2-alphavantage_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alpha Vantage API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "key = open('1-alphavantage.txt').read()\n",
    "from alpha_vantage.sectorperformance import SectorPerformances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = SectorPerformances(key, output_format='pandas')\n",
    "data, meta_data = sp.get_sector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Information': 'US Sector Performance (realtime & historical)',\n",
       " 'Last Refreshed': '2021-01-08 11:54:15 US/Eastern'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank A: Real-Time Performance</th>\n",
       "      <th>Rank B: Day Performance</th>\n",
       "      <th>Rank C: Day Performance</th>\n",
       "      <th>Rank D: Month Performance</th>\n",
       "      <th>Rank E: Month Performance</th>\n",
       "      <th>Rank F: Year-to-Date (YTD) Performance</th>\n",
       "      <th>Rank G: Year Performance</th>\n",
       "      <th>Rank H: Year Performance</th>\n",
       "      <th>Rank I: Year Performance</th>\n",
       "      <th>Rank J: Year Performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Consumer Discretionary</th>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.3365</td>\n",
       "      <td>0.6385</td>\n",
       "      <td>1.2042</td>\n",
       "      <td>3.4628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0852</td>\n",
       "      <td>0.0334</td>\n",
       "      <td>0.3879</td>\n",
       "      <td>0.0941</td>\n",
       "      <td>-0.3229</td>\n",
       "      <td>-0.4353</td>\n",
       "      <td>-0.2782</td>\n",
       "      <td>-0.3811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>-0.0238</td>\n",
       "      <td>-0.0481</td>\n",
       "      <td>-0.0255</td>\n",
       "      <td>-0.0360</td>\n",
       "      <td>-0.0818</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>0.0036</td>\n",
       "      <td>-0.0133</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>-0.0150</td>\n",
       "      <td>-0.0330</td>\n",
       "      <td>0.2055</td>\n",
       "      <td>0.4238</td>\n",
       "      <td>0.9647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information Technology</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0317</td>\n",
       "      <td>0.1228</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>0.4043</td>\n",
       "      <td>0.9812</td>\n",
       "      <td>2.2712</td>\n",
       "      <td>4.4857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Rank A: Real-Time Performance  \\\n",
       "Consumer Discretionary                         0.0135   \n",
       "Energy                                         0.0088   \n",
       "Real Estate                                    0.0055   \n",
       "Utilities                                      0.0036   \n",
       "Information Technology                         0.0030   \n",
       "\n",
       "                        Rank B: Day Performance  Rank C: Day Performance  \\\n",
       "Consumer Discretionary                   0.0180                   0.0199   \n",
       "Energy                                   0.0149                   0.0852   \n",
       "Real Estate                              0.0031                  -0.0238   \n",
       "Utilities                               -0.0133                   0.0006   \n",
       "Information Technology                   0.0265                  -0.0002   \n",
       "\n",
       "                        Rank D: Month Performance  Rank E: Month Performance  \\\n",
       "Consumer Discretionary                     0.0423                     0.1015   \n",
       "Energy                                     0.0334                     0.3879   \n",
       "Real Estate                               -0.0481                    -0.0255   \n",
       "Utilities                                 -0.0035                    -0.0023   \n",
       "Information Technology                     0.0317                     0.1228   \n",
       "\n",
       "                        Rank F: Year-to-Date (YTD) Performance  \\\n",
       "Consumer Discretionary                                  0.0200   \n",
       "Energy                                                  0.0941   \n",
       "Real Estate                                            -0.0360   \n",
       "Utilities                                              -0.0150   \n",
       "Information Technology                                 -0.0032   \n",
       "\n",
       "                        Rank G: Year Performance  Rank H: Year Performance  \\\n",
       "Consumer Discretionary                    0.3365                    0.6385   \n",
       "Energy                                   -0.3229                   -0.4353   \n",
       "Real Estate                              -0.0818                    0.0988   \n",
       "Utilities                                -0.0330                    0.2055   \n",
       "Information Technology                    0.4043                    0.9812   \n",
       "\n",
       "                        Rank I: Year Performance  Rank J: Year Performance  \n",
       "Consumer Discretionary                    1.2042                    3.4628  \n",
       "Energy                                   -0.2782                   -0.3811  \n",
       "Real Estate                                  NaN                       NaN  \n",
       "Utilities                                 0.4238                    0.9647  \n",
