ipython/06_Financial_Time_Series.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://pythonquants.com' target='_blank'>www.pythonquants.com</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:analytics@pythonquants.com'>analytics@pythonquants.com</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "uuid": "eda2a742-134d-4d47-8b30-557b846b9bb3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Steps with DataFrame Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "uuid": "f3be2d89-829a-49b2-96fc-07c475db1e3f"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "a       10\n",
       "b       20\n",
       "c       30\n",
       "d       40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([10, 20, 30, 40], columns=['numbers'],\n",
    "                  index=['a', 'b', 'c', 'd'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "uuid": "47b70a7b-710f-4c40-9a70-b09db7af1a12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'a', u'b', u'c', u'd'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index  # the index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "uuid": "a36c6695-520d-4df1-a6fa-5f8362af37a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'numbers'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns  # the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "uuid": "c93aed37-21de-429d-86ed-9849e4c3e23c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    30\n",
       "Name: c, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix['c']  # selection via index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "uuid": "8c7c2f69-3673-40d9-a568-0471c629810d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "a       10\n",
       "d       40"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[['a', 'd']]  # selection of multiple indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "uuid": "c3ce0cc3-26e8-4256-ab8c-9a2d4b181633"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "b       20\n",
       "c       30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[df.index[1:3]]  # selection via Index object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "uuid": "94b1d846-63df-49f4-8a7f-8fed03e5f4fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()  # sum per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "uuid": "4e73eb4f-352d-4527-b0c5-4f3a6e7eb354"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "a      100\n",
       "b      400\n",
       "c      900\n",
       "d     1600"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x ** 2)  # square of every element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "uuid": "75206a83-0154-4be2-88d0-7a82a190fda1"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers\n",
       "a      100\n",
       "b      400\n",
       "c      900\n",
       "d     1600"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df ** 2  # again square, this time NumPy-like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "uuid": "49a2633a-b3c0-4d00-a227-e0ff4a8cf81d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "      <th>floats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers  floats\n",
       "a       10     1.5\n",
       "b       20     2.5\n",
       "c       30     3.5\n",
       "d       40     4.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floats'] = (1.5, 2.5, 3.5, 4.5)\n",
    "  # new column is generated\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "uuid": "c49b9aea-417a-4c2b-8e27-0e8771a77c87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.5\n",
       "b    2.5\n",
       "c    3.5\n",
       "d    4.5\n",
       "Name: floats, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floats']  # selection of column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "uuid": "aa892c41-6637-45ed-876b-6a70285e4c0b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>30</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Francesc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>40</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Yves</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers  floats     names\n",
       "a       10     1.5     Guido\n",
       "b       20     2.5     Felix\n",
       "c       30     3.5  Francesc\n",
       "d       40     4.5      Yves"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['names'] = pd.DataFrame(['Yves', 'Guido', 'Felix', 'Francesc'],\n",
    "                           index=['d', 'a', 'b', 'c'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "uuid": "584ac18c-161f-4c7b-8ff1-1cd406fb8437"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numbers</th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numbers  floats     names\n",
       "0       10    1.50     Guido\n",
       "1       20    2.50     Felix\n",
       "2       30    3.50  Francesc\n",
       "3       40    4.50      Yves\n",
       "4      100    5.75     Henry"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append({'numbers': 100, 'floats': 5.75, 'names': 'Henry'},\n",
    "               ignore_index=True)\n",
    "  # temporary object; df not changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "uuid": "9068cd04-c6ff-4d0c-bd52-cf04cd89a0e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "      <th>numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floats     names  numbers\n",
       "a    1.50     Guido       10\n",
       "b    2.50     Felix       20\n",
       "c    3.50  Francesc       30\n",
       "d    4.50      Yves       40\n",
       "z    5.75     Henry      100"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.append(pd.DataFrame({'numbers': 100, 'floats': 5.75,\n",
    "                             'names': 'Henry'}, index=['z',]))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "uuid": "0d526ac2-8691-4a59-83c5-712c800f0464"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "      <th>numbers</th>\n",
       "      <th>squares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floats     names  numbers  squares\n",
       "a    1.50     Guido       10        1\n",
       "b    2.50     Felix       20        4\n",
       "c    3.50  Francesc       30        9\n",
       "d    4.50      Yves       40       16\n",
       "z    5.75     Henry      100      NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.join(pd.DataFrame([1, 4, 9, 16, 25],\n",
    "            index=['a', 'b', 'c', 'd', 'y'],\n",
    "            columns=['squares',]))\n",
    "  # temporary object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "uuid": "6558c7ff-f24e-4dd0-b57e-a30196bad1f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floats</th>\n",
       "      <th>names</th>\n",
       "      <th>numbers</th>\n",
       "      <th>squares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Guido</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.50</td>\n",
       "      <td>Felix</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Francesc</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.50</td>\n",
       "      <td>Yves</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>5.75</td>\n",
       "      <td>Henry</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   floats     names  numbers  squares\n",
       "a    1.50     Guido       10        1\n",
       "b    2.50     Felix       20        4\n",
       "c    3.50  Francesc       30        9\n",
       "d    4.50      Yves       40       16\n",
       "y     NaN       NaN      NaN       25\n",
       "z    5.75     Henry      100      NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.join(pd.DataFrame([1, 4, 9, 16, 25],\n",
    "                    index=['a', 'b', 'c', 'd', 'y'],\n",
    "                    columns=['squares',]),\n",
    "                    how='outer')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "uuid": "3e863c7f-7875-4911-997b-6e48123dc1e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    40\n",
       "squares    11\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['numbers', 'squares']].mean()\n",
    "  # column-wise mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "uuid": "c52173a0-485d-4eb2-b6b4-407d1ff2c30e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numbers    35.355339\n",
       "squares     9.669540\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['numbers', 'squares']].std()\n",
    "  # column-wise standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Steps with DataFrame Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "uuid": "d6f56a00-91e6-4221-a1ec-6093f416d1be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.395884, -0.211678, -1.496935, -1.599972],\n",
       "       [ 1.207983, -1.044791,  0.949148,  0.214076],\n",
       "       [-1.216668,  0.049338,  0.260951, -0.083694],\n",
       "       [-0.20149 , -1.907368, -0.182761,  2.380596],\n",
       "       [-0.08032 , -0.146086,  0.494233, -1.842956],\n",
       "       [-0.12713 , -0.850146, -0.81677 ,  1.02073 ],\n",
       "       [-0.928652, -0.376429, -0.598408, -1.152673],\n",
       "       [ 0.95279 ,  0.988867,  0.661644,  1.058102],\n",
       "       [-1.222361, -0.628496, -1.120179, -0.14629 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.standard_normal((9, 4))\n",
    "a.round(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "uuid": "450bd14d-7668-4f3f-a863-966f13562818"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>-0.211678</td>\n",
       "      <td>-1.496935</td>\n",
       "      <td>-1.599972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.207983</td>\n",
       "      <td>-1.044791</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.214076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.216668</td>\n",
       "      <td>0.049338</td>\n",
       "      <td>0.260951</td>\n",
       "      <td>-0.083694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.201490</td>\n",
       "      <td>-1.907368</td>\n",
       "      <td>-0.182761</td>\n",
       "      <td>2.380596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.080320</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>-1.842956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.127130</td>\n",
       "      <td>-0.850146</td>\n",
       "      <td>-0.816770</td>\n",
       "      <td>1.020730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.928652</td>\n",
       "      <td>-0.376429</td>\n",
       "      <td>-0.598408</td>\n",
       "      <td>-1.152673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.661644</td>\n",
       "      <td>1.058102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.222361</td>\n",
       "      <td>-0.628496</td>\n",
       "      <td>-1.120179</td>\n",
       "      <td>-0.146290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.395884 -0.211678 -1.496935 -1.599972\n",
       "1  1.207983 -1.044791  0.949148  0.214076\n",
       "2 -1.216668  0.049338  0.260951 -0.083694\n",
       "3 -0.201490 -1.907368 -0.182761  2.380596\n",
       "4 -0.080320 -0.146086  0.494233 -1.842956\n",
       "5 -0.127130 -0.850146 -0.816770  1.020730\n",
       "6 -0.928652 -0.376429 -0.598408 -1.152673\n",
       "7  0.952790  0.988867  0.661644  1.058102\n",
       "8 -1.222361 -0.628496 -1.120179 -0.146290"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(a)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "uuid": "968395a4-12bc-46d2-b486-6c767abce366"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>-0.211678</td>\n",
       "      <td>-1.496935</td>\n",
       "      <td>-1.599972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.207983</td>\n",
       "      <td>-1.044791</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.214076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.216668</td>\n",
       "      <td>0.049338</td>\n",
       "      <td>0.260951</td>\n",
       "      <td>-0.083694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.201490</td>\n",
       "      <td>-1.907368</td>\n",
       "      <td>-0.182761</td>\n",
       "      <td>2.380596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.080320</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>-1.842956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.127130</td>\n",
       "      <td>-0.850146</td>\n",
       "      <td>-0.816770</td>\n",
       "      <td>1.020730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.928652</td>\n",
       "      <td>-0.376429</td>\n",
       "      <td>-0.598408</td>\n",
       "      <td>-1.152673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.661644</td>\n",
       "      <td>1.058102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.222361</td>\n",
       "      <td>-0.628496</td>\n",
       "      <td>-1.120179</td>\n",
       "      <td>-0.146290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        No1       No2       No3       No4\n",
       "0  1.395884 -0.211678 -1.496935 -1.599972\n",
       "1  1.207983 -1.044791  0.949148  0.214076\n",
       "2 -1.216668  0.049338  0.260951 -0.083694\n",
       "3 -0.201490 -1.907368 -0.182761  2.380596\n",
       "4 -0.080320 -0.146086  0.494233 -1.842956\n",
       "5 -0.127130 -0.850146 -0.816770  1.020730\n",
       "6 -0.928652 -0.376429 -0.598408 -1.152673\n",
       "7  0.952790  0.988867  0.661644  1.058102\n",
       "8 -1.222361 -0.628496 -1.120179 -0.146290"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [['No1', 'No2', 'No3', 'No4']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "uuid": "68e8d73f-93d3-47ac-a656-1edbdebcd1ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.907367859224099"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No2'][3]  # value in column No2 at index position 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "uuid": "a80e1e88-d211-4ee4-a6d3-90403a7739a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-31', '2015-02-28', '2015-03-31', '2015-04-30',\n",
       "               '2015-05-31', '2015-06-30', '2015-07-31', '2015-08-31',\n",
       "               '2015-09-30'],\n",
       "              dtype='datetime64[ns]', freq='M', tz=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2015-1-1', periods=9, freq='M')\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "uuid": "d8fef9ed-25ca-4ae0-bd0c-026d340a903b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>-0.211678</td>\n",
