options_modeling_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "#https://www.macroption.com/historical-volatility-calculation/\n",
    "import json\n",
    "import os\n",
    "import requests\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.figure import Figure\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "import matplotlib.tri as mtri\n",
    "from scipy.interpolate import griddata\n",
    "from scipy.spatial import Delaunay\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.figure_factory as FF\n",
    "import plotly.io as pio\n",
    "from iexfinance.stocks import Stock, get_historical_data\n",
    "from iexfinance import get_stats_recent\n",
    "from fredapi import Fred\n",
    "from pytradier import *\n",
    "from options_ import *\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('images'):\n",
    "    os.mkdir('images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('config.json') as f:\n",
    "    data = json.load(f)\n",
    "fred_key = data['fred']\n",
    "tradier_key = data['tradier']\n",
    "plotly_cred = data['plotly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FRED \n",
    "tradier = Tradier(tradier_key)\n",
    "plotly.tools.set_credentials_file(username= plotly_cred['username'], api_key= plotly_cred['api-key'])\n",
    "plotly.tools.set_config_file(world_readable=True,\n",
    "                             sharing='public')\n",
    "fred = Fred(api_key=fred_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portfolio = ['MSFT', 'BA', 'AAPL', 'V', 'GOOGL', 'SPY']\n",
    "stocks = Stock(portfolio)\n",
    "p = stocks.get_price()\n",
    "options_dates = [1, 3, 6, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = timedelta(days = 61)\n",
    "yest = timedelta(days = 1)\n",
    "start = date.today()-d\n",
    "end = date.today()-yest\n",
    "historicals = get_historical_data(portfolio, start, end, output_format='pandas').reset_index()\n",
    "dates = historicals['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~jaisal1024/0 or inside your plot.ly account where it is named 'simple_candlestick'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jaisal1024/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Candlestick Graph\n",
    "df = historicals[portfolio[0]].join(dates)\n",
    "trace = go.Ohlc(x=df['date'],\n",
    "                open=df['open'],\n",
    "                high=df['high'],\n",
    "                low=df['low'],\n",
    "                close=df['close'])\n",
    "data = [trace]\n",
    "py.iplot(data, filename='simple_candlestick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21741f28>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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bvWlF3BAbzotXdcRNd/69yOP6MrYkKGK/LTGffHsvt3WAGwOi/RkQ7U/fVn7VWvm7Ugt4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\niDb+Go1G0wDRxl+j0WgaIP8fRwpxXy/4i1oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22d811d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_1_df_ = hv_1(historicals, portfolio)\n",
    "hv_1_df_.plot(title = 'HV Pct Change')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21c9deb8>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G7d1u55rIa9DWYjBTCMHq+EH4ZJXT+bNn8CmrinDITgSTXr5I7Sz7LguPgqUC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\nGiD/D2cWJj8Uxi1BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21c9ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_2_df_ = hv_2(historicals, portfolio)\n",
    "hv_2_df_.plot(title = 'HV Log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22b6a5f8>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0nqQq/pZGqwedz0nn+2u1WpKSkhgwYACDBg1i48aNtfLrc+usotJaUXv+nkJKxZHar09B\nebay6GrccxAU52nJzm0MgVCZp4y71BlY9/HxYceOHQAsX76c2bNn89tvv7nyT+bWWUWlNaL2/D1B\nzk749HJYNAN8Q2DGMpj8sar4WwM1M32qy09ZrLy8nJCQENd5jVvnF154gZSUlOaUUEWlSVB7/i2J\nsQhWvwBbP1X81V8xHwbdfGq/MyonsuxxxV1FUxLdDy6dB16+yqK46vITZleZTCaSkpIwm83k5OSw\natUqV15D3DqrqLQm1J5/S2C3wR8fwtsDYetnMPRueHArJM9QFX9rQwjwDlQGfeu4Pqkx+6Snp/PL\nL79w8803U+MeZeHChUydOrWWW2cVldaM2vNvbg6uUXqqBWlK+MIJ8yAywdNStW0unde81zcEKm6u\nLUbw9q+3yIgRIygsLKSgoIDc3NxTunVWUWmNqD3/5qIkExbeCP+ZpLhOnvoVTF+sKv62gHeAsj+F\n3T89PR273U5YWBgpKSnMmTOHzMxMMjMzOXbsGEePHlXdOqu0atSef1NTXQnr34SNbyu244ueghEP\ngJfB05KpNBSNTnH3YC6v5f20xuYPIKXks88+Q6vVsnDhQpYtW1brEjVunWfNmtWioquoNBRV+TcV\nUsJf38L/noGKY9B/Coyd0+ZcJ6s4MQSeEODFbrfXW/TQoUMnpL3xxhvNKp6KytmiKv+m4NgOWDYL\njvwOMUlw3adqgJS2jncgcEwx/ejCPS2NikqToyr/s6GyAFY9p4RE9AuHie9A0o2qP/j2gM6grPg1\nlyl/WxWVdkajtJQQYoIQIkMIsV8I8fgpyk0WQkghRLJb2mxnvQwhxPizEdrj2K1KNK23B8OOr2DE\n/crUzUHTVcXfXqgJ8FJdqQR4UVFpZzS45y+E0ALvAuOAbGCLEOIHKeWeOuUCgIeAP9zSegNTgT5A\nB2CFEKKnlLJ+I2prZv8K+GU2FO6F7mNh/EsQ0dPTUqk0B64AL5VKQ6Ci0o5oTDd1KLBfSnlQSmkB\nFgKT6in3PPAKYHZLmwQslFJWSykPAfud12s7FB2Ar6bCF9cqMXGnfQ03LlIVf3vGFeBFddSm0v5o\njPKPBY64nWc701wIIQYCHaWUSxtb11n/LiFEqhAitaCgoBGiNTPbv4D3hkPmOhj7LNz3O/SaoHrd\nbO9oNEoDUH3ial8VlbZOY5R/fZrO9R8hhNAAbwJ/b2xdV4KUC6SUyVLK5IiIiEaI1ozs+QF+eBA6\nn6fY9Uc+AjpvT0ul0lIYgsBuAZuZvLw8brjhBrp27crgwYMZMWIEixcvBmD9+vUMHTqUhIQEEhIS\nWLBgQa3LLFiwwJU3dOhQ1q9f78qz2Ww88cQT9OjRg6SkJJKSkpg7d64r39+//lXGKipnQ2Nm+2QD\nHd3O44BjbucBQF9gjVB6xNHAD0KIiQ2o2zo5tBa+ux1ik5UVuno/T0uk0tJ4K7Z+aSrjqquu5pZb\nbuGrr74CICsrix9++IHc3FxuuOEGlixZwqBBgygsLGT8+PHExsZy+eWXs3TpUj788EPWr19PeHg4\n27Zt46qrrmLz5s1ER0fz5JNPkpuby19//YXBYKCiooLXX3/dk0+tci4gpWzQhtJQHAS6AHrgT6DP\nKcqvAZKdx32c5b2d9Q8C2lPdb/DgwdKjHN0u5dxYKd8ZJqWxyLOyqMg9e/Z47uZ5aXLFd5/K0aNH\n15v95JNPyqeeeqpW2ooVK+TIkSOllFKOHDlSrly58oQ6Tz75pDQajTI0NFSWl5ef9PZ+fn6nFdGj\n70elVQGkygbo9Ab3/KWUNiHEA8ByQAt8LKXcLYR4znmzH05Rd7cQ4htgD2AD7peteaZP0QFlYNcn\nGKZ/rwZOb2W8vPll0ovTm/SaCaEJzBp6ElcMhkB2797NoIFJ9Wbv3r2bW265pVZacnIyu3fvduUP\nHjz4hPzPPvuM/fv306lTJwICAs7+IVRUGkGjJqVLKX+WUvaUUnaTUs51pj1dn+KXUl4opUx1O5/r\nrNdLSrmsbvlWQ3kOfH4VIGH6EtU9g8rxAC82iyvp/vvvZ8CAAQwZMgQpJaKewf/60mo4WZ1PPvmE\npKQkOnbsyJEjR+qpqaLSNKgrfN0xlcAX10BVMdzyI4R397REKvVw0h56c+HlS5+EHnz3y0eupHff\nfZfCwkKSk5MZP348qampTJw40ZW/detWevfuDUDv3r3ZunUrF110kSt/27Zt9O7dm+7du3P48GEq\nKioICAhgxowZzJgxg759+57Ul5CKSlOgLketwVIFX02Bov0w9UuIHeRpiVRaC0Jw0cVjMZuqeP+9\n91zJVVVVgPIV8Omnn7ri+xYVFTFr1ixmzpwJwMyZM5k1axZFRUUA7Nixg08//ZT77rsPX19fbr/9\ndh544AHMZmVpjN1ux2KxoKLSnKg9f1DcNXx7CxzZrDhl63qhK8vmsJFRksHB0oM4pLLMXzpnqUq3\nud/uaa5jZK0y9eXXTXO/7snyG3od92vZpA2r3YrFbsHisGCxW7A6rEqa89zisLjKjIobxX1J9zX0\nDbZ7hCGIJf9+nb+9tIBXXn2ViIgI/Pz8ePnll4mJieGLL77gzjvvpKKiAikljzzyCFdeeSUAEydO\n5OjRo5x33nkIIQgICOCLL74gJiYGgLlz5/LUU0/Rt29fAgIC8PHx4ZZbbqFDB8XkWFVVRVzc8fjO\njz76KI8++mjLvwSVdoWoq5xaC8nJyTI1NfX0Bc8WhwOW3AM7v4Yr3sQ6cDq7i3aTmpdKal4qO/J3\nYLQam1+OFkArtOi1erw0XnhpvNBr9a5zvVaPXqOcV1or2VO0h39d8i+Gxwz3tNgApKWlkZiY6DkB\nHDYlbrB/VKscB/L4+1FpNQghtkopk09X7tzu+UuJafnj7Ny7hK1JV7C1aAN/prxHtb0agO7B3bmi\n6xUMjhpMYmgiOo3yuoQQCOe6NdfebfDOPa2+/JoyNWnu1zqhnFuZutcSbmvnapU7yXVr5D8d1fZq\nJi2ZxLw/5vHtxG/x0ng1qF675iQBXlRU2irnnPKXUrKzcCerD69m674f2WXOwxYThaZ8F720vbi+\n1/UMjhrMoMhBhBhCPC2uR/DWejNzyEweXv0wC9MXMr33dE+L1DrwDlQC9bgFeFFRaaucM8r/YNlB\nfjr4Ez8f/Jnsymx0aOhjNnFLQDyDRz1JUtRAAvTqXOsaxnQcw/kdzue9He9xaZdLCfdRfdpjcCp/\nNcCLSjugXSv//Kp8lh1axk8HfyKtOA2N0DA0tC93a8IY+9fP+HcdA9enqL24ehBCMGvoLK754Rre\n2vYWz5//vKdF8jxqgBeVdkS7U/4VlgpWZK3gp0M/sTlnMxJJn9BEZsZewoQju4hIXQoaL+hzLVw5\nX1X8p6BLUBemJ07nk92fcF3P6+gf0d/TInkWIRTTj6lYmSigBu5RacO0eeVvdVjJr8onrSiNnw/9\nzG9HfsPisNAxoCN3d5/MZSUFdNn9I5iWQ0gXxSVz0o3g30q8hrZy7h5wN0sPLuWlP17iy8u/RCPO\ncYVnCIIqNcCLStunVSt/KSVl1WXkGHNcW64xt9a+oKrANbc91BDKdd2v5nLpS9/0XxE7X1d6+YlX\nwOAZED9K7a01Ej8vP/42+G88sf4J/rv/v1zd42pPi+QxhBDcdOONfP7qP8BUgk3nS0xMDMOGDWPp\n0qXk5eVx++23c+TIEaxWK/Hx8fz8889kZmaSmJhIr169XNe69957ef/99wHYs2cPvXr1QqvVMmHC\nBObNm+epR1QBsBhB59PudUWrVf77i9IY9p8kTNSOn6oXOmK8g4k2hDEiuBcxsWOICYijo86PgYe2\noPvtX2AuhdCuMO45GHCD2ss/S67oegXfZHzD/G3zubjzxQTqz80er5+fH7t278YkfPExFfO/DX8S\nG3s8JtHTTz/NuHHjePjhhwHYuXOnK69bt26uFcA13H333QDEx8ezevVqwsPVcQSPYDFC5nolROv+\nlVB8QIng5hPi3EKVvW+o23lw7fOaY71/mwny1GqVv7d0MNlYTYzFrGw2O9E2G6EOR72RYQCll997\nIgy+Venlt5E/QmtHCMHsYbOZunQq7+94v+V967QiLr30Un5au5XJIxNI+eIzpk2bxrp16wDIycnh\nkksucZXt3/8cHyNprUgJ+XsURb9/BRzepATs0flAl1GQNA2sZmVsx1Si+PqqyFHqmEoUk9/J0HjV\n01CEgG99DYnbsZdPyz2/k1ar/DuG92Hmfc4Vvnar8sItRqh27i0VbueVoNFCwhXqLIxmondYbyb3\nnExKegrX9LiGHiE9PCZL7osvUp3WtC6dvRMTiH7iidOWmzp1Ks899xxXjH6JnX/t4rY773Ep//vv\nv58pU6bwzjvvMHbsWGbMmOFy0XDgwAGSkhSX0Oeffz7vvvtuk8qvchqqiuHgGjiwUlH6FTlKemRv\nGHoXdB8LnUaAl+H017JVg6m0duNgKqnnvARKMuHYdiXPZj75NXWGOg1D8PHziAToP6XJzVCtVvnX\nQut1/BNMxWM8NPAhlmcuZ97meXx0yUendFncXunfvz+ZmZmkLF3NZReNVJSAk/Hjx3Pw4EF++eUX\nli1bxsCBA9m1axdQv9lHpRkpz4HsLcp2eBMc3QrSoQzYdx0D3S+GbhdD0AmhxE+PzhsCopStMVhN\np2goas6djUbhvuP5Div8mQJXfwiBMY2X92SP0WRXUmn3BBuCeXDgg8z9Yy6/Zv3K+PjxHpGjIT30\n5mTixIk8Nms2a374iqKcI7WCu4eGhnLDDTdwww03cMUVV7B27doTArmoNDFWM+TuPK7sj2yB8mwl\nT6uHmAEw+h+Kso8dDFoPqT0vH6WxaUyDIyVs/wKWzYT3z4Or3oNelzaJOKryV2kU1/W8jkV7F/Fa\n6muMih2Fr5evp0VqcW677TaCgoLoN/QC1vz3c3D6glq1ahXDhw/H19eXiooKDhw4QKdOnTwsbTtD\nSig97FT0qZC9GXJ2Kr1jgKBO0HEoxN2v7KP7KT31tooQMGg6dBoOi2ZAylQYcidc8vxZjxOoyl+l\nUWg1WmYPm82tv9zKv3f9mwcHPuhpkVqcuLg414we9AHKYKHdxtatW3nggQfQ6XQ4HA7uuOMOhgwZ\nQmZmpkflbdM47MpA6+HfIWujYsKpsdd7+UKHQTDifogbAnHJEBDtWXmbi/AecMdKWPkcbHoHsjbA\n5I8h8sw9uaounVXOiFlrZ7EiawVLrlpCx4COzX6/Vuuy2GqCgnTwj25Se2xjabXvp7FYzcoA6eGN\nkLVJibFRXabkBcYqg7Kdhiu9+sg+njPheJJ9KxQ39NUVMH4uJN9ea2aj6tJZpVl5dPCjrD6ymle2\nvMLbF73taXE8h5ePMohoLFDWkzTQbbaKE7tNmYWTtcE5MLvNZUYjIgH6Xg2dzoPOIyCoozp9G6DH\nWLh3Iyy5F376OxxYDRPfVmYJNYJG/VKFEBOAtwAt8JGUcl6d/HuA+wE7UAncJaXcI4SIB9KADGfR\n36WU9zRKUpVWRZRfFHf3v5v52+az/uh6RsaO9LRInsM/GswZYCxsv2aHpsZSBTu+hI1vQ2mW0mjG\nJMGwu5Tefcfh4BfmaSlbL/6RcMO38Mf78L9n4P3z4ZoFyjqFBtJg5S+E0ALvAuOAbGCLEOIHKeUe\nt2JfSSk/cJafCLwBTHDmHZBSJjVYMpVWz/Te01m8fzEvb36ZYROH4aU9R4O+6H0Vh2+V+eAXoaw5\nUakfUwls/gj++EDxkRQ3BC55QZl6qffztHRtC41GGe+IHwmLbofProRRDQ/v2ZhVA0OB/VLKg1JK\nC7AQmOReQEpZ7nbqB7TOAQWVJkGv1TNzyEwyyzN5NfVVjlYe9bRIniMgGqQdqoo8LUnrpOwoLP8n\nvNEHVr8AsYPg1p/h9v8pq/JVxX/mxAyAu3+DgTfButcbXK0xZp9Y4IjbeTYwrG4hIcT9wKOAHrjI\nLauLEGI7UA48KaVcV0/du4C7AHWKXBthdNxoLutyGSnpKaSkpxAfGM/I2JGMjB3J4KjBGHQNWDHZ\nHtD7KX5dKvPAN7zdOwVrMAUZsOEt2PmNssiq77Vw/sMQ3dfTkrUv9H4w6R3odhE8e22DqjR4to8Q\n4jpgvJTyDuf5dGColLLeuX5CiBuc5W8RQngD/lLKIiHEYGAJ0KfOl0It1Nk+bQcpJVnlWaw/up71\nx9aTmptKtb0ab603ydHJjOyTy8zWAAAgAElEQVSgNAadAzuf8argNjGbpboCivZDUJxi/mlBWt37\nObIZ1r8JGT8rPnMGTYcRD0BIZ09L1u5pjtk+2YD7nL444Ngpyi8E3geQUlYD1c7jrUKIA0BPQNXu\n7QAhBPFB8cQHxXNT75sw28yk5qWy4egG1h9dz8tbXublLS8T6x/LyNiRnN/hfIbGDMXPq2196s+d\nO5evvvoKrVaLRqPhww8/ZNasWeTk5GAwGPD39+fj157is5T3sBtCePmVVwDIyspizJgxbNu2jeDg\nYA8/RTNgs0DhXmU+ft5uZT5+9mbFHcsFsxTfOarPrVZHY5T/FqCHEKILcBSYCtzgXkAI0UNKuc95\nejmwz5keARRLKe1CiK5AD+Dg2Qqv0jox6Awu088sZpFdka00BMfW88OBH/g642u0QktCaALJUckM\njhrMoKhBBHkHeVr0k7Jp0yaWLl3Ktm3b8Pb2prCwEIvFAsCXX35JcnIyCxYs4B9z/4+v33qKgZfe\nzK0zZpCYmMjDDz/M888/3/YVv8MBZYchbw/k73bu9yhfOw6bUkbjpUzRnDAPBk4Hb3/PyqxyUhqs\n/KWUNiHEA8BylKmeH0spdwshngNSpZQ/AA8IIcYCVqAEuMVZfTTwnBDChjIN9B4pZXFTPohK6yUu\nII4pCVOYkjAFi93C9vztbM7dzNa8raSkp/DZns8QCHqE9GBw1GDX1pqCxufk5BAeHo63t+IqoD7f\n+6NHj2b+/Pn4BIbyxjOPct999zFz5kwqKiq48cYbW1rks8NY5Kbgd0N+mrK5uzMO7qQstEq4XPGO\nGdlbWYl6rs76amM0ap6/lPJn4Oc6aU+7HT98knrfAd+diYAq7Qu9Vs+wmGEMi1HmClTbq/mr4C+2\n5m1la95WluxfQkp6CgDxgfGuhqCLo4vrGuu+2UvhkVP4VD8Dwjv6M+r6nifNv+SSS3juuefo2bMn\nY8eOZcqUKVxwwQW1yvz444/069cP/KO4bMxw/v3tz9x8882sX7++SWVtUixVygrl/D3He/L5e5SB\n6xp8QiGqjxL+NDJROY5IUMNYtnHU5YgqHqVmUDg5WhmfsjqspBWluRqDXzN/5bt93zG/93z0JXr8\ndH5U26px4EDTqJnKZ4e/vz9bt25l3bp1rF69milTprjCLd544434+PgQHx/P22+/raz41flw//Sr\nMVnstcI3egyHHYoPKjb5/LTjvfrig7hmZOsMilLvdjFEOXvyUX3AP6rdrayVDgdV5WVUFBZQUVRI\nRVEBFcVFVBQWYKqswOAfgG9gEL5BQfgGBuMbGIRPzXFQEN6+fm3epbmq/FVaFV4aL/pH9Kd/RH9m\n9J2B3WFnX+k+KrMr8dH5UGGtIOYSDTH4oNPo8PXyxU/nh6+XL95a72b9h9RqtVx44YVceOGF9OvX\nj88++ww4bvOvRUAUGmlHI+0Nv4HDDuYyZRNCMZ9o9Mpe66W4J9boTq2IpYSK3OM9+BqzTUGGWzAR\noYQ5jeoN/a5zKvo+ENql3SxQczjslBcUUJJzlNK8HDclr2yVxUU47LZadXReevzDwvDxD6SisICq\n8lKqjcZ6r6/R6vANDMQnSGkYahoKH2fjoKQFuxoPL0Prm/KsKn+VVo1WowwMp+Wl0TGgI1JKLHYL\nRpuRKmsVRquR8upyV1lfnS9+XkpjYNAamqwxyMjIQKPR0KOHEsFsx44ddO7c2RWs5QQMwcrgp61a\nUcgnk0M6wFyurHytLlfONV5KebuVE9dJ1jQKbg2CVqfU/+RyRdGbSo4X949SevBD7nD25HtDeC9l\nVXIbR0qJsaSYkpyjlOQcoyT3mHJ87Chl+bnYbceVu0arIyAsjICwCGJ7JRIQFo5/WDgBYREEhIUT\nEBaOT0DgCb8Xu82KqbycqvIyqspKMZWXuY6rnMemsjJKc49RVV6O1WyqV1ad3vt4oxAUjE9AUO3z\nwNrHOq/mHzdRlb9Km0IIgbfOG2+dN6GGUKSUWB1WjFZnY2AzUmGpAEAjNPh6+boaBIPOgEacmamo\nsrKSBx98kNLSUnQ6Hd27d2fBggVMnjz5ZIIqcVulXVHuPm4zmaRU1gSYSpRevrQrPfqasH16P6W+\nlMosGrtV8VdvtyjHduex1XS8wbAYFYdoiRMVU03NAGw78o9jMVVxIPUPDmzbQvGxbEpzjmGtPh4a\nUevlRXBUDKGxHek2ZDgh0R0IielAcHQH/IKCEWew8E6r88I/NAz/0Ia9R2u1WWksykqpqiijqux4\nQ1HTcFSWFJOfdQhTWWmtBsodvY+v66shpEMsQyZeS1hs03rPVV06q7QJGrOIyWq3UmWrcjUI1U4v\nkRqhwUfno5iKvPzw0fmccWPQIGoChWt0EN5TUdCmEjCXKkpdaJXxAZ8Q8A44M7u6lCAdpKVnkNi7\nd9M/g4exVps5uC2VjE1rObQtFZvVgl9IKJGduxASE0twTAdCYmIJjYnFPywMTRsyW0kpsZhMVJWX\nUlVW0zgox1Xlpa5GJGf/XmzV1fS5cCwjJk8jMPzUCwhVl84q5yxeWi+CtEGudQM2h831VVBlraKg\nqoACChBCKI2BzpdQn1C8NE38qS2EYnYpOwJ5u5xz4TXKLBmfEMUZ3Nm6gRBCaUTa+OCjOzarlcw/\nt5GxcS0HUv/AWm3GNyiYfhePp9eIUXTomXBGvfjWhhACb19fvH19CYnucNJyVeVl/LH4G/789SfS\n1q8mafwVDLvqOnwCzm62lar8Vdo9Oo2OQO9AAr2Vfxa7w17ry6DQVEi1vZpOgc3gT8o3VOntC42i\n8A1B7WZQtSmx22wc2fUn6ZvWsX/zJqqrjBj8A0gceSG9zhtNXO8+bapX35T4BgYx5pY7GXzZJDZ+\n+xXbfvovf61czpArr2HQ5ZPQG84snKOq/FVqIe12TDt3og0KwrtrV0+L0yxoNVoC9AEE6AMAyDHm\nUGIuwe6wo21qBSM0ysInlXoxlpawaVEKe39fj6miHL2PLz2GjqDXeaPp1HcAWl3Lqihps1G9fz9e\nsbFoAwJa9N6nIzAikgn3PULylVez4evP2fDNF2xfvpTh10yh/9gJaHWN+3JVlX8DqNq+nYrlv6IL\nD0MXE4NXTAxe0dHoIiMRLfzjbA4cVVUYN26kfOUqSteupaqyHL+gYBJXrkS0wKwDTxOoD6TYVEyl\ntbJVu5hob+zbvJFfF7yD1Wyi+5ARJJw3mvgBg9Dp9R6Rx1ZYyNG/PUrVli0AeMXFYUhMwDshAUNi\nIoaEBHQxMR6f3x/esTOTHnuSY3vTWZfyKas++ZDUpUs4f8pNJJw/usHXafuaqxmRViuF779P4Qcf\nKrbZuiPzGg26yEilIYiJxis6Bq+YaKWBcB5rQ0Ob3D4pHQ5sNis4JFI6kFIiHRKJRDocIKUzzZkn\nHVjN1VSVlWAsK6WqrJSKY0cpy8ig8mg2VRXlVGsFFi8djrhgIBi91Yb/4u/odP3UJpW9NeKr80Wn\n0VFuKVeVfwtgMVWx+rN/sWv1/4js0o3LHniMsLiGz2SxOqxNPj5TtX07Rx9+BHt5OZEzZyItFszp\n6VSnp1OxYqUysA5ogoIw9OrlbBQSlX3XrggPNFgdeiZw/dMvkfXnNtal/Idl77zOlh8a7khBVf4n\nwZKVxdF/zMS8cydBV11F1JP/BMCWk4M1NxfrsRysuTnYcnKx5uZSvSeNypWrkE5nXzUILy9nYxCt\nNAzRMWgiI3CEhuAIDMTu74sVqK6qorrKSHWVEUuVEbPRua8yYqmqotpYSbWpimqjkWpTlevHeKYI\nKdHb7BiEBt/ISKLiOhLYtRt+oWHoDQbWfvA2P37/FTeMGkVITOxZ3au1I4QgQB9AWXUZDulo3hlA\n5zhHM9JY9u7rlOfnM+zq6xkxedpJzRVSSvKq8kgrSiO9OJ20YmWfY8whzBBG58DOdArspOwDlH3H\ngI74ejV8DYOUkpKUFPJemodXdDTxC1MwJCTUKuMwGjHv3Ut1ejrmtHTM6emUfP0N0uycZurlhXf3\n7rUbhYReaIOavyMhhCA+aTCd+w8k4/f1bPj684bXVad61kZKSemiReS9NA/h5UXMs3MInDDheL7D\ngcVsUpRwldGpjJW92WjEXFSIqbAAc0kx5rIyp9I2YbFasDrsWAGH5tSfjQLw0uvxNvjg7eePISgI\nb/8AvH39lM3PD53eG41GgxDC+WUhkFYrsrISh7ESWVGJo7ISR0UFjvIK7NlH0BUU4W2zE9QrgdAx\nFxF48UXou3ev9zN277yXWL5lLfqwcKa+8CpBkZ6NTdvc/uorLZVklWfRMaCja2DYnQsvvJDZs2cz\nfvx4V9r8+fPZu3cv7733