       "Information Technology                    2.2712                    4.4857  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Rank A: Real-Time Performance'].plot(kind='bar')\n",
    "plt.title(\"Performance by Sector\")\n",
    "plt.tight_layout()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpha_vantage.techindicators import TechIndicators\n",
    "from alpha_vantage.timeseries import TimeSeries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ta = TechIndicators(key, output_format='pandas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "goog, meta = ta.get_sma('GOOG', interval='daily', time_period=200, series_type='close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>552.7623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>552.4327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>552.1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>551.8332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>551.5063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SMA\n",
       "date                \n",
       "2015-01-09  552.7623\n",
       "2015-01-12  552.4327\n",
       "2015-01-13  552.1137\n",
       "2015-01-14  551.8332\n",
       "2015-01-15  551.5063"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117dc4f90>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(goog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = TimeSeries(key, output_format='pandas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices,meta = ts.get_daily('GOOG', outputsize='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</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>2014-03-27</th>\n",
       "      <td>568.000</td>\n",
       "      <td>568.00</td>\n",
       "      <td>552.92</td>\n",
       "      <td>558.46</td>\n",
       "      <td>13100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-28</th>\n",
       "      <td>561.200</td>\n",
       "      <td>566.43</td>\n",
       "      <td>558.67</td>\n",
       "      <td>559.99</td>\n",
       "      <td>41100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>566.890</td>\n",
       "      <td>567.00</td>\n",
       "      <td>556.93</td>\n",
       "      <td>556.97</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>558.710</td>\n",
       "      <td>568.45</td>\n",
       "      <td>558.71</td>\n",
       "      <td>567.16</td>\n",
       "      <td>7900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-02</th>\n",
       "      <td>565.106</td>\n",
       "      <td>604.83</td>\n",
       "      <td>562.19</td>\n",
       "      <td>567.00</td>\n",
       "      <td>146700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    high     low   close    volume\n",
       "date                                                 \n",
       "2014-03-27  568.000  568.00  552.92  558.46   13100.0\n",
       "2014-03-28  561.200  566.43  558.67  559.99   41100.0\n",
       "2014-03-31  566.890  567.00  556.93  556.97   10800.0\n",
       "2014-04-01  558.710  568.45  558.71  567.16    7900.0\n",
       "2014-04-02  565.106  604.83  562.19  567.00  146700.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.columns = ['open','high','low','close','volume']\n",
    "prices.sort_index(inplace=True)\n",
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = prices.loc['2015-01-09':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1182efd50>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1le53nox11lz8pKwK2/SzT8InN0BQF7hnQ7MM+FCLoK+13q217qi1jtFax2AE9BFa6xRgKXCLOYpnLJCttU4GlgNTlVKhZgfuVLNMCCHqhQbO7dXB7b4rh1evNdliK22AqGicvltFuUYN31Jo1PCbsKO2KtUZsvkJsAHop5RKUkrdXsnhy4CjQDzwDnAPgNY6E3gB2GJ+PW+WCSFEndlsmvTcIpdkaM6qO3LHeeHzmYPdpE5wpzAHPrwS0g/AVQugY//qnddEqmzT11pfX8X+GKdtDcyr4LiFwMIa3p8QQlTo5JkCzuQX8+GG4wDEJWS5Pa66Qd/qVNP/4+Q+VZ+QkwzvX2wsfjLrDRh4WbXepynJjFwhRIt13ku/uLwe1s39ilPVnY1bYi1NzezlJie+l3Mncd5pmH8RFGbDTUug14RqvUdTk6AvhGiR3A3BvP38Hm6Prc5s3I1HMziUerbC/Zv/NAlfT7MT11IE719ipFb43XKIGlm9m24GJOgLIVqkt8zx9M6C/dwPp8yvRk1/9vyNju3XZg8rt79jkF/pi2WPQPp+uPq9FhXwQbJsCiFakJX7UknMzCe/2MKCtceY0C+CJfec69hf0XDKmqRgePGKwcwaVslon9V/h20fwJi7YfCV1b5ucyE1fSFEi3HHojg8FKx5dAJWm2b64E6M6BbK368ewskz5RdFt3Nu3tFaUyaTgIt2vhWERUsx/PICrH/dyIM/9S+1fo6mJEFfCNEi2DtZbRrO5Bvt+e39jSGa18RGV3geuHbkllg1Pl4VB/0QNwnbyE2Bj6+BlF0w4FJjpSvPlhk+pXlHCNEi5BWVzpD9aW8KAMF+1Qu83TsEOradR+i4U26CV9ZxWDAZ0vbDVe/CdR+Bt5/7k1sACfpCiGbPZtMMe36F4/XrvxiduEEVdNyW9cHvRjOhn5HWxWJ1m/aL0ABvrh8djbfzUM2sBHhvBuSlw5ylcM7VtXuAZkSCvhCi2csw0yaX1a6aNf2IIF8mDogEoLiCmr4GfJwD/unD8N5MY1jmnG+h+7luz2tpWmajlBCiTTlb5D75WVA1gz6Aj6fRjl9R847V5tTBu/tL+O5BQMOt37e4YZmVkaAvhKi1HYlnABgW7X4mbH1YfSidOQs3A3BtbBQlVs1X208CEFKdBU1Mvl7GcM4iSwU1fQ1B1iz45l7Y/iF0HATXvAcR/er4BM2LBH0hRK1d/uY6ABJeurjB3mPuojjH9otXnIO3pwc3j+vOgeRct6kSKuLrZRx7MCWXHuGlHbs/7E7mo03HiSo+yq0H3oSSFBg7DyY8Cb7t6u9BmgkJ+kKIWnHOSNmQ7DXznhGBjk7WEd1CGdGtZumL7RO37vpoq8uH1Asf/8SD3l9yhc9vFJYEws1LoMeF9XT3zY8EfSGaubwiC/9aeYgHpvQlwKf5/Mqm5xY16vt1Cq7bMElfLw8UNqJVOhxdDWn7IGU3y32X4IOF/1kn8YXvbJa24oAPEvSFaPZe//kw7/x2jJjwQG4c072pb8fhirfWN/h7OP814eNVh8GGWtP5+Nds8X2OcJUDi8xy3/astQ3mH5ZriNdRROJbtxtuASToC9HMZeUbwxU9Kkkd0BTsaQ/8vBtu5Pc3O0pXVb2imqtflWMphh8fo0fcQnbqnrxsuY6Xb78EIvqjAzty95M/OA51zqffWsk4fSFaiKSsfH73/hayC0r45UAqv/8wruqTGlDvjkYnZ9cQ/2qf8+GGBGIe/77S4Prb4XSyC0qwWG388bMdACy4JbbyJGgVsVpg2UMQt5AzQ+/kyuLn+Nw6AXqOh6BOlJ2nZWkDQV9q+kK0EG+uOgLA9fM3si85B4D8YkuTtPPbbNqR/6YmcfK/a44CsPdUNkOiXId5Hs/IY39yLnd9tJWhUe35x7Wl6Y1r9UeOzQo/PArbFkHs7Zw973msm1a5HFI2yLeFmr4EfSGaOU8P14hnD/gAGWeLCQhrnF/j1JxCOgb5opRi98lsTp8twkOBxVZ5LhtnI7uHkpRVwM6k8kF/yqtrKDZH6uxMyiYxKx+A/p2CuKBPRM1utjgPvvo97