OrderImbalanceThesis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import IPython.core.display as di\n",
    "\n",
    "# This line will hide code by default when the notebook is exported as HTML\n",
    "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n",
    "\n",
    "# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n",
    "di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>''', raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn \n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "#Read and format the data\n",
    "data = pd.read_csv('IndexFutures.txt', delimiter='@')\n",
    "headerList = pd.read_excel('DataHeader.xlsx')\n",
    "data.columns = headerList.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1820b3d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0d5976d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.sort_values(['Publish Counter'], inplace=True)\n",
    "data.TTQ.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sanity check to ensure that the data is in correct order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'EsteeId', u'LTP', u'LTQ', u'LTT', u'Bid Price', u'Bid Quantity',\n",
       "       u'Ask Price', u'Ask Quantity', u'Bid Price 2', u'Bid Quantity 2',\n",
       "       u'Ask Price 2', u'Ask Quantity 2', u'Bid Price 3',\n",
       "       u'Bid Quantity 3', u'Ask Price 3', u'Ask Quantity 3',\n",
       "       u'Bid Price 4', u'Bid Quantity 4', u'Ask Price 4',\n",
       "       u'Ask Quantity 4', u'Bid Price 5', u'Bid Quantity 5',\n",
       "       u'Ask Price 5', u'Ask Quantity 5', u'VWAP', u'System Time', u'TTQ',\n",
       "       u'ATP', u'TTV', u'TBQ', u'TSQ', u'OI', u'Processed Time',\n",
       "       u'TickType', u'MsgType', u'BCSequence Number', u'Receive Counter',\n",
       "       u'Publish Counter', u'Receive by DL Counter', u'BidAskChange Flag'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "218345"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "233746"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "449844"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data.index= pd.to_datetime(data[])\n",
    "data.columns.values\n",
    "len(data['System Time'].unique())\n",
    "len(data['Processed Time'].unique())\n",
    "len(data)\n",
    "data.index = pd.to_datetime(data['Processed Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processed time has more unique entries than system time, so we index using the former"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1bdb0790>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1a43e350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Change the index to the SystemTime\n",
    "#Order of the data would remain unaffected\n",
    "data.index = pd.to_datetime(data['Processed Time'])\n",
    "data['TTQ'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb of NA count in the data are 9447.000000\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EsteeId</th>\n",
       "      <th>LTP</th>\n",
       "      <th>LTQ</th>\n",
       "      <th>LTT</th>\n",
       "      <th>Bid Price</th>\n",
       "      <th>Bid Quantity</th>\n",
       "      <th>Ask Price</th>\n",
       "      <th>Ask Quantity</th>\n",
       "      <th>Bid Price 2</th>\n",
       "      <th>Bid Quantity 2</th>\n",
       "      <th>...</th>\n",
       "      <th>OI</th>\n",
       "      <th>Processed Time</th>\n",
       "      <th>TickType</th>\n",
       "      <th>MsgType</th>\n",
       "      <th>BCSequence Number</th>\n",
       "      <th>Receive Counter</th>\n",
       "      <th>Publish Counter</th>\n",
       "      <th>Receive by DL Counter</th>\n",
       "      <th>BidAskChange Flag</th>\n",
       "      <th>TempTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Processed Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-22 09:15:30.000</th>\n",
       "      <td>IN0000000002IS0520171130</td>\n",
       "      <td>10379.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>22/11/2017 09:15:29.516000</td>\n",
       "      <td>10379.70</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10381.95</td>\n",
       "      <td>375.0</td>\n",
       "      <td>10379.65</td>\n",
       "      <td>375.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22/11/2017 09:15:29.995200</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.177784e+11</td>\n",
       "      <td>2.177784e+11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6291456.0</td>\n",
       "      <td>09:15:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 09:15:30.500</th>\n",
       "      <td>IN0000000002IS0520171130</td>\n",
       "      <td>10379.7</td>\n",
       "      <td>75.0</td>\n",
       "      <td>22/11/2017 09:15:30.397000</td>\n",
       "      <td>10379.65</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10381.95</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10379.50</td>\n",
       "      <td>525.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22/11/2017 09:15:30.477200</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.177853e+11</td>\n",
       "      <td>2.177853e+11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4194304.0</td>\n",
       "      <td>09:15:30.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 09:15:31.000</th>\n",
       "      <td>IN0000000002IS0520171130</td>\n",
       "      <td>10382.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>22/11/2017 09:15:30.892000</td>\n",
       "      <td>10380.95</td>\n",
       "      <td>150.0</td>\n",
       "      <td>10381.85</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10379.70</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22/11/2017 09:15:30.967200</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.177924e+11</td>\n",
       "      <td>2.177924e+11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6684672.0</td>\n",
       "      <td>09:15:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 09:15:31.500</th>\n",
       "      <td>IN0000000002IS0520171130</td>\n",
       "      <td>10381.7</td>\n",
       "      <td>75.0</td>\n",
       "      <td>22/11/2017 09:15:31.292000</td>\n",
       "      <td>10380.45</td>\n",
       "      <td>450.0</td>\n",
       "      <td>10381.75</td>\n",
       "      <td>675.0</td>\n",
       "      <td>10379.70</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22/11/2017 09:15:31.489200</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.177998e+11</td>\n",
       "      <td>2.177998e+11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>09:15:31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 09:15:32.000</th>\n",
       "      <td>IN0000000002IS0520171130</td>\n",
       "      <td>10380.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>22/11/2017 09:15:31.703000</td>\n",
       "      <td>10380.05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10381.75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10380.00</td>\n",
       "      <td>225.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22/11/2017 09:15:31.968200</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.178067e+11</td>\n",
       "      <td>2.178067e+11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6694496.0</td>\n",
       "      <td>09:15:32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          EsteeId      LTP    LTQ  \\\n",
       "Processed Time                                                      \n",
       "2017-11-22 09:15:30.000  IN0000000002IS0520171130  10379.5   75.0   \n",
       "2017-11-22 09:15:30.500  IN0000000002IS0520171130  10379.7   75.0   \n",
       "2017-11-22 09:15:31.000  IN0000000002IS0520171130  10382.0   75.0   \n",
       "2017-11-22 09:15:31.500  IN0000000002IS0520171130  10381.7   75.0   \n",
       "2017-11-22 09:15:32.000  IN0000000002IS0520171130  10380.0  675.0   \n",
       "\n",
       "                                                LTT  Bid Price  Bid Quantity  \\\n",
       "Processed Time                                                                 \n",