XrPJ1VBanT//OthtNjYtSmHzkm8JjIjg0vv/TmzC8ampdoedrIos0ovS\nXYo+oziDkmplwZpA0DmwM4mhiXQK7ER+VT5Z5VkcrjhMoamw1r0ifSNrNQgd/DsgEDikA7u045AO\nbA4b0mwm+t3/ErLmT8oGd+fgw1di9fNGp9HRP7w/iWGJ6DT194+l3Y4lKwtzWtrxRiEjHXvBcVm8\nOnRQTEYJCXgnKqYjr9jYZjUb2W02dF5eDZrqqSp/N2wlJeQ89RSVK1biO3w4Hea9hFd0NLvWrOD3\n71IwV1Y2qNet8/Y+rqh9fd2O/dD7+uLlkOisVnQmM5pKI5ryCjQlJYiCQkRePhQUUvfnoQ0ORtfB\naU6KjkYYDNgKCrDl52PLy8OWn4+jquoEWTR+fugiI9F37UrAmAvxv+ACdBGnDzRizc1l22WXsjmh\nM4bQMKbMeYnA8MjGvM4mpbmVm0M62FuyF38vf+IC4k7I//DDD/n999/55JNPXGnDhw/n1VdfZdSo\nhgfNbi5as/IvPpbNz2+/Tt7BffS5YCxjbr0LvY8Pe0v2subIGjYc20B6cTomm7I6VqfR0SO4B4lh\niSSEJpAYmkjPkJ4n7dEbrUYOlx8mqyJL2ZdnKQ1D+WFX41GXyBLJY9/b6ZQPi0Zq+G6kQNZRyr46\nXwZGDlR8T0Ul0yesz2njVNsKCjCnZ2BOT6M6LR1zRgaWQ4cUd9iAxt9faQwSElzjCd49eqBpQrNR\nQ+f5q8rfSeX6DeTMno29tJSIv/2N0FtvQWg0pK1bzc/vvkFMt57E9OiF3tcPg5+ixA2+/sreT9nX\nKPiznaHgsFiw5eZizcnFlpuDNScXa05tM5M0m9FFRro2r6jjx7qISHRRyl7rf+YBU47cex856bvZ\nHB+FT0AQ1895iYBQz7hZbgnldrTyKOXV5fQK7XWC6aeoqIiEhASys7Px9vYmMzOT0aNHk5WV5fEB\nQGidyl9KyZ+//sxvX3yMztubi+64h7LOetYcWcOaI2tcMZ/7hvWlf0R/RdGHJdItqNtplWxDKbeU\nk2vMBUAndGiEBrlxK+an5yEA/7lPYRh1HlqhRSM06DQ6Ki2VbM/fTmpeKqm5qRwoOwCAQWtgQOQA\nkqOUxqBfRD+8td6nlcFhMlG9b5/TZJRGdXoG5owMZE1nTafDu2tXpTHodbxR0IWEnNEzq8q/gTiq\nq8l//XVK/vM5+u7diH3tNZfNb9/mjfz45jziEvty9ePP4KU//R+6PVGxZg3Z99yL1z9ns2zlj/iF\nhDHlmZfwCz6zH+XZ4K7cVn+6gPyspo0FFNm5K8k3TONw+WE6BXZyTQN15/LLL+euu+5i0qRJzJs3\nj6KiIl599dUmleNMaW3K31hawvL353Nox1b8enQk8zwDa0v/oMJagbfWm+ExwxnTcQyj40YT4duw\nkJcOk4nK39Zi2r4Nfbdu+A4ahL5r1wZPqJAOB4Xvvkfhe+/h3asXcW//H/qOpx9oLjYXsy1vm6sx\n2FuyF4lEr9HTP6I/ydFKQKIBEQPw0TVszr10OLAePozZaTKqTlfGEmx5x11p66Kjla+EHt3RRUWj\ni4zAKzISXVQUuvDwk87EU1f4NgBzRgbHHvsH1fv2EXLTTUQ+9nc0Tu97h3ZsZen8V4ju3pOrZj51\nzil+AP9Ro9DFxKBf/RtXPz6H7198hm+f/yfXP/MSvoHtb1aMn5cfGqGh3FJer/KfNm0aCxcuZNKk\nSSxcuJCPP/7YA1K2fjav/Yn1H/8bu8VCap9S9nTKIrQilLGdx3JhxwsZHjO8wYOyDpOJyrXrKP9l\nGZVrfkOaTODlBVYroMy+8UkagO/AgfgMHIRPv75ofE+8tr2sjKMzZ2L8bS1BkyYSPWcOGp+GKepQ\ngyL72M5jASirLjveGOSlsmDnAhzSgU6jo29YX5eZaGDkwJM+p9Bo0MfHo4+PrzWmaCsudjYEx01H\nlRs2uJ73+AUE2tBQdFGReEU4v/ijotBFNjx29DnZ85d2O8Wff07B62+gCQqiw4tz8R99fH7skT1/\n8f2LzxAa25Hrnp6Lwe/cDUVX8O67FL79Dt1+XU5eRSnfz3uWkOgYrnv6xbNeXt4YWqpnm12RTaW1\nkp4hPU8w/VRWVtK1a1d++eUXpk2bRkZGRrPL01BaQ8/fbrPx4dszMf2+l8LAag6N9GFY3zGM6TiG\nfuH9GryAzmE2U7luHRXLfqFizRpkVRXa0FACLhlH4IRL8U0ejDU7m6pt2zFt307V9m1Y9iumGbRa\nDImJ+AwciO+ggfgMHIi9pITshx7GmptL9BOzCZ46tUlNdRWWCpeZaGveVvYU7sEmbWiFlt5hvRUz\nUbTSGNTXqTgd0uHAXlKCLT8fq3N8z5aXr5zn52HLL8CWl4e9WAmO2DsjXTX7uOOorsa4cSMVK1ZQ\nuXoN9uJi/C+6iJgXnkcXGuoqd2xvOovmPkVgeES77eE2BmteHvsvupiw22YQ+fe/k7lzO0teeY6w\nuE5c91TLNYwtpdzKq8s5UnGEzoGd8def+GzXX389e/fu5aqrrmLOnDnNLk9D8bTyrygv5v3nH0B7\nuJzKvkHcfP9zdAnt1uD6jupqjOvXU77sFypXrcJRVYU2JISASy4h8NIJ+CYnn3JBpb2sDNOOHVRt\n345p23ZMO3cen4opBLqICGLfmo/vwIFn+6inpcpaxY6CHaTmKo3BX4V/YXVY0QgNvUJ6ucxEyVHJ\nTbquRFos2AoK0MfFqcrfXl5O5W+/UbFiJZXr1iGrqtD4+eF/wQUEXnYp/hdfXKsHkJ95kG+em42P\nfyBT5sxrsBvX9s6R+x/AtGMHPVavQuj1HNy+hf++OpfILl2Z/M8X8K7nM7upaSnl5pAOMoozCPIO\nooP/ic62Fi9ezDXXXENaWhoJdeaDexJPKv+sA3v46qXZaCtt6Mb35pFbX27wWglbYSH5r75GxYoV\nOIxGtMHBBIwbpyj8oUPPeAW9tFoxp2dg2r4NW34+obfeiq6euMstgdlmZmfBTpeZaGfBTpen2R4h\nPVwDyIOjBhPmc/Y655wd8LXm5VG5ahUV/1uBcfNmsNnQRoQTcNHFBIwdi++wofVOqyrKPsLXzz6O\nzkvP1GdfJjDCc9MaWxuVv/3GkbvvIXb+my775P4tv/Pjmy8R3b0X1z7x7Bk7l2ooLancjlQcwWg1\n0iukV6uYydMQPKX8/1i7lN8+/ACL1k63WyYx7eJ7GlzXcuQIh2+/A1t+PoFXXE7ghEvxGza03bsU\nsdgt7Crc5RpA3lGwwzXNtWtQV5eZKDkqucGD4e60W+UvpURWVWEvKzu+lZZhycykYvUqzH/uBEDf\nuTMB48bif/HF+AwYcMoZAaW5OXw9ZxYOh4Mpc14mtEP7XtHaWKTdzv5x49B37kxnt3nue//YwNL5\nLxOX0EeZDeXdfKHqWlK5lVWXkV2RTXxQPH5eZz5VtiVpaeUvHQ4W/2c+h5atojjExoSHH2NU4tgG\n1zenp3P4zjvBYqXjgg/xGTCgGaVt3VgdVvYU7SE1V/ky2J6/HaNVCR/ZObAzw2OGc3X3q+kT3qdB\n12sW5S+EmAC8BWiBj6SU8+rk3wPcD9iBSuAuKeUeZ95s4HZn3kNSyuWnuteAuDi57KbptZW8czth\n5NuJoV8/Ai6+mICxF6Pv1q1BvbbywgK+njMLi8nElGdeIrxT/GnrnIsUvv8+BW/9H92W/4K+c2dX\netqG31j29uvEJw3i6plPN5uf9ZZUbnaHnYySDEIMIcT4xbTIPc+Wlnw/FlMVn77yOBV7DnIsXnLv\nP96ke3jPBtev2rKFI/feh8bfn07//gjvbg0fGzgXsDlsZBRnuMxEf+T8gclmIjE0kck9J3N518tP\n2SlpcuUvhNACe4FxQDawBZhWo9ydZQKllOXO44nAfVLKCUKI3kAKMBToAKwAekp58ujWfX395OLh\nw9AGBaMNCqq9BdfslTxNUBC6iIhGL4owlpbw9ZzHMZaWcP3TLxLVtXuj6p9LWPPy2X/RRYTNuJXI\nxx6rlbfj159Z+e/3OO/6Gxlx7bRmuX+Njb2lzDCHyw9jspnoGdKz1Zt+pJSkp6e3iPIvzjnKZ3Mf\nw1ZYTs4QP5687wNCfUJPX9FJxcqVHP3bo3jFxdHpo3/h1eHkQUxUFCosFfx08Ce+3fste0v24qvz\n5bKulzG552T6hJ34NdAc8/yHAvullAedN1gITAJcyr9G8Tvx43j06UnAQillNXBICLHfeb1NJ7uZ\noXciPVataoR4jcNUWcGiuU9RUVzItU88pyr+0+AVFYn/mAsp/X4xEQ89VMuL4YBxl3Jsbxobv/2K\nDj0S6dw/qcnvbzAYKCoqIiwsrEWUcaA+kApLBSabqVGOwloaKSVFRUUYDM1ncqth//bNLH5zLhaH\nBfPEbsyb8gZ6bcPdEpR+9x05Tz2NoV9fOn7wwRmvYD3XCNAHMDVhKlN6TeGvwr/4du+3LD2wlEV7\nF9E7rDeTe07msi6XNdpE2Zie/2RggpTyDuf5dGCYlPKBOuXuBx4F9MBFUsp9Qoh3gN+llF84y/wb\nWCalXFSn7l3AXQCdOnUanJWV1aiHaSjlhfn8+MZLFBzO5OqZzzSLsmqPVK5bx5E77yL2jdcJvOyy\nWnlWs5kv//koVeVlTH/5rSZ3A2G1WsnOzsZsNp++cBPgkA7yjHn4efnV6+itNWEwGIiLi8OrmQZK\npZSsX/wVf3yTQom/hQ43jee+Cx5tcCMspaToo48oeP0N/EaOJO6t+Wj82sZYSmul3FLu+hrYV7LP\n9TVwXc/r6BPep8nNPtcB4+so/6FSygdPUv4GZ/lbhBDvApvqKP+fpZQndT7d1PP8i7KPsG/zRvZv\n2UTewf1otFom/v0Jug0e1mT3aO9Ih4MD4y7BKy6Ozp99ekJ+0dEjfDn7b0TEd+X6p19s8ShMTc29\nK+7lUNkhll2zrNWbfpoLq6WaJe/M4/AfWzgcbWLsPQ9yZeJVDa4vHQ7yX3mV4k8/JfDyy+nw0ose\n8X3fXpFS8mfBnyzau4jlmcsx283sunVXk5t9sgF3RxhxwLFTlF8IvH+Gdak2VlJ4OJOg6Jgzcq0g\nHQ5yD+5j/+ZN7NvyOyXHsgGI6d6LUTfcSs9h5xMc3TYG81oLQqMh+LrrKJg/n+pDh/Du0qVWflhs\nRy65+0F++r9XWZfyGRdOv91DkjYN4zqP45mNz5BWnEbvsN6nr9DOKC8sIGXeE1QcOUZ672ruu+dV\nBkUNanB9abWS8+STlP33B0JuuomoJ2a3+sDrS7YfZd6ydEL99HQJ9yM+3Jcu4f50CfclPsyPUD99\nq+oICCFIikwiKTKJmUNn8uOBH7mJmxpWtxE9fx3KgO/FwFGUAd8bpJS73cr0kFLucx5fCTwjpUwW\nQvQBvuL4gO9KoMepBnw7hgbLR8aNBCEIDI8gJCaWkJgOzn0soR1iCQiPqBXU2W6zkZ22i/1bNrF/\ny+9UFheh0WqJ692PHkNG0G3IsEabI/YcK+f7bdnsOFKKj15LgEGHn16Hv0GHv7cOP29lX7P5eeuU\nMm5pBi9Nq/rBnA22ggL2jbmI0JtvJmrmP+ots/Lj99mx/CcmPvoEPYad18ISNh0l5hLGfDOG2/re\nxkODHvK0OC1KdvpuFr0yB1O1kb3naXlu+nt0DGx4tC2HyUT2I49g/G0tEY88TNjdd7f6/4GNBwq5\n5ePN9IwKIDLAm0OFRo6UmLA7juvIQIPO2Sj4ER/mR9cIZR8f7keQT+tYn9BcUz0vA+ajTPX8WEo5\nVwjxHJAqpfxBCPEWMBawAiXAAzWNgxDin8BtgA14REq57FT3GtCvn/z6g3coyTlGSc5RSnKOUnzs\nKBbTcZ/1Wp2O4OgOhMR0QKf3JnPHVszGSnR6b+IHDKLH0BF0HTQUg3/jXBDkl5v5745jfLctm/Tc\nCry0gqSOwVjtEmO1jUq3rSGvT6sR+Om1SmNgaFiDUauMQYeft5YAb69aDYnV7qDEaKGw0kKx0UKR\nsZoi17GFospqio3KebdIf964fgABhrP/gWY/+BBVqal0/21NvQvmbFYrXz8zk+JjR7lp3nxCotvu\njI47fr2DPGMeP1z1Q6tXXk3Fjv/9zMqP36fMYKFwQgwvXfVWo9wQ2EtLOXLvfZj+/JPoZ54hZMr1\nzSht07A/v5Jr3ttAZKCB7+49z6XIrXYH2SUmDhVWcqiwisxCI4ec27EyU63//zA/vatR6OL8Yoh3\nfjH4ebecCbRdLvKSUmIqL6M45yglx466GoWSnGOYjZV07pdE96EjiO8/sNELjkwWO7/uyeW7bUdZ\nv68Ah4SkjsFcOyiWK/p3IMTvRCUnpcRktVNprt0gVJptGC02Z7qdymorxmo7FWbbCY2H0Vm+0tKw\nhkQjwN/5Qyo3205aJtRPT6ifnjA/b4J8vFiRlkefDoF8dttQgn3PzuZauX4DR+64gw6vvUbQFZfX\nW6a8IJ/PZz1EQEQk055/tc16Rf06/Wte+OMFFk9cTPeQ9j0jzG6z8r+P32f3yl/JjjAReO1wnrxg\nTqPi5Zp27ybn8cexZGbR4bXXCBx/STNK3DQUVVZz1XsbMFnsLL7vfDqGNmx2l9lq53BxFYcKjWQW\nGsksMnKwQNnnlVfXKpM85wcAACAASURBVBsV6O1sFJSvhC7OrVOoLwavhjm8ayjtUvk3NQ6H5I9D\nxXy/LZuf/8rBaLETG+zDNYNiuXpgLF0jWs6bp5SSKosdY7WNCvdGwa2RcE+XQJifN6H+esL8nJu/\nnlA/b4J9vNDUCRW5Yk8e9325ja4RfnxxxzDC/c9cGUuHgwPjJ+AVE0Pn/3x20nIHt29h8bxn6XfR\nJVxyd+szm9htVoqdnYjYXr3rjVNQaCrkom8u4t4B93Jv0r0ekLJlMJaW8P3rz5O/dy9/dS1j9LQZ\nzOg3o8FfO7aiIgrmz6d00XdoQ0KIfeN1/IYPb2apzx6z1f7/7Z13eBzVuYffb5t2pVVbFVvFkgvu\nDRsbA6bb9J5A6DGhJkACIbnUVAiEElogJEAuhITewdjApQUIBlwCuPciW12rvtKutpz7x4zklW3Z\nktV2pfM+zzzTd34a7f7Ome98cw7nP/kVq0vqefHKQ5hW0DPpp75AiK1eH1urmtoVClurfHh9O8f5\nFoHcVFdb+0J0KGmYJxG7tettJNr8O0ApxaqSet5dWcqb35RQXNuMO8HGKZNzOGt6HgcP9+xmnAOF\nzzdUcsU/l5KX5uL5Kw5hSMr+54ZXPfEklQ88wMiFC0kYOaLD4/7z4j/5+o2XOeEn1zPp6M6//t+T\nRMJhaspK8O4ooqpomzHfvo3ashIiYaPZKW1IDhfc9QAu9+5d7s57dx4NwQZeP/31vpbeJ5Rt2sDr\n9/2exvoavp5az0/O+21b3/X7QrW0UP3c81T95S9E/H48F11E5tU/wZoS2+mxYFT+fvrCNyxcWcpj\nF0znpMl9kwBS1xxse1JoDSG1hpOin+atFiE/3WUWBC7y0xPJT3cxzJx31PiszT8KXyDEfzZW8fGa\nCj5ZV0FFQwCrRThidCbfm57PceOH4HL07KNXrPL1Zi+X/mMJmckJPHf5LPLT9+8FpraG34suYsjN\nN3V4XCQc5tU7f03phnVc8Ic/kVXYcUHRXVQkQl1FOVU7ivBu30bV9m14t2+jumQH4ZD5oxIhLXso\nGcMKyMgvIHNYIRarjYWP/IlhEyfzvZt/h8Xa/rvw7OpnuWfJPbxz1jsUphTu4crxy5rPP+Hdxx+m\n0RZg2WEt3Pm9h5mUOalT5zZ+9hnlf7ybli1bSDryCIbcfMteKwKxxj3vreWv/97ErSeP48oj+7+L\nCaUU1b4Ws1DY2b6w1etjR00zdc3tu7VJdFjbFQbDPMb8pMm5g9v8t1b5+HitYfZfb66mJRwh2Wnj\nyDFZHDs2m6PHZpHRjdBHPPNNUQ3znlqMO8HG81ccwvDM/XvhZsd119P01Vcc8NmnWBI6vpe+2hr+\ndfN1OJxOLrzroW53Aa2UosFbaZq7UYv37ijCW7ydUGBnrDU5M4vM/AIyhhWSaU6evPw9tgct/+h9\nPnjiEQ469azdUlTLfGUc9+pxXDf9Oi6ffHm3tMcKkUiYz59/hqXzX6fcE2DbMW4eOvkxctz7rv0G\ntmyh4u57aPz0UxyFhWTfcjPJRx/d+6J7kBcXF3Hz6yu4YFYBd545KS4a8+v9QXZUN7OjpontNebc\nXN9R00xjwKjgbLvn1MFl/i2hCEu3VvPx2go+XlvB5iqjV7wDst0cOy6bY8ZmM2N4+n7F0AYiq0rq\nuPh/F2OzCM9dPovRQ7o+wpBv0SKKLr2M3PvuI/W0U/d67I7VK3n5jlsZffBhnHr9TZ36sSml8NXW\n7GLyhtG3NDe3HZeU7iFzWGFbTT4j36jV71rIqFDIGBGpuppQVRXh6mpCVV7C1dUkHzeXRUu/4Nv3\nF3Dytb9g/BHHtDv3ggUXEFZhXjr1pS7codjE39jI/Ifvpmj5t6wprMc+dwJ/Oub+fXYPEG5ooOqx\nv1L97LNYHA4yr7kGz0UXxt1LW59vqOSSp5cw+4BMnpo3A9sA8ASlFHXNQbZXNzNlWFp8m/+UA6er\n5975hLrmIPX+IPXNwajl0G7by+r8+FrCOGwWDhmZwbFjszh23BAKMmK3X5b+ZkN5Axf8/WvCEcW/\nLjuYibldG1VIRSJsOvEk7NnZFD77r30ev/itV/n8+X9wzCVXMv2k09vta6qvM0I1bSEbY+73NbYd\n40pOMcx92E6T92QNwR5oIeT1EvJ6CXu9hKq8hKq9hKu8hKqrCXurCHmrCdfU0FFKlT03l8I33+D1\nB++ibMN6zrv93nb9PT218ikeXPYg733/PfLc8dvld9X2bbxx7+3UVZWzaKKXGcedxo0zb8Rm6TgV\nUUUi1L3xBhUPPEi4uprU751F9s9/3i+DoyilaAlHSLDtX5h2XVkDZ/91EXnpLl758aE9kvoca8R9\nzD8hZ7TKmffQbtutFiHVZSfFaTPmLjspTjtZyQnMPiCT2QdkkOiI724F+pItVT4ufPIrGgMhnrn0\n4C5nO1Q9+SSV9z/AyAXv7LNrXhWJ8Nb9d7Llm6Uc9oOL8NVU491hGH1TXW3bcQmJiXiyhpKelk6q\nM4lUq43kYAR7QyPh6vbmHvH59ngtS1IS1swMbJ4MbJkZWD0Z2DIydt+WmUFg3Tq2XfxDMq68Evfl\nl/LsLdejlOKiux5sywDaXr+dk984mV/O+CXzJs7r0j2KFTYs+ZKFj/6JJgJ8NK2cH51wPReOv3Cv\n5zT99xvK77wT/6pVuKZNY8itt+Ka3Lk2gf1BKUVVYws7apoorm1mR00zxWaIo7jWWG4KhplZ6OHk\nyUM5aXJOpxMXKhr8nPWXRQTDEd68Zja5ab07AFF/EffmP2rCFPX4qx+Q4rKbJm8jxWkn0WGNi/hc\nPLGjpokLnvwab2OApy6ZyayRnR9KLuT1suHoY3BNnYJr4iSwWRGrDbFZwWpFrK1zY1sgHGb+5+/T\n4GvEZrGSaneQHBGS/QGS6n0kVXpxNPvZ7T8sgjU9HVuGB2tGpmHkGR5sGZnmNsPcje0ZWLrYy2XJ\nTTdTt3AhI996k1pRvPibGxkychTn/PpOrDajdnjO/HNwWp386+R9P+XEEioS4cvXXuTLV5+nNj3C\n5zNquP3Eezgy/8gOzwmWlVHxp/upf+cdbEOGkP3LX5Jy6ik9/tvbVNnIM4u2sqXK12bugVCk3TGp\nLjt5aS7y013kpbtw2q18vKaCdeUNiMDM4R5OmZzDSZOGkt1BQdDcEubcJ75kQ3kjL191KJPzB+7Y\n3HFv/n2R56/ZSVmdnwv//hXFtc08+cMZHDG688PHlf/xj9S+8SaEwyhz6mjAHYCg1ULQYiERwZaZ\nic3jaV8j38XIbRkZWNPS9ns8184Qqqpi00kn45w4kYKnn2Ltos9Y+Of7mHrcScy9/BoAHv/ucR79\n9lE+PPtDhiQN6TUtPUlLcxPv/uUBNi75ii35zayfKTxy/F8Y6xm7x+Mjfj/VTz9N1RNPQjiM57JL\nybziCiw9PE5zvT/Inz/cwD8WbcVutTBmiJu8dCOdMdro89JcHYZmNlY0sGB5GQtWlLC+vBEROHi4\nh1Om5HDipKFkJxsFQTii+Mmzy/hgTTlPXDyD4ybEx/9uf9Hmr+kyVY0BLvr712yu9PHn86dx4qSh\n3fo8FYlAKISKRFChMIRDRuEQDGFxObEkJ8fUU1z1889TfvsdbW8uf/bc0yx5+zXmXn4NU487ic21\nmznjrTO45eBbuGD8Bf0td5/UlpXy5n134C3ezpLx1ajpeTw65y97HBdWKUXD/31Axb33EiwuJvn4\n48m+8X9w5Of3qKZwRPHK0u3c9/46qptaOHfGMH5x/FiykruXebehvIEFK0pZsLyUDRVGQTBrhPFE\nsLGikWe+3MZvTp3ApYfHTyrq/qLNX7Nf1Da1MO+pxXy3o45zDsrnV6dMIDVx4DWK+YNh/ruthkWb\nvCzdVs3sUZlcfeQIis47j2BFOaMWLkSSEnnjntspWvEt5/zmLvLHTeTMN8/E4/Lw1AlP9fefsFe2\nLv+Gdx66B3/Yz/9NKWbC9NncdfhdexyYxr9uHeV3/ZGmr78mYcwYhtx6K0mH9HxX50u2VvP7+atY\nWVzPjMJ0fnf6RCbl9Xz4ZX15AwuWl7JgRSkbK4yEgUsOG87vTu/cGLjxjjZ/zX7jD4b580cbePyz\nzWQkObjjzEmcMLF7TwH9TTAcYfmOOr7cVGUafg0toQhWi1CYkcjmSh+nTsnhznEWSi68gPSLL2Lo\nrbfib2zk+V/dQKCpiQvvepB/bn+JJ1c8ycfnfEyGq/NtI32FUoplC97ks2efwp9mZf7UbZwz82Ku\nP+h6LNI+pTFUU0PVI49Q8+JLWJOTybr+OtLOOafHw2sltc3c/e5a3v6uhJxUJzefNI7Tp+b2yVPf\n+vIGVpfUc9rUXKwD9M39XdHmr+k2K4vr+J9Xl7OmtJ5TpuTw+9MndqtPoL4kElGsLq3ny01eFm2q\nYvGWanwtRlcOE3JSOGxUBocdkMHM4R7cCTae+Gwzd7+3lgOHpfFA8fv433iNEa+9inP8eLw7tvP8\nr24gPSeP6dddzrnvn8/FEy7m2gOv7XCIRxWJUFm0laIV37Jj7SpUJILTnYzTnYzLnYwzOQWn243L\nnYIzuXVbMvYE536bYrAlwAdPPMqazz+hapjw4YRibjr8Vs4ec3Z7baEQNS+8SOWjjxJpbCT9/PPJ\nuvYarGlp+3XdjvAHwzzx2Wb++u9NRJTiqiNH8uOjR+lsvF5Gm7+mRwiGIzz+6Sb+/NFGkhKs/O70\niX1Wa+sKSik2Vfr4clMVX2z08tUWL7VNRqPzyKwkw+xHZXLIyAw8e+ihFeC9laVc/9K3FNhDPLzg\nLlwjhlP4/HOIxcLGpV/z1n13MP7wo1k4fgufFn+G2+7mzAPO5IJxF5CfnE9deRlFK79j28rv2L7y\nO5objCGt03PysDud+BsbaG5oIOhv3uP1weim3JmcsnMMi6G5pLWNY5GLw7nn9MT6qkrevv9Oyjdv\nZO34AKtG+7j/mPs5NPfQdsf5Fi2i/I9/JLBhI4mHHsKQW27BOWbMftzxjlFK8e7KMu5csIbi2mZO\nmZzDzSeN63RvmZruoc1f06NsKG/gxteW801RLXPGZfOHsyaRk9q/edLbq5vaavaLNnmpaDC6dshL\nc7XV7A8dmcnQ1M6nfX63vZbL/7mUWesWcc3iF8j5wx2knW3UnL987QUWvfwcR118GfZZI3lh2TOs\n++YrsqscjKhNxd5oPFm4PRkUTJpqTJOn7jaAUCgYJOBrpLmhHn9DA82+BmPeUG8WEPXUVZRTU1ZC\no7eq3blJ6R6jQDDHsUjPyUXEwgdPPorf38S/p1QQGOHmsTmPMTJtZNt5LUVFlN9zL40ffYR92DCG\n3HQj7jlzeqwQr21qYWVxPSuK6/h4bTlLttYwbmgyvz1tIoeOir3w2EBGm7+mxwlHFP9YtJX73l+L\n3WLh1lPGc97MYX32FFBR7+fLzV4WbfSyaHMV26uNGnSmO8Gs2Ru1+2EeV7c0Fdc2c/nTX3PZq/cw\nNlDN+A/ew5aejopEmP/g3Wxc8hWevHy8O4qMExJslGY0sy2tHsuITM6ceQFnHHBGhyGhrhAM+Kkt\nK6WmrMQYw6KshJrSEmrLStq9GGdJT+K1SRsYMXIiDx/7MB6nB4CIz0fV409Q/fTTYLeTedVVeC6Z\nt9e+mPZFja+FlSV1rCiuY2WxMW/9XwAUZiRyxREjOW/msAHRdUK8oc1f02ts8/q4+bUVfLnZy2Gj\nMrj7e1N6pRuN2qYWvtpc3dZIu8HM3Ehx2jhkpGn2B2QyOtvd4wVQgz/I7Q+/xbx//JaiWXM48amH\nsViEFn8zb/3pTgAKJk2lcPKBZI8YSUiFeX/r+zy35jlWeVe1hYTOH3c+BSkFPaqtlVLvdr5dv4hF\n6z5hQWgRc0efyB2H30GCNQEViVA/fz4Vf7qfUGUlqWecTtYNv8A+JLtL16jxtbCiuL3R76jZafQF\nnkQm56UyKS/VnKd0e7AgTffQ5q/pVZRSvLB4O3ctXEM4ojj/4AIm5qYwdmgyB2S792t0Il8gxOKt\n1W2hnFUl9SgFLruVg0d42mr2E3JT+iRzIxSOMP+qGxnzn4W8NO833PyLs/fZWKmUYnnVcp5b8xwf\nbP2AsApzRP4RHJpzKLnuXPLceeQn57frRE0pRWVDgKLqJoqqmyit8zM+J5nDD8jCYTNqzs2hZtZ4\n17CiagUrqlawsmolxY3FAFjFyhVTruDqqVcjIjQvX07ZnXfi/245zilTGHrrLbgOPHCff2+1afQr\ni+tYscMw+uLa9jX6VpOfnJfKpNzUAZkGHO9o89f0CaV1zfz2rVX8e10lLWHjtXwRKPQkMnpIMmOH\nJDN6iJuxQ5MZkZnUrkMufzDMf4tqTLP38t32WkIRhcNqYVpBGrMPyOSwURlMyU9rM8G+JtTQwKrj\nTqJIOXnygt/w5I8O7nxfMk0VvLL+FV5Z9wpev7fdPoe4sUUyCLd4aGpKJhhIJ9LiQQXTiQRTsThq\nSEouJm9oFSQUUda8lbAy2hTy3HlMypzE5MzJTMqcxHjPeBLtiQQrKqh84EHq3nwTa1Ym2Tf8gtQz\nTkcsu987b2OgXW1+ZXF9O6MfvovRT8xLjZkByjV7R5u/pk8JhiNs8/pYX97I+vIGc2pkS5WPcMT4\njlktwvCMRMYOTaauOcjSrTUEQhEsAlPy09pq9gcVpsfU4Dr1775L8c9v4O/TvseiKcfyv/NmMiF3\n50hVTS0hyur8lNX5Ka3zU1bvp7SuuW29tK6ZGn8NYq/BYq/B4qjGkVCLM7EOsddg81fhqQ+SWafI\nrIeMBkVEoMkpNNnsNEomfutQhg0dx1GTZ3Hk5IkkpKVgSUxELBYiLS1UP/MM3r/+DRUM4rlkHhlX\n/Rir23i6qGwItJl8q+GX1vnb9I/ITDKNPoVJealMzNVGH8/0ivmLyInAw4AV+LtS6u5d9t8AXA6E\ngErgUqXUNnNfGFhhHlqklGrfp+8uaPMfGARCYbZUmYVCWUNbweBy2NoaaWeO8JASw13rKqXYftll\nNH63nOtPvo1icTFjuIfyesPcdx1hCSAt0c7QFCc5qU6GW/yMaqkhr6WezKYakuu8WCrKCJWWEiwt\nJdLY2O7ciNWCKIVE9v7bVCJY3cY405GGBtzHHovt2utYY0llxY76NqMvq99p9CPbjN6I00/MS4np\ne6/pOj1u/iJiBdYDxwE7gCXA+Uqp1VHHHAN8rZRqEpGfAEcrpc419zUqpTo9Iro2f00sEdiyhS2n\nn4F9zvH8fsoPqPG1MMQ096Gp0XMX2XaFWv4Nvv98gW/RIgIbNrT7LGtqKra8XOw5udhzcrDn5mLP\nzWlbtmZkgAgRXxMRXyORhgbCDQ0E6upZvbGUleuL2batAqu/iXTVwgi3lZWjZ7IgoaDN6EWMGv3k\naKPPTRmQ/ddr2tNZ8+/Kq3YHAxuVUpvNC7wInAG0mb9S6pOo478CLurC52s0MUvCiBF4LrsU798e\n56/n/4CkWYe37VORCIG1a2n8+F18ixaxfekyVDCIOBy4DppO1umn4Rw3zjD5oUOxJHVu2EyrO8kI\n3QwxeqFMBGYfDbMxnqg+X1/FwhWl/HNdBRlJDg6JyrqZmJeKO0G/SavpmK7U/M8GTlRKXW6uXwzM\nUkpd28HxjwJlSqk/mOsh4FuMkNDdSqk393DOlcCVAAUFBQdt27at63+RRtNLRJqb2XzqaYjTybDH\nH6dpyRJ8Xxi1+3B1NQAJY8aQdNhhJM2eTeKMg7C4BuaAIZrYpTdq/nvKrdtjySEiFwEzgKOiNhco\npUpEZCTwsYisUEptavdhSj0BPAFG2KcL2jSaXsficjHkttvYcfXVbJo7FwBrRgZJs2eTNPswkg49\nrMt59BpNf9EV898BDItazwdKdj1IROYCtwFHKaUCrduVUiXmfLOI/BuYBmza9XyNJpZJPvYYsm+6\nCcIhkg4/nIQxY/aYSqnRxDpdMf8lwGgRGQEUA+cB7Ua0EJFpwOMY4aGKqO3pQJNSKiAimRhhy3u7\nK16j6Q8yfnRJf0vQaLpNp81fKRUSkWuB9zFSPZ9SSq0SkduBpUqpt4H7ADfwivm6fWtK53jgcRGJ\nABaMmP/qPV5Io9FoNL2OfslLo9FoBhCdbfDVwUqNRqMZhGjz12g0mkGINn+NRqMZhGjz12g0mkGI\nNn+NRqMZhGjz12g0mkGINn+NRqMZhGjz12g0mkGINn+NRqMZhGjz12g0mkGINn+NRqMZhGjz12g0\nmkGINn+NRqMZhGjz12g0mkGINn+NRqMZhGjz12g0mkGINn+NRqMZhGjz12g0mkGINn+NRqMZhGjz\n12g0mkFIl8xfRE4UkXUislFEbt7D/htEZLWILBeRj0SkMGrfPBHZYE7zekK8RqPRaPaPTpu/iFiB\nvwAnAROA80Vkwi6HfQPMUEpNAV4F7jXP9QC/BWYBBwO/FZH07svXaDQazf7QlZr/wcBGpdRmpVQL\n8CJwRvQBSqlPlFJN5upXQL65fALwgVKqWilVA3wAnNg96RqNRqPZX7pi/nnA9qj1Hea2jrgMeLcr\n54rIlSKyVESWVlZWdkGaRqMZDARbwtSWN9FYEyAYCKOU6m9JcYutC8fKHrbt8c6LyEXADOCorpyr\nlHoCeAJgxowZ+r+q0QxCAs0h6iqaqKts3jlVNFFf2YyvrqXdsWIRHC4rCS4bDpdtt7kj0YY9wQoK\nImFFJKJQEXNuru+6DJCQaMPltuNMsuN023G57SQk2XG5HSQk2bBa4z9XpivmvwMYFrWeD5TsepCI\nzAVuA45SSgWizj16l3P/3RWhGo1m4OH3BVn5aTE1Zb42o/c3Btsdk5jqIDXLxbAJHlKzEnF7Egi1\nRGhpDhFoDtFiTq3L9VXNxnJTiBZ/eLdrioBYBYuIMbcIFqsgFmNZKQg0BQm1RDrU7XDZcJqFg8tt\nb1tuLShal9vmbnvMFRhdMf8lwGgRGQEUA+cBF0QfICLTgMeBE5VSFVG73gfuimrkPR64Zb9VazSa\nuKdolZeP/7kGX30LyelOUrNdjJyWRWqWi7SsRFKyXKRmuYya+36iIopgSxiLRXYavmVPgYjdCbWE\n8fuCNDcG8TcG8fuMeXPUst8XpKm+BW9JI35fiFBg98KmFYfTahYEjp2FRlTh0K4gMdettt4rMDpt\n/kqpkIhci2HkVuAppdQqEbkdWKqUehu4D3ADr4gIQJFS6nSlVLWI3IFRgADcrpSq7tG/RKPRxAXB\nQJhFr29k5afFpOckcco1U8kqSO6Va4lFcDi7Usfdic1hxe2w4k53dvqc1gKjXaGxS8ER8AVpbmih\nptSH3xckuJcCw+604k5LYOjIVHIOSCVnVBqp2S5Mf+0WEqsNJjNmzFBLly7tbxkajaYHKdtSx4dP\nr6auspmpc4ZxyBkjsdn3v2Y/EAgFw/gbQ2YB0YLfF8Lf2NL2hFFf2Uzp5joCvhAArmQ7Q0emMnRU\nKrkHpJE1LBmrfecTgogsU0rN2Nd1969IHGSEgxECzSEcTis2x+D+omo0+0M4HGHpgq0se3crSekJ\nnHn9NPLG6ld9AGx2K+50K+70hA6PURFFTXkTZZvqKN1YS+mmOrZ8VwWA1WYhe3gyOaOMJ4NOX7fb\nygcA4XCExuoADd5m6r1+Grx+6r3NNFT5qff68dUF2nKTrDYLjkQjmyAhau5ItO/clmhmHCTaSHDZ\n223rzRieRhOLVJf4+PAfq6ksamDcIUM5/NwxJLi09XQFsQienCQ8OUlMODwXgKb6Fso21VGyqZay\nTXV8++F2/vt+Uec/czCFfQJNQSq3N1JZ1EB1cSP1psn7agJE3wYRcKc7Scl0kpzhJDnDhcttp8Vv\nZBP4m4xMgkBziECTmWnQFCTgC7WlinWEzWEx0tAS7TgTbTsLElfrsp2EpOj11gLFjsNlw9LJxiqN\npr9REcV3H2/nqzc3Y3daOebCcYycltXfsgYsoZYwFdvqyRvjie+wT215E5/8aw3JGS7TgJ2kZDhJ\nTE3olAE2N7ZQWdRgTo1UFtVTX+Vv25+U6iAly0Xe6HTjszMNk0/JcJKUnrBfaVlKKULBCC1NIfxN\nwT0XEOY245gQTXVGw0/rtn2VxXbnzpxmm8OK3WHB5rC2X06wYnNYsJvbbQ7L7k8cu1xo18tmF6Tg\nyU3q8j3QaAAaqv189MxqitfVMnxKJsdcNI7EFEd/yxrQ2BxWckd3PpQWs+avFGxZ4aW5vv1LHRar\n4PYYBUGyZ2ehYHfa8BY3thl+Y02g7ZyUTCdZBclMODyXrIJksoYl40ru+S+iiGB3WLE7rCSldRy/\n6wgVUQQD4agCI4jf11pwtBYkwbb85VBLmGBLmKb6lrblUEuEkDnvDjaHhbN+MZ3swpRufY5mcKGU\nYt1XZXz+0nqUgmMuHsf4w3J6JDtF07PEfNgn2BKmsdrfFos3pp2x+abowkEgLTuxzeCzCtxkDkvG\nmWTvvz+kn1ARRShkFATBQJhISLV7z3r33+LODcFAmIWPLSccinD2zTNI9nQ+1U0zOFFKsW2ll8Xz\nt1BZ1EDOAanMvWQCKZmu/pY26Ohstk/Mm/++CAXDNFYH8DcF8eQk7XdOr6Y93pJGXr93GW6Pk+//\nz0E4dAOdZg8opdi+upqv52+hYms9yRlOZpw8nHGH5uj2qX5i0KR62uxW0oYk9reMAUdGrpsTr5rM\nO498x3tPruSUa6bE3Ovpmv5DKcWOdTUsfnsLZZvrcHsSOPrCsYw7LEd/T+KEuDd/Te8xbLyHoy4c\nyyf/WstnL6zn6AvH6tithuL1NSyev4WSDbUkpSVw1PljGH9YbrsXjTSxjzZ/zV6ZMDuX+spmlr23\njdQsF9NPKNz3SZoBScnGWhbP30zxuloSUx0cce4YJhyeM+jf0I1XtPlr9sms00dSV9XMl29sIiXT\nxQEHZfe3JE0fUra5jsXzN7N9TQ2uZDuHnzOaiUfk6rfd4xxt/pp9IhZhzrzxNFYH+PDp1bjTjY6m\nNAOb8q31LJ6/TAYyBQAAE6NJREFUhaJVXpxuO4d97wAmHZXXrV42NbFD3Gf7aPqO5sYWXr1nGUF/\niO/fOIPULJ3GNxCpLGpg8fzNbF3hJSHJxvTjC5l0VJ7OpIsTBk2qp6ZvqS1v4tV7l+JyO/j+jQcN\nyncoBipVOxpYPH8LW76rIiHRxoFzC5hyTL5O840zBk2qp6ZvSRuSyMk/nsJbD3/De4+v4LSfHRjX\nndVFwhGa6oMkptixDNIURW9xI0sWbGHTfytxOK3MPHUEU+cM052vDXD0f1fTZXJHp3HsxeP58OnV\nfPLsWubMGx+TKaBKKZobgjTW+GmsCdBQbcwba/w0VhtzX10LKqJIzXZx5LljKJiY0d+y+4zqUh9L\nF2xhw7IK7AlWZpw8nKlzhumnuUGCNn/NfjF21lDqq5pZPH8LqVkuZp4yos81tDSH2ht6m8G3mnuA\ncKh9H0dWmwV3egJuTwJ5Y9NJ9jhxJtlZ+Vkx8x/5jpHTsjj8nNEDukuL2vImlizYwvol5dgcVqaf\nUMi0uQU43dr0BxPa/DX7zYyTh1NfaRQAoWCE7MLkts72nEn2bj0NhIJhw9SjzL0her3av9vg3CKQ\nlJaAO91JVmEyIw7MItljrLvTjbkrec+6Jh2Zx7cfFbF04VaeX+llxinDOXBOwYB6camusomlC7ay\n7usyrDYL0+YWMO34gl7p5FAT++gGX023CIciLPzrCopWedtttzksbQVBsseJ2+Nstw60mXhDjX83\no29uCO52LVeyfaeRe4x5ssfZti0p1dHtuH1DtZ8vXtnApm8qjVDQeWMomBDfoaD6qmaWvruVtV+W\nYbEKk47MY/oJhbqL5QGKzvbR9BlKKQI+IwTT4PUb89bJXPc37m7m0Tic1jZDd3ucJJs19bZt6Ql9\n+iZp0Sovn720nrqKZkZNy2J2HIaCGqr9LHt3K2u+KEUswsQjcpl+YiFJqV3vblwTP2jz18QUwUC4\nXYHQOlpaq9nHYmZJOBjhmw+LWLZwK4gR5jpwbkHMZzc11gRY9t5WVn9RAgomHJ7LQScW4k6Pr8JL\ns3/0ivmLyInAw4AV+LtS6u5d9h8JPARMAc5TSr0atS8MrDBXi5RSp+/tWtr8NbFCvbeZL17dyOZv\nKkkbksgR546OyVCQry7Af9/bxqrPS1ARxbjZOcw4aXjcPbFoukePm7+IWIH1wHHADmAJcL5SanXU\nMcOBFOCXwNu7mH+jUsrd2T9Am78m1ti2ysvnL66nrrKZ9KGJZA9PIbswmezCFDLz3d3q6yYcjlBb\n3kR1iY/a8iaUot1QnHZzaE6bOVJc23KClXAowncfb2flp8VEwopxhw5lxknD9UAqg5TeeMnrYGCj\nUmqzeYEXgTOANvNXSm0193VvDEGNJgYpnJhB/m9msfKzYnasraZodTXrvioDjP6PPLlJDClMJqsw\nhSHDjTGQdw0RRcIR6iqbqS7xUV3qa5vXljcRCe9/CFbESL+dccpwUrP0+BaafdMV888Dtket7wBm\ndeF8p4gsBULA3UqpN3c9QESuBK4EKCgo6MJHazR9g9VuYeqcYUydMwylFL7aABXbGqjYVk/FtgY2\nfVvJ6i9KAbDYhMw8N1kFybT4w1SX+Kgp9xlDagIIpGQ48eS6GT45E09uEp6cJNKHJmK1WdqNyRxs\nCRMKRHYbqzkYCBMORSiclEH60KR+vDOaeKMr5r+npO2uVFUKlFIlIjIS+FhEViilNrX7MKWeAJ4A\nI+zThc/WaPocETEbrZ2MPDALMDKfGrx+yrfWU7mtgYqiejYuq8DhtOHJS6JgomenyeckYd9LqMjh\ntOHQ4XpNL9EV898BDItazwdKOnuyUqrEnG8WkX8D04BNez1Jo4kzRISUTBcpmS5GzxjS33I0mg7p\nSs7aEmC0iIwQEQdwHvB2Z04UkXQRSTCXM4HZRLUVaDQajaZv6bT5K6VCwLXA+8Aa4GWl1CoRuV1E\nTgcQkZkisgM4B3hcRFaZp48HlorId8AnGDF/bf4ajUbTT+iXvDQajWYA0dlUz9h+VVGj0Wg0vYI2\nf41GoxmEaPPXaDSaQYg2f41GoxmExGyDr4hUAtt68RKZQFUvfn5X0Xr2jtazd7SejoklLdD7egqV\nUln7Oihmzb+3EZGlnWkR7yu0nr2j9ewdradjYkkLxI4eHfbRaDSaQYg2f41GoxmEDGbzf6K/BeyC\n1rN3tJ69o/V0TCxpgRjRM2hj/hqNRjOYGcw1f41Goxm0aPPXaDSaQYg2f42mA0RkTwMYaWIQ/b/q\nOgPa/EVknIgkmcv6yxGFGEwXka4M6NNriMg9InKEuRwr/6uE1oX+1iQi7hjSIiJylYjk9KeOXbC3\nLvT3/TE1JEct97uePTEgzV9EfiAiq4F7gdcBVD+2bIvI5SLycqu5xQjPA08BU/tThIhcKCIfA1eY\nU7/+r0xN54nIWuAhEbmhPzWZ92cpcJ+I3N6fWkw9JwBrgcMAR3/paEVEzheRZcCdInId9Pv9ucjU\n82cRebC/9eyNmKj19SQiMg64DrhUKfWViBSJyBFKqc/7Sc8JwA0YA+AcKiIrlVI1IiL9aCgCOIEN\nwEEislUp5e0rTSJiAVzAr4CZwG0YRnKqiLiVUo29rWEv2gqBnwGXAjXAqyJSpZT6Zx9qaP3//BI4\nFuP74wX+ISIvK6VW9pWWXXTZgJOBnyml3t9lX59/n0VkBvBT4BpgI/CRiDQopZ7qaz0iYgeuBr5n\naioy9XymlHqjP3/vHTEQa/5ZGENEFplDR34DBPtSQOuQlSbLgDnAoxjjHh8FfVsbiNYT9SX8CmNo\nztkYI631iSYRSVBKRZRSPuAZpdRcpdSXQDpwVH8Yv4i4olZbC8VVSqk1wPXAL0TE00danMqgGXhD\nKXWMUuozjMJxA1DcFzqi9bQum6P5jQW2i0iqiPxCRI7rS2OL1oPxvf1IKfWVUqoKeA64S0RS+9po\nlVJBDN85Wym1SCm1AyOff6y5P6aMHwaA+YvI70XklKhNVUAjhtluAhowHgkfFpGUPtBzC/C6iPxM\nRCYqpaqUUqXApxg/3BkiMtw8ttdjgVF6fioik5RSyjSyQ4BHgJXAMSJyhYiM6CMtPzO1rG29B0qp\nNwFXX4fGRORG4F3TyKYBzRgViERT1wfAeuBG8/he+82IyK+A96Luz0oRsYjIHOBZIBt4QER+2dta\ndtFzrYhMMTdvxHhaewPjPt2GER5L7uBjekPPz0RkGLAOOElExpuHRIB6jAK7L+7Prt7zH6VUZdR1\nDwJKelNDd4hb8xcRj4g8gfGIfpf52IVSao1S6ufAf4AHlFIXARdjxChH9qKeEWbseiLwJ4wS/5LW\nH4VSKgJ8CCQDc81tvVYb6EDPPLNWVA2sM2uXCsPYfgTU9ZGWMaYWd+s9MAvmj4EhvaFhD5pGicj7\nGG0etwGFwLlKqSKMysNVUYffDJwnImnm/7E39FyK8b24CaPXxz+IyHDzeqXAEUqpucDdwO9EJLO3\ntOxBTzZwh1lp2AL8EFiglLoZuBA4lF78be1BTyZG5W4NRpveTWacPRu4ADhNRJJ68X/Vkfc073oo\n8O0u58ZM42/cmj/gA95USqVj1KhvAKO0N2+wArYCKKVKMGoso3pRTzXwjlLqIqXUJ8DbQC4QjKrd\nLgP+C+SKyCUicnMf6plv6mkWkWzgWBFZBHwf+BfwOZDWx1pCUfemHsP4cwFExNpLWlopB/6glLpQ\nKfWFud7aze5twFlmTBml1CaMgtu9x0/qJuY9GAY8ppT6GiNRYSWG0aOUWm0W2Cil1mHcv+ze0LIX\nPWtMPY8AIcAhIi6lVDHGk1GvPTXuRc9DSqm7MNr4LlNK3YjxP1wEtPSi0XbkPa3f5YiIOIB8pdRy\nETlQRK4298VM+CduzV8pFQA+M1d/C1whIjlmPFkBVuBcMTJt7sOobS7rDS1mzLMOeDJq8yqML6zd\nDLW0fhG/AeZh/rD7QU+iUqoCo8b0klLqUIwGszSMeHdfamm9N63fwwXA+QBKqXBPa4lGKdWolPpc\nROxiZNH8FCP89WuMht4HgJ+JyE0i8leMioO3u9fdkyFFGcIPW7UBDwMjROToqHNtIvJnIAWzYtOH\neu4HpgPjgPswCp9fi8gD5rb/9oOeySJyrFKqTin1rWm4vwbCSqlgTxhtB3o68p7o3/lMIElE7gb+\nlxj02pgT1BWUUo2muSzBiKnfEbX7YeBljAbWCHCkUmprL+lQ5rwhavMsYHvrNvOL4QAewqhlj1RK\n9UoBsA89tea+u5VSD0cdf7VSanUfa2m9N62P52XAX3o7VruLviCwWCk1FLgcaAFuV0o9g2EwORhh\noFP38Fi/P7SZiZiYq3cDI0XkSHPdi9GAebx57EXA10AYOEcp1dQDWvZHz/eVUh8C92AUknUYDfVF\n/aDnWYxkCkRkOkbYEOCWHtKym57W5Y68J6rAyQVGm8tHKKUe7UFNPYNSKmYn4Ezgjj1sF8BiLtvM\neRZG/vFojPj+THO7o5/03ADcaC4fCkw0l1NjTI/EkBZrX39/9rDvhxhGIz2pCSNF8i2MJ4qjo7Zb\no+7LtcDXUfuuAX5pLk8ChvfgvemWnp787vTQ/ckAhvSBHss+vOdQYAJGG8jInvw+9/QUkzV/M25/\nOUbj4M2ySwaIMoiI8Yahw9xWCbyPkQHwWNSxLX2spzV0MhxIMRuGfo/RBoEyQiCxpKdbj8Y9rKVH\nQj1d0SQiiVHnZQDnAhWt96W7msyw0v3A74C/AbXA+SJycOvnK6VCZtjgUcAnIneLyOHA6ZhP50qp\nlaoHnlx7QE907bcnwio9okcp5VVKlfeBnsg+vOdvGKHVzUqpzd3V06v0d+nT0YQRrknGeOvzk132\nWTEafT7DiK0JcCpGJsKN/aznIPO4Eowvw3UDXU8sadkPTTMw0jr/BnwH/E8vaLkKGGUu5wEvAQeZ\n6zZTyyKMQnEk8GOMUMJtvXRvtJ7u6+kz7+mtqd8FRN3ws4FZUev2qOUlGK35retjMR7H0qO2jaZn\nQyrd1fMTIGMg6oklLT2o6SzA00taHBg1eIe5vhA4oSMtref04r3RenpQT097T19N/S/AyBr4FKM2\n+CY742nRseKTMDJE0vdwvi3G9PTYlzLW9MSSlh7UZO8DLZaoY9KBj4Chezi/p9s8tJ7e1dOj3tPX\nU7/H/JWRdvgWcCLGyyytL9eIMmJropR6FyOv90oRSRaRH0BbGmEoxvR0u40hVvXEkpYe1NRjXX/s\nRUt0bLwAqFNKlYlIvhhv77Zq6dH0Vq2n1/X0qPf0OX1Z0rBLdgA7S1o7RiztLOAdIKd1P2Zpj5Fn\nHcFIB/yp1tO7emJJSyxq2g8trZkhp2AYzk8x2hgu7Kd7o/X0o55YmPr2YkYr+N72D8HI0PjjLttH\nAf+H8WJSrtbT+3piSUssauqGlocwCqGHgbwYuDdaTz/oiYWpby5idCL2GvA0xksrrbUxiS6RMbIw\njjR/pPkYfXikAKnAGK2n9/XEkpZY1NQNLUOizp8bA/dG6+kHPbE09f4F4GiMV78vxOgg65/mjzK6\nUSUBSIhavwMjv3YNMFrr6Rs9saQlFjXFkhatJ/70xNrU+xcwer573FzOB14AnFH7b8foWGy4uf5j\noALj9fEey8TQeuJLSyxqiiUtWk/86Ym1qec/0HhMGhO1fiBGr46/xeg58d8Ywweei9ENw/PAAVHH\nz41e13p6T08saYlFTbGkReuJPz2xPvXcBxm9Qi7AGDzlV4A7at/B5k3/vrl+GUYvj1OjjunpHF6t\nJw60xKKmWNKi9cSfnniZejLPPwmjf4ufmstt/akopRZjdH60zdz0sfkPqwGjLxbV8134aj3xoSUW\nNcWSFq0n/vTEBd0yfxH5oYgcJSIpyhjU4QmMbpT9wCwRaR2YIwGjb46rzVPnAB7zOFQPjbij9cSH\nlljUFEtatJ740xOPtHZb2/kTRAQYihEvi2CMk5uE0UlXlXnMbOAHwFKl1L/MbRMxYm9DMQZUv1YZ\nA2R37w/QeuJCSyxqiiUtWk/86Yl7uhIjYmeO7BjgWXPZhjG02+u7HPtz4A8Yj1guc5uLHuzjWuuJ\nDy2xqCmWtGg98adnIEydvfE24C6MFKijgNOAZ6L2C0bfGEdFbXNjvB23BKOlvSff1tN64kBLLGqK\nJS1aT/zpGUjTPmP+InIUxti36RiDoN+B8eh0jOwc4EBh5Mz+LurUUzDibN8Ck5URl+s2Wk98aIlF\nTbGkReuJPz0Djk6UvEcAF0etP4bRH/slwDJzmwUjnvYyO1+YOANj3NweLa20nvjQEouaYkmL1hN/\negba1Jl/QCLGK9CtMbcLMTs/wihZf2ouzwBe6HXBWk9caIlFTbGkReuJPz0Dbdpn2Ecp1aSUCqid\nubDHAZXm8o+A8SLyDsar0/+Ftlb5XkHriQ8tsagplrRoPfGnZ6Bh6+yBImLFGORgCPC2ubkBuBWY\nBGxRZmxNmcVxb6L1xIeWWNQUS1q0nvjTM1DoykteEYyBD6qAKWaJ+2sgopT6j+r7RhWtJz60xKKm\nWNKi9cSfnoFBV2JEGB0nRYD/EDUgdn9NWk98aIlFTbGkReuJPz0DYerSG74ikg9cDDyglAp0pZDp\nDbSe+NDSSixpiiUtWk/86RkIdLl7B41Go9HEPz3Zq6dGo9Fo4gRt/hqNRjMI0eav0Wg0gxBt/hqN\nRjMI0eav0Wg0gxBt/hqNRjMI0eav0Wg0g5D/BxAyakUNjeXyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22d8fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_weighted_df_ = hv_weighted(historicals, portfolio, 1)\n",
    "hv_weighted_df_.plot(title = 'HV Intraday Weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a229c61d0>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5ubdNalcm9ZqEUzr5Ie8Hb5uiUHQIPVLhpJRUr1zJ/mnTKPr7c/idcw4pX35B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22106cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_hl_df_ = hv_hl(historicals, portfolio)\n",
    "hv_hl_df_.plot(title = 'HV Intraday High-Low')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a233eab70>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zFlK/Wwvc4CCQlwkrH9KibUf+CA1bsWZfDJ9sO8PDYb6M7dHkmpc293RgpdGb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\np3VjpxvzRBFCK3nm0UwLWDIatc3SqO3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GGpPzxYbFMn//fMqN5Ric7KBdX2t2fjaKtqX7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a217cdd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_all_ = hv_all('BA', hv_1_df_, hv_2_df_, hv_weighted_df_, hv_hl_df_)\n",
    "hv_all_.plot(title = 'HV All - BA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a225d7860>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RILJ2neqDSP0afn7X+AD/jsbw1sqH5cK7NXuAkIAghBBuaHiA6IElsicM/q0x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\nwDBInkDXIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21f0a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_all_ = hv_all('AAPL', hv_1_df_, hv_2_df_, hv_weighted_df_, hv_hl_df_)\n",
    "hv_all_.plot(title = 'HV All - AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def graph_option_1(chain, stock):\n",
    "    chain_ = chain.loc[chain['Stock'] == stock]\n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "    Axes3D.mouse_init(ax)\n",
    "    surf = ax.plot_trisurf(chain_[\"Strike\"], chain_[\"T\"], chain_[\"Call Premium\"], cmap=cm.RdBu, linewidth=0.5, antialiased=False)\n",
    "    ax.set_title('3-D Options Surface for '+ stock)\n",
    "    # fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def graph_option_subplot(chain, chain_2, stock, stock_2 = None): \n",
    "    if (stock_2 == None): \n",
    "        stock_2 = stock\n",
    "    chain_ = chain.loc[chain['Stock'] == stock]\n",
    "    chain_2 = chain.loc[chain['Stock'] == stock_2]\n",
    "    \n",
    "    x = chain_[\"Strike\"]\n",
    "    y = chain_[\"T\"]\n",
    "    tri = mtri.Triangulation(x, y)\n",
    "    \n",
    "    fig = plt.figure(figsize=plt.figaspect(0.25))\n",
    "    ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "    surf = ax.plot_trisurf(x,y, chain_[\"Call Premium\"], triangles=tri.triangles, cmap=plt.cm.CMRmap,linewidth=0, antialiased=False)\n",
    "    fig.colorbar(surf, shrink=0.75, aspect=10)\n",
    "    \n",
    "    x1 = chain_2[\"Strike\"]\n",
    "    y1 = chain_2[\"T\"]\n",
    "    tri = mtri.Triangulation(x1, y1)\n",
    "    \n",
    "    ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "    surf = ax.plot_trisurf(x1, y1, chain_2[\"Call Premium\"], triangles=tri.triangles, cmap=plt.cm.Spectral,linewidth=0, antialiased=False)\n",
    "    fig.colorbar(surf, shrink=0.75, aspect=10)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "def graph_real_option_subplot(chain, stock): \n",
    "    chain_ = chain.loc[((chain['underlying'] == stock) & (chain['option_type'] == 'call'))]\n",
    "    chain_2 = chain.loc[((chain['underlying'] == stock) & (chain['option_type'] == 'put'))]\n",
    "    \n",
    "    x = chain_[\"strike\"]\n",
    "    y = chain_[\"T\"]\n",
    "    tri = mtri.Triangulation(x, y)\n",
    "    \n",
    "    fig = plt.figure(figsize=plt.figaspect(0.25))\n",
    "    ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "    surf = ax.plot_trisurf(x,y, chain_[\"spread\"], triangles=tri.triangles, cmap=plt.cm.CMRmap,linewidth=0, antialiased=False)\n",
    "    fig.colorbar(surf, shrink=0.75, aspect=10)\n",
    "    \n",
    "    x1 = chain_2[\"strike\"]\n",