P8WRt0JF7+C39nyHc9lg76tDQR9ad4RopmrLL9MdRYHqQ/7TuUw5v9+5pPNiQDsTDImZZ3bKxxrBbls3OkQaHSUZrgJwMVlJk0Vms/26nXDataJm58JH10FB76Hyc/Bxa8ApeP0nVnKzM596pKB1X+fFkqCvhDNXNlg6Cy/2H16gvp2MNX462LzsQwAtp84Q0iAN1Gh/jVqB7fPpcp0k0snLNA1pbF94ZOKFkZxKyvBWLc2cTNc+jqc/0fHLnfXKSnzgTV7dLfqv1cLVWXQV0otVEqlKaX2OJX9XSl1QCm1Syn1lVIqxGnfE0qpeKXUQaXUNKfy6WZZvFLq8fp/FCFaJ+cFQG4Y4xqUzrhZJ7YhlFiM4FhQYuWmBZv4avtJLuobgZenqlE7uP3Q02Vq+ll5xWTmFXNR39JmnP+sNvowKlzUpKzUfbBwOuRnwez/wYibXXY7Z89MOJ0H4GhCgrrPA2gpqlPTfx+YXqZsBTBYaz0EOAQ8AaCUGgjMBgaZ57yllPJUSnkCbwIzgIHA9eaxQogqFFlsBPh48vW88xhZZhbqkbSKF/KuT/bMlMv3prI2/jQA0wZ1wsvDo0Y1fZs57v5ASq5LbX/4C0ba5PN7h/PGDcMBOJR6lgv6hBMRVMXY+dOHYclc+O8FxoLlN38F/cqGLFf2BG6vrTxMSIA3L181xGXZxdasyqCvtV4DZJYp+0lrbf+7ciMQZW7PAj7VWhdprY8B8cBo8ytea31Ua10MfGoeK4SoRHZBCeviT9MtLIBh0SHlskruT851bD/25S5uWrCpQe6jbNs3QIdAHzw9albTt8+1Opqex4gXVrAr6YxL52lMeCBRoQGO16HuVrFyFvcevDUW9n1jdNjevR6iR1V5H/YPn1NnChjbowPXjoqmSw2GnrZk9dHt/zvgM3O7K8aHgF2SWQaQWKZ8jLuLKaXmAnMBunVr/e1rQlTmqa/3kJZbRFSoEZDG9uqAt6fi0qFdSM8t4nCaEfS3Hs/is7jEyi5Va4UlVt5YFV+ufHDX9vxyIK1Go3fKfkBc9sY6pg/qBEBMhwAmD+jIodTSv14CK2rasdlg+ZOw6W3ofj5cvRCCImt8H/nFVgJ8a9Bn0ArUqSNXKfUnwAJ8bC9yc5iupLx8odbztdaxWuvYiIgaDtMSohUpsdpYutOYkWpvjgj28+bwizP557XD6BTsR1pOEUlZ+Vz1dsOlRHh37TFOny1tiuka4s+y+y8g0NcLpRSFJTb+8t2+al3L5iZJ24/m0ofn9Q5HKeUyyqZzezft7Mm7jNWsNr0NsbcbK1rVIODb70NrzckzBY6Uy21FrYO+UmoOcAlwoy5NkJEEOK9QHAWcqqRcCFEBe2cj4HY2aniQLyk5hS45cPxrMtKlmsouL9i7YzsGdgkGIN7sU1iw9hirDqRVea3KWoLsHa2+Tmkdfn9Rz9IDtIaN/4EFkyDjMFzyL7j4H+BR82e22kpX46pogZbWqlZBXyk1HXgMuExrne+0aykwWynlq5TqAfQBNgNbgD5KqR5KKR+Mzt6ldbt1IZre6kPpDHr6RxZvTaqX6x1MyeW9dccAyDWD0f2T+nD3Rb3KHdvBHOLonO2yoMRa7xOMtPlH+b0TegOuo2AKSkoD5m3vb2HfqRwqU1k6ZvuIHvuShZMHdCythVstxuImPz4G3c+DeVsg9rZaTtU1mnfso6Iu6BNeq2u0VFW26SulPgHGA+FKqSTgGYzROr7ACnN5sY1a67u01nuVUp8D+zCafeZpra3mde4FlgOewEKt9d4GeB4hGpV9RaeHvtjJVSOjXPYdSMmhd0S7Gi30Me1fawAYFRNGUYnRVj6uZwc8PMoHt4pGtRRarPWamuH02SLCAn0Y3NWo3UcGl76vfSinnT05XEXcNe/Y2dvZO7Tz5dO5Yzmna3tjR3YSfD4HTsbB+Q/CxKfAo25TjGxaO/L0u5u01ZpV+T9Da329m+J3Kzn+ReBFN+XLgGU1ujshWqjFW5N46IudvDZ7WK0ShR1JP0t7cylA3wqyWHbvEOjy+tZzY3h/fQIFxfUX9OMSMvlo4wk6BfsxaUAkD0zuy23nxzj2945sx+aE0sF9RZbKZwhX9EfIgM7BPHPpIMfrsT07GBv7voFv/wAlhXDlAhhyTa2fxdmC346y6mA6gLTpCyGqp7LZsMvNzsnsWk6eSs4uZMMRY/ZrRTXRYdEhLL57HAAeCoZGGzXj1Jz6W9zk378Yo3ZScgrx9vTgD5P7EOyUFuLpSwZyx/k9HK/XxWdUeK35a444ngmMnDp2S+4+l07OnbaWYvjxCfj8FgjuCnetrbeADzgCPlT8odpata2nFaIenTpTCBijWbw9S5tfUnMKHUPTnv6m+q2YzrNUj2fks3J/KgAxZWr0zkZ2D+PLu8bx68MTOK9XOErBLwdSa/AUrgpLrCxce8zR1GJP9lbRB4+ftyd/nNLX8frdtcfcttsfSMnh/5YdcOTgB3h4aj8uNGfg+vs41bYTt8B702HjWzDmLrhzFYT3rvUzVUWad4QQ1XLKDGDRYf6cyja2k7LyOf9vqyo7za2U7EIe/mKn43V6bhEZecXMHhVd8Vh1U2xMmGM7op0vJzLzKzm6cq//fJi3fj1CWKAPlw/vyo5EI7Ha5AFuhkRmJcC2DwnMS+duzyI22AayQ/ciu6CEkDKTqv636YTLa/sateP7RZTmvyk4A2v/CRveAr9guOYDGHR5rZ+lutpa844EfSFq6bfDRhNBt7AANh7NxGbT7DmZXatrvbz8gCO9AcCx02c5k19Cr4h2NbpOaICPI899bRw3PzDsg2JyC0vo3iGAJy8eYHSoJu+E9INwfD3ErwDlgfIP4zFv496P2DoT9/50Jt/yJ2gX4bhG2aBv5+XpgZeywqGf4IdHjA+SIdfBtL9CYIdaP0dNNOTKX82RBH0haumd34yhlV1DjLQBJTYbiZkFLsd4eii01qgqhhYmOZ03eUBHVu43xryHBlaRhsDOZoOD3/N0/lt0KTgNr2SDhxf0mgBDrzeGOVZjeGOO2Qfh5+1JscWGrzWPx6OP0/WbV+HY6tIDQ2Pggodg5K0Q0o0hj3/GTM/NXOqxgYlp78Ob38B598OoO3n0y4Mu+XkcK21ZiowFTja+DZlHIKQbzPkOelxQvWeuJ1LTF0JUKc4csRLg4+moKVpt2jHhx85q0+QUWhwjcSqSU1haO+8Z0Q7MoB8WWPl5AKTuhe8fghMb6O/Rgd0lUeSHDmBQuCfs/Rq2fwTtOoG3P3j5QVhP6HEh9J9pBFrHzVrwyDzM1Z670J++Srb3Mfb4nYYDQPtomPBn40MkvK/R/OJ8/7TjU+tETve9nucOxPFTzx9g5bNkr/w7U6zD6erZnVTvrjx602VEe2TA1/Pg4DIoyIQuI2DWmzDoSvAJoLFJm74QokqHzZmof796KMlme36JVbPtRFa5Y7PyiqsM+vYPiy/uGsdvh0ubecq2jbtI3Qe/vQJ7FoN/KMx8hTs392NbUi4ch4S7L4aSAtj1udEco21gKTDSGBz83pjoFDUKwvtByi5I288HthLwhmQdxtqSARy2TWL8hKmMnnhFpTNf37kllpScQv67+ghJOprsq77gi6+/oMOBjxnvsYMrPdcaB370svHdNxh6T4YRtxjr1Tbhou9tbfSOBH0hakhrzRNLdgNwYd9wvt5+EjAWO9l8rHTMeqCPJ3nFVrLyi4mh4hE49nNvGdedUTFh/LA7xVEe7OfmV9Rmhd/+Cav+At6BcO79MO5eCIpk25LvHYdl55fQPsAfRs6BkXMoLLHy12X7ue+OPoQXJsK+r4zVpeJXQkRfbGPn8ejqAnbbenBIR6HNwX33XDCtylQHUwYaHb0Wq43nvt1HVkEJO9QAviuZB8DICM3HV3bALysefAKh7/QmqdV/dc+5LmkrQJp3hBBV2HjUObB7OSZCZeS5jo/v1iGQ/ck5ZJx1P0v1xz0p/HIglRevOMdoPzebGS7oG85CMxVDudTCZ9Pgi1vh+DoYcClc/Kqjw7Ss+z/dzge/G+14/f76BCeKD6gAACAASURBVD7YcJz2AT48OKUvXPiI8QVsP5GFxab58pcN5a5T7UVMwJEWOaewhO92JTvKB/eOwa/HYOgxrtrXagi9OpbvGJfmHSFEpexjzd+5JRYPD+UYUll2hMqw6BAOpOSw91QOkweWH/J410dbAbh3Qh+KrTbHOrAT+nVky58mk5lXTId2TqkWshLgvZnGGrCXvm7U4Cux+lA6izYkEOjjxVUjo9h63Gh6sqdpdla29mt35wU93JZXJNAcb19QZu3eKhdCaSTebtI3+NQgTUZrIEFfiBrKNTtdR3Y3VrGy14T3ncpxBJBiq41RMaH8vD/V0eYPcMvCzQyPDuEPk/o4ynIKS7DaND6epc0MEUG+roEydR8susxIR3DL19BtbLXu1T457KqRUexPNpKhebnJ4+Ns5jmdmDwgkvH9OlbZF1GWnxn0H128y6W8T2SQu8MbnfMkut9f2JMnZg5owrtpGm3rI06IOjiafpaU7EKy8ktQqrS9PdBchCPueBbB/t6OpQVHxYTh5+3pSOwFsOZQOq/9fJhCpxw1l/zb6OSssEMxZbeRTlhrYynAagb8spKyjA+fsqmSD6bkuryeMjCSK0dEEWaujFUTAWbQP57hOkGsXzMJ+s7PU1nyt9ZMgr4Q1TTxH6sZ+9efyThbRFiAjyN7pvOM2fb+pdtRoUZ6hmIz6Dvnbc8vLp+YrOxSiAAkbYWFM4zhlnN/rXIpwP/ePLLc4ungmtK4yGnpw+V7UxyZPe3q0rEZ4O36DPa/hqLDGr/T1h2lFE/O7A9Untu/NZPmHSGqwTlH/emzRXRoV9rB6hr0vQlv58vps0UopfDxKq3pn3LKO3M8o3SBFLtyHabxK+GT6yGgA8z5FkKiy51T1rRBnZg2qFO5/oXle0vz8Tz19R5mDu5Eh3a+Lqkf7PLqsKiIn09pPfLNG0ZwUb8I0nOLavwXQ0OyD4PNOFt/ielaEqnpC1EN6U4BYvneVJdFtNs5pTEO9vfml4cvIu7PkwHw8fJwNPeUONWwj6YbQX90j9K8OX72Va+KcuGru+CjqyC0B9z5C4SX9gHUhr3T2G78K78CRgoJu9FmDh9LHarAzimd+3UKop2vFz3CKx+u2tj6mCN4nNfibUukpi9EJR78bAcouGG0a5OJY4EPStv0wajpO6ceLii2sDPxDMnZBY5mHoCfzRm3d1/Ui3duieXd344yoV9HyDgCi2YZeW7OfwAufLRBxrPnFhq1eT+n5RUfmd6PrcezuGpEVEWnVcl5ucYAn+Y5/r2v2b8wvl/bXINbgr4QFdBas8SceGVfUq97hwCOZ+QzqEtpGgLnlbEig10X8rbXJuevOcqMwZ0d5T+b6Y9jY0IJ8vPmwan9IGEdfHq90WF763cQc37DPJhpZ+IZxzBOAA+luMvNsow14dyM01yDfqCvF7ufnVqvq4u1JNK8I0QZB1JyuO29zY5UCwAPfGa0fX9w22huHtudC/q4ryVeMqSz2/IQfx+Xmn6JVTOhXwRBft5gLYFN/zVq+H7tjQ7bOgZ8++IqlZn15jrHdmSwL73dTFyqi5pM6mpsQX7ezaqfoTE135+KEE3AatNM/9dvgOvqSgDXj44mJjyQFy4fXOH5PStIhbz1RBavrjzkUrbqYJqxHOCKp42JV70mwtXvgX9I3R4CaOfrfnz91IGR/LSv/CIrm56cXOf3dNYh0KdGawOLxiM/FSGcxDmt91rWjWO6V3m+XwVT+tccKv0A6UQGN3mu4Eefx43lAD284dpFcOPiegn4AO3M4Z+hAd68OyfWUf7a7OHcdl5MvbxHRfY8N431T0xs0PcQtSc1fSFMNpvmuvkbK9xfnVQCldVuu6lU/uC1hMs91uKpNEk+PWHm23DOteBZv7+KAWaHamSwn8vas/4+nkaTUgNqzs06oho1faXUQqVUmlJqj1NZmFJqhVLqsPk91CxXSqnXlVLxSqldSqkRTufMMY8/rJSqPGmIEE0g12l8+tY/T8ZDwfBupTXvsOouaGJXnM+G6af4p/db/OLzIGt8H+By70380O5yJhX9Hf/7NsCwG+o94IOx+MpfrzyH924bxcDOwTw5sz/bnpoCwIQyo1acRyKJ1q86/9veB94AFjmVPQ78rLV+SSn1uPn6MWAG0Mf8GgO8DYxRSoUBzwCxgAa2KqWWaq3LJx8XoolYzHH0z146kA7tfDn614s5djqPCeaYdu+atFGf2g5f30PntH2c5xHCHlsPPi6ZzGMPPsYlHbpxSQPcf1nXOw0znXth6aic4d1CHWmfn710IDdUo9lKtB5V/i/WWq8ByjZ0zgI+MLc/AC53Kl+kDRuBEKVUZ2AasEJrnWkG+hXA9Pp4ACHqi31SkrdTu3xMh+qNkXcsAVhSAD+/AAumQEEWtus+5smYL7jD8gjn3fw0Ph3Kp0hoCvZmqJAAH0d2T9E21PbvykitdTKA1jpZKdXRLO8KJDodl2SWVVRejlJqLjAXoFu35vELIlo3q03z6Je7+H73KcA1/a5SihevGFxl+t1l951P0Z6l8NZYYyTOOdfC1BfwCOrEu80wkaN9oaq2lkte1H9HrruBr7qS8vKFWs8H5gPExsa20ZRIojq01rz04wEGd2nPpUO7uN1f1YLkAOviT7N4W5LjtZen6zlVjtopzKb9Dw/Cni8hoj/c8AX0nVq9h2hiUstve2r7E081m20wv6eZ5UmAc1aoKOBUJeVC1NpHG4/z39VHue+T7eX2bTiSQY8nlnHsdPnEZmX98bMdLq9rNL7cZoWl9xkB/4KHYe7qFhHw7R9rEvTbntr+xJcC9hE4c4BvnMpvMUfxjAWyzWag5cBUpVSoOdJnqlkmRK3EJWTylLlAiDvXv2MMvTxgLhxSEatNk5nnupyhd01maq77lzHBavwTMOkp8Par+pxm4PEZRnrhriHlV9ESrVt1hmx+AmwA+imlkpRStwMvAVOUUoeBKeZrgGXAUSAeeAe4B0BrnQm8AGwxv543y4SolcXbTjq2QwO8+XFPCg99bqRKsDpliayq1p5TYKyC1SnYz7G498iY0OrdxN6v4Ze/GIt8X/RYTW6/yV03qhvxL86ocAaxaL2qbNPXWl9fwa5Jbo7VwLwKrrMQWFijuxOiAtkFRu3cx8uDwhKbI3XwEzP7O9IWAxSWlF+sxJk9xfBDU/ty6dAu5BVZXNelrcjOz+CrudBxIFz+dmnPaAsiaRLaJvmpixYpM6+Y2O6h3H5+DwqcAvvOxDP8+evdjteVBX2L1Ua2WdNPyy3Cz9uz6oBvs8Hmd+Dru6DLCLj1ewgIq/wcIZoRCfqixdlwJIONRzPpFhZAWIDrLNnUnCKXzsktleTScW7Lv3lcNSYoZZ+E9y+GZQ9Dl+Fw45cS8EWLI0FftChpuYWOTtpeHdsxa7jrUM0nv9pN5tlirhxhTAP5PC7JZf/Vb6/npgWbWLEv1bEa1ps3jHBZ+MSt1L2wYDIk74QZL8PvlkNgh3p6KiEaj2RGEi3G8r0p/P7D0mX/pg2KpGOQH5uenMSOxDOOfam5RXR2SjL24Oc7+Mvlg8kttBBnLhqyNv60Y39oQCUBX2vY/hF8/5CR6/62741avhAtlAR90WI4B3yAXubIk8hgP6YMiHSUW22a3h3b8encscyev5El206yxGm0T1khARUkUivOg2WPwI6PIWqUkf44uPwkMCFaEmneES3G+b3DXV47z7b18FD0jDAW4PbyUEzsF8nYnh24xU1b/U1jXdN7dHeXXyfzKLw1DnZ+Yky6+t1PEvBFqyA1fdEiFFms7D6ZzZXDu3LPhN5uR+UMjQrhaHoeFpumvdlk8/yswSzacNzluPsm9sFDKRZtOM7PD11EYNn874dXwBe3gfKAOd82+Fq1QjQmCfqiRdhwJIPsghIuHdqlwrVcq5Pv/r1bRxEZ7Mdzlw3isen9XQO+1rD+dWP5wvC+cMPnENajvh5BiGZBgr5oETLOGsMr7U047gzuGgzA5cNcm2E2PDERb08PDqXmcm4vo4lIKeUa8M+mwRe3wvF10G8mXPUu+FQvrbIQLYkEfdEiFJsLnPh6eVZ4zGVDu5KdX8LVsdEu5Z3bG/llwiuaeHXkF1h8JxTlwsxXYNQdLXKGrRDVIUFftAhFZht+ZfnfPT0Ut55Xg+aYs+mw8lnY8RGEdIc5SyFyUB3vVIjmTYK+aBHsNf16SQVsH3v/05+M2v3YeWaGTMk4KVo/CfqiRVi81RhnX+eVno6tMZYzTNoMXWPhsn9D5MB6uEMhWgYJ+qJFOJVdQHt/79pnhjxzAlb91Rh3HxgOF/8TRt4GHjJVRbQtEvRFs2OzaTycFjKxWG3kF1u566KeNb9Y2gFY/29jZSubBcbeAxOeBF/JIy/aJgn6olmJTzvLpf9ey/OzBnGNOQonObsQq03TLawGQygLc4wFTuLeBQ8vGHo9XPAQhERXfa4QrZgEfdFs5BdbuHNRHAUlVt769Ygj6J/IzAcgOrSaQT8pDr66CzIOw4hbYOLT0C6ioW5biBZFgr5odPnFFv7z6xHuGt+LAB/jv+Dps0U88sVOx0LmAT6eXPbGWnpFtCPRDPoDOgdXfuGU3bDmFWPN2qDOcPPX0GtCgz6LEC2NBH3R6JZsO8nrv8SDUjw4pS9Pf7PHJT/OtEGRLN+bCsCupGwARseEEVpRmgVLEax+Gda+Cj6BMG6e0ZQjC5wIUY4EfdHojKWUISW7gAMpOS4Bf8rASIZGhziCvt3jM/u7v1jqPmPpwuSdMPhqmPE3Y3SOEMKtOgV9pdQDwB2ABnYDtwGdgU+BMGAbcLPWulgp5QssAkYCGcB1WuuEury/aB4OpuTSN7KdS6rjytjb6C1WzT0fbwPgL5cPJuNsMX+Y3Icf9yQ7jr1xTDfOFlkYGhXiehFLEWx51+is9faHa96HgZdL+gQhqlDroK+U6grcDwzUWhcopT4HZgMzgVe11p8qpf4D3A68bX7P0lr3VkrNBv4GXFfnJxBNatPRDK6bv5EXrxjMjWMqX2f2aPpZrnhrvWMx8m0nskjIyGd0TBg3jS0995yoEAJ9PPnX7OFMGRhZ/kLJu+Db++HUdoi5AGa9CaHVWONWCFHnRVS8AH+llBcQACQDE4Evzf0fAJeb27PM15j7J6nqVg1Fs/XKTwcBI/WxO/amHICJ/1jtCPgACRlGjf//rhzsck7XEH/2PDetfMC3lhht9/MvMhYpv/IdI9+9BHwhqq3WQV9rfRJ4BTiBEeyzga3AGa21xTwsCehqbncFEs1zLebx5VaWVkrNVUrFKaXi0tPTa3t7opFsSTDWnE3LKSq3L+NsET2eWEbM499z6kyBy76h0UZzTUiAN707BpU7t1x94MwJ+PAKWPUiDLgU7tkAQ66V5hwhaqjWQV8pFYpRe+8BdAECgRluDrVX9dz9dupyBVrP11rHaq1jIyJkbHVT0VpTUFx+daqE03msP2IsKr76UOmH8uaETIY//xPrnBYcf+qbPY7tc1/6BYAXZg0i4aWLeevGEUwZGMkvD42v/EbOpsPK5+CNUXBqB1z6mrFWrXTWClErdenInQwc01qnAyillgDnAiFKKS+zNh8FnDKPTwKigSSzOag9kFmH9xcNJC2nkKv+s57EzAIemdaPeRN6syvpDF4eHjz/3V42Hs3k1nNjeH99AgBXDO/KV9tPkpVfwvPf7mP5AxcCsD85F4D+nYI4kGJsj+5h/HHXNcSfd26JrfgmspNg83zY9F+j03bAJTDleQirRSoGIYRDXdr0TwBjlVIBZtv8JGAfsAq42jxmDvCNub3UfI25/xft3OArynnsy10s2pDQ6O/7xqp4EjON5pi/Lz/I9hNZXPbGOm7/YAsbjxqf0/aAD3D1yCjHdpBfaT3idG4Rt50Xw+K7z3WU9Y2sJOeN1nBkFXx4Jbw6CNa/Yaxidc9GuO4jCfhC1INa1/S11puUUl9iDMu0ANuB+cD3wKdKqb+YZe+ap7wLfKiUiseo4c+uy423RlsSMtlwJIN7J/TGw0PxWVwixMEt42Icx1htGg/lps27jLNFFnYnZTOuV7luk0qlZBc6xs3fP6kPr/98mCveWg8YOXDK+nreeQyLLh1OGXc8i5jHv3e8jgjydSxLGN7Ox/19lxTCwe+NIH9qGwRGwEWPG232HXrV6P6FEJWr0zh9rfUzwDNlio8Co90cWwhcU5f3a+2u+c8GALLyi3lsevnJSCVWG33+9APzJvTikWkVTFYyXfffDew9lcPWP0+mQ0XLBLqx+6QxA/aSIZ15cEpfxvQI48YFm1yOee6yQUwdFElkkJ9LNkx3As00C6seHk97f+/SHcX5kHkU9i6BTfOhOBdCusHF/4BhN4G3X7XvWQhRfTIjtxk4kn6WOz+Ic7x+b10C761LcLzWWqOUcgx3/HjTiSqD/t5TOQDc/+l2Pr5jbLXvpcBclvCPk/sCcF7vcBJeupj31x3j2W/3ERboww1juuFdJq/9Y9P787cfD5S7XmSwL9is9PBMh1OH4cQGOLnN+G4pBBQMnGUkRus5QfLbC9HAJOg3A/tO5XDUTDTmTmJmAd06BHAyy2hnb+fr/seWU1jCkGd/cikrsdas26TQHLHj7+O6APmcc2O4OjYaf29PPN3U7u8e34uV+1PZejyLv111Di8tXscoj4NMT/gZvv8KCsw+ew8v6DgARt4K3cZC56HSVi9EI5Kg3wwcSMmpdP/tH2xhxYMX8etBY4ikcxu63c7EMzz4+Y5y5WN6VD/p2E97U3h08S7AtUMWjD6Eij5ssJZA8i6utXzLVV77mfFbEtf5HTb27fCH/jOhx0VG+3znYbKAiRBNSIJ+EyossXLPx9v45UAaYDSRxKedZfG2JABWPzKei/7+K+lnjYlP9glO9nZyZzcu2MTZIku58rI1/U82n+C3w+m8et0wfL1ca/P3/m87AN6eimA/p/Z3mxVyTkJ+hvmVaXzPTYasBGPd2YIsrgMyPIMo8B5I8KSnoft50GU4eFW/T0EI0bAk