       "      <td>-1.496935</td>\n",
       "      <td>-1.599972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>1.207983</td>\n",
       "      <td>-1.044791</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.214076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>-1.216668</td>\n",
       "      <td>0.049338</td>\n",
       "      <td>0.260951</td>\n",
       "      <td>-0.083694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>-0.201490</td>\n",
       "      <td>-1.907368</td>\n",
       "      <td>-0.182761</td>\n",
       "      <td>2.380596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>-0.080320</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>-1.842956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>-0.127130</td>\n",
       "      <td>-0.850146</td>\n",
       "      <td>-0.816770</td>\n",
       "      <td>1.020730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>-0.928652</td>\n",
       "      <td>-0.376429</td>\n",
       "      <td>-0.598408</td>\n",
       "      <td>-1.152673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.661644</td>\n",
       "      <td>1.058102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>-1.222361</td>\n",
       "      <td>-0.628496</td>\n",
       "      <td>-1.120179</td>\n",
       "      <td>-0.146290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4\n",
       "2015-01-31  1.395884 -0.211678 -1.496935 -1.599972\n",
       "2015-02-28  1.207983 -1.044791  0.949148  0.214076\n",
       "2015-03-31 -1.216668  0.049338  0.260951 -0.083694\n",
       "2015-04-30 -0.201490 -1.907368 -0.182761  2.380596\n",
       "2015-05-31 -0.080320 -0.146086  0.494233 -1.842956\n",
       "2015-06-30 -0.127130 -0.850146 -0.816770  1.020730\n",
       "2015-07-31 -0.928652 -0.376429 -0.598408 -1.152673\n",
       "2015-08-31  0.952790  0.988867  0.661644  1.058102\n",
       "2015-09-30 -1.222361 -0.628496 -1.120179 -0.146290"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = dates\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "uuid": "bcc38d60-3e1c-49bb-b883-ea7564c136b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.395884, -0.211678, -1.496935, -1.599972],\n",
       "       [ 1.207983, -1.044791,  0.949148,  0.214076],\n",
       "       [-1.216668,  0.049338,  0.260951, -0.083694],\n",
       "       [-0.20149 , -1.907368, -0.182761,  2.380596],\n",
       "       [-0.08032 , -0.146086,  0.494233, -1.842956],\n",
       "       [-0.12713 , -0.850146, -0.81677 ,  1.02073 ],\n",
       "       [-0.928652, -0.376429, -0.598408, -1.152673],\n",
       "       [ 0.95279 ,  0.988867,  0.661644,  1.058102],\n",
       "       [-1.222361, -0.628496, -1.120179, -0.14629 ]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df).round(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "uuid": "f760ea25-c64c-4e70-9f91-b72701d919ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No1   -0.219963\n",
       "No2   -4.126790\n",
       "No3   -1.849076\n",
       "No4   -0.152081\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "uuid": "3dd9bd77-eb80-46cb-87f3-62c053a8e223"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No1   -0.024440\n",
       "No2   -0.458532\n",
       "No3   -0.205453\n",
       "No4   -0.016898\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "uuid": "8e167ea8-09b7-4585-8cac-28fe20eefe66"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>-0.211678</td>\n",
       "      <td>-1.496935</td>\n",
       "      <td>-1.599972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>2.603867</td>\n",
       "      <td>-1.256469</td>\n",
       "      <td>-0.547787</td>\n",
       "      <td>-1.385896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>1.387199</td>\n",
       "      <td>-1.207132</td>\n",
       "      <td>-0.286836</td>\n",
       "      <td>-1.469590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>1.185709</td>\n",
       "      <td>-3.114499</td>\n",
       "      <td>-0.469597</td>\n",
       "      <td>0.911006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>1.105389</td>\n",
       "      <td>-3.260586</td>\n",
       "      <td>0.024636</td>\n",
       "      <td>-0.931950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>0.978260</td>\n",
       "      <td>-4.110732</td>\n",
       "      <td>-0.792134</td>\n",
       "      <td>0.088780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>0.049608</td>\n",
       "      <td>-4.487161</td>\n",
       "      <td>-1.390541</td>\n",
       "      <td>-1.063893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>1.002398</td>\n",
       "      <td>-3.498294</td>\n",
       "      <td>-0.728897</td>\n",
       "      <td>-0.005791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>-0.219963</td>\n",
       "      <td>-4.126790</td>\n",
       "      <td>-1.849076</td>\n",
       "      <td>-0.152081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4\n",
       "2015-01-31  1.395884 -0.211678 -1.496935 -1.599972\n",
       "2015-02-28  2.603867 -1.256469 -0.547787 -1.385896\n",
       "2015-03-31  1.387199 -1.207132 -0.286836 -1.469590\n",
       "2015-04-30  1.185709 -3.114499 -0.469597  0.911006\n",
       "2015-05-31  1.105389 -3.260586  0.024636 -0.931950\n",
       "2015-06-30  0.978260 -4.110732 -0.792134  0.088780\n",
       "2015-07-31  0.049608 -4.487161 -1.390541 -1.063893\n",
       "2015-08-31  1.002398 -3.498294 -0.728897 -0.005791\n",
       "2015-09-30 -0.219963 -4.126790 -1.849076 -0.152081"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "uuid": "125980cc-91ec-4ab4-9a4a-cfd772dd1254"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.024440</td>\n",
       "      <td>-0.458532</td>\n",
       "      <td>-0.205453</td>\n",
       "      <td>-0.016898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.013052</td>\n",
       "      <td>0.801816</td>\n",
       "      <td>0.853327</td>\n",
       "      <td>1.377744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.222361</td>\n",
       "      <td>-1.907368</td>\n",
       "      <td>-1.496935</td>\n",
       "      <td>-1.842956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.928652</td>\n",
       "      <td>-0.850146</td>\n",
       "      <td>-0.816770</td>\n",
       "      <td>-1.152673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.127130</td>\n",
       "      <td>-0.376429</td>\n",
       "      <td>-0.182761</td>\n",
       "      <td>-0.083694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>1.020730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>2.380596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            No1       No2       No3       No4\n",
       "count  9.000000  9.000000  9.000000  9.000000\n",
       "mean  -0.024440 -0.458532 -0.205453 -0.016898\n",
       "std    1.013052  0.801816  0.853327  1.377744\n",
       "min   -1.222361 -1.907368 -1.496935 -1.842956\n",
       "25%   -0.928652 -0.850146 -0.816770 -1.152673\n",
       "50%   -0.127130 -0.376429 -0.182761 -0.083694\n",
       "75%    0.952790 -0.146086  0.494233  1.020730\n",
       "max    1.395884  0.988867  0.949148  2.380596"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "uuid": "9dfc1e40-c030-4a9c-9e3a-ff28c64a93df"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.181475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>1.099083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.974242</td>\n",
       "      <td>0.462683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.222121</td>\n",
       "      <td>0.510833</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.542918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.703017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.010312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.976109</td>\n",
       "      <td>0.994418</td>\n",
       "      <td>0.813415</td>\n",
       "      <td>1.028641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4\n",
       "2015-01-31  1.181475       NaN       NaN       NaN\n",
       "2015-02-28  1.099083       NaN  0.974242  0.462683\n",
       "2015-03-31       NaN  0.222121  0.510833       NaN\n",
       "2015-04-30       NaN       NaN       NaN  1.542918\n",
       "2015-05-31       NaN       NaN  0.703017       NaN\n",
       "2015-06-30       NaN       NaN       NaN  1.010312\n",
       "2015-07-31       NaN       NaN       NaN       NaN\n",
       "2015-08-31  0.976109  0.994418  0.813415  1.028641\n",
       "2015-09-30       NaN       NaN       NaN       NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "uuid": "a540362b-50d7-4ef0-89ba-0b6ee38033f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No1    3.256667\n",
       "No2    1.216539\n",
       "No3    3.001508\n",
       "No4    4.044554\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "uuid": "4b1834ec-9f9b-41d6-8d06-f2efc8433dc4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45db747d90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YPx769r3lQmsW4b/nzvH+qVOkilDD25vvgoPtKraXU1N5/tAhVl+/jhvwYc2a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\nJUELdXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45db747790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.cumsum().plot(lw=2.0, grid=True)\n",
    "# tag: dataframe_plot\n",
    "# title: Line plot of a DataFrame object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "uuid": "e86f82d1-5934-42d3-a986-f01bc829adaa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "uuid": "bcebc814-623d-4e8a-81e9-314ab36a7429"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-31    1.395884\n",
       "2015-02-28    1.207983\n",
       "2015-03-31   -1.216668\n",
       "2015-04-30   -0.201490\n",
       "2015-05-31   -0.080320\n",
       "2015-06-30   -0.127130\n",
       "2015-07-31   -0.928652\n",
       "2015-08-31    0.952790\n",
       "2015-09-30   -1.222361\n",
       "Freq: M, Name: No1, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "uuid": "ca241ef9-5359-4c89-bc92-be6346cb3959"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['No1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "uuid": "b3d4cc90-e499-459c-88a5-011fde80d864"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f45db5eca90>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8WhMN5vsAnwNvsmKDeSfgK3d3M+sDPOTupXVcS8lDpNDcfXckjfbtYfz4aAxO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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45db6766d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df['No1'].cumsum().plot(style='r', lw=2., grid=True)\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('value')\n",
    "# tag: time_series\n",
    "# title: Line plot of a Series object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "uuid": "4bc106dd-9590-4566-bc70-d410517c8223"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "      <th>Quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>-0.211678</td>\n",
       "      <td>-1.496935</td>\n",
       "      <td>-1.599972</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>1.207983</td>\n",
       "      <td>-1.044791</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.214076</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>-1.216668</td>\n",
       "      <td>0.049338</td>\n",
       "      <td>0.260951</td>\n",
       "      <td>-0.083694</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>-0.201490</td>\n",
       "      <td>-1.907368</td>\n",
       "      <td>-0.182761</td>\n",
       "      <td>2.380596</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>-0.080320</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>-1.842956</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>-0.127130</td>\n",
       "      <td>-0.850146</td>\n",
       "      <td>-0.816770</td>\n",
       "      <td>1.020730</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>-0.928652</td>\n",
       "      <td>-0.376429</td>\n",
       "      <td>-0.598408</td>\n",
       "      <td>-1.152673</td>\n",
       "      <td>Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.661644</td>\n",
       "      <td>1.058102</td>\n",
       "      <td>Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>-1.222361</td>\n",
       "      <td>-0.628496</td>\n",
       "      <td>-1.120179</td>\n",
       "      <td>-0.146290</td>\n",
       "      <td>Q3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 No1       No2       No3       No4 Quarter\n",
       "2015-01-31  1.395884 -0.211678 -1.496935 -1.599972      Q1\n",
       "2015-02-28  1.207983 -1.044791  0.949148  0.214076      Q1\n",
       "2015-03-31 -1.216668  0.049338  0.260951 -0.083694      Q1\n",
       "2015-04-30 -0.201490 -1.907368 -0.182761  2.380596      Q2\n",
       "2015-05-31 -0.080320 -0.146086  0.494233 -1.842956      Q2\n",
       "2015-06-30 -0.127130 -0.850146 -0.816770  1.020730      Q2\n",
       "2015-07-31 -0.928652 -0.376429 -0.598408 -1.152673      Q3\n",
       "2015-08-31  0.952790  0.988867  0.661644  1.058102      Q3\n",
       "2015-09-30 -1.222361 -0.628496 -1.120179 -0.146290      Q3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quarter'] = ['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2', 'Q3', 'Q3', 'Q3']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "uuid": "41c1962a-05ba-4c0f-b017-e6873e2d245e"
   },
   "outputs": [],
   "source": [
    "groups = df.groupby('Quarter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "uuid": "804e567f-6b74-4405-a10e-d19d914655e7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>0.462400</td>\n",
       "      <td>-0.402377</td>\n",
       "      <td>-0.095612</td>\n",
       "      <td>-0.489863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>-0.136313</td>\n",
       "      <td>-0.967867</td>\n",
       "      <td>-0.168433</td>\n",
       "      <td>0.519457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>-0.399408</td>\n",
       "      <td>-0.005353</td>\n",
       "      <td>-0.352314</td>\n",
       "      <td>-0.080287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              No1       No2       No3       No4\n",