    "    y1 = chain_2[\"T\"]\n",
    "    tri = mtri.Triangulation(x1, y1)\n",
    "    \n",
    "    ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "    surf = ax.plot_trisurf(x1, y1, chain_2[\"spread\"], triangles=tri.triangles, cmap=plt.cm.Spectral,linewidth=0, antialiased=False)\n",
    "    fig.colorbar(surf, shrink=0.75, aspect=10)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Each quarter, on the third Friday in March, June, September, and January*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_exp = {}\n",
    "for stock in portfolio:    \n",
    "    t_exp[stock] = t_(date.today(), options_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BA 2019-03-15\n",
      "2019,02,15\n",
      "BA 2020-06-19\n",
      "2021,01,15\n",
      "V 2020-06-19\n",
      "2021,01,15\n"
     ]
    }
   ],
   "source": [
    "for stock in portfolio: \n",
    "    for i in range(len(t_exp[stock])): \n",
    "        while(not tradier.check_expiration(t_exp[stock][i], stock)): \n",
    "            print(stock, t_exp[stock][i])\n",
    "            t_ = input()\n",
    "            t_ = t_.split(',')\n",
    "            t_ = date(int(t_[0]), int(t_[1]), int(t_[2]))\n",
    "            t_exp[stock][i] = t_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = list((fred.get_series('DGS1', sort_order = 'desc', limit = 1).to_dict()).values())[0]/100\n",
    "q_matrix = extractQ(p, stocks)\n",
    "strikes = strikes_(p, 5)\n",
    "default = list((fred.get_series('BAMLH0A0HYM2', sort_order = 'desc', limit = 1).to_dict()).values())[0]/100\n",
    "stats = stocks.get_key_stats()\n",
    "beta = {}\n",
    "for stock in portfolio:\n",
    "    beta[stock] = stats[stock]['beta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "chains_blackscholes = pd.DataFrame(columns = [\"Stock\", \"Underlying Price\", \"Call Premium\", \"Put Premium\", \"Strike\", \"Expiry\", \"T\"])\n",
    "i = 0\n",
    "sigma_matrix = hv_weighted_df_.iloc[len(hv_weighted_df_.index)-1].to_dict()\n",
    "for stock in strikes: \n",
    "    for t_d in t_exp[stock]:\n",
    "        for x in strikes[stock]:\n",
    "            sigma = sigma_matrix[stock]\n",
    "            q = q_matrix[stock]\n",
    "            t = ((t_d-date.today()).days)/365 \n",
    "            s = p[stock]\n",
    "            d1 = d1_(s, x, t, r, q, sigma)\n",
    "            d2 = d2_(d1, sigma, t)\n",
    "            obj = {\n",
    "                    \"Stock\": stock,\n",
    "                    \"Underlying Price\": s, \n",
    "                    \"Call Premium\": round(call_(s, x, t, r, q, d1, d2), 4),\n",
    "                    \"Put Premium\": round(put_(s, x, t, r, q, d1, d2), 4),\n",
    "                    \"Strike\": x,\n",
    "                    \"Expiry\": t_d, \n",
    "                    \"T\": t\n",
    "                }\n",
    "            temp = pd.DataFrame(obj, index = [i])\n",
    "            chains_blackscholes = pd.concat([chains_blackscholes, temp], ignore_index = True)  \n",
    "            i+=1       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "chains_extension = pd.DataFrame(columns = [\"Stock\", \"Underlying Price\", \"Call Premium\", \"Put Premium\", \"Strike\", \"Expiry\", \"T\"])\n",
    "i = 0\n",
    "sigma_matrix = hv_weighted_df_.iloc[len(hv_weighted_df_.index)-1].to_dict()\n",
    "for stock in strikes: \n",
    "    for t_d in t_exp[stock]:\n",
    "        for x in strikes[stock]:\n",
    "            sigma = sigma_matrix[stock]\n",
    "            q = q_matrix[stock]\n",
    "            t = ((t_d-date.today()).days)/365 \n",
    "            s = p[stock]\n",
    "            d1 = d1_(s, x, t, r, q, sigma)\n",
    "            d2 = d2_(d1, sigma, t)\n",
    "            obj = {\n",
    "                    \"Stock\": stock,\n",
    "                    \"Underlying Price\": s, \n",
    "                    \"Call Premium\": round(call_(s, x, t, r, q, d1, d2) * M_(.1, default, beta[stock], t), 4),\n",
    "                    \"Put Premium\": round(put_(s, x, t, r, q, d1, d2) * (1/M_(.1, default, beta[stock], t)), 4),\n",
    "                    \"Strike\": x,\n",
    "                    \"Expiry\": t_d, \n",
    "                    \"T\": t\n",
    "                }\n",
    "            temp = pd.DataFrame(obj, index = [i])\n",
    "            chains_extension = pd.concat([chains_extension, temp], ignore_index = True)  \n",
    "            i+=1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain_realtime = pd.DataFrame(columns = ['ask','bid', 'strike', 'underlying', 'volume', 'expiration_date', 'option_type'])\n",
    "for stock in portfolio: \n",
    "    for t in t_exp[stock]: \n",
    "        temp = tradier.get_option_chain(stock, t, \"pandas\")\n",
    "        temp = temp[temp['strike'].isin(strikes[stock])]\n",
    "        temp = temp[['ask','bid', 'strike', 'underlying', 'volume', 'expiration_date', 'option_type']]\n",
    "        chain_realtime = pd.concat([chain_realtime,temp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Call Premium</th>\n",
       "      <th>Expiry</th>\n",
       "      <th>Put Premium</th>\n",
       "      <th>Stock</th>\n",
       "      <th>Strike</th>\n",
       "      <th>T</th>\n",
       "      <th>Underlying Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.1279</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.2774</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>0.1708</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.7261</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>0.6066</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.7650</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>1.6326</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6624</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>3.5172</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.5175</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>6.3593</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.2188</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>10.0478</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.5298</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>14.3459</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.2077</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>19.0109</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0739</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>23.8642</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.7029</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>0.5079</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.3472</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>1.1187</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.4213</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>2.1595</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.0331</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>3.7378</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.2415</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>5.9128</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.0475</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>8.6854</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.4011</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>12.0056</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.2188</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>15.7898</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.4041</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>19.9417</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.8636</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>24.3678</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.249315</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.1182</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>1.7480</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.2878</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>2.8484</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.8497</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>4.3412</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.8325</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>6.2548</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10.2420</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>8.5952</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8.0641</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>11.3481</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.2687</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>14.4835</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.8153</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>17.9610</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.6584</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>21.7348</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.7514</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>25.7587</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.517808</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>34.7567</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>9.1175</td>\n",
       "      <td>SPY</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>31.6173</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>10.8333</td>\n",
       "      <td>SPY</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>28.6682</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>12.7392</td>\n",
       "      <td>SPY</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>25.9106</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>14.8367</td>\n",
       "      <td>SPY</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>23.3440</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>17.1253</td>\n",
       "      <td>SPY</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>20.9662</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>19.6025</td>\n",
       "      <td>SPY</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>18.7728</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>22.2643</td>\n",
       "      <td>SPY</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>16.7586</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>25.1052</td>\n",
       "      <td>SPY</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>14.9166</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>28.1183</td>\n",
       "      <td>SPY</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>13.2391</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>31.2959</td>\n",
       "      <td>SPY</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1.093151</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>37.5993</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>11.9024</td>\n",
       "      <td>SPY</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>34.6469</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>13.7503</td>\n",
       "      <td>SPY</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>31.8578</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>15.7615</td>\n",
       "      <td>SPY</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>29.2315</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>17.9356</td>\n",
       "      <td>SPY</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>26.7665</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>20.2708</td>\n",
       "      <td>SPY</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>24.4599</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>22.7646</td>\n",
       "      <td>SPY</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>22.3082</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>25.4132</td>\n",
       "      <td>SPY</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>20.3069</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>28.2122</td>\n",
       "      <td>SPY</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>18.4507</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>31.1564</td>\n",
       "      <td>SPY</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>16.7339</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>34.2399</td>\n",
       "      <td>SPY</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1.515068</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>40.8593</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>15.0902</td>\n",
       "      <td>SPY</td>\n",
       "      <td>235.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>38.0890</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>17.0465</td>\n",
       "      <td>SPY</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>35.4576</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>19.1417</td>\n",
       "      <td>SPY</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>32.9637</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>21.3745</td>\n",
       "      <td>SPY</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>30.6054</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>23.7427</td>\n",
       "      <td>SPY</td>\n",
       "      <td>255.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>28.3798</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>26.2438</td>\n",
       "      <td>SPY</td>\n",
       "      <td>260.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>26.2839</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>28.8744</td>\n",
       "      <td>SPY</td>\n",
       "      <td>265.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>24.3138</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>31.6310</td>\n",
       "      <td>SPY</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>22.4657</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>34.5094</td>\n",
       "      <td>SPY</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>20.7350</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>37.5054</td>\n",
       "      <td>SPY</td>\n",
       "      <td>280.0</td>\n",
       "      <td>2.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Call Premium      Expiry  Put Premium Stock  Strike         T  \\\n",
       "0         21.1279  2019-01-18       0.0342  MSFT    85.0  0.095890   \n",
       "1         16.2774  2019-01-18       0.1708  MSFT    90.0  0.095890   \n",
       "2         11.7261  2019-01-18       0.6066  MSFT    95.0  0.095890   \n",
       "3          7.7650  2019-01-18       1.6326  MSFT   100.0  0.095890   \n",