6Deh/2064Qj4YDSR2Gyax2b0I9DHi0BfLy4+pzPf704mMTOf+PSzABRZXCdNLdqQ4Aj43cICmD06GqtV8/bqI1isNsdx2QUlPLFkNwC/HV7JqofHE+7UyRvZ3heLVfP1PeMg/mc49KPR/p66FyyuM2oB8PA2VqLqPQX6zaCg00gWbS/ingm9oMwHihCieZCg34S2J54pV+bhoegYVDpyJdjf+BFd8PIqR1lhSWkg35KQyX9XHwVg3eMT6Rri79j3jxWHWLD2GOf1Cefej7fx0NR+jn25hRb+/uNB/nb1EAA+3JBAbmYa/x4cT+RHT0L6AfAOhC7DIPZ3ENHXGEoZ0MH48g8D/1CXjld/4IEpdfs3EUI0LAn6TeSfPx3k252nHK/dDdEEyjXBQGlNPyW70DHM85IhnV0CvrNnvtlLXrGV57/b51L+WdwJHon1IPzMbvy+/x8bfTfgF18Cnc4xkpkNnCVNM0K0MhL0m8jrv8QD8Mi0fvQMD2TaoE5uj5sxuJPL7FcwavrPf7uPheuOOcrC3YzFf+qSgbzw3T5H/nqDZtlNXdm/4n3OyVpB+PsnAZjp6cvPvpO5+PanIHJQ3R5OCNFsSdBvAsnZpe3j94zvVens2jE9y8+oLbRYXQI+uB/GObiLsaZsKDmM9jjAeI+djPfcSecvMxkIbKI/T5fcRsjACbyx24O3rhkFke4/fIQQrYME/SaQaqYhfndObLVXm3KWW1h+lE5abvkUCdHF8Xzm8zxjPIxJU2fxJ7vLBTBsKkfDLuC6BUeMA42+XToGS1OOEK2dBP0mYLUZHbFentWbffrJnWP5ZPMJ/nbVEB5bvIulZl9AO18vXrlmCB9vOsHcC8vkqNn/HZ0X34Gf8uQfJVdzy41ziOg7lnZexuLioXnFwBGXU4Z0bV+3BxNCNHsS9JuAfey8dxV5a+zG9ergSJwW6NSM8/NDFxEZ7Mf0wZ1LD9Ya1v0Lfn4e1WkIZ6Yu5L7oHvh4uX7AhAb68L87xnCDmVdn5zNTq/0hJIRoueS3vI7WxZ9mp5uhl5WxmEG/NkH21nNjAOgZHkhkcJmkZEW58NlNsPJZIyXxrd/Rs0evcgHfzrk5xyUZmhCi1ZKafh3ZM1Ae++vMarfP/xZvpFPw8qx5e36/TkFs/fNkQgJ8XHcc/RWW/B7OpsDEp+D8B6tMXhYdFlDj9xdCtGwS9OvJuvgMYsIDmPzP1RSW2Nj21BTCAn3KHZeYme+YTOVdy4ySLqmSSwph7T/ht39ASHe4+SvoNbFa1/H18uQ/N43AYpO1bIRoKyTo15GftweFJTaOnT7LsYw8x2zZjzce575Jfcod/0VcomO7NjV9F0lb4Zt5kL4fBl8FF/8T/MsnY6uMS3+AEKLVk6BfRyH+PqSUFJJXbCXPKeFZp/al7e32fPgnzxQ4JmUB+FbQ1l6lMydg3WsQ956xQPh1H0H/S6AWwz+FEG2LdOTWkbeXEWgPJOfwb6eAvu3EGSxWG/uTc+jxxDLWHj7N+L+vcjk3IqiG4+KtFvjpz/D6cIhbCMNvNNaPHXCpBHwhRLVITb+O7Eu7f73jlEv5J5tP0N7fG/va7ze9W7rk4Hf3nU9mXjFBfjUYMZO6D354FBJ+g+E3wUWPGcsLCiFEDUjQryNbmU7QoVHt2ZlkrDO76kAaiVn5Lvvvn9ibwTWZBJV3GlY+A9s/Ar/2cNkbMOLmOt+3EKJtkuadOnKO+eHtfPh63nmO1wdTc8kvds1936tjNVeNstmMJpw3RsHOT2HMXTBvswR8IUSd1Kmmr5QKARYAgwEN/A44CHwGxAAJwLVa6yxlDGJ/DZgJ5AO3aq231eX9mwOr1ozvF8Frs4dXOMFp6sBIftqXCkCEm2yY5WQlwNf3wPF1EDUKZr5i5LUXQog6qmtN/zXgR611f2AosB94HPhZa90H+Nl8DTAD6GN+zQXeruN7Nwtaa7qG+Fc6o/WpSwY6tsMr67zV2qjVv3UupOyGaX+FW7+XgC+EqDe1rukrpYKBC4FbAbTWxUCxUmoWMN487APgV+AxYBawSBs9mxuVUiFKqc5a6+Ra330zYLVpPKoYOeM887WDmwlbAFiK4JcXYP2/IXosXP6WsZC4EELUo7o07/QE0oH3lFJDga3AH4BIeyDXWicrpTqax3cFEp3OTzLLXIK+Umouxl8CdOvW/Een2DR4VpI47Yu7xgFGbpvsghJCy6ZPAMg4YuTMSdsHQ2bDrDfAU3LhCCHqX12CvhcwArhPa71JKfUapU057riLjOXm/2ut5wPzAWJjY5t9fgCbTZcbIh8a4E1WfglrHplAtw5GLf+j28dw9PRZPMp+QBxaDovvAA9PmP0/I1GajLkXQjSQurTpJwFJWmv7APQvMT4EUpVSnQHM72lOx0c7nR8FuA5ub0QWq63qg6rBqjWeZYL0eb3DAdfYfU5Ue2YN61paYLMa2TD/dy0Ed4XbV0D/iyXgCyEaVK2DvtY6BUhUSvUziyYB+4ClwByzbA7wjbm9FLhFGcYC2U3Vnn/v/7bR+08/kFdk4eSZArerTlWH1ppii61c6uJXrhnKh7ePrjiLZd5pI9ivfRVGzIG5qyC8fJ4eIYSob3WdnHUf8LFSygc4CtyG8UHyuVLqduAEcI157DKM4ZrxGEM2b6vje9eK1prvdhmfNU9/s5fF25IASHjpYsDIgrnhSAbXjoqu8Bp2RRYbFpt2WdgEwM/bkwv6RJQ/4UwibHzbGH9vs8Al/4LYJvlnEEK0UXUK+lrrHUCsm12T3ByrgXl1eb/6kJhZuii5PeA7e/TLXWw4msGYnmF07xBY6bWOpJ8FcKRacKswB/Z/C3u+hGO/GWUDL4MLH4GOA2r+AEIIUQetMw2DpRjem2GMbw/qDL7B4BtETl4ehacSedZrH+Eqm2CMFAkleJH9/iLatw/j2nwfQj2C2b3RQvcpU8Cn4oVG0nKNBc5dmnFsVji+3ljU5NhqOLUDbCVGrvuxd8Ho30NI1X9FCCFEQ2idQT8/A3wCYednUJzrKA42vzp5BpCu25ONUZP3xsKZxEyCM4q5IjeZK3yALcA2H6zh/bCG9cGn0wCj3T28nzF+3suXnIISAAZ1CYbsJNizBLYsgDPHQXlA11gYdw/0nQHdxkonrRCiyalKmyaaWGxsrI6Li6v1+fnFFu75YAMPXNiZ7YdOMH9dIhkEU4QPu5+dymdbErkmNpqhz/0EGGPpiwtyiVGpRKl0Rnsepi/H6amSifZId1zXigeW4G6c8OnNryk+/C4mE8+kjcZOe6DvNRH8Q+v0/EIIURtKqa1aa3dN7620pm86mp7Hr0dy+PVIjlkS7tgX5OfNHRf0BOCui3rxn9VHyC4oAfzYr7uzX3dnha3038yPInqqZHqrU/TyOMX5ljRi8ndzk9cZPEv6woQ/w+ArZRatEKJZa9VBPyOvuFzZnRf0oFuZDtp5E3oxY3AnZr25DoDnZw3i6W/2uhxTiC/7dAz7dAzY4NVMo3xEtxCW3H0eQgjRErTuoH+2yOX14rvHMbJ7WLnjgvy8GRodwqvXDeXRL3dxbWw0P+1NxcfLgzduGE6Ajxf5xRb+tfIw3p6KnuHteOiLnYCxQpYQQrQUrTroH88oXcBk/s0j3QZ8Z1cMj+KK4VEAfHTHGJd9AT5ePDnTGGJpsdo4daaAf6w4xDOXDix3HSGEaK5abdBPyynktZ8PO14PjQ6pt2t7eXpw36Q+3DuxN0pG5AghWpBWGfS11tz/6XbA6KR9bHq/BgnOEvCFEC1Nq1wu8ejpPDYeNXpaH5nWMAFfCCFaolZZ0+8V0Y63bxxBVn5JpbnuhRCirWmVQR9gxjmdm/oWhBCi2WmVzTtCCCHck6AvhBBtiAR9IYRoQyToCyFEGyJBXwgh2hAJ+kII0YZI0BdCiDZEgr4QQrQhzXrlr/jhGAAABRxJREFULKVUOnC8lqeHA6fr8XaaSmt5DpBnaa5ay7O0lueAuj9Ld611hLsdzTro14VSKq6i5cJaktbyHCDP0ly1lmdpLc8BDfss0rwjhBBtiAR9IYRoQ1pz0J/f1DdQT1rLc4A8S3PVWp6ltTwHNOCztNo2fSGEEOW15pq+EEKIMiToCyFEG9Jigr5SKloptUoptV8ptVcp9QezPEwptUIpddj8HmqW91dKbVBKFSmlHi5zrQSl1G6l1A6lVFwLfo4QpdSXSqkD5vXGtcRnUUr1M38W9q8cpdQfW+KzmPseMK+xRyn1iVLKrwU/yx/M59jbAn4mNyqldplf65VSQ52uNV0pdVApFa+Uerwxn6MBnmWhUipNKbWnVjejtW4RX0BnYIS5HQQcAgYCLwOPm+WPA38ztzsCo4AXgYfLXCsBCG8Fz/EBcIe57QOEtNRncbqmJ5CCMbmkxT0L0BU4Bvibrz8Hbm2hzzIY2AMEYKyytxLo04yf41wg1NyeAWxy+j91BOhp/p7sBAY285+J22cxX18IjAD21OZeWkxNX2udrLXeZm7nAvsxfsFmYQQ/zO+Xm8ekaa23ACVNcLsVqq/nUEoFY/zw3zWPK9Zan2mUhzA10M9kEnBEa13bmdi1Us/P4gX4K6W8MALmqQa+fRf1+CwDgI1a63yttQVYDVzRCI+AeV81fY71Wusss3wjEGVujwbitdZHtdbFwKfmNRpNPT4LWus1QGZt76XFBH1nSqkYYDiwCYjUWieD8Q+LUWupigZ+UkptVUrNbaj7rEodn6MnkA68p5TarpRaoJQKbMDbrVQ9/EzsZgOf1Pf91URdnkVrfRJ4BTgBJAPZWuufGvJ+K1PHn8se4EKlVAelVAAwE4huuLutWC2e43bgB3O7K5DotC/JLGsSdXyWOmtxQV8p1Q5YDPxRa51Ty8ucp7UegfFn0zyl1IX1doPVVA/P4YXxJ97bWuvhQB7Gn4eNrp5+JiilfIDLgC/q695qcQ91ehazTXYW0APoAgQqpW6q37us9r3U6Vm01vuBvwErgB8xmkUs9XqT1VDT51BKTcAIlI/Zi9wc1iRj1evhWeqsRQV9pZQ3xj/Yx1rrJWZxqlKqs7m/M5BW1XW01qfM72nAVxh//jWaenqOJCBJa73JfP0lxodAo6qvn4lpBrBNa51a/3datXp6lsnAMa11uta6BFiC0T7bqOrxd+VdrfUIrfWFGE0Khxvqnt2p6XMopYYAC4BZWusMszgJ179QomjkJjfz3urjWeqsxQR9pf6/nbtnaSiGwjj+P5NQUUFBnAQHcXV0cLvg0NFJB+ngx3DyC/gJujm4ibp3crSDFXxBcNHFRRRXhzjkCHdSamPqJc8PQkMTQg5p07xcakY8v74NIRzUis6Ajuc7wOkP7Uya2dRXHtggbmOzSBVHCOEZeDKzFX+rAm4Sd/dbqWKp2WZMRzsJY3kE1sys5W1WxPPbbFKOi5nN++sisEnG8Rk2Du/jMbATQriv1b8Als1syXeTW95GNgljGd1vbn/HkYB14pbsCrj01AbmgB5xBdIDZr3+AvEX/h148/w08Sx84Oka2GtiHF62CvS9rRP8tr+hsbSAF2CmyZ8vL9sH7oiLiUNgosGxnBMXEwOg+udxdIHXWt1+ra028YmZh9zf+T+I5Yh4X/ThY7U7TF/0NwwiIgVpzPGOiIiMTpO+iEhBNOmLiBREk76ISEE06YuIFESTvohIQTTpi4gU5BOpeVTPncIGpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(prices['close'])\n",
