.ipynb_checkpoints/ML Forecast-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basics libs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Classification ML Algorithms\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n",
    "\n",
    "    percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n",
    "    filledLength = int(length * iteration // total)\n",
    "    bar = fill * filledLength + '-' * (length - filledLength)\n",
    "    print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\n",
    "    # Print New Line on Complete\n",
    "    if iteration == total: \n",
    "        print()\n",
    "\n",
    "def import_data(csv): \n",
    "    \n",
    "    df = pd.read_csv(csv)\n",
    "    df.index = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
    "    df = df.drop('date', axis=1)\n",
    "    \n",
    "    close_columns = []\n",
    "    high_columns = []\n",
    "    low_columns = []\n",
    "    open_columns = []\n",
    "    volume_columns = []\n",
    "    open_int_columns = []\n",
    "\n",
    "    for i in df.columns:\n",
    "        if \"close\" in i:\n",
    "            close_columns.append(i)\n",
    "        elif \"high\" in i:\n",
    "            high_columns.append(i)\n",
    "        elif \"low\" in i:\n",
    "            low_columns.append(i)\n",
    "        elif \"open_int\" in i:\n",
    "            open_int_columns.append(i)\n",
    "        elif \"open\" in i:\n",
    "            open_columns.append(i)\n",
    "        elif \"volume\" in i:\n",
    "            volume_columns.append(i)\n",
    "\n",
    "    close_df = df[close_columns]\n",
    "    high_df = df[high_columns]\n",
    "    low_df = df[low_columns]\n",
    "    open_df = df[open_columns]\n",
    "    volume_df = df[volume_columns]\n",
    "    open_int_df = df[open_int_columns]\n",
    "    \n",
    "    return [close_df, high_df, low_df, open_df, volume_df, open_int_df]\n",
    "\n",
    "def garman_klass_vol(high_df, low_df, close_df, open_df, period=60):\n",
    "    \"\"\"\n",
    "    Estima a volatilidade a partir dos seguintes preços: alta, baixa, abertura e fechamento\n",
    "    \"\"\"\n",
    "    # Calculando parcelas internas da somatoria\n",
    "    x_hl = (1/2)*(np.log(np.divide(high_df, low_df))) ** 2\n",
    "    x_co = - (2 * np.log(2) - 1)* (np.log(np.divide(close_df, open_df))**2)\n",
    "    \n",
    "    # Somando parcelas calculadas\n",
    "    x = x_hl + x_co.values\n",
    "    \n",
    "    x.columns = [x[0:3] + \"gk\" for x in x.columns]\n",
    "    \n",
    "    # Criando dataframe para atribuir as volatilidades\n",
    "    gk = x.copy()\n",
    "    \n",
    "    # Termo constante fora da somatoria (Considerando vol diaria)\n",
    "    const = 1/period\n",
    "    \n",
    "    # Atribuindo not a number, para os valores iniciais\n",
    "    gk.iloc[:period,:] = np.nan\n",
    "    \n",
    "    # iteração do centro de massa da vol\n",
    "    for row in range(period, len(high_df)):\n",
    "        gk.iloc[row] = np.sqrt(const * np.sum(x.iloc[row-period:row,:]))\n",
    "        \n",
    "    return gk\n",
    "\n",
    "def parkinson_vol(high_df, low_df, period=60):\n",
    "    \"\"\"\n",
    "    Estimando a volatilidade a partir dos preço de Alta e de Baixa\n",
    "    \"\"\"\n",
    "    \n",
    "    # Calculando parcela interna da somatoria\n",
    "    x = np.log(np.divide(high_df, low_df)) ** 2\n",
    "    x.columns = [x[0:3] + \"pv\" for x in x.columns]\n",
    "    \n",
    "    # Criando dataframe para atribuir as volatilidades\n",
    "    pv = x.copy()\n",
    "    \n",
    "    # Termo constante fora da somatoria (Considerando vol diaria)\n",
    "    const = 1 / (4 * period * np.log(2))\n",
    "    \n",
    "    # Atribuindo not a number, para os valores iniciais\n",
    "    pv.iloc[:period,:] = np.nan\n",
    "        \n",
    "    # iteração do centro de massa da vol\n",
    "    for row in range(period, len(high_df)):\n",
    "        pv.iloc[row] = np.sqrt(const * np.sum(x.iloc[row-period:row,:]))\n",
    "        \n",
    "    return pv\n",
    "\n",
    "def monthly_volume(volume, period=20):\n",
    "    \n",
    "    daily_volume = volume.copy()\n",
    "    \n",
    "    for row in range(period, len(volume)):\n",
    "        daily_volume.iloc[row] = volume.iloc[row-period:row,:].cumsum().iloc[-1]\n",
    "        \n",
    "    monthly_volume = daily_volume.resample(\"BM\").last().ffill()\n",
    "    \n",
    "    return monthly_volume\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_df, high_df, low_df, open_df, volume_df, open_int_df = import_data(\"data.csv\")\n",
    "\n",
    "# Retornos diários\n",
    "returns_daily = close_df.pct_change().fillna(0)\n",
    "\n",
    "# Retornos mensais\n",
    "returns_monthly = close_df.pct_change(20).fillna(0).resample('BM').last().ffill()\n",
    "\n",
    "# Vol diária\n",
    "vol_daily = returns_daily.ewm(adjust=True, com=60, min_periods=0).std().dropna()\n",
    "\n",
    "# Vol de 261 dias, apenas o último dia de cada mês\n",
    "vol_monthly = (np.sqrt(261)*vol_daily).resample('BM').last().ffill()\n",
    "\n",
    "pv_df = parkinson_vol(high_df, low_df)\n",
    "pv_monthly = (np.sqrt(261)*pv_df).resample('BM').last().ffill()\n",
    "\n",
    "gk_df = garman_klass_vol(high_df, low_df, close_df, open_df)\n",
    "gk_monthly = (np.sqrt(261)*gk_df).resample('BM').last().ffill()\n",
    "\n",
    "monthly_volume = monthly_volume(volume_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test(stock):\n",
    "    train = pd.DataFrame()\n",
    "\n",
    "    train[\"Returns Monthly\"] = returns_monthly.iloc[:,stock]\n",
    "    train[\"EWMA\"] = vol_monthly.iloc[:,stock]\n",
    "    train[\"Parkinson\"] = pv_monthly.iloc[:,stock]\n",
    "    train[\"Garman-Klass\"] = gk_monthly.iloc[:,stock]\n",
    "    train[\"Monthly Volume\"] = monthly_volume.iloc[:,stock]\n",
    "\n",
    "    prices = close_df.iloc[:,stock]\n",
    "\n",
    "    for lag in [3, 6, 9, 12]:\n",
    "        train[\"Momentum \" + str(lag)] = prices.pct_change(lag * 21)\n",
    "\n",
    "    target = train[\"Returns Monthly\"].shift(-1)\n",
    "    \n",
    "    features = train.dropna()\n",
    "    target = target.dropna().loc['2000-07-31':]\n",
    "    \n",
    "    return (features, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "features, target = train_test(1)\n",
    "df = features\n",
    "df[\"target\"] = np.sign(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Returns Monthly</th>\n",
       "      <th>EWMA</th>\n",
       "      <th>Parkinson</th>\n",
       "      <th>Garman-Klass</th>\n",
       "      <th>Monthly Volume</th>\n",
       "      <th>Momentum 3</th>\n",
       "      <th>Momentum 6</th>\n",
       "      <th>Momentum 9</th>\n",
       "      <th>Momentum 12</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-07-31</th>\n",
       "      <td>-0.013500</td>\n",
       "      <td>0.081672</td>\n",
       "      <td>0.090117</td>\n",
       "      <td>0.087723</td>\n",
       "      <td>135249.0</td>\n",
       "      <td>-0.051498</td>\n",
       "      <td>-0.084389</td>\n",
       "      <td>-0.095780</td>\n",
       "      <td>-0.052628</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-08-31</th>\n",
       "      <td>-0.031493</td>\n",
       "      <td>0.076585</td>\n",
       "      <td>0.078957</td>\n",
       "      <td>0.080442</td>\n",
       "      <td>137686.0</td>\n",
       "      <td>-0.025209</td>\n",
       "      <td>-0.094248</td>\n",
       "      <td>-0.106080</td>\n",
       "      <td>-0.107423</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-29</th>\n",
       "      <td>0.010879</td>\n",
       "      <td>0.095101</td>\n",
       "      <td>0.092941</td>\n",
       "      <td>0.094567</td>\n",
       "      <td>227772.0</td>\n",
       "      <td>-0.017524</td>\n",
       "      <td>-0.063966</td>\n",
       "      <td>-0.096061</td>\n",
       "      <td>-0.084917</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10-31</th>\n",
       "      <td>-0.010391</td>\n",
       "      <td>0.097895</td>\n",
       "      <td>0.104744</td>\n",
       "      <td>0.107019</td>\n",
       "      <td>101142.0</td>\n",
       "      <td>-0.045012</td>\n",
       "      <td>-0.090034</td>\n",
       "      <td>-0.116647</td>\n",
       "      <td>-0.124354</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-11-30</th>\n",
       "      <td>-0.019273</td>\n",
       "      <td>0.092711</td>\n",
       "      <td>0.111326</td>\n",
       "      <td>0.112521</td>\n",
       "      <td>92974.0</td>\n",
       "      <td>-0.043417</td>\n",
       "      <td>-0.050007</td>\n",
       "      <td>-0.119602</td>\n",
       "      <td>-0.133054</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Returns Monthly      EWMA  Parkinson  Garman-Klass  \\\n",
       "date                                                             \n",
