{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({\"figure.figsize\":(16, 9), \"grid.alpha\":0.21})\n",
    "plt.style.use(\"seaborn-whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From yahoo https://finance.yahoo.com/quote/SPY?p=SPY\n",
    "spx = pd.read_csv(\"../Data/SPY.csv\", index_col=\"Date\")\n",
    "spx.index = pd.to_datetime(spx.index, format=\"%Y-%m-%d\")\n",
    "spx[\"gap_pct\"] = (spx[\"Open\"] / spx[\"Close\"].shift(1)) - 1 # gap percent\n",
    "spx[\"pct_cc\"] = (spx[\"Close\"] / spx[\"Close\"].shift(1)) - 1 # close to close percent\n",
    "spx[\"pct_oc\"] = (spx[\"Close\"] / spx[\"Open\"]) - 1 # open to close percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "instances = pd.DataFrame()\n",
    "\n",
    "for i in range(len(spx)):\n",
    "    gap_amount = -0.02 # 2% gap down\n",
    "    if spx[\"gap_pct\"].iloc[i] <= gap_amount and spx[\"gap_pct\"].iloc[i-1] > gap_amount:\n",
    "        rets = spx[\"pct_cc\"].iloc[i-1: i+15]\n",
    "        rets.iloc[0] = 0 # zeroing day before the gap\n",
    "        rets.iloc[1] = spx[\"pct_oc\"].iloc[i] # day 0 pct is open to close\n",
    "        rets = rets.reset_index(drop=True)\n",
    "        instances[spx.index[i]] = rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Days since gap')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xxx+sX7/e/FqA3r17U7FixefKY8n96Unmz5+f423evHmT4OBgTp8+bb7v119/5dq1a2zcuJG5c+cSHh4OwJgxYxg1ahRr1qzB2dmZrVu38tdff3HixAnWr1/PrFmzCA0NfWJ/Z8+eZdmyZURGRhIZGYmLiwtz584lODiYyMhI+vTpw6xZswDo0qULM2fOzPFtFkIIIYR4GVhbOkBB4OHhwd27d9m4cSPVqlWjQoUKbNy4EXjwZbNkyZJcu3YNRVGYPXs2kZGR+Pr60rlzZ+7fv09ISAibN282tzdv3jyOHz+ORqNh0qRJ/Pnnn2zbtg2VSkXTpk3p3LkzS5YsQafTUblyZVatWkVYWBilS5dm7dq1JCYm0rp1a/r164ebmxv16tVj3759lC9fnkuXLpGens7cuXPx8vJi4MCBpKeno9PpGDp0KDVq1DDnMBqNjB07ltjYWJKTk6lXrx7t27cnIiICnU5HoUKF+O6777CxseGtt95Cp9Mxe/Zs1Go1r732GuPHj2fr1q1s2rQJk8nEZ599Rq1atQCoXLkyFSpUyNaXtbU1jo6OaDQaNBoNDg4OLFy4kL59+z72vV+9ejUbNmzA29ubpKQkALKyshg1ahS3bt3CaDQSEhJCyZIlmTNnDosXL2bbtm0sWbKEH374gePHj/PTTz/h4+PD7du3SUpK4u7du4wcOZK6detm62vBggXs2rULDw8PtFotAwcOpESJEoSFhaHX60lJSWHAgAEEBATQtGlT3nvvPS5dukShQoWYNWtWtoIzOjqaMmXKYGtry4QJE/jtt99wdnamTZs2JCcn8+233xIcHJzt+XPmzMHOzg43NzcmT57MrFmzSE1NpV+/fkRERJifO2LECJo2bUpiYiJ79+5Fp9Nx8+ZNevXqRZs2bVi9ejXff/89VlZWVKlShSFDhmTbn1xcXMxFoU6nY+rUqdjY2DB48GD8/Py4desW77zzDuHh4SQlJTFixAjS0tJQFIWpU6fi6enJ6NGjSU5OBiA0NJQ33niDESNGcPPmTfR6PT169KBp06bmzHq9/pH7Yu3atYmOjqZLly7m/Tc1NZV58+ZRtGhRFi5cyK5duzAajXTs2JEOHToQGRmZ7fPStWvXbOOo0WiYOHEiS5cuNd93+fJl6tati5WVFR4eHqjVahISEoiLi6NKlSoAVKlShd27d9OqVSuWL1+OSqXi7t27eHl5AbB//36OHj2abbbXZDJx48YNxo4dS2JiIoGBgQQGBjJ8+HBcXFzM+76dnR0ApUqV4urVqyQnJ+Pu7v7Y/V4IIYQQ4lX01OLUZDIRFhbGhQsXsLW1ZeLEiZQoUcL8+Pr161m3bh3W1tb069cPf39/EhISGDJkCFlZWXh7ezNlyhQcHBzYs2cPCxYswNramrZt29K+ffvnDpwW9TOpa7Y/9+uexLVTM1yCmjz2cQ8PDyIiIvj222+ZP38+Dg4OfPHFFzRu3Bh48KV2/PjxrF69msWLF9OtWzcGDRpE586d2bZtGy1atHiozVKlShEaGsrly5f58ccfWbNmDSqViu7du1OnTh169+7N1atXadiwIatWrXpkroSEBDZt2oStrS379u2jYsWKjB49mtmzZ7N9+3b8/f1JTExk1apVJCUlcf369Wyvj4mJoVKlSrRr1w69Xk+9evX4/PPPzX3369cPg8GAl5cX77zzDk2aNGHNmjV4enoyZ84cvvvuO6ytrXF1dc1WPAHY2dlhZ2dHVlYWI0aMICgoCCcnJ4KDg5kyZQpeXl54eXnh6OjIX3/9xbJly6hXrx4NGzY0t5GWlsY333zD1q1bUalUtGnTBoCoqCjc3d2ZPn066enptGnThnXr1nH37l30ej2///47KpWKxMREfvvtNxo1asTJkyextbVl2bJlREdHs2LFimzF6fnz5/n999/ZuHEjWVlZ5jG7evUqISEh1KhRg2PHjjFv3jwCAgLQ6XS0aNGCatWqMW3aNKKioggJCTG3d+jQId544w3gwexcWloagwcPZv/+/QB89NFH5ucqisKYMWNYu3Ytvr6+fP3110RERBAWFsbOnTsfem//KT09neXLl3P9+nX69u1LmzZt2Lx5M2PGjKFSpUqsWbMGRVGy7U+rV69m+vTp+Pr6smjRIn7++WdatGjB9evXWb58OQ4ODgQEBJCQkMDixYtp0KABHTt2ZP/+/Zw6dYoLFy5Qs2ZNOnXqxPXr1xk5ciRLly7l4MGDbNq0CXhQbP/TzZs3n7gvAub9d9q0aWzfvp06deqwb98+NmzYQGZmJjNnzuTSpUuP/LyUKlXK3E758uUfartChQqsXLmSzp07Exsby+XLl9Fqtbz22mscOnSI6tWr8+uvv6LVagGwtrZm9uzZfPPNN4wZMwaAWrVqmX98+ZtGoyE4OJiQkBCMRiNdu3bl7bffNme4evUqU6dOZcGCBebXlCpVimPHjmXb14UQQgghxDMUp7t27SIzM5OoqChOnDjBlClTzF+WExISiIyMZNOmTej1ejp16kTt2rVZsmQJrVu35qOPPmLevHlERUXRuXNnvvzySzZu3IiDgwMdO3bE398fb2/vXN/If+vGjRs4Ozvz5ZdfotVquXz5Mr179zbPQtasWRN4UKTu2bOH1157DScnJy5fvszWrVtZuHDhQ22WLFkSeLDc8+/j0wDu37/PzZs3H5tFURTz38WKFcPW1tZ8+8033wTAz8+PxMREypYtS+fOnRk0aBAGg4EuXbpka8vNzY3Tp09z4MABnJ2dyczMfGy/9+7dIz4+ns8//xx4MONWu3Ztihcvbt6W/+/+/ft89tlnVK9e3Xy8pY+Pj3mJ4+DBgxk3bhwDBw5k5cqVfPzxx9m+sF+9etU8+wiYl7JeuXKF999/HwBnZ2dKly7NrVu3qFOnDgcPHiQmJoYWLVrw559/cuzYMYYOHcrJkyfNM7l+fn4PbeuVK1d45513UKvVqNVq3n77bQC8vb2JiIhg48aNqFQqDAYD8KB4qVatGvBg3P95bCNAcnIy7777LgATJkzg119/JSYmhmvXrlGkSBHS09PNM6fJyck4Ozvj6+sLQLVq1czv0dP8XQQVLlzYvE1ffvklK1asYMaMGVSqVCnbPgPg6+vLpEmTcHR0zDZzWLx4cZydnc3brdfruXbtGoGBgQDmwqxXr14cOHCAn376CYDU1FScnZ0ZM2YMY8aMIT09nZYtW2br82n7ImTff+/fv8+1a9eoWLEiarUaBwcHQkND+fHHHx/5eflncfooderU4fTp03Tr1o3y5cvz1ltvmWeoJ02axLJly3jnnXeyfZ6++OILevXqRVBQEO+99x7Fixd/qF0HBwe6du2Kg4MD8OB/wfnz5ylfvjwHDhwgPDycadOmZcvn7e1NSkrKE/MKIYQQQryKnlqcHj161DzDVKlSpWwnAjp16hSVK1fG1tYWW1tbihcvzvnz5xk1ahSKomAymYiJieH111/nypUrFC9enEKFCgFQtWpVjhw5wocffvhcgV2CmjxxljM3XLhwgbVr17Jo0SLgQWHp4uKCWq0G4MyZM/j5+XHs2DHKlCkDYF4e6+vri4eHx0NtWlk9ONy3VKlSlClThmXLlqFSqVi1ahXlypXj0KFD5pOz2NrakpCQQOnSpfnrr7/MRczfbTwpd0ZGBkuWLCE+Pp4OHTrg7+9vfnzz5s24uLgwfvx4bty4wfr16x8qZFQqFSaTCXd3d/z8/Fi4cCEuLi7s3r0bR0dHYmJiHplDp9PRvXt3QkJCHipUAPbu3UulSpVwdXVFr9cDPHSSoNdee43Lly+j0+mwsbHh3LlztGzZktKlS3PkyBEaNWpEeno6Fy9epFixYgQEBDBnzhzKly9PnTp1GDduHMWLF8fGxsa8LY9TpkwZIiMjMZlMGAwG/vrrLwDmzp1Lu3btqF+/Pps2beK7774DwGAwmIuQo0ePmsf9bx4eHqSlpQEwYMAAkpOTmTVrFt26dTOfJOlv7u7upKenEx8fj4+PD4cOHeL1119/bNZ/etQ2rV+/nvDwcOzs7OjRowfHjx/HysrKvD+Fhoaya9cunJ2dGT58uHnMH9VW6dKlOX36NOXLl+fw4cP89ttvlCpVipYtW9KiRQuSkpLYsGED8fHxnD17lgULFqDX66lfvz6tWrXC2vrBv5in7YuPUqpUKdauXYvJZMJoNNK7d2+GDx/+yM/L01y7dg1PT0/WrFlDTEwMw4YNw9XVlU2bNjF58mR8fX2ZMGEC9erVY//+/ezYsYNx48ZhZ2eHtbX1Y/ed69ev88UXX/Ddd99hMpk4duwYrVu35sCBA+ait2jRotlec//+fTw9PZ+aWQghhBDiVfPU4jQ9Pd08mwKgVqsxGAxYW1uTnp5uPq4KwMnJifT0dPMMU6tWrdDr9QwYMICYmJhHPvdR/l5al1/UrVuX8+fPExgYiJ2dHSqVioEDB2JtbY3RaGTjxo2sWLECe3t7Jk2ahFarpU6dOowfP958+5+ysrLIyspCq9VSokQJ3nvvPYKCgsjKyuKtt94iMDCQEiVKsHDhQsqWLUtQUBDh4eH4+vri4+NDVlYWOp0Ok8lkbttoNKLX69Fqteb2fX192b9/P5s3b8bGxoZ+/fply1K5cmVGjBjBoUOHcHBwoHjx4ty4cYPMzEwMBgNarZayZcsyZ84cXnvtNYYMGULPnj0xmUw4OzszceJEbty4YX7uP0VGRnLz5k3WrVvHunXrABg/fjxFixbFaDSydu1apk2bhlarpUaNGgQGBlK/fv1s7Tg4ONCvXz/at2+Pu7s7dnZ26PV6WrZsSXh4OEFBQej1enr37o2joyMVKlTg6tWrdOnShRIlSnDnzh06dOiQ7T3RarXo9XqMRmO2vooXL877779PYGAgbm5uqNVqjEYjDRo0YMKECeYfGpKSktBqtZhMJiIiIoiNjcXPz4++fftma69SpUrs2bOHxo0bc/z4ccqWLUtqaiq2traP3L/HjBnDgAEDUKlUuLq6Mn78eLRaLYqiPPR8g8FAZmZmtnHS6/Xm/aFkyZK0adMGd3d3fHx8KFeuHDY2Nub9qVmzZrRr1w4XFxc8PT1RqVQP7U8mkwmdTke3bt0ICwvju+++Q6VSERYWhrOzM2FhYaxdu5aMjAz69u2Ls7MzsbGxtGzZEkdHR7p27Wp+z4HH7ot/b9+j9t/XX3+dmjVrEhQUhMlkol27do/9vDzqPf37fdJqtbi7u/Pbb7+xfv16bG1tGTlyJFqtlsKFC9OzZ0/s7e2pVq0a1atXx2g0sn37dtq3b2/u18vLi71793L8+HHzKgCAIkWK0KRJEwIDA7G2tqZFixYUK1aMQYMGkZmZydChQwF4/fXXzcuDz549y4ABA/Ld/7kXpdPpLB1BPAcZr4JFxqtgkfEqeGTM8iHlKSZPnqxs377dfLtu3brmv3ft2qWMGzfOfLt///7KqVOnsr0+Ojpa6dy5s3Lu3DmlZ8+e5vsnTZqk/PTTTw/1d+TIkadFsiiNRpPtdnBwsHL58uVHPq9NmzaK0WjMq2jiEf7/eD1OYmKi8u233yqKoih6vV4JCAhQ7ty589jn+/v7Kzqd7rGPG41GJTg4WNHr9c8XWDzzmBVEly5dUkaNGmXpGDnqZR6vl5GMV8Ei41WwyHgVPDJmlvGkeu+pl5L55/F0J06cyLaErmLFihw9ehS9Xk9aWhpXrlyhXLlyhIWFceDAAeDBDKlKpaJ06dLcuHGDlJQUMjMzOXLkyEt3+ZC/HTt2jPbt29O/f/+nLr0V+YO7uztnzpyhbdu2dOrUiXbt2lGkSJEXbs/KyooBAwawZs2aHEwpCrrIyEgGDhxo6RhCCCGEEPmSSlH+30GG/8/fZ+u9ePEiiqIwefJk9u3bR/HixWnYsCHr168nKioKRVHo06cPjRs35sqVK4SFhQEPvqSPHTuW0qVLm8/WqygKbdu2pXPnzg/1d/ToUapWrZorG5sTtFqt+eQnIv+T8Sp4ZMwKFhmvgkXGq2CR8SpYZLwKHhkzy3hSvffU4jSvSXEqcpKMV8EjY1awyHgVLDJeBYuMV8Ei41XwyJhZxpPqPVlzKoQQQgghhBDC4qQ4FUIIIYQQQjzEpNOTtmknpnTN05+wIZB9AAAgAElEQVQsRA6Q4lQIIYQQIp9J/34PiR2HY0hItnQU8YrKuhnD3eYDiO87nrj+E1D+e710IXKTFKdCCCGEEPmIKV1DwsjZZEYfJ6bDEExpGZaOJF4xGbsOcLthD7Ku3cGlY1M0P/1BypxvLR1LvAKkOBVCCCGEyEdSFqzFlJiCy+CuZJ69Qmy3USj6TEvHEq8AxWjk3rQVxHYahnVRX4rtWob33BE4t23EvSnLyNh1wNIRxUtOilMhhBBCiHzCEJtIysJ1OLVqgMvAYHzmjkD7+zHi+k1AMRotHU+8xIxJKcR0HEby9JW4tG9M0R8jsClZFJVKhfesYdi+WZr4vuFkXbtj6ajiJSbFqRBCCCFEPpE8YxVKlgHP0b0BcAlqgmf4ADK2/kbiyDnksysAipeE7vg5bgf0RBt9HO9ZQ/GeNworR3vz41aO9vitmgQqFbEhozFlaC2YVrzMpDgVQgghhMgHMi9eJ/XbbRTq1gqbkkXN97v174Dbp51IXfk9yTNWWS6geOkoisL9Vd9zp/kAUKkoun0hrl1aolKpHnquzetF8F00jsy/rpIweLr8UCJyhRSnQgghhBD5QNLExagc7HAf3O2hxzzG9MWlY1OSp63g/srvLJBOvGxMGh3xn0wicehMHOtWpdju5dhXKv/E1zg2rIHHiJ6kb9rJ/cUb8iipeJVYWzqAEEIIIcSrTnvgFJqf/sBjZC/UXu4PPf7guL+hGO/dJ3H4bNQebji38rdAUvEyyLxyi7iPQ8k8dw33ET1w/6IrKqtnm7Ny+zwY/cnzJIUtxO6dsjjUrpzLacWrRGZOhRBCCCEsSFEUksIXovbzolDf9o99nsraGt8lYdhXf4e4fuPR7D2ShynFyyJ9+z7uNOqFITaJwlEz8Bjc/ZkLUwCVlRU+80djU7Iocb3GYbgbn4tpxatGilMhhBBCCAvK2L4P/ZGzeAz7ONtJaB7FytEev2+nYFumOLHdRqE7cT6PUoqCTjEYSApfSFz30diULU6x3ctx9K/+Qm1ZuTjh9/UkTBodsR+PkUsdiRwjxakQQgghhIUoWQbuTViEzRuv49Lxw2d6jdrNhcLrZ6L2KERMx6FkXrmZyylFQWeIS+Jum89Jmb8W15CPKPrDfGyK+f6rNm3LvY7P/NHoj/5F4qi5OZRUvOqkOBVCCCGEsJDUb7eSdfU2nmP6orJ+9lOBWPt5UXjDTFAUYtoNxhCbmIspRUGmPXCK2w17oD9xHp8Fo/GeNhiVnW2OtO3cvD5un3Um9ZsfSI3cmiNtilebFKdCCCGEEBZgSteQPH0l9rXexfE/7z/3621LF6fwuhkY790npv1gjClpuZBSFFSKopCyKIq7H32GlZMDRX9ejEv7Jjnej8eoXjh8UI2EEbPRHfsrx9sXrxYpToUQQgghLCBl4TqMCcl4hvV/5HUln4V9pfL4fTOZzCu3iA0egUmrz+GUoiAypWUQ12MsSWPm49SkNkV3LsXuzdK50pdKrcZ38Tis/byIDRmDISE5V/oRrwYpToUQQggh8pghNpGUBetwaumPfZU3/1VbjvXew3fhGHSHThPXaxyKwZBDKUVBlHn+Grf/05uMH3/HM6w/visnonZ1ztU+1R6F8Fs5EdO9FOJ6jpV9ULwwKU6FEEIIIfJY8oxVKJmZeI7unSPtObfyx2vqF2h+iSbhi2koipIj7YqCJW3TTm437o3pfjpFNs3GbUDHF56Vf152FcvhPXMouj9PkDR+UZ70KV4+z37kvRBCCCGE+NcyL90g9dttuHZvhU2pYjnWbqGQ1hgTkkmevhK1tzueY/vlWNsif1Mys0gcO5/U5Zuxr/kuvkvDsPbzyvMcLu2boD9+nvsRUdhVKo9Lm4A8zyAKNilOhRBCCCHy0L2Ji1E52OE+uHuOt+0+NARjYjIp89ag9nLHrX+HHO9D5C+GO3HE9hiL/uhfFOrfAc/QPqhsLPcV33P8J+hPXyLh8ynYvvE6dm+VsVgWUfDIsl4hhBBCiDyiPXiKjB9/x/2TTlh7u+d4+yqVCq8vP8eppT9J4xaQFvVzjvch8g/N3iPcatiDzAvX8V0+Hq/wARYtTAFUNtb4Lh+Plaszsd1Hy1mkxXOR4lQIIYQQIg8oikJSeARqX08K9W2fa/2o1Gp8F4biUK8q8QOnkLHjz1zrS1iGYjKRPOsbYtoNQu3tQbGdS3Fu6W/pWGbWvp74rZyI4U488X3Ho5hMlo4kCggpToUQQggh8kDGj7+jP3wGj+EfY+XkkKt9qexs8ft6MnZvlyGu51h0h07nan8i7xhT0ogNHsG9L5fi3CaAYj8vxrZMcUvHeoh9tbfxmvQZmt0HSJ62wtJxRAEhxakQQgghRC5Tsgzcm7AIm3IlcOnYNE/6tHJ2xG/tdKwLexPTeTiZ56/lSb8i9+hPXuB2QA80vx3Ga+ogfCLG5PoPHf+Ga/ePcOnYlOSZX5Px8x+WjiMKAClOhRBCCCFyWerqbWRduYXn2H6orPPumEBrb3cKr5+Jys6Wu+0Hk3UrNs/6Fjkr9dtt3GnWHyXLSNGt8yn0ces8u0zMi1KpVHhNG4Tdu28Q338imVduWjrSS01RFNK37cUQk2DpKC9MilMhhBBCiFxkSteQPG0l9jXfxfE/7+d5/zYlilA4agZKhpaY9oMxJqXkeQbx4kxaPfEDp5DwxVTsa73La7uXYV/1LUvHemZW9nb4rpoEttbEdhuNKV1j6UgvJZNOT/yAicSFhKL59ZCl47wwKU6FEEIIIXJRSkQUxoR7eIb1s9hMl91bZfD7dgqG27HEdBwmBUIBkXX9Lnea9iNtzXbcB3Wj8LrpqL1y/izPuc2mmC++S8PJunST+M++RFEUS0d6qRgSkolp8znpG3bgMaJnnh06kBukOBVCCCGEyCWGuCRS5q/FqcUHFp/tcqj1Lr7LxqM/dZHYkFCUzCyL5hFPlvFLNLcDemC4FYPf6ql4jOyJSq22dKwX5li3Kp5j+5Kx9TdS5q+xdJyXhv7cVe406YP+zCV8l43HfXC3fL/c+0mkOBVCCCGEyCXJM1aiZGbiMbq3paMA4NS4Nt6zhqL97TDxn0ySS3zkQ4rRSNKkJcQGj8C6eGGK7V6OkwWWg+eGQv074NSqAfcmLkGz94il4xR4GTv3c6dpPxR9JkV+mI9zq/xzOaEXJcWpEEIIIUQuyLx8k9TIbbh2bYVt6dcsHcfMtVMzPMb2Jf273SSN/kqWWOYjxsRkYtoPJmVOJC7BzSn6YwQ2JYpYOlaOUalU+MwZjk25EsT1DiPrZoylIxVIiqKQsmg9scEjsClZlGI7lmBfqbylY+UIKU6FEEIIIXLBvYmLUdnb4j6ku6WjPMTtk04U6hfE/WWbSJkdaek4AtAdOcuthj3RHTqN95wR+MwejpW9naVj5TgrZ0f8Vk0Cg5HYkFBMWr2lIxUoSpaBxCEzSBozD6cP61B06wKsi/hYOlaOkeJUCCGEECKH6Q6dJmP7Ptw+7YS1d/47gY1KpcIzrD/O7Rtz78ulpH7zg6UjvbIUReH+sk3cafkJKltrim6PwLVzM0vHylW2pV/DJyKUzFMXSRw6Q2bvn5ExOZWYoMGkfvMDbgOD8V0xIV9f5/ZF5N2FtoQQQgghXgGKopAUthC1jwdufYMsHeexVFZW+MwZgenefRKGzsTKoxDOzetbOtYrxZSuIWHwdNI378KxcW185o9G7eZi6Vh5wuk/tXEfGkLy9JXYVXmTQh+3tnSkfC3zyk1iOw0n61