    "plt.plot(goog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### https://www.alphavantage.co/documentation/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = 'GOOG'\n",
    "\n",
    "call = requests.get(f'https://www.alphavantage.co/query?function=OVERVIEW&symbol={symbol}&apikey={key}').text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "call = json.loads(call)\n",
    "call = pd.DataFrame.from_dict(call, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    GOOG\n",
       "Name: Symbol, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.loc['Symbol']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other API function calls: BALANCE_SHEET INCOME_STATEMENT  CASH_FLOW EARNINGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = 'GOOG'\n",
    "function = 'EARNINGS'\n",
    "call = requests.get(f\"https://www.alphavantage.co/query?function={function}&symbol={symbol}&apikey={key}\").text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'symbol': 'GOOG',\n",
       " 'annualEarnings': [{'fiscalDateEnding': '2020-09-30', 'reportedEPS': '36.4'},\n",
       "  {'fiscalDateEnding': '2019-12-31', 'reportedEPS': '52.38'},\n",
       "  {'fiscalDateEnding': '2018-12-31', 'reportedEPS': '47.0794'},\n",
       "  {'fiscalDateEnding': '2017-12-31', 'reportedEPS': '38.176'},\n",
       "  {'fiscalDateEnding': '2016-12-31', 'reportedEPS': '34.34'},\n",
       "  {'fiscalDateEnding': '2015-12-31', 'reportedEPS': '29.58'},\n",
       "  {'fiscalDateEnding': '2014-12-31', 'reportedEPS': '28.2406'}],\n",
       " 'quarterlyEarnings': [{'fiscalDateEnding': '2020-09-30',\n",
       "   'reportedDate': '2020-10-29',\n",
       "   'reportedEPS': '16.4',\n",
       "   'estimatedEPS': '11.1964',\n",
       "   'surprise': '5.2036',\n",
       "   'surprisePercentage': '46.4757'},\n",
       "  {'fiscalDateEnding': '2020-06-30',\n",
       "   'reportedDate': '2020-07-30',\n",
       "   'reportedEPS': '10.13',\n",
       "   'estimatedEPS': '8.2091',\n",
       "   'surprise': '1.9209',\n",
       "   'surprisePercentage': '23.3996'},\n",
       "  {'fiscalDateEnding': '2020-03-31',\n",
       "   'reportedDate': '2020-04-28',\n",
       "   'reportedEPS': '9.87',\n",
       "   'estimatedEPS': '10.762',\n",
       "   'surprise': '-0.892',\n",
       "   'surprisePercentage': '-8.2884'},\n",
       "  {'fiscalDateEnding': '2019-12-31',\n",
       "   'reportedDate': '2020-02-03',\n",
       "   'reportedEPS': '15.35',\n",
       "   'estimatedEPS': '12.5938',\n",
       "   'surprise': '2.7562',\n",
       "   'surprisePercentage': '21.8854'},\n",
       "  {'fiscalDateEnding': '2019-09-30',\n",
       "   'reportedDate': '2019-10-28',\n",
       "   'reportedEPS': '10.92',\n",
       "   'estimatedEPS': '12.709',\n",
       "   'surprise': '-1.789',\n",
       "   'surprisePercentage': '-14.0766'},\n",
       "  {'fiscalDateEnding': '2019-06-30',\n",
       "   'reportedDate': '2019-07-25',\n",
       "   'reportedEPS': '14.21',\n",
       "   'estimatedEPS': '11.471',\n",
       "   'surprise': '2.739',\n",
       "   'surprisePercentage': '23.8776'},\n",
       "  {'fiscalDateEnding': '2019-03-31',\n",
       "   'reportedDate': '2019-04-29',\n",
       "   'reportedEPS': '11.9',\n",
       "   'estimatedEPS': '10.3995',\n",
       "   'surprise': '1.5005',\n",
       "   'surprisePercentage': '14.4286'},\n",
       "  {'fiscalDateEnding': '2018-12-31',\n",
       "   'reportedDate': '2019-02-04',\n",
       "   'reportedEPS': '12.77',\n",
       "   'estimatedEPS': '10.9778',\n",
       "   'surprise': '1.7922',\n",
       "   'surprisePercentage': '16.3257'},\n",
       "  {'fiscalDateEnding': '2018-09-30',\n",
       "   'reportedDate': '2018-10-25',\n",
       "   'reportedEPS': '13.06',\n",
       "   'estimatedEPS': '10.4613',\n",
       "   'surprise': '2.5987',\n",
       "   'surprisePercentage': '24.8411'},\n",
       "  {'fiscalDateEnding': '2018-06-30',\n",
       "   'reportedDate': '2018-07-23',\n",
       "   'reportedEPS': '11.75',\n",
       "   'estimatedEPS': '9.5405',\n",
       "   'surprise': '2.2095',\n",
       "   'surprisePercentage': '23.1592'},\n",
       "  {'fiscalDateEnding': '2018-03-31',\n",
       "   'reportedDate': '2018-04-23',\n",
       "   'reportedEPS': '9.4994',\n",
       "   'estimatedEPS': '9.2878',\n",
       "   'surprise': '0.2116',\n",
       "   'surprisePercentage': '2.2783'},\n",
       "  {'fiscalDateEnding': '2017-12-31',\n",
       "   'reportedDate': '2018-02-01',\n",
       "   'reportedEPS': '9.7',\n",
       "   'estimatedEPS': '9.8995',\n",
       "   'surprise': '-0.1995',\n",
       "   'surprisePercentage': '-2.0153'},\n",
       "  {'fiscalDateEnding': '2017-09-30',\n",
       "   'reportedDate': '2017-10-26',\n",
       "   'reportedEPS': '9.57',\n",
       "   'estimatedEPS': '8.4592',\n",
       "   'surprise': '1.1108',\n",
       "   'surprisePercentage': '13.1313'},\n",
       "  {'fiscalDateEnding': '2017-06-30',\n",
       "   'reportedDate': '2017-07-24',\n",
       "   'reportedEPS': '8.9',\n",
       "   'estimatedEPS': '8.2892',\n",
       "   'surprise': '0.6108',\n",
       "   'surprisePercentage': '7.3686'},\n",
       "  {'fiscalDateEnding': '2017-03-31',\n",
       "   'reportedDate': '2017-04-27',\n",