       "2000-07-31        -0.013500  0.081672   0.090117      0.087723   \n",
       "2000-08-31        -0.031493  0.076585   0.078957      0.080442   \n",
       "2000-09-29         0.010879  0.095101   0.092941      0.094567   \n",
       "2000-10-31        -0.010391  0.097895   0.104744      0.107019   \n",
       "2000-11-30        -0.019273  0.092711   0.111326      0.112521   \n",
       "\n",
       "            Monthly Volume  Momentum 3  Momentum 6  Momentum 9  Momentum 12  \\\n",
       "date                                                                          \n",
       "2000-07-31        135249.0   -0.051498   -0.084389   -0.095780    -0.052628   \n",
       "2000-08-31        137686.0   -0.025209   -0.094248   -0.106080    -0.107423   \n",
       "2000-09-29        227772.0   -0.017524   -0.063966   -0.096061    -0.084917   \n",
       "2000-10-31        101142.0   -0.045012   -0.090034   -0.116647    -0.124354   \n",
       "2000-11-30         92974.0   -0.043417   -0.050007   -0.119602    -0.133054   \n",
       "\n",
       "            target  \n",
       "date                \n",
       "2000-07-31    -1.0  \n",
       "2000-08-31     1.0  \n",
       "2000-09-29    -1.0  \n",
       "2000-10-31    -1.0  \n",
       "2000-11-30     1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22c1e1d0>"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(features.corr(), annot=True, vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a201e9cd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JRLjuuut44oknWLNmDQ899BBr1qzpdM0Pf/hDzj//fN58802WLFnCtddem6RsP6CCWURERERERDp55vEX+fwZ13DWEefz+TOu4ZnHXzyk57322mtMmzaNKVOm4Pf7ueCCC1i6dGmna4wxNDQ0AFBfX09xcfEhtdkfvMlOQERERERERAaPZx5/kV/cejfBQAiAvbuq+MWtdwNw2ic/0qdn7tixg/Hjx7cfl5SUsHLlyk7X3HrrrZx55pn8+te/prm5maeffrqPf4P+ox5mERERERERaXf/Lx9sL5b3CQZC3P/LB/v8TGttl5gxptPxQw89xKWXXkpFRQXLly/n4osvJhqN9rnN/qCCWURERERERNpV7q7uVbwnSkpK2L59e/txRUVFlyHX9913H+effz4AJ5xwAoFAgKqqqj632R9UMIuIiIiIiEi7wjEFvYr3xLHHHsv69evZvHkzoVCIJUuWcPbZZ3e6ZsKECTzzzDMArF27lkAgQGFhYZ/b7A8qmEVERERERKTdZTd+jpRUf6dYSqqfy278XJ+f6fV6ufPOOznrrLMoLS3l/PPPZ9asWXznO99h2bJlAPzsZz/j3nvvZc6cOVx44YX84Q9/6DJse6CZeGPJ5QNlZWW2vLw82WmIiIiIiIj02dq1ayktLe3x9c88/iL3//JBKndXUzimgMtu/FyfF/waTOJ9H4wxq6y1ZfGu1yrZIiIiIiIi0slpn/zIsCiQD5WGZIuIiIiIiIjEoYJZREREREREJA4VzCIiIiIiIiJxqGAWERERERERiUMFs4iIiIiIiEgcCSuYjTGpxpjXjDH/Mca8a4z5Xiz+B2PMZmPM6tjX3FjcGGN+ZYzZYIx5yxhzdIdnXWKMWR/7uqRD/BhjzNuxe35lYpt0GWPyjTErYtevMMbkddeGiIiIiIiIJM7ll19OUVERs2fPPuA1zz33HHPnzmXWrFl89KMfHcDs4ktkD3MQONVaOweYC8w3xsyLnfuGtXZu7Gt1LPYxYHrs64vAb6Gt+AW+CxwPHAd8d18BHLvmix3umx+LLwKesdZOB56JHR+wDREREREREUmsSy+9lCeffPKA5+vq6rj22mtZtmwZ7777Ln/9618HMLv4ElYw2zZNsUNf7Mse5JYFwB9j970K5BpjxgJnASustTXW2lpgBW3F91gg21r7irXWAn8EzunwrMWx14v3i8drQ0RERERERGL++Y8VnPWh85kz6WTO+tD5/PMfKw75mSeddBL5+fkHPP/ggw/y6U9/mgkTJgBQVFR0yG0eqoTOYTbGeIwxq4G9tBW9K2OnbosNib7DGJMSi40Dtne4vSIWO1i8Ik4cYLS1dhdA7M993+kDPWv/vL9ojCk3xpRXVlb26u8sIiIiIiIylP3zHyv43qKfsGvHHqy17Nqxh+8t+km/FM0H8/7771NbW8vJJ5/MMcccwx//+MeEttcTCS2YrbURa+1coAQ4zhgzG/gmMAM4FsgHboldbuI9og/xg+nRPdbae6y1ZdbassLCwm4eKSIiIiIiMnz86sf3EmgNdooFWoP86sf3JrRd13VZtWoV//znP3nqqaf4wQ9+wPvvv5/QNrszIKtkW2vrgOeA+dbaXbEh0UHgftrmJUNbb+/4DreVADu7iZfEiQPs2TfUOvbn3m7aEBEREREREWD3zr29iveXkpIS5s+fT0ZGBqNGjeKkk07iP//5T0Lb7E4iV8kuNMbkxl6nAacD73UoZA1tc4vfid2yDPhCbCXreUB9bDj1U8CZxpi82GJfZwJPxc41GmPmxZ71BWBph2ftW037kv3i8doQERERERERYExx/LnDB4r3lwULFvDiiy/iui4tLS2sXLmS0tLShLbZHW8Cnz0WWGyM8dBWmD9srX3cGPOsMaaQtuHRq4GrY9cvBz4ObABagMsArLU1xpgfAK/Hrvu+tbYm9voa4A9AGvBE7AvgduBhY8wVwDbgvIO1ISIiIiIiIm1uuPkqvrfoJ52GZaempXDDzVcd0nMvvPBCnnvuOaqqqigpKeF73/se4XAYgKuvvprS0lLmz5/PkUceieM4XHnllQfdgmogmLYFpuVAysrKbHl5ebLTEBERERER6bO1a9f2qrf2n/9Ywa9+fC+7d+5lTHERN9x8FZ8454wEZjgw4n0fjDGrrLVl8a5PZA+ziIiIiIiIDEGfOOeMYVEgH6oBWfRLRERERCSZgoEgGlkpIr2lHmYRERERGbZqqut44dlXeOGZlznhI8dy2vyTyC/ITXZaIjJEqGAWERERkWGpqbGZX9z+O/7x8HIAnn7iBVa+/Abf+Z+vkZ2TleTsRAaetZa2DYZGpr6MMtGQbBEREREZllqaW3nsb091iq3453O0tgSSlJFI8qSmplJdXT1ipyZYa6muriY1NbVX96mHWURERESGJwNer4dIJNIe8nicEd3DJiNXSUkJFRUVVFZWJjuVpElNTaWkpKRX96hgFhEREZFhKSsrk8uv+Ry//cUf2mMXXb6QjMy05CUlkiQ+n4/JkycnO40hRwWziIiIiAxLaempXHjpp/nQScfy6kurOHbeUUyZNpGMzIxkpyYiQ4QKZhEREREZtnLzcsg9Joc5x8xOdioiMgRp0S8RERERERGROFQwi4iIiIiIiMShgllEREREREQkDhXMIiIiIiIiInGoYBYRERERERGJQwWziIiIiIiISBwqmEVERERERETiUMEsIiIiIiIiEocKZhEREREREZE4VDCLiIiIiIiIxKGCWURERCQBbDSKjUaTnYaIiBwCb7ITEBERERlOopEI0WCAQOVu8HhIKxyL4/NhHPVTiIgMNSqYRURERPpRNBigYcPa9uNQXQ05h83G4/cnMSsREekLfdQpIiIi0k+i0Qitlbv3DxJurE9OQiIickhUMIuIiIj0E4PBOJ6ucU/XmIiIDH4qmEVERET6iXEc0orGQIf5yo7Pjy8jK4lZiYhIX2kOs4iIiEg/cnx+cg6bTbixDuN48GVm4fh8yU5LRET6QAWziIiISD8yjoPH78dTUJTsVERE5BBpSLaIiIiIiIhIHCqYRUREREREROJQwSwiIiIiIiIShwpmERERERERkThUMIuIiIiIiIjEoYJZREREREREJA4VzCIiIiIiIiJxaB9mEREZkqKRCEQiRCNhHK8P4/FiHH0OLCIiIv1HBbOIiAw5Nhol3FhP8/bNYC0YQ+akafgyszBGRbOIiIj0D/1WISIiQ46NuLRUbG0rlgGspXn7ZqwbSW5iIiIiMqyoYBYRkSHHWouNdi6Oreti9xXQMqDC4TCNDU36/ouIyLCjIdkiIjLkGOPgSU0jEmhtj3nTMzCOSWJWI9PePVX88d6/sG7NBj6+4HROPuPD5OXnJDstERGRfqGCWUREhhzH5yNz0jRadmzDbWnGm5FJxrgJOF5fslMbUaora7jywpvYsnEbACtfeoMv3biHK6/7PCkp/iRnJyIicug0JFtERIYkjz+FjAmTyTlsJpnjJ+P4VKANtMbG5vZieZ+H//QPGuobk5SRiIhI/1IPs4iIDFmOxwueZGcxcvn9XXv0s3OycDQ0XkREhgn1MIuIiEifZGSk84lzTm8/Nsbwje9cT35BXhKz6pmoGyYSChIJBYm6brLTERGRQcpoRcuDKysrs+Xl5clOQ0REZFCqra5j29YdbHx/C2Xz5lIwKo+MzPRkp3VQ0XCYpm2bcJvbho77snPJKJmoOfAiIiOUMWaVtbYs3jkNyRYREZE+yyvIJa8glzlHz0p2Kj0Wqq9tL5YBwg11uM2j8OfkJjErEREZjDQkW0REREYMG43itjR1iceLiYiIqGAWERGREcM4Dv7c/C5x9S6LiEg8CSuYjTGpxpjXjDH/Mca8a4z5Xiw+2Riz0hiz3hjzF2OMPxZPiR1viJ2f1OFZ34zF1xljzuoQnx+LbTDGLOoQ73UbIiIiMjJ40zNJG12McTwYj4f04gk4/tRkpyUiIoNQInuYg8Cp1to5wFxgvjFmHvAj4A5r7XSgFrgidv0VQK21dhpwR+w6jDEzgQuAWcB84C5jjMcY4wF+A3wMmAlcGLuW3rYhIiIiI4fj9ZJaOIacw2eRc9gsUvJH4Xi1rIuIiHSVsILZttk3IcgX+7LAqcAjsfhi4JzY6wWxY2LnTzPGmFh8ibU2aK3dDGwAjot9bbDWbrLWhoAlwILYPb1tQ0REREYQ4zg4Pj+Oz49xNENNRETiS+j/IWI9wauBvcAKYCNQZ63dt+FhBTAu9nocsB0gdr4eKOgY3++eA8UL+tDG/nl/0RhTbowpr6ys7NtfXkRERERERIa0hBbM1tqItXYuUEJbj3BpvMtif8br6bX9GD9YG50D1t5jrS2z1pYVFhbGuUVERERERESGuwEZg2StrQOeA+YBucaYfROFSoCdsdcVwHiA2PkcoKZjfL97DhSv6kMbIiIiIiIiIp0kcpXsQmNMbux1GnA6sBb4F7AwdtklwNLY62WxY2Lnn7XW2lj8gtgK15OB6cBrwOvA9NiK2H7aFgZbFrunt22IiIiIiIiIdJLIJSHHAotjq1k7wMPW2seNMWuAJcaYHwJvAvfFrr8P+JMxZgNtvb4XAFhr3zXGPAysAVzgOmttBMAYcz3wFOAB/tda+27sWbf0pg0RERERERGR/Rl1sB5cWVmZLS8vT3YaIiKDSk11HXt3V9LY0MSkqRMoGJWHo5WGRUREZAgyxqyy1pbFO6dNB0VEpFdqqmu55cs/YOVLqwDIzcvhocd+x7jxY5OcmYiIiEj/UneAiIj0ypaN29uLZYC62np++4s/EGgNJDErERERkf6ngllERHpl9669XWI7K3YTCoWTkI2IiIhI4qhgFhGRXjn62CPwp/g7xc7//AKyc7KSlJFI/7LWEg2H2r5cN9npiIhIEmkOs4iI9EpeQR5/fvQufnbbXdTVNnDhJZ/mhA/HXSdDZMix0Sjh5kaat2/BumG8GZlkTpiC4/N3f7OIiAw7WiW7G1olW0Qkvob6RlzXJTcvRytky7ARDYeoe+9t6PD7kS87j4zxE3E86mcQERmOtEq2iIj0Ow3BluEoGg53KpYB3OZGiEbBk6SkkigaDhNqrCcaDpGSm4/x+XCcEfiNEJERSwWziIiISIzj8wEG+KBo9qanwwgcRRENh2nY+B7RUBCAwJ5dZE+bgZOekeTMBq/6ugasteTm5SQ7FRHpJyPv3V9ERETkQBwPGeMntRfITkoq6eNG5nBsN9DSXiy3sbTu2Uk0ooXQ9tfS0sqbr7/NV676L66//Js8/8zLNDY0JTstEekHI+/dX0REROQAHI8Hf04evswsrLUY48R6nUegOOvcWBtNQiKD397dlVx2/g1Eo23fny9f/k3+/I/fcuRRM5OcmYgcKvUwi4iIiHRgHAfH58fjTxm5xTLgTUvHeDv//dOKikdkb3t3nlj2bHuxvM9f/vgobli98SJDnd7xRERERKQL4/WRPa2UYE0l0VCI1FFFOCkpyU5rUCoZP7ZLbPzEcTge9U2JDHX6VywiIiIiXRhj8Pj9pI0uJqNkIt70DPUuH8AJJx3LtMMmtx+PKS7iMxd+UlvuiQwDetcTERERkQMyxoAxyU5jUBtVmM+9D91BxbaduGGXiVPGM6owP9lpiUg/UMEsIiIiInKICkblUTAqL9lpiEg/0zgREREREREZNqy1RCORZKchw4R6mEVEREREZFiIhsMEaiqJtLbgz83Hl5mF4x25q93LoVPBLCIiIiIiQ140HKZx8/tEAq0AhBvqSBs9jtTC0RgtwCZ9pJ8cEREREREZ8mw00l4s7xOo2oPV8Gw5BOphFhEREREZpBrqm2htacU4hsysDNLT05Kd0uAVZzV39SzLodJPkIiIyBATjUSIhkNEXTfZqYhIAtVU1/HDb/+MM084j499+AL+964HqautT3Zag5ZxHHzZuZ1iaWNLMF71EUrf6adHRERkCImGw7TsriDcUI8nNZWMcRNxUlLb9sodpILBEI31jXi8HvLyc7u/QUSw1vL0E8/z5LJnAQiHwtzz6z/ykVPnkZuXk+TsBifH6yOjZCKR1kLcQAu+rFwcr29Qvz/K4KceZhERkSEiGnFp3rmNUG01NuLiNq3+IwMAACAASURBVDfRsGkd1g0nO7UDqq2p43e/XMwFn/wi11+2iHffWkcoNHjzFRksgoEg/35uZZd4+Surk5DN0OF4ffiyckgrHIs3NQ1HvctyiFQwi4iIDBVRS7ihrlPIui42Ek1SQgfnhl0e/vNSfv+bP7N3TxVvr17LpQuvp66mrvubRUa4lNQUPnLKvC7xYz90VBKyERm5VDCLiIgMIZ6U1P0iZtAualNf39g+nHSfYDDExvVbkpOQyBBijOG0+SfxsQWn4TgO/hQ/X7rhC0yYNC7ZqYmMKBqjICIyxLS0tNLaEiArOxO/35fsdGQAOT4fGSWTaNi0DqJtvcppxSXg8SQ5s/hSUv1MnDK+S4E8emxRchISGWLyC3L59g+/yle/eQ3GGDKztUq2yEBTwSwiw4a1FuuGCdXVYKNR/HkFbYt9DNLet77YvWsvv/7J71nz1jpOOu0EvnDVZykYlZfstGQAeVLTyD18NlHXxXi8GI+DM0gL5szMDL72X9ewetU71FTVAnD+xQvIL9DCXyI9lZWdSVZ2ZrLTEBmxVDCLyLBh3TD176/BRtq22mndu4ucw2bFGcI6NFVX1XD1xV9n0/qtAGxcv4U9uyv59m1fJTMzI8nZyUAxjoNx/Dg+f7JT6ZFx48fy1yfuo7qyhsysDLKyM8nJzU52WiIiIj0yfLpdRGTECzXUtxfLAFhLoHI3Njo4F0TqrdaWQHuxvM9Tj/2LQEsgSRlJPIHWAKFgKNlpDBqO41BYVMCMWdMpmVCsYlkSrqG+iT279rJn114a6puSnY6IDHHqYRYZoqqraqitrsfjdcjJzdEQRwDbtTC21iYhkcTw+X14vR5cN9IeG1WYD9pfclBobmpm88Zt3H/3EtIz0rjqus8zdtxofJpnPqxFXbf9vcd4vMNqCshQVFtTx89+eBeP/f3/APjUp8/ka9++Vvt/i0if6V1dZAiqqqzhqgu/yqfPvJQFp36Br17931TH5geOZL7sPIzTcS6nIa1wzLD5BTYzM4Nrbrqs/djj8fCd279OXn5OErMaWaLhMG5rC+GmRqLhUKcPZLZuruCiBdewYvlzLP3rEyycfznVVTVJzFYSLRoO07x9M3Vr36L+/XcJ1dcS7TjKRQbcqpX/Ydnfnmpb08Jalv3tKVa99lay0xKRIWx4/BYpMoJYa1n2yJNseH9ze+yN197ijdf+k8SsBgfH5yP7sJmkFI4mpaCQnMNm4fhTkp1Wv8nITOezF5/DY889wF2Lf8zyfz/EMcfPwTNIF3wabqJumKZtG2lYv4bGTeuoX7+GaLht6HUoGGLxvX/pVEAHAkH+9X8vJStdSTAbjRKo3E24sb7tOBKheftmbIcRIDLwXnmxvGvshdeTkImIDBcqmEWGmIgb4f21G7vE31+7KQnZDC7GGDz+FNLHlJBePAFPauqw6V3eJzsni4mTS/jwyccztni0thcZQJFAK27zB/MhrevSuncXNhrFOIbsOKvYZuVoZdvhykYjhJsausQjgZYkZCP7nHLGh3sUExHpqeH1m6TICOD1eTn3sx/vEj/zEycPfDKDlDEGo3m90s+ioa4LeUVDQWw0is/n49IvXUhGZnr7ueKSMcw78ZiBTFEGkHE8eDO6fiDiSdWHWMk0a84MLrv6Qvwpfvwpfi67+kJmzZmR7LREZAgzw2lBnEQoKyuz5eVdh/eIJFN9XQPPPPkC9931ID6/lxu+cRXHfugosrLUmyWSKJFQkPp170CH/29mTJhCSm4+AK4bobqqhpeef42M9DSOOX4Oo4oKkpWuDIBoOETT1o24Lc1gDGljSkjJL8DxaE3VZGptaaWpsRmAzKwM0jQSR0S6YYxZZa0ti3tOBfPBqWCWwSoSiVBbU48B8kflqUdVJMFsNEok0ErLrgpsxCVl1Gj82bk4XhVHI1nUDbcNyzcGHA+O1hQQERlyDlYw6//yIkOUx+Np21JIRAaEcRy86RlkTpwK1mK8Xn1QJThebRsmIjKcqWAWERHpBfUoi4iIjBz6v76IiIjIIBR1XaJumEigFW96Bsbj1ZBvEZEBpoJZREREZJCJRlwCVXsI7N3VHsucOBVfdq6mAvSQ67q0tgRIz0jTfvUi0mcqmEVEREQGGRuNdiqWAZp3bCMnPRPj07zp7lRX1vDwn5fy+qur+cgp81hw3nzyC/KSnZaIDEEqmEWkR6LRCAaDcbR9u4hIwkW77mJiXRfQ7ibdqautZ9ENP2Dly28AUP7qatat2cC3b/sqmVkZSc5ORIYa/eYrIgcVdV3CjQ00b9tMy+4dREIhtB2diEhiGcfBk9p5/2B/Xj44GlrcndbWQHuxvM+Tjz1LS3NrkjISkaFMPcwiclBuUwNN2za1H4fqasiZPlNDAkVEEsjx+ciaPJ2WPbuItDbjy8oldVShFv3qAY/j4PV5ccNueyw9I42hNPXbdV1aWwNkZKTjaGSXSFLpX6CIHFDUDdNauadTzLphIkF9Si8ikmiOz096cQlZk6eTVjRGez73UEZWBldee1Gn2A03f5Hs3OwkZdQ7VZU13P3LxXzt6u/yyIOPUVtTl+yUREY09TCLyEHEn7NsNCRQRGRAOI5Hw7B7KSMjnYsuX8hp809izdvrOPLoWRQWjSIlxZ/s1LpVU13HDVd8k3f+8x4Ar/67nI3rt/CVW75IWnpaN3eLSCIkrIfZGDPeGPMvY8xaY8y7xpgbY/FbjTE7jDGrY18f73DPN40xG4wx64wxZ3WIz4/FNhhjFnWITzbGrDTGrDfG/MUY44/FU2LHG2LnJ3XXhoh05Xi9pI0dB3wwjs2Tlo7jH/y/dIiIDDWu67J7115+98vF/PLH91CxbSfBYCjZaQ1JObnZHD5zGud+9hNMnT6J7JzMZKfUI60tre3F8j5/e+hxmppakpSRiCSyh9kFvmatfcMYkwWsMsasiJ27w1r7044XG2NmAhcAs4Bi4GljzGGx078BzgAqgNeNMcustWuAH8WetcQYczdwBfDb2J+11tppxpgLYtd99kBtWGsjCfsuiAxxntQ0cg6fRbihHsfvx5ueqWGBIiIJUF1Vy2fOvIzGhiYA/vT7v/L3/7ufCZNKkpyZDBSPx4MxptPimpmZ6Qyh6dciw07CepittbustW/EXjcCa4FxB7llAbDEWhu01m4GNgDHxb42WGs3WWtDwBJggTHGAKcCj8TuXwyc0+FZi2OvHwFOi11/oDZE5AAcx4MnJZXUwtH4c/JwOiz2FY24REIh3EAr0bBWz5aB09TYzK4de3h79Vr27qlSL1wPuG6EqsoaqvZW09qidQgGoxXLn28vlgFCwRB/vu8RXFef6w9GUTdMNBwi6ob77ZkZmel89uJzOsW+9l/XkpuX029tiEjvDMgc5tiQ6KOAlcCJwPXGmC8A5bT1QtfSVky/2uG2Cj4osLfvFz8eKADqrLVunOvH7bvHWusaY+pj1x+sDRHphajrEqjaQ2DvLgCM10f21MPxpKQmOTMZ7pqbWnj04eX89Ae/wVpLamoK9zz4c+YeMzvZqQ1aTY3N/Pu5lfzoe7+msaGJT1/wCa6+8VLyC3KTnZp0w2rf5UEpEgrSvG0zbksTTkoqmROm4ElNwxziUtxZ2Zlcc9NlfOozZ7L2nfWUzZtLYVEBXp+WHRJJloSvkm2MyQT+BnzFWttA25DpqcBcYBfws32Xxrnd9iHel2ftn/MXjTHlxpjyysrKOLeIiI1E2otlaFs9u2XnNqIR9yB3iRy6pqZm7vif37aPaAgEgnznGz+iuqo2YW26rkt9XQPhcP/1JA2k6qoabr7+e1RX1hAKhliy+FGefOwZotFoslOTDs742EfJyv5grq0/xc/FV5yH16tFvwaTqBumedsm3Ja20QDRYIDGzeux/dTTnJefwxFzZ3L+5xcwZdrETj8TIjLwEvpxlTHGR1ux/IC19u8A1to9Hc7fCzweO6wAxne4vQTYGXsdL14F5BpjvLFe5o7X73tWhTHGC+QANd200c5aew9wD0BZWZk+2hWJI+p2HQIbCQQgGgX9bicJFGgNdhmiWrFtJzaamLfrmupa/v6X5bzwzMscVXYEF195PqMK8xPSVqK8+frbXWJPP/ECnzz3TLJzspKQ0dAWdcPY2IcNxnH6bV2HgsI8/vbU/Tz68HICrQHOu+hsCkeP6pdnSz+yFreluXOow8+EiAwvCSuYY3OG7wPWWmt/3iE+1lq7r1vqXOCd2OtlwIPGmJ/TtiDXdOA12nqFpxtjJgM7aFu063PWWmuM+RewkLZ5zZcASzs86xLgldj5Z2PXH6gNEekljz8FjAP2g18QfDm5GI+GjUliZWSkM6a4iN0797bHTjvrI6Sl9/90gKamZn78/d+w/B9ta1auLn+HN8vf5pf33kZefu+HM0ddl6gbJhJoxZuWjvF6cQbg38z0GVO6xI6YW0pa2vCbQhGNRCAaAePgePv/exsNh2naurG9d9GbmU3m+Mmd1nfoK6/Xy5jiIq75yqWH/CxJJIOTkko0GPgg4nggzjaM0sZai3VjI9A8Ttt2aSJDRCL/ZZ8IXAycut8WUj82xrxtjHkLOAW4CcBa+y7wMLAGeBK4zlobifUeXw88RdvCYQ/HrgW4BfiqMWYDbXOU74vF7wMKYvGvAosO1kYCvwciw5bxeMmaenjbnC3Hgz9/FGlFY+Pu2yzSnwoK87hvyS848aPHUVhUwGcu+CSLbr2BjMz0fm+rpbmVpx57tlNsdfk7tLYEDnDHgUUjEYLVlTS8/y7N2zZRv+4dwvV1A9IrNW78WD532Wfa51eWzp7O5684D59/eK14Hw2HaN6+mbp179C0ZQORQKB96H5TYzNVlTW0tvb+v11HoYa69mIZwG1qINzceEjPlKHF8fnInDAFs+8DGcchY8IUHE//F4GNDU1U7q2mvm7o/oxFIxHCjQ00bHyP+nXv0Lp7Z78ulCaSaEar2h5cWVmZLS8vT3YaIoNW1A2DteB4EvLLgsiBNNQ3EQwGyczKSFhPaeXeas49/RIa6j/4ZdXr9fDES39h9JjCXj0rGg5R997bbf9eYozHQ85hs/uld7I7DfWNtDS34rou6Rlp5BfkJbzNgRR1XZq3bSLc1NAeMz4fOdNmsntPNT/5/p2sfXc9HzllHl+68ZI+Dau31tJcsYVQbXWneMqo0WQUjz/AXTIctfWYxoZhOw6Ox9PWy9yP9uyq5Lb/voNVK//DzCMO57u3f52SCcX92sZAiISC1L/XeVpI+tgSUkaNPuRF0kT6izFmlbW2LN45dQWJyCFxvD4cn1/Fsgy47JxMCosKEjqsODc3m2/893WdYlde93kyMzN6/SxrbadiGdoWz4uz9mRCZOdkMaa4iJIJxcOuWAbA2k7FMoANh4m4Ya763Fd55qkX2Vmxm7/86R/c/t1f0tTYfIAHHZgxhpTcgi5xf84H38+o6xIJhzSfdZgzxuD4/HhSUvH4/P1eLNfV1nPLl7/PcyteorGhiZUvreLaS2+huqqmX9sZCG5zU5dYqL429v4nMvj1aHKPMebDwHRr7f3GmEIgM7aPsYiIyLDl8/s4df5HmFs2m7ffXMvhM6dRNGZUn4Z/G8fBm57ZaTivLztP8x77kZOSQjQY/CBgDBbD9q07Ol33zJMvsujWG8jM6v0HH560dNLGjm/bJcAY0kYX40lJxUajRIIBWnZsIxIK4s/NI61obL8tCCYjSzAY4o3X3+oU27JxG4HW4AHuGLw8qWldYk5quqZwyZDRbcFsjPkuUAYcDtwP+IA/0zZHWURERoDamnqCgSAer4e8vJwRtSdoVlYmWVmZTJx8aENuHa+PzIlTCVTuJtzchC8rh9SCwgFZ9GskcHw+MsdPpnHTemw0AsaQUTKJQDCM1+fFDX+w5d2Y4kLo41BQx+sltaCQlNx8wGI8XozjEA2HaNj4XttOAUCwai/GOKSNLlZhIL3m8TiMHlPInt0fbG+akZk+JNcdcHx+/PmjCNVUtR37U0jXmicyhPTkJ/Vc4GygGcBauxPQHhQiIiPE7l17+fLlizjzhPM4b/7lvPrSqiHZyzEYOD4faWPGkTV5GmlFYwZk7vJI4klNJ+fw2WQfNovcGUfgy87F4/Nx06Kr26/x+X3c+uNbyC/o/Srn+xjHwfG1TUfZ90t/JBRqL5b3CdXVYLU3vfRBXn4ut/3iv0hNTQHA6/PyvR/fTE5udpIz6z3H6yV9TAk5M45s+/c5dQaO35/stER6rNtFv4wxr1lrjzPGvGGtPdoYkwG8Yq09cmBSTC4t+iUiI1ljQxPf/ur/418r/t0e86f4Wf7iQxRpf1gZIhobmmhsaGL3zr2MmzCWnNzs9kKkv0SCAerXvdMp5s3MInPC1IRsbyXDXzAYpL6ukfraBnJys8jKzkrI9nkicvBFv3ryDv6wMeZ3QK4x5irgcuDe/kxQREQGp0Ag2GUeXSgYoqaqVgXzMFG5t5pnn3qRutp6Pr7gDIpGF5DSz8VksmVlZ5KVnUlxyZiEtWE8XlJHjSZQtSd27CGjeIKKZemzlJQUikan6L1WJMm6fRe31v7UGHMG0EDbPObvWGtXJDwzERFJutTUFOYcPYsXnn2lPeb1eQ9pOKsMHlV7q/nc2V9iz662eZL3/PpPPLz890ydPim5iQ1BjtdLatFYUkYVYSMujteH0YJfIiJDXo9m28cK5B8A/wOsMsb0fvNCEREZcrKyM/nWD7/CYaVT249/8ptbycrWUhYHEo1EiLphupvyNBiseu2t9mIZIBwKc8+v/kigNZDErIYux+vF40/Bm5bRNr9Ze8yKiAx5PVkl+0vA94FWIAoY2jaNnJLY1EREkstaS3VlDbt3VZKekUZefg55+SOvZ7V43BjueeBnBFqDeH1ecvNy8A/BlVoTzVpLNBSkdfdOouEg/rxR+HNyB/W2Qh1Xjt4nHBoaxb6IiMhA6MnEmq8Ds6y1VYlORkRkMNm9ay8Xn3Mte/e0vf2ddNoJfP8ni0bkcOT8grxkp3BA1VW1bN64ldrqOo48ehYFo/Lxej0Dnod1wzRsWIuNRABwW5rBRkkpKBq0PY3HfugocvNyqKutB8BxHK68/mLS0rvumyq9Ew2HsTaKMaZ96ykRERl6elIwbwRaEp2IiMhgEgwEufdXf2ovlgFeeOYVtm2pGJEF82BVXVXD9Zd9k3ffeg9o26d0yeP3HPKeyX0RCQbbi+V9gtWV+HPyMYN0+6hRhfk8vPz3PPznpdTW1PG5yz7DuAQujDVSREIhmrZuINLagnE8pJdMxJeVg+MZ+A9yRETk0PSkYP4m8LIxZiXQvvGmtfaGhGUlIpJkwWCILZu3d4lv37qDucfMTkJGEs/G9Vvbi2WA5qYW7vr5/3Lrj24e8F5SE6cYMl4fDNLeZWjrUR5TXMT1X78Cay0eFXSHzEYitOzcRqS1ra/BRiM0b9tMbukRkODvr41GsREXay3GOBivd9CObhARGSp6UjD/DngWeJu2OcwiIsNeVnYm55z/ccpfXd0e83o9HHPcnCRmJfurra7rEquuqiUcdhnoQcWOz4cvK4dwY9vwZowhvXj8kNhWyNFw4X4TjUZwW5r2i1qi4TCOz3/we10Xa6NgwXgcHE/Pf3ZsNEK4uYnmbZvbVun2p5A1eTqeFO3bKyJyKHryTuxaa7+a8ExERAYRYwwnnTqPW777ZR5a/Hdy83L4+n9fN6jn8o5Ec8tmk5aeRmtLa3vsossWkp0z8Kt4O14fGeMnEQ2FiIRD+NIyMEOgWJb+ZRwHb3oW4YbaDkHT7eJvUTdM6+6dBGvaVi335+bHPnDp2XB+G4nQtHUjRNv6NqKhIE3bNpE1efqgXnhORGSwM92thGmMuQ3YCjxG5yHZNYlNbXAoKyuz5eXlyU5DRJLEdSPU1dbj8TgjcoXswc4Nu1Rs38lvfva/VFfV8vkrzuOY4+eQk4SCWWSfaChE07aNuC3NGI+XjPGT8GZm4TgHHpIdaqinacv6TrGM8ZNJySvoUZuRYID6de90ieeWzsEZpHPoRUQGC2PMKmttWdxzPSiYN8cJW2vtiNhWSgWziMjg19Lciuu6SelZlpEj6rrYiEs04uLx+TFe3wHnCEddFxuNYgw9WiW7eed2glV7OsX8uflkjJ/co3nI0XCI+vff7bTwnDc9k8xJU9XDLCLSjYMVzN2OFbPWTu7/lERERPpPesbg2wapob6JlpYWotEo6Wlp5ObnJDslOQRR16Vlzw5C1W1Dpo3HQ/bUGXhS4//s9Xbuuj8rp0vB7MvO7fGiXcbjJXPSdJq3bSIaDuFJTSNjwmQVyyIih6jbd3NjzBfixa21f+z/dERERIa+2uo6fvWTe/n7kn9irWXeh8v4f7/8NgWjkj8Hvrm5ldbmFoxjyC/IS+oqyvW19UQt5A2BDxNsxG0vltuOIzTv3E7mhCn9srCbJy2d1MIxBKr2gIWU/FH4Mns+YqJt7nQG2dNmYC0Yp/t50yIi0r2evMMf2+F1KnAa8AaggllkBAiFwgD4/frFS6SnNry/mb899Hj78av/Lmf5P1Zw0eULk7oidXVVLXf8v7t56rFnGVNcxHd/9A1mzyklNTVlQPNobmphzTvvc+dPfk8kEuGLN3yBo8qOICs7c0Dz6I2oG+4aCwWhm6ltPeV4vaQWjSV1VFEs4On1vs3GGEw3K3GLAEQjEYjGhu/34WdNZCTpyZDsL3c8NsbkAH9KWEYiMigEAkF2bt/N/b97EIDLr76I4pLRpAzwL9YiQ9Fbb6zpElv12lucd9HZpKYlZ5ufYDDIH373EMseeRKArZsr+NLnv87yFx4idWzhgOayZ1clV17wFfato3L9ZYt4YOndHDG3dEDz6A2PPwUcp30VamibYxxv/+2+cjyehO/VLCOXjUaxrkskFMB4vIQb6mjdu4uUgkLSisZqRILIAfTlY+4WYHp/JyIig8ve3ZUs/NjlLP3rkyz965Ms/Njl7N1Tley0RIaEE07qum7I6fNPSlqxDNDU0MxzK17qFAuHwmzZvH3Ac3n8H//H/ouOPvLAMqIditHBxni8ZE+dgTcjE8fnJ7VoDKmjirpdzEtkMLDW4rY0UbfubRo3vU/D+jVYa0ktKCJYtRe3ef+9w0Vkn27f5Y0xjxljlsW+HgfWAUsTn5qIJNPDf16GG3bbj8OhMH//yz+TmJHI0DFufDGLbr2BrOxM/Cl+Pn/FeZx48nFJzSk1LZXpM6Z2iRePGz3guYyfOK5rbNK4pA5X745xHLxp6WROnEr2tBmkFRWrR06GDOu6NFds6TSFILB3N76ctnUVQg11XT7EEpE2PZnD/NMOr11gq7W2IkH5iMggkZmV0TWW2TUmIl3l5Gax8KJPceYnTsbS9u8pLYm9ywAZmel8/dvXsvaddezYvhvHcbj6K5eQk5s94LmcdMo8pk6fxMb1WwAYN34MCxZ+bMDz6AsVyTJURUP7z8O3sS/wZWUndQFAkcGs232YRzrtwywj1Z5de1k4/wrq6xqAtlVsH37iPkaPGdi5jiKDXXVVLXt3V+K6EcaOG82owvwBbd/aKFHXBWsxxsHxHbygq6qsoaW5hZSUFDIy0+N+ODYQqqtqqdi2k4gbYcLkkoN+31qaWwkGQ4TDYULBEP4UPzm5WaSkaE0FkZ6IRlxaKrYRqq9pjzn+FNKLxxOqqyG9eLw+DJIR7WD7MB+wYDbGNLLvY6f9TgHWWjvwH0kngQpmGakikQjVVbW8+OyrGGP4yCnHU1CYP6iHTIoMtOrKGq699GbWvrMegJIJxSx+5E4KRxcMSPs2GiXc3Pj/2bvv+Miq+v/jr3PvlMxMet3eC4uAoKsgKiCKFGmKwIIUEVnACvJTRCwoAiqIgl9F6WABBEFAlCIIqCCyiNK395ZeZzLtnt8fMxuSTTab7CaZSfJ+Ph7zyM5n7sz9ZJPM3M+953wOHetXY1Mp3OyQYTcwdgrJ2i113HHT7zni6A/xjQuvYO3qDYTCIb77o69x8IffRyicf2twi+QjL5kkVruZdKKTmL+QRNoSDAYoLi6kQH9HMs71VzDv8MjXWltkrS3u41Y0XoplkfHMdV2qayo54ZSj+cSij1FVU6liWWQ7/37+5a5iGWDDuk088PtHRmwuoE2naF+zEpvK9BtIx6J0bFiTueI8BjQ1NvOV875NVXU51199M2tXZ2aExaIxvnHhFbS2jo9GRV4yQWdDHbG6LaQTcWweN0eT/OX4/YQnTqHRhvj0ogv52EGncuyhp/PPvy+hMxbPdXoieWtAR7/GmHcaY76Qve0z3EmJiIiMBmtW9e4wvWrFWtKp9Ijs30unwfYsnlIdHVg7NgqqzlicV15+g+kzp/LGq0t7PJZKpmhpas1RZiPHSyZpXfEm0Y1riW3eQMvS1/GSiVynJTlgrcVLJjO39K