American_Options_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OptionMetrics data base available through the Wharton Research Data Services"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_options = pd.read_csv(\"D://Data/Options/f4c96ef74cf7492c.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_options = df_options[df_options[\"volume\"] != 0] # no volume, no real options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_options.date = pd.to_datetime(df_options.date, format=\"%Y%m%d\")\n",
    "df_options.exdate = pd.to_datetime(df_options.exdate, format=\"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_options.strike_price = df_options.strike_price / 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_options[\"to_matur\"] = df_options.exdate - df_options.date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exmaple of the avaliable data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>exdate</th>\n",
       "      <th>cp_flag</th>\n",
       "      <th>strike_price</th>\n",
       "      <th>best_bid</th>\n",
       "      <th>best_offer</th>\n",
       "      <th>volume</th>\n",
       "      <th>open_interest</th>\n",
       "      <th>impl_volatility</th>\n",
       "      <th>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "      <th>optionid</th>\n",
       "      <th>index_flag</th>\n",
       "      <th>issuer</th>\n",
       "      <th>exercise_style</th>\n",
       "      <th>to_matur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>2007-12-22</td>\n",
       "      <td>C</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.049434</td>\n",
       "      <td>0.418867</td>\n",
       "      <td>0.073463</td>\n",
       "      <td>40.82416</td>\n",
       "      <td>-4.565195</td>\n",
       "      <td>32549447</td>\n",
       "      <td>0</td>\n",
       "      <td>CURRENCYSHARES EURO TR</td>\n",
       "      <td>A</td>\n",
       "      <td>226 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31534</th>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>2007-12-22</td>\n",
       "      <td>C</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048378</td>\n",
       "      <td>0.741377</td>\n",
       "      <td>0.101836</td>\n",
       "      <td>20.41096</td>\n",
       "      <td>-3.907860</td>\n",
       "      <td>32549492</td>\n",
       "      <td>0</td>\n",
       "      <td>CURRENCYSHARES SWISS FRANC T</td>\n",
       "      <td>A</td>\n",
       "      <td>226 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31523</th>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>2007-09-22</td>\n",
       "      <td>C</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.90</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054671</td>\n",
       "      <td>0.675453</td>\n",
       "      <td>0.131345</td>\n",
       "      <td>17.71416</td>\n",
       "      <td>-4.199652</td>\n",
       "      <td>32549506</td>\n",
       "      <td>0</td>\n",
       "      <td>CURRENCYSHARES SWISS FRANC T</td>\n",
       "      <td>A</td>\n",
       "      <td>135 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>2007-12-22</td>\n",
       "      <td>C</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.40</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051073</td>\n",
       "      <td>0.353716</td>\n",
       "      <td>0.067857</td>\n",
       "      <td>38.89334</td>\n",
       "      <td>-4.089529</td>\n",
       "      <td>32549451</td>\n",
       "      <td>0</td>\n",
       "      <td>CURRENCYSHARES EURO TR</td>\n",
       "      <td>A</td>\n",
       "      <td>226 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>2007-12-22</td>\n",
       "      <td>C</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051163</td>\n",
       "      <td>0.290748</td>\n",
       "      <td>0.062580</td>\n",
       "      <td>35.94629</td>\n",
       "      <td>-3.532333</td>\n",
       "      <td>32549452</td>\n",
       "      <td>0</td>\n",
       "      <td>CURRENCYSHARES EURO TR</td>\n",
       "      <td>A</td>\n",
       "      <td>226 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     exdate cp_flag  strike_price  best_bid  best_offer  \\\n",
       "128   2007-05-10 2007-12-22       C          1.38      1.45        1.70   \n",
       "31534 2007-05-10 2007-12-22       C          0.82      2.25        2.65   \n",
       "31523 2007-05-10 2007-09-22       C          0.82      1.65        1.90   \n",
       "129   2007-05-10 2007-12-22       C          1.39      1.15        1.40   \n",
       "130   2007-05-10 2007-12-22       C          1.40      0.85        1.10   \n",
       "\n",
       "       volume  open_interest  impl_volatility     delta     gamma      vega  \\\n",
       "128         2              0         0.049434  0.418867  0.073463  40.82416   \n",