       "2017-11-22 09:15:30.000  22/11/2017 09:15:29.516000   10379.70          75.0   \n",
       "2017-11-22 09:15:30.500  22/11/2017 09:15:30.397000   10379.65         225.0   \n",
       "2017-11-22 09:15:31.000  22/11/2017 09:15:30.892000   10380.95         150.0   \n",
       "2017-11-22 09:15:31.500  22/11/2017 09:15:31.292000   10380.45         450.0   \n",
       "2017-11-22 09:15:32.000  22/11/2017 09:15:31.703000   10380.05          75.0   \n",
       "\n",
       "                         Ask Price  Ask Quantity  Bid Price 2  Bid Quantity 2  \\\n",
       "Processed Time                                                                  \n",
       "2017-11-22 09:15:30.000   10381.95         375.0     10379.65           375.0   \n",
       "2017-11-22 09:15:30.500   10381.95         225.0     10379.50           525.0   \n",
       "2017-11-22 09:15:31.000   10381.85          75.0     10379.70            75.0   \n",
       "2017-11-22 09:15:31.500   10381.75         675.0     10379.70            75.0   \n",
       "2017-11-22 09:15:32.000   10381.75          75.0     10380.00           225.0   \n",
       "\n",
       "                              ...          OI              Processed Time  \\\n",
       "Processed Time                ...                                           \n",
       "2017-11-22 09:15:30.000       ...         0.0  22/11/2017 09:15:29.995200   \n",
       "2017-11-22 09:15:30.500       ...         0.0  22/11/2017 09:15:30.477200   \n",
       "2017-11-22 09:15:31.000       ...         0.0  22/11/2017 09:15:30.967200   \n",
       "2017-11-22 09:15:31.500       ...         0.0  22/11/2017 09:15:31.489200   \n",
       "2017-11-22 09:15:32.000       ...         0.0  22/11/2017 09:15:31.968200   \n",
       "\n",
       "                         TickType  MsgType  BCSequence Number  \\\n",
       "Processed Time                                                  \n",
       "2017-11-22 09:15:30.000  Exchange      0.0                0.0   \n",
       "2017-11-22 09:15:30.500  Exchange      0.0                0.0   \n",
       "2017-11-22 09:15:31.000  Exchange      0.0                0.0   \n",
       "2017-11-22 09:15:31.500  Exchange      0.0                0.0   \n",
       "2017-11-22 09:15:32.000  Exchange      0.0                0.0   \n",
       "\n",
       "                         Receive Counter  Publish Counter  \\\n",
       "Processed Time                                              \n",
       "2017-11-22 09:15:30.000     2.177784e+11     2.177784e+11   \n",
       "2017-11-22 09:15:30.500     2.177853e+11     2.177853e+11   \n",
       "2017-11-22 09:15:31.000     2.177924e+11     2.177924e+11   \n",
       "2017-11-22 09:15:31.500     2.177998e+11     2.177998e+11   \n",
       "2017-11-22 09:15:32.000     2.178067e+11     2.178067e+11   \n",
       "\n",
       "                         Receive by DL Counter  BidAskChange Flag  \\\n",
       "Processed Time                                                      \n",
       "2017-11-22 09:15:30.000                    0.0          6291456.0   \n",
       "2017-11-22 09:15:30.500                    0.0          4194304.0   \n",
       "2017-11-22 09:15:31.000                    0.0          6684672.0   \n",
       "2017-11-22 09:15:31.500                    0.0               32.0   \n",
       "2017-11-22 09:15:32.000                    0.0          6694496.0   \n",
       "\n",
       "                                TempTime  \n",
       "Processed Time                            \n",
       "2017-11-22 09:15:30.000         09:15:30  \n",
       "2017-11-22 09:15:30.500  09:15:30.500000  \n",
       "2017-11-22 09:15:31.000         09:15:31  \n",
       "2017-11-22 09:15:31.500  09:15:31.500000  \n",
       "2017-11-22 09:15:32.000         09:15:32  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resample the data to a 500ms time frame- replicating the paper as it is\n",
    "#data.head(10)\n",
    "resampledDF = data.resample('500L',closed = 'left', label='right', how = 'last')\n",
    "\n",
    "#Exlcude first and last 30 seconds of trade data\n",
    "import datetime\n",
    "openTime  = datetime.time(9,15,30)\n",
    "closeTime = datetime.time(15,29,30)\n",
    "resampledDF['TempTime'] = resampledDF.index\n",
    "resampledDF['TempTime'] = resampledDF['TempTime'].apply( lambda x: x.time() )\n",
    "resampledDF = resampledDF[(resampledDF.TempTime >= openTime)&(resampledDF.TempTime <= closeTime)]\n",
    "\n",
    "#Number of instances where there is data within the 1 second window\n",
    "testDF = resampledDF.dropna()\n",
    "NACount = len(resampledDF) - len(testDF)\n",
    "\n",
    "print('Nb of NA count in the data are %f'%(NACount))\n",
    "\n",
    "#Fill all the NA values with the last observed values\n",
    "resampledDF = resampledDF.ffill()\n",
    "#resampledDF.dropna(inplace=True)\n",
    "resampledDF.head()\n",
    "#resampledDF.to_csv('ResampledFutures1Test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last available values are used where the no entry is found for the window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44881"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(resampledDF)\n",
    "resampledDF.to_csv('ResampledIndexFutures.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c126b91d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c12578f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#resampledDF[['bidVol1', 'askVol1']].describe()\n",
    "resampledDF[['Bid Quantity', 'Ask Quantity']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Feature VOI\n",
    "resampledDF['deltaVtB'] = 0*(resampledDF['Bid Price']<resampledDF['Bid Price'].shift(1)) + (resampledDF['Bid Quantity']-resampledDF['Bid Quantity'].shift(1))*(resampledDF['Bid Price']==resampledDF['Bid Price'].shift(1)) + \\\n",
    "                    resampledDF['Bid Quantity']*(resampledDF['Bid Price']>resampledDF['Bid Price'].shift(1))\n",
    "resampledDF['deltaVtA'] = resampledDF['Ask Quantity']*(resampledDF['Ask Price']<resampledDF['Ask Price'].shift(1)) + (resampledDF['Ask Quantity']-resampledDF['Ask Quantity'].shift(1))*(resampledDF['Ask Price']==resampledDF['Ask Price'].shift(1)) + \\\n",
    "                    0*(resampledDF['Ask Price']>resampledDF['Ask Price'].shift(1))\n",
    "resampledDF['VOI'] = resampledDF.deltaVtB - resampledDF.deltaVtA\n",
    "resampledDF['DeltaVOI'] = resampledDF.VOI.diff()\n",
    "#resampledDF.to_csv('ResampledFutures1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Don't resample\n",
    "data['deltaVtB'] = 0*(data['Bid Price']<data['Bid Price'].shift(1)) + (data['Bid Quantity']-data['Bid Quantity'].shift(1))*(data['Bid Price']==data['Bid Price'].shift(1)) + \\\n",
    "                    data['Bid Quantity']*(data['Bid Price']>data['Bid Price'].shift(1))\n",
    "data['deltaVtA'] = data['Ask Quantity']*(data['Ask Price']<data['Ask Price'].shift(1)) + (data['Ask Quantity']-data['Ask Quantity'].shift(1))*(data['Ask Price']==data['Ask Price'].shift(1)) + \\\n",
    "                    0*(data['Ask Price']>data['Ask Price'].shift(1))\n",
    "data['VOI'] = data.deltaVtB - data.deltaVtA\n",
    "data['DeltaVOI'] = data.VOI.diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    44880.000000\n",
       "mean         7.601939\n",
       "std        436.558354\n",
       "min      -7650.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max      12825.000000\n",
       "Name: VOI, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c16f019d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'VOI Plot -Resampled Data')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1680fd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c207bf150>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Last Traded Price')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c13ccfd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c26ae8cd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Delta VOI Plot- ResampledData')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c26ae1410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampledDF.VOI.describe()\n",