69x7S0tPHNi65i4/otQGa6w9e/+D1aW9uGMlWRMWUgXbK/DPwWqM7efmuM+WL/zxIRERn7Dv/YIb0a5Zx46rH4/APpqbn7HNcF0/Oj3BeOYMzYGA3i+lwihWGWvrGCd+//zh6PFRQEKSsvzVFmIyfZ3oqX7NasyXrEajfrKnOObSte0/FOvGRi2H8eXjpNsq2F1hVv0PzWK0Q3rev5ezFAqWSKN19b1iMWjyeItkeHKlWRMWcgn6hnA/tba79trf02cABwzvCmJSIyfjU1tvDGq8v4y0NPsmnDFmLRzlynJDtQM7GKX/3mx+z1zj2Yt2A2V//8O8zdY9aI7d+4PgpnzMa4mQLdLQgRmToDxzcyBftwKykt5tLvX8g9v3mQz37uUyw8YF8g8//+8zt+RHFpYY4zHH59FmKeh+2zzYyMlHRnjJblb9Cy9DValr5OsqMN6w3fyJLM9IsVmSLZWhJNDXQ21A66UA8EA7wn+3e0TWFRhEhheCjTFRlTdtol2xjzKvAea21n9n4B8KK1du8RecXcCgAAIABJREFUyC/n1PRLREZSS3Mr11z+cx6871EgM5f8V7/9Me993345zmzoJZNJmhtbSSQSBAuCVFSWjdplTZoaM2uYlpWXjvj3YD0Pm05hrcU4zpjrdNvRHqW1pY3arfXUTKjCGHBdH+WVpeOir4KXTNCy9PUexVjx3AX4QlrmL1e8VJK2lUtJx98+mWkcl+L578D1B4Zln4mWJtrXruwRcwtCFM2aN+i/+S2bavn6ly7nPy++wuSpE/nBdd9iz33m4d+uw77neTTWN9HZGScYDFBSVkIgMLbeX0S26a/p10BOQd8GvGCMeSB7/3jglqFKTkRE3tbe1tFVLEOmW/kPvn0dN9/1E8ory3KY2a5prG/C8zxKSovxdzvQiscTvPTC/7j4i9+jpbmVqdMn8/Pbf8iMWVNzmO2uy+XQYOM4GGd4DtLzQaQwTKQwzMTJNblOJSeMz0fxvD3prNuKTacoqJqAM4a6oI9K1vYoloHMCY1hHJbtBHuv4+6Gwr2mZAzEhEnV/PSm75OIJ3FcZ4cnK1evWMt5Z3yVrZvrKCou5OqfX8bCA/ZV0Szjzk7/yqy11wKfARqBJuAsa+1PhzsxEZHxqK9OpY2NzXijbL5itCPK839/kc+eciGnHHMud9x0D81NLV2PtzS38pXzvtW1zvf6tRu59MIraGpszlXKInnJGAc3ECQ8aQqRKTPwhcKZuevjSFNjM0vfWMGzTz7P1i11JBKDn7s7pIzBF+55hd/4fBhn+H4ujs9PQfWEt+8HCwjXTN7l34XSshKqJ1RSWVXeZ7HcWN/E1774PbZurgOgrbWdi87/Ni3d3sdFxosdXmE2xjwC/A74o7X2JeClEctKRGScKiktZvLUCV0dTAFOWHQ0RSVFOcxq8Brqmzj/jK91FfrX/+gmqidUcewJhwOZZYGiHbEez3ntf2+RTo+uEwMiI8UYB0bnjIXd0tzYwpXfvo7HHn4KgGAwwJ33/5wFe83LWU6Oz09k2iza160iHe3ACRZQOG0WZhh7Bzg+HwVVEygor8ZaD+O4OP7hu9Kb9jyWv7WqR6yjPUpMy0/JONTfFeYbgaOBNcaYe4wxxxtjxu6YLxGRPFBZXc5tv7+eExYdzTvf/Q6+/t0vc9rZJxIMjq63338/93Kvq+J/uv9x2rLr5obDYYqKezZs2m/h3vjHSLMqERkaTY3NXcUyZKZz/Oi7P8v5lU43EKRoxhxKF+xD8az5+ELhYe9f4Lg+nEAAN1gwrMUygN/vY9+Fe/WIlVeUEgr3HhouMtbtsGC21j5orT0FmAbcD5wJrDPG3GqMOWykEhQRGW8mTKrh4su+xM9u+QEnn34cZeUluU5p0GbOmd4rNnePmRSEMnMvS8uL+cXtP2LCpGoAFuw1jyt/eiklZcUjmqeI5LeWlt7rA9fXNZIcobXO++P4/Dj+wLAXr7lQWlbCVdd9i733XQDAtBmTueHOq8fFUm4i29vpqXxrbQy4B7jHGLMPcAeZ4nl8TaARERlBBaFgV3E5Gs2YNZWPHHkwf/3LMwBMmTaJM885uasLq9/vZ+/9FvC7B39FKp0iEAhQXjE0B2LWWhrrm7DWEiwI9rqSLZLPvHQavDTG9WHGQRfwnZkydSKlZSU9eiB8YtHRlOrk2rCbPGUC/3fbD0gmkriuS/koXslAZHcMZFmpGuAkYBEwEbgXuMta+9/hTy/3tKyUiMiuaW5qoa21nXhngtLyEiqryod9n/F4gjdeXco3v3IVG9dv5uCPHMg3r/gKVdUVw75vGRqxaGfm9yYeJxQOjerlxgYrnYgT3bSBdGcUf3EJoaqJY/Lq5WB4nsfG9Zu57oc3sn7tJo478QiOPPYjo3LkjYjkr/6WldphwWyMOQc4BZhPZkj23dbafw5blnlKBbOIyOhRt7Wejx10Kp2dbzemOfaTR/CN711AOBLKYWYyEB0dUf7652f4/qXXEo8nmDx1Ijf+9sdMnT55yPdlraWpsQXjGMrKcl98eckkrSvfwku8/bsbKCknPGUajqu5/R3tURKJBMUlRbjjrEu4iAy//grm/sb6HAj8AJhqrf3ieCyWRURGk9aWNtat2cA/nn6BzZu2Eot27vxJY0zt1oYexTLAP55+gY6OaI4yksFob+vgsouvJh5PALBx/Wa+d8mPaWnuPY91d7S2tPHXR5/l/DP+H1886+u88M+X6GjP7e+I9dI9imWAREsjeP2PBBwvIoVhyspLVSyLyIjb4SlLa+1ZI5mIiIjsuo72Du6+8wH+75pbAHBdl/+79Sred9B7cMbRPMiKyjIcx+nRoXv+gtmD6jLe2tJOLBbDYCgqjhAK68r0SGlubCGd7tnM6a3Xl5PIFtBDZeWyNVx03re77i/+1EU88Nc7mNVHs7qRkpmvbIC3C2THP7q644uIjEXj5yhKRGQMa2+PcsNPbu+6n06n+e4l19DY0Dzk+2ptaaO+toHG+qYhf+3dVVQU4RuXX4A/kJn3OWnKBL55xVcoHuA61o0NzXz/mz/mowecyFEHncJtv7yL5qbW4UxZuimvKCMU6rlszQEfXDikw+nTqTT3/e6hHjFrLY8+9OSQ7WOXOC6hCZO6BQzhKdOHdW1fERHZOb0Li4iMAYl4steVufraBnbW2HGwtm6p47KLr+b5Z19kzvyZXPmTS5k1dzq+PDmojxRFOPoTH+XgjxxIvDNOOBKmorIM63k77TjseR5P/PlpHn0os+ZrIp7gl9fdwQcPPYDSsj1HIv1xr7i0iBt+fTWXXnglmzZs4cCD38vXvvUFIoVhvHSqa3iy8fl2uRGY4zrMmD2tV3z6rKm7lfvuclyXYEUVgZJyvGQcJ1CA8bnjpuGZiEi+2ukRjjHmGuA2a+3rI5CPiIjsgnAkxIzZ01izcl1X7KNHH0p4CIcTt7a0cdnFV/PPp18AYNmbK1n8qa9w36O3UplHXajD4VDX9+2lkiTbWkg0N+ILFxIoKdth1+F4Z7zre+tuyb/+x977qmAeCcFggP0W7s2vH/hFZlmwYIDikiK8ZJKOjWtJtjbjBIJEpszAF47s0rJLxhiOP+koHrjnz2xYtwmA+XvO4YAP9NnrZUQ5rg9cH25w9C4pJyIy1gzkksBbwI3GGB9wG5klpVp28hwRERlBFZVl3PibH3P91Tfx5qvLOOjD7+OMc04mUhgesn0k4gmef/bFHrHGhmY6OmJUDtleho71POIN9cS2bgQg0dxIormRwhmzcXy9i+ZgQZD3H7I/T//1uR7xd+//zhHJVzKMMT2WIPPSaaJbNpJszUwv8BJx2lYvo3SPvTHOrs3xraqu4Nf3/5wN6zfj9/uomVhNRWXZkOQvIiJjy04LZmvtzcDNxpj5wFnAK8aYfwI3WWv/NtwJiojIwEyYVM03r/gKsWgnRcWFBAJDu36r4zrMmT+TZW+u7IoFgwFC4YJ+npU7Np2ms35Lj1gq2o5Ne31++jmOw2FHHcKSF/7H43/6G/6An7POXcT0GVNGKGPpk+eRbN/uPL21pBOJHk2xrLWDGr5cUVVOxQisDS4iIqPbDtdh7rGRMS5wNJmCeSrwe+ADQIe1dtGwZphjWodZRIZCKpWmqbGZaHuUUDhEUXFh3haa/dk2DLuxoZlgMMD3rvk6hxz2/l6NmvKBl0zSsux1bDrVI16yx964gR0PeW1taScWjWGMobA4MqTD2scaay02lQLHDNtawV46Rce61STbehbN236OXipJqr2dRFsz/qIS/IVFfY4gyCdeOg1Yra8sIpIn+luHeacFszHmWuBY4EngFmvtv7s9ttRaO38ok803KphFZCi8+doyFn/qIlqaWwkEA1yex4Vmf9LpNE3ZYdihcEGm8M/T78F6HvGmBqIb13bF/MWlRKZMz/uCajTwUkniTY0kmupx/H7CE6fiBIMYM/QLcKTjcdpWL8VLJMAYQhOnECyrAAyxLRuIN9R1bRsoLSc8eVpeFqPW8/CSCWJbNuGlkxRU1uALF+LkSdM8EZHxqr+CeSCfaq8B+1hrz+1eLGe9t5+dTjXG/M0Y86Yx5nVjzJez8XJjzBPGmOXZr2XZuDHGXG+MWWGMecUY865ur3Vmdvvlxpgzu8XfbYx5Nfuc6012LNau7ENEZLg01DdxyQXfp6U5szxRIp7gO1/9IW2t7TnObPBc16WyuoLpM6dQXVOZt8UyZNa1DZSUUTxnAQVVEyicPlvF8hCx1hJvaiS2eT3pzhhpC7Wbt9JQ1zTkndkB3GCQ4tkLKJm/FyV77E2wrDJTEHtp4g31PbZNNDdmht3nIZtK0bL8DRItjaTa22hfs4JUdPS9D4iIjCc7LJiNMe/KFpT/BfbYdr9bnJ00/0oBF1lrFwAHAJ83xuwJfB140lo7l8xV669ntz8SmJu9LQZuyOZRDnwH2J9Mgf6dbQVwdpvF3Z53RDY+qH2IiAwnz/NYvWJdj1hnZ5zOaGeOMhq9vGQSL5nIDmndOcfnwxeOEJ44JdMhW8XykLCpFImmTKGaLq7kyeff4uwzLuHsUy7kqcf+Tlvb0BeBjt+PGyzA9QdwXPftB/qatpynKzElo+3g9SzmO+u3ZpbMkt3S0tRKY0MTnpefJ0tEZPTqbwzQj/t5zAKH9vfC1trNwObsv9uMMW8Ck4HjgEOym90BPA1cnI3faTOnpv9ljCk1xkzMbvuEtbYRwBjzBHCEMeZpoNha+3w2fidwPPCXwe4jm6uIyLAIBAMc8IF38/zf357eUVldTjiiubEDZb00qViM6IY1pJMJAiVlhCdM2eESUTLMHJM5+RC0LFtXy3e/8fYhw4Xnfov7Hr2VogWFI5CHS0FFTY/mboHyKhzH7edJudPX0GvH9ZO3Ff4oEIt1suzNlVx7xQ10dEQ545yTOPjDB1JSWpzr1ERkjNjhFWZr7Yf6ufVbLG/PGDMD2A94AajZVqBmv1ZnN5sMrO/2tA3ZWH/xDX3E2YV9bJ/vYmPMEmPMkrq6uu0fFhEZlJKSIr53zdc56ND34fP72HPv+dz0259QVlGa69RGDZtO07ZqKel4J3geiaYGOuu2YHU1KScc10d40lQIRfjjHx7r9fjjjzw9Ij8bx3UpqK6haOZcghXVFM6YQ3jCJIybnwWzGwzhhsIY18VfVIwbLiQ0YVLPK+YyKA11jZx14hd5ecmrLHtzJd/8ylX896XXcp2WiIwhA+oyYYw5EJjRfXtr7Z0DfG4h8AfgAmttaz9LPvT1gN2FeL/pDOQ51tobgRsh0/RrJ68pIrJTNROquPKnlxKPJ/D5XMrKVSwPRjoeh+3mxiZamymomoBxhr7JlOycEwxSOHEKe+w5hz//8a89Hps7bzrxpgYCJaXDPgze8flxikrwF5UM635amttIpZKUlpXg7mKB6/j9uDXTaKpr5IlHnmHa9Mm8u6Sayqr87QWQ7/75zIukUj2naNz7m4dYeMC+RCJDtw69iIxfOy2YjTG/BmaTmcu87R3JAjstmI0xfjLF8m+ttfdnw1u3DYPODrmuzcY3kFmyapspwKZs/JDt4k9n41P62H5X9iEiMuyKS4pyncKo1dfQazdYoGI5h4xxcP0OR3/icB7+w+MsX7oKgHe9Z2/2e9eeRDeuxaZTFFTWjOqfUzyeYNXyNVx9+c9pbGji5NOO58jjPkxp2a4V6G+9uYLPLrqwa67tgr3mcsMdV1NeWbaTZ0pfpkyb2Cs2bcbkIV+HXkTGr4FcYV4I7GkH2fYy27H6FuBNa+213R56CDgT+EH264Pd4l8wxtxNpsFXS7bgfQy4slujr48Cl1hrG40xbcaYA8gM9T4D+Nmu7GMw35eIiIw84/ooqJ5IZ23mLdvx+wlPmpq3Q2/Hk8qqcm783bU01TdhsBSF/PhbarFAoqmBYFnlqC6YmxqbOf0TnycRTwBw1XeuoyBUwPEnHUk/o+b61NzUwnU/vLFHY6o3X1vOls21Kph30YJ3zOVd79mH/7z4CgDVNZWccc7J+NXfQESGyEAK5teACWQbeA3C+4HTgVeNMf/Nxr5Bpoj9vTHmbGAdcGL2sT8DRwErgChwFkC2ML4ceDG73fe2NQADzgduB0Jkmn39JRsf1D5ERCS/OT4fBZU1BMurwPMwroNRx+u8UVFZRklhAa3L34CE7Zrr5ASCMIqLZYBXX36jq1je5v57/sSHPvoBSssG11jK82yv1wJIxJO7leN4Vl5ZxrW/+h61W+qJRTuZOmMylVXluU5LRMaQHRbMxpiHyQy9LgLeMMb8G4hve9xae2x/L2yt/Qc7bvv44T62t8Dnd/BatwK39hFfAuzVR7xhsPsQEZH81leHYckfjs9PoKScRHMDAMZ1CU+aOuobWk2YVNMrNnnqJILBwZ+wKSsv4TPnn8pXP//drtjEyTVMmT5pt3Ic78oryiiv0BV6ERke/R19XDNiWYiIiMio5vh8hCdNIVQzES+Vwg0EBjUKwEsmSbQ2k453EiyrwAkEcNzcnySZPHUiB334QJ598jkgU/R+4aLPEAoPflm42i31JJMpfnzDd3n8kaeZNmMKJ59+nK6IiojksR1+EllrnwEwxvzQWntx98eMMT8Enhnm3ERERGQUcXx+8Plxg4N7npdM0rZ6GenOGADx+q0UzphDoDj33eTLK0q5/JqLaahrorW1janTJ1OxC/ON29s6uOrb1/HU439n1tzpvPfAd7F54xYY5DxoEREZWQM5dXsYcPF2sSP7iImIiIgMmpdKdhXL28S2bsIXivTZIX2klZWX7vZScLFYJy/+62UAVi1fy6rlawH49LmnUF1Tuds5iojI8NhhJw5jzPnGmFeB+caYV7rdVgOvjlyKIiJjj/U8vGSCVKyDdCKBl07lOiWR3OlrIY7BLc6R9wKBAO/YZ48eMZ/fR2n58K4fLZJLnudRX9fIpg1bqK9r7NEhXmS06K915e+AY8gsxXRMt9u7rbWfGoHcRETGrFRnlOalr9G6/E1a3nqFeFMDXjq98yeKjEGOP4ATLOgRC1VPwoyhRm8lpUV8+6qLmDFrKgCRwjA/uO6bFBUV5jgzkeHheR7Ll67mtOPP54j3n8zpHz+fFUtXq2iWUccMZHllY4wL1NBtCLe1dt0w5pU3Fi5caJcsWZLrNERkDPGSSVpXLcWLd3aLGkoX7I3jD+QsLxkftl3xeeGf/8Hnc1l4wL5UVpUPek3hHWltaSMYDBAsGNxEZi+ZJNHSSCoWo6CiCicYzIumX0Otob6Rzlgcf8BPSWkxwaD+5mVsqq9t5LSPn8+mDVu6YpOnTuDXD9ygRneSd4wxL1lrF/b12E4/iYwxXwAuA7YC204JWWCfoUpQRGS88RLx7SIWq7PuMgJqt9Zz0pGfpbmpBYDqmkruevhXVO3mPNrmphb++cy/uf/uR5g2fTLnfOkMJk6qHnAh7vj9FFTWYK0dsuI9H1VUqlCQ8SGRSPQolgE2rt9CItF7LXKRfNbfkOxtLgDmW2vfYa3dO3tTsSwisouM4+Av6tlAyPj8GGcgb8njk/U80skEXiq526/VGeukrraB1pa2IchsZHnpFF4yscsnV6y13Pfbh7uKZcgU0E/8ZfcWvkin0/zloSe55Mvf58XnX+YPd/+J044/j/q6xkG/1lgulvvS1NjCG68u5cH7HmXdmo10tHXkOiWRIREIBpg0ZUKP2OSpEwgENKpCRpeBHJ2tB1p2upWIiAyIcV0ik6cRKKvAuD58kSKKZ88f1Jq144mXShLdspHW5W/StnoFqWjHLheMDfWNXH35zznl6MV89fOXsXb1hlExn85aSzoep2P9alpXLiW2ZSNecvAnD6y1PYrlbVqaWncrv+bGFu6+8489YvW1jZllk8aQof5daW1p4xfX3sqioxfzrYuu4phDPsXz/3iJtPoZyBhQXlHKdTdf0VU0T5oygZ/edCXlFblfLk5kMAYyOWgV8LQx5hGgawyhtfbaYctKRGSMc/x+wpOmwYQ0GAdnDDU3GkrW8+isryVevxWAdCpJ68qllO6xF8YZ3FWKaEeUH19xA3+6/3Egc2X17JO/zN2P3JT38+lsKkXbqqV4ycxQxs76rXjpFOFJ03Bcd8Cv4zgOp5z5ce773cNdxZ8/4OeYEw7frfxcn0txSe/mVeFweEDPt9bDplKZCV9O/v091G6t58Hf/4XNG7dy0unHMWXaJAqLIrv9utGOGL//zYNd9621/PC717Pvwr1y/jvppVMYDGYQv18i3TmOw9z5s/jNH28gEU8QCAYoryjF0WgqGWUG8om0LnsLZG8iIjIEHNcFHYz2y6bTJFq2G9ZrPdLxzkE3SIt2xPjrn3sOPa7dWk97W0fOi5OdsV66q1jeJtHSRHjC5EH/Dk2aMoG7H7mRW37+W3w+H+d84TSqqit2K7/SshK++q0v8OkTv0gqmVki7QOH7E9FVdlOn2s9j2RHGx3rV2NTKXyRQgqnzcqbBnj1tQ186rjz2Lq5DoD77nqYW+65jvccsO9uv3YymWL75