       "4          4.6624  2019-01-18       3.5172  MSFT   105.0  0.095890   \n",
       "5          2.5175  2019-01-18       6.3593  MSFT   110.0  0.095890   \n",
       "6          1.2188  2019-01-18      10.0478  MSFT   115.0  0.095890   \n",
       "7          0.5298  2019-01-18      14.3459  MSFT   120.0  0.095890   \n",
       "8          0.2077  2019-01-18      19.0109  MSFT   125.0  0.095890   \n",
       "9          0.0739  2019-01-18      23.8642  MSFT   130.0  0.095890   \n",
       "10        21.7029  2019-03-15       0.5079  MSFT    85.0  0.249315   \n",
       "11        17.3472  2019-03-15       1.1187  MSFT    90.0  0.249315   \n",
       "12        13.4213  2019-03-15       2.1595  MSFT    95.0  0.249315   \n",
       "13        10.0331  2019-03-15       3.7378  MSFT   100.0  0.249315   \n",
       "14         7.2415  2019-03-15       5.9128  MSFT   105.0  0.249315   \n",
       "15         5.0475  2019-03-15       8.6854  MSFT   110.0  0.249315   \n",
       "16         3.4011  2019-03-15      12.0056  MSFT   115.0  0.249315   \n",
       "17         2.2188  2019-03-15      15.7898  MSFT   120.0  0.249315   \n",
       "18         1.4041  2019-03-15      19.9417  MSFT   125.0  0.249315   \n",
       "19         0.8636  2019-03-15      24.3678  MSFT   130.0  0.249315   \n",
       "20        23.1182  2019-06-21       1.7480  MSFT    85.0  0.517808   \n",
       "21        19.2878  2019-06-21       2.8484  MSFT    90.0  0.517808   \n",
       "22        15.8497  2019-06-21       4.3412  MSFT    95.0  0.517808   \n",
       "23        12.8325  2019-06-21       6.2548  MSFT   100.0  0.517808   \n",
       "24        10.2420  2019-06-21       8.5952  MSFT   105.0  0.517808   \n",
       "25         8.0641  2019-06-21      11.3481  MSFT   110.0  0.517808   \n",
       "26         6.2687  2019-06-21      14.4835  MSFT   115.0  0.517808   \n",
       "27         4.8153  2019-06-21      17.9610  MSFT   120.0  0.517808   \n",
       "28         3.6584  2019-06-21      21.7348  MSFT   125.0  0.517808   \n",
       "29         2.7514  2019-06-21      25.7587  MSFT   130.0  0.517808   \n",
       "..            ...         ...          ...   ...     ...       ...   \n",
       "330       34.7567  2020-01-17       9.1175   SPY   235.0  1.093151   \n",
       "331       31.6173  2020-01-17      10.8333   SPY   240.0  1.093151   \n",
       "332       28.6682  2020-01-17      12.7392   SPY   245.0  1.093151   \n",
       "333       25.9106  2020-01-17      14.8367   SPY   250.0  1.093151   \n",
       "334       23.3440  2020-01-17      17.1253   SPY   255.0  1.093151   \n",
       "335       20.9662  2020-01-17      19.6025   SPY   260.0  1.093151   \n",
       "336       18.7728  2020-01-17      22.2643   SPY   265.0  1.093151   \n",
       "337       16.7586  2020-01-17      25.1052   SPY   270.0  1.093151   \n",
       "338       14.9166  2020-01-17      28.1183   SPY   275.0  1.093151   \n",
       "339       13.2391  2020-01-17      31.2959   SPY   280.0  1.093151   \n",
       "340       37.5993  2020-06-19      11.9024   SPY   235.0  1.515068   \n",
       "341       34.6469  2020-06-19      13.7503   SPY   240.0  1.515068   \n",
       "342       31.8578  2020-06-19      15.7615   SPY   245.0  1.515068   \n",
       "343       29.2315  2020-06-19      17.9356   SPY   250.0  1.515068   \n",
       "344       26.7665  2020-06-19      20.2708   SPY   255.0  1.515068   \n",
       "345       24.4599  2020-06-19      22.7646   SPY   260.0  1.515068   \n",
       "346       22.3082  2020-06-19      25.4132   SPY   265.0  1.515068   \n",
       "347       20.3069  2020-06-19      28.2122   SPY   270.0  1.515068   \n",
       "348       18.4507  2020-06-19      31.1564   SPY   275.0  1.515068   \n",
       "349       16.7339  2020-06-19      34.2399   SPY   280.0  1.515068   \n",
       "350       40.8593  2021-01-15      15.0902   SPY   235.0  2.090411   \n",
       "351       38.0890  2021-01-15      17.0465   SPY   240.0  2.090411   \n",
       "352       35.4576  2021-01-15      19.1417   SPY   245.0  2.090411   \n",
       "353       32.9637  2021-01-15      21.3745   SPY   250.0  2.090411   \n",
       "354       30.6054  2021-01-15      23.7427   SPY   255.0  2.090411   \n",
       "355       28.3798  2021-01-15      26.2438   SPY   260.0  2.090411   \n",
       "356       26.2839  2021-01-15      28.8744   SPY   265.0  2.090411   \n",
       "357       24.3138  2021-01-15      31.6310   SPY   270.0  2.090411   \n",
       "358       22.4657  2021-01-15      34.5094   SPY   275.0  2.090411   \n",
       "359       20.7350  2021-01-15      37.5054   SPY   280.0  2.090411   \n",
       "\n",
       "     Underlying Price  \n",
       "0              106.03  \n",
       "1              106.03  \n",
       "2              106.03  \n",
       "3              106.03  \n",
       "4              106.03  \n",
       "5              106.03  \n",
       "6              106.03  \n",
       "7              106.03  \n",
       "8              106.03  \n",
       "9              106.03  \n",
       "10             106.03  \n",
       "11             106.03  \n",
       "12             106.03  \n",
       "13             106.03  \n",
       "14             106.03  \n",
       "15             106.03  \n",
       "16             106.03  \n",
       "17             106.03  \n",
       "18             106.03  \n",
       "19             106.03  \n",
       "20             106.03  \n",
       "21             106.03  \n",
       "22             106.03  \n",
       "23             106.03  \n",
       "24             106.03  \n",
       "25             106.03  \n",
       "26             106.03  \n",
       "27             106.03  \n",
       "28             106.03  \n",
       "29             106.03  \n",
       "..                ...  \n",
       "330            260.47  \n",
       "331            260.47  \n",
       "332            260.47  \n",
       "333            260.47  \n",
       "334            260.47  \n",
       "335            260.47  \n",
       "336            260.47  \n",
       "337            260.47  \n",
       "338            260.47  \n",
       "339            260.47  \n",
       "340            260.47  \n",
       "341            260.47  \n",
       "342            260.47  \n",
       "343            260.47  \n",
       "344            260.47  \n",
       "345            260.47  \n",
       "346            260.47  \n",
       "347            260.47  \n",
       "348            260.47  \n",
       "349            260.47  \n",
       "350            260.47  \n",
       "351            260.47  \n",
       "352            260.47  \n",
       "353            260.47  \n",
       "354            260.47  \n",
       "355            260.47  \n",
       "356            260.47  \n",
       "357            260.47  \n",
       "358            260.47  \n",
       "359            260.47  \n",
       "\n",
       "[360 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chains_blackscholes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Call Premium</th>\n",
       "      <th>Expiry</th>\n",
       "      <th>Put Premium</th>\n",
       "      <th>Stock</th>\n",
       "      <th>Strike</th>\n",
       "      <th>T</th>\n",
       "      <th>Underlying Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.9904</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.6173</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>0.1699</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.5037</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>0.6285</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.9176</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>1.7397</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.1136</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>3.8183</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.1884</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>6.9839</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0380</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>11.1079</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.4395</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>15.9146</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1669</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>21.1244</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0572</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>26.5358</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20.5044</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>0.5102</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16.3686</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>1.1391</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.6391</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>2.2220</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.4212</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>3.8765</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.7735</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>6.1684</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.6982</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>9.0999</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.1468</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>12.6169</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0385</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>16.6291</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.2797</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>21.0318</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.7801</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>25.7242</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.2185</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>1.6905</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.3497</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>2.7706</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.8774</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>4.2423</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.8317</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>6.1351</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10.2193</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>8.4557</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8.0260</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>11.1901</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.2213</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>14.3080</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.7637</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>17.7686</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.6065</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>21.5259</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.7022</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>25.5327</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.515068</td>\n",
       "      <td>106.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>35.6046</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>8.3796</td>\n",
       "      <td>SPY</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>32.3202</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>10.0078</td>\n",
       "      <td>SPY</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>29.2373</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>11.8234</td>\n",
       "      <td>SPY</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>26.3579</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>13.8281</td>\n",
       "      <td>SPY</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>23.6817</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>16.0217</td>\n",
       "      <td>SPY</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>21.2062</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>18.4019</td>\n",
       "      <td>SPY</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>18.9273</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>20.9649</td>\n",
       "      <td>SPY</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>16.8390</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>23.7051</td>\n",
       "      <td>SPY</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>14.9340</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>26.6157</td>\n",
       "      <td>SPY</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>13.2039</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>29.6889</td>\n",
       "      <td>SPY</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1.090411</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>39.8688</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>10.6003</td>\n",
       "      <td>SPY</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>36.6681</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>12.2963</td>\n",
       "      <td>SPY</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>33.6470</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>14.1477</td>\n",
       "      <td>SPY</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>30.8052</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>16.1543</td>\n",
       "      <td>SPY</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>28.1411</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>18.3147</td>\n",
       "      <td>SPY</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>25.6518</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>20.6264</td>\n",
       "      <td>SPY</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>23.3333</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>23.0859</td>\n",
       "      <td>SPY</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>21.1808</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>25.6890</td>\n",
       "      <td>SPY</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>19.1883</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>28.4307</td>\n",
       "      <td>SPY</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>17.3494</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>31.3054</td>\n",
       "      <td>SPY</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1.512329</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>44.8974</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>12.9971</td>\n",
       "      <td>SPY</td>\n",
       "      <td>235.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>41.7820</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>14.7306</td>\n",