    "\n",
    "resampledDF.VOI.plot()\n",
    "plt.title('VOI Plot -Resampled Data')\n",
    "plt.show()\n",
    "resampledDF.LTP.plot()\n",
    "plt.title('Last Traded Price')\n",
    "plt.show()\n",
    "resampledDF.DeltaVOI.plot()\n",
    "plt.title('Delta VOI Plot- ResampledData')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c26aff850>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c26aff850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##ACF function for the returns at different frequencies\n",
    "data.dropna(inplace=True)\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plt.figure(1, figsize=(10,10))\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "a=plot_acf(data.VOI, ax = ax1, lags = np.arange(1,11), \\\n",
    "        title = 'VOI Autocorrelation')\n",
    "\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "a=plot_acf(data.DeltaVOI, ax = ax2, lags = np.arange(1,11), \\\n",
    "        title = 'DeltaVOI Autocorrelation')\n",
    "\n",
    "a=plt.suptitle('VOI Autocorrelation Structure-RawData', fontsize = 24)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27968810>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27968810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACF function for the returns at different frequencies\n",
    "resampledDF.dropna(inplace=True)\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plt.figure(1, figsize=(10,10))\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "a=plot_acf(resampledDF.VOI, ax = ax1, lags = np.arange(1,11), \\\n",
    "        title = 'VOI Autocorrelation')\n",
    "\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "a=plot_acf(resampledDF.DeltaVOI, ax = ax2, lags = np.arange(1,11), \\\n",
    "        title = 'DeltaVOI Autocorrelation')\n",
    "\n",
    "a=plt.suptitle('VOI Autocorrelation Structure-ResampledData', fontsize = 24)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2.1 in Paper\n",
    "When the data was resampled with a sampling rate of 1s, we are not able to reproduce the results of the paper in terms of VOI correlation structure, however that is not true in case if we consider raw tick data. The resampling rate considered in the paper is 500ms, here we go for 1000ms since 500ms was again not leading to the desired autocorrelation structure.\n",
    "\n",
    "The time here used comes from 'SytemTime' in raw data- accurate time stamps would help."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    44879.000000\n",
       "mean         7.598766\n",
       "std        436.562701\n",
       "min      -7650.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max      12825.000000\n",
       "Name: VOI, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampledDF.VOI.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5382027228770695"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.*sum(resampledDF.VOI == 0) / len(resampledDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note here that for almost 50% cases, when resampled at 0.5 sec frequency, value of VOI is 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c27794c90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'PriceChange')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'VOI vs Contemporaneous Price Change - Sampled Data')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xIiIiogAxGCMiIiIKEIMxIiIiogAxGCMiIiIKEIMxIiIiogAxGCMiIiIKEIMxIiIiogAxGCMiIiIKEIMxIiIiogAxGCMiIiIKEIMxIiIiogAxGCMiIiIKEIMxIiIiogAxGCMiAEBPXwzrNm1HT18s6KEQEdWVUNADIKLg7RhIYO61yxHWNCRSKVx35nQsnDkh6GEREdUFZsaI6lxPXwzdvQMYTKSwK5bEYCKFy+5/mRkyIqIKYTBGVOe6ewcgGZeFNQ3dvQOBjIeIqN4wGCOqcx1jG6EyLkukUugY2xjIeIiI6g1rxojqXFtzFB1jG9EQTqbVjLU1R4MeGhFRXWAwRkRobQxj1ZJj0d07gI6xjQzEiIgqiMEYEQEwM2QMwoiIKo81Y0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGREREFCAGY0REREQBYjBGRACAnr4Y1m3ajp6+WNBDISKqK6GgB0BEwdsxkMDca5cjrGlIpFK47szpWDhzQtDDIiKqC8yMEdW5nr4YunsHMJhIYVcsicFECpfd/zIzZEREFcJgjKjOdfcOQDIuC2saunsHAhkPEVG9YTBGVOc6xjZCZVyWSKXQMbYxkPEQEdUb1owR1bm25ig6xjaiIZxMqxlra44GPTQiorrAYIyI0NoYxqolx6K7dwAdYxsZiBERVRCDMSICYGbIGIQREVUea8aIiIiIAsRgjIgAsOkrEVFQOE1JRGz6SkQUIGbGiOocm74SEQWLwRhRnWPTVyKiYDEYI6pzbPpKRBQs1owR1Tk2fSUiChaDMSJi01ciogAxGCMiAGz6SkQUFNaMEVEa9hsjIqosZsaIaMiDazdjyf0vs98YEVEFMTNGRADMjNiS+19mvzEiogpjMEZEAMx+Y2Et/S2B/caIiMqPwRgRATD7jSVSqbTL2G+MiKj8GIwREQDzbMrrzpyOhrCGlmgIDWGN/caIiCqABfxENGThzAmY2zmO/caIiCqIwRgRpWG/MSKiyuI0JREREVGAGIwRERERBYjBGBGVFDv4ExEVhjVjRFQy7OBPRFQ4ZsaIqCTYwZ+IqDgMxoioJNjBn4ioOAzGiKgk2MGfiKg4DMaIqCTYwZ+IqDgs4CeikmEHfyKiwjEYIyJPPX2xggMrdvAnIioMgzEicsU2FURElcGaMSLKwjYVRESVw2CMiLKwTQURUeUwGCOiLGxTQURUOQzGiCgL21SUB9ftJCI3gRXwi8hEALcB2BdACsDNSqlfBDUeIkrnbFPRFNHRHzfQ0xdjQFYknhBBRF6CPJsyCeBSpdSLItICYI2I/K9SamOAYyIih7bmKFZ2bWMQMUzOEyIGYU7/Xnb/y5jbOY7BLREFN02plPqbUupF6+ddAF4FwHd4oirCsypLgydEEFEuopQKegwQkckAngRwmFJqZ8Z1XwbwZQAYP378EXfffXfFx1eP+vr60NzcHPQwqEK8Hu+BhIG3P+yH4Xif0EVwwN5NaAzrlRxiTTNSCq+9vwspx3HURHDIvi3QNan4ePj6ri98vINz3HHHrVFKzcp3u8CDMRFpBvAXAD9SSv2/XLedNWuWWr16dWUGVudWrFiB+fPnBz0MqhCvx7unL4a51y7HYGLPmZUNYQ2rlizg9FqBHlq7GZdVyXQvX9/1hY93cETEVzAWaAd+EQkDuB/AHfkCMSKqPPusyswggoFY4bhuJxF5CfJsSgHwnwBeVUr9W1DjIKLcCg0iilnPsl5w3U4ichNkZmwugM8BeEVE1lqXXa6UeizAMRGRC79BBNs3EBEVLrBgTCm1EkDlK1eJqCzYvoGIqDjswE9EWYrpFJ+vfQO7zxMRuQu0gJ+Iqo+fqUa7LszuzN8xtjHnepb1MH3JWjkiKhaDMSIa4meq0Q6sAGAwkUJUF4gmuO7M6a5nXgIY8dOX9RBsElH5MBgjIgBmIPbEax9Al/RSTnuqsa05mhas2WKGAgyFy+5/GauWLMCqJQvSMkTrNm1HWNOGArHMbdY61soR0XAxGCMi7BhIYO61yxHSBP1xI+26waQ5DQnsqQtzBlY2O8CaMXFMWhCSa/pyJHA7JiMp2CSi8mMBP1Gds2udBhMp9MWMrOuVUujtj2Pdpu1oiuhZgZXNK8CyG8c2hDW0RENoCGsjqnFsqYJNnuBAVL+YGSOqc929Azl7zOgiOOWXKxHVzXqoRbM6sGx1N4DsmjGvAGskd58vxSoFrDkjqm8MxojqXMfYRuRaoTZmKAAK8aSZ/Vm2uhuPXDQP/XEj7WzKfMHHSO4+P5xgs5ZrzvycQcqzTInyYzBGVOfamqMYOyoMuNSB6QKEQ1pawX5Y07BlxwBaGyMY2xRB53h+wALFB5u1WnPmJ5vHjB+RPwzGiOpcT18MH+1OANCzrtM1gcpImw0mDVxw22pEdJ0fsCVQiyc4+Mnm1XLGj6jSWMBPVOc2bNkBlRlxWaIhHRcd1zlUfB8NaVBKIZZU2BVLYjCRwmX3v8yi82GoxRMc8q224Pc2lcATI6gWMDNGVPe8y/cTqRTOnT0J586ehO7eAewYSODCO17Erlhy6Da1MKVW7WrtBAc/2bxqyPhxmpRqBTNjRHWuvbXB9fKwJrjitKlDtVAzJo7BtPbRvj9gaz0jUenx28e42gMxwF82L+iMn3OalFlcqnbMjBHVuf644ZobC2mCax7ZiJZoaCib4LeNQ61nJGp9/JXgJ5sXZMavVk+MoPrEYIyozjVFdNfWFgNJ96LrfB+wtV64nW/8bNWwh58zSINqaVIN06REfnGakqjO9ccNiHjXjTmLru2pOwCeU2r5Crerffoy1/gfXLsZc69djvNueQ5zr12Oh9ZuDmiUlE/Q06REhWBmjKjONUV0z7MpASBumE1d803d2RkjtyWT7IxELUz/eWVUmiJ6TWf86lGtnRhB9YuZMaI6t2VH7lYDFx33MQDIWQztzBiddtNKLJrVkZWRyLeNauGVUemPG1XRqoEKU0snRlD9YmaMqO55T1FGQ9pQWwuvYmgAuOy+lxFL7skYOZdMsjMS6zZtr5mCareMSk9fjDVIRFQWzIwR1blp7aNdw7FoSMP1Z5k1NrmKoe947j3EkunXhTUN/XEDMyaOAQCs27Q95/RlNcrMqNRjDVK11/cRjRTMjBHVubbmKKLh9KWQJo1txAMXzs0KRDJbWgDA0ifeyNpm3HCvEVs0qwPLVnfnbItRzeqpBqkW6vuIRgoGY0R1bvXbPRhMGHC+HbzXO4C3P+xLCzYWzpyAqfuNxtpN2zFz4hh0jm/Buk3bEdF1xJLJtG1edFwnAGQVvDunL5siOvrjBnr6YjUV1ATVqqGSar09CVGtYTBGVOeefGMbwh6Xzzqgbeh3t0zJ3M5xWVOP0ZDkrDPrjxt4p6efWZcqxoapRJXFmjGiOjejo9X18r7BBLq27gLgvbQMgKw6quvPmpGzzszZIsLe1rfvW4cnX/+AtUlVgg1TiSqLmTGiOhcO6a6X3/r0u7j16XexeM4knPmJiZ6ZEq86Kq86M7tFhHNbsaTCV29/ESmlmCWrAn6XvSKi0mAwRlT3vBu+AsBtz7yHhdPbszIlsaRZ9wV411H5bREBALvjBgDWJlWLejpZgShonKYkqnPT2lshOXqNAcD6LTtx4fxOREMaorp5W00TnHbTyrxLAuVqETEqnJ2VYyPV6sGGqUSVwcwYEUHlyY7962OvIhrSoVQKKWUGY4OJ4s+ys7MuG7bsxAW3rU7rU8baJCKqN8yMEdW5DVt25r1N3FDYFUsibgDJVHrgZmey7AahXVt3+WoU2tYcxbFT9sb1Z+VvpMrmo0Q0kjEzRlT3cmfF8kmkUli/eQfOufkZAGbGLKoLRBNfxfj5apPYfJSIRjpmxojqXHtrYVOCmdVlC2e045pHN5oNQq2py5ih8i4E7sx2edUmebXUYIaMiEYSZsaI6tyy1ZvQUsDtRQDlSKb97qXNiITcv9d5NQr1m+1i81EiqgfMjBHVuaff2lbQ7TNKxhDWNcQN96lOt2L8QrJdw2k+yjozIqoVDMaI6ty8A8cN6+8NpfDpme1pl+mCrGJ8OzjasGUnwlr6W49XO4u25igWHdGRdtmiWR05s2I9fTHc+Oc38MmfLMd5tzyHudcuz9t+g4goSJymJKpzF3zqINz54Bue12dOS9rCukATwRc/ORn/ufLttOt0XcPt/3QUwiEdPX0xrOzaNjQtGTdSMHxmu3r6Yli2pjvtsmWru/H146e4BmQPrt2Mb9+7Flb/2KGWGWwkS0TVjMEYUZ3r7Y/nvN4tEAOAhKEAKPz6L28hs5++KIVz//N5RHUNccNASpm3t2u/wrogGgIiup5zqZ1CasZ6+mK4dNlaJLOb+7POjIiqGoMxojq39Ik3cOAw/t4l9kHMCtTibpERgIaQjqWf/ThaGyM5l9oppGZsw5adroFYrr8hIqoGrBkjqnNdH/RV/D4HkwamtbfmXWrHuXRSrqawALBzIOG6jbAuXOSaiKoaM