       "Quarter                                        \n",
       "Q1       0.462400 -0.402377 -0.095612 -0.489863\n",
       "Q2      -0.136313 -0.967867 -0.168433  0.519457\n",
       "Q3      -0.399408 -0.005353 -0.352314 -0.080287"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "uuid": "7eb45e5c-b86f-4464-afd9-d5a3665e0f8e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>1.395884</td>\n",
       "      <td>0.049338</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.214076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>-0.080320</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>2.380596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.661644</td>\n",
       "      <td>1.058102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              No1       No2       No3       No4\n",
       "Quarter                                        \n",
       "Q1       1.395884  0.049338  0.949148  0.214076\n",
       "Q2      -0.080320 -0.146086  0.494233  2.380596\n",
       "Q3       0.952790  0.988867  0.661644  1.058102"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "uuid": "a871b95e-5946-4b09-b8dc-bc9503d2ff14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quarter\n",
       "Q1    3\n",
       "Q2    3\n",
       "Q3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "uuid": "542cf99a-bbf8-447e-9643-d6887ac74be7"
   },
   "outputs": [],
   "source": [
    "df['Odd_Even'] = ['Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even',\n",
    "                  'Odd', 'Even', 'Odd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "uuid": "f5144c9f-ff37-4e35-9417-e39debdcd45b"
   },
   "outputs": [],
   "source": [
    "groups = df.groupby(['Quarter', 'Odd_Even'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "uuid": "06904508-dbf1-431f-a3a2-681f29f03c51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quarter  Odd_Even\n",
       "Q1       Even        1\n",
       "         Odd         2\n",
       "Q2       Even        2\n",
       "         Odd         1\n",
       "Q3       Even        1\n",
       "         Odd         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "uuid": "b8471956-40fc-4203-a54a-aaa45f5a3c00"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No1</th>\n",
       "      <th>No2</th>\n",
       "      <th>No3</th>\n",
       "      <th>No4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <th>Odd_Even</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q1</th>\n",
       "      <th>Even</th>\n",
       "      <td>1.207983</td>\n",
       "      <td>-1.044791</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.214076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odd</th>\n",
       "      <td>0.089608</td>\n",
       "      <td>-0.081170</td>\n",
       "      <td>-0.617992</td>\n",
       "      <td>-0.841833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q2</th>\n",
       "      <th>Even</th>\n",
       "      <td>-0.164310</td>\n",
       "      <td>-1.378757</td>\n",
       "      <td>-0.499765</td>\n",
       "      <td>1.700663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odd</th>\n",
       "      <td>-0.080320</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.494233</td>\n",
       "      <td>-1.842956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q3</th>\n",
       "      <th>Even</th>\n",
       "      <td>0.952790</td>\n",
       "      <td>0.988867</td>\n",
       "      <td>0.661644</td>\n",
       "      <td>1.058102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odd</th>\n",
       "      <td>-1.075506</td>\n",
       "      <td>-0.502462</td>\n",
       "      <td>-0.859293</td>\n",
       "      <td>-0.649482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No1       No2       No3       No4\n",
       "Quarter Odd_Even                                        \n",
       "Q1      Even      1.207983 -1.044791  0.949148  0.214076\n",
       "        Odd       0.089608 -0.081170 -0.617992 -0.841833\n",
       "Q2      Even     -0.164310 -1.378757 -0.499765  1.700663\n",
       "        Odd      -0.080320 -0.146086  0.494233 -1.842956\n",
       "Q3      Even      0.952790  0.988867  0.661644  1.058102\n",
       "        Odd      -1.075506 -0.502462 -0.859293 -0.649482"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "uuid": "9805e014-8a17-4e54-b6fd-1c77db7b6b78"
   },
   "outputs": [],
   "source": [
    "import pandas.io.data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "uuid": "53a33e39-a3ff-4c95-b0f2-a94d727ae0da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3975 entries, 2000-01-03 to 2015-08-07\n",
      "Data columns (total 6 columns):\n",
      "Open         3975 non-null float64\n",
      "High         3975 non-null float64\n",
      "Low          3975 non-null float64\n",
      "Close        3975 non-null float64\n",
      "Volume       3975 non-null int64\n",
      "Adj Close    3975 non-null float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 217.4 KB\n"
     ]
    }
   ],
   "source": [
    "DAX = web.DataReader(name='^GDAXI', data_source='yahoo',\n",
    "                     start='2000-1-1')\n",
    "DAX.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "uuid": "11984b1c-5248-4640-8f3b-a85040eb5683"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>11295.500000</td>\n",
       "      <td>11460.400391</td>\n",
       "      <td>11248.269531</td>\n",
       "      <td>11443.719727</td>\n",
       "      <td>77981000</td>\n",
       "      <td>11443.719727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>11408.230469</td>\n",
       "      <td>11476.900391</td>\n",
       "      <td>11380.809570</td>\n",
       "      <td>11456.070312</td>\n",
       "      <td>66699900</td>\n",
       "      <td>11456.070312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>11503.209961</td>\n",
       "      <td>11655.740234</td>\n",
       "      <td>11497.919922</td>\n",
       "      <td>11636.299805</td>\n",
       "      <td>73800000</td>\n",
       "      <td>11636.299805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>11605.589844</td>\n",
       "      <td>11669.860352</td>\n",
       "      <td>11574.969727</td>\n",
       "      <td>11585.099609</td>\n",
       "      <td>67470900</td>\n",
       "      <td>11585.099609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>11561.370117</td>\n",
       "      <td>11582.900391</td>\n",
       "      <td>11484.849609</td>\n",
       "      <td>11490.830078</td>\n",
       "      <td>60211600</td>\n",
       "      <td>11490.830078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close    Volume  \\\n",
       "Date                                                                           \n",
       "2015-08-03  11295.500000  11460.400391  11248.269531  11443.719727  77981000   \n",
       "2015-08-04  11408.230469  11476.900391  11380.809570  11456.070312  66699900   \n",
       "2015-08-05  11503.209961  11655.740234  11497.919922  11636.299805  73800000   \n",
       "2015-08-06  11605.589844  11669.860352  11574.969727  11585.099609  67470900   \n",
       "2015-08-07  11561.370117  11582.900391  11484.849609  11490.830078  60211600   \n",
       "\n",
       "               Adj Close  \n",
       "Date                      \n",
       "2015-08-03  11443.719727  \n",
       "2015-08-04  11456.070312  \n",
       "2015-08-05  11636.299805  \n",
       "2015-08-06  11585.099609  \n",
       "2015-08-07  11490.830078  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAX.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "uuid": "6185abc6-54c5-4711-b273-3252938f3e5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45db5ec650>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qy+LFotT+JhtL2SnuTTcN9isqEj0TIfAuHzhQs/SApmbcf/9gthEuzm4YUcd3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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45db5635d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DAX['Close'].plot(figsize=(8, 5), grid=True)\n",
    "# tag: dax\n",
    "# title: Historical DAX index levels"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%%time\n",
    "DAX['Ret_Loop'] = 0.0\n",
    "for i in range(1, len(DAX)):\n",
    "    DAX['Ret_Loop'][i] = np.log(DAX['Close'][i] /\n",
    "                                DAX['Close'][i - 1])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "DAX[['Close', 'Ret_Loop']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "uuid": "9b45b8c2-3b95-4c80-94a0-14f891cdd161"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27 ms, sys: 0 ns, total: 27 ms\n",
      "Wall time: 26.6 ms\n"
     ]
    }
   ],
   "source": [
    "%time DAX['Return'] = np.log(DAX['Close'] / DAX['Close'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "uuid": "5fbf45e9-dd56-40ba-8a75-086a80a04d5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Return</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>11443.719727</td>\n",
       "      <td>0.011843</td>\n",
       "      <td>0.011843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>11456.070312</td>\n",
       "      <td>0.001079</td>\n",
       "      <td>0.001079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>11636.299805</td>\n",
       "      <td>0.015610</td>\n",
       "      <td>0.015610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>11585.099609</td>\n",
       "      <td>-0.004410</td>\n",
       "      <td>-0.004410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>11490.830078</td>\n",
       "      <td>-0.008170</td>\n",
       "      <td>-0.008170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Close    Return    Return\n",
       "Date                                        \n",
       "2015-08-03  11443.719727  0.011843  0.011843\n",
       "2015-08-04  11456.070312  0.001079  0.001079\n",
       "2015-08-05  11636.299805  0.015610  0.015610\n",
       "2015-08-06  11585.099609 -0.004410 -0.004410\n",
       "2015-08-07  11490.830078 -0.008170 -0.008170"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAX[['Close', 'Return', 'Return']].tail()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "del DAX['Ret_Loop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "uuid": "8995980e-7fa1-482e-8996-3a0cc1050359"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f45db498210>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f45db382190>], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WmTZscPPUquVO3xjtByRT+d//3LJPmSLfD2ut/dOfZFhWaam1I0bI9tmzpfOZ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\n77OfDTtSt9yS3KnyHf1CdXDTJtcGDBrk/rJQKMd5Syru+A0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NKGeptJqMFa2cpqpvAHMS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\nNBGmuA3DMAyjifgfi7bQH/H4K1QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45db58d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DAX[['Close', 'Return']].plot(subplots=True, style='b',\n",
    "                              figsize=(8, 5), grid=True)\n",
    "# tag: dax_returns\n",
    "# title: The DAX index and daily log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "uuid": "956890ca-7927-4fac-a99a-af7a15cac58f"
   },
   "outputs": [],
   "source": [
    "DAX['42d'] = pd.rolling_mean(DAX['Close'], window=42)\n",
    "DAX['252d'] = pd.rolling_mean(DAX['Close'], window=252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "uuid": "f5440e3f-b808-4685-9bec-5f6ca39609c5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>42d</th>\n",
       "      <th>252d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>11443.719727</td>\n",
       "      <td>11255.769485</td>\n",
       "      <td>10515.471021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>11456.070312</td>\n",
       "      <td>11261.934245</td>\n",
       "      <td>10524.383681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>11636.299805</td>\n",
       "      <td>11275.538528</td>\n",
       "      <td>10534.233522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>11585.099609</td>\n",
       "      <td>11289.438756</td>\n",
       "      <td>10543.738917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>11490.830078</td>\n",
       "      <td>11294.806385</td>\n",
       "      <td>10553.107131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Close           42d          252d\n",
       "Date                                                \n",
       "2015-08-03  11443.719727  11255.769485  10515.471021\n",
       "2015-08-04  11456.070312  11261.934245  10524.383681\n",
       "2015-08-05  11636.299805  11275.538528  10534.233522\n",
       "2015-08-06  11585.099609  11289.438756  10543.738917\n",
       "2015-08-07  11490.830078  11294.806385  10553.107131"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DAX[['Close', '42d', '252d']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "uuid": "281a5820-2a77-46b4-b399-8c0913423bc3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45db46f8d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bxe+zy2S5fFpx2wj2bGeyxBHk1DLaB44gI9innPv2qevNm+DhIRk+Zzi9nlQH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\ndGdrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45db27a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DAX[['Close', '42d', '252d']].plot(figsize=(8, 5), grid=True)\n",
    "# tag: dax_trends\n",
    "# title: The DAX index and moving averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "uuid": "1b12ae02-5e35-47ff-a9c9-8563e468b489"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "DAX['Mov_Vol'] = pd.rolling_std(DAX['Return'],\n",
    "                                window=252) * math.sqrt(252)\n",
    "  # moving annual volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "uuid": "2e75f8fd-bcf8-4c36-93b9-974ef94366c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f45db1e86d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f45db124dd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f45db075110>], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mf/5piv3rr63uiSegcWPbgmX/+vprqFbNr6wvv9yfgzscxHXkNIfD4XA4sjNhAvTpY0r6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\nRVGUHEIVt6IoiqLkEP8PiOFyRM7ulAUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45db1a1d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DAX[['Close', 'Mov_Vol', 'Return']].plot(subplots=True, style='b',\n",