       "   'reportedEPS': '10.006',\n",
       "   'estimatedEPS': '9.3552',\n",
       "   'surprise': '0.6508',\n",
       "   'surprisePercentage': '6.9566'},\n",
       "  {'fiscalDateEnding': '2016-12-31',\n",
       "   'reportedDate': '2017-01-26',\n",
       "   'reportedEPS': '9.36',\n",
       "   'estimatedEPS': '9.6114',\n",
       "   'surprise': '-0.2514',\n",
       "   'surprisePercentage': '-2.6156'},\n",
       "  {'fiscalDateEnding': '2016-09-30',\n",
       "   'reportedDate': '2016-10-27',\n",
       "   'reportedEPS': '9.06',\n",
       "   'estimatedEPS': '8.6021',\n",
       "   'surprise': '0.4579',\n",
       "   'surprisePercentage': '5.3231'},\n",
       "  {'fiscalDateEnding': '2016-06-30',\n",
       "   'reportedDate': '2016-07-28',\n",
       "   'reportedEPS': '8.42',\n",
       "   'estimatedEPS': '8.0364',\n",
       "   'surprise': '0.3836',\n",
       "   'surprisePercentage': '4.7733'},\n",
       "  {'fiscalDateEnding': '2016-03-31',\n",
       "   'reportedDate': '2016-04-21',\n",
       "   'reportedEPS': '7.5',\n",
       "   'estimatedEPS': '7.9623',\n",
       "   'surprise': '-0.4623',\n",
       "   'surprisePercentage': '-5.8061'},\n",
       "  {'fiscalDateEnding': '2015-12-31',\n",
       "   'reportedDate': '2016-02-01',\n",
       "   'reportedEPS': '8.67',\n",
       "   'estimatedEPS': '8.0851',\n",
       "   'surprise': '0.5849',\n",
       "   'surprisePercentage': '7.2343'},\n",
       "  {'fiscalDateEnding': '2015-09-30',\n",
       "   'reportedDate': '2015-10-22',\n",
       "   'reportedEPS': '7.35',\n",
       "   'estimatedEPS': '7.21',\n",
       "   'surprise': '0.14',\n",
       "   'surprisePercentage': '1.9417'},\n",
       "  {'fiscalDateEnding': '2015-06-30',\n",
       "   'reportedDate': '2015-07-16',\n",
       "   'reportedEPS': '6.99',\n",
       "   'estimatedEPS': '6.74',\n",
       "   'surprise': '0.25',\n",
       "   'surprisePercentage': '3.7092'},\n",
       "  {'fiscalDateEnding': '2015-03-31',\n",
       "   'reportedDate': '2015-04-23',\n",
       "   'reportedEPS': '6.57',\n",
       "   'estimatedEPS': 'None',\n",
       "   'surprise': 'None',\n",
       "   'surprisePercentage': 'None'},\n",
       "  {'fiscalDateEnding': '2014-12-31',\n",
       "   'reportedDate': '2015-01-29',\n",
       "   'reportedEPS': '6.88',\n",
       "   'estimatedEPS': 'None',\n",
       "   'surprise': 'None',\n",
       "   'surprisePercentage': 'None'},\n",
       "  {'fiscalDateEnding': '2014-09-30',\n",
       "   'reportedDate': '2014-10-16',\n",
       "   'reportedEPS': '6.35',\n",
       "   'estimatedEPS': 'None',\n",
       "   'surprise': 'None',\n",
       "   'surprisePercentage': 'None'},\n",
       "  {'fiscalDateEnding': '2014-06-30',\n",
       "   'reportedDate': '2014-07-18',\n",
       "   'reportedEPS': '4.972',\n",
       "   'estimatedEPS': 'None',\n",
       "   'surprise': 'None',\n",
       "   'surprisePercentage': 'None'},\n",
       "  {'fiscalDateEnding': '2014-03-31',\n",
       "   'reportedDate': '2014-04-17',\n",
       "   'reportedEPS': '10.0386',\n",
       "   'estimatedEPS': 'None',\n",
       "   'surprise': 'None',\n",
       "   'surprisePercentage': 'None'}]}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call = json.loads(call)\n",
    "call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fiscalDateEnding</th>\n",
       "      <th>reportedDate</th>\n",
       "      <th>reportedEPS</th>\n",
       "      <th>estimatedEPS</th>\n",
       "      <th>surprise</th>\n",
       "      <th>surprisePercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>2020-10-29</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.1964</td>\n",
       "      <td>5.2036</td>\n",
       "      <td>46.4757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>10.13</td>\n",
       "      <td>8.2091</td>\n",
       "      <td>1.9209</td>\n",
       "      <td>23.3996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.762</td>\n",
       "      <td>-0.892</td>\n",
       "      <td>-8.2884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>15.35</td>\n",
       "      <td>12.5938</td>\n",
       "      <td>2.7562</td>\n",
       "      <td>21.8854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>10.92</td>\n",
       "      <td>12.709</td>\n",
       "      <td>-1.789</td>\n",
       "      <td>-14.0766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>2019-07-25</td>\n",
       "      <td>14.21</td>\n",
       "      <td>11.471</td>\n",
       "      <td>2.739</td>\n",
       "      <td>23.8776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-03-31</td>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>11.9</td>\n",
       "      <td>10.3995</td>\n",
       "      <td>1.5005</td>\n",
       "      <td>14.4286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>12.77</td>\n",
       "      <td>10.9778</td>\n",
       "      <td>1.7922</td>\n",
       "      <td>16.3257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>13.06</td>\n",
       "      <td>10.4613</td>\n",
       "      <td>2.5987</td>\n",
       "      <td>24.8411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>2018-07-23</td>\n",
       "      <td>11.75</td>\n",
       "      <td>9.5405</td>\n",
       "      <td>2.2095</td>\n",
       "      <td>23.1592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>9.4994</td>\n",
       "      <td>9.2878</td>\n",
       "      <td>0.2116</td>\n",
       "      <td>2.2783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.8995</td>\n",