2NEdW7BwXsD23orep/Ka+7ThZ8Fqx9cuxmX3bcu6/KQJnj84mPQOb6Q5h1ERJXFYIyozk1sa8bf8nS3CGvAolmTcOTkMXh8/ft44q8fQtNkqMlrLiFdoMOeujQ1hkNDZ0/mCrJsbc1Rz+vtVhmxZHqAFw1puP6s6QzEiKjqcZqSqM7NnDgm720UBHe98B6+sexl/GHjB4gbCofs24yQltmPP50uwN3nz4Zk3M5eQmnutcNvP7Fhy05oGesCjArruOHsGdi/rWnYfcaC7lcW9P0TUfkxM0ZU5zrHt6A5mvutIHNxcABYuyn/AuMigjGjIrjuzOm4zNFx/4pTp+5ZQgn520/09MVcM2jm9OTLQy0sbHHDwKX3rkNEH956lkGvixn0/RONVF7vKUFhMEZU53r6YuiPJVGOt4NkSuGUG5/CT8+egVVLFgy9+RXSssIrINkzPZkeiEV0M5MXS6aG1WfMuVKAn4Cx1IK+f6KRqhq/5HCakqjObdiyE/7L6QsXNxQuu/9lABg6e9Jvy4pcSyfZAZ3TqIiOy085FA0hPe1yrw7/mZxTgm7b97udUgj6/olGokKWY6skBmNEda+coZgprGnYsGUn1m3ajq6tu9DdO4BLTpiCSEhDU1T3bFmRKyBxC+hSSmFe595FrWf54NrNaTVs67fsKHpdzFIYzrqctYr1cVRu1folh9OURHXuoyI/+DTJXjR86DqkN4MdSCRxwW2rIQIMJlIIaUAyBUQ0IKEEV54+zXWaIFdAYvcguyxjuqFzfIvr5fnWs8ycErzmkY1DtW1+t1NKXvs3Uqcoq3HqiEaeav2Sw2CMqM79fsNWTPOZI9c1waiwPrS+ZGYwJgDOn3cADu9oHQoiYskkDIW02i77x3gKABSueXQjTjps37RFxe3sV66AxKsHmZ/eZE5eNWyHTWhNq3WrdCBU6H54qbZi5Uysj6NKqdYvOQzGiOrcfq0NwC5/t/3S3MmYMr4FDWEd3/1/r2BXLJl2vQLwm5Vv45jONjxy0Tzcuupt3LumG4ZXCs3BLt53y5DkCoi8epDl6k2WKV8GLsg36uHev9vxHF3C8ZVCISd0EA1Xqb7klBKDMaIRLl9WJJE0fG/r5qfehi5mhixueAdYT3X14KSfP4mkz3K0wUQKTRHdM0OyaskCzPDRDw3I3l8/WaFSfFvOdT9BZaa8jufS4xoqNgY/OsY2Im6kPw+rYeqIRq6gv2RlYjBGNILlq8N5cO1m3P58Ny493P82DQUYOQIxm99AzPb4+vdx7JS9h5UhydzfRUd0YNmabl91SMP5tpzrOAdZC+WVcYob+VdOqKSVXdvSprxDGqpi6oioUng2JdEIle8Ubvv6anHTE11oiuhFF9e67e9tz77n6xR2+yw+AHkXL/dzv/b9BH0avdf0a0Svnrd++xglHAG+rmmY2zkuwFERVVb1vCKJqKTyncLtdn2QdE3QHzfoDB1bAAAgAElEQVRw3ZnT0RDW0BINeba8ALLbIPjZH7dT2DNbWngty+TVdiHXcQ76NHp7+jXzeOp5lrGqJLdjFEumcOdz7wU0IqLK4zQl0QiV7xRut+srIaK715sZKYWOsY2YMXFM3ulCt6m/uZ3j8u5PZpbN71l8uaYa8x3noE+jd5t+XbHijYrdfz5mvVj243bTE2/g3NmTOFVJdaF6vhYTUcFyNcn0yorYH2729ZV22YkHw22W7AenT00bm9d0odfUH4Cs/V08Z1LOLJufzFW+qcZcxznfY1CMYhqj5jqeQWtrjuKi4zqzLo/oeuCNOIkqhZkxohrlpzA8X1F6+XvvZ/vx468hHNJgODJGTREdh7W3+vr7XG0Q3Pb368dPwYYtOwAIprWnN3Xw0wDST9uFXMe5lKfRF3syQLX3GTt39iTc9MQbiDnO+uDZlFRPGIwR1aBCmmR6ncIdVAG/oQAjkcq4TPn+4HULoGKG2RoDyN7flV3bPAMYPy0t/HbsznWqfClOoy+2MWot9Blra47i+rNmVF0jTqJKYTBGVINK0STTbRuVEtUFSgRRvfAPXmcABZg9ykQpnHbTyqxMkZ8AJl/mqlo6dhfzmNdKnzGgOhtxElUKgzGiGlSK9dWCKuAHANEEd/zTUXinZzdmThyDsU0RrNu03feH8MKZEzB1v9E45canAAAxQwGGygq0/AYw+TJX1RAoFPOY10qfMVu1NeIkqhQGY0Q1qBTZGnsbF9+9towjdXfk/mNx3q3Pm4FC0kAqpRAN6TBUCtefNWMou5Wr1mnLjgGEdC2tc3tmoFXKRYGDDhTcHvMrTps6VOTuNrZa6DNGRAzGiGpWKbI1QRTwA+ZySQDSMja7E2ZQdcmytZi632g8tv59LH2iCxHdvav9ZfetSyv4BrIDrbbmKBYd0YHbnt3Ts2rRrI6SLXNUac7HfP3mHbjmkY05i/m9gnZ9e/W0tiAiBmNENW042Zqevhi+de+6Eo9o+JIp4KRfPImkFafFkum1XgCw5P6XswKxaCi7bURPXwzL1nSn3W7Z6m58/fgpRS2vVMmljLzY4z7n5md8FfNXe58xImKfMaK69cybPWlL0FSTpEtJU66u9qPCOn6zeFZWoDScDvhBL2WUS6H7Vc19xoiIwRhRXXpw7WZ8856XAh1DoSvy2FOQHWMb0R9Ppl03mDSyeogBw6sZC3opo1xKWQs3HMU0oCWibAzGiOrM0MLMAZ9QlyowKWfXevX2x7P+NqWA3v541t8MpwN+tQQ8bsrR2b9Qftf0JKL8WDNGVGeC7C82HMtWd2Px0ZPx0Lotrtev3bQdneNbsi6f2zkON39uFgCFae2tRfUzC6K/WL4TB4Jst1FsA1oicsdgjKjOBNlfbDhUSuGUX65E2GN+c+bEMVmXDbcAP6iAx++4g2q3UYqmw0S0B6cpiUa4zLoeO+MTrrFXf8xQiCdT6I8bWdfpVoDm3M9SFeCXovi9kNqqaj5xwBb0FC5r1WikYWaMaATzyrAsnDkBmgAX3VX5hq+A+S0wX25OkN4HLayL59mfYV1wyo1PIRrSh/Zz/7amqsjeFJqdq4WsU5BTuNXYboRouGrsuzFR/cj17d9PZiBfhuX9HYNlG7uXiC747olTfN02M+zK1YZjMJFC3FBp+9kU0QMvwC8myxV01smvhTMnYNWSBbj9/NlYtWRBRQKiWsgaEhWDmTGiKpTr27/fzECuDMsv/vR6Wlf6SkkaCj/+w+sl2ZYugmhYQ9JQEChzfUpLWNPQHzewaFYHbnum+O77w1VMlquYrFNQqwRUumatFrKGRMVgMEZUZXKdqQbA91lsXhmWRNIIJBAD8k9NFsJQCkopAAqGyl4WqSmi467nN6Vdftfzm3x33y+FYrNchZw4UE/TdrWSNSQqFKcpiapMrmajhTQi9epF9dzbH5V1/MPVENbQENaweM4kRPTcnWEHrOlJEUFENzvxR0OC686cjtfe35k1tZkwFB5et7li01rD6Qfm58SBYqbtevpiGEgYNTm1Zx/PaEjSHmtmxajWMTNGVGXyffsvJDOQmWFZ2bUNP/9z9a5L+P1TDsGYURHMnDgGneNbsHB6Oz5zy3NI5lm2SSkAIlbVv2D1ux/h7ue7XW977e9fx09+/9eKZZDK2R6j0Gk7O4t28aEJfPPa5TWZRTOfCXsea6KRgMEYUZXJVzNUaD2RXdcz1Hm/StejBICfPP4aGiMhxJJJzNp/L6x+9yOENC1vMJa0WvInDLPthbNOLNNAwrxNJZuU5qqtyqz3KqT+q5BpO2cWzVBqKIs2nGNQ6Vo1ex9ijsVL2WyWRgIGY0RVKFc2pdhMSy103k8qYFfMXHfy6bes6VSjPOOthsLvzHqvRUd0YNmabt/1X4UU+5e6+D2IWjUW8NNIxWCMqEplZlMysxBuHz5uWZYNW3YAELS3NtRk5/1SCGkCgUpbj9OZQeraugtrN23H5LZRCIf0imR63E7UsE+sKGSJIb/BuVsWLWaYJzqUYuyVyFCxgJ9GKgZjRDXAKwvhDL5Wdm3DZfe9DF0TGCmFc2Z14K4XNg1NS+oCnDRtX/xh49ahab2RKBrScM6RHbjnhU1IpYBESiGkC4wUENIUGsOhtAzSD373StrZpSENCOkarjhtKg5rb/WcPhzO9CLgL1PpN+uTr8WEPbYrTpuKax7ZCE3MWitRCqfdtLLgrFZQGapCMoF2gG3XHxJVMwZjRFXOKwuxazCJax7diLCmIW4YSKYAwxFkZbavMBTw6Pr3Kzr2chkV0WCkgBMO2Qd/eu0DRHTzGFx03Mdw7uxJWNm1DXc/vwkJ63gMWimxaEjD0s9+AtPaR6OtOYqurbuyjlMyBSRTKXzvgfVoiugwlMqaPsz6fVYHlq32P70I+FsjtBRZn8xA/pITpiC+eQMAc4kpGKrgrFaQGSo/mcDMAHvxnEm4+ozDyz42omIxGCOqcm5ZCF0EP3xkI+LJVFXXgJVaNCT4wenT8masltz/MuIuRf8iQGtjeOgDfO2m7Tnvz14HM3P6MOv3ZwqfXmxrjmLRER1pQcMxnW144d3eki0x5BbI//SPf8U3pqXfrtCsVpDLIdn373VfbgH2bc+8h8VHT2aGjKoWgzGiKueahTBSiIQ0xJMBDSoAoyI6rjvzcEzcq8k1I9LbH0d37wA2fdQ/NA2XaTCRXiM1c+KYsozVT3DT0xfDsjXp7TdeeLcXj1w0D/1xoyR1a67TibqWFb4Xk9UqZ8uO4fAKsNdu2s5gjKoWgzGiKmdnIb7tqAf7wWlTcdXDG4IeWkXFkwYuvfdlRHQzE3PFaVPR0xfH0ie6IGIGWrqY07FedNmT7QKAzvEtWDxnUlorjHzb8MNPcONVd9UfNzCjREGiWyBvKIWxjWE410ModpmoSi+H5MfktlGul5cr8CYqBQZjRDVA2f9VYv5fYC0FtIcmZiCRGKGzlikFJJOpoR5T33tgfdZt8gVRhkLW2YNXn3E4Fh89Oe1syvWbdwzV48WSSTjityHRkDYUGLrVjJWyR1ix3KYTrzh1Kra/tQ7AnuOwbHV3RZeJKpeh+jhd0vrpLZ4ziVkxqmoMxoh8Cmox5j2NLhUAMyr44cMbEQ3pSDqihIawjgvnH4SlK97EbrfoocaV4gTQhrCWlhmzdY5vSfuwnjFxDE46bF909w7gpfc+wlUPv5r1N989+RB8fNLYoefD14+fUtDzo1J1V5nTid29A1j1VvptwpqGDVt2oLUxUlVTjoVw1sfZwrrgrvNnY9YBbQGOjCg/BmNEPpSzwWW+IM+97kcwmEgPKnbHDfziz69DofaWiInqgpTC0NmP5eTMPOU69vYUnJlJyw7G5nWOSwvgipmyK1XdVb7nUObYMo/yYNLABbetRkTXa3axcbfXSUNIRzhUeB81okpjMEaURzkbXPoJ8poiOmLJ9MAraSi4dUWIG2YvrWhIQ0gX9MdqI0MWM0ofQkZDgnOOnIh7XugeqrVzZp78BthudWWlnPYabt1VoV8U2pqj6BjbiIZw0mqLkoKRSiFmALGkeUZIJRq4ljrTzIawVMsYjBHlUa4Gl36CPPuDVtPMqvKoLhBN8MVPTsZ//OUt1+02hkNY+tlPYOdAAhfd9VLR46u0UuXEwrrg4gV7+o3d88KmPbV2lkIDbGddWTU1ES32i0JrYxirlhyL7t4B7BhI4MI7XhxahgoofwPXcmSag263QTQcDMaI8ijXN+58QZ5bDYwSwaMXzcOWHQOewVgilUJ7awOefP3DYY2vVl29cBr+cfb+rrV2dqBSTICdWVdWbn4yR8P5ouBcQL6SGaVyZpqrtd0GUT5a0AMgqnb2N+6GsIaWaAgNYa0k37jzBXn2B61TVDcL0Ke1t0JzmdeL6IIFB++DU3+5Ev/19NvDGl+tmjDWbG3gdvx0ETzx2gdoiuglD0B6+mJYt2k7evpiRW/D9uDazZh77XKcd8tzmHvtcjy0drPr7UrxRaFcz28vbo+LHUCWQltzFDMmjmEgRjWFmTEiH8rxjTvftEquD9re/rjr2YWplMJjI2TJo2KENGBa+2gA7sevP27gqoc3IJlSRbWj8FLKabdCMkelmpqrZEaJtV1E2RiMEflUjgaXuT4EXXtEnTbVarfQ67q95Mhd/zvNMQe14cwjJmBT7yB+ufx1hDQdhkrh+rNmAADWbdqOjrGNQ8dPE2B33AwA+qyTGpat7i5Jt/tST7sVOvVYaCDV0xfDQMJAT18s6/lWiWxSpWq7gmpFQ1QMBmNEAcv1Iej8oF2/eQeuecRsRDqYqKN1kFw89WYPnnqzBw1hDSIavjb/oKGC/bnXLk/7kL/i1Km48qHsBrHFdrvP/JAv9QkeHWMbMZDx+A4kkmiK6ENBplcbjnzsDN7FhybwzWuXB9bCotyZuHK2oiEqBwZjRFXO/qA65+Zn0rIvhKGTG25c/gam7teSlaH61r0vA1BIuhyyfFNjbpkVtw/5uZ3jSj7tJpJ+9mdKAaf+cuVQx/9iggtnBs9QCoOJVEVaWHgpVyaunCcIEJULC/iJaoBb0TPtkTAUvnTbGiQyoq64kULcZY2kSCh3kbpbAb3zQ35XLDkUzAAoaQF8d+8AGjIalaYUEEum32+hJwqUu3C+WtTLftLIwswY1aVqrCfJNSa3omfK5meB74gueOz/zvNsU+GVWbn5c7M8pyNLOe3m57EuZhq0Xgrn62U/aWThV22qO/naBpSyRcFwxuQch130HA3V3lJH1WJUREdDWMNPz56Rs1+YV2YFUDk/5EvVUiGz1UQ0JAjr6Y97McGFc7u6CKIhDRfO7xzWWKtRpVt1EJUCM2NUV/LVkwRR+Os2pkuWrYWuaVk1QlP3G40fPLgeT7/10dDfH9PZhmff6kGCiTNPER247MQpmNe5d97GrV6ZlWntrRXr8J6ZaVvVtW1Y92tnXed2jsOqJQvw5ydWADBw85NvYemKrhFX4O48fk0RHf3x7LNHiaoJgzGqK7nOfAMQSOGv25iSKSCZSiGW3DOOXYNJXP3IBquj/B7Pvv2Ra88xMmkCKAhu+OMb+Mnv/+pr7UavoKuS/bicBe7Dud/MLxhXnDoV23fFEEvqFV2LstLamqNY2bWNZ1VSTWAwRnUlVz1JudagBIZfD6Zrgh8+vMG1GD3hp1CqjumaIGEoJAwz8PjWveswdb/ROTNk+fq/2csIebWaKIdizj50y7pe9dB6fOOw9NuVey3KIPCsSqolrBmjupKrnqRchb/5atTamqNYNKsj7TI9Y60jM0PGoKtQmgDRUPrbXNxQOOXGp/DLP7+Rsy7QrgEDkFVD6He5oqC51b8lUkBKqYzLRl6BO8+qpFrCzBjVHa+sRzk6g/v5dt7TF8Oy1d1pfydQZhChFGKGgi6SNT1J+QncM4dxQ+GG/30dNz3xBq4/a4bn1JVXX7Faybj4PQt30ayOqhv7cPGsSqolzIxRXfI6823hzAlYtWQBbj9/NlYtWTDs+hI/387dbtMYDuGGs2dAiZkhi7l1LSUAQFgTfPaoSfjeKYcgqqcfx1GREC46rjPrbERbLKk8e3Z59RXbsGVHzWRc2pqjuOLUqXlvt2x1d0XPHq4EnlVJtYSZMaIMhdbmFFoPZn87t/8ukTQQM9JvE0ua6ydGdQ1xBmKe7D71967pRtzIPk6xpIFoSIPkmOL1qpfyqiEEpKYyLodNaEVzVB9ak9PNSKwZAzB0BvLaTdsxuW0UwiGdZ1VSVQo0GBORkwD8AoAO4Bal1E+CHI/N/pC0T4mupsagtkKalvq5bdfWXVi7aTtmThyT99T/4YylHLweL+e4AKSNMXN/nb8DwNpN2zF2VBi9uxNDb+KJpIF3enan/c2tq97BPc+/Z+aYFfCleZPREA5hRkcrxrU04O0Pd+HjHWPw3NsfIaxriBkpxBIpLLzxKWzZaWYi3MKEuKFw0V0vVegI1i4FIJlS8KqnMxTwr4+/lnMbA4kkEsn0QKWnL4YdAwkMZlw+mDQwKqzhHz4+Afe/uDmt9Ui1vUfYOsY2WsfIWzUHk37lWr5Kpazpfg3QRXDlwmk4rL21Kt/bqT4FFoyJiA5gKYC/A9AN4AUReUgptTGoMQF7XryAue5dVBeIJlV1SnQhvbD83PYHv3sFtz373tDvi+dMwoLW0o+lHLwer0VHdGDZmm6ENQ0DiSREBA0hHYlUCkfuPxZPdfUMbePg8U3469b+gu4362+sRMnNT73j+Td29ksB2LxzZE0JVSvDR8+PZAo469fPYvGcSbj6jMOHnlO6SFa9WcJQOOvXzw79fvyhe+OaMw6v6g/0zFrIuGGgtTGMaAiI6HrVB5N+5KvtsxkpwIDC9x5Yj6aIDkOpqnpvp/olSgVTFCwicwBcpZQ60fr9uwCglPqx19/MmjVLrV69umxj6umLYe61y9NevLaGsIZVSxYUdWp5KbNGbmP0Gpuf23Zt3YUTfvZk1v38+oRGnHjCgpKNJdc2ij0+uR4vKsylhydxwyusWrjvK0fjvFufL+g59advHltwNjkIztfaK6ufweGz5lT9DIAfXu9DN3/uCFx4x0vYFUvm/Pti39tryYoVKzB//vygh1GXRGSNUmpW3tsFGIydBeAkpdT51u+fAzBbKXVRxu2+DODLADB+/Pgj7r777rKNaSBh4O0P+2G4HBNdBAfs3YTGsO7yl+52DCTQ3TswVNfSMbYRrY3hko/Ra2x+btu7O4Hu3t1Z9zOxRcOY0bk/YAoZi5vhHp9cjxcVZnwjsLX66s8rbp+WBvT0xQp6TnWMHYWxo4b3uq60vr4+NDc3l+U9qtK83ocmtY3Cuz27s9p4ZCrmvb3W2I83Vd5xxx3nKxgL8quw2+lNWa8apdTNAG4GzMxYOaP7nr4YvumRaQlpwHOXzyso42N+W9vzAm8IJ7FqybHDbpWQOcaGsIZVC7PH5ue2XVt34ZsembF8x7qQsbj97XCPT67HiwrDzJjpvq/MwpUumbGmqI5EMuXadPdP3zy6JjJjTitWrMDhs+aU5T2q0jzfh5Ycg1TXNlxyz1rk6grj9z2rljEzVv2CbG3RDWCi4/cOAFsCGguAPbUVbosxixS2QHO5Gg4Wcrq2n9t2jm/B4jmT0v5u8ZxJWY0yhzuWTKU4Ps77bwib24rqgoawhlMOH+97O1T7Ju3V4Pu29lM7s9vF4jmTMOuAtqzn9I8+fRjuPP9oPPPd411fK7UWiNlGSlPUXO9DC2dOwK1fPMr17+yF42u9Xo5GhiC/Cr8A4GMicgCAzQA+A+DcAMcDwDwVesyoML56+4vYHd9zJlVDSC/o1O9yNhwsZJ06P7e9+ozDsfjoyWlnF65YsaLkY3Eq1fFxWxDY3safNn7gmsnwEtIk71lnVFkTRkdx6IRWaCqFP762Le26sAZ879SpmNc5Lu2M2MF4Eq9s2Yk5B+6FA/ZuwbNv9eCGP74OXQNSCrjouE6cfNi+6I8bWWfIAt7P6Z6+GM78xEQsnN6e9Te1aCQ1Rc31PjStfTQawlpa5iwaEvzqvE9gWnsrAzGqCoEFY0qppIhcBOAPMFtb3KqU2hDUeJymtbcOe7mQUnRzz1XcXkgvLD+37RzfUvQHSzFr5pWy271zrcD+uPmtfmXXtrQ575AGnDt7Eu55odu1gWo0pOFrnzoQP/9zV8H3T8MTDQl+cPo0XPPIxqyppocuPmboOfHQ2s1ZzxfnWXBuz+Gevhh+9qfXzR5k1nerpSu6cO7sSegcb2531gFtWWPKfE67na1Xy4EYUJ4VJ4Lk9T7ktZ/HTtkngFESuQu0SEQp9RiAx4Icg5tSvUkVmzUCgm8ZUQnDOT6ZnMcrbhhIptLbGogIvn78FCw+ejJO/PmTcCbMdAEe/b/z8NC68sySHzy+CW9+0J+zbmUk0wWIhnQkDAO6np6hGBXR8avzPoFjp+yDlmgo52uumOdLKRZ/H8kLTpfyNVjN6mU/qXaxYtdDqV68xWSNRvKbf6Zijk8mt+OVKWEobNiyA62NEYyKhNJOdx8VCeG193ch6WMNPwD4l4VT8cNHX3Vd8xAAwrrgWyd8DC+8tx3zPzYO//L4a3UbiAHAPV8+GuGQjqaIjtNuWpl2XUopTGs3m9r5ec0V+nwpxVRcKQK6alaK12AtqJf9pNrEYCyHoF68I/3Nv9Tcjpc7cf1w7oslPbvda2LWGdkawhoaIiGzgazh3r9IKeDHf3gdAPCnVz/wXBexHthF8bZ8GedSv+ZKkeUeSbVVRFSdGIxVIb75F8bteGUKaWYhb+aHcyyZRNxlyb7zZk/CcQfvjQvveimrzcHMiWNy3l/mSQBeGbSR6owZ++GYj+3tWuAexHTRcO9zpNVWEVH1CbK1BVl6+mJYt2k7evrMJXKG0zKiHrkdL7M9h2BUWEc0JPi3RTOHjt/CmROwaskC3H7+bFx+yqGu2+zcpxnHT93X9XHoHN+Sdrku5pmYTVEdIc09C1ZPubHHXvkb+mJJbN8dT3te29qao5gxcUxFn8/DvU/nc2bVkgUjrn6TiILFzFjAvAr1WXDqze0sU7fj9fXjp2DDlh0ABNPaR6dtw54Oa4roAF7Nuo9/fexV7NUU8XwcMi8HzOnSjVt24LsPrC/r/le7RAq46mFzidmQBoR0bUScgMKaIyIqFwZjAcpXqO/25l/qtS6LFdQ4cp1lmnm8VnZtw2X3rYMuGgyVwvVnzRi6rXP8i+dMwm3PvJd2P3FDZT0WNvtvzUAu3ZGT98qqMwNclpaocdGQ5toiJFMyBSRTqZKegFItrwEqHz7GVG8YjAWou3cAKuNTW6WUZ6F+tbS7CGochZxl2tMXw6XL1sKMF8yisEuWrcXcznFY2bUta/xHTd4Ll977clqA4XbShL3vKqUQMxQawhqSRgoigoaQjriRygrERpqwLvjuyYfgut//FbsTLgV3bn9TohNQquU1QOXDx5jqEWvGAtQU0RHLKO6OGco14+IMRHbFkhhMmNmGzHqccgtyHIUs37Jhy05kJm6SKeCZN3tcx3/IvqORueJV3DDSTpro2roL377P/Fv7cRtMpJBMmUX6u2JJX9miWpcwFMY0hny3AgFKcwJKtbwGqHz4GFO9YjAWoP64MbSmoq0hrKHf5fS+allHzm0cukhFxlHYWabu6al3e/pcj2N/3MB1Z06Hc0nOlAJWdW1DT18MN/75DZz8iycRr4Ngy49v3/9K1lmjAiBzSdOQhpKdgFItr4HME26odKrlMSaqNE5TBsgrU+B2ebW0u3AbR3/cwPotOzBj4hhf2yi2HqSQFgPT2lsR1iWrrcSNy9+E4XEcO8Y2Qte0oYxPwlC49N51EKCgNS7rgVu7DgXg5+fMxL6jG/BOz25MbhuFcEgvWd1POV4DhT4XOYVWXtXyPkdUacyMBaiQFhbV0u6irTmKK06dmnX5NY9s9JUpeHDtZsy9djnOu+U5zL12OR5au7mg+/fbYqCtOYobzp6BSEbD1VjSrO+KhiTrOHb3DiCip78kEobKGYg1hDXzjEGPlhb1ZjCRwqwD2nDWrImYdUBbSVtYlPo1UOhzkVNo5Vct73NElcbMWA6VOKOnkBYW1dLu4rAJrWiO6uiL7ZlO9VOgXaplnvy2GFg4cwLGjArjq7e/iN2Oqd+GkI6ln/04WhsjacfRT/NYW0gDfn3eERjX0oCOsY3o7Y/jlBufqvsM2kyf2dFileo1UMxzkStjVEa1vM8RVRIzYx6Gm8Gx+akvsRtSAvB92yDfoDrGNmZ3mfcxlVDpehDzOEraguGAOdZp7a1ZxzHzW3k0pGXVQNlCuoYL73oJ7/b0o605is7xLfjMURPLsh/VRhcgogsyV3laPGcSOse3lL2mqhSvgWKei5xCq5xqeJ8jqiRmxlyUKoNTSH1JLdWiFLs8TCU/zJzH00ilENbN1hP5xpr5rXxV1zbH0kkGUspc7sheIsl+XgDAstXdJd+PaqRpAkAhGtKRMAwsmjUJX5w7GZ3jW2rmeVzMc5HLIhFRuTAYc1GK6YhCe2KVIvirpGKmEir1YeZ2PKMhYOlnP4721kb0xw309MVc7zdzatq5nzsG4rjwjpewK7ZngfCwpmHDlp1468M+SMYMZVQXJFMKtT5zqQtgKHN/IAIjlUIiZbb+AID7X+rGJX8/paaex8U+FzmFRkTlwGDMRSkyOIUEdNVUi2IHI5lTe26KWR6mEh9mbsczoutYu2kHvvw/azyzNl5ZHXs/e/piWc+LgUQSX/rvF1zPLowbCiFdYNR4NHbiYeNxyQkHoz9uYMdAAhfe8WJWQGpP71XL89iPYp+LXBaJiEqNNWMuSnFGTyEBXSG3LWc9jrNO7rX3dxVdJ5dPuetBzGa66cczbhhY+kSX55lwfs6Ua2uOYtERHWnbTSn3Ng+A2erB67pa8tgrW7F9dxwAMCpsTtc6OVuD1FpNFWuTiKgaMDPmYZyldcMAACAASURBVLgZnEKmQfzetpz1OJlTTCmlqnaKKRf7GIkygyC7qe6F8ztx85NveS535Cc72dMXw7I16XVhI33pI9s/3vIcdE0wmEghs4vHolkdQ8eINVVERIVjMJbDcKcjStm2otz1OJWYKnXWY9n3WcqpSucxsqVSCo9dfAzGNkWwdEVX2u2dWRs/WR23Y1QvEoYayvJlBqB3P78Ji482C/hZU0VEVDgGY2VWSECX67blDpY6xjZiMGP6aTBplGyKyZnVG0gkhxbWLmWGz+0YRUM6+uMGOsenZx/jhoEL53cO3c7OTn77vpcBACmlcMVpU9OOrVvAFtYFSqmsdTD3XIesNiAjTdxQOOWXK/HTs9Jr7IiIyB/WjNWIStTjKKVy/l6szHos58Lapexinu8Y2d37Lzj2QACCm598K62H3Op3PkIsmUIsmULCUPj+A+vT6ubcaglvOHsGnrv8BNz0jzOhZ8zfGSk14gMxWzzJbvRERMViZqxGlLstRHfvABrDobSz5BrDoZJk3vJN75Uqw+f3GP37iq6hoAswp3vbWxtw27Pvpd1OAfjWvWvTpoK9puHmHDQOkrE4eZ3EYUOq+cxJIqJqxmCshpSzHqecmbd8ywyVcjrU6xjZ9WqbPuqHJukZrLCm4ck3trluT5AdYGROw/X0xfDEax+gIZy+RNRIFdU1fOvEKbj+j68j7pifrfYzJ4mIqhWDsRpTrnqczKySJkbJMm/ObeuaoD8jYCnVdKjz/pzjtuvVAKQV99sSqRSO/dg43Li8K+s6hdwBhr1tXQT98ZEfiAFAzEjhp398HZ85sgPLVnfzzEkiomFiMEZDnFmlbW+8hONLuIyNve0nXvsAVz60IS1wKdV0qBu3MyydoiGzh9wBezdDk+ypxStPP8xzXPm27UUDaup8TLsDv1MsmcKy1d145KJ56I8bPHOSiGgYWMBfBwppFGs3wcwsRh/udu1tH3fIPjBU4YuMF8ttQWjbqLCOG86egf3bmvDMmz2uNV4T9/Iel9u2G0IaGrxWF7fUUiAGZAditrCmoT9usGkqEdEwMTM2wpWrUWyx2630Ysu56tUGkwYuvXcdIrqW1dZjD++g1G3bCirn34wkrBEjIioNZsZGGGe2ys8SP8Xex3C2u3DmBDxy0TxcefpUPHLRPF9BXLHLQNnBX9QlW5VS5nTbrljSddmikAZMax/ted9urS6uP2sGrj9rOiL6yAvIQpo5rVvsEmFEROSOmbERJDNbdeH8zrI0ih1uA9pCs2rDze4tnDkBY0ZF8NX/WYPdCe8i+4awBsNIIazrMFQK1581Ayu7tuW8b7ezNx9cu3lEtbUYFdaRgsJ1Z05nd30iojJgMFZjnEsKZbZXyFwu6aYn3kDmlFkpppaG0waj0GWdCrm917EBzAxXCrkjpFRK4a4LjkY4pA/ty9xrlxe0BFXX1l349r3rRkyz12hIw68+dwSmtY8e2mcGYUREpcVgrIbkyhC5Zasiuo4vH3sglq7oKml91p6lg9ZBFw2G8r/dQrNqfm+fL3vW2x/HP3x8Au5/cTMiunmbRbPM1gwqpRAzFDRNcN6tz+OK06YCAHYMxLPuW4Ngw5adOHbK3ln3O5g0kEopz4L3WhPRgevPmj60r0REVB4MxmpEvgyRV7bq3NmTcO7sSSWfWjLjDTETb8p/fVShWTU/t893bH7wu1fSuusff+jeuOaMw9HWHMXioyfjlF+uBKCGWlR874H1aIroSKZSWdONuxMGLrhtNa4/y5yyy7zfXDQx20T46YQR1gWayNAqAZUW1gUiI6/ujYioGvku4BeR/UXkBOvnRhFpKd+wKJNbGwU7QwS4F5Pb2Sq7XUWpAjE7+IklU9gdNxArYF3CXOMs9vZex2bDlh14ZN3mrGWOHntlK3r74wCA/riBqJ79MuiPG4glFZRSWcX49v5u2LLDs21GprAu+Pk5M/Hs5SfgC3P297ydLsB9Xzka9331k/jN4lloiQbzfSlhqIIeVyIiKp6vd3oRuQDAlwHsBeAgAB0AfgXg+PINjZz8ZIiGs1xSrnqrTMMt4C90nPlu73ZsBpNmBsurzcTaTdvROb4FTREdMcM7+9QYDuHSv5+C6/7wV+x2NKo1gzDJucyTk3MUoxvDnrcbFQkhHNIxY+IY9PTFfG+/XAp5XAt5DhER0R5+v3ZfCOAoAM8BgFLqDRHZp2yjoix++3MVs1ySW73V6By3L8U6loWOM9ftM49N3EjBSKVgrrrkXsA1c+KYof0WqwltNKRlTQsmUinM6xyHn/z+tazLp7WPxsIZ7Vi2ujvv+OOGwqX3roMArm00bLvjSTRF9KHfFxy8Nx5bvzXv9svF7+Narn52RET1wG8wFlNKxe0aEhEJwetTjsqmHAuFe9VbLT2uwfNvKt241Q/nsdkxkMCFd7yIXbGk620Xz5mEsU2RrKWMlFK4/ORD8G9/ej1tvzrHt7juLwD8bu2WrO03hDT83wWd+MWfuxB3ZN1yBWE2QwGn/nIlzjmyA/e80B1IzVhIFzSGdN+Pa6FnyBIRUTq/wdhfRORyAI0i8ncA/hnAw+UbFnkp9ULhXlOO8RxTd0B68NMU0dEfN9DTFwv0w9c+Nm7TexFdcPkph2Je5zh0jm/Buk3bs/Y7GtIx+8A2rFqyIGu/3ALhdZu2I6IL4hkxX0opzD5gr7zH0EssmcJtz7yX/4Zlcvf5s4fae5TjDFkiIkrnNxj7DoAvAXgFwFcAPAbglnINqhaMlPoYrynHiEtRu5O9/+s378A1j27MOT1V6WPllblzjivXVGtbcxQru7bhMkfrjuvPmoGFMyekjb9jbKNrP7ErT5+GcEhHVBfEaqzPxTGdbZh1QFvW5fZjaAeozseyFNPWRET1zFcwppRKAfiN9a/ujaT6GK/ARd/+huff2Psf0gR9ZmGW5/RUUMcq35RurqnWnr4YLl22FuYMobl/lyxbmzXt5tyGrgkShsKVp0/FZ2fvj56+WNaC6LXghXd7szKc9mNo92NrCJuBuv1YVuO0NRFRLfF7NuUryK4R2wFgNYB/UUr1lHpg1Wok1se4BS4rVrgHY879d+Ocnir1sSo0w5ZvStdtv3v6Ynh43RZklmolU0hr9pprGzazxtI7IAvrgp8tmok5B7Xhzufew01PdCGia4gZKaRSqbQxhDRkjakcMqcX3R5v+2fnY1mOekYionrhd5rycZgpgjut3z9j/X8ngN8COL20w6pe5aiPqYYpT7+1aG777+ScnirlsSpXhs2530NnV3re2j2wcjt23b0DaAjpSBjuJxEAgK4J5hxkTgkeO2VvnHzYvuiPG2iK6DjtppVIOqb+8gV2pZI5vZjr8c58LAutZ6yG5z0RUTXwG4zNVUrNdfz+ioisUkrNFZHzyjGwalXq+pham/J0238AaIroMJRKm54q1bHq6YvhsvvWIZZURWfY8n3w58v4AUB7q/e4M7fvtu9mV31zmapEKoUrTpuKO557D0utjJjz8XdO+8WSBk6f3o4H120p+5qXmdOLXo83UF/PeyKicvIbjDWLyGyl1HMAICJHAWi2rvP+6j8ClbI+phanPN32/4rTpuKw9tasQKdUx+qO595DLJkehBSSYfPzwZ8v4xfVBf2Opq9+tu+27