    "                                         figsize=(8, 7), grid=True)\n",
    "# tag: dax_mov_std\n",
    "# title: The DAX index and moving, annualized volatility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "uuid": "85bf9df2-d445-4600-a02e-37cf2b7dc9ff"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from urllib import urlretrieve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "uuid": "17a2e317-7047-4c9f-9faf-7e0e4132bd01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 trial_yves 661200 Aug  8 11:37 ./data/es.txt\r\n",
      "-rw-r--r-- 1 trial_yves 352571 Aug  8 11:37 ./data/vs.txt\r\n"
     ]
    }
   ],
   "source": [
    "es_url = 'https://www.stoxx.com/document/Indices/Current/HistoricalData/hbrbcpe.txt'\n",
    "vs_url = 'https://www.stoxx.com/document/Indices/Current/HistoricalData/h_vstoxx.txt'\n",
    "urlretrieve(es_url, './data/es.txt')\n",
    "urlretrieve(vs_url, './data/vs.txt')\n",
    "!ls -o ./data/*.txt\n",
    "# Windows: use dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "uuid": "3bdd1237-41d5-4e92-8d2c-3c4ffd25e7e9"
   },
   "outputs": [],
   "source": [
    "lines = open('./data/es.txt', 'r').readlines()\n",
    "lines = [line.replace(' ', '') for line in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "uuid": "6c7769ea-4fb8-49ef-bdc8-4e06b986fb3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PriceIndices-EUROCurrency\\n',\n",
       " 'Date;Blue-Chip;Blue-Chip;Broad;Broad;ExUK;ExEuroZone;Blue-Chip;Broad\\n',\n",
       " ';Europe;Euro-Zone;Europe;Euro-Zone;;;Nordic;Nordic\\n',\n",
       " ';SX5P;SX5E;SXXP;SXXE;SXXF;SXXA;DK5F;DKXF\\n',\n",
       " '31.12.1986;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n',\n",
       " '01.01.1987;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "uuid": "b5edc764-13a4-4e0c-b6d3-ac615b4a530b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "317.10;267.23;5268.36;363.19\n",
      "322.55;272.18;5360.52;370.94\n",
      "322.69;272.95;5360.52;370.94\n",
      "327.57;277.68;5479.59;378.69;\n",
      "329.94;278.87;5585.35;386.99;\n",
      "326.77;272.38;5522.25;380.09;\n",
      "332.62;277.08;5722.57;396.12;\n"
     ]
    }
   ],
   "source": [
    "for line in lines[3883:3890]:\n",
    "    print line[41:],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "uuid": "ea43adac-94fb-4b11-8af5-153c6fb4cebe"
   },
   "outputs": [],
   "source": [
    "new_file = open('./data/es50.txt', 'w')\n",
    "    # opens a new file\n",
    "new_file.writelines('date' + lines[3][:-1]\n",
    "                    + ';DEL' + lines[3][-1])\n",
    "    # writes the corrected third line of the orginal file\n",
    "    # as first line of new file\n",
    "new_file.writelines(lines[4:])\n",
    "    # writes the remaining lines of the orginial file\n",
    "new_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "uuid": "aca0ad29-cce1-4da5-b39e-ace9bafe3077"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['date;SX5P;SX5E;SXXP;SXXE;SXXF;SXXA;DK5F;DKXF;DEL\\n',\n",
       " '31.12.1986;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n',\n",
       " '01.01.1987;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n',\n",
       " '02.01.1987;770.89;891.78;82.57;97.80;97.43;69.37;647.62;65.81\\n',\n",
       " '05.01.1987;771.89;898.33;82.82;98.60;98.19;69.16;649.94;65.82\\n']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lines = open('./data/es50.txt', 'r').readlines()\n",
    "new_lines[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "uuid": "0bc55f0d-cd99-45b9-955e-3a126360e94f"
   },
   "outputs": [],
   "source": [
    "es = pd.read_csv('./data/es50.txt', index_col=0,\n",
    "                 parse_dates=True, sep=';', dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "uuid": "73526ac3-4bf0-4455-89b2-f6aa614ffdca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5P</th>\n",
       "      <th>SX5E</th>\n",
       "      <th>SXXP</th>\n",
       "      <th>SXXE</th>\n",
       "      <th>SXXF</th>\n",
       "      <th>SXXA</th>\n",
       "      <th>DK5F</th>\n",
       "      <th>DKXF</th>\n",
       "      <th>DEL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>3456</td>\n",
       "      <td>3635</td>\n",
       "      <td>399</td>\n",
       "      <td>375</td>\n",
       "      <td>470</td>\n",
       "      <td>412</td>\n",
       "      <td>10729</td>\n",
       "      <td>646</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>3449</td>\n",
       "      <td>3619</td>\n",
       "      <td>399</td>\n",
       "      <td>374</td>\n",
       "      <td>468</td>\n",
       "      <td>411</td>\n",
       "      <td>10687</td>\n",
       "      <td>644</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>3501</td>\n",
       "      <td>3677</td>\n",
       "      <td>404</td>\n",
       "      <td>379</td>\n",
       "      <td>473</td>\n",
       "      <td>417</td>\n",
       "      <td>10782</td>\n",
       "      <td>649</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>3472</td>\n",
       "      <td>3668</td>\n",
       "      <td>401</td>\n",
       "      <td>378</td>\n",
       "      <td>471</td>\n",
       "      <td>412</td>\n",
       "      <td>10637</td>\n",
       "      <td>641</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>3441</td>\n",
       "      <td>3638</td>\n",
       "      <td>397</td>\n",
       "      <td>375</td>\n",
       "      <td>466</td>\n",
       "      <td>408</td>\n",
       "      <td>10520</td>\n",
       "      <td>633</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SX5P  SX5E  SXXP  SXXE  SXXF  SXXA   DK5F  DKXF  DEL\n",
       "date                                                            \n",
       "2015-08-03  3456  3635   399   375   470   412  10729   646  NaN\n",
       "2015-08-04  3449  3619   399   374   468   411  10687   644  NaN\n",
       "2015-08-05  3501  3677   404   379   473   417  10782   649  NaN\n",
       "2015-08-06  3472  3668   401   378   471   412  10637   641  NaN\n",
       "2015-08-07  3441  3638   397   375   466   408  10520   633  NaN"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(es.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "uuid": "e6e3100a-8296-494f-9758-bbb0006c5df4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 7373 entries, 1986-12-31 to 2015-08-07\n",
      "Data columns (total 8 columns):\n",
      "SX5P    7373 non-null float64\n",
      "SX5E    7373 non-null float64\n",
      "SXXP    7373 non-null float64\n",
      "SXXE    7373 non-null float64\n",
      "SXXF    7373 non-null float64\n",
      "SXXA    7373 non-null float64\n",
      "DK5F    7373 non-null float64\n",
      "DKXF    7373 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 518.4 KB\n"
     ]
    }
   ],
   "source": [
    "del es['DEL'] \n",
    "es.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "uuid": "fff2d2a1-dce8-4f4c-bab9-0b990a1f7b5f"
   },
   "outputs": [],
   "source": [
    "cols = ['SX5P', 'SX5E', 'SXXP', 'SXXE', 'SXXF',\n",
    "        'SXXA', 'DK5F', 'DKXF']\n",
    "es = pd.read_csv(es_url, index_col=0, parse_dates=True,\n",
    "                 sep=';', dayfirst=True, header=None,\n",
    "                 skiprows=4, names=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "uuid": "76793f6a-1625-4fc2-8063-38536b46b15e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5P</th>\n",
       "      <th>SX5E</th>\n",
       "      <th>SXXP</th>\n",
       "      <th>SXXE</th>\n",
       "      <th>SXXF</th>\n",
       "      <th>SXXA</th>\n",
       "      <th>DK5F</th>\n",
       "      <th>DKXF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>3456.29</td>\n",
       "      <td>3635.40</td>\n",
       "      <td>399.44</td>\n",
       "      <td>374.80</td>\n",
       "      <td>469.70</td>\n",
       "      <td>411.85</td>\n",
       "      <td>10728.61</td>\n",
       "      <td>646.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>3448.82</td>\n",
       "      <td>3619.31</td>\n",
       "      <td>398.75</td>\n",
       "      <td>373.94</td>\n",
       "      <td>468.45</td>\n",
       "      <td>411.34</td>\n",
       "      <td>10686.96</td>\n",
       "      <td>644.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>3501.11</td>\n",
       "      <td>3676.75</td>\n",
       "      <td>403.93</td>\n",
       "      <td>378.75</td>\n",
       "      <td>473.48</td>\n",
       "      <td>416.71</td>\n",
       "      <td>10781.72</td>\n",
       "      <td>649.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>3471.95</td>\n",
       "      <td>3668.47</td>\n",
       "      <td>400.70</td>\n",
       "      <td>377.67</td>\n",
       "      <td>470.56</td>\n",
       "      <td>411.54</td>\n",
       "      <td>10637.46</td>\n",
       "      <td>640.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>3441.25</td>\n",
       "      <td>3637.80</td>\n",
       "      <td>397.07</td>\n",
       "      <td>374.54</td>\n",
       "      <td>466.37</td>\n",
       "      <td>407.53</td>\n",
       "      <td>10520.07</td>\n",
       "      <td>633.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5P     SX5E    SXXP    SXXE    SXXF    SXXA      DK5F    DKXF\n",
       "2015-08-03  3456.29  3635.40  399.44  374.80  469.70  411.85  10728.61  646.17\n",
       "2015-08-04  3448.82  3619.31  398.75  373.94  468.45  411.34  10686.96  644.50\n",
       "2015-08-05  3501.11  3676.75  403.93  378.75  473.48  416.71  10781.72  649.46\n",
       "2015-08-06  3471.95  3668.47  400.70  377.67  470.56  411.54  10637.46  640.67\n",
       "2015-08-07  3441.25  3637.80  397.07  374.54  466.37  407.53  10520.07  633.10"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "uuid": "3a1920c2-8c61-4720-941e-afdb983350aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4226 entries, 1999-01-04 to 2015-08-07\n",
      "Data columns (total 9 columns):\n",
      "V2TX    4226 non-null float64\n",
      "V6I1    3787 non-null float64\n",
      "V6I2    4226 non-null float64\n",
      "V6I3    4169 non-null float64\n",
      "V6I4    4226 non-null float64\n",
      "V6I5    4226 non-null float64\n",
      "V6I6    4210 non-null float64\n",
      "V6I7    4226 non-null float64\n",
      "V6I8    4213 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 330.2 KB\n"
     ]
    }
   ],
   "source": [
    "vs = pd.read_csv('./data/vs.txt', index_col=0, header=2,\n",
    "                 parse_dates=True, dayfirst=True)\n",
    "vs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "uuid": "3a437278-4466-41bf-b7f2-f9c17d2c44a7"
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "data = pd.DataFrame({'EUROSTOXX' :\n",
    "                     es['SX5E'][es.index > dt.datetime(1999, 1, 1)]})\n",
    "data = data.join(pd.DataFrame({'VSTOXX' :\n",
    "                     vs['V2TX'][vs.index > dt.datetime(1999, 1, 1)]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "uuid": "9223e142-d574-40c9-92e7-149d86628458"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4254 entries, 1999-01-04 to 2015-08-07\n",
      "Data columns (total 2 columns):\n",
      "EUROSTOXX    4254 non-null float64\n",
      "VSTOXX       4254 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 99.7 KB\n"
     ]
    }
   ],
   "source": [
    "data = data.fillna(method='ffill')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "uuid": "fc5fc92a-3475-4e4b-a5fe-145809d35919"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>3635.40</td>\n",
       "      <td>18.5935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>3619.31</td>\n",
       "      <td>18.2536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>3676.75</td>\n",
       "      <td>17.5482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>3668.47</td>\n",
       "      <td>18.2603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>3637.80</td>\n",
       "      <td>18.7066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EUROSTOXX   VSTOXX\n",
       "2015-08-03    3635.40  18.5935\n",
       "2015-08-04    3619.31  18.2536\n",
       "2015-08-05    3676.75  17.5482\n",
       "2015-08-06    3668.47  18.2603\n",
