rx9dkPBKbuC4iJC9NOPLh117f5rF2hHRau+/OR+/fVz6xXqsKnZ2a8vPblPu6ra9fIXL1BWQuSY92p3DegfR5t193l5stkmZ008AAAAgPcQ/ABAACmpd6pv0Ktl2TNGXb+0KFXP7juufTXtWp6ToN4Bj373VqU2zEnWQ3etUptrQH97t167qtrlNc2R5y6fn6YrFqZPuI6EqFB99/qFuufR3Xpgc6lW5iXoi0/sl7NvUG6vqUe3H9O/X1E05rm/vdsgt9fkmBcAAAAwRQh+ACCA7KlulyQtyxkd/Fw4J1kOm6FXDjdpeU6CnninWq2uAX12w9AMnoSoUN26Oke3rs7xWS2XzU/TDUtn6oEtZfJulmalROsPn1itn20u1eM7q/Uvl8xSVNjov2ae21+n/KQozc+IPcW7AgAAAPAl1qkAQADZU92u1NgwZcSFj7oeGx6iNfmJerW4UQNur379jwqtyJmhVXmTO0fnm1fP17yMWN2yOlvPff48zUmL0d3r8+Tsc+uZPbWj7q3r6NWOyjZdsyTjlMfKAAAAAPgWwQ8ABJA91e1alj3jpMHJpUUpKmvq1s9eK9Xxzr6z2rg1UXGRIdp07/n67vULFRFqlyQty47X4sw4/X5rlbzeoWNlHq+prz61X6F2m25YmjnpdQEAAAAYQvADAAGi2dmvmrbeMfN9TrikKFWS9MCWMhWlx2rDnOSpLG+EYRi6a32eKppdeqO0WZL001eP6u2yVt1/7QJlJ44d+gwAAABgchD8AECAeG++T/xJX89KiNTctBhJ0mdOs6VrKlyxMF0pMWH6/dtV2nKkST/bXKablmfqwyuzLKsJAAAAmI4Y7gwAAWJPdbtC7IbmZ8Sd8p7b1uTohQP1umJB2hRWNlaow6aPrcnRj145qr3V7ZqbFqP7rl1gaU0AAADAdETHDwAEiL3HOjQ/I07hIfZT3nPbmhw9fs8aOezW//F+y+pshTpsMk3pF7ctH5kBBAAAAGDq0PEDAAFg0OPVgboO3bLKd+vYJ1tidJh+cesyJUSFKi8pyupyAAAAgGmJ4AcA/NDuY+2aERmi/ORoSVJxfZf6Br2nnO/jr04MnAYAAABgDevPAgAAxvj8H/fopl9uU01bjyRpz7Hhwc6n2OgFAAAAACdD8AMAfsbZN6j6zj61ugZ05+93qrNnUHuqO5QWG66M+AirywMAAAAQQAh+AMDPVLa4JEl3rc9VdVuPPvXYLu0+1h5wx7wAAAAAWI/gBwD8TEXzUPBzy6ps/fBDi7S9ok11Hb0c8wIAAAAwYQx3BgA/U9HcLZshZSdGalZqjKpbe/U/rx7VmvxEq0sDAAAAEGAIfgDAz5S3uJSdEKkwh12S9P9dUqibVmQy3wcAAADAhHHUCwD8THlT98gad0kyDIPQBwAAAMBZIfgBAD/i9ZqqanUpPynK6lIAAAAABAGCHwDwI8c7e9U36B3V8QMAAAAAZ4vgBwD8yImNXvnJdPwAAAAAOHcEPwDgRyqauyUR/AAAAADwDYIfAPAj5c0uxYQ7lBwdZnUpAAAAAIIAwQ8A+JGKlqGNXoZhWF0KAAAAgCBA8AMAfqSi2aUCNnoBAAAA8BGCHwDwE65+t+o7+5jvAwAAAMBnCH4AwE9UtpzY6MUqdwAAAAC+QfADAH6inI1eAAAAAHyM4AcA/ERFs0uGIeUmEvwAAAAA8A2CHwDwExUtLmXOiFB4iN3qUgAAAAAECYIfAPATFc3dyk9ivg8AAAAA3yH4AQA/4PWaqmh2Md8HAAAAgE8R/ACAH2jo6lPvoIeNXgAAAAB8iuAHAPxARfPQKvcCOn4AAAAA+BDBDwBYrN/t0R92HJMkFdLxAwAAAMCHHFYXAADTWWt3vz716G7tOtaur35gjlJiw60uCQAAAEAQIfgBAIuUNjp198PvqKmrXw/cslRXLcqwuiQAAAAAQYbgBwAs4PZ4dfNvdkiSnvjUWi3Jire4IgAAAADBiBk/AGCB6rYetXT3618/MIfQBwAAAMCkIfgBAAuUNnVLkmanxlhcCQAAAIBgRvADABYoGw5+ClLY4gUAAABg8pwx+DEMI9wwjJ2GYew3DOOQYRjfHr6eYBjGK4ZhlA7/OmPyywWA4FDa6NTM+AhFhzFqDQAAAMDkGU/HT7+ki03TXCxpiaQPGIaxRtLXJL1mmuYsSa8Nfw4AGIfSpm4V0u0DAAAAYJKdMfgxh3QPfxoy/I8p6VpJDw9ff1jSdZNSIQAEGY/XVFlTt2YR/AAAAACYZOOa8WMYht0wjH2SmiS9YprmDkmppmnWS9LwrymTVyYABI+69l71u72alUrwAwAAAGByjSv4MU3TY5rmEkmZklYZhrFgvF/AMIx7DMPYZRjGrubm5rOtEwCCRmmTU5JUmMJGLwAAAACTa0JbvUzT7JD0uqQPSGo0DCNdkoZ/bTrFM782TXOFaZorkpOTz7FcAAh8J1a5M+MHAAAAwGQbz1avZMMw4oc/jpB0qaQSSc9JumP4tjskPTtZRQJAMClr6lZqbJjiIkKsLgUAAABAkBvPHuF0SQ8bhmHXUFD0pGmamwzD2CbpScMwPi6pWtJNk1gnAAQ9+TPMAAAgAElEQVSN0qZuzeKYFwAAAIApcMbgxzTNA5KWnuR6q6RLJqMoAAhWpmmqrNGpm1ZkWV0KAAAAgGlgQjN+AADnpr6zT64BD/N9AAAAAEwJgh8AmEInBjvPIvgBAAAAMAUIfgBgCpU2Dq1yn5XKjB8AAAAAk4/gBwCmUFlTtxKjQpUQFWp1KQAAAACmAYIfAJhCpU3dzPcBAAAAMGUIfgBgipimqdJGp2alEvwAAAAAmBoEPwAwRZqd/erqc2tWCvN9AAAAAEwNgh8AmCJs9AIAAAAw1Qh+AGCKnNjoVchRLwAAAABThOAHAKZAu2tAj++sUVJ0mJKjw6wuBwAAAMA04bC6AAAIdp09g7rttztU2erSb+9YIcMwrC4JAAAAwDRBxw8ATKKuvkHd/rsdKm3s1q8+tlznz0q2uiQAAAAA0wjBDwBMkgG3V3f+bqcOHe/Sz29dpovmpFhdEgAAAIBphqNeADBBrn63ShqcqmpxqbLFpa6+QX3l8jmKCQ8Zdd+mA8e1p7pDP/nIEl06L9WiagEAAABMZwQ/ADBBN/5iq0oahjZ02W2GPF5TqbHh+txFhaPue3hrlQqSo3TtkgwrygQAAAAAjnoBwETUdfSqpMGpu9fnafOXL1TJ/R/QRXOS9du3KtUz4B65b19Nh/bXduqOdbkMcwYAAABgGYIfAJiArWUtkqQPr8xUfnK0Quw2fe6iQrW5BvSnnTUj9z2ytUrRYQ7dsCzTqlIBAAAAgOAHACZiW3mrEqNCNTslZuTaitwErcpL0K//UaEBt1ct3f3adKBeNy6bqegwTtQCAAAAsA7BDwCMk2ma2lreqjUFibLZRh/f+vxFhWro6tNf9tbqTzurNeDx6mNrc60pFAAAAACG8aNoABinyhaXGrr6tK4gccxr589K0sKZcfrF6+XqG/Tq/FlJKkyJtqBKAAAAAHgPHT8AME5by1slSesKksa8ZhiGPndRgapae9TQ1afb6fYBAAAA4Afo+AGAcdpW3qqMuHDlJkae9PXL5qVpVkq0+tweXTw3ZYqrAwAAAICxCH4AYBy8XlPbKlp10ZyUU65nt9kMPXz3Knm8puw2VrgDAAAAsB7BDwCMQ0mDU22ugZPO93m/jPiIKaoIAAAAAM6MGT8AMA5by1skSWvPEPwAAAAAgD8h+AGAcdhW3qq8pCg6egAAAAAEFIIfADgDt8erHZVtdPsAAAAACDgEPwBwBgfrOtXd7z7jfB8AAAAA8DcEPwBwBptLmmQY0tp8gh8AAAAAgYXgBwBOw+M19fTuWp0/K1mJ0WFWlwMAAAAAE0LwAwCn8XZZi4539unDKzKtLgUAAAAAJozgBwBO44ldNYqPDNHGealWlwIAAAAAE0bwAwCn0O4a0CuHGnXdkpkKc9itLgcAAAAAJozgBwBO4dl9dRrwePXhFVlWlwIAAAAAZ4XgBwBO4cldtVo4M07zMmKtLgUAAAAAzgrBDwCcxLt1nTpc38VQZwAAAAABjeAHAE7iyV01CnPYdM2SmVaXAgAAAABnzWF1AQDgL5qd/XrjaLO2HGnSK4cb9cEFaYqLCLG6LAAAAAA4awQ/ACDp929X6tvPH5YkpcSE6bolGfrSxjkWVwUAAAAA54bgBwAkbS5pUm5ipB68dZnmpcfKMAyrSwIAAACAc8aMHwCQVNLg1PKcBM3PiCP0AQAAABA0CH4ATHst3f1qdvarKD3G6lIAAAAAwKcIfgAElT/vqtFbpS0TeuZIg1OSNDctdjJKAgAAAADLEPwACBqmaeq+TYf18Yff0Z7q9nE/V3Ii+KHjBwAAAECQIfgBEDSanP1y9rk14PHqkw/vUnVrz7ieK6nvUlJ0mJKiwya5QgAAAACYWgQ/AIJGWVO3JOm+axfI7TV150M71dEzcMbnShqczPcBAAAAEJQIfgAEjRPBz2XzUvXrjy1XbVuvPvXobvW7Pad8xu3x6mijU3PTCH4AAAAABB+CHwBBo6ypWzFhDqXEhGl1fqJ++KFF2lHZpn97+qBM0zzpM1WtPep3ezWHwc4AAAAAgpDD6gIAwFfKmrpVkBItwzAkSdctnanqth79+JWjykqI1Bc3zh7zTElDlyTR8QMAAAAgKNHxAyBolDd3qzAletS1ey8u1IeWZ+qnr5Xq6d21Y54pqXfKbjPGPAcAAAAAwYCOHwBBoatvUE3O/jEBjmEY+u71C3W8o1dfe+aAMuIjtLYgceT1koYu5SdFKTzEPtUlAwAAAMCko+MHQFA4Mdi5MHls506ow6Zf3LZc2QmR+j9/3q8Bt3fkteJ6p+amM98HAAAAQHAi+AEQFEaCn1Mc2YqLCNHXr5qnuo5ePbNn6MhXV9+g6jp6me8DAAAAIGgR/AAICuVN3Qp12JSVEHnKezbMTtaizDj9/PXyoTXuDU5JUlE6wQ8AAACA4ETwAyAolDV1Kz8pSnabccp7DMPQ5y8qVHVbj57dd1zFw8HPXFa5AwAAAAhSBD8AgkJZ89Aq9zPZOC9Vc9Ni9OCWMh0+3qmYcIfS48KnoEIAAAAAmHoEPwACXt+gRzVtPScd7PzPDMPQvRfPUkWLS8/sqVNRWqwM49RdQgAAAAAQyAh+AAS8yhaXvOapBzv/sw8uSFNhSrT63V7NZb4PAAAAgCBG8AMg4J1po9c/s9mGZv1IUhGr3AEAAAAEMYfVBQCYGoMerz77hz26a12u1hUmWV2OT5U1dctmSHlJUeN+5urFGTJl6vL5aZNYGQAAAABYi44fYJrYVt6qVw436uXDjVaX4nNlzd3KSohUeIh93M/YbYauX5qpyFDybwAAAADBi+AHmCZePFgvSapqdVlcie+VN3WPa7AzAAAAAEw3BD/ANDDo8ervhxokSVUtwRX8eLymKlpc457vAwAAAADTCcEPMA1sr2hVe8+gZqdGq6a9V4Mer9Ul+UxNW48G3F4VEPwAAAAAwBgEP8A08OLBekWF2nX72lx5vKZq23utLslnJrrRCwAAAACmE4IfIMi5PV79/VCjLi5K1dy0GEnBddzrSKNTklTAjB8AAAAAGIPgBwhwZU1O7a1ul2maJ319e0Wb2lwDunJhmnKH151XBlHws7e6XfnJUYqLCLG6FAAAAADwO+wxBgLYoeOd+uivtsvZ79bCmXG6a32urlyUrjDHe2vNXzhYr8hQuzbMSVGYw6aYMIeOBclmL9M0tae6QxfPTbG6FAAAAADwS3T8AAGqurVHd/7+HUWHO/SNq+apd9CjLz25X+u/v0U/f71Mzr7B4WNeDbp4borCQ+wyDEM5SZGqbO2xunyfONbaozbXgJZlz7C6FAAAAADwS3T8AAGopbtft/9uhwY9Xj3+ybUqTInR3etz9WZpi37zZoV++NIR/fL1cl1SlDp8zCt95NncxCgdqO20sHrf2VPdLklalhNvcSUAAAAA4J8IfoAA0zPg1p2/36mGrj794RNrVJgyNLDZMAxdMDtZF8xO1oHaDj2wuUx/2Vs3cszrhLykKL14sF4Dbq9CHYHd9Lenul3RYQ7NGv5vAAAAAAAYjeAHCDB/P9Sgd+u69Mvblmt5zsmPOC3KjNevb1+ho41O9Q16FBH63syf3MQoeU2ppr0n4Ddh7TnWoSVZ8bLbDKtLAQAAAAC/FNg/7gemoeJ6p0IdNl1adOaBxrNTY7Qoc/QxqBObvQJ9pbur362Shi4ty+aYFwAAAACcCsEPEGCK67s0OzVaDvvZ/fbNC5KV7vtrO+Q1paWn6HoCAAAAABD8AAGnpMGpuWmxZ/38jMgQxYQ7VBXgK933VndIkpZlEfwAAAAAwKkQ/AABpKW7X83Ofs1NO/thxoZhKC8pSscCfKX7nmPtKkiOUlxkiNWlAAAAAIDfIvgBAsiRBqckqSj97Dt+pKEBz1Yc9frxy0f0u7cqZZrmmNcOH+9SSUPXuN7HNE3trenQsmy6fQAAAADgdNjqBQSQ4vqhYORcOn6koQHPmw4cV7/bozDH0MavdteAIkLtCg+xn+Hps7Orqk0/21w29PGxNv3wQ4sVHebQoMern71Wqge3lCkq1KHn7j1vZA7RqVS19qjNNaBlzPcBAAAAgNOi4wcIICUNTiXHhCkxOuyc3icvKXJopXvb0HGvzt5Bbfyff+hrTx/wRZkn9aOXjyopOlRfuXyOXnq3Qdc/+La2lDTphp9v1f9uLtPVizNksxn6zGO71TfoOe177TnWLkl0/AAAAADAGRD8AAGkpKHrnLt9pKGjXpJU2TIU/PzstVK1dPfrhYP1anb2n/P7/7Ot5S3aVtGqz2wo1OcuKtQjd69WS3e/7nroHdW29+iXty3TTz+6VD/5yBKVNDj1jb++e9r321PdrugwhwpTon1eKwAAAAAEE456AQHC7fHqaGO37lyXe87vdSL4qWpxqazJqYe3Vun8WUl6s7RFT+6q0ecuKjznr3GCaZr68ctHlRYbrltXZ0uSzpuVpOc+f56e2l2rW9dkKyUmXJJ00dwU3Xtxof53c5lW5M7QRXNS9PdDDXrpUIPqO/o0OzVG8zJi9VZZi5ZkxctuM3xWJwAAAAAEI4IfIEBUtbo04Pb6pONnRlSo4iJCVNnq0pubWhQRatdPPrJE9z6+V3/cUa1PX1jgs1DlH6Ut2nWsXfdft2DU/KCshEh9cePsMfd/4dLZ2lvdoX//y7vymqZMU8pPjtKs1GgdaXTq74cbZJrSTcszfVIfAAAAAAQzgh8gQBTXD230mpt2bhu9TshNitKLB+vV0TOob1w1T4nRYbptTY4++4c9ev1Iky4pSp3Q+w24vXpiV40eertSaXHh+uCCdF02P1U/evmIZsZH6CMrssb1PnaboZ98dIm++ewhzU6N0QcXpmlWSrQMYyiIcvW7Vdni0qxUjnkBAAAAwJkQ/AABoqShSw6boYKU02+8Gq+8xEjtr+lQYUq0bl+bI0naOC9VyTFh+sOO6nEHP26PV0/vqdXPXitTXUevFmfFq76jT1//67v6+vCsnh/cuFChjvGPFEuKDtODty476WtRYQ4tmBk37vcCAAAAgOnsjMGPYRhZkh6RlCbJK+nXpmn+1DCMBElPSMqVVCXpw6Zptk9eqcD0VlLvVEFy9Mj69XOVnzzUMfONq+YpxD4UyoTYbfroyiw9sKVMNW09ykqIVHVrj/716QOy2wz99KNLRm0U6+gZ0Ccf2aV3qtq1ODNO371hoS6YlSRJOtrYrb+9W68mZ79uWMaxLAAAAACwwng6ftySvmya5h7DMGIk7TYM4xVJd0p6zTTN7xuG8TVJX5P0r5NXKjC9lTQ4tSLXd+vLb1uTo3npsbpwdvKo6zevytaDW8r0+M5q5SRG6r7nD8tmGBrweHXdz9/Wb+9YqdmpMapp69Edv9+p2rZe/eimxbph2cyR41iSNCctRnN8MI8IAAAAAHD2zhj8mKZZL6l++GOnYRjFkmZKulbShuHbHpb0ugh+gEnR1Teouo5e3ZaW47P3TIgK1aXzxh7nyoiP0MVzU/XLN8rlNaW1+Yn6vx9erGZnvz75yC7d8POt+tLG2fr562UacHv16MdXaXV+os/qAgAAAAD4zviHbkgyDCNX0lJJOySlDodCJ8KhFF8XB2DIkYbhwc7pU9NBc88F+YqPDNV/XFGkP3xitWbGR2hJVrye+/x6ZSdE6r5NhxXmsOuZz64j9AEAAAAAPzbu4c6GYURLelrSF0zT7Hr/kY4zPHePpHskKTs7+2xqBKa9kvouSVKRjzZ6ncmqvATt+cbGMdfT4yL01GfW6ol3anTlonSlxIRPST0AAAAAgLMzro4fwzBCNBT6/ME0zWeGLzcahpE+/Hq6pKaTPWua5q9N01xhmuaK5OTkk90C4AyKG5yKjwxRamzYmW+eZJGhDt21Po/QBwAAAAACwBmDH2Ootee3kopN0/zx+156TtIdwx/fIelZ35cHQBrq+JmbFqPxdtoBAAAAACCNr+NnvaSPSbrYMIx9w/9cIen7kjYahlEqaePw5wB8bH9Nh/bWdGhlboLVpQAAAAAAAsx4tnq9JelUbQaX+LYcAO/n8Zr6j78eVHJ0mO65IN/qcgAAAAAAAWZCW70ATK1Ht1Xp3bou/efV8xQTHmJ1OQAAAACAAEPwA/ippq4+/ejlozp/VpKuXJhudTkAAAAAgAA07nXuACbPgNurux7aqfiIUH1gQZounpui+18oVr/Hq/uvXcBQZwAAAADAWSH4AfzAO1VterusVVGhdr1wsF6hDpsG3F594dJZyk2Ksro8AAAAAECAIvgB/MArhxsV5rBpx39cqkN1nfrbuw1q7u7Xpy8ssLo0AAAAAEAAI/gBLGaapl4radT6wiRFhzm0Oj9Rq/MTrS4LAAAAABAEGO4MWKy0qVs1bb26pCjF6lIAAAAAAEGG4Aew2KvFjZKkS+amWlwJAAAAACDYEPwAFnutuEkLZsYqLS7c6lIAAAAAAEGG4AewUEt3v/ZUt+vSIrp9AAAAAAC+R/ADWGhLSZNMUwQ/AAAAAIBJQfADWOi14ialxYZrfkas1aUAAAAAAIIQwQ9gkX63R2+WNuviohQZhmF1OQAAAACAIETwA1hke0WbXAMebeSYFwAAAABgkhD8ABbZXNyoiBC71hYkWl0KAAAAACBIEfwAFnm7vFWr8hIUHmK3uhQAAAAAQJAi+AEs0OTsU1lTN90+AAAAAIBJRfADWGB7RZskaW0+wQ8AAAAAYPIQ/AAW2FbeqpgwB2vcAQAAAACTiuAHmEQ1bT2qaesZc317xdB8H4ed34IAAAAAgMnDd53AJPrcH/fo9t/tlNdrjlxr6OxTZYuL+T4AAAAAgElH8ANMknbXgA7WdaqyxaU3SptHrm+raJEkrWG+DwAAAABgkhH8AJNke0WrTFMKtdv08NaqkevbylsVFxGieenM9wEAAAAATC6CH2CSvF3eoqhQu+65IF+vH2lWZYtLkrStolWr8xJksxkWVwgAAAAACHYEP8Ak2Vo+NMD59nU5CrEbemRblWrbe1TT1st8HwAAAADAlHBYXQAQjBo6+1TR7NLNK7OVEhOuKxam66ldtcpNjJIkgh8AAAAAwJSg4weYBFvLhwY4ryscCnjuXJcrZ79b//flI0qICtXslBgrywMAAAAATBMEP8Ak2FreqhmRISpKGxrgvDR7hhZnxsnZ59aafOb7AAAAAACmBsEP4GOmaWprWYvWFiSOCnjuWJcrSVrLGncAAAAAwBRhxg/gY8dae3S8s0+fKUgadf2axRlyDXh0w9KZFlUGAAAAAJhuCH4AH3t7eL7P+n8a4Oyw2/SxNTlWlAQAAAAAmKY46gX42NbyVqXFhisvKcrqUgAAAAAA0xzBD+BDXq+pbeWtWleYKMNggDMAAAAAwFoEP4APHWl0qs01oHX/NN8HAAAAAAArEPwAPvTc/uOyGdL5swh+AAAAAADWI/gBfGTA7dWT79TokqJUpcaGW10OAAAAAAAEP4Cv/P1Qg1pdA7qNzV0AAAAAAD9B8AP4yGPbjyk7IVLnF3LMCwAAAADgHwh+AB8oa3JqR2WbblmdLZuNbV4AAAAAAP9A8AP4wGPbqxVqt+mm5ZlWlwIAAAAAwAiCH+Ac9Q549PSeWn1wYZoSo8OsLgcAAAAAgBEEP8A5en7/cTn73Lp1NUOdAQAAAAD+heAHOEd/3Fmt2anRWpk7w+pSAAAAAAAYheAHOAd9gx4dqO3Q5fPTZBgMdQYAAAAA+BeCH+AclDZ2y2tK89JjrS4FAAAAAIAxCH6Ac1Bc3yVJmkvwAwAAAADwQwQ/wDk4XN+lyFC7chIirS4FAAAAAIAxCH6Ac1Bc36U5aTGy2ZjvAwAAAADwPwQ/wFkyTVPF9V0q4pgXAAAAAMBPEfwAZ6m+s09dfW4VpcVYXQoAAAAAACdF8AOcpRODnen4AQAAAAD4K4If4Cyx0QsAAAAA4O8IfoCzVFzvVHZCpKLDHFaXAgAAAADASRH8AGepuKFLc5nvAwAAAADwYwQ/wFnoHfCoqsXFfB8AAAAAgF8j+AHOwpFGp7wmg50BAAAAAP6N4Ac4A9M09du3KlXV4hq5dmKw8zyCHwAAAACAHyP4ATR0dOt7Lxarpbt/zGvF9U7dv+mwPv3Ybg24vZKkkvouRYc5lDkjYqpLBQAAAABg3Ah+AElP7qrRr/5RoT/uqB7z2suHGyRJJQ1OPbC5VNJQGDQnLUY2mzGldQIAAAAAMBEEP5j2vF5TD2+rkiRtOnB8zOuvHG7UipwZumHZTD34erkO1HaouKFLRels9AIAAAAA+DeCH0x7b5a1qKLZpVW5CTra2K0jDc6R12rbe3ToeJcum5+qb149X8nRYfr0o7vl7HMz2BkAAAAA4PcIfjDtPby1SknRYfrJR5fIZozu+nnlcKMkaeO8NMVFhOj7Ny7U8c4+SWz0AgAAAAD4P4IfTGtVLS5tOdKkW1ZnKyM+QmvyE7XpQL1M05QkvXyoUbNTo5WXFCVJ2jAnRTevylZEiF1zUjnqBQAAAADwbwQ/mNYe2XZMdsPQbauzJUlXLcpQZYtLh453qd01oJ1VbbpsXtqoZ/7rugXa/H8uVFSYw4qSAQAAAAAYN4IfTFuufrf+vKtGVyxMV0psuCTpAwvSZLcZ2nSgXptLmuTxmrpsfuqo5+w2Q+lxrHEHAAAAAPg/WhYwbT2zp1bOfrfuXJ87ci0hKlTrC5O06cBxzUuPVVpsuBbOjLOuSAAAAAAAzgEdP5i2ntpTpwUzY7U0K37U9asWpau2vVevFjfqsvmpMgzDogoBAAAAADg3BD+Ylpx9gzpY26GL56SMCXYun5emELshr6kx830AAAAAAAgkBD8Iat9+/pCe2398zPVdVe3ymtKa/MQxr8VFhujC2SmKiwjR6vyEqSgTAAAAAIBJwYwfBC23x6tHtx3Tzso2XbM4Y9Rr2ytaFWq3aWn2jJM++70bFqq9Z0AhdrJRAAAAAEDg4rtaBK36zj65vaYOHe9SfWfvqNe2V7RqSVa8IkLtJ302OSZMs1NjpqJMAAAAAAAmDcEPgtax1p6Rj18rbhr52Nk3qHePd3GMCwAAAAAQ9Ah+ELSqWl2SpNhwh14rbhy5vutYuzxe86TzfQAAAAAACCYEPwha1W09CnXYdMOyTL1d3qqeAbekoWNeIXZDy04x3wcAAAAAgGBB8IOgdazVpeyESG2cl6oBt1dvlbZIkrZXtJ12vg8AAAAAAMGC4AdB61hrj3ISIrUyN0ExYQ5tLmlSd79b79Z1anUex7wAAAAAAMGP4AdByTRNVbf1KDsxUqEOmy6Yk6zXSpr0TmUb830AAAAAANMGwQ+CUnN3v3oGPMpNjJIkXVqUomZnv37zZsXQfJ+ceIsrBAAAAABg8hH8IChVD69yz06MlCRtmJ0imyFtLW/V4sx4RYY6rCwPAAAAAIApQfCDoFQ1HPzkJAwFPzOiQrUiJ0GStDo/wbK6AAAAAACYSgQ/CErVrS7ZDClzRuTItUuKUiSJwc4AAAAAgGmD8y4ISsfaepQRH6FQx3vZ5s2rs2W3GVpfmGRhZQAAAAAATB2CHwSlY609ykmMHHUtNjxEnzg/36KKAAAAAACYehz1QlA61upSdkKU1WUAAAAAAGApgh8Ena6+QbX3DI7p+AEAAAD+X3t3Hhx5ed95/PNV61brPmZ0zYyG0VzMBQwD5rYxxjhbHHZMjHddOCY2Sdkue5PdDZvsVuKtFHHF2aRqtzBrO0CIl2CTAAYCjtchjIfBYU6YA83JjEbSSKOrdbRuqfvZP9QaRqPWNTq6+6f3q4qS9PTvJ33bfuqn0aee5/sAwFIzbfBjZk+bWYuZHb1krMDMfmlmpyIf8xe2TGDmxo5yX0XwAwAAAABY4may4udvJX36srHHJL3pnKuW9GbkayAunIsEP2z1AgAAAAAsddMGP865XZIClw3fJ+nZyOfPSrp/nusCrlhte68kaQUrfgAAAAAAS9yV9vhZ5pxrkqTIx5L5KwmYm7r2PhX5U+VP49A6AAAAAMDStuDNnc3sa2a238z2t7a2LvSPA3Qu0KuVhWzzAgAAAADgSoOfZjMrlaTIx5bJLnTO/dA5t905t724uPgKfxwwc+fa+7SygG1eAAAAAABcafDzqqSHI58/LOmV+SkHmJuB4ZAudA/Q3wcAAAAAAM3sOPfnJf2bpHVm1mBmj0j6rqS7zOyUpLsiXwMx19DRJ+eklQQ/AAAAAABo2u63zrmHJnnpznmuBZizsaPc6fEDAAAAAMAiNHcGFlPtWPBDjx8AAAAAAAh+4C31gT5lpfpUkJUa61IAAAAAAIg5gh94Sn2gT5UFmTKzWJcCAAAAAEDMEfzAU+o7+lSRzzYvAAAAAAAkgh94iHNO9YF+raC/DwAAAAAAkgh+4CHtvUPqHw6psiAj1qUAAAAAABAXCH7gGfWB0RO9KtnqBQAAAACAJIIfeEjdWPDDVi8AAAAAACQR/MBDGjr6JUkV+Wz1AgAAAABAIviBh9QH+lTkT1VWWnKsSwEAAAAAIC4Q/MAzOModAAAAAIDxCH7gGfWBfvr7AAAAAABwCYIfeMJIKKzznf2qpL8PAAAAAAAXEfzAE5q6BhQKO61gxQ8AAAAAABcR/MAT6js4yh0AAAAAgMtx/BHi0n//2VEdrOvQvVvLdO+2MpXmTr2FqyEwepR7Jc2dAQAAAAC4iBU/iDsDwyH9w4F6ne/s15///Lhu+u6/6ktP7VFn39Ck99R39CnJpNK89EWsFAAAAACA+Ebwg7izrzaggeGw/urBrXrrP92h37v9Kr19qk1vHLkw6T11gT6V5mYoxceUBgAAAABgDH8lI+7sOtmqVF+SblxdqKqiLP3nu9cpOy1ZNU1dk95TH+ijsTMAAAAAAJch+EHc2XWyTdtX5SszdbQFlZlpQ1mOahq7J72