/ZU3vrNO3D1wxuG9stuYWE//va03/d/tx6Pr38f97+0Oe9+DtcB4xqzjovzMXSrGauX5z0RUTn5DcbOB3CriDTDbCa+E8D5ItIE4MflGly1KlV9TK22BChk/4d7rHr6Ylj6RFfW5XHD8JWV8fvBnysDBACiiev95dp+rn0/5+ZnsgJMYM8yTq2NEbz94S48vv79vPtYKoMefdnc2pjU4/OeiKhc/J5N+QKAw0WkFYAopbY7rl5WlpFVuVL0+6rllgCF7P9wjlV374BZ1J5RvX7RcR8raQ+stDMjRTCYMKBpYtZ95cjoFRNYdPcOIKS5V6bZyzglDYVKd8UI6brndaXsb1fLz3sionLwezZlFMCZACYDCNmd+JVSV5dtZHWALQHyc/vgjoY0nDt7UtF/7/XBv3DmBOwaTOKHj2xEY0RHwkjhy8ceiHNnT/J8THJt32v6cv3mHUMtQTL3a88yTpUnFVpUg897IqJ0fqcpH4TZymINgFj5hlN/qrGTfTUZ7gd3IX/f0xfDNY9uRDyZGuqqv3RFV87Az2v7AFynL6fuNxrXPLoxaztfm38g5hw4LucyTuW2aXvlXtpshUFEtIffYKxDKXVSWUdSx+yO70GdXVaqFgPlalUw3A9uv39fbC2T11JJbtta2fUhNEmfomwMCQ4a14z21oacdWvlNipS2fsr9dJeRES1ym8w9rSIHK6UeqWso6lTQZ5dVqoWA27bKWXmY7gf3H7+fji1TJnbd9vWYNLAvz72KjJPyhxIKlz+wCsQEZxzZAeWre5GIplKqxnTBPDT1SIkgMt5Ab4cOWmv4v6QiIiGxW8wNg/AF0TkbZjTlAJAKaWml21kdSSos8tyBYHD3c4ly9ZC17Ss/lnVrJS1TJnbihspGKkUvNqYxQ0FQOGu5zfh8YuPQX/cQCJp4J2e3Zg5cQzuf7Eb//GXt3Lepy4wTzrQJGe/NC8b399V8N8QEdHw+Q3GTi7rKOpcUGeX5QoCh7udZApIplJZ/bMqMS3lNl2aeZnXlGopa5mc29oxkPBVD5YwFLbsGMCxU/ZBT18M4ZCOsU0RzDloXN5gzFCAYSgUexpmY8Tv2wEREZWS39YW7wKAiOwDoKGsI6pDQZ1d5hYExg0zCOwd5nYyVaqPlNt0qQLSLls0y5wK9JqaLWUtk72tnr4YBhJ+C/MlbT/ihoF/mncAdPGOs3QBwiGtqIyYbfqE1qL/loiIiue3tcVCADcAaAfwAYD9AbwKYFr5hlZfgji7zA4CL713HRLWp7yRSmFV1zaMLmI7lzmCh5TC0DaBymT63KZLv33fOgCCWHLPZbc98x4AVLw+z8/6kroAo8IavpyxH/+x4i2EdYEylOsaATecPR3feWD9sMa372gW0xMRBUHzebtrABwN4HWl1AEAjgewqmyjqlNtzVHMmDimomeYze0cB2f/0WTKDE6MAtdAXDhzAlYtWYDbz5+Np79zPG44ewYawhpaoiE0hLWKZPrs6VInXTToHg1WbcVMzRbKXjg8ky7mi9BaYQjhkIZzb3nOdRsJQyGkZ++LJsABe7fgujP/f3v3Hid3Xd97/P2Z2UvCbkKWBAIkQbEJngYIe3S9YJAmgXIrTXpM2kNbHnB6UKqVammVqJzaCj3nQYLKEdG2oDw0Vo0IxSBCD4GYw0VAQs8mEFRIBckGQRMSzOayl5nP+WN+s5mdnevuzHxnZ17PxyOP7Pzm9/vtd+a7s3nne12kthzPl+qJF18f97UAgPErdZDIkLvvMbOYmcXc/YdmtqaqJUNNpFa4j2tg+EgXWnrAebkyu/dCtPTl6i5NeFLywgGlFq12+bpyE64oQJmk5JFuxjz9kS3xVMtjpqRLu/cf1vLuOXr9wKD+/vtj1zErRWu81P+bAQAqqdTfvvuifSkflvRNM/uCmmyD8EaVb/JAWwX+YS6lpW9P/4C27tynPf0TX3A03V2a2SJ346ozdOOq0ccuO/OkmrfapcuWq+VqOOnKWnpM7XFTa65zE0nlavy6Yt3T+vSGZ3RWmTNhR33P1vzbIQEAqqfUlrEVkg5LulrSn0o6WhJbITWAfJMH4vteqPr3rtQaZ5nytchlH/voOafUfPX35d1z1NEW1xXrnh51POkas/emxUz3X3WW7n/2Vd3ywx1qi8d0eDihRDL/npXrHn9ZyxedWPKaZNl+a9ZR5V8EAJiwUmdTHsh4+PUqlQWB5AowmzdXN4xVc6HbXLMhs49VcsZkOTsP7D04VPR+7S2mtSsXqaujTWefcqwuPO14vfLGIb3/61uKrlrx8Au71dHWMq4tlX7KOmMAEESpsynfJ2mNpOOUGtySXvS1nEl3qGO13pom1EK3lZavdS9fQOueN6Pg/Y5qi+ufLn2b9h4c0uI1m0buu+ytx0YLwxZ29oJZuvWRwuuR5XNMx+R53wGgkZTaTblW0u+7+0+qWRg0j1AL3VZSvta9/YeHdf0PnsvZ/Tp/9jRdduZJI8trZEu668Sjp+rKbzw96r73Pfta0fJcduZJ6jl5ptauXKSP37ltTNdnMRedfnxZ5wMAKqPUUdqvEcRQSbkG29diIH0l5V5Kw/SZe5/T4aGk9g8M6/BQUtfctW3UBIXrVpyuB68+W59dtUifuvA/jXkPDgwmxty3kAXHdujBq8/WdStOl5Tqdr7tsp6R5TJKtbXvjfIuAABURMGWsah7UpK2mNl3JH1Pqb0pJUnu/q9VLFvNlTP2p5LXVlK9lKMUIZa/mIjs9zbnDgbDCbW3xjWYMWQrJtP2V36js085duRYV0ebFsyeprldU7Xy7XPHbNVUbEeDTDv3pdZI27pz38g9Tj1x+pgZmsU89wphDABCKNZN+fsZXx+UdF7GY5fUMGFsIjP7qjErcDzqpRzlqPVYtfHK996uXblIf31Hr0Z6BM10eGj0OmAHhxL6wLotunFV6ppi9ZQ9w3WgyCzK4URSF938iFriMSWSPvJ9et40Uz/6+Z6SX+Nx0+q/HgCgERUMY+7+Z7UqSEgTmdlXzVmB5aiXcjSiQu/t4vmzFI/FNBy1ZA0lXK1xU1vcNZiRyQaGU92VC0+YXlI9ZbYadrTFdd5ND+ctXyoI+shisH/z3a1aeMJ0PfFi6UFMkk47kb0pASCEgqNKzGytmX0wx/GrG2kF/lxjf0rdImci11ZSvZSjERV6b1M7GIx+bkpLXJ+66Ld1VFt8zDW9O/eVXE/pRXO7OtrK6nIcSri+9eQvyl5r7MmXytkeHgBQKcWG+F4s6dYcx78g6fcqX5wwJjKzr15mBdZLORpRofc233NnzT9WQ1lbSh0eTqh73gwdGhq9BtihoeGC9ZQr8BXztR/9oqzzJemoNrZDAoAQiv32dXcfM5I4Ojb+HYnrzERm9tXLrMB6KUcjKvTe5nuuq6NN7qObptKPLauZK/txtrldU5VUec1c5e8sKp3727PHcRUAYKKKDeA/aGYL3H3UcuxmtkBSQ/V/TWRmX73MCqyXcjSiQu9true27tynqa2jV8Kf2tqi3p37NKUlrqHEkeNTWuIFF7ud2dmuG1edMXqiQAaT1BK31ISARELxeOzIhuNl2HeI7WYBIIRiYezTku43s3+QlN5Qr0fSJyX9VTULFsJEZvbVy6zAeilHIyr03mY/l6/7snvejHF1J6cD3wPbX9Un73521HMu6dvvf5daW+LqaIvr4lseLeNVHTGLnxsACKJgN6W73y/pDyQtlfS16M8SSSvd/b4qlw2YtPJ1X86fPW1CXeILTzxaU7JWc53SGlNrS1xnzJsx6v7ZEwgKMUln/tbMcl8mAKACim6H5O7PSrpcksys0937q14qoAHk69ostzt5y4t79PALu3X2glk6+djOnOdktqyl77/9lTd0+e1PlTnaDABQa6VuFH6mpK9K6pR0kpmdIenP3f0vqlk4YLLL17VZanfypV95Qo/uSK0XdvOmHXrv/JmjFoRNLxqbfa+Zne068eippQcxs0m3STsANIpSNwr/35LOl3SPJLn7VjM7u2qlQl6TabsjTMyWF/eMBLG0R3bs0UfPWaB7rzpLvTv3qTvqmsyld+e+kr+Xu2toOFH8RABAxZUaxuTuO7Om4PObu8ZCbHdE+Ku+fO/xwy/sznn+bY+8qP/7wq+L/hy8fmCwrHK8tOegek5m3BgA1FqpYWynmb1HkptZm6SPSPpJ9YqFbCG2O5qMe11ONoXe47MXzNLNm3aMuWbTT1/TUFIFfw729A/o8w8+X1ZZ3jzzqAm8EgDAeJW65PYHJX1Y0hxJfZK6o8eokVpvd5RI+kj42z8wrMNDqb0V9/QPVOX7NaPMgJ1+jz/23a3a8dp+SVLPyTP13vmjW6q65x6tKa2j/w+V6+cg189LocmVJqm1pfTZlwCAyimpZczdd0v60yqXBQXUerujwURSrbHYSOuLdOQf/XrprpzsXajpwJT5Hg8mXBd98VF9dlWqhewb73/3mNmU7/pfD466T67tlHL9vLhMyjOk38zYOgsAAimpZczMvm5mMzIed5nZ7dUrFrLVerujtnisrve63NC7S4vXbNKlX3lSi9ds0j29u0IXqWy5ApMkDQ6PbSH76/PeOjKeq9TtlJa99bhRjy887Xi1xMae2xrTpA20ANAISu2mXOTuI1Oz3H2vpP9cnSIhn+Xdc/TY6mX6l/e/S4+tXlbV8VvxmNXtXpe5uvcmYxdqOmC3xccGpHQLWXbI7Nt7SPGsQBWP2ahuyg29u/SeGx7Sfc++Ouq8e7b+UulLM+8wlJQODLIVEgCEUuoA/piZdUUhTGZ2TBnXooJqud1Rve51mat7r966UEu1vHuOFp4wXRd98VENZm08OTicHDM4v6MtPmbfycNDyZFlKXa8tl8f/+5WDSZyd0emj2c/u6d/UDte2593mQwAQPWUGqg+J+lHZnZn9PgPJf3P6hQJ9aQe97qs9fi5aps/e5o+u2qRPpYjRGWHzAODCbXENGbD8D/56o91yTvmav1TfXmDWDG9O/cRxgAggJK6Kd19naSVkl6T9CtJ73P3b1SzYEA+tR4/VwvLu+fovo+8V20toz+S2SGzoy0+JohJqVa0dY+/PKZ1rRxdR7WO+1oAwPgVbBkzs+nu/puoW/JVSd/KeO4Yd3+92gUEcqnXLtSJSLeQFdrq6MBgQu1x00CJrV+tcekjy07RMR1tuv4Hz6k1FtOhoeGcgW7vwaFKvRQAQBmKdVN+S9LFkp7W6GEm6Tnyb6lSuYCi6rELdaKKhcy5XVNlMZNKCGNtLTHd95dnqaujTX17D+neq87SgcGEhoYTWvXPT4w5v3vejBx3AQBUW8Ew5u4XW2re/O+4+8s1KhPQ1AqFzHQX7TV3bZMnXQMJ15TWVNfmH/XM1R1b+ka1qj354uv6zL3PqS1uGkq4rlo6XxeednzOe3d1tFXtNQEA8is6gN/d3czulvT2GpQHQBGZrWcdbXEdGEyMtKJ99JxTRlrV/u3ZV3Xt956VJKVXrvjcxud108bc2yQ9sP1V/fG73lSrlwEAiJQ6m/IJM3uHuz9V1dIAKEmxLtq9Bwb1me9vz/lcviH+P999oAIlAwCUq9QwtlTSB83sJUkHFI0Zc/dF1SoYgPJkbjo+kEhKXt4SF+cvnF2lkgEACik1jF1Y1VIAmJDMXQkO5237KuzkYzsrXCoAQCmKLW0xRdIHJc2X9Iykr7o7+6YAeYTavDzXrgRTWmNKJl2t8ZgODyfkyfxdlHGzSbmDAQA0gmItY1+XNCTpEaVaxxZK+mi1CwVMRpndhOnZjJXYP7SUgJdv0/H7PvLekQH+ew8M5tx2SZKS7pN2BwMAmOyKrcC/0N0vdfd/lrRK0ntrUCZg0qnG5uV7+gd080Mv6D03PKRLv/KkFq/ZNGbj8LTsXQnaW2L68JL56upo0xnzZmhmZ/vIorJt8Rw3sLGblQMAaqNYGBtZkpvuSSC/dDdhpvS+kuOxoXeX3nPDJn1+4/MaGPaSAt7y7jl6bPUyfeDst0hy3frwz8cEOJfkGhu8LHoNAIDaK9ZNeYaZ/Sb62iRNjR6nZ1NOr2rpgEmikpuXp1vZBnJ0J2YGvHxdl1/evEMDw66B4dT/n665a5sWz58lSVp91zYN5Vi9P+mujpxNZgCAaiu2Aj+/nYE8ssdypVfGz7evZLHr03INxk8bSib17K439F9vfTzn2LRc12YGuHz3NTMdGEyM+70AAIxfqUtbAMiQb7B+qZuXFxrsn28wfnuL6W9/b6Gu/8Fzo5awSLd8zexsz3ntwHBCHW1xdXW05byvJDktYwAQTLExYwCyFBqsP7OzfWTA/Hiul3IPxv+b3z1FP/rEOTptztEFx6ZlXtseT40Ni8VMF9/yqB7bsVtrVy5SW8vYj32MljEACIaWMaBMhboCS1mnq5TrC7WyFRubtrx7jhaeMF0XffFRSa7DQ0da0B5bvUzfuuKduuS2J5Q9JI2lLQAgDFrGgDKVM1h/T/+Atu7cN2oGZKnX52ply241m9Iayzk27cBgQu3xsS1o33zyZV16+4/VEj3XHjdNaY3VfJFaAMARtIwBZSp1sH6+cWHjGeyfqZSxabkC32AiqS/98AUNDB+ZTelm+sFVZ6nvJ0+P450AAFQCYQwYh2KBKNdekZkD7csZ7J/LzM72gtfkCnwfXjJftz7885ElLySpxUyvvHG4rO8NAKgswhgwToUCUSnjwooFqonKDnyS9KXNO0adc3AooQ+s26K1i/lVAAChBBkzZmY3mtlPzWybmd1tZjNClAOolkouAjsRmePO0q1l7S2jV+AfGE6qb++hCW3dBAAYv1AD+DdKOs3dF0l6XtInA5UDqIpSB9rX2vLuObrtsh4dlbWmGNshAUA4Qfom3P2BjIdPKLUJORpUvpXmG91Ex4VVy6knHq2kj94SycXSFgAQirmP3aeupgUw+76k77j7v+R5/kpJV0rS7Nmz375+/fpaFq9p9ff3q7Ozc8L3eePQkPr2HkptZqrUP/hHT22d8H1RukTSNZhIqi0eUzyW6qIcUy+dpqOnTwtaTtROpT7fmByo73CWLl36tLv3FDuvamHMzB6UdHyOp6519w3ROddK6pH0Pi+hID09Pb5ly5bKFhQ5bd68WUuWLJnQPfb0D2jxmk0ji45K0pTWmB5bvaxuWokaXaFtlzJbLJ/Z8viE6xuTRyU+35g8qO9wzKykMFa1bkp3P7fQ82Z2uaSLJZ1TShDD5DPRleoxVjldvsWW1wAA1IcgY8bM7AJJqyX9jrsfDFEGVF+9zChsFIVauXIpFIYf3bFb19y5TfGYKZF0wYV4vQAAD1VJREFUlrYAgIBCzaa8RdI0SRvNrNfM/ilQOVBF9TqjcDIqtrl4LvnCcEdbXB/77lYNDCd1cDChgeGkdrK0BQAEE2o25fwQ3xe1V68zCieb8XT55tt26ZU3DmkokTWb0l3bX3lDZ59yXFVfBwBgLPomUHXVXmm+GYy3yzdXGH74+V/nOdvyHAcAVFOobkoAZZhIl2/mKvySdOqJ09WS9cm36DgAoPZoGQMmiUp1+c7sbNfn/6hbH79zq+IWU8KTmnfMUbReAkAghDFgEqlUl292sHtmy+MVKB0AYDwIY0CTYiwfANQHxowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjQIPb0z+grTv3sRE4ANQp1hkDGtiG3l1anbVR+MITpqt35z51z5uh+bOnhS4iADQ9whjQoPb0D2j1Xdt0eCipw0ptMv7R9b3yjHMuO/MkXbfi9DAFBABIopsSaFh9ew+pNTb6I+5Z56x7/GXteG1/7QoFABiDMIam0IzjpuZ2TdVQMln0vN6d+2pQGgBAPoQxNLwNvbu0eM0mXfqVJ7V4zSbd07srdJGqJjN0zuxs19qVizSlNaZp7S1qi+e+pnvejNoWEgAwCmPG0NByjZu65q5tWjx/VsNtkp1rsP7y7jlaPH+W+vYe0tyuqfrCQ89r3eMvj1xz2Zknaf7saer7ScCCA0CTI4yhoaXHTaWDmCS1xmLq23uoocJYsdCZfq3XrThdl737zcymBIA6QhhDQ8s1bmoomdTcrqmBSlQd5YTO+bOnEcIAoI4wZgwNLXvc1JTWmNauXNRQrWJS84ROAGhEtIyh4WWPm2q0ICYdCZ3XZI0Za8TXCgCNhjCGppA5bqpRNUPoBIBGRBgDGkgzhE4AaDSMGQMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABBQ1jZvYxM3MzmxWyHAAAAKEEC2NmNk/S70p6OVQZAAAAQgvZMnaTpGskecAyAAAABNUS4pua2XJJu9x9q5kVO/dKSVdK0uzZs7V58+bqFxDq7+/nvW4i1Hdzob6bC/Vd/8y9Og1TZvagpONzPHWtpE9JOs/d3zCzlyT1uPvuYvfs6enxLVu2VLagyGnz5s1asmRJ6GKgRqjv5kJ9NxfqOxwze9rde4qdV7WWMXc/N9dxMztd0smS0q1icyX9u5m9091frVZ5AAAA6lHNuynd/RlJx6Ufl9MyBgAA0GhYZwwAACCgIAP4M7n7m0OXAQAAIBRaxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGAAAQEGEMAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIijAEAAAREGAMAAAiIMAYAABAQYQwAACAgwhgAAEBAhDEAAICACGMAAAABEcYAAAACIowBAAAERBgDAAAIiDAGNLg9/QPaunOf9vQPhC4KACCHltAFAFA9G3p3afVd29Qai2komdTalYu0vHtO6GIBADLQMgY0qD39A1p91zYdHkpq/8CwDg8ldc1d22ghA4A6QxgDGlTf3kNqjY3+iLfGYurbeyhQiQAAuRDGgAY1t2uqhpLJUceGkknN7ZoaqEQAgFwIY0CDmtnZrrUrF2lKa0zT2ls0pTWmtSsXaWZne+iiAQAyMIAfaGDLu+do8fxZ6tt7SHO7phLEAKAOEcaABjezs50QBgB1jG5KAACAgAhjAAAAARHGAAAAAiKMAQAABEQYAwAACIgwBgAAEBBhDAAAICDCGAAAQECEMQAAgIAIYwAAAAERxgAAAAIKFsbM7C/N7Gdmtt3M1oYqBwAAQEhBNgo3s6WSVkha5O4DZnZciHIAAACEFqpl7EOSbnD3AUly918FKgcAAEBQ5u61/6ZmvZI2SLpA0mFJH3P3p/Kce6WkKyVp9uzZb1+/fn3NytnM+vv71dnZGboYqBHqu7lQ382F+g5n6dKlT7t7T7HzqtZNaWYPSjo+x1PXRt+3S9K7Jb1D0h1m9hbPkQzd/VZJt0pST0+PL1mypFpFRobNmzeL97p5UN/NhfpuLtR3/ataGHP3c/M9Z2YfkvSvUfj6sZklJc2S9OtqlQcAAKAehRoz9j1JyyTJzE6R1CZpd6CyAAAABBNkNqWk2yXdbmbPShqUdHmuLkoAAIBGFySMufugpEtDfG8AAIB6EmQ25XiZ2a8l/SJ0OZrELNF13Eyo7+ZCfTcX6jucN7n7scVOmlRhDLVjZltKmY6LxkB9Nxfqu7lQ3/WPvSkBAAACIowBAAAERBhDPreGLgBqivpuLtR3c6G+6xxjxgAAAAKiZQwAACAgwhgAAEBAhLEmZGZ/b2a7zKw3+nNRxnOfNLMdZvYzMzs/4/gF0bEdZvaJjOMnm9mTZvaCmX3HzNpq/XowfvnqFZOPmb1kZs9En+kt0bFjzGxj9PncaGZd0XEzs5ujet9mZm/LuM/l0fkvmNnloV4PRjOz283sV9HONeljFatfM3t79POzI7rWavsKmxthrHnd5O7d0Z/7JMnMFkq6RNKpki6Q9GUzi5tZXNKXJF0oaaGkP47OlaQ10b0WSNor6YpavxCMT5F6xeS0NPpMp9eU+oSkh6LP50PRYylV5wuiP1dK+kcp9Y+7pL+T9C5J75T0d+l/4BHc15T6vZypkvX7j9G56euyvxeqiDCGTCskrXf3AXd/UdIOpT6w75S0w91/Hm1ltV7Siuh/Tssk3Rld/3VJfxCg3BifnPUauEyorBVKfS6l0Z/PFZLWecoTkmaY2QmSzpe00d1fd/e9kjaKf5Trgrs/LOn1rMMVqd/ouenu/ni0T/Q68bu8pghjzeuqqPn69oz/Gc2RtDPjnL7oWL7jMyXtc/fhrOOYHPLVKyYnl/SAmT1tZldGx2a7+y8lKfr7uOh4uZ911KdK1e+c6Ovs46iRIBuFo/rM7EFJx+d46lqlmqOvV+qX9/WSPifpv0vKNUbAlTu0e4HzMTlQf41lsbu/YmbHSdpoZj8tcG6+uudnojGUW7/Ue2CEsQbl7ueWcp6Z3Sbp3uhhn6R5GU/PlfRK9HWu47uVav5uiVrHMs9H/StU35hk3P2V6O9fmdndSnVDv2ZmJ7j7L6OuqF9Fp+er+z5JS7KOb65y0TF+larfvujr7PNRI3RTNqHoQ5v2XySlZ+fcI+kSM2s3s5OVGsT5Y0lPSVoQzZxsU2qQ/z3R2IIfSloVXX+5pA21eA2oiJz1GrhMGAcz6zCzaemvJZ2n1Of6HqU+l9Loz+c9ki6LZt29W9IbUTfX/5F0npl1RcMXzouOoT5VpH6j5/ab2bujscCXid/lNUXLWHNaa2bdSjVDvyTpzyXJ3beb2R2SnpM0LOnD7p6QJDO7SqkPclzS7e6+PbrXaknrzewfJP0/SV+t5QvB+Ln7cIF6xeQyW9Ld0WoELZK+5e7/ZmZPSbrDzK6Q9LKkP4zOv0/SRUpN0jko6c8kyd1fN7PrlQrqknSdu2cPGkcAZvZtpVq1ZplZn1KzIm9Q5er3Q0rN2Jwq6f7oD2qE7ZAAAAACopsSAAAgIMIYAABAQIQxAACAgAhjAAAAARHGAAAAAiKMAQAABEQYA9BwzOzNZnbIzHrNbLOZnZ/1/F+Z2Zejr081s01m9ryZvWBmfxstfCkz+29mdkv09dVm9nL6MQBUCmEMQKP6D3fvlvRtpXYXyHSJpG+b2VSlViu/wd1PkXSGpPdI+ovsm7n7TZI+Xd0iA2hGhDEAje5OSRebWbuUajWTdKKkRyX9iaTH3P0BSXL3g5KukvSJICUF0JQIYwAamrvvUWqP1QuiQ5dI+k60t+qpkp7OOv8/JHWa2fSaFhRA0yKMAWgGmV2Vl0SPJcmU2qM1F/aKA1AThDEAzeB7ks4xs7dJmuru/x4d3y6pJ/NEM3uLpH5331/jMgJoUoQxAA3P3fslbZZ0u460iknSNyWdZWbnSlI0oP9mSWtrXUYAzYswBqBZfFup2ZLr0wfc/ZCkFZL+h5n9TNIzkp6SxPIVAGqmJXQBAKAW3P1upcaIZR9/RtKSPNd8TdLXqlkuAKBlDEAjSkg62sx6K3VDM7ta0icl/aZS9wQASbLU7G4AAACEQMsYAABAQIQxAACAgAhjAAAAARHGAAAAAvr/aBkhRu6Uxt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c26ad7810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analyze relation with Mt, where Mt is the mid point price\n",
    "data['Mt'] = (data['Bid Price'] +data['Ask Price'])/2.\n",
    "resampledDF['Mt'] = (resampledDF['Bid Price'] +resampledDF['Ask Price'])/2.\n",
    "resampledDF['DeltaMt'] = resampledDF['Mt'].diff()\n",
    "resampledDF.plot(x = ['VOI'], y = ['DeltaMt'], kind = 'scatter', grid = True, figsize = (10,10))\n",
    "plt.ylabel('PriceChange')\n",
    "plt.title('VOI vs Contemporaneous Price Change - Sampled Data')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship is evident here, a positive regression slope is strong suspect. Next we do the regression to see what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>DeltaMt</td>     <th>  R-squared:         </th>  <td>   0.144</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.144</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   7565.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 08 May 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:25:10</td>     <th>  Log-Likelihood:    </th>  <td>  7609.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 44878</td>      <th>  AIC:               </th> <td>-1.522e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 44876</td>      <th>  BIC:               </th> <td>-1.520e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0019</td> <td>    0.001</td> <td>   -1.999</td> <td> 0.046</td> <td>   -0.004</td> <td>-3.78e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI</th>   <td>    0.0002</td> <td> 2.21e-06</td> <td>   86.977</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>15771.481</td> <th>  Durbin-Watson:     </th>  <td>   2.062</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>2135633.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.637</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>36.771</td>   <th>  Cond. No.          </th>  <td>    437.</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                DeltaMt   R-squared:                       0.144\n",
       "Model:                            OLS   Adj. R-squared:                  0.144\n",
       "Method:                 Least Squares   F-statistic:                     7565.\n",
       "Date:                Tue, 08 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        11:25:10   Log-Likelihood:                 7609.7\n",
       "No. Observations:               44878   AIC:                        -1.522e+04\n",
       "Df Residuals:                   44876   BIC:                        -1.520e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0019      0.001     -1.999      0.046      -0.004   -3.78e-05\n",
       "VOI            0.0002   2.21e-06     86.977      0.000       0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                    15771.481   Durbin-Watson:                   2.062\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2135633.610\n",
       "Skew:                          -0.637   Prob(JB):                         0.00\n",
       "Kurtosis:                      36.771   Cond. No.                         437.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c4244fd50>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27853210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Regression Analysis\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "resampledDF.dropna(inplace=True)\n",
    "X = pd.DataFrame(resampledDF.VOI, index = resampledDF.index)\n",
    "X = sm.add_constant(X) ## add an intercept (beta_0) to our model\n",
    "y = resampledDF[\"DeltaMt\"] \n",
    "\n",
    "# Calibrate\n",
    "model = sm.OLS(y, X).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()\n",
    "\n",
    "#Plot the regression line as well alongside the data\n",
    "import statsmodels.api as sm\n",
    "sns.lmplot(x='VOI',y='DeltaMt',data=resampledDF,fit_reg=True) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Corresponds to Figure 2.2 of paper\n",
    "R^2 observed here is almost similar to that observed in the paper. \n",
    "\n",
    "Next we add the other two Volume Imbalance Indicators mentioned in the paper and reproduce the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.5015375016712"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100.*sum(resampledDF.DeltaMt == 0)/len(resampledDF)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change in Mid Price is non-zero for only 36% of the cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add OIR to both resampled and original data, compute the autocorrelation Structures\n",