       "      <td>SPY</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>38.8251</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>16.5917</td>\n",
       "      <td>SPY</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>36.0254</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>18.5792</td>\n",
       "      <td>SPY</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>33.3807</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>20.6913</td>\n",
       "      <td>SPY</td>\n",
       "      <td>255.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>30.8879</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>22.9256</td>\n",
       "      <td>SPY</td>\n",
       "      <td>260.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>28.5435</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>25.2792</td>\n",
       "      <td>SPY</td>\n",
       "      <td>265.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>26.3431</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>27.7487</td>\n",
       "      <td>SPY</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>24.2822</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>30.3303</td>\n",
       "      <td>SPY</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>22.3557</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>33.0199</td>\n",
       "      <td>SPY</td>\n",
       "      <td>280.0</td>\n",
       "      <td>2.087671</td>\n",
       "      <td>260.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Call Premium      Expiry  Put Premium Stock  Strike         T  \\\n",
       "0         18.9904  2019-01-18       0.0322  MSFT    85.0  0.093151   \n",
       "1         14.6173  2019-01-18       0.1699  MSFT    90.0  0.093151   \n",
       "2         10.5037  2019-01-18       0.6285  MSFT    95.0  0.093151   \n",
       "3          6.9176  2019-01-18       1.7397  MSFT   100.0  0.093151   \n",
       "4          4.1136  2019-01-18       3.8183  MSFT   105.0  0.093151   \n",
       "5          2.1884  2019-01-18       6.9839  MSFT   110.0  0.093151   \n",
       "6          1.0380  2019-01-18      11.1079  MSFT   115.0  0.093151   \n",
       "7          0.4395  2019-01-18      15.9146  MSFT   120.0  0.093151   \n",
       "8          0.1669  2019-01-18      21.1244  MSFT   125.0  0.093151   \n",
       "9          0.0572  2019-01-18      26.5358  MSFT   130.0  0.093151   \n",
       "10        20.5044  2019-03-15       0.5102  MSFT    85.0  0.246575   \n",
       "11        16.3686  2019-03-15       1.1391  MSFT    90.0  0.246575   \n",
       "12        12.6391  2019-03-15       2.2220  MSFT    95.0  0.246575   \n",
       "13         9.4212  2019-03-15       3.8765  MSFT   100.0  0.246575   \n",
       "14         6.7735  2019-03-15       6.1684  MSFT   105.0  0.246575   \n",
       "15         4.6982  2019-03-15       9.0999  MSFT   110.0  0.246575   \n",
       "16         3.1468  2019-03-15      12.6169  MSFT   115.0  0.246575   \n",
       "17         2.0385  2019-03-15      16.6291  MSFT   120.0  0.246575   \n",
       "18         1.2797  2019-03-15      21.0318  MSFT   125.0  0.246575   \n",
       "19         0.7801  2019-03-15      25.7242  MSFT   130.0  0.246575   \n",
       "20        23.2185  2019-06-21       1.6905  MSFT    85.0  0.515068   \n",
       "21        19.3497  2019-06-21       2.7706  MSFT    90.0  0.515068   \n",
       "22        15.8774  2019-06-21       4.2423  MSFT    95.0  0.515068   \n",
       "23        12.8317  2019-06-21       6.1351  MSFT   100.0  0.515068   \n",
       "24        10.2193  2019-06-21       8.4557  MSFT   105.0  0.515068   \n",
       "25         8.0260  2019-06-21      11.1901  MSFT   110.0  0.515068   \n",
       "26         6.2213  2019-06-21      14.3080  MSFT   115.0  0.515068   \n",
       "27         4.7637  2019-06-21      17.7686  MSFT   120.0  0.515068   \n",
       "28         3.6065  2019-06-21      21.5259  MSFT   125.0  0.515068   \n",
       "29         2.7022  2019-06-21      25.5327  MSFT   130.0  0.515068   \n",
       "..            ...         ...          ...   ...     ...       ...   \n",
       "330       35.6046  2020-01-17       8.3796   SPY   235.0  1.090411   \n",
       "331       32.3202  2020-01-17      10.0078   SPY   240.0  1.090411   \n",
       "332       29.2373  2020-01-17      11.8234   SPY   245.0  1.090411   \n",
       "333       26.3579  2020-01-17      13.8281   SPY   250.0  1.090411   \n",
       "334       23.6817  2020-01-17      16.0217   SPY   255.0  1.090411   \n",
       "335       21.2062  2020-01-17      18.4019   SPY   260.0  1.090411   \n",
       "336       18.9273  2020-01-17      20.9649   SPY   265.0  1.090411   \n",
       "337       16.8390  2020-01-17      23.7051   SPY   270.0  1.090411   \n",
       "338       14.9340  2020-01-17      26.6157   SPY   275.0  1.090411   \n",
       "339       13.2039  2020-01-17      29.6889   SPY   280.0  1.090411   \n",
       "340       39.8688  2020-06-19      10.6003   SPY   235.0  1.512329   \n",
       "341       36.6681  2020-06-19      12.2963   SPY   240.0  1.512329   \n",
       "342       33.6470  2020-06-19      14.1477   SPY   245.0  1.512329   \n",
       "343       30.8052  2020-06-19      16.1543   SPY   250.0  1.512329   \n",
       "344       28.1411  2020-06-19      18.3147   SPY   255.0  1.512329   \n",
       "345       25.6518  2020-06-19      20.6264   SPY   260.0  1.512329   \n",
       "346       23.3333  2020-06-19      23.0859   SPY   265.0  1.512329   \n",
       "347       21.1808  2020-06-19      25.6890   SPY   270.0  1.512329   \n",
       "348       19.1883  2020-06-19      28.4307   SPY   275.0  1.512329   \n",
       "349       17.3494  2020-06-19      31.3054   SPY   280.0  1.512329   \n",
       "350       44.8974  2021-01-15      12.9971   SPY   235.0  2.087671   \n",
       "351       41.7820  2021-01-15      14.7306   SPY   240.0  2.087671   \n",
       "352       38.8251  2021-01-15      16.5917   SPY   245.0  2.087671   \n",
       "353       36.0254  2021-01-15      18.5792   SPY   250.0  2.087671   \n",
       "354       33.3807  2021-01-15      20.6913   SPY   255.0  2.087671   \n",
       "355       30.8879  2021-01-15      22.9256   SPY   260.0  2.087671   \n",
       "356       28.5435  2021-01-15      25.2792   SPY   265.0  2.087671   \n",
       "357       26.3431  2021-01-15      27.7487   SPY   270.0  2.087671   \n",
       "358       24.2822  2021-01-15      30.3303   SPY   275.0  2.087671   \n",
       "359       22.3557  2021-01-15      33.0199   SPY   280.0  2.087671   \n",
       "\n",
       "     Underlying Price  \n",
       "0              106.03  \n",
       "1              106.03  \n",
       "2              106.03  \n",
       "3              106.03  \n",
       "4              106.03  \n",
       "5              106.03  \n",
       "6              106.03  \n",
       "7              106.03  \n",
       "8              106.03  \n",
       "9              106.03  \n",
       "10             106.03  \n",
       "11             106.03  \n",
       "12             106.03  \n",
       "13             106.03  \n",
       "14             106.03  \n",
       "15             106.03  \n",
       "16             106.03  \n",
       "17             106.03  \n",
       "18             106.03  \n",
       "19             106.03  \n",
       "20             106.03  \n",
       "21             106.03  \n",
       "22             106.03  \n",
       "23             106.03  \n",
       "24             106.03  \n",
       "25             106.03  \n",
       "26             106.03  \n",
       "27             106.03  \n",
       "28             106.03  \n",
       "29             106.03  \n",
       "..                ...  \n",
       "330            260.47  \n",
       "331            260.47  \n",
       "332            260.47  \n",
       "333            260.47  \n",
       "334            260.47  \n",
       "335            260.47  \n",
       "336            260.47  \n",
       "337            260.47  \n",
       "338            260.47  \n",
       "339            260.47  \n",
       "340            260.47  \n",
       "341            260.47  \n",
       "342            260.47  \n",
       "343            260.47  \n",
       "344            260.47  \n",
       "345            260.47  \n",
       "346            260.47  \n",
       "347            260.47  \n",
       "348            260.47  \n",
       "349            260.47  \n",
       "350            260.47  \n",
       "351            260.47  \n",
       "352            260.47  \n",
       "353            260.47  \n",
       "354            260.47  \n",
       "355            260.47  \n",
       "356            260.47  \n",
       "357            260.47  \n",
       "358            260.47  \n",
       "359            260.47  \n",
       "\n",
       "[360 rows x 7 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chains_extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain_realtime['spread'] = (chain_realtime['bid'] + chain_realtime['ask'])/2\n",
    "chain_realtime['T']  = chain_realtime.loc[:,'expiration_date'].apply(lambda x: x.split('-')) \n",
    "chain_realtime['T']  = chain_realtime.loc[:,'T'].apply(lambda x: ((date(int(x[0]),int(x[1]),int(x[2]))-date.today()).days)/365) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ask</th>\n",
       "      <th>bid</th>\n",
       "      <th>strike</th>\n",
       "      <th>underlying</th>\n",
       "      <th>volume</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>option_type</th>\n",
       "      <th>spread</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.18</td>\n",
       "      <td>85.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>35</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>21.65</td>\n",
       "      <td>21.35</td>\n",
       "      <td>85.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>12</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>21.500</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.40</td>\n",
       "      <td>90.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>242</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>16.85</td>\n",
       "      <td>16.55</td>\n",
       "      <td>90.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>146</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>16.700</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>12.35</td>\n",
       "      <td>12.05</td>\n",
       "      <td>95.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>58</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>12.200</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.82</td>\n",
       "      <td>95.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1538</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.75</td>\n",
       "      <td>1.63</td>\n",
       "      <td>100.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>3128</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>1.690</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>8.15</td>\n",
       "      <td>7.90</td>\n",
       "      <td>100.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>184</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>8.025</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>3.35</td>\n",
       "      <td>3.15</td>\n",
       "      <td>105.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>12306</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>3.250</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4.70</td>\n",
       "      <td>4.50</td>\n",
       "      <td>105.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>8174</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>4.600</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.95</td>\n",
       "      <td>5.80</td>\n",
       "      <td>110.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1178</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>5.875</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2.28</td>\n",
       "      <td>2.20</td>\n",
       "      <td>110.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>15595</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>2.240</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>9.75</td>\n",
       "      <td>9.45</td>\n",
       "      <td>115.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>84</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>9.600</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>115.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>13034</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>14.30</td>\n",
       "      <td>13.90</td>\n",
       "      <td>120.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>14.100</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.28</td>\n",
       "      <td>120.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>2331</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>19.40</td>\n",
       "      <td>18.75</td>\n",
       "      <td>125.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>19.075</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.07</td>\n",
       "      <td>125.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>425</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>26.15</td>\n",
       "      <td>22.40</td>\n",
       "      <td>130.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>put</td>\n",
       "      <td>24.275</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>130.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>449</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>call</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.095890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.05</td>\n",
       "      <td>0.95</td>\n",
       "      <td>85.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>put</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22.50</td>\n",
       "      <td>22.15</td>\n",
       "      <td>85.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>19</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>call</td>\n",
       "      <td>22.325</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.62</td>\n",
       "      <td>1.51</td>\n",
       "      <td>90.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>107</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>put</td>\n",