nvqNflQUc5Q4AAAAAwKUIfhBXLnQN6ERzUPXjLQkAABa0SURBVLetLR43vrE0R8cvBBUKuwn3DAyH1BocpLEzAAAAAACXIfhBXNl1qlWSdFv1ZcFPWY76hkI619474Z4GjnIHAAAAACAqgh/ElV0nW1WcnaYNpdnjxjeW5kiSapombveqHzvKna1eAAAAAACMQ/CDuBEKO+0+3abbqotlZuNeq17mV3KSRe3zUxdgxQ8AAAAAANEQ/CBuHDnfpc6+Yd22tmjCa2nJPq0p8U+y4qdP6SlJKvanLUaZAAAAAAAkDIIfxI1dJ1tlJt16WX+fMRsnOdmrvqNPFfmZE1YJAQAAAACw1BH8IG7sOtmqzeW5KshKjfr6xtIctQQH1RocHDdeH+hXZT79fQAAAAAAuBzBD+JC98Cw3qvvnHCa16U2lo02eD52yXYv55zqA31aQX8fAAAAAAAmIPhBXNhzJqBQ2OnW6on9fcZEO9nrZHOPgoMjWl3sX/AaAQAAAABINAQ/iAtjq3g2V+ROek1eZqrK8zLG9fn5wa4PlZHi071byxa8RgAAAAAAEg3BD+LCieagKgsylJmaPOV1G0pzLoZE5zv79er7jfrCjkrlT9IXCAAAAACApYzgB3HhVHNQ65ZlT3vdxrIcfdjao4HhkJ7efVZO0iO3VC18gQAAAAAAJCCCH8TccCiss229qp5J8FOao7CT9pwN6Pm9dbp3a5kq8mnsDAAAAABANAQ/iLnatl4Nh5zWLpu+QfPVkZO9vvPqB+obCulrt61e6PIAAAAAAEhYBD8J4EhDl/5+T12sy1gwJ5qDkqTqkulX/FTkZyg7LVln2np1+9pibYic9AUAAAAAACaaupMuYu6t4y36vecOaGA4rFuri1RZ4L1tTSebe5Rk0pqS6Vf8mJk2lOVo79mAfvf2qxahOgAAAAAAEhcrfuLYSwcb9Dt/t1/Lc9IlSe+cbotxRQvjVHNQKwuzlJ7im9H1928r1wPXlOvG1QULXBkAAAAAAImN4CdO/c3bZ/T7LxzSDVUFeu2bt2hZTpre9mjwc7I5qOoZrPYZ88UbVuivf2ubzGwBqwIAAAAAIPER/MShC10D+rPXj+lTG5fpmd++XtnpKbplTbF+fbpN4bCLdXnzanAkpNr2Pq2dwYleAAAAAABgdgh+4tC+2oAk6RufWKO05NHtT7dWF6mjb1g1Td2xLG3enWntVSjsVD2DE70AAAAAAMDsEPzEoQPnOpSR4ht3YtVNawolSW+f8tZ2r5ORE73WLWfFDwAAAAAA843gJw4drOvQ1spcpfg++r+nJDtd65dna/fp1hhWNv9ONffIl2SqKsqKdSkAAAAAAHgOwU+c6Rsa0QeN3bpuZf6E125ZU6R9tR0aGA5N+33aegb1N2+f0W8/s1d17X0LUeq8ONkc1KrCzItb2gAAAAAAwPwh+Ikzh+q7FAo7bV858ajym6uLNDQSvtgDKJojDV169Mf7dePjb+rPXj+mnSdb9eSvTi9kyXNyqqWHxs4AAAAAACwQgp84c7CuQ5J0zYq8Ca/dUFWgVF+Sdk/S5yccdnr0x/u192xAX7mlSr/8j7fpoR0r9OLB82rvGVzQuq/EwHBIte29qib4AQAAAABgQRD8xJn9tQGtKfErLzN1wmuZqcm6dmWedp+OHvzsORtQY9eAvnPfJv3RZzaoelm2vnJzlYZGwvq/79bNqo4n3jo9acA0X0639Mg5aR3BDwAAAAAAC4LgJ46Ew04H6zq1PUp/nzG3rCnSB43dUVfw/Oy988pK9emuDcsujq0p8evj64r143drZ9QbSJLq2vv0vV+c0B//7IhCYTf7NzJDp1pGT/Ray1HuAAAAAAAsCIKfOHKmrUdd/cO6dqrgp7pYkvTrD9vHjQ8Mh/TGkSZ9elOpMlLHN0r+nVtXq61nSK8eapxRHS+/d16SdK69T28caZrNW5iVk809SvGZVnGiFwAAAAAAC4LgJ47srx3t7xPtRK8xm8tzlZ+Zop/uq5dzH63GefNYi4KDI3rgmvIJ99x0VaHWL8/WU2+fHXdPNM45vfReg26oKtDq4ix9f+eH095zpU5eCKqqKGvcsfUAAAAAAGD+8Bd3HDlwrkP5mSlaPcUKGF+S6dufXKvdp9v02uGPVuO8/N55LctJ08euKpxwj5npkVuqdKI5OGl/oDEH6zp1rr1Pv3ldhX739qt0rKlbO0+2XvmbmoRzTscvBGnsDAAAAADAAiL4iSMH6jp03cp8mdmU1/2HG1dqS0Wu/sdrNerqH1agd0g7T7Tovm3l8iVFv/febWUq8qfpf795espePy8dbFB6SpLu2Vyq+7eVqzQ3XU/u/HBO7yuaDxq7db6zXzdFCaoAAAAAAMD8IPiJE4HeIZ1p7Z2yv88YX5Lp8Qc2K9A7qO/94rheP9KkkbDT/dsmbvMak5bs03+5e5321gb0xR+9q9bgxObQgyMh/dPhJt199XL505KVmpykr966WnvPBnTgXOCK3teLBxrUEhyYMP7aoUYlJ5k+s6n0ir4vAAAAAACYHsFPjDjndLCuQ+fae0dP8zo32t9n+8qCGd2/qTxXD9+0Ss/tqdP/2fmh1i/P1saynCnvefD6Sj35769VTVO37n/iHZ24EBz3+lvHW9TVPzyuT9AXdlQqPzNF339r9qt+6gN9+oN/OKTHXz82bjwcdnrtUKNurS5SftbEY+sBAAAAAMD8IPiJkX893qLPfv/Xuv17O3X1n/xCj710RMlJpi0VuTP+Hr9/11qVZKfpfGe/7o/S1DmaezaX6oVHP6ahUFife/LXem7POQ2NhCVJLx48r+LsNN2ypuji9ZmpyXr4plV683iLGjv7Z/UeDzV0SpJeO9w07t4DdR1q7BrQfVOsUAIAAAAAAHNH8BMjP9h1RuV5GfqLz23RF29YofXLs/XwTauUnuKb/uaI7PQUPf7AZpXnZUQ9zWsyWyry9MrXb9aG0mz98ctH9fG/3Kln3jk72idoa5mSLztl69oVo9vPzs8y+Dnc0KUU32jPoad3n704/ur7jUpPSdJdG5fN6vsBAAAAAIDZSY51AUvRofpO7T0b0H/7jQ168PrKOX2vOzcs050bZh+glOVl6IVHP6ZfnWzVX//LKX3ntRpJ0mevrZhwbZE/TZLU3jOxL9BUDtV3amNZrlYVZur5vXX65p3Vykz16fUjTbpzwzJlpTH9AAAAAABYSPzlHQM/evuMstOS9VtzDH3mysx0x7oS3b62WDtPtKq+oy9qn6Ai/2gfntaeoRl/71DY6ej5Ln3uugo9uL1Sr7zfqOf31mn98mwFeod079ayeXsfAAAAAAAgOoKfRdbQ0aefH72gR26pUnZ6SqzLkTQaAH18fcmkrxdEGjC3RTkJbDIftvaodyikLRV52lSeq5vXFOqZd85qR1WhstOTdce64jnXDQAAAAAApkaPn0X2zDu1MklfvmlVrEuZsWRfkvIzU9TeO/Pg51D9aGPnrZFm1V+9dbWauwf12qFG3bNpudKSZ97LCAAAAAAAXBmCn0XU1T+sn+yt029sKVVZXkasy5mVIn+a2oIz3+p1uKFLWak+rS72S5JuX1us9cuzJUn3buU0LwAAAAAAFgPBzyL6yd469Q6F9NVbV8e6lFkr8qfNasXP4YZObSrPlS9p9FQvM9Nj96zX3Vcv042rCxaqTAAAAAAAcAmCn0X0jwcatGNVgTaV58a6lFkr9KeqbYbNnYdGwjrWFNS2yrxx43esK9EPvrR9wnHxAAAAAABgYfAX+CJpDQ7qVEvPlE2U49noVq+Zrfg5fqFbQ6GwtlTkTX8xAAAAAABYMAQ/i2Tv2YAkJew2pyJ/qoKDIxoYDk177aGGLknSlorEW9kEAAAAAICXEPwsknfPtCsr1ZeQ27yk0RU/ktTeO/12r8P1nSrISlVFfmI1sAYAAAAAwGsIfhbJu2fatX1VgVIStL/NxeCnZ/rtXocburSlIldmttBlAQAAAACAKSRmCpFg2npG+/vcuLow1qVcsUJ/qqTR9zKVvqERnWoJ0t8HAAAAAIA4QPCzCPacSez+PtJHK37aglNv9Tp6vlthJ22lvw8AAAAAADFH8LMI9pxtV2YC9/eRLgl+eqde8XO4oVOSWPEDAAAAAEAcIPhZBIne30eSMlJ9ykr1Tbvi5736TpXnZag4O22RKgMAAAAAAJNJ3CQiQbT1DOpkc09Cb/MaU+hPU/sUK36cc9pfG9B1K/MXsSoAAAAAADAZgp8FtvfsWH+fxG3sPKbInzplc+eGjn41dw/q+lUEPwAAAAAAxAOCnwX27pnR/j6bE7i/z5gif9qUW7321Y6GXNdXJf7qJgAAAAAAvIDgZ4HtORNI+P4+Y6bb6rWvNqDs9GStLclexKoAAAAAAMBkEj+NiGPtPYM60Rz0RH8fSSr2pyrQO6RQ2EV9fV9th7avzFdSki1yZQAAAAAAIBqCnwX08nvnJUk3VCV+fx9pdMVP2EmB3onbvQK9Qzrd0qPtq7wRcgEAAAAA4AUEP3NU09itx984pq7+4XHjvzrZqj//+XF9Yn2JrqnMi1F186vIP3pEe7TtXgfOdUiSdtDfBwAAAACAuEHwM0c/frdWP9x1Rvc/8Y5ONQclSScuBPWN5w6qusSv//XQNZ7Z+lToT5WkqA2e99UGlOpL8kQTawAAAAAAvILgZ45qGrtVVZSl4MCI7n/iHf10X52+8rf7lJHq09Nfvl7+tORYlzhvplrxs682oC0VuUpP8S12WQAAAAAAYBIEP3MwEgrr+IWgPrG+RK9982atKfHrD188okDvkJ56+HqV5WXEusR5VRwJflqD44Of/qGQjp7vor8PAAAAAABxxjvLUWLgbFuvBkfCurosR6W5Gfrpox/TE2+d1g1Vhdpc4b0tTzkZyUrxmdp6xm/1er++U8Mhpx1V+TGqDAAAAAAAREPwMwc1Td2SpI1lOZKk9BSf/uBT62JZ0oIyMxVmpam9Z/yKn/21AUnSdStY8QMAAAAAQDxhq9cc1DR2K9WXpKuK/bEuZdEU+lPVdlnws+9ch9Yty1ZuZkqMqgIAAAAAANEQ/MxBTVO31i73K8W3dP5nLPKnqb33o61eobDTwXMdup5tXgAAAAAAxJ2lk1jMM+ecahq7tbE0J9alLKoif5raLmnufLihUz2DI7qexs4AAAAAAMSdOQU/ZvZpMzthZqfN7LH5KioRtAQH1d47tASDn1S19QzJOSdJevm980pNTtId60piXBkAAAAAALjcFQc/ZuaT9ISkeyRtlPSQmW2cr8LiXU3jWGNn753eNZUif5qGQmEFB0c0MBzSK+836u6rlys3g/4+AAAAAADEm7mc6rVD0mnn3BlJMrOfSLpPUs18FBbvxk70Wl+aHeNKFlehP