qutzW05XVLLS6dJd0aJbd2MAQomTMINhgZ1YkZkG8dx8v79VWRndlowW2u/C2CMKcrcte3DnpWIiAiA4+AGCvDiPZuk7Uox5TgOU6dPZvnSVV0x13UJhQv6eVaeML2vyGT+DwY/3zcUDrHHnnO5/JqvY4wZdDfrHZm3YDaPPPM7lrzwX6ZMncT0WVMoK9/50EubTtG+ZkVXkZjqaKdj03oiU6bnRZfsZW+t6iqWt/nVdbczf8HlFJcU7dZrhyMh5u4xi+Vvvf07ecKpxwzJ1etd5SXitK1c2nU/uaKVknnvADeUs5ykf9bzsOkUXjqN47oY16eeGCJDaCBdsvcCfg2UZ+/XA2dYa18f5txERGScc1yX8KSptK7owKYzVy6DFVWYXSikyivLuOxHX+Oziy4gFuvEGMOXvnYOkcjAhg3nknEcCqon0lm7ORswmYLSv+vz3gtCQ3uioKAgyMTJNRzzicEN7/aSyV5XVFMdbZl56nlwUdN1+zhZ4bhD0pysorKMX955NXfc9Htef+UtDjvqYI489sND/rMZKGst8Ya6XvHOpnoiE6fmICPZGWszIzS6TjoZQ+H02fgLi1U0iwyRgRxx3Ah8xVr7NwBjzCHATcCBw5iXiIgIAE4gSMm8PfFSKYzrYhx3l+e4zt9zDn965rds2VxHRVU5RUWRnF7NGyjH56OgsoZgWQVeMoEbLNilkwb5qK9md75QJG8O9ufMm8nU6ZNZv3YjkBmp8IWLPkNRce8527uiqqaSL33tHDpjnUQKw7g5HPpsjMHpo4OxmyfD46U3m0rRsX712yedrKVj/WpK5r1j0H0eRKRvA/m0jWwrlgGstU8bY/L/6EJERMYEYwzGHxiSOa2BgJ+qmsrdXnM4FxyfD3w+3OAoGEI+CMZ1iUydScfGteB5uMECwpOn5cVwbICKqnLuuO9nPPX4P9iyqZZjTjicmglVQ7qPQMBPIJAfXfKDZRV0NtRis13YHX+AQMnO56JLblhrMw3zusfS6V5z40Vk1w2oS7Yx5ltkhmUDnAasHr6URERkLNo2z856HsZxMD4fpo+5uTK+OK5LoKQMX2ERWIsxzm4NNR8OldUVnHTacblOY0Q4/gAlc/YkFYuCAV9BOO9+HvnOS6UyjfoScZxAAcbn4jjDM3LAOA5uKEw6Fu2KuQWhvBmhITIWDKRg/gzwXeB+Mt1FngXOGs6kRERkbLGeR6qjnfa1K7FeGuP6KJo5FzcUHpK5oDK6GcfB1fDRvOH4/QT8JblOY1Ty0mniTQ3ENq/PRgyFM+bgLyoelvc6x+encPpsohvWkoy24wsXZvob9DHVQUR2zUC6ZDcBXxqBXEREZIyy6RTt6zLF8tv3V1E8ew+Mrl6JyBhhvTSxLRu6R+jYuIaSOXsO23udGwgSmTYLrAcm/9YxFxntdvgXZYx5qL8nWmuPHfp0RERkLLKexabTPWJeIg5onp2IjCGe7dX1PTMffHjf61Qkiwyf/v663gesB+4CXmBXFnsUEREhM+TW8QfwkomumBsKg4Zji8gwa2pqwXoeZeWlwz4FxDgOTrAAL97ZFfMXl4DmFIuMWv0VzBOAw4BTgFOBR4C7tP6yiIgMlvFl5iy3r1tFujOGLxwhMm2W5tnJkEin01gLPl8eLNwseSPaEeWNV5fx0x/8is7OOGeddwofOOQASkqLhm2fjt9P0cy5RDdvIB2L4i8qJlQ9KW+6vovI4JmBtJ03xgTJFM5XA9+z1v5suBPLFwsXLrRLlizJdRoiImOCt21oojGjqlj2UklSHe0kWpvxF5XgLywaVfmPVclkkq2b6/jNLfeSSqU57ewTmTipmmBBMNepSR5Yu3o9xx16Bp7ndcVuvvunvPd9+w37vr10GjwPXGfYOmSLyNAxxrxkrV3Y12P9nu7KFsofI1MszwCuJ9MtW0REZNBG4/I0XjpNrHYz8fpaABJNDQRKy/NqreDxqq62gRM+ehaxWGb46/33PMIDT9zO9JlTc5yZ5IOnHv9nV7FsjOFLXzuHUEGQpx7/O/MXzKG0rIRIYXhY9u24LrgqlEXGgh1OqDDG3AE8B7wL+K619j3W2suttRtHLDsREZFc89LEG+p6hBLNjdi0t4MnyEj50wNPdBXLAKlkit/dfn+PK4oyfk2bPqnr36edfSLRaIxPHX8+F5zzTY764Cn845kXSCVTOcxQREaD/joQnA7MA74MPGeMac3e2owxrSOTnoiIiEjfCoK9h14HgwGt7S0A7Ltwb/bZb08APnLkQdz2y7u6HrPWctW3fkpzU0uu0hORUWKHBbO11rHWFmVvxd1uRdba4pFMUkREJGccl4KKmh6hQFklxlXX21w7/JgPUVpW0nU/HAmx6IyPq2AWACoqy7j+liv5/Z9vpqy8tNfV5OamVrwB9PIRkfFNk69EZMyw1sOmUqSiUYzPhxsIjso5s5JfHNeloLoGf1FRV9P5jFu+AAAgAElEQVQvXzii+ct5oKq6gvsevZW/PvoMyUSKw4/5EJVVFblOS/JIeUUZ5RVlNDY0s2Cvebz52rKuxz5y5EGEQgU5zE5ERoMBdckez9QlW2T0SMc7aVn+RqYzKeALF1I4fbaKZhERoXZrPTdefyf/+8/rfOCQ/Tnt7BOpqCzb4fbWWmwqBVhw3EwjLxEZk3a5S7aIyGjhpdNEt2zsKpYBUtF20sk4GJOJG4Px+TRcU0RkHKquqeT/ffPzRKNRiooL8fdzMtVLp0lF24luXIuXTBIoqyA8YbKWkxMZh1Qwi8jYYC02lewRcvx+HNdH25rlpKMdOP4Akakz8YUjGEfzT0VExpuCUJCC0M7X6bbpFO2rl3fdTzTW4/gChKon6PNDZJzRX7yIjAmOz0eworpHLFhZQ8fGdaSjHQB4yQRta5Zj01pGREREdiwdi/WKJVubsF46B9mISC6pYBaRMcNfWExk2kx84UL8xaUESspIdbT13MjzsGkd8IiIyI45fSxZ5obCYHToLDLeaEi2iIwZjs9HsLQCf2Fx5qDGWnyhCKlo+9sbGQejxi0ishus9bBpD+M4Gp47Rjk+PwVVE+is25K5HwgSqpmkxl8i45AKZhEZc7o3ZYlMnUHb6uV4iTjGcYlMm4nRckAisou8ZJJ4Uz3J1hZ8kUIKKmvUiX8Mcnw+CqonZqb6WA/juPo5i4xTOmoUkTHNCQQpnr0H1vMwjsG4Pl0REpFdYtNpopvXk2huBDKd+FMdbRTOmKPuyWOQ47qgK8oi454KZhEZ04wxGF0VEJEhYL10V7G8TSrage22nJ2IiIwtuswiIiIiMkC9eyCY7E1ERMYiFcwiIiIiA2BcH+FJU3vECqonqJGgiMgYpiHZIiIiIgNgHAd/USkl8/ciFe3ALQjh+APqnCwiMoapYBYREREZIMfnA58PN1iQ61REdsh6HjadAuNkfmdFZJfpL0hERCRPeek0Np3KXM0MFuD4/erGLDIAsVgntZvrePC+R6moKuOjRx1CVU3lkL1+KpmiqbGZjRu2UFFRRnFpMSWlRUP2+rvDSyXprK8l0dSAEwgQnjQNN1igFSJEdpEKZhERkTxkrSXV0Ub7mhVdsWBFFaEJk3G0lriQ+R1pqGuisaGJwqIIkcJI3hRtubZu9QZOOWYxqVQagNt/eTd3/elGKqvKh+T1V69cxxknfJ6O9igAp519Iud++UxKSnL7/289j3hDHZ21mwHwkglaV75F6fy9ME4gp7mJjFY61SQjLt4Zp25rPevXbqS+thFPy3GIyAiwnjeqlv+xqRTRTet6xOINddj06PkeZHhtXL+Zkz52Np884jMc8f6T+b9rbqa5qSXXaeVcNBrjl9fd3lUsA2zdUscr/3l9SF6/uamFK7/1k65iGeA3t9xLW0vbkLz+7rDpNPHtlj7D80jHO3OTkMgYoIJZRlQs1smzTz3P0Qd/io8ddCqnHLOYNavW5zotERnDbPZgsWPTOjo2rSPdGRs1hbOXSvUO2tGRuwyvjvYoP77iBupr3y6O7vn1H2lsaM5hVvnBWvA82yvevYDeHalkig3rN/eKNze1DsnrD4S1lvraBpYvXcWmDVto2VasOwY3EOy1vdluKoeXSpJoaaJjwxoSLU14qeRIpC0yKqlglhHV1trOJRdcQSyWOdO5dUsd3/zKlfqAF5Fh46WStCx7nURjPYnGelqWv4GXTOQ6rZ1zHIJlPedcOsECjKOOzAKdnXHW9nHCecumrTnIJr9EIiEWf/F0nG5zdiuqytnvPXsPyesXlxRx1HEf6RErKi6kZmLVkLz+QGzasIVFRy/mhI+elRldcHVmdIHj+ghPnNpjqbNAeWWP3gc2nSa6eSPta1cSb6ynfe1KYls34aWH5oSCyFijSVAyomIdMRLxngeqb72+XG/SIjJs4o31mUtO21hLZ30t4UlTMcbkLrGdcFyXUM1EHH+ARGsTbkGYcM1EHL+afgmUlBRx+DGHsuLHt3TFAsEAs+fNzGFW+WPmnOn84bFbufvOP1JZVc7xJx81ZPOXA8EAZy5ehOM4PPrwk0yZNolLvvtlyitKh+T1d6ajPcpPf/ArarfWd8Xu+fUfWXTmxyktK8EJBimZtxfpZBzH9WFcX49O2Z6XJtFU3+M14w31hKongpZIE+lFBbOMqEhhmKLiQtpa27ti+39gIYGgGlGIyPDo64qscdy8Lpa3cXx+CqpqCJZXZHJWl1vJ8vl9nPSpY4lFYzx436PUTKji0u9fSGlZSa5TywvhcIjZ82byjcsvGJa/9fKKUs674NN86qwTCAQDFI9gs694Z5xVK9b2im/euJXZc2dgjMH4/YM7udbHf1FrSxuxaAyMIRIJU1gU2Y2sRUavYfvkNcbcaoypNca81i12mTFmozHmv9nbUd0eu8QYs8IYs9QYc3i3+BHZ2ApjzNe7xWcaY14wxiw3xtxjjAlk48Hs/RXZx2fsbB8yckrLSrjxt9cyY9ZUAN574H5c9oOvjugHjYiML8Gycky3rtLGcQlWDN3yMsPNGIPj86tYll7KKkr53IVnce+fb+YXd/yIvfddQFAnoHsYzhNjwWCAyuqKET+GKS4p4ohjDu0R8wf8zN1j1oCe7zgugfKe74HB8irodnKxsaGZyy+9lo++7ySOOPBkfnbNzTQ1qqGcjE/G2t5NEYbkhY05CGgH7rTW7pWNXQa0W2uv2W7bPYG7gPcCk4C/AvOyDy8DDgM2AC8Cp1hr3zDG/B6431p7tzHml8D/rLU3GGM+B+xjrT3PGLMI+Li19uQd7cNa2+9Y4IULF9olS5bs9v+H9NRQ34iX9ggEA5SUFuc6HREZw6y12FSSRGszWIu/uAzH7x/yA+nMflKkOqMYx8ENFGj4tIgMi6aGZm771V089IfHqK6p5NLvX8gee84hWNC74VdfvFSSVEcHybYW/MUl+MKRHvOc/3jvX/j2//tBj+fc9Ltr2f/97x7S70MkXxhjXrLWLuzrsWEbkm2tfbb71d2dOA6421obB1YbY1aQKWwBVlhrVwEYY+4GjjPGvAkcCpya3eYO4DLghuxrXZaN3wf8n8kcFe1oH8/v6vcou66iMjOPyPM86rY2sHzpSgqLIkyZOonyyrIcZyciY0lmeGKAgorqYd2Pl0zQuvxNbDrT2doNhSmaMVdFswxIvDNOOp0mHAnnOhUZBcoqSvn8V87mjM+ehOO6g54/7fj8BEpKCZT0fl4ymeS5Z/7dK/7v515WwSzjUi7mMH/BGHMGsAS4yFrbBEwG/tVtmw3ZGMD67eL7AxVAs7U21cf2k7c9x1qbMsa0ZLfvbx89GGMWA4sBpk2btgvfogzUlk21LDp6cde6kfvstyfX3XwlFSqaRWQUsZ5HZ+2WrmIZIB2Lkop1EPCPTCMgGZ1SyRSbNm7hxp/9mubGFs5YfBIL3jGPouLCXKcmeS5YECBYUDHkr+v3+znksPfz6MNP9YgfePB7hnxfIqPBSE+IugGYDewLbAZ+nI33NS7O7kJ8V16rd9DaG621C621C6uqRm6JgPEmEU9w889/01UsA7zy8hsse3NFDrMSERk8a22fS1WNiuWrhM5YJ+kcrdbQ0NDESUd9lofue5Rnn3qezy66kDdfW9bntk2NLaxfu5FVy9fQUNfY5zYiQ+F9H1zISacdh8/nEgwGOPfLZzJ77oxcpyWSEyN6hdla27U4oDHmJuBP2bsbgKndNp0CbMr+u694PVBqjPFlrzJ3337ba20wxviAEqBxJ/uQHEgmU2zZVNsr3ldMRCSfOa5LsLKaZFu3pjjG4C9Sx+J81tzUwgvP/Ye/PPgke+27gONPPHLIlh4aqBefe5loR6xH7PYb7+Yd++xBpPDt4dmNDU1cdvHVPP3EPwGYMXsat9z1U6pqhv4Ko0hZeSkXXnIei794BpjMOtOhUEGu0xLJiRG9wmyMmdjt7seBbR20HwIWZTtczwTmAv8m0+RrbrYjdgBYBDxkM53K/gZ8Mvv8M4EHu73Wmdl/fxJ4Krv9jvYhORIpDLPojON7xPwBPwd8oM/59iIiec0XilA4fTa+cCH+omKK5yzo0URH8ks8nuCeO//IVz93GU899neu/+GNfP7TF9PY0DyiefTV+LK0tBjX7XmItvyt1V3FMsCaleu46477SaVS2z9dZEhECsNUT6ikuqZSxbKMa8N2hdkYcxdwCFBpjNkAfAc4xBizL5mh0GuAcwGsta9nu16/AaSAz2/rXm2M+QLwGOACt1prX8/u4mLgbmPM94GXgVuy8VuAX2ebejWSKbL73Yfkzr7v3ourf/4dfn3zvRQWRbjgknPV9EtERiXH5yNQUoYvUgjGwXF7r/8s+aOtpY1f33Jvj9ibry2joz066AZKu2PB3vOYPXcGK5evASAUDnHulz9NwXYFysplq3s9d+mbK4jHE/h8uWhJI2OdtZaGukbWrFpPKFzAxMk1lFfoGE3Gn2FbVmqs0LJSI6OpsQWfzx32JieJRBKsJaB1KkVExrX6ukZOOXoxW7fU9Yg/9tzvmTi5ZkRzaahr5PVXltLc1ML+7383FZVl+Pw9i+A1q9Zz3KGn0/247epfXMbhH/vQiOYq48eWTbWcety51Ndm5svvve8CfnbLVbqwIWNSf8tKjXTTL5E+lZWXDGuxnEgkWbd6A1d+6yd852s/YtlbK4lFO4dtfyIikt/Kyku44JJze8QOO+pgQpHQiOdSUVXOQR9+H8d+8ghqJlb1KpYBqqor+NmtVzFzznRqJlRx4SXnsf+B7xrxXGV8SCaT3HHTPV3FMsCr/32T1195K4dZieSGxvDIuFBf28AJh59FPJ7pWPvow09x76O3MGfezBxnJiIiueC6Lgcd+j7+8Pht/P2pf7Fgr3nM33MOpX3MKR4O1lqM6WsBj75FCsN88EMH8I595mM9S2lZSZ+FtchQSCZTbFzXszeu67ojPsdfJB/onVbGhcf+9LeuYhkgnU7z21vu5dIrLsLn0zxDEZHxqKi4kKLiQubOnzVi+/RSKdKxKPHmBnzhQgIlpQNuDmeMoaJyZLt4y/gUDoc46fTjefqvzwFw8unHcva5p1BaHCadiGMcF0dz52Wc0G+6jAvdl+bYJlwYwXEGfnZfRERGN2stNpUCLDjuiDdms55HvKme2OYNACSaGkg0N1A4fbY6qkve2Xu/PfnRz77Ny0te47zzT8LWbSLalJlDH5o4lWB55ZD9DXnpNHhprOdhHAfj8w9qBIbIcNIcZhkXDjns/VRVv71WZWFRhE995gQcR38Cw8Xz0pkPQBGRPOB5aVLtbbSufIvmpa8R27IRL5kc0RxsOkVn7ZYesVRHOzbtjWgeIgNRUlLE4cccykWXLIamWjKL3GTEtmzAekPzGe+l0ySaG2l+61Valr5Gy/I38OLqMyP5Q1eYZVyoqq7g7kdu4rlnXyTRGeegjxxIZZWGtQ0Haz28RIJY7WZsKkVBZQ1uOIzj6u1GRHLHplK0rV7WdT/eUIvj91FQOQEzYidPDfR10UwX0iRPGWPw+VxsaruTS9aCN0Qr7aTTRDeuY1tBblMp2jesoWjGHI28kLygI1gZF4wxVFVXcNwnj8h1KmOeTaZoWf4GeJkrJsm2FgpnziVQVJLjzERkPEvHor1iiZZmguVVI1YwG9clVD2J6KZ1XTFfYTHGUS8NyWOOg7+4hGRrS1fIDRYM2d+N56XpfvUaIN0ZyxTlInlABbOIDKlktK2rWN4mXrcVXyiiBiEikjNOsKBXzC0IgRm5qTnGcQiUluOGwiRamvCFI/gjRXpvlLzmuD4ik2cQ828i2daKGwoTnjgFxz80V38d18W4LrbbNC5/YQlo2pzkCb1Di8iQMn0MvTauC2reISI55Pj8BCuqiTfUZu4HgoRqJo144y/H58PxFeKPFI7ofkV2h+P3E5o4hVC1B44zpH83xvVRNGs+HetXk+7sxF9cQmTyVE3lkryh30QRGVK+gjBuQSgznArAOIRqJo/4QamISHeOz0dowiQKqiaA9TLL4gzRFTKR8cBxXBiG6QPGcfCFwhTNnJfdkVGxLHlFv40iMqQcv5+imfNIxaLYVBJfYbGGG4pIXnBcH+jcnUhe0gksyVc6ihWRIef4/QT8avIlIiIiIqObZtOLiIiIiIiI9EEFs4iIiIjIdqy1WC1tJDLuaUi2iIjknYa6Rl757xvUb23g/R86gIrKMoLBQK7TEpFxIJlIsnVLHXfdfj8Ap3z6E1RPqCIQ0BxbkfFIBbOIiOSVhvomFp92EcvfWgVAIBjg7odvZM78mTnOTETGg9qt9XzisE/T2RkH4Pe/eZA/Pnknk6dOzHFmIpILGpItIiJ5ZeWy1V3FMkAinuBn19xMR3s0h1mJyHhx/92PdBXLAPF4gvt+91AOMxKRXNIVZhERySt9FcbtbR2k0+kcZCPdtbW1s3HdZh689y/MnDOdQw//IJVV5blOS2RI+fy9D499WvJIZNzSFWYREckre71zD0pKi3vEzjrvFIpLinKUkWzznxde4aSjPstvb/sD37/0Whaf