       "      <td>1.565</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.05</td>\n",
       "      <td>17.70</td>\n",
       "      <td>90.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>call</td>\n",
       "      <td>17.875</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.52</td>\n",
       "      <td>2.39</td>\n",
       "      <td>95.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>71</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>put</td>\n",
       "      <td>2.455</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13.95</td>\n",
       "      <td>13.65</td>\n",
       "      <td>95.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>11</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>call</td>\n",
       "      <td>13.800</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.85</td>\n",
       "      <td>3.75</td>\n",
       "      <td>100.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>256</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>put</td>\n",
       "      <td>3.800</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10.25</td>\n",
       "      <td>10.05</td>\n",
       "      <td>100.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>34</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>call</td>\n",
       "      <td>10.150</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.80</td>\n",
       "      <td>5.65</td>\n",
       "      <td>105.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>364</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>put</td>\n",
       "      <td>5.725</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.20</td>\n",
       "      <td>6.95</td>\n",
       "      <td>105.0</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>235</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>call</td>\n",
       "      <td>7.075</td>\n",
       "      <td>0.249315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>14.27</td>\n",
       "      <td>13.93</td>\n",
       "      <td>235.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>put</td>\n",
       "      <td>14.100</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>42.02</td>\n",
       "      <td>40.74</td>\n",
       "      <td>235.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>call</td>\n",
       "      <td>41.380</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>32.64</td>\n",
       "      <td>31.92</td>\n",
       "      <td>280.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>put</td>\n",
       "      <td>32.280</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>15.97</td>\n",
       "      <td>15.48</td>\n",
       "      <td>280.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>201</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>call</td>\n",
       "      <td>15.725</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>29.92</td>\n",
       "      <td>29.27</td>\n",
       "      <td>275.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>put</td>\n",
       "      <td>29.595</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>18.23</td>\n",
       "      <td>17.70</td>\n",
       "      <td>275.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>call</td>\n",
       "      <td>17.965</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>27.39</td>\n",
       "      <td>26.81</td>\n",
       "      <td>270.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>put</td>\n",
       "      <td>27.100</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20.66</td>\n",
       "      <td>20.08</td>\n",
       "      <td>270.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>call</td>\n",
       "      <td>20.370</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>25.05</td>\n",
       "      <td>24.52</td>\n",
       "      <td>265.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>put</td>\n",
       "      <td>24.785</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>23.26</td>\n",
       "      <td>22.64</td>\n",
       "      <td>265.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>call</td>\n",
       "      <td>22.950</td>\n",
       "      <td>1.515068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.20</td>\n",
       "      <td>25.95</td>\n",
       "      <td>260.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>103</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>26.075</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31.41</td>\n",
       "      <td>29.00</td>\n",
       "      <td>260.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>62</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>30.205</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24.97</td>\n",
       "      <td>23.05</td>\n",
       "      <td>255.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>24.010</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>34.21</td>\n",
       "      <td>30.99</td>\n",
       "      <td>255.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>32.600</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.08</td>\n",
       "      <td>21.18</td>\n",
       "      <td>250.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>22.130</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>37.12</td>\n",
       "      <td>33.80</td>\n",
       "      <td>250.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>35.460</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21.27</td>\n",
       "      <td>19.42</td>\n",
       "      <td>245.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>20.345</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>40.14</td>\n",
       "      <td>36.73</td>\n",
       "      <td>245.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>38.435</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>19.58</td>\n",
       "      <td>17.81</td>\n",
       "      <td>240.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>47</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>18.695</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>43.27</td>\n",
       "      <td>40.00</td>\n",
       "      <td>240.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>41.635</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>18.02</td>\n",
       "      <td>16.27</td>\n",
       "      <td>235.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>17.145</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>46.50</td>\n",
       "      <td>42.90</td>\n",
       "      <td>235.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>44.700</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>36.59</td>\n",
       "      <td>34.22</td>\n",
       "      <td>280.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>35.405</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>21.47</td>\n",
       "      <td>18.75</td>\n",
       "      <td>280.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>20.110</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>33.99</td>\n",
       "      <td>31.69</td>\n",
       "      <td>275.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>32.840</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23.76</td>\n",
       "      <td>20.94</td>\n",
       "      <td>275.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>22.350</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>31.51</td>\n",
       "      <td>29.34</td>\n",
       "      <td>270.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>74</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>30.425</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>26.18</td>\n",
       "      <td>23.26</td>\n",
       "      <td>270.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>9</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>24.720</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>28.50</td>\n",
       "      <td>27.11</td>\n",
       "      <td>265.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>put</td>\n",
       "      <td>27.805</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>28.74</td>\n",
       "      <td>25.71</td>\n",
       "      <td>265.0</td>\n",
       "      <td>SPY</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>call</td>\n",
       "      <td>27.225</td>\n",
       "      <td>2.090411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>698 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ask    bid  strike underlying volume expiration_date option_type  \\\n",
       "44    0.27   0.18    85.0       MSFT     35      2019-01-18         put   \n",
       "45   21.65  21.35    85.0       MSFT     12      2019-01-18        call   \n",
       "48    0.48   0.40    90.0       MSFT    242      2019-01-18         put   \n",
       "49   16.85  16.55    90.0       MSFT    146      2019-01-18        call   \n",
       "52   12.35  12.05    95.0       MSFT     58      2019-01-18        call   \n",
       "53    0.90   0.82    95.0       MSFT   1538      2019-01-18         put   \n",
       "56    1.75   1.63   100.0       MSFT   3128      2019-01-18         put   \n",
       "57    8.15   7.90   100.0       MSFT    184      2019-01-18        call   \n",
       "58    3.35   3.15   105.0       MSFT  12306      2019-01-18         put   \n",
       "59    4.70   4.50   105.0       MSFT   8174      2019-01-18        call   \n",
       "60    5.95   5.80   110.0       MSFT   1178      2019-01-18         put   \n",
       "61    2.28   2.20   110.0       MSFT  15595      2019-01-18        call   \n",
       "62    9.75   9.45   115.0       MSFT     84      2019-01-18         put   \n",
       "63    0.91   0.87   115.0       MSFT  13034      2019-01-18        call   \n",
       "64   14.30  13.90   120.0       MSFT     22      2019-01-18         put   \n",
       "65    0.32   0.28   120.0       MSFT   2331      2019-01-18        call   \n",
       "66   19.40  18.75   125.0       MSFT      1      2019-01-18         put   \n",
       "67    0.10   0.07   125.0       MSFT    425      2019-01-18        call   \n",
       "68   26.15  22.40   130.0       MSFT      0      2019-01-18         put   \n",
       "69    0.06   0.04   130.0       MSFT    449      2019-01-18        call   \n",
       "14    1.05   0.95    85.0       MSFT     60      2019-03-15         put   \n",
       "15   22.50  22.15    85.0       MSFT     19      2019-03-15        call   \n",
       "16    1.62   1.51    90.0       MSFT    107      2019-03-15         put   \n",
       "17   18.05  17.70    90.0       MSFT     14      2019-03-15        call   \n",
       "20    2.52   2.39    95.0       MSFT     71      2019-03-15         put   \n",
       "21   13.95  13.65    95.0       MSFT     11      2019-03-15        call   \n",
       "24    3.85   3.75   100.0       MSFT    256      2019-03-15         put   \n",
       "25   10.25  10.05   100.0       MSFT     34      2019-03-15        call   \n",
       "26    5.80   5.65   105.0       MSFT    364      2019-03-15         put   \n",
       "27    7.20   6.95   105.0       MSFT    235      2019-03-15        call   \n",
       "..     ...    ...     ...        ...    ...             ...         ...   \n",
       "39   14.27  13.93   235.0        SPY      0      2020-06-19         put   \n",
       "44   42.02  40.74   235.0        SPY      0      2020-06-19        call   \n",
       "80   32.64  31.92   280.0        SPY      0      2020-06-19         put   \n",
       "88   15.97  15.48   280.0        SPY    201      2020-06-19        call   \n",
       "89   29.92  29.27   275.0        SPY      0      2020-06-19         put   \n",
       "98   18.23  17.70   275.0        SPY      0      2020-06-19        call   \n",
       "104  27.39  26.81   270.0        SPY      0      2020-06-19         put   \n",
       "112  20.66  20.08   270.0        SPY      0      2020-06-19        call   \n",
       "113  25.05  24.52   265.0        SPY      5      2020-06-19         put   \n",
       "121  23.26  22.64   265.0        SPY      5      2020-06-19        call   \n",
       "2    26.20  25.95   260.0        SPY    103      2021-01-15         put   \n",
       "7    31.41  29.00   260.0        SPY     62      2021-01-15        call   \n",
       "8    24.97  23.05   255.0        SPY      2      2021-01-15         put   \n",
       "13   34.21  30.99   255.0        SPY      0      2021-01-15        call   \n",
       "19   23.08  21.18   250.0        SPY      0      2021-01-15         put   \n",
       "22   37.12  33.80   250.0        SPY      0      2021-01-15        call   \n",
       "23   21.27  19.42   245.0        SPY      0      2021-01-15         put   \n",
       "26   40.14  36.73   245.0        SPY      0      2021-01-15        call   \n",
       "31   19.58  17.81   240.0        SPY     47      2021-01-15         put   \n",
       "35   43.27  40.00   240.0        SPY      0      2021-01-15        call   \n",
       "36   18.02  16.27   235.0        SPY      0      2021-01-15         put   \n",
       "41   46.50  42.90   235.0        SPY      1      2021-01-15        call   \n",
       "77   36.59  34.22   280.0        SPY      1      2021-01-15         put   \n",
       "85   21.47  18.75   280.0        SPY      4      2021-01-15        call   \n",
       "86   33.99  31.69   275.0        SPY      1      2021-01-15         put   \n",
       "95   23.76  20.94   275.0        SPY      0      2021-01-15        call   \n",
       "101  31.51  29.34   270.0        SPY     74      2021-01-15         put   \n",
       "109  26.18  23.26   270.0        SPY      9      2021-01-15        call   \n",
       "110  28.50  27.11   265.0        SPY      6      2021-01-15         put   \n",
       "118  28.74  25.71   265.0        SPY      1      2021-01-15        call   \n",
       "\n",
       "     spread         T  \n",
       "44    0.225  0.095890  \n",
       "45   21.500  0.095890  \n",
       "48    0.440  0.095890  \n",
       "49   16.700  0.095890  \n",
       "52   12.200  0.095890  \n",
       "53    0.860  0.095890  \n",
       "56    1.690  0.095890  \n",
       "57    8.025  0.095890  \n",
       "58    3.250  0.095890  \n",
       "59    4.600  0.095890  \n",
       "60    5.875  0.095890  \n",
       "61    2.240  0.095890  \n",
       "62    9.600  0.095890  \n",
       "63    0.890  0.095890  \n",
       "64   14.100  0.095890  \n",
       "65    0.300  0.095890  \n",
       "66   19.075  0.095890  \n",
       "67    0.085  0.095890  \n",
       "68   24.275  0.095890  \n",