       "2015-08-07    3637.80  18.7066"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "uuid": "07158c72-907f-4636-ad40-95182b7728e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f45dae75610>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f45dac5e610>], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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89NtvN8pnltTDtaMNRqlwyBC47TYTDh0lctmwITjumvlMRXbvDvacNXIk/PGP\nZgQh1F2rxZIpPP+82Z5+esWPkTaNtOtY3uWMM8z28cc9Lz4nnRR532efDU9LhfmKWHn4YbN97bXg\n9NBRBaOYE6BFCzMPGMqjj8KVVyagglVAOt2v8hAIBLj8crj5Zi+tcWNj4euxx6BtW+PJKnQJ3tq1\n5p3o3BnGjTO2uevXr9q6RyPSvdqxI1iJ7fzzjXc68OyMpzqZ/AxmIsmUK9Kqioo6d0yLRlo1uBfp\nCt+pE9x6qxkOzM/3TG3Omxe8/5dfVk09E0n37uFp118fHC8uNpq0YJRz/MOItWqZbWWVGCzxZ+xY\ns83PN4pUfn/j7jrmP/wh+CXv1g1+/BEWLoR+/WDixKqrb3k57zyzhtu/brpTJ+PtDeDUU5NTL4sl\nUbwXwQrIDz8Y/RH3PVaFF14wyyxLJZqVk2T9iGCGa906z5KS/7dkicnftctL+8MfTNoDD3hpkyaV\n0/xLCjJ3rmrdusFp++0Xfk2OP94Lb96s2rChKTt4sElr1arq626JzC23BN+7aLj5a9aoPvdc+D1P\nRTp0MHXr3Nmr5113me1RR3nlrr3WyGSxZBL33Wee9Q4dVGvXDv5vnjvXlJk50/8eV9DiWCrw3ntm\nSNvlaZ8n6haOWw+/Jx/Xc9SAAV6a27tMZxo2NPOTfiItX/GPImzaZPYDbzTBatOmDm+95YXLWjMP\nRqfgttuC01L1frqjXf/9r5fmenMrKPDS6tUz66YtlkzinnuM0u+SJZ5OifvfvGMHvPFGsLJoaZTa\nSItIHRGZKyIFIrJYREY66SNEZJWILHB+Z/n2GSYiS0VkiYic6UvvKiILnbynI53P5f/+zxsS6NPH\nGC0xTiaCvfk0ahS+r9tgH3igd4xQD0CQfvMwtWoFe8Pyz3cEK5QFSkIHH+w10gc6Psfq1oWffiJM\n+zbVSbf7VRYvveTegwAQm8vI6dODjdnMn5+aywsDgUDQ8+m+uxDsLhNMIx06PZWqZNoz6GLlii9L\nlpjtgAGRrenVqGGW1v75z7Edryzb3buA01Q1F+gCnCYi3QEFnlDVo53fVAAR6QT0AzoBvYDnHFvd\nAM8DV6pqB6CDiPSKdt6LL4aVK2HvXi+tXz/T6LoNc79+ka0p1awZHF+3Drp2LU3K9KBWLW/OPZSz\nnE+kJ58Mz3P/xF991Sx52bABOnQIV8SzVC2TJ5utq+xYmmUwl8MO85bjgeeSMhU55BDvXXRHtyBc\nibNDB5g23McIAAAgAElEQVQ6terqZbEkmr/9zWzXrPHS3FFf8Ez8ho6MRqPM4W5VdU1o1AL2Axyd\nTCL9rVwAjFXVPaq6HPgJ6CYirYAGqup+M48Bepd23nfeCW5wDzjAbC+9FBYsMBqtftw1mKEadM2b\nRz5+KqyhKw/RGul+/cx2/Hj4619hx468oIfD1fjdf//gYcaVKxNX10SQbverNPbtM0Y+Jk2CU0/N\nK7N8kyZmKwJjxnjpqeo0Ji8vj3fe8UZrOnb08kI/oo85xiyfvP32qqtfRcmkZ9CPlSu+uBb1HnnE\nS9uyJbzcqlWxHa/MRlpEskSkAFgH5KvqIifrRhH5VkReFhH37+IAwH/qVcCBEdJXO+lR+eKL4Li7\nlnK//SA3N7y8q8l86KFlSZSe1KzpNdKzZ3vp99xjtpdcYkYZsrON0wIX/xpbf29t/fr0MSKRSezd\n602/9O5tzHiWZZFo9my48cZwgwip7JgiO9v7iChtaZir8R3Jmp7Fko60a2dGLjt18tJWrjSjmP6O\nUqxLssp0sKGqxUCuiDQCPhaRPMzQ9f9zitwHPA7EcQXuQGbObOeEc4BcbrstD4juRPvmm/M44wyY\nNy9yfiSn23l5eSnjBLys+HHH5VFUZOLGspTJ//HHABs2eOWfeuopcnNzS/ILCwMEAl7+uecG+M9/\nzP6vvgoHH5wa8mXa/YoWnzTJxA880MTfffcp7r3Xu1/R9u/SJc+5CgG6d4fZs1NDnkjxgoICbnYW\nfnv3L3L5DRtM/OCDU6f+0eKhz2Ky6xOveKT7lUr1q2g8GfcrPz/AvHnw+uuR82fP9uoEAY47bjkt\nWxrLglGJpvYd6QfcA/w9JK0dsNAJ3wHc4cv7COgGtAS+96VfBrwQ5RxBy0tycxOzzCSdHIKrqhYV\nqWZlqe7bF7z8prg4uJwr15w5kZfo/PvfXvrzz1dN3eNBut0vl3//W3XgQC8eek9ilWvKlOB7XlgY\n33rGk0gy7b9/9PfYXYrmsnGj6o4dialbZUjXZ7AsrFzxY+nSyP/LLqH/35Mnm3RKWYJVVqPcFMhx\nwtnAZ8DpQEtfmVuAt51wJ6AAM3/dHliG57N6rtNgCzAF6BXlnEFCXHBB/C9kunLooarffBP7GtlI\nZXbsUJ0wwayX7tcvcXW1GPz3oLi44mub3X0feSS+9asqTjlFtUGDyHlXXhl8TfbbT7V/f9Xffqua\nulks8WDrVtVDDon9fxlUp0510yq+TroVMNOZk54LfKCqM4BHROS/IvItcKrTUKOqi4EJwGJgKjDY\nqQDAYGA0sBT4SVU/inbSm27ywrFqwFUHGjeObT2ty4IF4etos7ONO8Q1a4yymaXqOPtss62ILoCr\nT+BfzpROfPghLF8eOa9HD7NVx473vn2webNRFvUvO7RYUpmGDc3y1pEjSy+3ZQu8/bYJhypSRqKs\nJVgLVfUYVc1V1S6q+qiT/hcnfpSq9lbVdb59HlTVQ1T1MFX92Jf+tap2dvKGlHbe/fc3244dE2fX\n1z9fkS5kZXmesE48MbLigV+u3Nzo62hDTYqmOulyvwIBePNN0xCfcIKXXlBgvJV16BCswFceufbt\n85bbpTKRZKpf39NSD+Xyy812xw5vPb/Rmwg2JZps0uUZLC9Wrsrjf05dU77RaNjQc5oUy9LLMhXH\nkoGrxXzVVZlhLSxe7Lef10jfd1/ljnXttcZWtCV+rFvnuYtcsSLYC9vRR5vtK69U/PhZKazNHQ8i\naYHv2pU6jkMslmj43QhHW/brx3UdHM3DnR/RSN2xJCIiOm2acvbZRvBYhgOqC3l5MGiQcWO4eXPl\njrVkCVxwgTH6bqk8e/Z4TkxC6dLFM4+ZYq9byhCtR/Hrr3bKy5J4broJPv00eIlUeVi1yrhiXbrU\nWHWMpYcsYqaBzj4bRARVjbhXSjbSqVanVMF/4yt7iZYtM1ahiotje6AspfP113DsscFpQ4dC//7m\ng8q1MmQf7ci4z2DdusFrpnv3NkZfLJZE8fvvwaM1/nf0t9+MLkXLlsYTnZ9vvjF6Ey1bwqJFRtdn\n8eLYzytiPNmZKbDojXSGD6BFp7rPw7i9vnSZm071+/XSS6bHN22aMVACxuf5kUcac54Q2fF7qstV\nESoikzv1cvnlsG2bl/7ee+G+tJNFJt4rsHIF+z4I/kh84QVjuvfgg4PL7NxpzE2fcYaJb95c9lx0\nKKqmgS6LattIV3cOOshsQx9QS/kpKoIXXzTOL3r29CyIuYpjTZuaKYpbbkleHVMd14Ljtm2mV/Pm\nm17eE08kpUqWDKVfP/j4Y2P9b+NGT4mrTx+znT7dmwb0T2H5zXy6H5XffWe2//tf4sz02uHuNCKe\nw93+49nLXTnOOMO82MuWmSGxHTvMsFfo8LeldEKfx3g/7xYLRJ7e27PHLFdt2dJb9rdrF9SpY4ax\n33nHpLnP4a23eg6Nnn3WmPzcvh0efbSidbLD3RnFSy/F5zinnmq25fFtagnmqKNMAw3mhQYzr2ob\n6Irht73ft2/y6mFJf0aPhoceMuF164xtiBUrIpetUcMsEfSvy3edEJ12GnTuDEcc4eX98gs0a2bC\n119vVnK0bRt/GaAaN9LpPA+TnR09rzxyuY4e/vIXePjhytUp0aTi/Vq82NPaPu88z1NbeUhFuSpL\nRWWqV89bqgaVW66WCDLxXkHmynXzzQGGDTMNb8uW5v388MPwcu++64XPPdcLuw36NdcY74uLFnl5\nhYXBdhCmTg12rRxPSm2kRaSOiMwVkQIRWSwiI530JiIyTUR+FJFPfF6wEJFhIrJURJaIyJm+9K4i\nstDJezox4lQPSmuky4N/2Kc8lsyqE7//bpS/fv89fMjV/bKeOhWef77q65ZprF0b7Hu6bl0v7I5W\nWCylsW+f2dap461d9vsrnzbNG62ZMcO80xdd5OV/8IHR2gZPKSwry1hvBLN0VQQ++yz83K++Gj85\ngohmL9T9AXWdbQ3gS6A78Ahwu5M+FHhIg21318Q43vgJb957HnC8Ey7VdrclMr16GXuvU6bE53in\nnebZkL366vgcM5M491zv+nTpotq0qWeXd/v2itvhtsSOe42vuSbZNUkea9ZEd9hgMaxcqdq3r3lW\nfvjBe25q1zYOXHr2DLaZDaq//BL9eG6Zww4z8Q0bTHzs2GC726Bap47Z3nprxetPJWx3o6quQnot\nYD/gf8D5gPvN+zrQ2wlfAIxV1T2qutxppLuJSCuggarOc8qN8e1jiRF3LjpePel27bywVcwJZu9e\nzzQlmGHtjRu9+N//brbr11dtvaorof7lqwu//mqsLrqKS5Zw/v53aNPGu0auQheYHvWsWXD44V5a\nixbm/87//xdKx45m+9RTZtu0qdnPP1zeubPZtmxptvfeWykxolJmIy0iWY6DjXVAvqouAlqoZ697\nHdDCCR8ArPLtvgo4MEL6aic9aaTjPIxrfS1ec9LpNERb1ffLf23cuadDDjFLMr75xtjovvNOT3mk\noqTjc1gWiZApFWx4/+lPAX78MXLe6tWJUb5s08Zs/R+M7vnWrQsvXxHS+RlcswYef9yLd+oE8+fD\nP/5hfDtv2WLirVvD8OGmzPffl33c334zW3/j3qiRtzTwP//xbNG7DXqizNfG0pMuVtVcoDVwioic\nFpKvgO2HVQHumj3/XF1lqF07PsfJJPbtM3+As2aZ+Jo1JqxqFMWKiowRgyVLTKNtqRrOPLPsMonk\nk0/M79BDw72YrVljGoF4+xnwm6h84w2jAFVcbDSJ+/Y1ZiirO3PmmO1XX5nGuF07s3UdC7Vvb7aX\nXWZsGDRvHpvRkaefNuXdjySg5AMtJwfOOcfrLF14IZx/flzEiUjMDjZUdYuIfAh0BdaJSEtVXesM\nZbuDfquBg3y7tcb0oFc7YX/66mjnGjhwIO2csYicnBxyc3PJc6wduF991TFuetIB/vtf6Nw5cnk3\nLdbjQ8DZJl++8sS/+CKPq6+GhQvjd/x9+6BGDROHPG64AZYs8fLd6+/mn3565eVx05J9PeMd98tW\nmePl5wd47DE44oiqq/+6ddC3bx5ZWV7+n/6Uh3lHAjz9NNxyi1f+1lvBfX/y8wOIxKc+RtM9wCmn\nwGef5TF7NrzwQoAJE7zzpdr9qur4uHEBLrgAjj3WxKdMMflvvZXHPffk8cwzAc47Dxo0yKN7dxg/\nPkAgUPbxBw3KY9Cg4PyzzoKpUwO89hq41/+sswK0aQOTJ5ev/m54eTT/rX6iTVabDjJNgRwnnA18\nBpyOURwb6qTfQbjiWC2gPbAMT3FsLtANEKziWIXYtcsoKKxaFb9jukoQgwbF75iJpKhINTfX1PnN\nN+NzzBUrVD/6KFyx5Pvvw8u6eUuWxOfclrK5/nrVUaOq5lx795r7W7eu6rJlqmvXqm7aZNKefNJs\nP//cK//WW8HPzGuvxace27aZ47Vpo/rhh+HPpvubODE+50s3VqxQbdzYXIM33vDSn3jCpF1yiZf2\nxRfxOWfnzubYe/fG53h+qITiWCtgpjMnPRf4QFVnAA8BZ4jIj0APJ46qLgYmAIuBqcBgpwIAg4HR\nwFLgJ1X9qOxPiMQR+gWZDsR7ThrM0paWLVPf21ggEGD6dJg40RsG3LvXrO+eMcObQyovn35qjBD0\n6mXil19u1o3PmePZ3Pazb59x2O43ulEZ0vE5LIt4y1Szpue+NpHMnw/332/CO3YYe80tW3r+7bt0\nCXDMMd78+IoV8Oc/m/CyZWYbi+vBSGzdGhx3l/MsWWK8JD38cOQ5T/+StYqSjs/g1KnGFCcEu4m8\n+Wb44x/htdc8ufzrmSuD+867biarilKHu1V1IXBMhPRCoGeUfR4EHoyQ/jXQuWLVtIDnTziec8mn\nn258S993nzEmv2EDfPQRXHFF/M4RDyZOhFGjgtM2bYI77vDiFdFQ9406A3D77caKWDSyssz8lqXq\nqFUr2BJUIigqMrbXXbp3h9mzg8tkZRkNX7dReO89s23f3pv7dOdCY+W668x7B+b53bgRbrsNTjzR\nfAC4H+R5ecar2qGHGo3lb78186uTJ8OWLUap6f77YfBgT6Epk5nnrBPKzjYf1S4i8PnniTlno0aJ\nOW5ZVFuLY3mh/85pwtKlxjJTNCoi1/HHmx7ihg1wzz3mof/114rXMRG4cz4uffqYRtqPa8gA4Oef\nvd5NJNavN/KCMR2Yn2/+JEtroBNBuj6HpRFvmaqiJx2qKe33xAXmozUvL4969YyNZjDPmOsrXAQG\nDCifFvru3V4DDabH3KwZjBljPpxbtPDyOnUy2x9+8BQWXYctOTlGoeyeeypmOTAVn8Gvv/ausx8R\n+Ne/zEf7xIlmxCPayGK85Xr8cVi4MK6HjI1o4+DJ+mHnpKucQMDMtXz2mTfX9eOPya6Vx/ffmzqd\neqo3J/Tww54hgV9+MeGBA035n3/25hUj8c03Jv/KK60xknTg3ntV77knccf//XfVoUO9Z3/IkPC5\n388+M2Xd+KxZGmYEqDTjNv/8p8nbtcv8fvjBzJVGm2sG1dtuCz4GqD7wgOrmzaq//qq6Z49Xdv16\ns23ZMjHXqCp54QUjS+/ewemvvOLJ26iRec8zBSpjzCRTScd5mFioiFzukq4avsmPSJ5ikoVZfhPg\nX/8y5vj2288bcty1y1sm8dprJu46Z9+xI7wHdtVVcIwzgfPyy6b3kkwy8TlMtznpevVMD/Tll03c\nfZ6aNzfbGjXM8HcgECjxFX7NNWYb6/PjruXduNE8z4ceauZOIXyaxp1OiaRn0bixGXZt3Tr4fXXr\neuml5Z8aSKVncN8+M4oA3nSCy6BBXnjLlrJtFKSSXJWh2jbSFg93uMj/ZxG6FjSZtG1rGtYjj4ST\nTzZp7rxbgwZmrvCGG4wN3mHDgvf1v9jbtxvPOH78xgosqUmtWvDMM0ZRK3SeOJ64tti7dDHb9evN\ne7Bnj/fR6q5NXrzYbLt29fafNs1sBw8221694EFHO8f9qNy6NbLd55tv9sJmiU+wL2OXUBsJoVM+\nTz3lWcBKVTZs8DxMQbDil+u1KhR3OsI/nJ8o4yEpR7QudrJ+2PHHKuenn8wQUocO3nDS6NFmOC3Z\nuMtP1q0LTndtZ3fvbuKhS6h++0113DgNWjLTv3/4kOIDD1StPJby8/TTWrIcKRF/D+6z8L//qS5a\nZOxkRxu6njYt+PnxL8dxp13c/fxTNNF+M2aYsgUFJn7jjSb+66+qW7eG13Ps2Oj19/9Sld9+C67j\nM88E1/f8842NfHfqbds2k56TY+Kl3Zt0BjvcbSmNhg3NdulSL+1vfzOKGclk1ixj2eegg7zhPBdX\nec7tbfh7GBdfbKw/udq6J55obD+/+abx/TpkiFnGtW6d0Zi1pDaR7nE8KC4OntZp2NAoaInAJZfA\n1VeH79OzZ7CvcP9ynEirLtxeretr3MVdquXu4yosur3K1q3DNcVvusmcP5SNG80wtzvN48qWatx1\nV7g71yFDzPbRR82IxfvvmympE0806e412LzZ+C4QMdbAXK9U1YJorXeyflTRJ1J+fn6VnKeqqahc\nzZuHf42//XZwmb17jWGJRCzm97Ntm1Hmceuxdm1kuVatMoYmVMO/0F3cNNcQxTPPJLbu5SUTn8N4\ny/Tyy+beHXlkfHtQX37pPR8ff1x2eVeuaD1W1+iJX5HL/bVoYYzvgOp555ny8+er7tvn7R9qhKO8\nbN3qnW/jxtj3q4pncO9e1euuM3X78ENjiMRV4Az9bd5s9vGPdvnTYyWd3i1sT9pSFjUirJgPVR57\n4AF49ln48sv4nXf27GAD+WC+nv3LzPxLUfwceKA3N92qFfzznzgmEz06dDBb14NYpN6RJbVxDe1k\nxfnfym/koiK2wTVE4cvfkx43LjivqMjTf3CNcHTtGixTnTpG76Ki+Hvefo9tqcDf/mac1vToAWed\nZa6Bq8DpXynVrp23Htm9hnfdZbbuiF+1I1rr7f4wtrjzgUXAd8AQJ30Exi73Aud3lm+fYRjLYkuA\nM33pXYGFTt7TUc5XZV8vFo8DD/S+XA85xGzHjfPyhw/38qdOjd953WMuWGDiW7YEf1VX1o/uypXe\nsSZNqnx9LVVPqOnNDRvic9z69Ss2vzlyZORnyb8kKvR3552m11za+fbtq/zz7h6/sqYw333XjE7F\ngx07vHqNGWPS/Ndm1KjooxNu2t/+Fp+6pCpUsie9B7hFVY8ATgCuF5HDAQWeUNWjnd9UABHpBPTD\n2PHuBTwnUtInex64UlU7AB1EpFeFviwsccffa3a/wv1f+X5fqZGMDFSEyy/3wkcfbYw7uFqzACNH\nVn4p2EEHwbnnmnAK2mywxEBoj3XHDi/81VdmOU552bnTPMcXXlh+vYQ77oDevcPTQ81FnnSS2d54\noxmFct+nZ56JfNysrMo/78XF8Kc/eb31inLxxcb8bXl45BEz6qFKkEtP9zoUF3uWDHNzvfxrrind\ntzMY3ZTqSpmNtKquVdUCJ7wd+B7PF3SkR+oCYKyq7lHV5cBPQDfHW1YDVXUMujEGiPCoVw2ZsoYu\nlIrK5f45dOkCpznOSN0/lY9CrKyHWmOqKGPHBsfr1DFrR8891wxT+xvVytyviRPhrbeMZaZUIxOf\nw3jLFNpI+9cBH388jBhR/mO6FvWGD4++7CeUsuQKbWDdJUL+BnP5cqO8mChEzBRQYWHs+/jl+vRT\neOcdEy7vkPnQocae/nXXmXXg+fnG6t+CBTBzZvD1WbDAmD/t0sU07F98YdKjLYn805/KVxfInHer\nXLM8ItIOOBpwZyVvFJFvReRlEXH/Ag/ADIO7rMI06qHpq/Eae0uScV+g2rVh0iQT3rULFi0yc0h+\nBg0yhv3LY0Lzt9+MxuzOnaa3nJ/v5V11VXAvpFEj8yUeL8P4NWsG99ot6UW0RtrVYI6msxCKiLdO\nfscO00C4a6ITgbuG2b8OuG3b+M+th5KTY7Shy8v775sP40suMfFYP14g+B69+KLZ9ujhmTDt3j18\nn8ceMzbIwWjBf/edcfgTygUXlO5UKNOJ+XERkfrAu8BNTo/6eYw7ylxgDfB4KbunHKlorzYeVFQu\nt5HeudNb5vHFF+FD266CzcCBxmbxPffEdvwXXjBf6D17GqMRPXqYL35Vs/zCb3c7km1ye7/Sh3jL\nFKmRnjjRfEDGilv2qqvM9scfg5csxUJ55broIrONl8e0WGnQoHxTUnl5eaiaxjCU0FG0SOza5RmZ\n6d8/PH/48Ni87B1xRPgSrcsuM1MSFSFT3q1SvWC5iEhN4P+AN1X1PQBVXe/LHw184ERXY5TNXFpj\netCrnbA/fXWk8w0cOJB2ziRFTk4Oubm5KeNkPFPjIia+aVOAUaPgiivy+OQTGDzY5LtOzmvVCo7/\n+GNsTtTvu8/EP//c27+w0OSbP2GT36ZNwNFwTe71sPHUiRs9BROHAJ9+CjfemOdY/wo4qw2i779o\nEdxwg7d/IADffpvHUUfFv75jxwYoKIDTT8/jjDPghBMCznkTd31C4xs3Qs2a4fnTp8NFFwWYPDl8\n//33964PQNu2eaxYASNHBqhTp/TzDRsGX35p4rNnB8sLAccNZ8Xkufrq4OOlwvMYj7gbXr58OWUS\nTaPM/WHmnccAT4akt/KFbwHedsKdgAKgFqanvQwQJ28u0M055hSgV4TzJU6Fzkc6raErDxWVKzfX\naFFedpmJT5igeuGFqr16BWteus4u3N+tt8Z2/Ghar/78Aw6Iv1ypTibKFW+Z/I4VQLVWreB4jx6l\n7x/puRNRfeON8tUjXe7VU08ZJyGhuJbbQpkxIz/ofaxdW/Xii0080nH8/PBD8HV1nWPcf7/JB9VX\nX62EMJUgXe6Xauna3bH0pE8C+gP/FRHXzsudwGUikovR8v4FuMZpYReLyARgMbAXGOxUAmAw8BqQ\nDUxR1RgGUyxVwUcfGYWwgw828exsM/R9xRXekNdtt8Fhh3n79OoVm3KJqzhz2mneXPQ//2n85fqJ\nl0KaJbPoHOKFPtTZRmna3f6yH37oaQmrZu662/r1I79L0fxMr18fHF+71rz/xx0XnhfKp58Gx6+5\nxnM+AkYJ1FVEtVSMMhtpVZ1N5LnrqaXs8yDwYIT0r4HO4XtUPe7wQ6ZRUblatAhWwMnOhvnzg+ep\nHnvMbOfNM1q17doFG8r3U1wMTz4J775rlleBmQN0/TaHMnRo6drX9n6lD/GW6dhjvWcuEjt3Rt/3\n55+9cKjHqlCzm2WRLvcq0pz01q3wyy8mvGeP0cLevt2845ddlkebNsY5B3jvYZMmRgt79+7IJk/B\n5F1+Ofz1r+GmewE++CA8rapIl/tVFgnWM7SkK9nZxlvNtdeaxthdsgKeTeyzzorek37wQfj73411\nsuefN2nXXhts99jPQw+Z9acWSyRqOfa7/UpY774Ln3xiGoo1a8Jtze/d6y3tcenls8zgWrbKNBo0\nCO9Jn3SSGb0CY7N+wADTqLqeu2rVgj59gvc55BBj9atOneiuQmfONO90z56J1ZSvzlTbRto/gZ9J\nxEsu/5KH5cuNwX8/qsYZQaRG+rffwrW+n3rKvMxffVWx+tj7lT4kQibXuYZrIhLg9NNNo71li9EK\ndrWpXUaNMssFO3Uy63XBNOSuYZ5WrcpXh3S5Vw0amCkq/zv4009e+KCDzAcOuD3uQESzwB07euFn\nnw3OGzPG7DtpkjfClmqky/0qi2rbSFtKx78M6oYbIpdp2tRrpDds8NLd4W0/Bx0UnmaxxErbtmbr\n9yFcr57p5UUbzXENeixe7C23ys42PW+AZs0SU9dkk+X8q99/v3fdQu0EuFNOrk17d+vH/xETOvc8\nYIA3XWB9sicW0UgThElERDTV6lQd2bjR+xObMAH69g0vo2r+ENzGWtX8Afrd8h1+OHz/vVlH6ZoH\ntFgqwhdfmPW27nSLqlFK9CtE+f863LX/zZoFK0Bt2GCGejP1b2bVquCPYlUzNVXamuf//S+yTsig\nQfDqqyZcVGSuf2Eh7L+/V+b882Hy5PjUvboiIqhqRKOwtidtiYj/hY1miMD9E3R7Mnv2BDfQu3d7\nVsPatIl/HS3Viz/+0VNgcnvGofPK/obXzVuyJLhMs2aZ20BDZK31bduMNa9IXuC++Sa60ubIkV7Y\ntUD222/BZV55pWL1tMRGtW2kM2W+IpR4yeWfo9q7N7Z9/Eb1s7ONMspDD5mXuLLD3fZ+pQ9VIZM7\nx5yVFTw1M3++2T75pJmrXrEi+tKj8pIu9yrU0cfYsTBnjvloefFFM1Tt0qgRbNkSiHqsFi28Dxp3\n+mDIkOAy/l51KpEu96ssqm0jbYmdstZKurz1lhd2tUabNzfLMyyWeODa6/bjflAefbRplMHYgYbg\nOezqgv8DOyvLm492lUHdueYZM2JfIvXEE0b5c8ECz9aBfa+rhpjMgmYimbKGLpR4yqVqhrQjaX66\nNG7sGSuZO9dLd7Vx44W9X+lDImVq3x5OPjk4rXlzM7WSm+sZNnGHYOPp+Sxd7pX/ffV/1LjvZNOm\nZnvaae6UVV6Zx3SnF445xkvr1KkytUw86XK/yqLMnrSIHCQi+SKySES+E5EhTnoTEZkmIj+KyCc+\nL1iIyDARWSoiS0TkTF96VxFZ6OQ9nRiRLPHk55+N8kg0Vvn8ms2c6YUjOcmwWCpLw4bw2WfBaZ99\nZuadGzY0nqfcNb1nnJF4j1OpyH77GS9YrvESF7cn7eqNlMd39emnB8cPOABuusmsT7ckllge4T3A\nLap6BHACcL2IHA7cAUxT1Y7ADCeOiHQC+mFsePcCnhMpeRyeB65U1Q5ABxHpRZLIlPmKUOItV/v2\npfek69Y16yVDlVXi3ZO29yt9qGqZWrY0S40OOMAY6nA/HCMNjVeGdLpXjRqFK9W5CqChowuxyFW/\nvqcdvmwZrF5tjteyZeXrmijS6X6VRpmNtKquVdUCJ7wd+B7jB/p84HWn2OtAbyd8ATBWVfeo6nLg\nJz/aqvkAACAASURBVKCbiLQCGqjqPKfcGN8+ljSmXj3PxZy7tT1pS1VTu7ZZYeA6FnLXQ1dX/I10\n376egle/flBQUP7jucPkdqVG1VKuddIi0g74FDgSWKmqjZ10AQpVtbGIjAK+VNW3nLzRGDvfy4GH\nVPUMJ/1k4HZVPS/kHHaddBrirs38wx/MEPneveFaphZLInn+eXjvPWPC9vvvzXrqefPK3i+Tcccw\nn3sOrruu8sfbsyc239CW8lHaOumYFcdEpD7Gp/RNqrpNfBMaqqoiYlvWaoxrNrS42MwL2gbaUtXU\nrWtsebtEsxNfHYmXAp1toKuemBppEamJaaDfUNX3nOR1ItJSVdc6Q9nuQp3VgH9VbGtglZPeOiR9\ndaTzDRw4kHbt2gGQk5NDbm5uQpxu5+XlpYwT8HjFn3rqqYRcr1jj69YF+O9/E+Mk3d6v9IgXFBRw\n8803V/n5u3YFCFCvHkyfnke3bvE9fuizWNXyVSQ+YkSAmTPhnHOil0/W/Up0PJXvlxte7s7NlEY0\nR9PuDxDM/PGTIemPAEOd8B2YoWwwCmMFQC2gPbAMb1h9LtDNOeYUoFeE88XJjXbppJND8PKQTLka\nNozsVD4e2PuVPiRLpr17zfMHqqtWxf/4mXivVK1cqYDT7kVsg8uckxaR7sBnwH8Bt/AwYB4wAWiD\nmW++RFU3O/vcCQwC9mKGxz920rsCrwHZwBRVDbFdY+ek05lDDzVWx+ztsyQLdxbO6kRY0onS5qSt\ngw1L3OjUySjs2NtnSRZuI22fQUs6YR1sRMA/N5BJJFOurAQ+TfZ+pQ+ZKBNYudKNTJGr2jbSlvgz\nejSMH5/sWliqM8OHJ7sGFkt8scPdFoslY5g2zTiU2LAh2TWxWGLHzklbLBaLxZKi2DnpCGTKfEUo\nVq70IhPlykSZwMqVbmSKXNW2kbZYLBaLJdWxw90Wi8VisSQRO9xtsVgsFksaUm0b6UyZrwjFypVe\nZKJcmSgTWLnSjUyRq8xGWkReEZF1IrLQlzZCRFaJyALnd5Yvb5iILBWRJSJypi+9q4gsdPKejr8o\n5aOgIg5V0wArV3qRiXJlokxg5Uo3MkWuWHrSrwK9QtIUeEJVj3Z+UwFEpBPQD+NkoxfwnHg+LZ8H\nrlTVDkAHEQk9ZpWyefPmZJ4+YVi50otMlCsTZQIrV7qRKXKV2Uir6izgfxGyIk1yXwCMVdU9qroc\n+Ano5riybKCqrgv2MUDvilW5bDJlmCPR2OsUG/Y6lY29RrFhr1Ns2OvkUZk56RtF5FsReVlEXJfi\nB2B8R7usAg6MkL7aSU8IsdzgmPx4piHlkSudXoRk3q9EXqdMeQ791yhTZAolHnKl4juXivcrHtcp\nFeWqCDEtwRKRdsAHqtrZiTcHXMN79wGtVPVKERkFfKmqbznlRgNTMa4sH1LVM5z0k4HbVfW8COey\n668sFovFUq2ItgSrRgUPtt4NOw3xB050NXCQr2hrTA96tRP2p68uT0UtFovFYqluVGi425ljdukD\nuJrf7wOXikgtEWkPdADmqepaYKuIdHMUya4A3qtEvS0Wi8ViyXjK7EmLyFjgVKCpiPwKDAfyRCQX\no+X9C3ANgKouFpEJwGJgLzDYZz5sMPAakA1MUdWP4iyLxWKxWCwZRcqZBbVYLBaLxWKothbHLBaL\nxWJJdWwjbbFYLBZLimIbaYvFYrFYUhTbSFssFovFkqLYRtpisVgslhTFNtIWi8VisaQotpG2WCwW\niyVFsY20xWKxWCwpim2kLRaLxWJJUWwjbbFYLBZLimIbaYvFYrFYUhTbSFssFovFkqLYRtpisVgs\nlhTFNtIWi8VisaQotpG2WCwWiyVFsY20xWKxWCwpim2kLRaLxWJJUWwjbbFYLBZLipKQRlpEbhKR\nhSLynYjc5KQ1EZFpIvKjiHwiIjmJOLfFYrFYLJlC3BtpETkS+BtwHHAUcK6IHAzcAUxT1Y7ADCdu\nsVgsFoslConoSR8GzFXVXaq6D/gUuAg4H3jdKfM60DsB57ZYLBaLJWNIRCP9HXCyM7xdFzgbaA20\nUNV1Tpl1QIsEnNtisVgsloyhRrwPqKpLRORh4BPgd6AA2BdSRkVE431ui8VisVgyibg30gCq+grw\nCoCIPACsAtaJSEtVXSsirYD1kfa1jbfFYrFYqhuqKpHSE6Xd3dzZtgEuBN4G3gcGOEUGAO9F219V\nK/UbPnx4mWUGDBhQ6fOk4q88csVynVLll8z7lcjrlCnPof8aZYpMibhXqfjOpeL9isd1SkW5ov1K\nIyE9aeBdEdkf2AMMVtUtIvIQMEFErgSWA5ck6Nzk5eWVWaZdu3aJOn1SKY9csVynVCGZ9yuR1ylT\nnkP/NcoUmUKJh1yp+M6l4v2Kx3VKRbkqQqKGu0+JkFYI9EzE+UJJxRchFbHXKTbsdSqbRF+jUaOg\nTx9o3Tqhp0k49lmKDXudPKqtxbGcnMy0pWLlSi8yUa5EyDRkCPz733E/bLnIxHsFVq5Up9o20rm5\nucmuQkKwcqUXmShXJsoEVq50I1PkkrImrasaEdFUq5PFYkkuInD33XDffcmuSeYhElGp2JIgIrVv\nIoJG0e5OlOKYxWKxWNIE2zGqGiryQVRth7sDgUCyq5AQrFzpRSbKlSiZkt2OZOK9sqQ+1baRtlgs\nFosl1bFz0haLJeURgTvvhAceSHZNMg9nPjTZ1agWRLvWpc1J2560xWKxWCwpSqLMgg4TkUUislBE\n3haR2o5XrGki8qOIfCIiSV3ElqnzS1au9CIT5bJz0pZ40KtXL4YPHx6WPnnyZFq1asXq1au56KKL\naNasGTk5OXTu3JnXX3+d2bNn06BBAxo0aED9+vXJysoqiTds2JBVq1YB8J///Ifjjz+e+vXr07Rp\nU/r378/q1asBWLBgAY0aNWLZsmUl5/36669p3LgxK1euZPv27bRv35633367JH/btm20adOGiRMn\nxvU6xL2RFpF2wFXAMaraGdgPuBS4A5imqh2BGU7cYrFYLJYwBg4cyJtvvhmW/sYbb9C/f3/69+9P\n27ZtWblyJYWFhbzxxhu0aNGC7t27s23bNrZt28aiRYsA2LJlC9u2bWPr1q20bt2ad999lz//+c/c\neuutbNq0iUWLFlG7dm26d+/O5s2bOfroo7nhhhu46qqrANizZw+DBg3ivvvuo02bNtSvX58XX3yR\nm2++mY0bNwJw++23c/zxx3PhhRfG90IkwFB4E+AHoDFmidcHwBnAEoxPaYCWwJIo+6vFYrG47Nql\nCqp33JHsmmQmqfqfu2PHDm3UqJF+9tlnJWmFhYVap04d/fbbb7V+/fpaUFBQ6jF++eUXFRHdt29f\nSVpxcbG2adNGH3300aCyxcXFeuSRR+o///lPVVXdvXu3HnbYYfriiy/qiBEjtHv37mHHHzhwoF52\n2WWan5+v+++/v65bt67U+kS71k56xDY17j1pNTa6HwdWAr8Bm1V1mtNAr3OKrQNaxPvcFosl8xg3\nLtk1sCSD7OxsLrnkEsaMGVOSNmHCBA4//HC6dOnCCSecwPXXX8/48eNZuXJlzMf94Ycf+PXXX+nb\nt29Quohw0UUXMW3aNABq1arFyy+/zO23384TTzzByy+/HHasJ598kvz8fPr27cvjjz9O8+bNKyht\ndBIx3H0wcDPQDjgAqC8i/f1l3C+HeJ+7PGTq/JKVK73IRLniLZNr/8HOSScHkfj8KsKAAQN49913\nKSoqAmDMmDEMGGA8Hr/zzjucfPLJ3HffffzhD3/g6KOPZv78+WUe0x2ebtWqVVhey5YtS/IBjjji\nCGrWrEmXLl3o2LFjWPmcnByOOOIIdu7cSZ8+fSokY1kkwuLYscDnqroJQEQmAn8E1opIS1VdKyKt\ngPXRDjBw4MASN2M5OTnk5uaWeEVxX5TKxl3idbxUiRcUFKRUfez9Kj2eiferoKAgrsdbsgQgD9XU\nkC8T46WRzI+jk046iaZNmzJp0iSOPfZYvvrqK9577z3AtA0jR45k5MiRbNq0ib///e/07t27RDEs\nGk2bNgVgzZo1tG3bNihvzZo1NGvWrCR+2223ceqppzJ//nzGjx9Pv379gsq/+eabrFixgp49ezJ0\n6FCef/75MmVyr3kgEGD58uVllk/EnPRRwHdANiDA68D1wCPAUKfMHcBDUfYvdUzfYrFUL95808xJ\n/+Mfya5JZpLq/7n/7//9Pz377LN1xIgRet5550Utt3DhQhURLSwsLEmLNid90EEH6SOPPBK0/759\n+/SII47Qe+65R1VVp02bps2bN9dNmzbplClTtEWLFkHHXrdunTZr1kwDgYCuWbNGmzRporNmzSpV\nlmjXmlLmpOPeSJvzcTuwCFjoNNI1MQpl04EfgU+AnCj7liqkxWKpXrz1lm2kE0mq/+cuX75ca9as\nqa1bt9Z33323JP3222/X7777Tvfs2aNbt27VwYMHa8eOHYP2jdRIq6qOHz9eGzZsqG+//bbu3LlT\n16xZo3/961+1bdu2WlhYqNu3b9f27dvr22+/XbJP//79dcCAASXxvn376tVXX10SHz16tB522GG6\ne/fuqLKkTCNdmV9VPTD5+flVcp6qxsqVXmSiXPGWaexY80/197/H9bDlJhPvlWrqN9Kqqnl5edqk\nSRMtKioqSbvxxhu1Q4cOWr9+fW3WrJmed955umTJkqD9fvnlF83KygprpFVVJ0+erMcdd5zWq1dP\nmzRpopdffrmuWrVKVVWHDBmi55xzTlD5jRs3avPmzXX69Ok6adIkPfDAA3XLli1BZXr06KF33313\nVDkq0khbL1gWiyWlSRXFMUvyyM/PD0t75plnytyvXbt27Nu3L2Le+eefz/nnnx8x7+mnnw5L23//\n/Vm3bl1JvHfv3mFlZsyYUWadyou13W2xWFKad96BSy4x4R07IDs7ufXJNKzt7qrD2u62WCwZR5bv\nX2rLluTVw2JJBtW2kY5l6UE6YuVKLzJRrnjLlJUi/1KZeK8sqU+KPP4Wi8USGb8hjL17k1cPiyUZ\n2Dlpi8WS0rz/PlxwgQl//z28/jqMHJncOmUSdk666rBz0haLJePw96QXLoSHHvr/7Z13mBRF+se/\nL7tklQUlKSrcKSioLGdGxQUBE2bF8/QEPT3TmQOeniceIpxyipjzDznzmcMpQUYMSFBBBREUlnDA\nSpa4u+y+vz/eLrq6p3umZ7Yn9Gx9nmee7upYNVVdb1W9b72Vu7gYDNmm3grpQtUvmXRFi0JMVyZ1\n0rkc7i7EvFIQkfll4ZcO9VZIGwyGaKDXbT5TXg11YPLkyTl3YpWJX76mK1VC10kTURcA+uJyvwFw\nB4B/A3gFwN4AygEMZOb1HvcbnbTBYNjBhx8CJ54o+889B1x0kXFsYigssqqTZuYfmbkHM/cAcDCA\nLQDehCyqMYGZOwOYZIUNBoMhIfky3G0w5IJMD3f3BfATMy8FcCpksQ1Y23ifalmkUPVLJl3RohDT\nZXTS0cKkK7/JtJD+PYCXrP22zKwcn1YAaJvhdxsMhgJA10mbYW5DfSNjC2wQUSMApwAY4j7HzExE\nvp/b4MGD0bFjRwCysHdpaWnOF0WPSlgdy5f4mHDisDqWL/EJK6ynra7P+/ZbAJDwvHlyvra2DA0a\nZDd9ZWVlefP/5nN+5Us4n/NL7ZeXlyMZGXNmQkSnAbiCmU+wwvMAlDHzSiJqD2AyM+/ncZ8xHDMY\nDDuIxYDevWX//vuBG24AqqqAhg1zGi2DITRy5czkPNhD3QDwDoBB1v4gAG9l8N1JcbcgCwWTrmhR\niOkKO00NtFqqqkq2uZiKVYh5BZh05TsZEdJE1BxiNPaGdngkgH5ENB9AHytsMBgMCdF10tXVsjVW\n3ob6gvHdbTAY8prPPgOOOUb2b78dGD4cWL8eaNEit/EyGMLC+O42GAyRRW+zDx8uW9OTNtQX6q2Q\nLhR9hRuTrmhRiOnKRpqMTjo8TLrym3orpA0GQzTw0n4ZH96G+oLRSRsMhrxmyhTg2GOdx5YsAfbc\nMzfxMRjCxuikDQZDZDE9aUN9pt4K6ULRV7gx6YoWhZguo5OOFiZd+U29FdIGgyEaePWkjXW3ob5g\ndNIGAMBVVwHTpwMzZuQ6JgaDE90tqGLOHKBr15xEx2AInazrpImohIj+Q0Q/ENFcIjqciFoR0QQi\nmk9E44moJBPvNqTHRx8BM2fGH7/3XtsVo8GQC0xP2lCfydRw94MAPmDm/QEcBGAegFsBTGDmzgAm\nWeGcUSj6Cjfppos82nCffgoMGQL89FPd4hQGJr+iQ6bT1KCB0UmHiUlXfhO6kCaiFgCOYeZnAYCZ\ntzPzBgCnAhhrXTYWwOlhv9uQPl5C+rTTZFtbm924GAw6FRXOcKNGxrrbUH8IXSdNRKUAngAwF0B3\nAF8BuA7AMmZuaV1DANaqsOt+o5POAV26APPnO4cWleCeNQvo3j038TIYdtsNWLPGDu+8MzB+PHDE\nEbmLk8EQJol00sUZeF8xgN8B+AszzyCi0XANbTMzE5GvJB48eDA6duwIACgpKUFpaWnOF+ku9DCR\n93kghmnTgO7d8yu+Jlx/wmITIWEgBiKgpiZ/4mfCJpxqWO2Xl5cjKcwc6g9AOwCLtPDRAN4H8AOA\ndtax9gDm+dzP2WDy5MlZeU+2STdd++3H7P7rpV/NPH163eNVV0x+RYew07TbbnZZBJjbtWN+6aVQ\nXxGIQswrZpOufMCSe54yNXSdNDOvBLCUiDpbh/oCmAPgXQCDrGODALwV9rsNmcHopA25pHFjZ3jt\nWuC883ITF4Mh22RknjQRdQfwNIBGAH4GcBGAIgCvAtgLQDmAgcy83uNezkScCoWqKjGcCRtdJ33b\nbcAHHwCzZ8u5zz8HevYM/50GQxDGjAGuvdYON2wIVFd7T80yGKJItnXSYObZAA71ONU3E++rTzRu\nDGzdCjRpEu5zdWvZKVNsAe0+ZzBkG3dZr66WLbP3rASDoZCot25BdQV+1EjkXCTddOlD2u7hxXwQ\n0lHOr0QUYrrCTpNf+VPCOlsUYl4BJl35Tr0V0lEmE0JTCennn48fTs8HIW2ov/iVv6oqYNMm05s2\nFDbGd3eEYBZvS6tWydzRMNlzT2DZMtk/5RTg3Xftcx99BPTvH+77DIagPPggMHeu/D77zD6+erUI\n8LZtjX7aEG2yrpM2ZAbV283EMJ+XBfeuuwKHHGJ60obcUlMDNGsGtGnjPF5ZCRQV5SZOBkO2qLfD\n3VHUV/z8s2wTLS4Qhk5a0bOnVIL5IKSjmF9BKMR0hZ2m2loZQSp2dSmyvfBLIeYVYNKV79RbIR1F\nunSRbSZ60itX2vtKx1dcLJWjmSdtyCU1NdJYdOueKyvtYe5sG5FFhbVrgc2bcx0LQ12ot0JauWmL\nIokqpHTStWWLvb/LLvZ+UVHynvSECYDu2S5TSwhGOb8SUYjpCjtNfkJ69GhbSN+ahTX1ophXrVsD\nZ5+d+JoopisIhZKueiuko0zYglB/nj79qrg4uZDu3x+44go73LAhsGhRuPEz1G/8hPTjj9tC+ocf\nsh+vKFBbC/zvf7mOhaEuZERIE1E5EX1LRN8Q0XTrWCsimkBE84loPBGVZOLdQYmyviJRTzqddOnP\nW7XKOdxdVASccw4waZL//Vu3OsMbN6YchaREOb8SUYjpysQ8aT8DsWwOd0c1r5J5KIxqupJRKOnK\nVE+aAZQxcw9mPsw6diuACczcGcAkuFbGMgQn7J60u4JbbzlrLS4GXntN9idOdF5TUWHHw93TzoTb\nUkP9pbbWuycN2EI620ZkUcJ8j9Emk8Pd7k/qVABjrf2xAE7P4LuTEmV9Rdg6affz1q2T7c4728fc\nPZl27YBHHpF9ZVimhHUmpsVEOb8SUYjpyoROukEDW0jr6hRV9iorQ32lJ1HNq2RCOqrpSkahpCuT\nPemJRDSTiC61jrVl5gprvwJA2wy9u+AJe2jP3QvZtk22HTrYxw44wN7va3lgdwtlJdyDLJFqMATF\nPdxtLTW/4xwg38S6dcA992Q1apGgUSNp4OgGoobokClnJkcx8woiag1gAhHN008yMxORr4+gwYMH\no6P1JZaUlKC0tDQji26XlZXlzSLgQcNADDNnAr16eZ8fPXp0yv/XkiUAYD9fpmOVoXVrCQNAVZV9\nveiny9CihZz/9VcJn366hPv3B5jDTb86luv/P+xwOvmV7+FZs2bhuuuuC+15ixYBBx9cZvWkY5BL\n5PwXX8j1TZuW4cMPgdtvj6FlS+CKK8JPn7ssel1/770xDBkCrF8v30eu8qNDhzLssw+gf5+bNwPT\np8dfH3Z+5Us4SH7lKqz2y4P0aPwWmg7rB+BOADcCmAegnXWsPYB5PteHuJS2P1FaEFyhFr3/8EP/\na9JJ1+zZ9rNbtLD3n3uOuVMn2T/ssPh4PPusbPv0YT7zTPt4JrIwivkVhEJMV9hpuv565lGjmP/4\nR7tsqXI2f75sjz/eLo+ZqkKCpEu9f+HCzMQhCNXVEge1Vb/Vq72vL8QyyBytdFlyz1OGhj7cTUTN\niGhna785gP4AvgPwDoBB1mWDALwV9rtTQbVsokgmddJNm9pOU4jsdaSnT4fVc7aRFrroC994I+XX\npkSU8ysRhZiusNOkDMf0cnr99fY5AGjVypRBhVJfuZ2Y+Dklikq6UqVQ0pUJnXRbAJ8S0SwA0wC8\nx8zjAYwE0I+I5gPoY4UNaZAJ6+4DD5T9pk3lB4iQHj7cvk4JZYWqBFQlqmMWPDCEhTIca9jQPnbN\nNcDee9uC56efgPfey038vMhl+VeNGXdjPlOOhgyZJXQhzcyLmLnU+h3AzCOs42uZuS8zd2bm/sy8\nPux3p4KuG4gaYc+TrqqyBXPTpsCsWfa5vfeWClKht8Y3bZLtxx/H+1VWxmdhEeX8SkQhpivsNCnD\nsTFjgDlz5FijRlJuVXmcMSPUV3qSSrpy6UpX1Q9vucYq/ZwSFWIZBAonXZmcgmXIEJnoSTdrJvtq\nC9hTXnr1so898YS9r4Q0ED8FxliSGsJCCemSEqBrVzmmhHQ+LP7iRS570mq4+9JLncfz9b8yJKbe\nCuko6yuqq0WAVlTEn0snXVVVtjtQ1aMGbCH96KP2Md0YMZHj/rCFdJTzKxGFmK6w0+Tlcczdk84G\nqaQrH4a73fg17guxDAKFk656K6SjjPoIV6wI73kNG0pl2KSJfXyPPWSr965V5dOxI/Dss/7PND1p\nQ1h42TwoIe0neHJtE5HLVbn83m160tGk3grpKOsrVMXk9TGmk67qaqn0GjSwdctffAH07i37uuBW\nPRe3v25FURFQWhq+kI5yfiWiENOVCZ10A1dN1bChCOnKSueiMIrZs71HmupCKunKpVMVPxepRicd\nTeqtkI4yftab6VJVZVvOKiHdvLl9fqed7H31of/ud97P+vRTudesYWsIC6/h7qIiEdxbtzqXV1X0\n6AGccUZ24ufFiy/m7t1+9ULXrsCXX2Y3Loa6U2+FdJT1FWrpOa+PMZ10ffml7d9XCWm9UtQFtnL9\nqVyDuikuluHxsIV0lPMrEYWYrmzopNXxW27xFtJA/S2DiRrvn38OfPut81hU0pUqhZKueiuko8xI\na4Z5WFbeY8bYH7aXkNZp00a2fhVg587S8zY6aUNYVFX5l8dvvvEX0l6rZtUHvIa7xUWo2JF0757d\n+BjqRl4K6REjMv+OQtBX9O8ffyzddCnhrLZuHeC//y2W38rrmNea0W++Ked32sk5PSsMCiG/vCjE\ndIWdpjfeAD74wP+8vuBGJolKXnn1pJXx59y58eeikq5UKZR0ZUxIE1EREX1DRO9a4VZENIGI5hPR\neCIq8bv3ttsyFavCIozpJ+oZbiHt7rmcfLLorVevlvDZZ8u2Wzdg//1lXxmYZUJIG+o3sgiMN8qN\nreLJJ2U7e3bm4uOFblF+ySXZfbdOIiFtiB6Z7ElfC2AuZNlKALgVwARm7gxgkhXOGYWir3CTarqU\nExIllP160o0bixexykpZR/qww+T4HnsA48fLvtJXZ0JIm/yKDplIk7KZ8EIf3v3lF3tWQtgkS5fe\naH7tNWDcuMzEIxnu4e4vv0y8xnshlkGgcNKVESFNRB0AnATgaQBKM3QqgLHW/lgAp2fi3YbUUOVY\nCWW/nrTqJW/Y4PShvHmzfU71sps3Nz1pQ7gceaT/uTVrxMq7qgpo3Rpo2dI+d+GF2ZszrQvpDRuA\nW3PUDXH3pA8/PN5tb33V10eRTPWkHwBwMwB9QLYtM6uZixWQhTh8kTWLM0cU9RUtWgBXXuk85p77\nmGq6pk+XrRLSSgC7e9JEstJQRYWzV7NokX1Pnz6yNTrp4BRiumKxGCoqZNGLMGjVCrj6av/zDRtK\nQ1GVwxJNkTZuXHhlMVleudVPun+BbOI13O0W0jqFWAaBwklXJpaqHADgF2b+BnYv2oFaPzPRc/ym\n+NRnunUDTjrJecztMztdlFBWLkC9hsd23RVYudIppDduFOva//xH4gcYnbQBGDAA2HffcJ61ZYvT\nXa1i3Djgb38DhgxxHneX3XfeCSceycgXIe1l3W100tElQfsqbXoCOJWITgLQBMAuRDQOQAURtWPm\nlUTUHsAv/o8YDKAjhg4FSkpKUFpaukO/oFpH9TU8d66EAQk3bx7D5MlO/UssFgv8PEDCRUXOsHq+\nfn2rVsDs2THMn2+f37Qphk8+Ac46y75+yRJg06bs/B9RD6tj+RKfsMLK+r+uz5s0KYaqKqBJk/jz\nF1wg4WXLgH32cZ5X5ROI4YILgPPPr3v6ysrKEp4XIW2/v0mT3Pz/Mg/aTn8sZv9/evyKiuT/1cmX\n8hNGOFl+5TKs9sv1xRD8YOaM/QAcC+Bda/9eAEOs/VsBjPS5h0WLxAYXRx7JHIvxjv8njP9JPePW\nW53hRYvirz3hBOaiIuZ33pFwo0be73/rLeZTT61bvAzRpkuXcL7h8nLmDh1Sv2/gwPC+kaD8ExC2\n3AAAIABJREFU+qvznT17Zue9bp55Jj7tbdvG1xumjs0fYA0we/2yMU9aDWuPBNCPiOYD6GOFc4be\nookKzKJ3u/12/2vSTZfbIUSHDvHXNG4sOnCl+7vpJuDyy+OvMzrp4BRiusJM04oVQPv2qd/3yivO\n8PoQVq9Plq58Ge728tGdyI95IZZBoHDSlVEhzcyfMPOp1v5aZu7LzJ2ZuT8zJ/1sli/PZOyiCRFw\n993A2rXhPlcJ6aoqMTzxMjRRCxk0snTSw4cDjz0Wf53RSRvCsqju2xdYurTuz/n3v+v+jGS4hbTX\nwh/ZIJn/hC++yE48DOGQjZ502qxZk7ln6zrBqKBXfPo0E91QJN107byzbBs29LcEdQtpP8w86eAU\nYrq80nT33cAf/5j6szZvFmPFdNAtwsMwHkuWV9lc2zoRyeKhDDwVhVgGgcJJV14K6QsukO3jj+c2\nHvmIPr/xt7+V7eLFdX/uqacmv0Y5LdEbCF7oQnrzZtNyNwBPPJGd3qzO5ZcDf/qT7E+YINuqqsyN\n8riHmZllNaxt2zLzvqDxcNNAq/VTWUmvogLo1MmsS51t8lJIq5be7rtn7h1R1Fe4hxC9HI+kk66r\nr3bOLfVD6bU6dUp8nS6k77sPOOqolKMURxTzKwiFmK5YLBZXVrPlUESna1fg6aedxy66SKYSpkOq\nOmkAOP984KOP0ntfuiTrSesN/Zkzg5fBhQuB8nL/teTzjUL5tvJSSA8aJIV7771zHZP8Q//AVIs4\nkcu/ZDRtaq+qlQz1nmRzLnUhne1ehCE/yYWQ9mLOHOlNZ6JcuoWjUkM1CKmWPfbYYPp5PR7ffCPb\n4cPtY0SyGA7gXCs+GSo9YflmMAQjL4V0+/ZSeLxWWgqLKOortm1z6oO9nBakkq6HH5ZWse7mMxFd\nuwa7rlEjGRKrqgpvOc0o5lcQopKu6dODlxOvNOVSSN90k2w3brQX3fByjpKMVHXS6vsMq0EwZQow\nbVry62pqgKOPlmtLS+XYwIG2cWijRrZ3wOrqYGVwzRpg4kTZj0rDOyrfVjLyUkgD2bMQZgauuCJ/\nWvqJWLfOqQ9W6oB0DVZefVW2iVwG6gR11Uok/rt/+AH417/Si5shv5g1K7UGVz4MdyvOOEO2f/5z\nZt/j/g6VMEtmw5EKQf7H2lrx160WwQFkPennnpP94mIR2IcfHrxXfNttYvwHyIIdhuyRt0J6550z\nK6Sfey4GQFq7jz+emgFFWCiXAkFZt078GCtOPFG2uiFHOnqYoM72W7cOHt+GDYFbbgkeh2HDgDFj\n/M8Xin7JTVTSlcqQbSwWixNMuRTSqhH68st1e06qOmklpJPNhgib2lpvFZhbIDduLMeClEE9/9Qy\ntflOVL6tZOStkE61Jz1jBrDffsF6lT/+CFx8sewrI4hcGEMcdJDo34NQUyOW0mqqFAD89a+ylnO6\nOqJMThlZs8a2BgfsZSz9+PvfgTvvzFx8DHUjVbsHtca4Ih0hHZZgz5bfav17OvdcoHNn2Q9jnneq\n8fBqVHkJaS+VmRdm1azcEbqQJqImRDSNiGYR0VwiGmEdb0VEE4hoPhGNJ6KE9sQ77QTcf78MmQbh\nxRdF+Irf2sSI96EyAPYcTNXq3bwZWLAg2DvryvffAx98EOzaykr5qNwfS+vWsoauIhU9TDZ7N0Hm\nvCfSdRWKfslNVNIVVB8NSJqUkFBOd35J4KnfDyVA/v731O/V8fKep1i71tmYTEQqOunTT5flKoFw\nnTIF+WZraoIL6YkTgbZty5I+Myzjt2ySz9/Wk08Gb/iE/tcz8zYAvZm5FMBBAHoT0dEQf90TmLkz\ngElW2Bf1UQedA9w24cKXTo44QrZz5tgt/q1bRe924412CziTbNki26B6PiWk3bRtm9jlXyLCMury\nwu2AQqU3EQMGpPaO884DPvsstXsM6bHbbrIN2rBTAmvXXdPvSVZViW3DXXeld7+iRQvv49u3i9Xz\n8ccD7pHR225L3Z5C/w5l8QrZD1L2gxJUJx10uPu+++JX1vPC9KSl0RWkExiEyy6TbZDOS0baR8ys\nimUjAEUA1gE4FcBY6/hYAKcneobqQXsN5Xq1ytV0reAtvhhWrLBDc+YAPXpIjxzIvI5aOVlIRUh7\n+QJ2C+lU9DCZdErQti3wu9/Z4Rkzkt/zn//4n/NK18sv113PmGuiojdTZS+ID+xYLOYo14cckt47\nq6rCca3pJ2BWr7bP9e7tPDdihG0VrkiWV0cfLdvFi516aF2VtnKlv6B9+mnb+tyPZEL644+lPvCq\nB90jVco3Qnl5LPFD4e3+N98J+9u64Qage/dQH4nXXkt+TUaENBE1IKJZACoATGbmOQDaMrMSJxUA\nEvZ9L7xQtmrISPHNN4l7zUH0rKpXoFckqhenhHOme9P/+5/zfcnw60m3bp1+T7pfv/jKKUz0hREu\nuSTYPak2HKJglV8IqHKyeXOw6/V8TGeoGxAhHZbRlVc5Wbs2XoDrdULXrsCHH8bbU4waBbz9tv+7\n9trLaTuiC+n27f0bo5deCvzjH/7PDcJxx8ma8F5C2m13c9xx9r7Xd7dtm38DJ5OjcPlKmCMiiiCd\nykz1pGut4e4OAHoRUW/XeYa9OpYnJ5wgQuSFF5zDXUq4uVGFLFklX1OjPvwy3HCDffzII2WrWpvl\n5VJAX3ghuCB4/vnghhhqOCro9X5CumlTafUTAd99Z+thxo4Ffv018TNra4H+/YO9Px0OPdQZZrbn\nvj/1FPDss7Kv55mfEZyffinqQjqf9WZeBCmvZWVlaVXiv/+9zN1XVFZm1jLaSwg1bCjfESCjcyee\naM+oUHl18832SJgf+qpyqnJX71q1yv++VJyLJMJruHvQIOCf/7TDdmenDHPmxF+vDHePOcY+dtZZ\nso2C17Gwvy2/uqamBvj669TqItVACmKQGXCGbHow8wYieh/AwQAqiKgdM68kovYAfNvXgwcPRseO\nHS1/uyUYP74Ud95ZBgCYNi1mXSVhNaRRVWWf37jRzqB99onhnnuAgQMl3L17DMuXA8XFZdaQutw/\ndWqZ9Vzn8y+4IIZ164C//MX5Pq9FvQcNAtaujaG0NPmi30VF9vtiseTXN2lShpYt488vWWLH97HH\ngO3bY2jdGrjnnjJUV0v6vZ5XWlqGe+8FLr882PvTCd95JzB0qB2/668HHnwwhpEjgVtvFeOi3/wm\nZn3wcv/EiTHssgvAXIZevYBPP038vmXLMhd/E9aHDCV8110xLFkCTJ6c+Prt28us+5z3T5wYQ3Gx\n9/teeQWYMSOGAw6QsDjDCS9/+/aV96v4PPoo8Mwzdlgq2Zil8iqzRrmc8Y/FYmjWDFizxvt96nve\nfXcJFxfHUF5u3w/EcNddwJVXet+/YUPi9M6Zk/i8im+TJvHnO3YEDjvMvl8aH3J+69b46+W7jFl2\nH2X405+kPpwwQa7feef8KZ/ZCKvyof//RDH06wdMmFCGgw6S+i3I82SKYgzjxpXj00+RGL+FptP9\nAdgNQIm13xTAFADHAbgXwBDr+K0ARvrcv2Mh7H/9K35x8iee8F6s/IEH5Pjnn7sX02b+73/tcEmJ\neuZkz0XQvX5Tpviu1b2DjRvl2kmTnMcrK+PjxMzct69cf+CByZ994YXMzZoxDxgQf+7RR93xdabr\n5pu9nzltmpy/+OLk768LelwGDPBedH71ajv8v//Z9330kf2cyZMnO55bXi7XXHZZZuOfadzpyldU\n/jRvLtvp0/2vnTx5smdeA8zz5iV+R+vWdvi775i7dQsvDU895YxLq1bOcGWlbB9+WLZHHOEspyqv\n9t/fuw7Sr12xwg4feyzzmjXx5d7r/ltu8Y8/wPzCC4nTqJ7/4ouJr2O2vyFgMv/ud/bxJUuYN2xg\n/uEHZ5xvu03Od+gg99bWMm/blvw9uSLsb+ucc+LzDmDeY4/E+Rofr/h8suSep0zNxHB3ewAfWzrp\naQDeZeZJAEYC6EdE8wH0scIJOfZYe18Nr3i5Cq2qAq6/Xvb1oVM1zKQbXJ3uYa62336J4xFED/fE\nE7KtrRWH+kQyVHbjjd4LTCgXe4nmDxOJN7SJEyUtbdrEX5NMp33ffd7H1X+StBVXR8aPlzQAwHvv\neV+jG7To+4mG1N54Q7ZRH+6OGupbSLaeuXu4++67RaWUzH5C2aIAosueOzf1OCZj9Wr5Nt1pUKoW\n9U3p6qIlS2S7Zo1t1OrWtZ9zDvCXv8i+Ptz9ySfBF/VINqfbT40wf75zKdA99kj+rjZt7JkuX39t\nH99rL/HS5q73Dj9ctmvWiIrtoYekHkk0fF9IuOsad1i5YE2GbgcURH2UiSlY3zHz75i5lJkPYub7\nrONrmbkvM3dm5v7MnNROVJ86oT4Sr49c/5iUodlPP8n0DcCpyy0uVgK1DIBYBw8cKOd0Qycdt/Ga\nFz//LNv580WfDojRia5j82LZMuCaa+KPv/66bB9/3NbL/fe/8dfFG3aUJY8sbC9Mzz8f6PK06ddP\nDFl0T2ludJ3Xb39rT9/SjQDt4TxBxT/qQtqdrnzEqyGpyqQXZR466YMOku8w2ZQTPT8vvzzc/NWn\nhXkJFuXuUjXudUOhhQslXapxCMQL6UaNbFecStj+5jfx7+nXL/5YUONPv4brww87lwL1q8t0mjYF\npk4FvvmmDPvvL5b7Kk1LljiF9KWX2svZbt0qdahqQN11l8yO0Rsm+UDY35a7LLobde3aBXuO0us3\naxbMcDgjhmNhoWf6kiXAokV2z7BnT/uc7pnslFOcW8DZ+ty2TQqnEm7nnisW0oC0hL0IIqTV2s5X\nXeV9fupU/3sfeij+mD7/VzmSUOts6+g9Dy90C06dYcNkq1rSmSZRz2vRIu9wogKsRkz0D2f9+mgY\ntKTL9u25aZQMGRJ/LJnBi8of5UVu+3aZi3zmmd7Xe42Qhe1U6IILbOdBM2c6z+2/v+3u8vbbZavP\nIlH/+6hR9jF3Waupif9fvGaJqN7TBRfYjRZVPyTLX7/y7a5D/OaGe9GqlTSMd9vNTjOzU0gn6uE3\nbQoccEBmF0TKB9x5o/4f1UkMOvNBlZFddslRTzpMWrcW6+qDDxYrZL1Vqgs9L/eh8+bZ+/ofsXWr\nFKpBg2I7jqnh2IMPtq+rqbF7uEEcqiSrtPSWu2rRJ3LyoD9PCWllga7TooUMIyvn+bahi+D10a9e\nnT/zi3WVhkLNxdUrJNuASVANL723veuuziG/KOBOVyIaNnT2lrKF1xCrfmzGDKfXrpg1T/qTT4Ch\nQ+VYsh6DapBn0lVts2a2v3s3y5fHz4bQV5xilnTNn28fc0/J8RLSGzcCV15ph5s0kfqoulrqNvW8\njz9OHHf1v/h55XOnKxVXqF99Jcaxuqqwtja5kFYdHS//DflAKt9WENx1qcp/3VtlEFRetmhRAEKa\nCPjDH+xeqh/uFpzbQ5E+pLt1qxQqXQCoD0sNob73nsxfGz4c6NUrmF4sUSXUrJlzapGaTpXIXaH+\nsav3q+F7NyefDAwebJmATHaecxesb76xRw6yid8KWlOm2PtqpEANuSVyE6ryT6Vv5Uop/D/+WLd4\n5ju5SJ+X61q9vB92mHjt0tm+3VmGa2rsnqqXeke/T+fqq1OLa1AOPdQeOZsxI77nqTvi8cNuGAtu\nIb1kCfDKK8Ajj9hCbvx4+f4XLpSwKr+qJ6wq8G+/dQ6nq9E8v7m67qmLqSzF6aW6qK11zuf2EtJq\nFCCovt3N1Km2Dj8KuOtSd1ndvFmc0STzC7BsmWwLQkgr/LwOqSHUzZtFGa/0wqr1rtCF9IIFQMeO\nQJ8+Zb7CXxld7LST+A0OMty9ZYu/Z6UzznAKHL85z35xVgSZQ6nrYf72NxHao0fb5/XKx8ugLVP0\n6SOF/J13nKoIHaWX+/572eoNKbd+SZ1TTiNkmkv0SFVvlgv3jF76Uq/e9UcfyVbppPUlUJs1s4e6\n9aFZZnHHqzwGVlbaedm9O3DRRXWOvifTpokAZZbv9p135Pi998pWVaSKmTPtaUyKsWOd17iF9J57\n2nXJ+efLtqREjLSUsermzc4lJdX/2r277QCopsYezfPrrbl74qksiNKvX1ncMWb5fxReo3jKW1a6\nC5g884w0YBJx7722kW2qhK2Tdv+n+qjPY49J3pSWxjvbWrBADHQ//1w6lMr2oSCGuxXKaMyNKtCV\nldJyDGLRuGwZ0KmTrC3700/OczI/12mlV1IiQkPv8bkhko9efYiAsyJq0sRfSP/tb7LVW2lr18b7\nvgZS0zMB9tCvsnx3F4hu3VJ7Xhicckp8D0ShhuCVu9ZE+mV1TvUsVE9CCfhCYsoUe5GJfPChrBu8\n6OXWPWKlenPTp0u+v/lm/LMefljc8S5eLA3Ip56S75NZ3pHKwh6pQOT8L7t3l3fefLOEf/nFqaJS\nenn36l4KZvFC1sCnRv3rX2UpVnfjfMsWp8vcESPi7x02TP4jIF61V1pqd07Sxes/VgLonHMkbbpt\nizJqVXgZtAbB77/SGTLEbjjlGl0dCjiF9JFHxjfsFJ07y4js0UeLMbGiIAzHFH6ZpBK4bZsIwmS9\nU2b7Wre+YuFCGTZ206KFfKxeulOd8eOdFsz6MM7SpcC119rvmTfPjuuwYTLcpITMggUyfKRbPCuS\nDfsDdrr23FMWetfjoLf8d9rJ22AtG7RsCdxzjx3u1Em2rVpJYVYjF4l00mqITekRo2owFkRvduyx\ntqFfqktGhonqTVVX2w1kXY+pehCxWAzff2+Pchx6qFTIXuuL6x4E9cZbgwaZFdJBcM9IuOMO0bV/\n+KEdL9WgVA1gv/h26iRD92rEQOFlT6PKv+qh6t7A3NfPng3L6VP6fPJJDAcd5DymBJBXneo2/nvr\nLXs/qF4WCL7Ogntt8qAo24gg/rGDoEaGVJnXhfReezmv9fOcqGYSAVLvF0xP+rDDRMAOG+YUlkOH\nylDZtm3ehcn9wRx1lDzHq6Lr1Mm7l6LrgZXgrK2Ndz9aU2O/Tw1TDRggOuBVq0SI1NTIsbIyp+W6\nWnwdsI3JPv0UePVV+5qvvgpuoPHVV6LTatBAPuJ99wVuvVVGD5o2lQp23brsL0avaNBAehYqPfp/\nfOGFYtgGJFYzNGkiglr9R4mE1/btwEsv1S3O+UJxiD4CH31UhuCCcumlsq2uthvIX31ln99zT3u/\nceP4aUBe05F0+4h993Weq6oKN71BefBBYORIW4ioeN19t9Q1DRvaKiTVe1KVbTI3u40bO1d7U8Ps\nOmr1LSWkdT20l1BPRTD64bZ2VyMkqdYRZ54p88jnzJG6L9GCLEGFtKrbFyyQuvfVV+1R0NmzEzfQ\nv/zSFoyqk5YuSqWg8loX0iWuhZf98kRXNzZvHtAttJ+Xk1z9EMBty/vvOz3hjBrFfNFFynOL/I4+\nWjzjqPC2bal5hVG4PQVt38580knMhx9uX6POff45888/M2/d6nzGsmVy/vXXmbt2lX3dww/APGSI\n7L/7rv28119PL846P/3E3LEjc69edX9W2KxYwbx0KfNxx9nxeu21+P/bi+HDnekZOdIO19Y6r33m\nmfxKd6ro/8c11zCvXcv8ySfhPLdXr+TXHX64XPvUU8yffcZ87rnMzz/vzKshQ5hHjJDra2qYieLz\ngVnu1T3LDRvmzLe773amd/HiuqezLtTWMr/xhjNOygNh7962h0Hl8TAIH3/M/Kc/ya9tW+ezW7Vi\nvvde2b/uOua5c5n79ZPwPfcw9+nDXFHB/OOPzO+847w3mUezZHz/PfNBBzmfo+pVN3Pnyvm//c0/\nDsnictVVzvOLFjGvX++8BrA9LQLM//mP87kA8513+r9j0iT72ldfrVs90LKl3L/LLsxVVfJ/ueOi\nfqp8u48fdJB4wPz6a+YxY5ivvFJdA2YfmRh6T5qI9iSiyUQ0h4i+J6JrrOOtiGgCEc0novFEVJLs\nWX641z+96aZ4C+9ffxUdkJpH/MAD6b2rRQt7ihYgK+N88IFzeoaiZ0/pLbh7vKpHcdZZ9rxktwXg\nP/8pRl5eVud1oXFjaX1nYgWXutKunVi4v/66PeypVihT+Dl5qKy056T//vcyUqBwGw6qxRCCLmaS\nz6xcCdxxR3L1S1DYY4qeGzXKU1sro1FNm0pPWu99NW5s91Luv1+e6zUypYbtlR5T18kRyTrOOrkc\n7gYkTmec4ZyyqOLUpInoaj/7zLb7CELv3rIs5ZYt8eV740ZbLfDtt1LfKLVVWZn05tq2Bbp0sZ2L\nhEW3bqJu0vHzcKa8HyaaoaLwm1an96RnzZLRTDXDg1nsEwCZbaP+f68eaqKetP7Np7uuuULNef/1\nV+nJ+6WrTRv/+vbbb0UN1KOHjJQEUdNlYri7GsD1zNwNwBEAriKi/SH+uicwc2cAk6xw2riHLVRY\nGWGUlMjHpdx1/vWvslU6pKBz6IqKZFhQORfR50kCwYaFGjSw9d0qU7w8HvXp4zSSU0L6uusCRRVA\nfLp2202Gj1WFGkSvnW1atAB231323c4fNmyQiuzDD2OO45WV0vghclqhAv7L/SVzZZkLgpRDPc+W\nLg3HOG7WLNkGEdLq21J6t4YNRbjqjcgmTaSc/fOfwM03x3yfpYavv/1WGtvuvHIL9jDWkw6Dc88F\nlA8CJaRVQ8PtjCco7gbJNdc480OpIiZPFktot97Ti+uuS31pUL0MPvSQ0wDKT+WkbEKWLXOqObzw\nMyxTdeCXX9qGcap+rqwU9ZwdR9mmYjgZi8Xw9NOyv2mTuGiuC3qDZft2W+j36eO8rlWrxJ0i9Zzm\nzcUOQ3kg8yMTbkFXMvMsa38TgB8A7AHgVADKdGksAA8v2sFxf7wqfMgh0vJSrbAmTZw+r4O6bnNz\n1FF2a5lI/uD166Xl27x5cl2H0p0m043qC80XFcmHWpc1Zt29+nxxYuLH7rvLx6kqh65dpXIaMkRc\npKqPtKpK0hZEyCiUrjtq6GsTT50qTkLqinJX62fgolNZKcaOyt1tcbE0NpWQvvlmsbd45BHniIYX\nquc1Zoyz8tZ7FHqe+vkGyAVKJ+/Wk6druOWejfLgg2LQ2aWLWFWrvNm6VRqyQWav7LFH3f0g6HOs\nk0373LxZ5r9ff73/dDn1fw0Y4LReV3WRPr1LCTDd1gGwy0SqsxuUJXoQh1Q677zjjGtFhTNOK1ZI\nGQacPiCefFK+V2WDpNsVKVQjT9kc6K5mvcio4RgRdQTQA7LQRltmVoM7FQDa+txWZ04+2dkj81r7\nNZ05dMoJA7OY40+fLh/P5s3JC497LqE+nWrIEO+5hk2byhC6XkknwytdyhvRRx/5z+XOJ554wmlY\nV1UFfPttmUPtkGyuuXIpOW2aXdEvXx5+XOtKkHKoBJha4EBxySWJpwYmQrX0k605Dsh/rVfWL70k\nlbJalGHbNud0xl13LfPtXTZsaE87VMybF9+YXLxYDKjypScNAN26lQGwjbdUeRw3Lr3ntWwpdYne\ncN5zT3FY88039rHFi5NPv3zmGdmmUlco3GVQ5cWgQdIwTkRNjag37r8/3qENIAJaNTbef1+mqQH+\nDYnZs2WrD2tffbXdW9d7+Upw+xmg6enSDdiCNOyHDnWOdFx2mfP88cfLtD2d6mppyDVrJiO6Eyeq\nERhBqTpVQzXo/PKMCWki2gnA6wCuZWaHxlgpyuv6Dn36RiLuuUd0Lbq/73TQe+E9e3oXyiD8/LPT\nrenIkc7KUjkZSbfX70bNufaa1pXPPPaYM6xa+MwiuBNV4EccIT3OU06RD764OLqr9aipQO4W9zPP\nxHvXC4qy4SgvT26v4J49cfnlsn3/fdk+9BDQt699vqoq8bQZfRgT8PZYtddewA03JI5XtlHxVsPO\njz7qPJ+KXlrnnHNsN8bKknyka41A1ftyC6TZs0VtcPHFEk5HSLtRQnq//RI/b+BAp28I98IhL70k\nQ7l63aZsEBKNat15p3OEp107u7wqtSUAfPedbN3/lRf6sL2Xhbwb1fNW71Dv10c1DzgAOO00O6xG\nDJo1k4aFGnkCpA5W16ryE3SUKCNCmogaQgT0OGZWs+gqiKiddb49AF/NyeDBgzF06FAMHToUo0eP\nduhMYrHYjvDuuwOPPBLDUUfFdvjH1c/r4YkTRa+hwuoav+u9wlJ4YzjllBgGDdpxBS67LNj9UsnG\nsGRJbMd0FHXe1u/FsGGDXN++fWrxA+D5f6n4NW2a+vNyGb7sMqBjxxhEFxiz9P4xnH12DFOnYkdY\n6QrHjwdGjJDwunViaLNqlYQbNxbhoT9/4UJgwoTcpjdR+VbhysoYxo+X8t6ggZ1eAPj44/TebzsC\niuH4453n77gjtmPayttvx7B2bWyHkI7FYujUyb4eiOGuu2L4wx/s8MaNo3dUQF7vnzfPef/s2anH\nPxfhadNimDw5hp9/ts9/8EEMQAyLFklvMp3nT5kSQ5cuEl69Wp7Xu7f0ZP/yFwmr/19sM+z7166N\n7bAB6NIF2Lw59feP1lwSqvgAMjqY6P5XXgG2bLHD0tuP4R//kPDJJ8v5mTPt+9etcz5P/35VeMGC\n2A6juCuuiKGiIqY5fJHrd9lFqVac9+vxk638bHe6MYwYEQORfP9+6VP2K5MmSVj15I85xn7fhRfK\n/+++f/78+PQ1aqSEcmzHO8aOHQpgsPVLgJ/Zd7o/AATgeQAPuI7fC2CItX8rgJE+96dvI58C6S4I\nPmsW8+rVsr/XXs6pWHWlooJ3TClLF790rViR/jNzydatzLffzgxMjpva8dJL8VM93FPm9N+55zJv\n3mw/G2AeOjTzadiwQaYueRGkHPbsyfzpp7LfuXM4021OPdX5jKlTmaur5dwpp9j/l9dUOP0/Xr7c\nnmplPy95mn75Ra79+OP04p8L0q0zUkH9h1VV9rEDD5T/3H1Nhw7hvNMrXWrKV6r88Y/yzappqLfc\nYk/NA5j328/e13/6dFl9emVtrXN6ZdBpXk89xXz55ZN5jz3k/Omnx99z/fX+6VDXfPj2qVZMAAAR\n6klEQVShhM88k/nkk2V/r72cU6rc7LZb/Ls2bZI8XbXKvm7BAv0a/ylYmRDSRwOoBTALwDfW7wQA\nrQBMBDAfwHgAJT73+/9z9YCqKu/5pfUZ97x49Xv9deYDDoj/UL2uveEG2d53n/O67t1Ti8ugQak3\neEaPTl+YMjO3bs08c6bsl5Y603XIIdLwWL48tWeefbb9jAMPtPeXLpX5u8kqQSXkdVJpOGzfLtdl\nQe5FCiVUEvHVV8xFRSIAMwXA/NhjdX/On/9spwdg/u1vpTGoykmXLrKdNk1+N94ogh5gPv54uU+/\nPpmQds/bbteOeeBA5hNPDC7gq6rsc2+9ZcddXav8BgDMnTrF36/SpP8qK+Ov0313ZFVI1/VX34W0\nIZ7aWrswz5lj77/3npx/8klx8qFQ508+Wba//z3z00/L/rBh4nDmgguCCxQdgPnll1O7Z9dd5b4z\nz0ztPmY77aonfdFFEm7cOHllk4iBA/0rrVGjkj/b63iqcZkyRSoqg82mTcy//prrWDCvW2ePrNSF\nk06S8jBlimzLypg3bpRGxvff28Jz3jy5Xjl7AuScwl0elYOZkhKnoHRf17p1cgGvj1rU1DjPNWli\nN7JVuf7lF+bx4yW8227xaS4piX+HX8criJCOhFvQTODUixQOhZguItENLVjgtDJWxjSXXirTVxSr\nV8tiD3fcIeFHH7WnryxcKPOO67Iuc6r3qiX9vKZaJMsvZbCiDLEuu0x0Yeed5zTWUs/XHYysXy/O\nQ5jFqlZ3PFFd7T09BIifTnj//fHXLF0qBj46550n28svT5wmxTHH5Jf1djKy8W01bx6O8VcqeKWr\npCQcl6zK2E45SVm6VMpXSYk4T1EGZMp6/YUX7Hv1fZ2ePW3jq0MPlf9r6FCvVf1iWLXKtsL3swLX\nrbjdzlK2bbP9VCiL+9atbSM56Vc6OfNMmXnRsaMsKjNmjP/sn4ED7SVTffGT3rn6IUs96Wzol3JB\nfUjX998zjxsnut5kqJ7a0qXerejWrZm3bBH9axASDV8pZsywW92bNiXuYU6aNJl//pl54kSJh5uf\nf2Zu0yZ5fPTfokVyTo0WVFba55RuecAAcSv52GP2Od0lbbo99LVrmSdOnJzaTRGhPnxbmcBdnu6/\n3y5XM2fKvm4rsssu8b3PBQtk6BoQFdfixewYLfP+TWZAhtDVsfLy+OuOPFJ02MzMl1zi/zzV29fT\n5dWTTu8/8u9J51wox0UoS0LaUP8YM8b50W3YwNy8OfNDD/GOIblE6MPuEyY4hwOXLbP3jzpKrvns\nM+aPPpL9K6+U7dKlzmcqnR3A3Lev6OV0br6Zed99/eOk+2RXv1at5JwK//KLDAmq8LRpzP37iw/h\nVavk2BVXOO9JR0AbDF589pn3EDCz+LB2C+SxY/3L3jPP2N/Qm29KWdafSWTv77OPLZiLiuzhc4D5\n4oud37N6X6Jh8iVLnHF5/HFpXIeBEdIGg8X27cznncd82mnx+qdkRW/zZue1Y8bY5wAR1LrTffU7\n8UQx7AJk4Rcd/boWLWS7YoWtw0sWr19+Ye7Rwzsdav/998VgR4W7d7cXh1BGMmrBjj/8QcJnny1p\nMRjC4IUXxDZEN6piZv7ii7o1BlUj0+v3xRf27A29EfDJJ/LdMDu/C2Xvob7FzZull52NBqsR0h6Y\noatokal0NWokX8F990lrf+vW+KHs6moZXlu3zlkJPPmkfQ0gK475VRjqGr14V1Uxd+kymQHnlKih\nQ5l33jl444FZDOq+/NL73Wp40f1TIwcrVzqf9cEH3kPvQTFlMFpkO116mV6+nLl9+7o9r00bZ7me\nMUOmyQZJ15Yt3t+GmvVRXc384ovMxxxTtzgmI5GQrreGYwYDYHuJuvFGMbTabz8xZlLGHAsXip/r\nvfcWv8KtWtmLgbjXFP/iC3u/Y0d7Xy3yogxbZs+WqqBRI3EBecMNTj/BQ4fGr+qWjK5dxW3oBx84\nj/foYXvush2OCMrwrq3LQe+JJzr9NxsMYbJmjb0ISPv2dXfXW1Eh35Py8FVa6u3FzoumTePfP2oU\n8Omnsl9cLAaR6brfDQU/6Z2rH8xwtyGL1NTYumV3a1oZtSg9V/fusq96uW3bMr/9tm3Eon433SQt\n+XHjnO/SdWBFRfa+6rmPGsXcrZt9/PXXpVetHCoERXf+cN11sj3sMDn38sv2ufnz6/bfGQz5hDIO\nTRW3A6Qffww/bslAgp40yfn8gYg43+JkqB+4p0kMGCArqrm56SaZVuG3PrXb17VOy5a2s/82bYAP\nP7SX6QNkCtWhh8q65HPnpj9FafFi6c3fc49MMRk5UhZyAWT1qVmznH6QDYb6yvbtznXLN25MvvpX\n2BARmNlzola9He4uxPnEgElXXTjuOBneUss4egloALjvvsTD0YkE66pVsroTIEN+yk+74pBDZHWp\nefPqNod4771le9ZZMpytr+Jz4omZFdCmDEaL+p6u4mJ7ePu227IvoJORqQU2niWiCiL6TjvWiogm\nENF8IhpPRCWZeHdQZqlV7wsMk670mThRnHxcdRV2LDChUOuTKxo1ksGxiRPtYz/+KMcSUVxs68F7\n9/ZOV48ezpZ9ujDLkq0rV4rziGxhymC0MOkCjj5avpfhwzMYoTTJVE/6OYi/bp1bAUxg5s4AJlnh\nnLFeX2C0gDDpCoe77xaPSLW1wN//Ll6+pk2DtpqOcNxxwJw5Ynyir2GeCCKpED7+uDDzqxDTBJh0\nRY1CSVdGhDQzfwpgnevwqQDGWvtjAZyeiXcDhTt8Ezbmf/Jn332B778Xgdq7tyw1d9hh3oK4a1ex\nUq3PmLIUDPM/BcP8TzbZ1Em3ZeYKa78CQNtEF9eFIBlcXl6eqdfnlFTSFaUPIZf5lcn/qVDKof4f\nFUqa3ISRrnz85vIxv8L4n/IxXemQMetuIuoI4F1mPtAKr2Pmltr5tczcyuM+Y9ptMBgMhnqFn3V3\nCOucBKaCiNox80oiag/gF6+L/CJqMBgMBkN9I5vD3e8AGGTtDwLwVhbfbTAYDAZD5MjIcDcRvQTg\nWAC7QfTPfwfwNoBXAewFoBzAQGYuDPM7g8FgMBgyQN55HDMkh4iKmXl7ruNhMBiig6k3oklBexwj\noka5jkOYENHhRPRvACOI6EAityPLaENEra1tNm0lMg4RHUJEbXIdj7DJtUOiTGHqjehQqHWGTkEK\naSI6koheAzCKiLoSUVGu41QXSBgK4GkA/4UY/F0FoEei+6KAlbbmRPQyRCUCZt5eCBUJEXUjoqkA\nhgJomeTyyGBV+m8DeIqI/kRETXIdpzAw9UY0KOQ6w4uCE9JWj+VhAB8AWAPgWgAX5zRSdcRacWQZ\ngEHM/AKAuwHsDSDSlQiwY82azVZwVyK60tovhLJ5HYA3mXkAM/8ISAWT4zjVCSI6GMBjAP5j/XoD\n2CenkQqBAq43FqPA6o0CrzPiKMRElQKYz8zPARgF4A0ApxFRQKeN+QER/YGI/kFEp1mHXgAwm4ia\nMPMaABsBRN7PFREVW1PyKgBcAuAKImrJzDVR7slYw3C1AB6ywmcS0Z4AmlrhqArrIwD8zMzjAIyH\npGdJbqMUCgeiMOqNMiI6Qjv0EqTeaFwI9QbZFFyd4UfkhbSHMPsGwCFEtI/V2poJ4CsAl+cskilg\nFcArANwMsYK/j4guAlDMzDXMvI2IGgLYE8C8HEY1LbT8OgWQYSpmXgGgI4BFAGIAbrXyryZ3MU0N\nLV2nWoc2A+gF4DgiegHAZQCGAXgQ2NHLyXs8vq/XIWkaBmAOgD0APEhEOfXFnyoewmw2pN74bUTr\njZ2J6A0AbwK4jIiUo6gqq96ojGq94corsnrSka8zghJZIe0jzC6BtBTHAbjGunQdgIkAmlmtr7zG\nqryPAPBPZn4WwJUA+gLopfW+ugKoYOb5RLQLER2Wo+gGxiO//kVEFxHRTiTe6cqZeRmACQCuAPAa\nETW2Kpa8xSNdo4joz8y8BcATAB4FMJ6ZjwdwO4ADiOiknEU4ID7f15+ZeSWk/DUBcAszHwHg/wAc\nRURH5iq+QfETZsy8GjJFNJL1BoAqAJMBnA9gOYBzAICZa7Vr9keE6g2vvFLpsUY4FkaxzkiVyApp\nH2FWBuA4AO8B2IeI+lmZugbS4t+Qo+gmhIguJKJjtdbvDwD2IJkyMRHAdwCOhrQcAWBXAFusHvYX\nkKG6vMYnv/pB0rUWwN5E9C5kqPETiNCuZObqXMU5CH7lkIhOgKwGVwygtXXt/wB8BiDvW/sJ0nWS\nJaj7AlhtXf41xINgVU4imxqewsxqAL8GYD8i6huheqPMGuqtBPAUpGExH8DBaqheE1pRqzc888pi\nOaSOfwcRqzNSJVJCOoAwmwPgSMjH9RKAB4hoHwB9ABCAvJlaYfVUdieiGIDBkIL4MBG1gBiJtYZt\nkPMypBWsprycAOD3kOHU85n5mSxGPTAB8utbAMcA6AJgBWTo6mBmPgXAXiRGSnlHwHT1gVQyVwO4\nkIhKrZ5pX0jPNO8ImK4yImoHEQi3EFEDAOcCOADy3eUdAYTZflaj5DtIvTE6QvXGHwA8QkStmXkb\nM1cBmApgFSRfoAmt/sjzeiNAXnWxLt0ZwEpEpM6oC3k/t8xq4bYH8CLEEOcnADtZFd4ySOWwD0TP\n8jKABwDsyszjiKgDZN3q/QD8OV88nFmV3nYi2hnA/5j5fJJ5fmMghkaXQKxmDyOiFcxcTkQbAJwN\n0bm/DeArZn4lV2nwI8X8egXA/ZBhxmutCkZxXL7kF5BWukYD2J+ZXyeZojQQQDcAf1SW3vlAGul6\nAEA7Zn6URE/4FsR47CJmLs9+CrzxSdefiehaZl5lXTMVQGdID22Ypc/8PxJL779CGo9RqDdGQ1Qr\nZwKANZw9E0A/q7GxwtKzvwtgVr7VGynm1UBIXq0goptdeZNXdUZY5LWQTkOYLbKE2VkAvmbmESRW\njZW5S4UNieXh3QAaENF/Ia3B7cCOeX5XQ3qUXSEt+jMAdABwD2SIdKp17efZj31y0syvXwGczcx3\nWL0yMHNtPn1saaZrPexy+AIRNXDpB3NOHb6vswHMAvAnACWqIs0X6iDMVgLYysz3RqzeuBbAciI6\nlpk/sY6/SUT7A/gI0ujqzcxf5CgJvqSZV/tCGpBbLQFP+VZnhEleDncTURERjQAwnIjKIC2oHYUS\nMoR4Amxhdjhkkj6gCTPr+nz50I6FWIuWQFqKwwBUA+hNlgGH1ZK/C6IHnAgppEcR0TSIM4xYDqKe\nlBDya5p1bW0+CbKQy2EhpetL69rqfBLQAdJ1LeR7Olbdw8xvQir8jwD8bN0TxXpjKKTuUPcNhBgq\nTgZwIDPPzW7ME1PHvPoQojL6DQt5821lgrwT0gUszBjAv5j5CmZ+CsD3ADoBuBPiHEK1mF8HsJGI\nOjHztwAuAHAmM5/BzJtyFHdfCjW/TLoKNl1DkViY/ZDdmCclaL3xJoBVRNTJum8lgBOZ+RJm9lwW\nOFcUcF5lBmbOqx/EqOGPWvgxiHn9RRA9LCAec9pBPB51so61BLBHruOfIF1NIdNWiqzw+QBGWPuz\nAFxj7R8C4KVcx9fkl0lXAafrNS1dvQD0ynX8E6Sr4OqNQs2rTP3yricNYAZkvpvyHPMZgL1YPAEV\nEdE1LK2sDgCqmXkRADDzOpYpLnkJM29lsb5U02/6wZ7CcjGA/Ynofcjw4te5iGOaFGR+waSrkNO1\nXUvXFGaekpsoJ6dA642CzKtMkXdCukAL5Q5I3GAWAWgLyzk8gF8B3AZgBIDezHxfruKXKoWaXyZd\nJl35RCHVG4WeV2GTt9bdloUfQwrl3dZhVSi7wfZQFSlYLBmbQArlQUT0oLV/NTN/ltvYpU+h5pdJ\nV7Qo1HQVYr1RqHkVNnkrpAuxUGr0gOiWOgF4jvPQqUCqFGp+mXRFi0JNl0VB1RsFnlehQcz56+ef\nxBfw5xAXdpEvlAoSJysXAhjFTgcekaaA88ukK0IUcLoKrt4o1LwKk3wX0gVXKAuZQs0vk65oUajp\nKkRMXiUnr4W0wWAwGAz1mbyz7jYYDAaDwSAYIW0wGAwGQ55ihLTBYDAYDHmKEdIGg8FgMOQpRkgb\nDAaDwZCnGCFtMBgMBkOeYoS0wWAwGAx5yv8DhCJOYdAYb+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45daea3810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(subplots=True, grid=True, style='b', figsize=(8, 6))\n",
    "# tag: es50_vs\n",
    "# title: The EURO STOXX 50 Index and the VSTOXX volatility index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "uuid": "17ae59ff-9863-4c30-9493-22d44c0e5edf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-05</th>\n",
       "      <td>0.017228</td>\n",
       "      <td>0.489248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-06</th>\n",
       "      <td>0.022138</td>\n",
       "      <td>-0.165317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-07</th>\n",
       "      <td>-0.015723</td>\n",
       "      <td>0.256337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-08</th>\n",
       "      <td>-0.003120</td>\n",
       "      <td>0.021570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EUROSTOXX    VSTOXX\n",
       "1999-01-04        NaN       NaN\n",
       "1999-01-05   0.017228  0.489248\n",
       "1999-01-06   0.022138 -0.165317\n",
       "1999-01-07  -0.015723  0.256337\n",
       "1999-01-08  -0.003120  0.021570"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = np.log(data / data.shift(1)) \n",
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "uuid": "771c53bf-78fb-4260-865b-39307973cb77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f45dab79a10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f45dab329d0>], dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ofo0Q4mchxE9CiB/TDUu6RKG/w4p05CIC1q+X15MnAz16+PcjDKXNcZVbBCVXKs3j11wj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\nRwo0noc6YWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45daa769d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot(subplots=True, grid=True, style='b', figsize=(8, 6))\n",
    "# tag: es50_vs_rets\n",
    "# title: Log returns of EURO STOXX 50 and VSTOXX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "uuid": "709bc1e8-03a8-47c6-9b21-4efa08052dab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-------------------------Summary of Regression Analysis-------------------------\n",
       "\n",
       "Formula: Y ~ <x> + <intercept>\n",
       "\n",
       "Number of Observations:         4253\n",
       "Number of Degrees of Freedom:   2\n",
       "\n",
       "R-squared:         0.5335\n",
       "Adj R-squared:     0.5334\n",
       "\n",
       "Rmse:              0.0394\n",
       "\n",
       "F-stat (1, 4251):  4861.3892, p-value:     0.0000\n",
       "\n",
       "Degrees of Freedom: model 1, resid 4251\n",
       "\n",
       "-----------------------Summary of Estimated Coefficients------------------------\n",
       "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
       "--------------------------------------------------------------------------------\n",
       "             x    -2.8064     0.0403     -69.72     0.0000    -2.8853    -2.7276\n",
       "     intercept     0.0000     0.0006       0.04     0.9685    -0.0012     0.0012\n",