+hUa6ptynZbIkPr4SUdRWBTpuh8pDPOJRR/LYUYikku6wiwiInmlvLKMex65idt+eRe1W+s57exPMn/B3FynNe41NTTzi5/c2iO2Ytlqtm6uo6KyLEdZiQy9yuoK7n/idv78x79isXzsuMOorK7IdVoikiMqmMcYay31tY08+9TzdMY6+fARH6Siqhy/hhKJyCjhui6Tpkzgq9/+AulUilA4lOuUBNjx4jpadkfGFp/PZcLEaj5z/qm5TkVE8oAK5jGmvraRRUefQ11tAwDXX30zf3jsVqZMm5TjzERkrEkmktTXNfL4I08TKQxx8EfeT2VVOcaYIXn9QMAPWsYlb5RXlHLeBZ/my5+9tCs2a+50aiZW5TArERGR4aWCeYz5+9/+1VUsA8SiMe686R6+9p0v4vPpxy0iQ2fL5lo+efhniMU6Aaj6ye3c/chNVGno4pi1cP99uftPN/LAPY8we+5MPnLUwVRUqumXiIiMXaqgxpjOzs5esVi0E2s1ZE5Ehk4ymeS2X97VVSwD1NU28Nwz/+a4E4/MYWYynIqKC9lz7/nsuff8XKciIiIyItQle4w59KMf7DHfz3Eczlh8suYwi8iQstb2WKd0m1isd0xERERktFLBPMZUVpXzh8duY9EZH+e4Tx7BvY/ewpSpmr8sIkMrEAjw6XMX4Thvf4xECsMcctiBOcxKREREZGgZDdXt38KFC+2SJUtyncagpVIprLW6siwiwyYW7WTj+k3cefO9RArDnPaZT1IzsRqfz811aiIiIiIDZox5yVq7sM/HVDD3b7QWzCIiIyWVTIExKpRFRERkVOqvYFbTLxER2S0+vz5KREREZGzSHGYRERERERGRPqhgFhEREREREemDxtGJiIiIjAItza1EozGwEAoXUFpWkuuU+mWtxaZSmTvG4Ph02Ckio4/euURERETyXGNDM1d95zoee/gpAA469H1875qLKa8oy3FmfbOeR6ozSse61XiJOP6iEiJTpuP4A7lOTURkUDQkW0RERCTP/felV7uKZYBnn3qeZ5/6Vw4z6p9Np2hbtQwvEQcg2dZCx6b1eOlUjjMTERkcFcwiIiIiee6lF17pFXvxuZfxPC8H2eycl0rBdrml2lt7xURE8p0KZhEREZE89+HDP9grdvgxh+I4+Xko57i9Z/25BSEw+ZmviMiO6F1LREREJM/NmjudCy85j8KiCKFwiHO/fCb77Lcg12ntmOsSmTIdjAHA8fuJTJmhxl8iMuoYa22uc8hrCxcutEuWLMl1GiIiIjLOxeMJ2lraACgqLiRYEMxxRv3z0mmslwbPwzguxufDZAtoEZF8Yox5yVq7sK/HdJpPREREZBQIBgMEqytyncaAOa4LrpvrNEREdouGZIuIiOQZL51WN2EREZE8oCvMIiIiecJLp/HincS2bgKgoHoibkEoc6VORERERpwKZhERkTxhU0laV7zZdT/Z1kLxvHfguKEcZiUiIjJ+aUi2iIhInuhsrO8VizfUogadIiIiuaGCWUREJE+4fn+vmOPzq7OwiIhIjqhgFhERyROBkjIcf6DrvvH5CZZXDui5XjpNOpkgnYjjpZLDlaKIiMi4ojnMIiIiecLxByies4BUrAOsxRcuxPh2/lHtpVLEG+syzcKsxQ2FKZoxp0fxLSIiIoM3bFeYjTG3GmNqjTGvdYuVG2OeMMYsz34ty8aNMeZ6Y8wKY8wrxph3dXvOmdntlxtjzuwWf7cx5tXsc6432fFqu7IPERGRfOH4/QSKS7NXmwc2HNumUsS2bITsXOd0LEqsdjPW84Y7XZF+1dU28KcHHufuOx9g86atJJMa/SAio8twDsm+HThiu9jXgSettXOBJ7P3AY4E5mZvi4EbIFP8At8B9gfeC3xnWwGc3WZxt+cdsSv7EBERGe1S8VjvWEc71kvnIBuRjPraBk7/+Of4xgVXcOW3fsrxh57B5o1bc52WiMigDFvBbK19FmjcLnwccEf233cAx3eL32kz/gWUGmMmAocDT1hrG621TcATwBHZx4qttc/bTOvQO7d7rcHsQ0REZFTzhcK9Yv6iEoyj9ZtzraW5lc0bt7JuzUYa6rc/LBrbXvzXf9m0YUvX/Visk1tv+B2JRCKHWYmIDM5IN/2qsdZuBsh+rc7GJwPru223IRvrL76hj/iu7ENERGRUM66PyLRZGDcz39lfXEpBZQ3GUW/PXGpqbObaK3/J4QeexNEHn8riUy+ibmtDrtMaMR3t0V6x9rYOPE/LpInI6JEvn6R9TdCyuxDflX303tCYxcaYJcaYJXV1dTt5WRERkdxyXJdAcSkl895B6YJ9iEydgdPHElUystav3cQD9zzSdX/50lX85tZ7x8083g9+6ADCkVDXfWMMZ513KgUFwRxmJSIyOCNdMG/dNgw6+7U2G98ATO223RRg007iU/qI78o+erHW3mitXWitXVhVVTWob1BERCQXjOPg+P04/gCOq0Uw8sGKpat7xV5/5S06Y/EcZDPyKirLuO/RWznptOM48tgPc9fDv2LGrKk7f6KISB4Z6YL5IWBbp+szgQe7xc/IdrI+AGjJDqd+DPioMaYs2+zro8Bj2cfajDEHZLtjn7Hdaw1mHyIiIiJDbuH+7+zV5fzwow8lUth7zvlY5PP7mDJtEhdf9kUuv+br7Ln3/HHzvYvI2DGcy0rdBTwPzDfGbDDGnA38ADjMGLMcOCx7H+DPwCpgBXAT8DkAa20jcDnwYvb2vWwM4Hzg5uxzVgJ/ycYHtQ8RERGR4VBRWca1v7yciZNriBSG+fS5i/jIkQfhjLO55X6/n0BQa4KLyOhkrFXjhf4sXLjQLlmyJNdpiIiIyCiUTqdpamwBaykqLiSo+bsiInnHGPOStXZhX49pkpOIiIjIMHFdl8qq8lynISIiu2h8jQkSERERERERGSAVzCIiIiIjSNPhRERGDw3JFhERERkBXipJsr2NZFsL/qIS/IVFOD6tly0iks9UMIuIiIgMMy+dIrplE4nGOgASTQ0EyioIT5qG47o5zk5ERHZEQ7JFREREhpvnkWis7xFKNDVgvXSOEhIRkYFQwSwiIiIyEsyAQiIikkdUMIuIiIgMN8clWFHVIxSsqAJHh2L/v707D4+zLPc4/r3fWTKTPZOke2mBFkqhCFKKAgIqIMtRFpFFZDmCgIACorIqCrJ4QFlEEI6sCgKiIEdkB0V2ClQKhdLSFrrSNvsySWbmfc4fmYaknTZpm8xMkt/nunolc8+73JSnydzvs4mI5DPNYRYREREZYF4gQHTEaEIlZSQaGwiVlBIsLMIL6KOYiEg+009pERERkSzwgiHCJWWES8pynYqIiPSRxgGJiIiIiIiIZKCCWURERERERCQDFcwiIiIiIiIiGahgFhEREREREclABbOIiIiIiIhIBiqYRURERERERDJQwSwiIiIiIiKSgQpmERERERERkQxUMIuIiIiIiIhkoIJZREREREREJAMVzCIiIiIiIiIZqGAWERERERERyUAFs4iIiIiIiEgGKphFREREREREMlDBLCIiIiIiIpJBMNcJiIiIiGSbcw6XTHS+MA8vqI9EIiKyLv12EBERkWHF+T6JliZalizCJRKESsooGjcRLxTKdWoiIpJnNCRbREREhhWXTNK8aD4u0dnDnGhqoHXFUvxUKseZiYhIvlHBLCIiIsNKKtEOzvWIJZsbwVfBLCIiPalgFhERkWHFC4XXiQWiheDpY5GIiPSk3wwiIiIyrFggQOHYLcAMAK8gQuGY8XgBLe0iIiI96TeDiIiIDCteIEi4vJJwaTnO9zEvoAW/REQkIxXMIiIiMqBaW+M0NjTx0YLFjNtiDKVlJZSUFuc0Jy8QgEAgpzmIiEj+U8EsIiIiAyaVSvH6S29x9ikXk0qvQn3+z8/isCMPJFoYzXF2IiIiG6Y5zCIiIjJg6mrq+fkFV3cVywC/vuJmmhpbcpiViIhI36hgFhERkQHjO0fNqroesY72DhLpPZBFRETymQpmERERGTCRSAF777t7j9jkbbciEo3kKCMREZG+0xxmERERGTClZSX89MofMmrMCF7612vssNNUzj7/FCqrKnKdWr9xvo+fTNBRX4sFgoRLyzLu9SyZxeNtNDc2A1BcWkxUD1NEJI+Ycy7XOeS16dOnu5kzZ+Y6DRERkUGtLd5Oc3ML0cIIRUWFuU6nX6Xa4jTMmwPpz1ReKEzppO20VVUf1NU2cNet93HP7Q9iZpxwylEc+99HUB4ry3VqIjKMmNkbzrnpmd7TkGwREREZcJFoAVXVsSFXLPupFPFPlnUVywB+ooNEa3MOsxo8Zs+aw+0330t7ewdtbe3ccsPdzH1vfq7TEhHpooJZREREZDNkHK3n+9lPZBB65rHn18rhG/0AACAASURBVIk99+QLOchERCQzFcwiIiIim8gLBIiOGN0jZoEAoeKSHGU0uOy25y7rxGbs/tkcZCIikpkKZhEREZHN4BVEKJ08lXBFJQVVIymdPBULav5yX3xuz+ns/19fBMDM+K/D92en6dNynJWIyKe06FcvtOiXiIiI9IXzfTDDzHKdyqDS2NBEa0scgMKiKKVl6p0Xkeza0KJf2lZKREREpB+Yp4F7m6K0rERFsojkLf1kFxEREREREclAPcwiIiIikjXOOVwyiZ/swLwAFgjgac63iOSpnPQwm9kiM5ttZrPMbGY6FjOzp8xsXvprRTpuZnaDmc03s7fN7LPdrnNC+vh5ZnZCt/gu6evPT59rG7qHiIiIiGSHn+igYd4cGue9R8Pcd2hZ+jF+MpHrtEREMsrlkOwvOud26ja5+nzgGefcZOCZ9GuAA4HJ6T+nADdDZ/ELXALsBswALulWAN+cPnbNeQf0cg8RERERGWB+KkV8xVJctwI50VCHn1DBLCL5KZ/mMB8C3JX+/i7g0G7xu12nV4ByMxsNfAV4yjlX65yrA54CDki/V+qce9l1LgF+91rXynQPERERERlozifV3r5OONWxbkxEJB/kqmB2wJNm9oaZnZKOjXTOLQdIfx2Rjo8FFnc7d0k6tqH4kgzxDd2jBzM7xcxmmtnMVatWbeJ/ooiIiIh0Z4EgBRWxtYJGMFqUm4RERHqRq0W/9nDOLTOzEcBTZvb+Bo7NtJmh24R4nznnbgVuhc59mDfmXBERERHJzMwIl8dwqRTttauxYIjCsePxglqHVkTyU056mJ1zy9JfVwIP0TkH+ZP0cGrSX1emD18CjO92+jhgWS/xcRnibOAeIiIiIpIFXjBEpHoUpZO2o2TLSYQKi7WHtUgWpVIpmhqbSSaTuU5lUMj6TyczKzKzkjXfA/sD7wCPAGtWuj4B+Fv6+0eA49OrZX8OaEgPp34C2N/MKtKLfe0PPJF+r8nMPpdeHfv4ta6V6R4iIiIikiXmeXihUM63k3K+j5/owE900Ln0jcjQVltTz5/u/CvnnHoxt95wNzWranOdUt7LxfiXkcBD6Z2egsC9zrnHzex14AEzOwn4GPhG+vh/AAcB84FW4L8BnHO1ZnYZ8Hr6uEudc2v+j38XuBOIAo+l/wBctZ57iIiIiMgw4ieTtNfV0LZqBWZGdNRYQiVlGh4uQ1ZLcyvXXXULDz/wDwBee+ktXn9lFr/+3WVUxMpynF3+Mj1N27Dp06e7mTNn5joNEREREelHiaYGmhbO6xErnbQdwUItQCZD06pPVvOV3Y8kmUz1iD/+4v2MGTcqR1nlBzN7o9t2xz1owoiIiIiIDCvO+bTX1awT72ioy0E2IlliRrQw2iMUCAQIBAM5SmhwUMEsIiIiIsOKmZexJzmwGb3Lzk/hJzpItDSR6mjHT2lBpb5obGhi9coa4q3xXo/taO/A9/0sZDU0lZWX8oMLT+sRO/47R1JUXLhZ1/WTSVId7aTa2/ATic26Vj7SJA0RERERGXbCZTHa62pIxVsBCBaXECoq3uTrJeOtNC34ANLTHaOjxlJQWY0X0MftTJxzLF28nKsuuYH3353Hnl/cjTN/eDJV1bF1jq2va+Ct12fz94eeYtpOU/ivw7+S8TjZsHA4xH4H7cNO06fx1szZTN1hG8aMG0Vx8aY/KPKTCVqWfkwiPTrDK4hQutU2eKFwf6Wdc5rD3AvNYRYREREZmvxkApfqnM9pgcAmr9rtJxI0LpiL397WI14+ZUe88NApHPrT6lW1HHfYd1m6eEVXbN8D9+bSq8+juOTTAq6jI8Efb/sz1111S1ds6rRtuenOXxKrqshqzrKuREsTTR/O7RGLVI0iOmrMoNouTnOYRURERETW4gVDBAoiBAoim7nFlcPvaF836jR8eH3iLfEexTLAc0++QFu850OHhvpG7v7f+3vE5syeS3Nz64DnKL1Lta07lD7Z1jqk2r7GiIiIiIiIbA4vQLisgo76T/e09UJhzOt9MSXnHDWralm6ZAXFxYXEqiqoiJUPZLZ5IRwJEwwFSSY+nes9Ztwo0lvPdjEzQuF1H2YEAur3yweh4tJ1YuHyWJ/a/mChliYiIiIishm8QIDC0eM75yyHwoRKyijZahusD3s6L1+2kiMPPpnjDjudw/Y7kYvOuYLamqG/WndxSRHnXnR6V4EcLghz6TXnrTPMuiJWxlnnndIjts9+e1BUtHkLVUn/sGCI4omT8MIFWCBIZMRowqVl6zz4GMw0h7kXmsMsIiIiIn3h+ylIpcDz+rTYV3tbO1decj1/ve/RHvG7HryRnXedNlBp5o3mphaaGptZtbKGUWNGUFZeSkHBunO+G+obWLF0Jc899SJTp23LDp+ZovnLecQ5h0t2jhSwQGBQzV1eY0NzmDUkW0RERESkH3heADZiKGp7eweLP1q2TnzpkuXDomAuLimiuKSI0WNHrvcYP5nAVi9nhLVx7CF74Cc6iAR8nHNDqhdzMDMzLLQ5awDkt8FX/ouIiIiIDAElpcUcdtRBPWLBUJDPztgxRxnlH5dKkWxtxiWTJJoaSLXFiX+yDJdMkEqlWPXJama98Q4LP/yY+rqGXKcrQ5B6mEVEREREcsDM2HOf3bjg0rP4010PUV5Ryo8uPoNYpYYbd8k0fdQ5wFjy8XKOPeQ0GhuaADj40H358SXfpyJWlt0c03zfp6GukVA41GNrLBncNIe5F5rDLCIiIiIDKZVKUV/bSCDoUV6Rm2IvX/nJBE0L55GKf7qNVOHYCSTCUS465wr++dSLPY7/65N3MmnbLbOdJvV1DTz1j3/x4L2PUD2iinMuPI0JE8cRDKl/cjDQHGYRERERkTwVCASorFavciZeMETJxMl0NNSSjLdSUFFFIBKltb6JpYuXr3P8yk9WZb1g9n2fpx/7F5dd+CsA3mMeM1+dxSPP/pERo6qymksmzjlqa+rxfZ9wOExZeUmuUxpUNIdZRERERETylhcKEakaSdG4iYSKS/CCQUrLS/na1w/ocVw0GmHStltlPb+G+kYevPfvPWKtLXHmvjc/67msLZFI8O7b73PiEWfy5V0P58dn/oxPVqzKdVqDigpmERERERHJe91XxQ4GA3ztiK/w/R9/hy0mjmX653bi7r/+Nifzl0PhUMae5KrqWNZzWVt9bQOnHHsuHy1cAsDL/57JLy76NU2NzTnObPDQkGwRERERERl0KmLlnHjK0Rx65EEEQ0HKy0uzev/6ukba29oJBoOcc/6pvP7yW7Q0d861/sIXd2Pk6BF9uk483kZLUwtmRkVlOV4/7mPc3NRCc1NLj9irL7xBW1s7JaXF/XafoUwFs4iIiIiIDErBUDAnPbmrV9ZwwdmX8+qLb1BSWsyvbvoZDz99F/M/WEhlVYyRo6qpqCzv9Tq1NfXcfO0dPPLg48SqyrnwsnPYZcZnKCyK9kueRcVFhMIhEh2Jrtg2UycR0mJkfaYh2SIiIiIiQ1gikaBmdR0tLa29Hyy9isfbuPm6O3n1xTcAaGps5tTjfkSivZ3tRxYyJuJTVtr7tlLJZIqHHniU+//wMPF4G0sXr+B7376gX/eTLikt5rJrzicSKQBg5OhqLrvmfK3GvhH0aEFEREREZIiqranjntv/wrNP/JvJ223F2eedyphxo3Kd1qDW2tzK6y+/1SPmnOPjhUuYOqqIZEsTqfY4Xii0wes0NTbzzGPP94j5vs+7b7/fb/+PooURvrT/F5i+2060tbVTWBghVqUV2TeGCmYREZE81NGRoKW5leLiQkLhDX/oEpHBq2Z1Lb7vKCqKUlhU2K/Xbm2Nc+PVt/Hgn/4PgA/nLWL2W+/xx4duojIPFqQarAqLC9l512ksWrC4R3z8xLH4zZ0rUPuJRKZTe4hGI0zZfjLv/Of9HvGJW23Rf8kCkWgBkWhBv15zONGQbBERkTyzelUtv7n6fznjxB9z83V3UrO6LtcpiUg/a29vZ9Yb7/Dto87moD2P5vKfXEfN6tp+vUdrcyuPPvxUj9jSxctpbm5ZzxnSF9FohDPOPYkdd57a+bowyk+v+AERvwOcA/MIFvW+13EkWsCpZ53AxK07C2Qz47iTv0F1HuzdLJ9SD7OIiAw6fioJvg8YFghg/biiaK7V1zbwozN+xhuv/geAd/7zPvPmLuDyX19IaVnvH8BEZHBoqGviO8ecQ3t7BwD/95cnKCkp5pwLTqUg0j+9geYZI0ZVdW0pBOB5Xr9dfzgbMbKK39x+JW3xdgIBj6KCIKmaT7DScgpHjcUL9q3MGjmqmjseuJ7WljihUIjCoqh+1ueZofMJQ0REhgU/kaBl8SLq33ubhg/epaOxHj+VynVa/Sbe1tZVLK/x/DMvE4+35SgjERkISxcv7yqW1/jn0y/26/64scoKfnLlDwl2WxH529/9JsXFvS9IJb2riJUzeuxIRoyqpqiiguKJkygavyWBSHSjHuRWVsUYP2Eso8aMULGch9TDLCIig4bzfdpWfUKisb7zdSpJy8cLKJsyDQKBHGfXPwJegIKCcI8P0kXFhXhmOcxKRPrbiAzDbidts2W/9v6aGTvuNJXH/n0f8z9YyNjxo6mIlVNcooJ5IPS1