       "69    0.050  0.095890  \n",
       "14    1.000  0.249315  \n",
       "15   22.325  0.249315  \n",
       "16    1.565  0.249315  \n",
       "17   17.875  0.249315  \n",
       "20    2.455  0.249315  \n",
       "21   13.800  0.249315  \n",
       "24    3.800  0.249315  \n",
       "25   10.150  0.249315  \n",
       "26    5.725  0.249315  \n",
       "27    7.075  0.249315  \n",
       "..      ...       ...  \n",
       "39   14.100  1.515068  \n",
       "44   41.380  1.515068  \n",
       "80   32.280  1.515068  \n",
       "88   15.725  1.515068  \n",
       "89   29.595  1.515068  \n",
       "98   17.965  1.515068  \n",
       "104  27.100  1.515068  \n",
       "112  20.370  1.515068  \n",
       "113  24.785  1.515068  \n",
       "121  22.950  1.515068  \n",
       "2    26.075  2.090411  \n",
       "7    30.205  2.090411  \n",
       "8    24.010  2.090411  \n",
       "13   32.600  2.090411  \n",
       "19   22.130  2.090411  \n",
       "22   35.460  2.090411  \n",
       "23   20.345  2.090411  \n",
       "26   38.435  2.090411  \n",
       "31   18.695  2.090411  \n",
       "35   41.635  2.090411  \n",
       "36   17.145  2.090411  \n",
       "41   44.700  2.090411  \n",
       "77   35.405  2.090411  \n",
       "85   20.110  2.090411  \n",
       "86   32.840  2.090411  \n",
       "95   22.350  2.090411  \n",
       "101  30.425  2.090411  \n",
       "109  24.720  2.090411  \n",
       "110  27.805  2.090411  \n",
       "118  27.225  2.090411  \n",
       "\n",
       "[698 rows x 9 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain_realtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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we+WeADrxi6MHb7/kcMoB+TRDtWw2MwCFEIKRkRFMTExgzZo1mtmsdU2nhM5I5gB7Qien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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a223d4a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_option_subplot(chains_blackscholes, chains_extension, portfolio[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OKs/p/7kdJpMJJSUlKCkpQW1tbcJzCYfDvCydTicCgQA4jkNpaWlCClbu2KVMIZZcLBZD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keGC/DHz88P06dPx/Tp0wH0nev65ptvory8nN0M4u/vj8mTJ7ON+Nxlv3V1dWwRDyNJHx8fDBs2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22366da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_option_1(chains_extension, portfolio[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LSKfTuHz5MlpbW8tO7pwezlJPqdMKHFqo/cyyNxvKAQLs9soyEWUTuACwofxyiVJ6q9Gd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Gy2tGBS6JuxXq+H3++H3+9HT08PbDZbVkdvaGgI9fX1ePnll3H48GE8/fTT2LKlOLWXpcpC98vC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2162ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_real_option_subplot(chain_realtime, portfolio[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface_graph_call = {}\n",
    "for stock in portfolio: \n",
    "    temp = chain_realtime[((chain_realtime['underlying'] == stock) & (chain_realtime['option_type'] == 'call'))]\n",
    "    temp2 = chains_blackscholes[(chains_blackscholes['Stock'] == stock)]\n",
    "    temp3 = chains_extension[(chains_extension['Stock'] == stock)]\n",
    "    x = temp['strike']\n",
    "    y = temp['T']\n",
    "    z = temp['spread']\n",
    "    x1 = temp2['Strike']\n",
    "    y1 = temp2['T']\n",
    "    z1 = temp2['Call Premium']\n",
    "    x2 = temp3['Strike']\n",
    "    y2 = temp3['T']\n",
    "    z2 = temp3['Call Premium']\n",
    "\n",
    "    points2D = np.vstack([x,y]).T\n",
    "    tri = Delaunay(points2D)\n",
    "    simplices = tri.simplices\n",
    "\n",
    "    points2D1 = np.vstack([x1,y1]).T\n",
    "    tri1 = Delaunay(points2D1)\n",
    "    simplices1 = tri1.simplices\n",
    "\n",
    "    points2D2 = np.vstack([x2,y2]).T\n",
    "    tri2 = Delaunay(points2D2)\n",
    "    simplices2 = tri2.simplices\n",
    "\n",
    "    fig = FF.create_trisurf(x=x, y=y, z=z,colormap=['rgb(56, 96, 56)', 'rgb(0, 39, 0)', ],\n",
    "                             simplices=simplices, show_colorbar=False,\n",
    "                             title=stock + \" Call Volatility Surface \", plot_edges=False, aspectratio=dict(x=1, y=1, z=0.3)).data[0]\n",
    "    fig2 = FF.create_trisurf(x=x1, y=y1, z=z1,colormap=['rgb(0, 155, 150)', 'rgb(105, 153, 255)', ],\n",
    "                             simplices=simplices1, show_colorbar=False,\n",
    "                             title=stock + \" Call Volatility Surface \", plot_edges=False, aspectratio=dict(x=1, y=1, z=0.3)).data[0]\n",
    "    fig3 = FF.create_trisurf(x=x2, y=y2, z=z2,colormap=['rgb(240, 128, 128)', 'rgb(139, 0, 0)', ],\n",
    "                             simplices=simplices2, show_colorbar=False,\n",
    "                             title=stock + \" Call Volatility Surface \", plot_edges=False, aspectratio=dict(x=1, y=1, z=0.3)).data[0]\n",
    "    fig[\"name\"] = \"Spread\"\n",
    "    fig2[\"name\"] = \"BS\"\n",
    "    fig3[\"name\"] = \"BS Extension\"\n",
    "    data = (fig, fig2, fig3)\n",
    "    layout = go.Layout(\n",
    "        title = stock + \" Call Volatility Surface \",\n",
    "        showlegend = True,\n",
    "        scene = dict(\n",
    "            xaxis = dict(\n",
    "                title='Strike'),\n",
    "            yaxis = dict(\n",
    "                title='Time'),\n",
    "            zaxis = dict(\n",
    "                title='Premium'),))\n",
    "        \n",
    "    \n",
    "    surface_graph_call[stock] = go.Figure(\n",
    "        data = data,\n",
    "        layout = layout)\n",
    "    \n",
    "    py.plot(surface_graph_call[stock], filename=stock+\"CVolSurface\")\n",
    "    path = \"images/\" + stock + \"CVolSurface.png\"\n",
    "#     pio.write_image(surface_graph_call[stock], path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surface_graph_put = {}\n",
    "for stock in portfolio: \n",
    "    temp = chain_realtime[((chain_realtime['underlying'] == stock) & (chain_realtime['option_type'] == 'put'))]\n",
    "    temp2 = chains_blackscholes[(chains_blackscholes['Stock'] == stock)]\n",
    "    temp3 = chains_extension[(chains_extension['Stock'] == stock)]\n",
    "    x = temp['strike']\n",
    "    y = temp['T']\n",
    "    z = temp['spread']\n",
    "    x1 = temp2['Strike']\n",
    "    y1 = temp2['T']\n",
    "    z1 = temp2['Put Premium']\n",
    "    x2 = temp3['Strike']\n",
    "    y2 = temp3['T']\n",
    "    z2 = temp3['Put Premium']\n",
    "\n",
    "    points2D = np.vstack([x,y]).T\n",
    "    tri = Delaunay(points2D)\n",
    "    simplices = tri.simplices\n",
    "\n",
    "    points2D1 = np.vstack([x1,y1]).T\n",
    "    tri1 = Delaunay(points2D1)\n",
    "    simplices1 = tri1.simplices\n",
    "\n",
    "    points2D2 = np.vstack([x2,y2]).T\n",
    "    tri2 = Delaunay(points2D2)\n",
    "    simplices2 = tri2.simplices\n",
    "\n",
    "    fig = FF.create_trisurf(x=x, y=y, z=z,colormap=['rgb(56, 96, 56)', 'rgb(0, 39, 0)', ],\n",
    "                             simplices=simplices, show_colorbar=False,\n",
    "                             title=stock + \" Put Volatility Surface \", plot_edges=False, aspectratio=dict(x=1, y=1, z=0.3)).data[0]\n",
    "    fig2 = FF.create_trisurf(x=x1, y=y1, z=z1,colormap=['rgb(0, 155, 150)', 'rgb(105, 153, 255)', ],\n",
    "                             simplices=simplices1, show_colorbar=False,\n",
    "                             title=stock + \" Put Volatility Surface \", plot_edges=False, aspectratio=dict(x=1, y=1, z=0.3)).data[0]\n",
    "    fig3 = FF.create_trisurf(x=x2, y=y2, z=z2,colormap=['rgb(240, 128, 128)', 'rgb(139, 0, 0)', ],\n",
    "                             simplices=simplices2, show_colorbar=False,\n",
    "                             title=stock + \" Put Volatility Surface \", plot_edges=False, aspectratio=dict(x=1, y=1, z=0.3)).data[0]\n",
    "    fig[\"name\"] = \"Spread\"\n",
    "    fig2[\"name\"] = \"BS\"\n",
    "    fig3[\"name\"] = \"BS Extension\"\n",
    "    data = (fig, fig2, fig3)\n",
    "    layout = go.Layout(\n",
    "        title = stock + \" Put Volatility Surface \",\n",
    "        showlegend = True,\n",
    "        scene = dict(\n",
    "            xaxis = dict(\n",
    "                title='Strike'),\n",
    "            yaxis = dict(\n",
    "                title='Time'),\n",
    "            zaxis = dict(\n",
    "                title='Premium'),))\n",
    "        \n",
    "    \n",
    "    surface_graph_put[stock] = go.Figure(\n",
    "        data = data,\n",
    "        layout = layout)\n",
    "    \n",
    "    py.plot(surface_graph_put[stock], filename=stock+\"PVolSurface\")\n",
    "    path = \"images/\" + stock + \"PVolSurface.png\"\n",
    "#     pio.write_image(surface_graph_call[stock], path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plotly.io.orca.config.executable = '/Users/37138/anaconda3/lib/python3.6/site-packages/orca.app/Contents/MacOS/orca'\n",
    "plotly.io.orca.config.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jaisal1024/19.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock = 'GOOGL'\n",
    "py.iplot(surface_graph_call[stock], filename=stock+\"CVolSurface\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-196-34d63eef0e17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'GOOGL'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurface_graph_put\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"PVolSurface\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py\u001b[0m in \u001b[0;36miplot\u001b[0;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'auto_open'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'auto_open'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m     \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/plotly/plotly/plotly.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\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[1;32m    238\u001b[0m     \u001b[0mplot_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'layout'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'layout'\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--> 239\u001b[0;31m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclientresp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[0;31m# Check if the url needs a secret key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/plotly/api/v1/clientresp.py\u001b[0m in \u001b[0;36mclientresp\u001b[0;34m(data, **kwargs)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{plotly_domain}/clientresp'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'post'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpayload\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[0;31m# Old functionality, just keeping it around.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;34m@\u001b[0m\u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/retrying.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_reject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mattempt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_exception\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0mdelay_since_first_attempt_ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/retrying.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, wrap_exception)\u001b[0m\n\u001b[1;32m    245\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m                 \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\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    248\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/retrying.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m                 \u001b[0mattempt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m                 \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/plotly/api/v1/utils.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'verify'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'plotly_ssl_verification'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mRequestException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;31m# The message can be an exception. E.g., MaxRetryError.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\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[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    506\u001b[0m         }\n\u001b[1;32m    507\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    438\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                     \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m                 )\n\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    599\u001b[0m                                                   \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m                                                   chunked=chunked)\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;31m# If we're going to release the connection in ``finally:``, then\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    344\u001b[0m         \u001b[0;31m# Trigger any extra validation we need to do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0;31m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0;31m# Force connect early to allow us to validate the connection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sock'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 850\u001b[0;31m             \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\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    851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_verified\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    282\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0;31m# Add certificate verification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m         \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\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    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0mhostname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m             conn = connection.create_connection(\n\u001b[0;32m--> 141\u001b[0;31m                 (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSocketTimeout\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msource_address\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "stock = 'GOOGL'\n",
    "py.iplot(surface_graph_put[stock], filename=stock+\"PVolSurface\")"
   ]
  }
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