YsPvNH4xLUxNKRcoXMnAohhBBC5CDNT7+jO3wGjxE98v2shsrGGt/lE7Cr+iZxfcLR/nHM0pFeGZmXbnC7SR/Sv9+Dx6he+H0z+ZUpTP/mPqQ7jo1qkTh6LrpDpy0dJ9/S/H6UO036YryfRpHNc1/awhSkOBVCCCGEyDGKwUDShMXYlC1RYK41aOVoT+HVU7EpWZSYLiPRn7po6UgvvfQtv3K7US+MickUXj8T9y+6orJ69b6Wq6ys8IkYg3UxP2JDQjHEJlo6Ur6T+s0PxLQfjNrXk2I/L8GhZkVLR8pVr96nQAghhBAil6Su3k7W5Zt4ju2LyrrgHD2ldnelyPqZqAs5E9NhCFlXb1s60ktJyTKQOGYecT3HYluhFK/tWYFj/fcsHcui1IVc8PtmMqZ0DXE9xsr1d/9LMRpJHDOPhMHTcaj33oMzN7/+8py5+XGkOBVCCCGEyAGmdA3JU1dgX6Mijo1rWzrOc7Mu4kPhDbNQjCbuBg3GEJdk6UgvFUNsInc/+oz7i9ZTqGdbim6Z91KdZfXfsKtQCp85I9AdOk3imPmWjmNxprQMYruMfLCv9Aqk8OopqF2dLR0rT0hxKoQQQgiRA1IiojAm3MMzrD8qlcrScV6IbdkSFF47DWN8MjFBQzCmpls60ktBG32c2w0+Rn/mEj6Lx+H15eeobG0sHStfcW7dkEL9O5C6YjOp636ydByLyboZw51m/dHsOYTX9MF4TR5YoFZh/FtSnAohhBBC/EuG+HukzF+LU/P62L/3lqXj/Cv2Vd7Eb9VEMi9eJzZ4BCadXIfyRSmKQvK81dxt8zlWhVwo9ssSXNoEWDpWvuU5pg/2daqQOHQG+pMXLB0nz+kOneZ2494Y7sRTOGoGhbp/ZOlIeU6KUyGEEEKIfyl5xkqUzEw8QntbOkqOcPSvjs/80egOnCK+TziKwWDpSAWOMTWduO6juTd+EU7N61Ns51Jsy5e0dKx8TWVtjd/SMNSebsSGhGJMSrF0pDyTtnEHd1oPxMrZiaI/L3plj0WW4lQIIYQQ4l/IvHyT1G+24tq1Fbali1s6To5xaROA16TPyPjxdxKGzkRRFEtHKjD0Zy9zJ6AXGTv+xHPCp/guC8fK2dHSsQoEtZc7vqsmYYy/R1yfcBSj0dKRcpViMpE0eSnx/SZgX+1tiv2yGNuyJSwdy2KkOBVCCCGE+BfuTVyCyt4W9yHdLR0lxxXqFYj7oG6kfbuNe5OXWjpOgZC2/mfufNgXk0ZLke++wq1v+wJ7DLKl2Fcqj9fUQWj3HuHepJd3vzNpdMT1HEfK7G9w6dzswRmzPQpZOpZFvTpH1wohhBBC5DDd4TNkbN+L+4geWHu7WzpOrnAf0QNjYjIpcyJRe7nj1qedpSPlS4o+k4SxC0hdtQX72pXxXTwOa19PS8cqsFw7N0N//Bwp81ZjV7k8zi0+sHSkHGWITSS2y0j0Jy/gGT6AQv2C5EcMpDgVQgghhHghiqKQFLYQtY8Hbn2DLB0n16hUKrymDcKYdJ+k0K9Qe7nh0raRpWPlGyadHu2egyTN/JqsUxdx+7QzHqN6vlJnWM0tXpM+Q3/2MvGfTsa2XAls33g5jtnVn7xATPAITGkZ+EV+iVMBvPRUbpFPjRBCCCHEC9D8/Ae6Q6fxnjkUKycHS8fJVSq1Gp9FY4jpkEr8J5NQu7ni2LCGpWNZjEmrR7PnIBk//ErGL9EoGVqsPAvh9/UknJrWs3S8l4bKzha/FRO43bAnsd1GU3THkgJ/vc/0bXuJHzARtUchim5fiN1bZSwdKV+RY06FEEIIIZ6TYjCQNGExNmVL4NKpqaXj5AkrezsKR36JbflSxH4ciu7oWUtHylMmrZ707fuI6xPO9QotiOs+Gs1vh3FuE0DhDbPwPbRWCtNcYF3YG9/l48m6cZf4AZNQTCZLR3ohiqKQPCeSuJBQbN8sTdFflkhh+ggycyqEEEII8ZzS1vxI1qUb+H0z+ZVavmnl4kThqBncadafmI7DKLptAbblXrd0rFxj0urR7D7wYIZ0x58PZkg9CuHcJgDnlv441K6MyubB+Gu1WgunfXk51HoXz/BPSBo9l5TZkbgP7mbpSM9F0WcSP2g66et/xrlNAN5zR2Blb2fpWPnSq/PfVAghhBAiB5jSNdybuhz76u/g2KSOpePkOWsfD4psmMWdpv2IaT+YotsXYl3U19Kxcky2gvSXP1E0D5bsOrcJwLlVAxxqV3qlfpDILwr1aov+xDnuTV2O7btv4BRQ09KRnokxMZnYbqPRHTqN+4geuA/qJic+egL5ZAkhhBBCPIeURVEY4+/ht2rSK/sl0+b1IhSOmsHdVp9yt/1gim5dUKAvgWHS6P4xQ7rfXJC6BDbCqaW/FKT5gEqlwnvGUDLPXSO+bzjFdi7DpmRRS8d6oszz14jpPBxjfBK+S8Nx/qiBpSPle/IpE0IIIYR4Rob4e6TMX4tT8/rYV3vb0nEsyu6dsvhFfklM0BBiOg+nyMbZBerEUE8sSFv54/C+FKT5jZWjPX6rJnI7oCex3UdR9MdF+Xafy9h1gLhe47BytKfIlnnYV3nT0pEKBPnECSGEEEI8o+QZq1B0mXiE9rZ0lHzBoXZlfBaPI+7jMcT1GItf5JfmYzDzo78L0vQtv6LZ+d+C1MsNl3b/wanlB1KQFgA2JYrgu3gcMR2GkjBoGj6LxuarFQyKonB/yUaSxs7H9s3SFF49BesiPpaOVWDIp08IIYQQ4hlkXrlJauQPuHZtiW3p4paOk284N6uHacYQEgZNI37gl/jMH43KKv9cEMKk0aHZtZ/0H35Ds/NPFI3ufwVpK38car0rBWkB49igBh6jenFv0hLsKlfArW97S0cCQMkykDhqDqmrtuDUtC4+C0Kxcna0dKwCRT6JQgghhBDP4N7EJajsbHEf0t3SUfId1y4tMCYmc2/yUtQehfCc8KlFZ7PMBemWX9Hs2o+i0aH2dselfeMHx5BKQVrguQ0MRn/iPElhC7F7pywOtStbNI8xJY24HmPQ7juK26ed8Qjtna9+pCko5FMphBBCCPEUuiNnydi2F/fhH2Pt42HpOPmS2+ddMCamcH/xBtTeHrgPDM7T/k0ZWjS7DpD+w8MFqXOrBtjXeheVWp2nmUTuUalU+Mwbxe3GfYjtOZbXdi+32PLZzCu3iO08nKybMXh/NRLXjq/GtY9zgxSnQgghhBBPoCgKSWELUXt74NY3yNJx8i2VSoXnhE8wJqVwb+Ji1J5uuAY3z9U+zQXplj1odh/4X0Ea1ATnlv5SkL7krFyc8Pt6Erf/05vYkFCK/jAflZ1tnmbQRh8ntvtosLKiyKY5ONR6N0/7f9lIcSqEEOKFKIqCoiiWjiFErtP8Eo3u4Cm8ZgyR48eeQmVlhc9XIzHeu0/C4OmoPQvh9GHdHO3DlKFFs3P//2ZItfr/FaStGmBfs6IUpK8Q27Il8Jk/mrjuo0kYOQefWcPyrO/UyK0kDJuJTaliFF49DZvXi+RZ3y8rKU6FEEI8t8zz14jpMhLFYMCpUS0cA2riUKcqVo72lo4mRI5SDAaSxi/CpkxxXDs3s3ScAkFla4Pfyoncbfs5cb3DKBw1E4f3K/2rNs0F6d8zpFo9am8PXDo0xbmVvxSkrzjnZvXQf96FlDmR2Fcuj2uXlrnan2I0khQewf2IKBz8q+O7LBy1q3Ou9vmqkOJUCCHEc9GfvMDd9oNR2Vhj82450qJ+IXXl96jsbLGvXRmngJo4NnpffkEWL4W0NT+SdekGfl9PkhPoPAcrJwcKr5nGneYDiO0ykiI/zMPurTLP1YYpXfO/GVIpSMVTeIzogf7kBRJGzMH2zdLYV30rV/oxpWuI6xOOZsefFOrZFs8Jn8j/hhykUvLZmqyjR49StWpVS8d4LK1Wi4ND/rzYr3iYjFfBI2OWv2kPnCK20zCs3FwosmkOBj8P7K3UaPefRLNrP5pdB8i6cgsAmzLFcQyoiWOjWjjUfBeVrY2F0wv5fD0fU4aWm9U7YPN6UYpsW5DnZ599GcYr63Ycd5r1B4OBoj9GYFPiyT9amQvSLb+i2fO/gtSpxQc4t/wgXxekL8N4FXTG5FRuN+qJkmmg2K5lTz152fOOWdatWGKDh5N54QZekwdS6OPW/zbyK+lJ9Z4Up89J/vEULDJeBY+MWf6l+e0wsV1HYl3EhyKbZmNd1PeR45V19TYZuw6g2fkn2j9PQGYWKicHHD+ohmPDmjgG1MS6sLeFtuLVJp+v53Nv5iqSpyyn6PaF2Fd/J8/7f1nGK/PCNe60+AS1mytFti14qGAwpWvI2PknGVt+Q7N7P4ouE7WPB07N839B+k8vy3gVdPrTl7jTrB92lStQZONsVDaPn9V8njHTHT5DbLdRKPosfJePx/GDajkV+ZUjxWkOkn88BYuMV8EjY5Y/Zfz0O7E9x2FbtjiF188yf7l82niZMrRo/ziGZud+NLv2Y7gTD4Dt22UfzKoG1MT+vbcKxBfPl4F8vp6dISGZm9WCcPygOn6rJlokw8s0XrrDZ7gb+AU2pV+j6JZ5oFL9tyD975LdvwvSFh88OMtujXcK3P+Fl2m8Crq0Db8Q338ihfq2x2vCp4993rOOWdqmnSQMnIK6sDeF10zFtmyJnIz7ynlSvScLpIUQQjxR2qadxA+YhF2lNyi8djpqd9dnfq2VkwNOjWvj1Lg2iqKQef6auVBNmbeGlDmRWLm74uhfHcdGtXD0r47a0y0Xt0aIZ5M8YxWKLhOP0N6WjvJSsK/2Nr7LJxDbZQS3/EMwxiU9KEh9PXHp3LzAFqQif3Jp1xj98fPcX7Qeu8oVcGkT8ELtKCYTydNWkDzza+xrvYvfqkmoPQrlcFrxT1KcCiGEeKzUb34gYcgM7N+vROFvp/yry2ioVCrsKpTCrkIp3D/rjDElDe1vhx8cq7rnIOmbd4FKhV3VN3EKeHAGYNuK5fL8OD8hMq/cIvWbLbh2bYFtmeKWjvPScAqoic+CUJKnr3xQkLZqgH31t6UgFbnCM3wA+tOXSPh8CrZvvP78J+TS6Ij/dDIZP/yKS6dmeE8fLOdOyAOyrPc5yZKNgkXGq+CRMcs/UiLWkTR2AY4Na+K7ciJWDnYPPSenxksxmdCfvPDfWdUD6I+fA0Dt62k+TtXxg2pYuTj9675eZfL5ejaxH49Bs/sgxQ+ve+oJVXKTjFfBIuOV/xjikrgd0BOVvS3Fdi5D7eaS7fHHjZkhNpHYLiPRn7yA57h+FOrfQX4ozUGyrFcIIcQzUxSF5BmrSJ62AqcWH+C7aGyu/1qssrLCvnIF7CtXwGPYxxji76HZcxDNrgNkbNtL2prtYK3Gvua75uuq2pQtIV8WRI7THT1LxtbfcB/2sUULUyHEv2ft64nfigncafUp8X3H47d6ylNn6vWnLhITPALT/XT8vpmMU5M6eZRWAFhZOoAQQoj8Q1EUksIWkjxtBS4dPsR3yTiLLGOy9vHAtcOH+C0L5/XzWymyZR5u/TpgSkohadwCbtXuws33gkgYPpuMnfsxafV5nlG8fB7s/xGovT1w6xdk6ThCiBxgX+1tvL78HM3uAyRPX/nE56Zv38edFgNQWakoun2hFKYWIDOnQgghgAdLaxOHzSL16y249miD1+SBqKws/xumysYah/cr4fB+JTzH9iXrdhya3QfQ7NxP2rofSV2xGZW9LQ51quD432NVn3YtRSEeRbPjT3QHTuI1ffC/Or5aCJG/uHZtif7YOZJnfo3du2/g9GHdbI8rikLKvDXcm7AIuyoV8PvmS6x9PS2U9tX21OLUZDIRFhbGhQsXsLW1ZeLEiZQo8b/TJ69fv55169ZhbW1Nv3798Pf35+7du4waNQqj0YiiKIwfP55SpUqxcuVKNm7ciIfHg2Uy4eHhlCpVKve2TgghxDNRDAbiP51M+saduH3WGY/QPvl2yaxNMV8KdWtFoW6tMOn06P488WD573+PVwWwKVcCx4BaODWqhX31d+QkFuKpFIOBpPER2JQpjmvn5paOI4TIQSqVCq+pX5D51xXi+k+k2M6l5pOdKfpMEgZPJy3qZ5xbN8R77shHnmNB5I2nFqe7du0iMzOTqKgoTpw4wZQpU4iIiAAgISGByMhINm3ahF6vp1OnTtSuXZu5c+cSHBxMQEAAv//+O7NmzWL+/PmcPXuWqVOn8vbbb+f6hgkhhHg2ij6TuN5hZPz4Ox6je+P+eRdLR3pmVvZ2ODaogWODGnhNHkjmlZtodh5As2s/95du5P7CdaicHXGsX+3BpWoa1sDaz8vSsUU+lLb2R7Iu3sB31SRUNrKwTIiXjZW9Hb4rJ3I7oAex3UdT7OfFGFPuc7fvRHQHT+E+7GPch3TPtz/Mviqe+t/36NGj1K37YOq7UqVKnDlzxvzYqVOnqFy5Mra2ttja2lK8eHHOnz/P8OHDcXF5cDYso9GInd2DXx/Onj3LkiVLSEhI4IMPPqBPnz65sU1CCCGekSlDS2z30Wh/O4zX5IEU6hVo6Uj/im3p4tiWLo5b3/aY0jVofz9qnlHN2L73wXPeKYtTo/dxDKiJXZUKchkLgSlDy72pK7Cv9jZOTes+/QVCiALJppgvfsvGc7ftF8T2GEvmpRuYEu7huyQM59YNLR1P8AzFaXp6Os7OzubbarUag8GAtbU16enp5iIUwMnJifT0dPOy3atXrzJ16lQWLFgAQLNmzejUqRPOzs588skn/Prrr/j7+z/Up1ar/dcbllt0Op2lI4jnIONV8MiY5R1Tagb3QsaQefQv3GYMxrZ94+f+/5uvx0utwuqD93D54D2cJ36C4fw1dLsPov/1MMlzIkme9TVW7q7YffAedv7Vsa//HlburpZOnavy9XhZUNr81RjjknCPCM1X71F+yiKeTsargKhaAddRPUmduASVlxueUdNRV66Qr+uPV8lTi9P/Y+++46Mq8/aPX5OZTHolHY0rKq4NKfqoi4osoqtYUQEjARULYIFQBJTee28qCIoFEMW6u66ubR9cXEFZVhRXQVmVJKQnkzIlc35/uD8e2QVDSXLPZD7v/4YTJtd5fSlz5b7PObGxsaqurj742u/3y+FwHPZYdXX1wbK6detWTZo0SbNnz1abNm1kWZb69+9/8HiXLl30xRdfHLacBvozogI9Hw7FvIIPM2t69fHFdPcAACAASURBVKUVyr9jtDy7vlH64xMUe9Nvj/u9gmZeHc9RfMdzpJF3q76sUjXv/00172xVzZ+3qnbzu1JYmCI7na3o7pcorvfv5MhKM524SQTNvJqJr6hMrpUvKqbH5Uq47ALTcf4L8wouzCs4RD7cV1HZWdL5Zyi2TbbpOPiZBm/D2LFjR3344YeSpB07dqht27YHj7Vr107bt2+X2+1WVVWV9uzZo7Zt22rr1q2aNm2aVq1apfPOO0/STyuw1113naqrq2VZlj7++GOuPQUAA3wFxfrxxofk2f2tMp6efkLFNFjZk+IVd/OVSl82Vr/a9apa/3GlkvJyZXm8Kp3+pH646j559/5gOiaaQdm8tbJq3Uoey6VGQKiw2WyKvbmb7JmppqPgPzS4ctq9e3dt2bJFffr0kWVZmj59utasWaPs7Gx169ZNubm5ysnJkWVZysvLU0REhKZPny6v16vRo0dLkk499VRNnjxZeXl56tevn5xOpy655BJ16dKlyU8QAPB/vN8XaH/Poao/UKrM9XMUdWlH05GMs9ntiux0jiI7naPk0ffIvesb7b9lqPb3HKKs15YqPDvTdEQ0Ee/eH356dFLudQfv3AkAMMdmWZZlOsTPbd++XZ06dTId44hqa2vZshFEmFfwYWZNx7PnX9rfM09WdY0y189V5AXnnPB7ttR5uf/xtfb3HKKwhFi1fnWJHK3TTUdqFC11XserYMB41byzVdl/eyEgn2nIvIIL8wo+zMyMX+p75p+uDgBocu5d32j/9Q/J8niUtXlxoxTTlizivDOUuXGe/KWV2t9zqHwFxaYjoZHVbd+l6tfeU+IDfQKymAJAKKKcAkALV/fpF9p/08OSw67Wry1VxHlnmI4UFCI7nKXM9XPkKyjR/luGyldUZjoSGollWSqZtEL21GQlDu5jOg4A4N8opwDQgtVu+Uz7ew5VWGKcWr++VM4zTjEdKahE/s95ynxhtnzfFyj/1qGqL60wHQmNoOZPH6nur39X0sg7FRYbbToOAODfKKcA0EJVv7NV+X1GyNE6Ta1fX6bwU7JMRwpKUb9pr4x1M+Xd84P23zZM9RVVpiPhBFg+n0qmrFT4aScrvu/1puMAAH6GcgoALZDr9fdV0G+Mws845acb+mSkmI4U1KK7XKD0NVPl+XKv8nsNl7+quuHfhIBU9cIf5P3qOyWPvV+28AYfWgAAaEaUUwBoYSrX/0GF90xQZPtfK2vzItlTkkxHahFiul+ijNWT5d75T+X3GSm/q8Z0JBwjf3WtSmc/pYgLz1VMj8tNxwEA/AfKKQC0IBVPbVbRQ9MVdWkHZb44X/aEONORWpSYay5T+soJqtu2S/m5Y+SvqTMdCceg4vEXVV9QrFYTBslms5mOAwD4D5RTAGghyhY/p+JR8xX9u0uV8dwshcXw7LamEHtjV6Ute0x1Wz5TQf9H5a9zm46Eo1BfXKayJc8p5trLFHVRO9NxAACHQTkFgCBnWZZKpj+p0ikrFXtzN2U8NUVhkRGmY7VocbdepdQFo1T7/icqHDBelsdrOhIaUDbvaVm1biWPvd90FADAEVBOASCIWX6/Sh5brPIFzyiu73VKWzGOm7w0k/g7eihlznDV/OkjFd43SZbXZzoSjsC79wdVrH1F8X2v43FKABDAKKcAEKSs+noV5c1WxZOblDCwl1LnPyKb3W46VkhJuPMmtZr6sKrf/EAHHpgqq77edCQcRsn0J2VzOpU08i7TUQAAv4AfrwNAELK8Ph0YPEWuV95V0og7lfTI3dzgxZDE+2+T5fGodPJKKTxcaUvGyBbGz34DRd2nX6j61Z/+njjSW5mOAwD4BZRTAAgy/jq3CgeMV82fPlLyhEFKejDHdKSQl/TQHbI8XpXNXC2b06HUeSMpqAHAsiyVTFohe2qSEh+43XQcAEADKKcAEET8rhoV9Buj2v/9TClzhivhzptMR8K/JQ+/U5bbq/IFz8jmdCpl5lBWsw2refsj1X20QymzhiksNtp0HABAAyinABAk6surlH/7SLk/2620ZY8p7rarTUfCf0gec48sj0cVy9bLFhGuVpMeoKAaYvl8Kpm8UuFtTlJ87vWm4wAAjgLlFACCQH1xmfbfNlyer75V+urJiu1xuelIOAybzaZWEwZLbq8qVmyQzRmu5Mfuo6AaULX+j/J+9Z3Sn5rCHawBIEjwrzUABDhffpH235In3w8Fynx2pqJ/e5HpSPgFNptNraYPkeXxqnzRs7JFOpU8grvENid/TZ1KZ61WxAXnKOa6LqbjAACOEuUUAAKY97v92n/rUNWXVChz/VxF/aa96Ug4CjabTSlzhv90k6RZT/30GJOH7zAdK2RUPL5R9QXFSn9yEqvWABBEKKcAEKA8//xO+2/Jk+X2KOvlhYrscJbpSDgGtrAwpS4cJcvjVemUlbI5HUoc2Nt0rBavvrhMZYufU/Q1lyrq4nam4wAAjgHlFAACkHvnP7W/1zDZwuzKenWJIs5qYzoSjoPNblfassdkebwqGbdUNqdTCXffbDpWi1Y2/xlZtW61GjfQdBQAwDHiIWwAEGDqPvlc+28eorDICGW9vpRiGuRsDofSH5+g6Ks7q3jUfFU++4bpSC2W99sfVbH2FcXf0UPOM04xHQcAcIwopwAQQGo+3Kb9tw6TvVWist5YLudpJ5uOhEZgc4YrY/VkRf32IhUNm62qjX80HalFKp3+hGzhDiWN5AZUABCMKKcAECCq39qigpxRCj8lU1mvL1X4SemmI6ER2SKcylg7TVGXdtCBh2bI9cq7piO1KHWffSnXK+8qcVBvOTJSTMcBABwHyikABADX5j+r4M7H5DyrjbJeWSxHeivTkdAEwqIilLFupiL/5zwVDpys6t9/aDpSi2BZlkomLldYSqISH8wxHQcAcJwopwBgWOWzb6jw/kmKvOBcZb28UPbkBNOR0ITCYqKU+cJsRXT4tQrumaDqP31kOlLQq3n7r6r7aIeSR9ylsNho03EAAMeJcgoABpU//qKK8mYp6ooLlblhrsLiYkxHQjMIi41W5vo5ijj7NBXePU417/3NdKSgZdXXq2TKSoW3OUnx/W4wHQcAcAIopwBggGVZKpv/jErGLlZMjy7KXDdDYdGRpmOhGdkT4pT54nyFn36yCvo/qtotn5mOFHT8dW6VzV4j7+5vlTz2ftnCeUIeAAQzyikANDPLslQ65XGVznhSsb2uVvqqibJFOE3HggH2pHhlbVogR3am8nNGqfbjnaYjBQW/q0bly17Qvzr1Utn8pxV9zaWKua6L6VgAgBNEOQWAZmT5/SoetUDlS55T/J03Km3Jo7I5WO0JZfaUpJ8KakYr5fcZqbpPvzAdKWDVl1epdO4a7et4m0omLpfz16cq65XFynh6umw2m+l4AIATxCciAGgmls+nA0NmybXxj0p88HYljx/EB2pIkhwZKcravEg/3viQ8nsNV9bLixTRrq3pWAHDd6BUFSs3qmLNZlmuGkVf3VlJebmK7HSO6WgAgEZEOQWAZmB5vCq8f5Kq3/hAyaPvUeKwfhRTHMKRlaaslxdp/w0Pav9tw5S1eZEizj7NdCyjfD8WqnzpC6p89nVZbq9ib+yqxKG5ijjndNPRAABNgHIKAE3MX1OngrvGqvbdj9VqykNKHNjLdCQEqPCTM5T18iL9eMODyr81T1mvLpHzjFNMx2p23r0/qGzxs6ra+JZkWYq77WolDrlDztOyTUcDADQhyikANCHvD4U6MHCy6v72D6XOf0TxudebjoQAF35qa2VtXqT9Nzyk/TcP+amgnnay6VjNwv3lXpUvelauzX+WLdyh+NwblPjg7Qo/OcN0NABAM6CcAkATsOrrVbHqZZVOf1KSpbTHxyvu5itNx0KQcJ6erayXF+rHmx7S/p5D1fq1JQo/Jct0rCZTt2O3yhc8o+rf/0W2mCglDu6thIG95UhvZToaAKAZUU4BoJG5P/9GRcNmy/3Zl4rudrFS5gxn5QfHzPnrU5W1aaH23/zwwYLqaJ1uOlajqv1oh8oWPKPa9z9RWGKckkbepYR