V1kGF/Uwi4jIoOH8FInmdbfbSMWHzlYpJWXFnPnDk3vEzr34dMoqSnOUkYgMhNKyEr7/4+/gpXsiR46u5ryffY+S0uJ+vU8kWsDI0dXssfcMJm41nrLywd2DmUgkaKhvJJkcOiOLJL+Zcy7XOeS16dOnu5kzZ+Y6DRERAXw/ReuyxXTUru4RL9t2BwIFkRxl1f8aG5qoWV3HB+99yHY7TKYiVt7vH6JFpJPzfVwqiZ9MYoEgFgjgZWnESnNTCy3NrbS1tVFUVEhldQzTaJL1Wr2ylj/c9gBvzZzNXl/encOPOohYpbZIks1nZm8456Znek/jBkREZNDwvACFI8eQird29iqbER05BgsMrV9npWUllJaVsOXW/bu1SL5yzqlIkJxJtbfRtGAuLr0WQnTMeAoqqrJSNBeXFOV0eHRtTT3z5y7gvXfnscdeMxg5ujpvH87V1dZzzmkX85833gVg1sx3+PCDhVx02TkaYi4DSj3MvVAPs4jIpxobmkkmk5RXlHYNI8wFP5nA+T6YYV72eoMGo5bmVhobm1i2eAVjx4+mtLyEwsJoj2Ma6hppbm6hvb2DsrKSrO3P6qdS+IkO2mtWYsEQkVgVFgypeJas8ZMJmhbOW2daR/mUHfHC4Rxl1X/q6xqY/8FC/v3sK+z6+Z2ZuuO2xGLlADTUN3LlJdfzj4ef7jr+yusv5oCvfolAHv5MXbZkBQfscVSPWCAQ4ImXH2DESG3DJJtHPcwiIrJZ2ts7WPThx1zzi5tobGjkmBMO54v77ZmzebVeMJST+w6keGuc5qZWwFFcWkw0uvlDzNvbO3juqRe4+AdX4vs+nudx9Y2XsM9+exAKd/4d1tXWc/WlN/L3hzr3ah23xRjueOAGRo6u3uz79ybVHqdp/vuf5luzirLJU7HQ0Pv/K3nKOVLt665A7/zBPz82Hm/jT3c9xM3X3gHAHb/7E4cfdTDnXnw6JaXFtLbEexTLANde8Tt222MXqrL00GxjBIIBgsFAj7nLxSVFesAmA06LfomISK/qaur57a9v56wffZtrbvgJlbEy3p41J9dpbTY/lSTV3kZ7XQ3JeCt+MpmTPOpqG7jp2js58AtHc/Be3+T2m++lvnbdxc02VkN9I5dffC2+7wPg+z4/v+AaalbXdR2zdPGKrmIZYMnHy7jt5nvoWGu7m/7mp1K0fbK8R8wlEyRbe99Sp7mphbraeobjKLlVK2u47+6HufGa2/howWLirfFcpzS4eR7h0vIeoTXzmNfHTyZJdbSTaG7C7+jAz9PiurmxmTtuvrdH7OE/P0ZLS2dveirDdnytrXHI4j8r3/epramjob6p12OLi4s46fRv9Yj96CdnUF6uBRFlYKmHWUREerVsyXJ++rMzCDSuxrWsYsctykmVjaCpsTlv57v1xvk+icYGWhYv7IpFRowmUj0SL8tzomfNnM1dt97X9fqW6+9ixud3ZtfP77xZ100lU7Q09xxq2tjQRFNTMxVt5RREwiz88KN1zpv33ofE29oIFwzwkNRMHUMb6C1KJBJ8vGgp117xO2pW13HMCYex95d3HzYriK9eWcNxh53OsiUrALjtpnu4/YHrKS8vo7K6os/7t/rJJH5HO4mWRoKFxQQKIkNy1EZfeIEghaPHAZBoaiBQEKFw3ERsPX8ffipFe10N8eWLOwNmFE+cRKi4NC97OlPph2Vr+L7fVRAXFkWZOm1b5sye2/X+MSccTmlZdn6mN9Q18vTjz3PvnX+hrLyUcy86nUnbTFzvtlpFxYV866Qj2P/gfZg7Zz7Tdt6OWFVF12iZjdUWbyMYChLcxK2galbV8uyTL/DhvEUccsQBjNtizKD9fSgbpoJZRETWq7amHuf7TNlua9o++qBz3jCQaosTDNQSKZ+Y2wQ3g0slaV22uEesbeUKCmLVkMXpe77v88zjz68T/+fTL212wRyJFjBtp+2YPeu9rtguM3bk7TffpbyijOpIJTvvuiOe53X1QgMcdOh+lJQM7Ac/LxAgOnIMicZPe9K9UJhgdP2L99TV1PPNr55KPN45hPbic6/kiusu4uBD98vLYqW/zXnng65iGTp7CG+/6V622W4rRo8dxSFHHNBr8eB8n/a61cSXL+mKFVSOIDpqTNYfFOULLxSmcOwE8FNgtsGHB85PEV+xpFvA0bpkEaWTcj+VIJFIEG9to6i4kEAgQFFJEUcfdyh/uO3PXcfsd9DeRNNrGMQqK7jxjqv46/2PMvvNORz4tS/z+b12HfgHZWkvvzCTn59/ddfr479+Bn//5z2MHjtyveeUlZdSVl7K5ClbbfJ9G+obefutOTx47/8xYctxHPvtIxg5auOmoNSsruOUY89l3twFANx7x1+44fdXsM9+e2xyXpK/NCRbRGQQ85NJ/FT/DyN2zrFowWK+e/wP+eL0wzAcrNVTkWxpJuAN7iLFrTMk0ZHV8YiA53l87gvrrjOy626fIZXo2KxhxxWxcq6+6ecc8NUvMWbcKA46dF/OueA07rz1/q7O3VisnJvv/h+2nDSBWGU5p37/ePY9cK+sLOrmFUQo23YHItWjKBwzntJJU/A2UHTMnvVeV7G8xgN//FufhnMOBakM+86m/BSeF+D6X95KQ31jr9dwqSTxT5b1iLXXrMSl/PWcMTx4gQBeKNx7T7vvYK1/k34iQbZ/bqxt1coafnP17znr5Au5546/UFdTT2FhlJPP/Ba//t2lHHzYflz2qwu46Bfn9NiHuao6xknf/Sa/vPGnHHTovlTEyrKSb1NjMw/e+0iPWKIjwcxXZw3ofX3f5/lnX+aME8/juSdf4M5b7uO4w05n9arajbrOyk9WdxXLa9x07R3U1tT3Z7qSJ4bno0QRkUHOTyZJtjTTtnoF5gWIjh5HIBzGvP7pGq2tqePM/z6PjxctBWD1qjqKPK9H0RwsLAIbxM9dPY9weQUd9Z9+UApEi7AcrP69+14z2P/gfXjy0X9iZnz16/szdcpEGue9t9mLYBUVFbL73ruy5z67Mf+DhZx+4nkc/52juobvFhZF+dye07n9/utxvk9JWQkFWeph8rwAFAS6hsT2pmpE5TqxkaOqCW/ikMzBZoedtqNqRIzVKzvbrJlx1LcO5bqrbqEt3tb3ks3PdOTwmw++Kczz8AoK8Nvbu2Kh0nLI4a4BnT+vz+e9dz4A4I3X3uajBYs596LTqYiVs++Be/PF/fdc78rXgUBgnZXzB1o4HGLsFmPgpbd6xEePGUEy3pp+eNH/ZUp9XQN33nJfj9iKZStZunj5xi10luFBZufDTf07GooG8ScdEZHhKxVvofmj+SRbmkk0NdA4bw5+ht6nTdXe1tFVLAPcdN1d+GUjuvY79goiFI2fmPEDjXMOP9FBR2M9iabGdO9L/vECQQrHjCc6aizBwiIiVSMpmbh1TuZyxirL+cnlP+Dx5+/hsX/9kbPOOJpgw0pcMkGqo733C2xAWXkJe31pdwoiBbS2xPnV7y7lyG99rcewSzOjsqqCqhGVWSuWN8X4CWPZbY9dul6XlBbzvR+dTGFRdj/s50pVdYw//e0WTvv+CRx+9MHc8sdrePXFN/hw3iKOPO5QSvqyF60XIBzr+eAhVFzabw/bhjovFKJky20IlVbghcIUxKopGjshp8PZW1viXcXyGt0X9wLybpuogkgBp37veGKVny64NmP3z7LFmCoa582hbeWKARk95Xle15D07iLrmTe9PiNGVbP15Ik9YqedfSIVsfLMJ8igpn2Ye6F9mEUkX/ipFPgpHNC65CMSTT1XUS4cN4FIrH+2Alq1soZvHHgStd1WUz70yAO5+NKzCAQ8zLz1Dp1NdXTQOH8OLr3itFdQQOlWGx5qm0vO+biUj3leTnqX1/CTSZoXzV9nlejSyVMJRgtzlFX+qa2pZ8WyT6irrWebKVsTq6rIu2JgIK1472MWv/4+E770GZ587Hlee+VN9vnS59l7/z37vH+2n0zQ0VBPoqmeUFEJ4fLKvP33ma/8VLKzpz7gdY6UyKEVy1dywO5H9ViHoLI6xp8fuy0vt4daw/d9alfX8dHCJRRFC4iVFRJqrklPlTHKpkwjMAB7Yb/52tucdPTZXauEf2aX7bn+fy8nVlmxUddZvaqWpx/7F/PnLuCwow5mi4njtOjXIKZ9mEVE8kRLcyutLa2YGaXlpX0eSuonErQuX0xHYz2R6lEZh+j2Z89oRaycX930c75/8oU0NTZTVl7K14/5KoFweIPFiXOucz5kt+2Z/PZ2Ek0NFMSq+i2//mTmYcHcD7jygkGiY8bR9OHcruF+waISFTJriVWW9+iVGm6Kq0pZ+NIcFr3yPhOmTWTb/fenqKSI0o34oO4FQxTEqgiXxzofFA2DBdP6mxcIZnVxwA0pKirk2G9/nT/8/tPFvc776ZmUV2RnPvKm8jyPqhGVxCpKaJg7B9fQ3G1A88ANb95uh8n87dk/8PwzL7HFxHFs/5kpG10sQ+eIj6OPP2wAMpR8ox7mXqiHWUT6S21NPdde+TsefehJooVRfnDRd9nvwL173QrGT6VoXfYxHXU1AFggQMmWk2laOK9r0apAJErJltv0a3GV6EhQX99IvLWNaGGEiooygqENP2d1vk/LkkU95gUDREeNJTpidL/lNlQ538dPJkg2N+GFCzq3+1HBLN10tLax4IV3eOdvL+GnfEpGVbD39w+nMNa3LaVkaKqva2DFspXMfe9DdtpleyqrYhT3ZYh+HvDTOxas+R0HnQ8LiydsNWy3O5Ps21APswrmXqhgFpH+kEqluPeOv3D1Zb/tEX/4mbvZatKEDZ7rJzpo+ODdHis6BwuLKdpiS1JtccwL5FVhlWxtpnH++90iRtm22xMoiOQsJ5GhJNHWQSLegZ9MESwIESnVkH0Z3Pxkgo76WjoaGwgVFVMQq86b32kyPGhItohIjrW2xPnn0y+tE3/r9dm9FsyYESiIkGxt6Qp1znM1wqX5NzTVK4hQstU2xFcux8wjOnJMzvcnFRlKQpEwoUj+Ls4msrG8YIiCyhGEKyrT0wRyP01GZA21RhGRLIhGI+wyY8d14lOnbdPruV4wROG4iV0rVANER4/HAvn5I9wLBAkVl1I8YWuKttiSYGFRzhfFERGR/GZmeIGgimXJO+phFhHJgmAoyFHHHcprL7/Fm6+9TSAQ4IRTjmL02JF9Oj9QEKFsm6n4qRTmBbBAAC/PVwbO5TYrIiIiIv1Bn2ZERLKksjrGtbdcRry1jUAgQHFJEUXFfZt7aGZYKIynkc0iIiIiWaOCWUQkiypi5VTk77aYIiIiItKNJgmIiIiIiIiIZKCCWURERERERCQDFcwiIiIiIiIiGQzLgtnMDjCzuWY238zOz3U+IiIiIiIikn+GXcFsZgHgt8CBwFTgGDObmtusREREREREJN8Mu4IZmAHMd84tcM51APcBh+Q4JxEREREREckzw7FgHgss7vZ6STrWxcxOMbOZZjZz1apVWU1ORERERERE8sNwLJgtQ8z1eOHcrc656c656dXV1VlKS0RERERERPLJcCyYlwDju70eByzLUS4iIiIiIiKSp4Zjwfw6MNnMtjSzMHA08EiOcxIREREREZE8E8x1AtnmnEua2ZnAE0AAuN05926O0xIREREREZE8M+wKZgDn3D+Af+Q6DxEREREREclfw3FItoiIiIiIiEivVDCLiIiIiIiIZKCCWURERERERCQDFcwiIiIiIiIiGahgFhEREREREclABbOIiIiIiIhIBuacy3UOec3MVgEf5ToPkW6qgNW5TkKkF2qnMhioncpgobYqg8FgbqcTnHPVmd5QwSwyyJjZTOfc9FznIbIhaqcyGKidymChtiqDwVBtpxqSLSIiIiIiIpKBCmYRERERERGRDFQwiww+t+Y6AZE+UDuVwUDtVAYLtVUZDIZkO9UcZhEREREREZEM1MMsIiIiIiIikoEKZpE8Z2YxM3vKzOalv1ZkOGYnM3vZzN41s7fN7Khc5CrDV1/aafq4x82s3sz+nu0cZfgyswPMbK6ZzTez8zO8X2Bm96fff9XMJmY/Sxnu+tBO9zKzN80saWZH5CJHkT600x+Y2Zz059FnzGxCLvLsTyqYRfLf+cAzzrnJwDPp12trBY53zm0PHABcZ2blWcxRpC/tFOBq4LisZSXDnpkFgN8CBwJTgWPMbOpah50E1DnnJgHXAr/MbpYy3PWxnX4MnAjcm93sRDr1sZ2+BUx3zu0IPAj8T3az7H8qmEXy3yHAXenv7wIOXfsA59wHzrl56e+XASuBjJuviwyQXtspgHPuGaApW0mJADOA+c65Bc65STcmfgAABYZJREFUDuA+Ottrd93b74PAl83MspijSK/t1Dm3yDn3NuDnIkER+tZOn3POtaZfvgKMy3KO/U4Fs0j+G+mcWw6Q/jpiQweb2QwgDHyYhdxE1tiodiqSRWOBxd1eL0nHMh7jnEsCDUBlVrIT6dSXdiqSaxvbTk8CHhvQjLIgmOsERATM7GlgVIa3LtrI64wG/gCc4JzTE2jpV/3VTkWyLFNP8dpbhPTlGJGBpDYog0Gf26mZfQuYDuw9oBllgQpmkTzgnNt3fe+Z2SdmNto5tzxdEK9cz3GlwKPAxc65VwYoVRnG+qOdiuTAEmB8t9fjgGXrOWaJmQWBMqA2O+mJAH1rpyK51qd2amb70vkwfW/nXHuWchswGpItkv8eAU5If38C8Le1DzCzMPAQcLdz7s9ZzE1kjV7bqUiOvA5MNrMt0z8rj6azvXbXvf0eATzrnFPvnmRTX9qpSK712k7NbGfgFuBrzrkh8fDc9PtAJL+ZWSXwALAFnStkfsM5V2tm04HTnHMnp4e93AG82+3UE51zs7KfsQxHfWmn6eP+DUwBioEa4CTn3BM5SluGCTM7CLgOCAC3O+cuN7NLgZnOuUfMLELndJad6exZPto5tyB3Gctw1Id2uiudD8crgDZgRXp3DJGs6UM7fRqYBixPn/Kxc+5rOUq3X6hgFhEREREREclAQ7JFREREREREMlDBLCIiIiIiIpKBCmYRERERERGRDFQwi4iIiIiIiGSggllEREREREQkAxXMIiIiOWZmzsz+0O110MxWmdnfc5TPTumtQwbi2pVm9pyZNZvZjd3ihWb2qJm9b2bvmtlVA3F/ERGRjaGCWUREJPdagB3MLJp+vR+wNIf57AQMSMFM5/6xPwF+mOG9a5xzU+jcD3kPMztwgHIQERHpExXMIiIi+eEx4OD098cAf1rzhpnFzOxhM3vbzF4xsx3T8Z+Z2V1m9qSZLTKzw83sf8xstpk9bmah9HG7mNm/zOwNM3vCzEan4/80s1+a2Wtm9oGZfcHMwsClwFFmNsvMjkrf54fd8nnHzCam/7xvZr9Px+4xs33N7EUzm2dmM9b+j3TOtTjnXqCzcO4eb3XOPZf+vgN4ExjXf3+9IiIiG08Fs4iISH64DzjazCLAjsCr3d77OfCWc25H4ELg7m7vbU1noX0I8EfgOefcNCAOHJwumn8DHOGc2wW4Hbi82/lB59wM4GzgknSx+lPgfufcTs65+3vJexJwfTrnKcA3gT3p7EG+cCP/DgAws3Lgq8Azm3K+iIhIfwnmOgEREREB59zbZjaRzt7lf6z19p7A19PHPZueB1yWfu8x51zCzGYDAeDxdHw2MBHYFtgBeMrMSB+zvNu1/5r++kb6+I210Dk3G8DM3gWecc65dD4bfT0zC9LZu36Dc27BJuQjIiLSb1Qwi4iI5I9HgGuAfYDKbnHLcKxLf20HcM75ZpZwzq2J+3T+njfgXefc59dzz/b01xTr/1yQpOeotEiG89fcs73b95vyOeNWYJ5z7rpNOFdERKRfaUi2iIhI/rgduHRNj203zwPHApjZPsBq51xjH685F6g2s8+nzw+Z2fa9nNMElHR7vQj4bPr8zwJb9vHeG8XMfgGU0Tk8XEREJOdUMIuIiOQJ59wS59z1Gd76GTDdzN4GrgJO2IhrdgBHAL80s/8As4DdezntOWDqmkW/gL8AMTObBXwX+KCv98/EzBYBvwZONLMlZjbVzMYBFwFTgTfT9z55c+4jIiKyuezTkVsiIiIiIiIisoZ6mEVEREREREQyUMEsIiIiIiIikoEKZhEREREREZEMVDCLiIiIiIiIZKCCWURERERERCQDFcwiIiIiIiIiGahgFhEREREREclABbOIiIiIiIhIBv8PZgy2rcC7SD4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.scatterplot('Momentum 12', 'Monthly Volume',\n",
    "                hue='target',  data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame()\n",
    "X[\"close\"] = close_df.iloc[:,0]\n",
    "X[\"open\"] = open_df.iloc[:,0]\n",
    "X[\"high\"] = high_df.iloc[:,0]\n",
    "X[\"volume\"] = volume_df.iloc[:,0]\n",
    "X[\"low\"] = low_df.iloc[:,0]\n",
    "\n",
    "y = np.sign(X[\"close\"].pct_change().shift(-21).dropna())\n",
    "\n",
    "X = X.iloc[:-21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-14</th>\n",
       "      <td>69.65</td>\n",
       "      <td>69.64</td>\n",
       "      <td>69.81</td>\n",
       "      <td>87378.0</td>\n",
       "      <td>69.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-15</th>\n",