       "31534       2              0         0.048378  0.741377  0.101836  20.41096   \n",
       "31523      10              0         0.054671  0.675453  0.131345  17.71416   \n",
       "129         5              0         0.051073  0.353716  0.067857  38.89334   \n",
       "130         2              0         0.051163  0.290748  0.062580  35.94629   \n",
       "\n",
       "          theta  optionid  index_flag                        issuer  \\\n",
       "128   -4.565195  32549447           0        CURRENCYSHARES EURO TR   \n",
       "31534 -3.907860  32549492           0  CURRENCYSHARES SWISS FRANC T   \n",
       "31523 -4.199652  32549506           0  CURRENCYSHARES SWISS FRANC T   \n",
       "129   -4.089529  32549451           0        CURRENCYSHARES EURO TR   \n",
       "130   -3.532333  32549452           0        CURRENCYSHARES EURO TR   \n",
       "\n",
       "      exercise_style to_matur  \n",
       "128                A 226 days  \n",
       "31534              A 226 days  \n",
       "31523              A 135 days  \n",
       "129                A 226 days  \n",
       "130                A 226 days  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_options.sort_values(\"date\", ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issuer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>234761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURRENCYSHARES SWISS FRANC T</th>\n",
       "      <td>23528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURRENCYSHS BRIT POUND STER</th>\n",
       "      <td>40332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                date\n",
       "issuer                              \n",
       "CURRENCYSHARES EURO TR        234761\n",
       "CURRENCYSHARES SWISS FRANC T   23528\n",
       "CURRENCYSHS BRIT POUND STER    40332"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_options.groupby(\"issuer\").date.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result: th emost popular type is EUR/USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp_flag</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>130080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>168541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date\n",
       "cp_flag        \n",
       "C        130080\n",
       "P        168541"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_options.groupby(\"cp_flag\").date.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result: different number of Call and Put options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-05-17</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-18</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-21</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-22</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-23</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date\n",
       "date            \n",
       "2007-05-17    13\n",
       "2007-05-18     5\n",
       "2007-05-21     3\n",
       "2007-05-22    11\n",
       "2007-05-23     1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_options[df_options[\"issuer\"] == \"CURRENCYSHS BRIT POUND STER\"].groupby([\"date\"]).date.count().head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result: the same type of options (EUR) has several prices per day. Just different levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to_matur</th>\n",
       "      <th>issuer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8 days</th>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>4851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 days</th>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>4716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days</th>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>4589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 days</th>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>4556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 days</th>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>4509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 date\n",
       "to_matur issuer                      \n",
       "8 days   CURRENCYSHARES EURO TR  4851\n",
       "3 days   CURRENCYSHARES EURO TR  4716\n",
       "1 days   CURRENCYSHARES EURO TR  4589\n",
       "2 days   CURRENCYSHARES EURO TR  4556\n",
       "4 days   CURRENCYSHARES EURO TR  4509"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_options.groupby([\"to_matur\", \"issuer\"]).date.count()).sort_values([\"date\"], ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result: the most frequent time to maturity is 8 days. Will use it for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>issuer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CURRENCYSHARES EURO TR</th>\n",
       "      <td>234761.0</td>\n",
       "      <td>1.258453</td>\n",
       "      <td>0.151441</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURRENCYSHARES SWISS FRANC T</th>\n",
       "      <td>23528.0</td>\n",
       "      <td>1.042337</td>\n",
       "      <td>0.122616</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURRENCYSHS BRIT POUND STER</th>\n",
       "      <td>40332.0</td>\n",
       "      <td>1.592202</td>\n",
       "      <td>0.185935</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.490</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 count      mean       std   min    25%   50%  \\\n",
       "issuer                                                                          \n",
       "CURRENCYSHARES EURO TR        234761.0  1.258453  0.151441  0.65  1.125  1.28   \n",
       "CURRENCYSHARES SWISS FRANC T   23528.0  1.042337  0.122616  0.65  0.960  1.04   \n",
       "CURRENCYSHS BRIT POUND STER    40332.0  1.592202  0.185935  0.95  1.490  1.56   \n",
       "\n",
       "                               75%  max  \n",
       "issuer                                   \n",
       "CURRENCYSHARES EURO TR        1.36  2.1  \n",
       "CURRENCYSHARES SWISS FRANC T  1.11  1.6  \n",
       "CURRENCYSHS BRIT POUND STER   1.65  2.2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_options.groupby([\"issuer\"]).strike_price.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result: here we have a wide range of avaliable strike prices (caution: the presented information covers the whole avaliable data set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Just an exchange rates:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The most frequent: EUR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read EUR/USD prices from a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_EUR = pd.read_csv(\"D://Data/Options/Exchange_Rate/EURUSD_EET_Daily_Bid_2007.05.09_2016.04.27.csv\", delimiter=\",\")\n",
    "df_EUR[\"Time (UTC)\"] = pd.to_datetime(df_EUR[\"Time (UTC)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selected_df = df_options[(df_options[\"issuer\"] == \"CURRENCYSHARES EURO TR\") & (df_options[\"to_matur\"] == \"8 days\") & (df_options[\"cp_flag\"] == \"P\")]\n",
    "uniqDates = selected_df.groupby([\"date\"]).date.count().index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we try to find the closest to the at-the-money options for each given time step (day):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here we try to find the closest to the at-the-money options:\n",
    "df_atTheMoney = pd.DataFrame()\n",
    "\n",
    "for i, aDate in enumerate(uniqDates):\n",
    "        \n",
    "    openPrice = df_EUR[(pd.DatetimeIndex(df_EUR[\"Time (UTC)\"]).year == uniqDates[0].year) & (pd.DatetimeIndex(df_EUR[\"Time (UTC)\"]).month == uniqDates[0].month) & (pd.DatetimeIndex(df_EUR[\"Time (UTC)\"]).day == uniqDates[0].day)].Open\n",
    "    \n",
    "    atTheMoney = selected_df[selected_df[\"date\"] == aDate].iloc[(selected_df[selected_df[\"date\"] == aDate]['strike_price']-openPrice).abs().argsort()[:1]]\n",
    "    \n",
    "    df_atTheMoney = pd.concat([df_atTheMoney, atTheMoney])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a plot for EUR/USD prices (black) and a curve for Put prices (brown and blue):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nn3ziV8YmpLXUTvfmk1QRI4QQEiveqYntsSJGmkdBjESMIAiw2WxQqVQQBCHoMaIoQqU6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RaIWy98LfAhSRP57ZTD4uYT58Dd+23st3bvrujP0cnmgQpR8+duJZaCmE17A8zphXqe2pNbcf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\n+u4Ly6WUBLa/QBwbxrj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      "text/plain": [
       "<matplotlib.figure.Figure at 0xfe58780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14,6))\n",
    "\n",
    "ax.plot(df_EUR[\"Time (UTC)\"], df_EUR.Open, linewidth=0.5, color=\"black\")\n",
    "#ax.plot(df_GBP[\"Time (UTC)\"], df_GBP.High, linewidth=0.5)\n",
    "\n",
    "ax.set_ylabel(\"Underl. Price\")\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "ax2.plot(df_atTheMoney[\"date\"], df_atTheMoney.best_offer, label=\"Put, Ask\")\n",
    "ax2.plot(df_atTheMoney[\"date\"], df_atTheMoney.best_bid, label=\"Put, Bid\")\n",
    "\n",
    "ax2.set_ylabel(\"Options Price\")\n",
    "\n",
    "ax2.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "** Results: the avaliable data is very badly fragmented, there is no sufficient information to make a decent research. We need real data on the option prices. Day by day, at-the-money. Various periods of maturity. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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  "latex_envs": {
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   "cite_by": "apalike",
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}