7b5U9Kd50NACAAZRTAGgk/lq3yuauUfmy9bInxyvtiQmKvakbNz7CcYs493Rlvjhf+T2Hav/NQ5X12hI5MlJMxzohlmWp9t2/qWzBM6r7eKfsqUlKHj9QCXfdrLDYaNPxAAAGUU4BoBHUfLhNRcPnyvfdj4rL6aFWEwez+oNGEdn+18rcMFf7bxum/T2HKuvVJXKkJpmOdcwsv1/Vv/+Lyheuk/vvX8nROk0p04coru/1CouKMB0PABAAKKcAcALqSytUMn6pqjb8UeGnnqSszYsUdWlH07HQwkReeK4yn5+t/NtHKv/WocravFj25ATTsY6K5fPJtfnPKlv0rLxffafwU09S6oJRiut1tWzOcNPxAAABhHIKAMfBsiy5Xn5HxWMXy19epcQhfZU0/E5WgNBkon7TXhnrZqggZ5T235qnrJcXyZ4YZzrWEVluj6o2/FFlS56T77v9cp7VRmmPT1DsDVfI5uDjBwDgv/G/AwAcI++/8lU0cp5q3/1YER3PUuqmBYo453TTsRACoi+/QOlrp6mg/6PK7z1cWZsWKCwuxnSsQ/hr6lT5zGsqX75e9flFiuhwllImP6joqzvLFhZmOh4AIIBRTgHgKFk+nyqe3KTSmasl2dRq2hAlDLhZNrvddDSEkJgrL1bG6skquGus8vuMVOaGuQFxI6H6SpcqV7+s8sc3yl9SocjftFfa4jGK6nIBNwUDABwVyikAHAX3zn/+9HiYv3+l6Kt+o5RZwxR+Ust6rAeCR8zvLlX64xNUeO9E5fcdrcznZyssOtJIlvqSclU8sUkVq16Sv9Kl6G4XKzEvV1EXtTOSBwAQvCinAPAL/DV1KpvzlMpXbJQ9OUHpT05SzI1dWQmCcbE3dJXl9enAoCkq6P+oMtbNUFhk813z7CsoVvny9ap8+lVZtW7F9LhcSUNzFXH+mc2WAQDQslBOAeAIat7/REUj5si3L19xfa9TqwmDA/oGNAg9cbd0l+X2qGjITBXePU4Za6c1+R1wvfv2q3zp86p8/vdSvV+xPbspaUhfOc88tUm/LwCg5aOcAsB/qC8pV/H4pXJtfEvhp52srFcWK6pzB9OxgMOKz+khy+tT8Yi5KrxvotKfnCRbeOP/9+75ep/KFj4r10tvS/Ywxfe5RokP3aHwX2U1+vcCAIQmyikA/JtlWXK9+JaKxy2Vv9KlpGH9lZiX26xbJYHjkdD/Rllur0oeW6QDg6cobcW4Rntci3vnP1W2cJ2q3/hAtkinEu7pqcQHbpcjM7VR3h8AgP+PcgoAkrzf7VfRyLmqff8TRVx4rlLnjVTEWW1MxwKOWuJ9t8ryeFQ6aYXkDFfakkdP6NEtdX/7h8oWPKOad7YqLC5GiUP6KvH+22RPSWrE1AAA/B/KKYCQZvl8Kl+5UWWzn5LsdqXMzFP8XTfxPEYEpaQHcyS3V6UzV8nmDFfqvJHH9GfZsizVfrhdZQueUd2WzxSWnKDkMfcqfsDNsidwvTUAoGlRTgGELPffv9KBobPk+fxrRf/uUqXOypMjK810LOCEJA3vL8vjVdn8p2ULD1fKrLwG7y5tWZZq/vSRyuY/LfenX8qekaJWUx5UfO4NCouJaqbkAIBQRzkFEHL81bUqnbVaFY+/KHtKotKfmqKY67rweBi0GEmjB8jvdqti2XrZIsLVavKDh/3zbdXXq/q191W2aJ08u/bIkZ2plLkjFN/nGtkinAaSAwBCGeUUQEip+fPHKho5V77vCxTf/0Ylj7uf7YpocWw2m1pNGCx5fKpYuVE2Z7iSx95/8Ljl8arqxT+pfPGz8u79QeFnnKK0pY8ptueVTXKnXwAAjgb/AwEICb6iMpWMWyLXS28r/IxTlPXaUkVdcr7pWECTsdlsajXtYVker8oXPydbZIQi77lZFc//QeVLn5fvh0I5zztD6asn/7RzgOusAQCGUU4BtGiWZalqwx9VMn6p/K4aJY28S0lD+rJlESHBZrMpZfYwWW6PymY/JdvKjbIqXYq88FylzB6u6CsvZjs7ACBgUE4BtFjevT/89HiYD7cr8n/OU+r8kXKeearpWECzsoWFKXXhKNkinXLv269WQ3IV+Zv2lFIAQMChnAJocSyvT+XL16ts7pqf7lY6Z7ji+93AtkWELJvdrtQ5I1RbW6uoKO6+CwAITJRTAC1K3Wdfqihvtjy7vlFMjy5KmTFEjsxU07EAAADQAMopgBbB76pR6YxVqlj1kuxpycp4eppirr3cdCwAAAAcJcopgKBX/fZfVfzIPPl+KFT8XTcpeez9ssfHmo4FAACAY0A5BRC0fAdKVfLYIrleeVfhZ/5KWW8sU9RF7UzHAgAAwHGgnAIIOpZlqeq5N1UycZn8tW4ljbpbSQ/dweNhAAAAghjlFEBQ8ez5l4qGz1Xdls8UefH5Pz0e5oxTTMcCAADACaKcAggKlser8qUvqGz+07JFOJU6f6Ti7riOx8MAAAC0EJRTAAGvbtsuFQ2bLc+XexVz/RVKmT5EjowU07EAAADQiCinAAKW31WjkqmPq/KpzbJnpipj3QzF/O5S07EAAADQBCinAAJS9VtbVPTIfNXnFyn+7pvV6rH7FBYXYzoWAAAAmgjlFEBAqS8sUcGUJ1T92ntyntVGGasnK/KCc0zHAgAAQBOjnAIICJZlqerZN1Q8cZnk9ir50XuV+MDtsjnDTUcDAABAM2iwnPr9fk2cOFFfffWVnE6npk6dqlNO+b/HNmzcuFHr16+Xw+HQoEGD1LVrV+3fv1+PPvqo6uvrZVmWJk+erDZt2ujdd9/VsmXL5HA4dMstt6hXr15NenIAgkfVutdVNHyOnJecr/QFj8h5WrbpSAAAAGhGDZbTd955Rx6PRxs2bNCOHTs0c+ZMrVixQpJUVFSkdevW6aWXXpLb7VZOTo46d+6sRYsWqW/fvrryyiv1l7/8RfPnz9eCBQs0Y8YMbdq0SVFRUbr99tvVtWtXpaamNvlJAghs7p3/VPGjixR1xYVKXDtFzhiuLQUAAAg1DT4gcPv27brsssskSe3bt9fnn39+8NjOnTvVoUMHOZ1OxcXFKTs7W7t379aoUaPUpUsXSVJ9fb0iIiK0Z88eZWdnKyEhQU6nU506ddK2bdua6LQABIv6SpcKBoxXWHKC0leM47mlAAAAIarBlVOXy6XY2NiDr+12u3w+nxwOh1wul+Li4g4ei4mJkcvlUnJysiRp7969mjVrlpYtW6bS0tLDfi2A0GVZloqGzJTv+wJlvbpY9pQkqbbWdCwAAAAY0GA5jY2NVXV19cHXfr9fDofjsMeqq6sPFtCtW7dq0qRJmj17ttq0aSOPx3PEr/1PtQH84bSurs50BBwD5hXYXE9tVvUbHyj+sXuldmeotraWmQUZ5hVcmFdwYV7BhXkFH2YWeBospx07dtR7772na6+9Vjt27FDbtm0PHmvXrp0WLlwot9stj8ejPXv2qG3bttq6daumTZumVatWqXXr1pKk0047Tfv27VN5ebmio6O1bds2DRgw4LDfMyoqqpFOr2kEej4cinkFprrtu1Q57UlFX91ZKUNyZbPZDh5jZsGFeQUX5hVcmFdwYV7Bh5kFlgbLaffu3bVlyxb16dNHlmVp+vTpWrNmjbKzs9WtWzfl5uYqJydHlmUpLy9PERERmj59urxer0aPHi1JOvXUUzV58mSNHj1aAwYMkGVZuuWWW5Sent7kJwgg8NSXVarwnglyZKYqbeljhxRTAAAAhCabZVmW6RA/t337dnXq1Ml0jCOqra3lJyxBhHkFHsvvV0Hf0ar5YJtav7FMkR3OOuQ4MwsuzCu4MK/gwryCC/MKPszMjF/qew2unAJAYypf+oJq3v6rUmbm/VcxBQAAQOjimQ0Amk3tRztUOv1Jxdz4W8XffbPpOAAAAAgglFMAzcJXVKbC+yYq/JRMpS14hOtMAQAAcAi29QJoclZ9vQ4Mmix/RZUy189VWFyM6UgAAAAIMJRTAE2ubP4zqv1gm1IXjFLEuaebjgMAAIAAxLZeAE2q5oNtKpuzRrG9fqe4O3qYjgMAAIAARTkF0GR8+UUqHDhJ4Wf+Sqmzh3GdKQAAAI6IcgqgSVg+nwrvnSirxq2M1ZMVFsNzxAAAAHBklFMATaJ0+irVfbxTqfNHytn2V6bjAAAAIMBRTgE0uuo/bVH5kucU3/9Gxd3S3XQcAAAABAHKKYBG5f2+QAcemCbnuWeo1dSHTMcBAABAkKCcAmg0lserwnvGS/X+n64zjYwwHQkAAABBguecAmg0JZNWyP3pl0p/aorC25xkOg4AAACCCCunABqF6/X3VfHEi0q47zbFXn+F6TgAAAAIMpRTACfMu/cHFQ2ZqYhOZ6vVhEGm4wAAACAIUU4BnBB/rVsFA8ZLDrvSV02WzRluOhIAAACCENecAjghJWMXy/P518p4bpbCT0o3HQcAAABBipVTAMet6sW3VPnMa0p8+A7FXPUb03EAAAAQxCinAI6L56tvVTRiriIvPl/JY+4xHQcAAABBjnIK4Jj5q2tVMGC8bNGRSn9yomwOrhAAAADAieETJYBjYlmWih6ZJ+8/9ynzxflyZKSYjgQAAIAWgJVTAMek6tk35Nr4lpIeuUvRXS4wHQcAAAAtBOUUwFFz/+NrFY9ZqKgrLlRSXj/TcQAAANCCUE4BHBV/VbUKB4xXWFK80paPk81uNx0JAAAALQjXnAJokGVZOjB0lrz/ylfW5kVypCaZjgQAAIAWhpVTAA2qXP2yql97T8mP3auoS843HQcAAAAtEOUUwC+q++xLFY9fquirfqPEB243HQcAAAAtFOUUwBHVl1epcMB4OTJSlLb0MdnC+CcDAAAATYNrTgEclmVZOvDgNPkKitX6jWWyJ8WbjgQAAIAWjHIK4LDKl72gmre2KGX6EEV2PNt0HAAAALRw7NED8F9qt+5U6dQnFHP9FYq/5xbTcQAAABACKKcADlFfXKbCeycoPDtTqQtHyWazmY4EAACAEMC2XgAHWfX1Khw0Rf6ySmU+P1v2+FjTkQAAABAiWDkFcFDZwnWqff8TpcwYoojzzjAdBwAAACGEcgpAklTz4TaVzXpKsbddpbi+15uOAwAAgBBDOQUgX0GxDgycrPC2pyh1zgiuMwUAAECz45pTIMRZPp8K75sof3WtsjYvUlhMlOlIAAAACEGUUyDElc5crbq//l1py8fKeeappuMAAAAgRLGtFwhh1W//VeWLnlVc7vWKu+1q03EAAAAQwiinQIjy/lCoAw9MlfOc05UybYjpOAAAAAhxlFMgBFkerwrvnSDL61P66skKi4owHQkAAAAhjmtOgRBUMmWl3Nt2KX31ZDlPO9l0HAAAAICVUyDUuN78UBUrNyrh3lsVe0NX03EAAAAASZRTIKR4v/1RRQ/PUETHs9Rq4mDTcQAAAICDKKdAiPDXuVUwYJwUZlP6k5Nkc4abjgQAAAAcxDWnQIgoGbdEnn98rYznZio8O9N0HAAAAOAQrJwCIaDqpbdVufZVJT6Uo5irOpuOAwAAAPwXyinQwnm+3qeiYXMUeVE7JY+513QcAAAA4LAop0AL5q+pU8Hd42SLjlD6kxNlC2cnPwAAAAITn1SBFqx41Hx