       "      <td>69.49</td>\n",
       "      <td>69.63</td>\n",
       "      <td>69.66</td>\n",
       "      <td>97195.0</td>\n",
       "      <td>69.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-16</th>\n",
       "      <td>69.13</td>\n",
       "      <td>69.50</td>\n",
       "      <td>69.66</td>\n",
       "      <td>100453.0</td>\n",
       "      <td>69.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-17</th>\n",
       "      <td>68.88</td>\n",
       "      <td>69.12</td>\n",
       "      <td>69.18</td>\n",
       "      <td>90210.0</td>\n",
       "      <td>68.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21</th>\n",
       "      <td>69.03</td>\n",
       "      <td>69.29</td>\n",
       "      <td>69.50</td>\n",
       "      <td>119111.0</td>\n",
       "      <td>68.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close   open   high    volume    low\n",
       "date                                            \n",
       "2019-05-14  69.65  69.64  69.81   87378.0  69.57\n",
       "2019-05-15  69.49  69.63  69.66   97195.0  69.36\n",
       "2019-05-16  69.13  69.50  69.66  100453.0  69.07\n",
       "2019-05-17  68.88  69.12  69.18   90210.0  68.85\n",
       "2019-05-21  69.03  69.29  69.50  119111.0  68.87"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2019-05-14   -1.0\n",
       "2019-05-15    1.0\n",
       "2019-05-16    1.0\n",
       "2019-05-17    1.0\n",
       "2019-05-21    1.0\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trainning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtesting(model, name=\"MODEL\"):\n",
    "    momentum = []\n",
    "    prediction = []\n",
    "    real = []\n",
    "\n",
    "    for stock in range(54):\n",
    "\n",
    "        printProgressBar(stock, 53)\n",
    "\n",
    "        features, target = train_test(stock)\n",
    "\n",
    "        pred = []\n",
    "        true = []\n",
    "        mom = []\n",
    "\n",
    "        for date in range(100,190):\n",
    "            \n",
    "            # **TRAIN DATA**\n",
    "            # Features\n",
    "            X = features.iloc[date-36:date]\n",
    "            \n",
    "            # Target\n",
    "            y = np.sign(target.iloc[date-36:date])\n",
    "\n",
    "            # **TEST DATA**\n",
    "            # Features\n",
    "            X_test = features.iloc[date:date+1]\n",
    "            \n",
    "            # Target\n",
    "            y_test = np.sign(target.iloc[date:date+1])\n",
    "\n",
    "            # Fitting the model\n",
    "            model.fit(X, y)\n",
    "\n",
    "            y_pred = model.predict(X_test)\n",
    "\n",
    "            pred.append(y_pred[0])\n",
    "            true.append(y_test[0])\n",
    "            mom.append(np.sign(features[\"Lagged Momentum 12\"].iloc[date-1]))\n",
    "            \n",
    "        #print(accuracy_score(mom, true))\n",
    "\n",
    "        momentum.append(mom)\n",
    "        prediction.append(pred)\n",
    "        real.append(true)\n",
    "\n",
    "    mom_array = np.array(momentum)\n",
    "    pred_array = np.array(prediction)\n",
    "    real_array = np.array(real)\n",
    "    \n",
    "    \n",
    "    # Reshaping\n",
    "    true = real_array.reshape([54*90, 1])\n",
    "    pred = pred_array.reshape([54*90, 1])\n",
    "    mom = mom_array.reshape([54*90, 1])\n",
    "\n",
    "    model_ac = accuracy_score(true, pred)\n",
    "    model_f1 = f1_score(true, pred)\n",
    "\n",
    "    mom_ac = accuracy_score(true, mom)\n",
    "    mom_f1 = f1_score(true, mom)\n",
    "\n",
    "    print(\"MOMENTUM\")\n",
    "    print( \"Accuracy: \", mom_ac)\n",
    "    print(\"F1 Score: \", mom_f1)\n",
    "    print()\n",
    "    print(name)\n",
    "    print(\"Accuracy: \", model_ac)\n",
    "    print(\"F1 Score: \", model_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " |----------------------------------------------------------------------------------------------------| 0.0% \r"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'Lagged Momentum 12'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Lagged Momentum 12'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-e692ceef6c45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbacktesting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-19-bb03e10861ce>\u001b[0m in \u001b[0;36mbacktesting\u001b[0;34m(model, name)\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0mtrue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0mmom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Lagged Momentum 12\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;31m#print(accuracy_score(mom, true))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Lagged Momentum 12'"
     ]
    }
   ],
   "source": [
    "backtesting(SVC())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " |████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0% \n",
      "MOMENTUM\n",
      "Accuracy:  0.5333333333333333\n",
      "F1 Score:  0.6315789473684211\n",
      "\n",
      "MODEL\n",
      "Accuracy:  0.5666666666666667\n",
      "F1 Score:  0.7194244604316546\n"
     ]
    }
   ],
   "source": [
    "backtesting(RidgeClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " |████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0% \n",
      "MOMENTUM\n",
      "Accuracy:  0.5333333333333333\n",
      "F1 Score:  0.6315789473684211\n",
      "\n",
      "MODEL\n",
      "Accuracy:  0.5347736625514403\n",
      "F1 Score:  0.6349103826901341\n"
     ]
    }
   ],
   "source": [
    "backtesting(SGDClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " |████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0% \n",
      "MOMENTUM\n",
      "Accuracy:  0.5333333333333333\n",
      "F1 Score:  0.6315789473684211\n",
      "\n",
      "MODEL\n",
      "Accuracy:  0.6111111111111112\n",
      "F1 Score:  0.7586206896551725\n"
     ]
    }
   ],
   "source": [
    "backtesting(MLPClassifier(solver='lbfgs', \n",
    "                          alpha=1e-3, \n",
    "                          activation='relu',\n",
    "                          hidden_layer_sizes=(5, 3), \n",
    "                          random_state=1),\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "0.7586206896551725"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " |----------------------------------------------------------------------------------------------------| 0.0% \r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mEmpty\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    796\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 797\u001b[0;31m                 \u001b[0mtasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ready_batches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEmpty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mEmpty\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-56695774b9e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbacktesting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-17-bb03e10861ce>\u001b[0m in \u001b[0;36mbacktesting\u001b[0;34m(model, name)\u001b[0m\n\u001b[1;32m     33\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m             \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    610\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m         \"\"\"\n\u001b[0;32m--> 612\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    667\u001b[0m             delayed(_accumulate_prediction)(e.predict_proba, X, all_proba,\n\u001b[1;32m    668\u001b[0m                                             lock)\n\u001b[0;32m--> 669\u001b[0;31m             for e in self.estimators_)\n\u001b[0m\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mproba\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_proba\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1005\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    806\u001b[0m                 \u001b[0mbig_batch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 808\u001b[0;31m                 \u001b[0mislice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mislice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbig_batch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    809\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mislice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/momentum_env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    667\u001b[0m             delayed(_accumulate_prediction)(e.predict_proba, X, all_proba,\n\u001b[1;32m    668\u001b[0m                                             lock)\n\u001b[0;32m--> 669\u001b[0;31m             for e in self.estimators_)\n\u001b[0m\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mproba\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_proba\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "backtesting(RandomForestClassifier())"
   ]
  }
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