1WS1BYc1LGmoLr6h/X56ypiXBUAAAAAAIhmLlu9yiXVX/J1Q2RsSahp7NaKgkzlpC+tlS5F/jRJUnvvkP7xQL1Kc9N185qiGFcFAAAAAACimUvwY1HG3ISLzL5mZvvNbH9ra+scflx8qWlaeo2dpY9W/NQ0dutXJ1v12WvL5UuKNhUAAAAAAECszSX4aZBUecnXFZIaL7/IOfdD59x259z24uLiOfy4+NEzOKLa9l5tLFt6wU9xZMXPU7vPKuyk37yucpo7AAAAAABArMwl+NknqdrMqswsVdIXJL06P2XFtxMXuuWcluSKn4Ks0RU/dYE+bV+Zr6qirBhXBAAAAAAAJnPFwY9zbkTSNyT9QtIxSS845z6Yr8Li2Ucnei294CfZl6T8zNG+Rp/fTlNnAAAAAADi2VxO9ZJz7g1Jb8xTLQmjpqlbeZkpKs1Nj3UpMVHkT1P/cEif2Vwa61IAAAAAAMAU5hT8LFU1jaONnc2WZlPjf7elTEkmZS+xE80AAAAAAEg0BD+zNBIK6/iFoL5048pYlxIz3/pkdaxLAAAAAAAAMzCX5s5LUqB3SFVFWdpckRvrUgAAAAAAAKbEip9ZKslJ1z9/+7ZYlwEAAAAAADAtVvwAAAAAAAB4FMEPAAAAAACARxH8AAAAAAAAeBTBDwAAAAAAgEcR/AAAAAAAAHgUwQ8AAAAAAIBHEfwAAAAAAAB4FMEPAAAAAACARxH8AAAAAAAAeBTBDwAAAAAAgEcR/AAAAAAAAHgUwQ8AAAAAAIBHEfwAAAAAAAB4FMEPAAAAAACARxH8AAAAAAAAeBTBDwAAAAAAgEcR/AAAAAAAAHgUwQ8AAAAAAIBHEfwAAAAAAAB4FMEPAAAAAACARxH8AAAAAAAAeBTBDwAAAAAAgEcR/AAAAAAAAHiUOecW74eZtUo6t2g/EJhfRZLaYl0EMEfMY3gFcxlewVyGFzCP4RWJPJdXOueKo72wqMEPkMjMbL9zbnus6wDmgnkMr2AuwyuYy/AC5jG8wqtzma1eAAAAAAAAHkXwAwAAAAAA4FEEP8DM/TDWBQDzgHkMr2AuwyuYy/AC5jG8wpNzmR4/AAAAAAAAHsWKHwAAAAAAAI8i+AGiMLNaMztiZu+b2f7IWIGZ/dLMTkU+5se6TuByZva0mbWY2dFLxiadu2b2X83stJmdMLO7Y1M1MNEkc/lPzex85Nn8vpl95pLXmMuIO2ZWaWZvmdkxM/vAzL4VGee5jIQxxTzmmYyEYmbpZrbXzA5F5vJ3IuOefyaz1QuIwsxqJW13zrVdMvYXkgLOue+a2WOS8p1zfxirGoFozOw2ST2S/s45tykyFnXumtlGSc9L2iGpTNK/SFrrnAvFqHzgoknm8p9K6nHO/eVl1zKXEZfMrFRSqXPuoJllSzog6X5JXxbPZSSIKebxg+KZjARiZiYpyznXY2YpknZL+pakz8rjz2RW/AAzd5+kZyOfP6vRX3hAXHHO7ZIUuGx4srl7n6SfOOcGnXNnJZ3W6C82IOYmmcuTYS4jLjnnmpxzByOfByUdk1QunstIIFPM48kwjxGX3KieyJcpkf+clsAzmeAHiM5J+n9mdsDMvhYZW+aca5JGfwFKKolZdcDsTDZ3yyXVX3Jdg6b+hxwQD75hZocjW8HGlmIzlxH3zGyVpGsk7RHPZSSoy+axxDMZCcbMfGb2vqQWSb90zi2JZzLBDxDdzc65ayXdI+nrkS0HgNdYlDH2/yKePSnpKknbJDVJ+p+RceYy4pqZ+SW9KOnbzrnuqS6NMsZcRlyIMo95JiPhOOdCzrltkiok7TCzTVNc7pm5TPADROGca4x8bJH0skaX9DVH9jiP7XVuiV2FwKxMNncbJFVecl2FpMZFrg2YMedcc+QfbGFJP9JHy62Zy4hbkT4SL0p6zjn3UmSY5zISSrR5zDMZicw51ylpp6RPawk8kwl+gMuYWVakcZ3MLEvSpyQdlfSqpIcjlz0s6ZXYVAjM2mRz91VJXzCzNDOrklQtaW8M6gNmZOwfZREPaPTZLDGXEacijUSfknTMOfdXl7zEcxkJY7J5zDMZicbMis0sL/J5hqRPSjquJfBMTo51AUAcWibp5dHfcUqW9PfOuX82s32SXjCzRyTVSfp8DGsEojKz5yXdIanIzBok/Ymk7yrK3HXOfWBmL0iqkTQi6euJeEoBvGmSuXyHmW3T6DLrWkmPSsxlxLWbJX1J0pFITwlJ+iPxXEZimWweP8QzGQmmVNKzZubT6CKYF5xz/2Rm/yaPP5M5zh0AAAAAAMCj2OoFAAAAAADgUQQ/AAAAAAAAHkXwAwAAAAAA4FEEPwAAAAAAAB5F8AMAAAAAAOBRBD8AAAAAAAAeRfADAAAAAADgUQQ/AAAAAAAAHvX/AY6c0zbG644kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Net Cumulative returns'] = (((df['Open Price'] - df['Close Price'])/(df['Open Price'])) * 100).cumsum()\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(df['Net Cumulative returns'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}