5v/pOmRvnyZGZajoOAAAAcESsnAItVOXzb6pq/R+UNOJORV9xoek4AAAAwC+inAItkHvXNyoeNV9Rl3dS0vD+puMAAAAADaKcAi2Mv6pahXePU1hCnNJWjJfNbjcdCQAAAGgQ15wCLYhlWSoaNlve7/Yra/MiOdKSTUcCAAAAjgorp0ALUrnmFbleeVfJj96rqN+0Nx0HAAAAOGqUU6CFqNuxW8Xjlij6youV+FCO6TgAAADAMaGcAi1AfXmVCgeMlyMtWWnLxsoWxl9tAAAABBeuOQWCnGVZOvDwdPnyi9T69aWyJyeYjgQAAAAcM5ZXgCBXsXKDav7wv2o1YbAiO51jOg4AAABwXCinQBCr+9s/VDJ5pWJ6dFHCfbeajgMAAAAcN8opEKTqi8tUcM8EOU5KV+ri0bLZbKYjAQAAAMeNa06BIGT5/SocPFX+0gq1fnO57PGxpiMBAAAAJ4SVUyAIlS98VrXv/U2tpj2siPPPNB0HAAAAOGGUUyDI1P717yqdtVqxt3ZXfL8bTMcBAAAAGgXlFAgi/qpqHXhgqsJPyVLqnBFcZwoAAIAWg2tOgSBS/Ogi+X48oNZvLldYbLTpOAAAAECjYeUUCBKuNz5Q1fo/KCkvV5EX8DxTAAAAtCyUUyAI+ApLVDR8jiLOP1NJw+80HQcAAABodA2WU7/fr/Hjx6t3797Kzc3Vvn37Djm+ceNG9ezZU7169dJ77713yLG1a9dq7ty5B1+vWbNGPXr0UG5urnJzc7V3795GOg2g5bIsS0VDZsqqqVXainGyhbMbHwAAAC1Pg59y33nnHXk8Hm3YsEE7duzQzJkztWLFCklSUVGR1q1bp5deeklut1s5OTnq3Lmz/H6/xo4dq507d+qqq646+F67du3SrFmzdO655zbdGQEtTOXTr6rmz1uVMmOonGecYjoOAAAA0CQaXDndvn27LrvsMklS+/bt9fnnnx88tnPnTnXo0EFOp1NxcXHKzs7W7t275Xa7ddNNN2ngwIGHvNeuXbv0xBNP6Pbbb9fjjz/eyKcCtDyePf9SyYRlirriQsXffbPpOAAAAECTaXDl1OVyKTY29uBru90un88nh8Mhl8uluLi4g8diYmLkcrmUkJCgSy+9VC+//PIh79WjRw/l5OQoNjZWDz74oN577z117dr1v75nbW3tiZxTk6qrqzMdAccgmOdl+epVPHCyFB6u+Nl5qnO7TUdqFsE8s1DEvIIL8wouzCu4MK/gw8wCT4PlNDY2NzGGWwAAIABJREFUVtXV1Qdf+/1+ORyOwx6rrq4+pKz+nGVZ6t+//8HjXbp00RdffHHYchoVFXVsZ9HMAj0fDhWs8yqdu0beHV8pfdVkxZ56suk4zSpYZxaqmFdwYV7BhXkFF+YVfJhZYGlwW2/Hjh314YcfSpJ27Nihtm3bHjzWrl07bd++XW63W1VVVdqzZ88hx3/O5XLpuuuuU3V1tSzL0scff8y1p8AR1H32pcrmPq3Y265S7I3//QMcAAAAoKVpcOW0e/fu2rJli/r06SPLsjR9+nStWbNG2dnZ6tatm3Jzc5WTkyPLspSXl6eIiIjDvk9cXJzy8vLUr18/OZ1OXXLJJerSpUujnxAQ7PzVtTowaIocGa2UMmOo6TgAAABAs7BZlmWZDvFz27dvV6dOnUzHOKLa2lqW/4NIMM6r6JH5qlyzWVmbFynq0o6m4zS7YJxZKGNewYV5BRfmFVyYV/BhZmb8Ut9rcFsvgOZT/c5WVa7ZrIRBvUOymAIAACB0UU6BAFFfWqGiITPkPKuNkh+913QcAAAAoFk1eM0pgKZnWZaKhs9RfVmlMjfMU1jk4a/dBgAAAFoqVk6BAOB68S1Vv/GBksfco4hzTzcdBwAAAGh2lFPAMO/3BSoevVCRF5+vxMF9TMcBAAAAjKCcAgZZ9fU68OA0WZaltGWPyWa3m44EAAAAGME1p4BBFSs3qu6jHUpdPEbh2Zmm4wAAAADGsHIKGOLe9Y1Kpj+pmB6XK67PNabjAAAAAEZRTgEDLLdHBwZPkT0hTqlzR8hms5mOBAAAABjFtl7AgNKZq+T5Yq8ynp8te0qS6TgAAACAcaycAs2sdstnKl+2XvF33qiY7peYjgMAAAAEBMop0IzqK1068OA0hf+qtVpNfMB0HAAAACBgsK0XaEbFYxbJl1+s1m8uU1hMlOk4AAAAQMBg5RRoJq7X3pNr4x+VlJeryE7nmI4DAAAABBTKKdAMfAXFKhoxVxHtf62kYf1NxwEAAAACDuUUaGKWZenAkJmy6txKWzFOtnB20wMAAAD/iU/JQBOrXPuKat/9WCmzhsl5erbpOAAAAEBAYuUUaEKeb/6lkgnLFNX1fxR/102m4wAAAAABi3IKNBHL69OBwVNli4xQ2uIxstlspiMBAAAAAYttvUATKVvwjNyffan01ZPlyEgxHQcAAAAIaKycAk2gbvsulc1/RrG9rlbsDV1NxwEAAAACHuUUaGT+6lodGDxVjswUpcwYajoOAAAAEBTY1gs0spJJy+X99kdlbV4ke3ys6TgAAABAUGDlFGhE1W//VZVrXlHC4N6K6tzBdBwAAAAgaFBOgUZSX1KuoiEz5Ty7jVqNudd0HAAAACCosK0XaASWZalo+BzVV1Qp88V5skU4TUcCAAAAggorp0AjqNrwR1W/+aGSx9yjiHNONx0HAAAACDqUU+AEef+Vr+IxCxV5yflKHNTbdBwAAAAgKFFOgRNg1dfrwAPTJElpSx+TzW43nAgAAAAITlxzCpyA8uXrVbf170pb+pjCszNNxwEAAACCFiunwHFyf/6NSmesUsx1XRTb62rTcQAAAICgRjkFjoO/zq0Dg6fInhSv1LkjZLPZTEcCAAAAghrbeoHjUDpjlTxf7lXGC3Nkb5VoOg4AAAAQ9Fg5BY5R7f9+qooVGxR/102KufJi03EAAACAFoFyChyD+kqXDjw4TeGntlarCYNNxwEAAABaDLb1AsegePQC+QpK1Pr3yxUWE2U6DgAAANBisHIKHCXXK+/K9eKflDS8vyI7nm06DgAAANCiUE6Bo+DLL1LRyLmK6HiWkobmmo4DAAAAtDiUU6ABlmXpwMMzZLk9Sls+VrZwdsMDAAAAjY1P2UADKp/arNr3P1HK7GFynpZtOg4AAADQIrFyCvwCz9f7VDJpuaJ+e5Hi77zJdBwAAACgxaKcAkdgeX06MHiqbJERSls0WjabzXQkAAAAoMViWy9wBGXz1sq9Y7fSn5oiR0aK6TgAAABAi8bKKXAYddt2qWzBOsX1/p1ir7/CdBwAAACgxaOcAv/B76rRgcFT5WidplbTh5iOAwAAAIQEtvUC/6Fk4nJ5v/tRWZsXyR4fazoOAAAAEBJYOQV+pvpPH6ny6VeV+EAfRXXuYDoOAAAAEDIop8C/1ReXqWjoLDnPbqPk0feYjgMAAACEFLb1ApIsy1LR8Dmqr6hS5qb5skU4TUcCAAAAQgorp4Ckqhd+r+rf/0WtHrtPEWefZjoOAAAAEHIopwh53n37VfzoIkV27qCEgb1MxwEAAABCEuUUIc2qr9eBB6bJFhamtCWPyhbGXwkAAADABD6JI6SVL1uvuo93KmXmUIWfnGE6DgAAABCyKKcIWe5/fK3SmasUc/0Vir3tatNxAAAAgJBGOUVI8te5VTh4iuzJCUqdO0I2m810JAAAACCk8SgZhKTSaU/Iu/tbZa6fK3tyguk4AAAAQMhj5RQhp+bDbapYuVHxd/dUdLeLTMcBAAAAIMopQkx9RZUOPDRD4aedrFYTBpmOAwAAAODf2NaLkFI8eoHqC0uU8YcVCouONB0HAAAAwL+xcoqQ4dr8Z7k2va2kEf0V2eEs03EAAAAA/AzlFCHBl1+kokfmKaLjWUoamms6DgAAAID/QDlFi2f5/Trw8AxZHq/Slo+VzcFudgAAACDQ8CkdLV7l6pdV+/4nSpkzXM7Tsk3HAQAAAHAYrJyiRfN+/S+VTF6h6CsvVnz/G03HAQAAAHAElFO0WJbHq/IhM2WLjlLqwtGy2WymIwEAAAA4Arb1okWyLEtFoxfI+/k3Sl8zVY70VqYjAQAAAPgFrJyiRSqb/ZSq1r2u2AdvV+x1XUzHAQAAANCABsup3+/X+PHj1bt3b+Xm5mrfvn2HHN+4caN69uypXr166b333jvk2Nq1azV37tyDr999913dcsst6t27tzZu3NhIpwAcqmLtKyqbu1ZxOT0UN/JO03EAAAAAHIUGt/W+88478ng82rBhg3bs2KGZM2dqxYoVkqSioiKtW7dOL730ktxut3JyctS5c2f5/X6NHTtWO3fu1FVXXSVJ8nq9mjFjhjZt2qSoqCjdfvvt6tq1q1JTU5v2DBFSXG98oOJH5iu6+yVKnTdCdV6v6UgAAAAAjkKDK6fbt2/XZZddJklq3769Pv/884PHdu7cqQ4dOsjpdCouLk7Z2dnavXu33G63brrpJg0cOPDg1+7Zs0fZ2dlKSEiQ0+lUp06dtG3btiY4JYSq2i2f6cDAyYrodLbSV03meaYAAABAEGnw07vL5VJsbOzB13a7XT6fTw6HQy6XS3FxcQePxcTEyOVyKSEhQZdeeqlefvnlQ97ncF97OLW1tcd1Ms2hrq7OdAQchnf3tyruN0b2k9OVtHqS3DZLqq1lXkGImQUX5hVcmFdwYV7BhXkFH2YWeBosp7Gxsaqurj742u/3y/HvFan/PFZdXX1IAf2l9/mlr42Kijq69IYEer5Q4/2+QIX9HlNYdJSyXlyg8Kz0Q44zr+DDzIIL8wouzCu4MK/gwryCDzMLLA1u6+3YsaM+/PBDSdKOHTvUtm3bg8fatWun7du3y+12q6qqSnv27Dnk+M+ddtpp2rdvn8rLy+XxeLRt2zZ16NChkU4Doaq+pFz5vYbLqqlT5oa5Cj8pveHfBAAAACDgNLhy2r17d23ZskV9+vSRZVmaPn261qxZo+zsbHXr1k25ubnKycmRZVnKy8tTRETEYd8nPDxco0eP1oABA2RZlm655Ralp1MkcPz81bXKv2OUfN8XKHPjPEWcfZrpSAAAAACOk82yLMt0iJ/bvn27OnXqZDrGEdXW1rL8HwAsr08F/cao5t2/Kf2pKYrtcflhv455BR9mFlyYV3BhXsGFeQUX5hV8mJkZv9T3GtzWCwQay7JUlDdLNe9sVcrsYUcspgAAAACCB+UUQad06uOq2vBHJY28Swn9bzQdBwAAAEAjoJwiqJQ/sUnli59TfL8blDTyLtNxAAAAADQSyimChmvzn1UydrFirr1MKbOHyWazmY4EAAAAoJFQThEUav6yXYUPTFXkRe2UtnKCbHa76UgAAAAAGhHlFAHPvfOfKuj3qJynZytj3QyFRR3+cUUAAAAAghflFAHN+91+5fcZKXtCrDLXz5E9Mc50JAAAAABNwGE6AHAkvqIy5fcaLsvnU9Yri+TISjMdCQAAAEAToZwiIPldNSrIeUS+giJlvbRQzra/Mh0JAAAAQBOinCLgWB6vCu4aK/c/vlbGM9MUeeG5piMBAAAAaGJcc4qAYvn9OjBkhmrf/0Sp80cq5qrOpiMBAAAAaAaUUwSUkkkr5Nr0tpIfvVfxOT1MxwEAAADQTCinCBjly9erYvl6xQ/oqcShuabjAAAAAGhGlFMEhKoX31LJhGWKuf4KpUx7WDabzXQkAAAAAM2Icgrjat79WAcenqHIzh2UtnysbHa76UgAAAAAmhnlFEbVffalCu4aJ+eZpyrjmekKi4wwHQkAAACAAZRTGOPZ873ybx8pe0qiMtfPkT0+1nQkAAAAAIZQTmGEr7BE+b2HS5IyN86VIyPFcCIAAAAAJjlMB0Do8VdVK7/PSNUXlSlr8yI5T8s2HQkAAACAYZRTNCvL7VFB/0fl2b1Xmc/OUmTHs01HAgAAABAAKKdoNpbfr8IHpqn2L58qbdljiu52kelIAAAAAAIE15yiWViWpZLHFqv61XeVPGGQ4nr9znQkAAAAAAGEcopmUb74OVWsekkJA3sp8YHbTccBAAAAEGAop2hylS/8XqVTH1fsLd3VatIDstlspiMBAAAACDCUUzSp6j99pKK82Yq64kKlLR4jWxh/5AAAAAD8N5oCmkzdtl0qvGe8Is49XRlrpsrmDDcdCQAAAECAopyiSXi+3qf8nEdkT09RxgtzFBYbbToSAAAAgABGOUWj8+UXKb/XcNkcdmVtnCdHapLpSAAAAAACHM85RaOqr6hSfu8Rqi+rVOtXlyj81NamIwEAAAAIApRTNBp/nVsFuWPk+eZfynxhjiLOP9N0JAAAAABBgnKKRmHV1+vAwCmq++vflfbEBEV3ucB0JAAAAABBhGtOccIsy1Lx6AWqfvMDtZr6sOJuvtJ0JAAAAABBhnKKE1Y2b60q176qxIdylHj/babjAAAAAAhClFOckMpnXlPZrKcU2+t3Sh430HQcAAAAAEGKcorjVv37D1U0cp6iu12stIWjZLPZTEcCAAAAEKQopzgutVt3qvD+SYpof6bSV0+WLZx7awEAAAA4fpRTHDP3l3tV0HeUHK3Tlfn8bIXFRJmOBAAAACDIUU5xTLw/FCq/9wjZIiOUuXGe7K0STUcCAAAA0AKwFxNHrb6sUvm9h8ty1Sjr9aUKz840HQkAAABAC0E5xVHx19Qp/45R8n63X1kb5yninNNNRwIAAADQglBO0SDL51PhfRPl3rZL6asmKapzB9ORAAAAALQwXHOKX2RZlopGzFXNW1uUMjNPsTd0NR0JAAAAQAtEOcUvKpu5WlXPvamkYf2VcPfNpuMAAAAAaKEopziiiqc2q2z+04rre52SRg8wHQcAAABAC0Y5xWG5XntPxaMXKPp3lyp1znDZbDbTkQAAAAC0YJRT/JfaLZ+pcNAURV5wjtIfnyCbg/tmAQAAAGhalFMcwv35NyrIHaPwU1sr47lZCouONB0JAAAAQAignOIg77/yld97uGxxMcraMFf2pHjTkQAAAACECPZrQpJUX1ym/F7DZbk9av3SQjlap5uOBAAAACCEUE4hf3Wt8u8YJd+PhcrctEDOX59qOhIAAACAEEM5hUomLZd7x1fKWDtVURe1Mx0HAAAAQAjimtMQ5/5yryqffk3xd92kmGsuMx0HAAAAQIiinIYwy7JUMn6pwuKilfzI3abjAAAAAAhhlNMQVvPOVtW+/4mSRtwpe3KC6TgAAAAAQhjlNERZXp9KJixTeJuTlHB3T9NxAAAAAIQ4bogUoiqfflXer/cp45npsjnDTccBAAAAEOJYOQ1B9eVVKp39lCIv7ajo311qOg4AAAAAUE5DUdm8tfKXVyll8oOy2Wym4wAAAAAA5TTUePZ8r4rVLysu51pFnHeG6TgAAAAAIIlyGnJKJi2XzRmu5DH3mo4CAAAAAAdRTkNI7f9+qpo//K+ShvSVI72V6TgAAAAAcBDlNERY9fUqHrtEjpPSlTCwt+k4AAAAAHAIymmIqHrhD/Ls+kbJ4wcqLCrCdBwAAAAAOATlNAT4XTUqnf6kIi48V7E3dTMdBwAAAAD+i8N0ADS9skXPqr6oVBnrpvPoGAAAAAABiZXTFs77fYEqVmxQ7C3dFdnpHNNxAAAAAOCwKKctXOmUlZJNSh57v+koAAAAAHBEDW7r9fv9mjhxor766is5nU5NnTpVp5xyysHjGzdu1Pr16+VwODRo0CB17dpVpaWlGjFihOrq6pSWlqYZM2YoKipKU6dO1aeffqqYmBhJ0vLlyxUXF9d0Zxfi6j75XK7Nf1bSsP4KPynddBwAAAAAOKIGy+k777wjj8ejDRs2aMeOHZo5c6ZWrFghSSoqKtK6dev00ksvye12KycnR507d9by5ct13XXXqWfPnnriiSe0YcMG3Xnnndq1a5dWrVql5OTkJj+xUGf5/Soet0T29FZKfCjHdBwAAAAA+EUNbuvdvn27LrvsMklS+/bt9fnnnx88tnPnTnXo0EFOp1NxcXHKzs7W7t27D/k9l19+uT766CP5/X7t27dP48ePV58+fbRp06YmOiVIkmvzn+Xe/oWSH71XYbHRpuMAAAAAwC9qcOXU5XIpNjb24Gu73S6fzyeHwyGXy3XIttyYmBi5XK5Dfj0mJkZVVVWqqalR3759ddddd6m+vl79+vXTueeeq1//+tdNcFqhzV9Tp9IpK+U87wzF9bnGdBwAAAAAaFCD5TQ2NlbV1dUHX/v9fjkcjsMeq66uVlxc3MFfj4yMVHV1teLj4xUVFaV+/fopKipKknTxxRdr9+7dhy2ntbW1J3xiTaWurs50hAZVLXlOvh8PqNWCR1TndpuOY1QwzAuHYmbBhXkFF+YVXJhXcGFewYeZBZ4Gy2nHjh313nvv6dprr9WOHTvUtm3bg8fatWunhQsXyu12y+PxaM+ePWrbtq06duyoDz74QD179tSHH36oTp066bvvvlNeXp42b94sv9+vTz/9VDfffPNhv+f/L7CBKpDz+QqK5Vq+UTE9Lldi14tMxwkIgTwvHB4zCy7MK7gwr+DCvIIL8wo+zCywNFhOu3fvri1btqhPnz6yLEvTp0/XmjVrlJ2drW7duik3N1c5OTmyLEt5eXmKiIjQoEGDNGrUKG3cuFFJSUmaN2+eoqOjdf3116tXr14KDw/XjTfeqDPOOKM5zjGklE5/UpbXq1bjB5mOAgAAAABHzWZZlmU6xM9t375dnTp1Mh3jiGprawP2Jyzuv3+lH7rfq4RBvZUy6QHTcQJCIM8Lh8fMggvzCi7MK7gwr+DCvIIPMzPjl/peg3frRXCwLEvF45cqLDleScP6mY4DAAAAAMeEctpC1PzhL6r7aIeSHxkge0Jcw78BAAAAAAII5bQFsDxelUxcofAzf6X4ftebjgMAAAAAx6zBGyIh8FWsfkneb39Q5vq5sjkYKQAAAIDgw8ppkKsvKVfZ3KcV9duLFN2NR8cAAAAACE6U0yBXOnuN/NW1SpnM3XkBAAAABC/KaRDzfPWtKp9+VfH9bpDzzFNNxwEAAACA40Y5DWIlE5YrLCZKyaPuNh0FAAAAAE4I5TRI1bz7sWr+vFVJw/vL3irRdBwAAAAAOCGU0yBk+XwqHr9Ujl+1VsKAnqbjAAAAAMAJ47kjQahy3evyfvWd0tdOky3CaToOAAAAAJwwVk6DTH1FlUpnrVbkb9or5trLTMcBAAAAgEZBOQ0yZQuekb+0UilTHpLNZjMdBwAAAAAaBeU0iHi//VEVT2xSXJ9rFNGurek4AAAAANBoKKdBpGTSCtnCw5X86L2mowAAAABAo6KcBonaLZ+p+s0PlPTwHXJkpJiOAwAAAACNinIaBCy//6dHx7ROU8LgPqbjAAAAAECjo5wGgaoNf5Rn5z+VPG6gwqIiTMcBAAAAgEZHOQ1wfleNSqc9oYhOZyu255Wm4wAAAABAk3CYDoBfVr70edUXlihjzVQeHQMAAACgxWLlNID5fixU+fL1ir25myIvPNd0HAAAAABoMpTTAFYy9XHJspQ8bqDpKAAAAADQpCinAaru0y/k2vS2Egb2VvjJGabjAAAAAECTopwGIMuyVDJ2iexpyUoa0td0HAAAAABocpTTAFT9yruq++RzJY+5V2Gx0abjAAAAAECTo5wGGH+dWyVTVsp57hmKu/0a03EAAAAAoFlQTgNMxcqN8n1foJQpD8pmt5uOAwAAAADNgnIaQHyFJSpbuE7R11yqqEs7mo4DAAAAAM2GchpASmeukuXxqtWEwaajAAAAAECzopwGCPfn36jquTeVMKCnnKedbDoOAAAAADQrymkAsCxLJeOXKCwpXknD7zQdBwAAAACaHeU0ANS8tUW1f/lUySPvkj0xznQcAAAAAGh2lFPDLI9XJROWKfyMUxTf/0bTcQAAAADACIfpAKGu4qnN8u79QRnPz5YtnHEAAAAACE2snBpUX1qhsrlrFHXFhYq+8mLTcQAAAADAGMqpQWVz1shfVaNWkx+UzWYzHQcAAAAAjKGcGuL5ep8q1ryi+H7XK+KsNqbjAAAAAIBRlFNDSiYsU1h0pJJHDTAdBQAAAACMo5waUPP+J6p5+69KHNZP9pQk03EAAAAAwDjKaTOzfD6VjF8qx6+ylHjvrabjAAAAAEBA4NklzazyuTfl+XKv0p+aIluE03QcAAAAAAgIrJw2o/pKl0pnrlLkxecr5roupuMAAAAAQMBg5bQZlS9cJ3/x/2vv7qOiqvc9jn8YUJQBAvEhzDCoKL1rEWIPepZSrTLNk7d0cYVoTcuHYyevR0OtMAx8OOJDGT3dkIcslygIFCsty661Spd69BapZdfuSlriMjURsRhCYWb2/aMTRzw8eEz6zdT79d+evWf2x/kuhM/ee2afUUTJs9w6BgAAAADOw5nTX0nz4WM6k1+u4Ilj1CP+RtNxAAAAAMCrUE5/JbWLV8kvwF8RTz9iOgoAAAAAeB3K6a+g8W/71fD2xwr7S6oCIvuYjgMAAAAAXody2sUsj0e1mS/LP7KPwv4zxXQcAAAAAPBKlNMu5iz/b53b/3+KePoR2ew9TccBAAAAAK9EOe1CnoZG1S7JV2D8jQpOusd0HAAAAADwWtxKpgudeaVE7hOn1K9wkfxsHAcAAAAAgPbQmLqI69hJnfmvYtn//U71HBZnOg4AAAAAeDXKaRepzS6Q5fYoIutR01EAAAAAwOtRTrvA2b0H5Sx7X2F/nqhuA/ubjgMAAAAAXo9yeplZlqXap1+Wf59whc92mI4DAAAAAD6BcnqZNWz6WGf/5wv1mvcn2ULspuMAAAAAgE+gnF5GnrPnVLt4lboPjlHIQ380HQcAAAAAfAa3krmMvi98Q64jxxX5xvPy8/c3HQcAAAAAfAZnTi8TV02d6nLWKuiePyjo9ptNxwEAAAAAn0I5vUzqVqyWdfacIhbNMB0FAAAAAHwO5fQyOPe/Vfqh6G1dMXm8ul8XZToOAAAAAPgcyukvZFmWahe8IluoXeFPTDYdBwAAAAB8EuX0F/px69/U+PEnCn98svzDQ03HAQAAAACfRDn9Baxml2oXvKJu116tK6aMNx0HAAAAAHwWt5L5BX5Y85aaDx3RleuWy68bbyUAAAAAXCrOnF4i95l6nX72dfVMHKqge/5gOg4AAAAA+DTK6SWqW/m6PN87FbH4L/Lz8zMdBwAAAAB8GuX0EjRVHdH3qysU8tAfFfhv15mOAwAAAAA+j3J6CWoXrpJfj0D1mvcn01EAAAAA4Deh03Lq8XiUlZWl5ORkORwOVVdXt1pfVlamCRMmaOLEifroo48kSadPn9aUKVOUmpqqtLQ0NTY2trutrzm3c69+3LJD4WkOBfTtZToOAAAAAPwmdFpOP/jgAzU1Nam0tFRz587V8uXLW9bV1NSoqKhIGzZs0OrVq5WTk6Ompibl5ubqvvvuU3FxsQYPHqzS0tJ2t/Ulltut7xfnKyAqUlf8+T9MxwEAAACA34xOy2llZaVGjhwpSYqPj9eBAwda1n3++ecaMmSIunfvrpCQEEVFRemrr75q9ZzExETt2rWr3W19ifPNrXId/EYRmY/K1iPQdBwAAAAA+M3o9OacTqdTwcHBLcv+/v5yuVwKCAiQ0+lUSEhIyzq73S6n09nqcbvdrvr6+na3bcvPlwF7G1dwTwU+NFa2e4Z5bUa0dvbsWdMR8C9iZr6FefkW5uVbmJdvYV6+h5l5n07LaXBwsBoaGlqWPR6PAgIC2lzX0NCgkJCQlsd79OihhoYGhYaGtrttW3r27HnJ/6Cu1HPs7epx561emw9tY16+h5n5FublW5iXb2FevoV5+R5m5l06vaw3ISFB27dvlyTt27dPsbGxLevi4uJUWVmpc+fOqb6+XlVVVYqNjVVCQoK2bdsmSdq+fbuGDh3a7rYAAAAAAHR65nTUqFHauXOnUlJSZFmWli5dqtdff11RUVG666675HA4lJqaKsuyNHv2bAUGBmr69OlKT09XWVmZwsPD9dxzzykoKKjNbQEAAAAA8LMsyzId4nyVlZUaOnSo6Rjtamxs5PS/D2FevoeZ+Rbm5VuYl29hXr6FefkeZmZGR32v08t6AQAAAADoapRTAAAAAIBxlFMAAAAAgHGUUwAAAACAcZRTAAAAAIBxlFMAAAAAgHGUUwAAAACAcZRTAAAAAIBxlFMAAAAAgHGUUwAAAACAcZRTAAAAAIBxlFMAAAAAgHFc6OZNAAAKHElEQVSUUwAAAACAcZRTAAAAAIBxlFMAAAAAgHGUUwAAAACAcZRTAAAAAIBxAaYDtKWystJ0BAAAAADAr8jPsizLdAgAAAAAwO8bl/UCAAAAAIyjnAIAAAAAjKOcXiSPx6OsrCwlJyfL4XCourradCR0oLm5WU888YRSU1OVlJSkDz/80HQkXITa2lrdfvvtqqqqMh0FncjPz1dycrImTJig8vJy03HQiebmZs2dO1cpKSlKTU3lZ8yL7d+/Xw6HQ5JUXV2tBx98UKmpqVqwYIE8Ho/hdLjQ+fM6ePCgUlNT5XA4NHXqVJ06dcpwOlzo/Hn97O2331ZycrKhRLgQ5fQiffDBB2pqalJpaanmzp2r5cuXm46EDmzatElhYWEqLi5WYWGh/vrXv5qOhE40NzcrKytLPXr0MB0FndizZ4/27t2rkpISFRUV6cSJE6YjoRPbtm2Ty+XShg0bNGPGDL3wwgumI6ENhYWFevrpp3Xu3DlJ0rJly5SWlqbi4mJZlsWBVi9z4byys7OVmZmpoqIijRo1SoWFhYYT4nwXzkv66YDCG2+8Ib6Cx3tQTi9SZWWlRo4cKUmKj4/XgQMHDCdCR8aMGaPHHnusZdnf399gGlyMFStWKCUlRX379jUdBZ3YsWOHYmNjNWPGDD366KO64447TEdCJ6Kjo+V2u+XxeOR0OhUQ4JVf1v+7FxUVpZdffrll+csvv9Stt94qSUpMTNSuXbtMRUMbLpxXTk6OBg0aJElyu90KDAw0FQ1tuHBedXV1WrlypTIyMgymwoX47XSRnE6ngoODW5b9/f3lcrn4Be+l7Ha7pJ/mNmvWLKWlpRlOhI5UVFSoV69eGjlypAoKCkzHQSfq6up07Ngx5eXl6ejRo5o+fbq2bNkiPz8/09HQjqCgIH377be69957VVdXp7y8PNOR0IbRo0fr6NGjLcuWZbX8XNntdtXX15uKhjZcOK+fD65+9tlnWrdundavX28qGtpw/rzcbrfmz5+vjIwMDiJ4Gc6cXqTg4GA1NDS0LHs8Hoqplzt+/Lgefvhh3X///Ro3bpzpOOjAm2++qV27dsnhcOjgwYNKT09XTU2N6VhoR1hYmEaMGKHu3bsrJiZGgYGBOn36tOlY6MCaNWs0YsQIvf/++9q4caPmzZvX6tI2eCeb7R9/pjU0NCg0NNRgGlyMd999VwsWLFBBQYF69eplOg7a8eWXX6q6uloLFy7UnDlzdOjQIWVnZ5uOBXHm9KIlJCToo48+0tixY7Vv3z7FxsaajoQOnDp1SlOmTFFWVpaGDx9uOg46cf7RZYfDoYULF6pPnz4GE6EjQ4cO1dq1azV58mSdPHlSjY2NCgsLMx0LHQgNDVW3bt0kSVdccYVcLpfcbrfhVOjM4MGDtWfPHt12223avn27hg0bZjoSOrBx40aVlpaqqKiI/xO9XFxcnDZv3ixJOnr0qObMmaP58+cbTgWJcnrRRo0apZ07dyolJUWWZWnp0qWmI6EDeXl5+uGHH5Sbm6vc3FxJP30Qni/bAX65O++8U5988omSkpJkWZaysrL4XLeXmzRpkjIyMpSamqrm5mbNnj1bQUFBpmOhE+np6crMzFROTo5iYmI0evRo05HQDrfbrezsbEVGRmrmzJmSpFtuuUWzZs0ynAzwLX4WX08FAAAAADCMz5wCAAAAAIyjnAIAAAAAjKOcAgAAAACMo5wCAAAAAIyjnAIAAAAAjONWMgCA36U9e/YoLS1N1113nSzLksvl0sMPP6yxY8d2+b4LCgo0bNgwxcXFdfm+AADwFZRTAMDv1rBhw/T8889LkhoaGuRwOBQdHa1BgwZ16X4feeSRLn19AAB8EeUUAABJdrtdycnJ2rJli2JjY5WVlaUTJ06orq5OiYmJmjVrlkaPHq3y8nKFhYWpuLhYP/74o6KiolRYWKiAgABdddVVeuaZZ2Sz/eNTM+vXr9dbb70lm82mhIQEpaena968eRo7dqxOnTqlbdu26ezZszpy5IimTZumCRMmaP/+/crOzpZlWerXr59Wrlyp6upqLVmyRJIUFhampUuXKiQkpGU/p0+f1uOPP66mpiZFR0dr9+7d2rp1q7Zs2aL169e3bPfiiy/q66+/Vl5enmw2m2pqapScnKyHHnro13uzAQBoA585BQDg7yIiIlRXV6fjx48rPj5eq1evVklJiUpKSmSz2TRu3Dht3rxZkrRp0yY98MADeueddzRp0iSVlJRoxIgRcjqdrV6zoqJC8+fPV2lpqa6++mq5XK5W651Op/Lz87Vq1SoVFBRIkjIzM7Vs2TKVl5dr+PDhqqqqUmZmphYsWKCioiIlJibq1VdfbfU6eXl5uuuuu7Ru3TqNGTNGbrdbknT48GEVFBSoqKhI0dHR2rFjhyTpu+++06pVq1RWVqY1a9aotra2S95TAAAuFmdOAQD4u2PHjunKK69UWFiYvvjiC+3evVvBwcFqamqSJCUlJWn27Nm65ZZb1Lt3b/Xu3VtPPfWU8vPzVVJSopiYGN19992tXnPZsmV67bXXtHLlSsXHx8uyrFbrb7zxRklSZGRky35qa2t17bXXSlLLGc2qqiotWrRIktTc3Kzo6OhWr1NVVaXx48dLkm6++eaWxyMiIpSeni673a5vvvlG8fHxkqQhQ4aoe/fukqTrr79eR44cUURExC98BwEAuHSUUwAA9NMZzPLycr344ouqqKhQSEiIFi9erOrqapWVlcmyLPXv318hISHKy8tTUlKSJKm0tFQzZ85URESEsrKytHXr1paSKEllZWVatGiRAgMDNXXqVO3du7fVfv38/P4pS9++fXX48GFdc801KigoUHR0tKKjo7VixQr1799flZWVqqmpafWc2NhY7d27V4MGDdK+ffskSfX19XrppZf08ccfS5ImT57cUo4PHjwot9utpqYmHTp0SAMHDrxs7yUAAJeCcgoA+N3avXu3HA6HbDab3G63Zs6cqZiYGLndbs2ZM0eVlZXq2bOnBg4cqJMnT6pfv36aOHGilixZomeffVaSFBcXp8mTJyssLEx2u1133HFHq33ccMMNSkpKUnh4uPr166ebbrpJFRUVHeZatGiRMjIyZLPZ1KdPH02aNEmRkZFKT09vuVw3Ozu71XOmTZumJ598Uu+995769u2rgIAABQcHKyEhQePHj1dQUJBCQ0N18uRJDRgwQC6XS9OmTdOZM2c0ffp09erV6/K9sQAAXAI/68LriwAAQLveffddff3113rsscdMR2ll27ZtCg8PV1xcnHbt2qW8vDytXbu2zW337NmjDRs2tHxTMQAA3oAzpwAAXKScnBx9+umnys3NNR3lnwwYMEAZGRny9/eXx+PR/PnzTUcCAOBfwplTAAAAAIBx3EoGAAAAAGAc5RQAAAAAYBzlFAAAAABgHOUUAAAAAGAc5RQAAAAAYBzlFAAAAABg3P8DyKvvlbe9PrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11717c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Spy returns after 2% down gap\")\n",
    "plt.plot(instances.mean(axis=1).cumsum(), color=\"crimson\", label=\"Spy returns after 2% down gap (# of instances since {}: {})\".format(spx.index.year[0], len(instances.columns)))\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Days since gap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Buy open(t+0) sell close(t+0): pct profitable 0.54\n",
      "Buy open(t+0) sell close(t+1): pct profitable 0.67\n",
      "Buy open(t+0) sell close(t+2): pct profitable 0.69\n",
      "Buy open(t+0) sell close(t+3): pct profitable 0.77\n",
      "Buy open(t+0) sell close(t+4): pct profitable 0.73\n",
      "Buy open(t+0) sell close(t+5): pct profitable 0.69\n"
     ]
    }
   ],
   "source": [
    "for i in range(2, 8):\n",
    "    all_rets = instances.iloc[1:i].cumsum().iloc[-1, :]\n",
    "    pct_profitable = len(all_rets[all_rets > 0]) / len(all_rets)\n",
    "    print(\"Buy open(t+0) sell close(t+{}): pct profitable {}\".format(i-2, round(pct_profitable, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}