       "      <td>-0.1995</td>\n",
       "      <td>-2.0153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>2017-10-26</td>\n",
       "      <td>9.57</td>\n",
       "      <td>8.4592</td>\n",
       "      <td>1.1108</td>\n",
       "      <td>13.1313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.2892</td>\n",
       "      <td>0.6108</td>\n",
       "      <td>7.3686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>10.006</td>\n",
       "      <td>9.3552</td>\n",
       "      <td>0.6508</td>\n",
       "      <td>6.9566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>9.36</td>\n",
       "      <td>9.6114</td>\n",
       "      <td>-0.2514</td>\n",
       "      <td>-2.6156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>9.06</td>\n",
       "      <td>8.6021</td>\n",
       "      <td>0.4579</td>\n",
       "      <td>5.3231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>2016-07-28</td>\n",
       "      <td>8.42</td>\n",
       "      <td>8.0364</td>\n",
       "      <td>0.3836</td>\n",
       "      <td>4.7733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>2016-04-21</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.9623</td>\n",
       "      <td>-0.4623</td>\n",
       "      <td>-5.8061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>8.67</td>\n",
       "      <td>8.0851</td>\n",
       "      <td>0.5849</td>\n",
       "      <td>7.2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.21</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.9417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>2015-07-16</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.7092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>6.57</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>6.88</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>2014-10-16</td>\n",
       "      <td>6.35</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>4.972</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>10.0386</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fiscalDateEnding reportedDate reportedEPS estimatedEPS surprise  \\\n",
       "0        2020-09-30   2020-10-29        16.4      11.1964   5.2036   \n",
       "1        2020-06-30   2020-07-30       10.13       8.2091   1.9209   \n",
       "2        2020-03-31   2020-04-28        9.87       10.762   -0.892   \n",
       "3        2019-12-31   2020-02-03       15.35      12.5938   2.7562   \n",
       "4        2019-09-30   2019-10-28       10.92       12.709   -1.789   \n",
       "5        2019-06-30   2019-07-25       14.21       11.471    2.739   \n",
       "6        2019-03-31   2019-04-29        11.9      10.3995   1.5005   \n",
       "7        2018-12-31   2019-02-04       12.77      10.9778   1.7922   \n",
       "8        2018-09-30   2018-10-25       13.06      10.4613   2.5987   \n",
       "9        2018-06-30   2018-07-23       11.75       9.5405   2.2095   \n",
       "10       2018-03-31   2018-04-23      9.4994       9.2878   0.2116   \n",
       "11       2017-12-31   2018-02-01         9.7       9.8995  -0.1995   \n",
       "12       2017-09-30   2017-10-26        9.57       8.4592   1.1108   \n",
       "13       2017-06-30   2017-07-24         8.9       8.2892   0.6108   \n",
       "14       2017-03-31   2017-04-27      10.006       9.3552   0.6508   \n",
       "15       2016-12-31   2017-01-26        9.36       9.6114  -0.2514   \n",
       "16       2016-09-30   2016-10-27        9.06       8.6021   0.4579   \n",
       "17       2016-06-30   2016-07-28        8.42       8.0364   0.3836   \n",
       "18       2016-03-31   2016-04-21         7.5       7.9623  -0.4623   \n",
       "19       2015-12-31   2016-02-01        8.67       8.0851   0.5849   \n",
       "20       2015-09-30   2015-10-22        7.35         7.21     0.14   \n",
       "21       2015-06-30   2015-07-16        6.99         6.74     0.25   \n",
       "22       2015-03-31   2015-04-23        6.57         None     None   \n",
       "23       2014-12-31   2015-01-29        6.88         None     None   \n",
       "24       2014-09-30   2014-10-16        6.35         None     None   \n",
       "25       2014-06-30   2014-07-18       4.972         None     None   \n",
       "26       2014-03-31   2014-04-17     10.0386         None     None   \n",
       "\n",
       "   surprisePercentage  \n",
       "0             46.4757  \n",
       "1             23.3996  \n",
       "2             -8.2884  \n",
       "3             21.8854  \n",
       "4            -14.0766  \n",
       "5             23.8776  \n",
       "6             14.4286  \n",
       "7             16.3257  \n",
       "8             24.8411  \n",
       "9             23.1592  \n",
       "10             2.2783  \n",
       "11            -2.0153  \n",
       "12            13.1313  \n",
       "13             7.3686  \n",
       "14             6.9566  \n",
       "15            -2.6156  \n",
       "16             5.3231  \n",
       "17             4.7733  \n",
       "18            -5.8061  \n",
       "19             7.2343  \n",
       "20             1.9417  \n",
       "21             3.7092  \n",
       "22               None  \n",
       "23               None  \n",
       "24               None  \n",
       "25               None  \n",
       "26               None  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(call['quarterlyEarnings'])"
   ]
  },
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