       "---------------------------------End of Summary---------------------------------"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdat = rets['EUROSTOXX']\n",
    "ydat = rets['VSTOXX']\n",
    "model = pd.ols(y=ydat, x=xdat)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "uuid": "ff00a7f1-173c-40f8-b077-9892ad310f4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x           -2.806444\n",
       "intercept    0.000024\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "uuid": "24c708df-1e81-48c6-b1c2-890dd52e541f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f45cfe02990>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GrgiIu00tFzGAEq1MwG5nPfhgkdatszZBf0O1/I4LIyK5HsUz8hb7ZWe7qt/W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\nUBRFUSJQwaAoiqJE8P8BPsa+TPXV5UYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45cfe52390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xdat, ydat, 'r.')\n",
    "ax = plt.axis()  # grab axis values\n",
    "x = np.linspace(ax[0], ax[1] + 0.01)\n",
    "plt.plot(x, model.beta[1] + model.beta[0] * x, 'b', lw=2)\n",
    "plt.grid(True)\n",
    "plt.axis('tight')\n",
    "plt.xlabel('EURO STOXX 50 returns')\n",
    "plt.ylabel('VSTOXX returns')\n",
    "# tag: scatter_rets\n",
    "# title: Scatter plot of log returns and regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "uuid": "e1f9009e-5b73-4e04-9b10-deea36f4e508"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EUROSTOXX</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.730406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VSTOXX</th>\n",
       "      <td>-0.730406</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           EUROSTOXX    VSTOXX\n",
       "EUROSTOXX   1.000000 -0.730406\n",
       "VSTOXX     -0.730406  1.000000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "uuid": "a534d3db-df59-4a31-b0b7-1ab3c19d77d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45dab5e650>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5dz9Q1f/GtiX4v1l9coVmfuih6mtJgjBMw/PPw777ln6efIO/SmXlSuuKCVbz\nD/38jz7aerC8desiwx/GBRo3LnmNbZG4Mc8XRx5qM1NUJQjnHgC7jxYssB5f8TkoVK3tLqzZl9K4\nWygbbFDZyZ2yUY7h76+qYSSWeUD/fJmBk4D7M7adKyJTROT32VxFlSBJ32rXrtm7g5UTZrWWvt+1\na80dcsAB+cPMZtM4Y4Yti23YLZTvfjeqrS9YEPlnTzyx5axfcX2ffmoPdfhW0qWLLWvts3bFx5+r\n/3hcXzhY0aWJh0opv88+s4Bro0dHHRIGDoz2//nP0X0RxpgK34JEzDjfeCMcdlhhhr9QjR075n/D\nrgR5o0iLyFgg23CXK+IJVVURyXlbiEhn4Ejgktjm24DQq3wtMBo4K9vxo0aNYnDgSG9sbGTYsGHr\nX6PCwi00PXny5KLy50u/8QbsvXcqUNkULC39v/810bdvbfUVm160CJYtS5FK5c8/efLknPubm1s/\nvpR0r15NgU/f0vHyXrIEDj+8CVW45JJ0ff36pfjc56ChwdIvv2zHqyarr9h0SK2uH6bHjg315Na3\nzz7w3HNWvrXWW075vf8+TJiQYsIEOPZY2//+++nfv2fPFM88Ay+/bOnp01NMnw7Dh1v65z+3/M3N\nyX2fWbNgwIDyz5dKpRgzZgzAenuZE1Ut6QO8AwwI1jcB3smT92jgmTz7BwNTc+xTV7E6UPbPM8/U\nWl3xhNpLpWPH8o7Px+TJdu7hw/OXeybZ9p1xhuqdd1ZGZ1tj+nQrm9Gjc+eZNMnyLF1aPV2V4Mkn\no3vhgAOi7b//fXTvhvfJZZepjhwZ5Vm2LP1e2mOP5HT99Keql16a3PlCAtuZ1eaW4+p5HAgDtp4O\nPJon78lA2hTXIhKf5uRYIEdvWnc59dTc+6ZNq56OJClnIvJKxnkJe14MHQr3ZzoMgR13tOUnn9hS\nJPfreHxy+HondFWGE5NkY9gwc/e45OophWOPjdZvvDFaP/NMa0PabLPI9bN4cXp8qO7drQE4HDWe\n5FwcDQ3Vd/WUY/ivBw4RkRnAgUEaERkoIusj2YhID6xh9+GM438uIm+KyBRgf+DCMrQUTOarYjmE\nAcYeeqhlg+73v1/aOZPUVyzdusGRR7aeL5fG22/PPQqyXMI2h622sjjqoRHaay+LcBgasHffbb0M\nu3evveGv5e8cJ2zczWzMz9QX79XiAqWUXzwkSDxcOFjD9oQJUaPvp5+mzzMNZuw/+CBaT0pjmzL8\nqrpAVQ/hzWCcAAAgAElEQVRW1aGqeqiqLgq2z1bVEbF8y1S1r6ouzTh+pKrurKq7qOoxGjUUtxnC\nWujGG1tMj1zMmmV9pV1m2bLy4uwAfOUr+UfglkNY1pmuyw03tK5wYWPcFVfQgnBGsZAePQofA9De\naW62qKX5QnSAe4a/FMIuk7mmVN1oIzP4W21lYdc33jh9f7ybdj3X+NskYaNIEoRx3Tt3Tt/+4INR\nDWraNOvbv9deNulCNfUVw3PP2Y3eUMAdUQuNYY0/bvg7d4YvftG0h71TXnwx0vfoo9ZHe8iQ9HNt\ns000UXatqNXvnElzc/YBTJn6amGc8lFK+S1ZAj/5iY0uz0Z4j4XRXzMNfzyWVCG9wgrV2NBQ/a6y\ndWf4kyTsrx7WIC4NhrD16xf5l0PfM+SfzrDWLFkSua5cJFuNf9UqyPVs9epl3VKzTa234YbmmmvP\nc8oWwqJF9rsXOnK1rdf4m5vN3ZOvLeqOO6L1TMMfL4MkDXWHDr7GX3GS9K2GEzOEQax697ZlOLAr\n80EJ/YPV0lcMrcXgj1MLjeEf6WabpW/fZ59o/ZFHbDBMKpVi1aqoz34m4UNbbHjnJHHBx9+7t1VW\nMt9YoX36+KdObd3FN2hQtJ5p+OMUYvjbpY/fY+6c+KQgF1xgA5k6d7aGovHja6etWN54I9nRiEmz\n0UZw9dUtjVQ42vSf/zSXzpIlVqtbvTp3TTb0zxYz0XZ7I+jujWr91Pgh/Q08G2FPnvHj849kTjIe\nVy0Mv6jjv6aIqOsaM3nnHdhuu5bbBwyIAo25hog18r34Yq2VFI+ITZHXr1/6H0Ou22bCBBvB+e1v\nt+8JR3Lx0UfRm9MOO9intciyffuabzxfLdhl1q61e2PNmtYrOCJ2j+Sa7rNLF6tYJGWWbr8dJk+2\nZZKICKqa9dv6Gn8FyHRHgMW3nzvXrUBXcYYOhd/+ttYqSkPVXtE7dco+i1cme+xh37XWYRtqRXh/\nnn22VVIKmfavQ4e2XV5Llpgrs5C32vffzz/Hc65eQaWSdI3/7bfhscdauWZyl2sbVMO3Gp+I/Y9/\ntAdmp52sMXheK51Wa+H7festc1EVOuGGC/7pXFio3VSr+TLnUK02tSrD0MA89JD92a9dm33+6Ex9\ngwZFE4+7QLHlt2SJtf8UQmttP/niWMWplY//W99qGb+qxTWTu5wnG4cearUlEQsyNndurRW1ZKed\nbBn/w2qrHHWUde9s7TW81oa/VsyebZOMH3987sbvbMydCz/4QeV0VZrFiws3/K1RSJtIMSRp+CdN\nghdeKOCayVyu7VCt/tMLF5phiftEN9ig9Z491e7fPWFC8ce40gc9Fwcd1NRqnk6dajthTqXLMJxl\nKhxrEnLnnVH8/Y02yn18pr54iAMXKLb8lixJLqRIoTX+YvrxJ2X4P/3UlvFuqVmvmczlPJk0Nra8\nQVIpCzcwa1YtFGXnueds2cbaz8tm000t3np7JXyz7N3bwljMmWMux6uvjkaZ77KLLQuJzzRwoNu9\nvlpj/vyou3W5FGr4CyXJAVwNDXDggdZ+kzdfMpdrO9TSP/3mm7YMjW02qq1v5cr0EYmF4LKPHwrT\nN2SIhXKoFZUuw/i8wttua4Y7dFEceKAte/a0ZSH9+Lt0sfYpVyoIxZbfV7+anMEu1NVTqMYkB3Dl\nG78Sp+4Mfy0Jfelf/3ptdaxZYz7PpUttnt2keym0Bfr1sx5WLky8Xgmuuca658YHuIEZwE03tfV8\nhj+TLl2st8izzyarsxqsXWtu1wHZZhYpgUrU+L3hrzC19k8/E8wsnKtbZ6X1PfwwHH20uaLCxq6T\nTy7uHLUuw9YoRJ8I7LprNKdqtal0GfboAeecA6+8YrX0uXOt4S8e0jpszC8kVk84s5orwe2KKb/X\nXrNlUv3kXfbxF2r4E/7v8rTGYYdBnz5W467FYJh4FNGDD4Zbb61sHH2X6dmzsD7sbZE1a9Ijrfbv\nb584oTEvZLrM8B454QR33D2F8uMfJ3s+l338vsafAxf805065R4MUy19228PY8faqM1icaEM81Go\nvp49kw2vWwyVLMPJk20AT2u+aBGbdP6LX2y5L1Nf6B5yhWLKL1+bWilUoh9/qX+mt9ySbku84XeY\nSo6CVLUHOtsk8HGuvLIy129LtMUa/3vvtf7bhjNNFTLZzIEHFhaKG8xVlK8LaL3gio//T3+C730v\nPQxMxQ2/iPQRkbEiMkNEnhWRxhz5LhORaSIyVUTuF5EuxRyfNC74p2fPjiJ7ZlKuvu98x5ZvvJF9\n/557wvXXw0knlX4NF8owH4Xq69Urmpyj2pRahkOHWpfMfITdhfv2LekSQHZ9G2xQeATXShPXt3Rp\n/qk2k6bQXj2F/sYipRn+c86x5bnnWkVy6VL7c650jf9SYKyqDgXGBek0RGQw8HVgN1XdCegAnFTo\n8Z7iCf/9Tz89+/4ePSxWjcfcXdOn11pF8RRSk//449xzFZRKGG7cNc48M1qvRvtDnz7Jnq8UV8/F\nF0eD8x57DE47zf6Yx4wprJdWOYb/KOCeYP0eIFt0iCVAM9BdRDoC3YFw+otCjk8cF/zT11+fe1+5\n+sIaWa7eKrlmXCoGF8owH4XqCxvZa0E5ZZjvwZ471wxJuQ322fR16WIVCxdi9owfn+KuuyzOUDjf\ndTgNZ5x337VunI8/nty1f/lLi23VGpWM1TNtmi3DXoLx6KqVrvH3j82TOw/on5lBVRcAo4H/ALOB\nxar6XKHHt1e23LJy5453t3vrLevKFg+1nIThby907w6ffNL2eqnkMvyvv25jMtatK2/u5FyE940L\nI55nzYKzzoIjjrD0jTdaILn4rGqXXGKD1+bOjWbLS4JevWDrrZM7X9zwL11a2B/r4MH2B3TYYdHM\nfyH55hFYj6rm/ABjgalZPkcBCzPyLshy/FbAdGAjrOvoI8Cpwb5Wjw+2a3tj4ULVhgbVdeuSP/eB\nB6r++c+qZs6iz+zZtn+33VQnTEj+um2Rp56ysrnzTtWrrlJ9771aK2odUP3Rj1puX7ky/feuBOvW\n2blfeqky5y+G8HsedJDq0qW27ZBD7DfNzAOqL79cG52F8PTTqocdZuuXXlrY7weqo0fb+tq1qsuX\nq06datvvvTfMg2oO2563fVpVD8m1T0TmicgAVZ0rIpsAn2TJtjvwiqrOD455GPg8cB9QyPEAjBo1\nisHBZKuNjY0MGzZsfcNJ+DrVltLLl8O6dU3cfz8MGpTs+adNs2kHBw1qCmo/tn/gwCZUYdGiFJMn\nw+67u1MetUrvthtAih/8ABYubGL+fDjuOHf0ZUtDKqgRpu/feGNLn3deitmzW+5P4voisMsuKV57\nDfbdN/nzP/kkdO2aokOH/PnNndPEM89Aly4pJk60/WPHwiuvpHjiCUtvthkcemiKf/8btt46eb1J\npadONXsAMHmy7c/3+1mf/yZ22SV9///+l+Lkk8cwbhx88MFg8pLrH6G1D3ADcEmwfilwfZY8uwBv\nAd0AwXz53yn0eK1AjX/8+PGJnq8UPvvM/pmvvrrlvnL0rV6t2qGD1cw+/rhlrf/TT1W320512rTS\ntZersRoUo2/MmKh8Lr64cpo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p78ADI4Me0qlTuuF/8UWr/YvYZ+lSgBT77RfF+O/QwRo7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45dab56190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_corr(rets['EUROSTOXX'], rets['VSTOXX'],\n",
    "                window=252).plot(grid=True, style='b')\n",
    "# tag: roll_corr\n",
    "# title: Rolling correlation between EURO STOXX 50 and VSTOXX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High Frequency Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "uuid": "cd3bd5f2-565a-4158-a796-e8ec41f76d88"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from urllib import urlretrieve\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "uuid": "63521c3e-8197-464a-a5bc-dc9a15550893"
   },
   "outputs": [],
   "source": [
    "url1 = 'http://www.netfonds.no/quotes/posdump.php?'\n",
    "url2 = 'date=%s%s%s&paper=NKE.N&csv_format=csv'\n",
    "url = url1 + url2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "uuid": "8fa05c35-a914-49b2-8da1-841146b43931"
   },
   "outputs": [],
   "source": [
    "year = '2015'\n",
    "month = '08'\n",
    "days = ['03', '04', '05', '06', '07']\n",
    "  # dates might need to be updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "uuid": "946e1882-04bb-4809-869f-1e7dd8ad396a"
   },
   "outputs": [],
   "source": [
    "NKE = pd.DataFrame()\n",
    "for day in days:\n",
    "    NKE = NKE.append(pd.read_csv(url % (year, month, day),\n",
    "                       index_col=0, header=0, parse_dates=True))\n",
    "NKE.columns = ['bid', 'bdepth', 'bdeptht', 'offer', 'odepth', 'odeptht']\n",
    "  # shorter colummn names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "uuid": "1fce3fb2-c664-4f77-80dc-0e4907f86dac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 50962 entries, 2015-08-03 10:00:01 to 2015-08-07 22:00:00\n",
      "Data columns (total 6 columns):\n",
      "bid        50962 non-null float64\n",
      "bdepth     50962 non-null int64\n",
      "bdeptht    50962 non-null int64\n",
      "offer      50962 non-null float64\n",
      "odepth     50962 non-null int64\n",
      "odeptht    50962 non-null int64\n",
      "dtypes: float64(2), int64(4)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "NKE.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "uuid": "b0fa9c58-2087-4f99-a7f1-efeb490bb456"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45cf71f310>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vFkvFWuR//Wv7XlHz59uXFsuWxdsh6Oyz0+evvDKe84ANr/zlL/ZJ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45da9a5d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NKE['bid'].plot(grid=True)\n",
    "# tag: aapl\n",
    "# title: Nike stock tick data for a week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "uuid": "73447893-dc77-488a-8252-64c29a92e4c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f45cf36ad10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f45cebced50>], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iKrNmOYM9ZYobRX755a5L10idBQv83QVuGEZ4MGOdRYLkjxFxARomT3YhO3fY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\nftBA2DDdgkl4dYtUyAmLEcjHM9uwoaa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MdVm2AIcCu6vqKuAZABEZDFwInCsiR3tl9wWW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\ngiAISk446yAIgiAoOeGsgyAIgqDk/B/nxOeknjOZQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45cf36a7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_plot = NKE[['bid', 'bdeptht']][\n",
    "    (NKE.index > dt.datetime(2015, 8, 4, 0, 0))\n",
    " &  (NKE.index < dt.datetime(2015, 8, 5, 2, 59))]\n",
    "  # adjust dates to given data set\n",
    "to_plot.plot(subplots=True, style='b', figsize=(8, 5), grid=True)\n",
    "# tag: aapl_day\n",
    "# title: Apple stock tick data and volume for a trading day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "uuid": "47a27b89-f6a1-4b3c-9944-8a6109642c72"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bid</th>\n",
       "      <th>bdepth</th>\n",
       "      <th>bdeptht</th>\n",
       "      <th>offer</th>\n",
       "      <th>odepth</th>\n",
       "      <th>odeptht</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03 10:00:00</th>\n",
       "      <td>113.56</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>116.82</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03 10:05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03 10:10:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03 10:15:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03 10:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        bid  bdepth  bdeptht   offer  odepth  odeptht\n",
       "time                                                                 \n",
       "2015-08-03 10:00:00  113.56     100      100  116.82     100      100\n",
       "2015-08-03 10:05:00     NaN     NaN      NaN     NaN     NaN      NaN\n",
       "2015-08-03 10:10:00     NaN     NaN      NaN     NaN     NaN      NaN\n",
       "2015-08-03 10:15:00     NaN     NaN      NaN     NaN     NaN      NaN\n",
       "2015-08-03 10:20:00     NaN     NaN      NaN     NaN     NaN      NaN"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NKE_resam = NKE.resample(rule='5min', how='mean')\n",
    "np.round(NKE_resam.head(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "uuid": "7730056f-4ce8-4a23-853e-f5206eb86ea7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45cf36a5d0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mraWkceTJHnn6GuWBUaPg3/89XJB/6EN+ZNN+XX0gMgryOrZjhw/pceP8cqXpfx6vWeMf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\n6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45cf2c8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NKE_resam['bid'].fillna(method='ffill').plot(grid=True)\n",
    "# tag: aapl_resam\n",
    "# title: Resampled Apple stock tick data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "uuid": "d8d2f963-3909-4434-95fa-69f932b49451"
   },
   "outputs": [],
   "source": [
    "def reversal(x):\n",
    "    return 2 * 95 - x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "uuid": "e6d72fc4-0617-480c-84a3-56892f0e4b01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45cf194110>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W+ayQESN6bt9rr5652Js2SbAbmVxxHjCgeB52/jHl6OryefdDhtTGTlE5sRZs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\n/YCX8QK0xMxextdFecrMxkd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45cf19c110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NKE_resam['bid'].fillna(method='ffill').apply(reversal).plot(grid=True)\n",
    "# tag: aapl_resam_apply\n",
    "# title: Resampled Apple stock tick data with function applied to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "uuid": "ac44afa5-337a-4aed-b811-1b76db21682d"
   },
   "outputs": [],
   "source": [
    "!rm ./data/*\n",
    "  # Windows: del /data/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://shop.oreilly.com/product/0636920032441.do\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
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 },
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}