    "data['OIR'] = (data['Bid Quantity'] - data['Ask Quantity'])/(data['Bid Quantity'] + data['Ask Quantity'])\n",
    "resampledDF['OIR'] = (resampledDF['Bid Quantity'] - resampledDF['Ask Quantity'])/(resampledDF['Bid Quantity'] + resampledDF['Ask Quantity'])\n",
    "data['DeltaOIR'] = data['OIR'].diff()\n",
    "resampledDF['DeltaOIR'] = resampledDF['OIR'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1928d350>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1928d350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##ACF function for the OIR-RawData\n",
    "data.dropna(inplace=True)\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plt.figure(1, figsize=(10,10))\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "a=plot_acf(data.OIR, ax = ax1, lags = np.arange(1,11), \\\n",
    "        title = 'OIR Autocorrelation')\n",
    "\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "a=plot_acf(data.DeltaOIR, ax = ax2, lags = np.arange(1,11), \\\n",
    "        title = 'DeltaOIR Autocorrelation')\n",
    "\n",
    "a=plt.suptitle('OIR Autocorrelation Structure-RawData', fontsize = 24)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c18bfd190>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c18bfd190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##ACF function for the OIR-ResampledData\n",
    "resampledDF.dropna(inplace=True)\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plt.figure(1, figsize=(10,10))\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "a=plot_acf(resampledDF.OIR, ax = ax1, lags = np.arange(1,11), \\\n",
    "        title = 'OIR Autocorrelation')\n",
    "\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "a=plot_acf(resampledDF.DeltaOIR, ax = ax2, lags = np.arange(1,11), \\\n",
    "        title = 'DeltaOIR Autocorrelation')\n",
    "\n",
    "a=plt.suptitle('OIR Autocorrelation Structure-SampledData', fontsize = 24)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much stronger relationship than that seen in the paper on lag-1. DeltaOIR shows similar negative AC on lag1 as in paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2d710890>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'VWAP of Resampled Data')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y55xLvH6Tg6SbJFVKWhNXVibpYUnrwnNpKF8i6SVJqyStlHRqKH9nKIs9WiSdF+bdLGlj3LwFw7Wzyertc8r55gXHUlXfyrX3v5rocJxzbkA1h5uBc7uVXQUsM7PDgWXhNWH6ODNbAFwG3AhgZo+Z2YJQfibQBDwUt71/ic03s1WD3psUduGiqZw6p5w/r9mR6FCcc67/5GBmy4HuN0deAtwSpm8BzgvLNtj+GxnkA721kXwQeMDMfLChOJKYP6WYXfWttLR3Jjoc59wYN9g+hwlmtgMgPI+PzZB0vqS1wP1EtYfuPgzc3q3s2tAc9V1J2X29qaQrQnPVyqqqqkGGnryOn15CZ5fxgg+z4ZxLsCHvkDaze8xsHlFt4pr4eZImAccAD8YVXw3MAxYDZcCXD7DtG8xskZktqqioGOrQE+7k2eMAeHpD94qac86NrMEmh13hQB874Fd2XyA0R82WVB5X/CHgHjNrj1tuh0VagV8CJw4yppRXlJNJcW4mtU1tiQ7FOTfGDTY53AcsDdNLgXsBJM2RpDC9EMgCquPWu5huTUpxSUZEtY01jGHj8rPYVtOS6DCcc2NcRn8LSLodOAMol7QV+DpwHXCnpMuBzcCFYfELgI9KageagYtiHdSSZgDTgL90e4vbJFUAAlYBnzq0XUptsyry2VTdmOgwnHNjXL/Jwcwu7mPWWb0sez1wfR/b2QRM6aX8zP5iGEtmVxTw6NpK2jq6yMrwaxSdc4nhR58ks2BaCV0G//v0m4kOxTk3hnlySDLvPnoiJ80q4zsPvcbeRu+Yds4lhieHJJOWJr76vqNobOvkvhe3Jzoc59wY5ckhCc2fUkxhTgbrKv0+D865xPDkkKSmlOSyw09pdc4liCeHJDWlJJfNe3z4KedcYnhySFIzyvPZsreJ/eMYOufcyPHkkKQmFefQ0t5FbXN7/ws759wQ8+SQpCYV5wKws877HZxzI8+TQ5KaWByNXL6j1pODc27keXJIUhNDzWGLd0o75xLAk0OSmlycw8SiHJ7Z6Pd2cM6NPE8OSUoSp88t5/G1lTS0diQ6HOfcGOPJIYldtHgajW2d/ObZzYkOxTk3xnhySGInHFbGsVOLuXeVj7HknBtZnhyS3DuPGM+a7bVsq2lOdCjOuTHEk0OSu2DhVNIkvvPQa4kOxTk3hnhySHLTx+WxZMFkHnllF11dPpSGc25keHJIAYtnlFHX0uED8TnnRownhxTwtpllAPzBb/7jnBshnhxSwKyKAuZPKeLZTX5BnHNuZHhySBGleVnUt/jFcM65keHJIUXkZqbT0t6Z6DCcc2OEJ4cUkZeVTrMnB+fcCOk3OUi6SVKlpDVxZWWSHpa0LjyXhvIlkl6StErSSkmnhvJ3hrLYo0XSeWHeTEnPhG3dISlruHY2lZXkZVHd0OZ3hnPOjYiB1BxuBs7tVnYVsMzMDgeWhdeE6ePMbAFwGXAjgJk9ZmYLQvmZQBPwUFjneuC7YVt7gcsHvzuj1/SyPBpaO9jb5HeGc84Nv36Tg5ktB7qfJrMEuCVM3wKcF5ZtsP0/bfOB3n7mfhB4wMyaJIkoWdzVfVvurXKz0gFo7+xKcCTOubFgsH0OE8xsB0B4Hh+bIel8SWuB+4lqD919GLg9TI8DaswsdhrOVmDKIGMa1dIlADr8Kmnn3AgY8g5pM7vHzOYR1QCuiZ8naRJwDPBgrKi3TfS1bUlXhL6MlVVVVUMVckpIT4s+qs5OTw7OueE32OSwKxzoYwf8yu4LhOao2ZLK44o/BNxjZrGG891AiaSM8Hoq0OdlwGZ2g5ktMrNFFRUVgww9Ne1LDt4h7ZwbAYNNDvcBS8P0UuBeAElzQj8CkhYCWUB13HoXs79JidA/8RhRP8RbtuXeal9y6PI+B+fc8BvIqay3AyuAIyRtlXQ5cB1wtqR1wNnhNcAFwBpJq4AfAxfFOqglzQCmAX/p9hZfBr4o6Q2iPohfHOpOjUb7k0OCA3HOjQkZ/S1gZhf3MeusXpa9nujU1N62s4leOpvNbANwYn9xjHWx5NDhNQfn3AjwK6RTRMa+moP3OTjnhp8nhxSR5snBOTeCPDmkCK85OOdGkieHFOEXwTnnRpInhxQR65D2+0g750aCJ4cUsf9sJU8Ozrnh58khRaR7n4NzbgR5ckgRGWnRn8qTg3NuJHhySBGxmoMP2e2cGwmeHFJEXrifg98q1Dk3Ejw5pIjCnGikk7pmvxOcc274eXJIEcW5mQDUNnf0s6Rzzh06Tw4pIiM9jcKcDPY2tSU6FOfcGODJIYUU5WRS1+LNSs654efJIYUU52ZS3eA1B+fc8PPkkELGFWRR7zUH59wI8OSQQgqyM2ho9Q5p59zw8+SQQgqyM6hv8eTgnBt+nhxSSEFOBnXN7YTbcjvn3LDx5JBCZozLp7Gtk511LYkOxTk3ynlySCFzJxQCsKGqMcGROOdGO08OKaSiMAuA3Q2tCY7EOTfaeXJIIYU50RAafsaSc264eXJIIbGRWZtafWRW59zw8uSQQvKyopFZvebgnBtu/SYHSTdJqpS0Jq6sTNLDktaF59JQvkTSS5JWSVop6dS4daZLekjSq5JekTQjlN8saWNYZ5WkBUO/m6NDeprIzUynqc2Tg3NueA2k5nAzcG63squAZWZ2OLAsvCZMH2dmC4DLgBvj1rkV+JaZHQmcCFTGzfsXM1sQHqsOfjfGjoIcvxDOOTf8+k0OZrYc2NOteAlwS5i+BTgvLNtg+6/QygcMQNJRQIaZPRy3XNOhhz/2FOdmUus3/HHODbPB9jlMMLMdAOF5fGyGpPMlrQXuJ6o9AMwFaiTdLelvkr4lKT1ue9eG5qjvSsru600lXRGaq1ZWVVUNMvTUVuQ1B+fcCBjyDmkzu8fM5hHVJq4JxRnAacCVwGJgFvCxMO9qYF4oLwO+fIBt32Bmi8xsUUVFxVCHnhLK8rP9Ogfn3LAbbHLYJWkSQHiu7L5AaI6aLakc2Ar8zcw2mFkH8HtgYVhuh0VagV8S9Ue4PozLz/K7wTnnht1gk8N9wNIwvRS4F0DSHEkK0wuBLKAaeA4olRT7uX8m8EpYLpZkRFTb2HdWlOupJC+Tmibvc3DODa+M/haQdDtwBlAuaSvwdeA64E5JlwObgQvD4hcAH5XUDjQDF4UO6k5JVwLLQhJ4Hvh5WOe2kDQErAI+NVQ7NxqV5GXR2tFFc1snuVnp/a/gnHOD0G9yMLOL+5h1Vi/LXg9c38d2HgaO7aX8zP5icPuV5EVDaNQ0t5GblZvgaJxzo5VfIZ1iSkNy2NvoTUvOueHjySHFFOdGI7PWNHuntHNu+HhySDGl+aFZyTulnXPDyJNDiimJ1Rw8OTjnhpEnhxQT65D2ax2cc8PJk0OKyclMJyczzcdXcs4NK08OKag0L4u9jV5zcM4NH08OKajQB99zLqm1tKf+3Ro9OaSg/OwMGv2GP84lpcq6Fub925/54bJ1iQ7lkHhySEEF2RnUec3BuaT0k8fXA/Cdh19PcCSHxpNDCirOzaS+xTuknUtGN/91EwCzKvITG8gh8uSQggqyM2jwmoNzSW1DVWOiQzgknhxSUEF2Bo2tnhycS0aTinMAyExXgiM5NJ4cUlBBTgaNbZ10dln/CzvnRlR7Z1d4Nlo7UvesJU8OKaggOxpp3c9Yci75tHV0oVBpaGz15OBGUH5IDt7v4FxyMTPqWjooy4uNgZa6F6t6ckhB2RnRny1WfXXOJYfYsDaTSqJ+h6/csyZlm5Y8OaSgzPToz9bW4cnBuWRS1xzV5j/+9plcec5cVmyo5lcr3kxwVIPjySEFZYWaQ5vXHJxLKlUNrUA0xM2nz5hDflY6v3thW4KjGhxPDiloX3LwmoNzSSX2P1mQk0F6mjh+emnKXrDqySEFZaXH+hz8VFbnkpGITleaO6GQyrpWzFLvf9WTQwrKyUwHoMlPZXUuqRhvTQJTS3Np6+xiZ11LgiIaPE8OKSh2Nzi/VahzSSbkhth1DrlZ0Q+5VPxf9eSQgkpHwTnUzo1GsXpDbOCMioJsADpSsAm43+Qg6SZJlZLWxJWVSXpY0rrwXBrKl0h6SdIqSSslnRq3znRJD0l6VdIrkmaE8pmSngnbukNS1tDv5uhSlBNdBLc3BX+NODea2b6aQ5QeMsL4Sql4ZuFAag43A+d2K7sKWGZmhwPLwmvC9HFmtgC4DLgxbp1bgW+Z2ZHAiUBlKL8e+G7Y1l7g8kHsx5iSkZ5GWX4WlfWp147p3FgQa1aKXZPUMRqTg5ktB/Z0K14C3BKmbwHOC8s22P5u+XxCLUvSUUCGmT0ct1yTovR6JnBX9225A5telseWPc2JDsM5F6d7h/S+5BAGyfyn3/yNz9z2/IjHNRgZg1xvgpntADCzHZLGx2ZIOh/4L2A88L5QPBeokXQ3MBN4hKi2UQrUmFnstJutwJRBxjSmTCjKTvnx4p0bbfY1K4XXsWG7W9o72bq3iXtXbU9MYIMw5B3SZnaPmc0jqgFcE4ozgNOAK4HFwCzgY+z/DN+yib62LemK0JexsqqqakjjTjUVhdn7rsZ0ziWHfR3S3c5Wamnv4g8v7ti33J9W7yDZDTY57JI0CSA8V3ZfIDRHzZZUTlQj+JuZbQi1hN8DC4HdQImkWA1mKtBnajWzG8xskZktqqioGGToo8PsigJqmtp5s9prD84li/2t6lF2yM2MJYdObl2xiVnl+cwsz+fa+19N+gvjBpsc7gOWhumlwL0AkuaEfgQkLQSygGrgOaBUUuyIfibwSuifeAz4YPdtuQM7fW70Ua5YX53gSJxz3cVqDtkZUXLYVN3IjtoWPrR4Gp84bSbbappZn+TNwgM5lfV2YAVwhKStki4HrgPOlrQOODu8BrgAWCNpFfBj4CKLdBI1KS2TtJoorf48rPNl4IuS3gDGAb8Yut0bvWaOyycvK521O+sTHYpzLuheFyjJyyQzXfzw0TcAOHZKMacfXoEE965K7gH5+u2QNrOL+5h1Vi/LXk90ampv23kYOLaX8g1Ep7a6g5CWJo6YWMgr2+sSHYpzLqZbh3ROZjrTSvPYsLuRdx05npNnj0MSi2eU8afVO/jnc45IWKj98SukU9j8ycW8vL3WR2d1LknETmWNXQQH8JG3TSc7I43Pv2vuvvJT55SzvqqRuiQesdWTQwo7c954Gts6Wf762D5zy7lk0f1UVoBPnDaLNf/xbuZPKd5XtnB6KQD3v5S8Zy15ckhhpx5eTmleJve+mDrnTjs3FqjbSfqxi+FiYv+7T67bPYJRHRxPDiksMz2N9x07iYdf2Uljqw/f7VyiHczZqUsWTOHPL+9kb2NyDqDpySHFnTVvAi3tXazZVpvoUJwb8/Zf5dDb9b1vdeGiqXR2GTc9tXF4gxokTw4p7shJRQCs9uTgXMLFLmzr3qzUm6MnF3PCYaXc/9IOapuTr2Pak0OKm1CUzdTSXL8YzrkU9KFFU9mwu5FTrnuU7z78Ot96cC0LvvEQn7jlOarqEzs8zmAH3nNJQhLHTSvx6x2cSwIHOyDGRYunk5OZzufvWMX3l63bV/7Iq5WU/Hkt377wuKEN8CB4chgFKgqyE/4rwzkXf7Ofga+zZMEUzp0/ka4u2F7bzISiHK69/xVuf3YLHz35MI6dWrJv2daOTu5dtZ0LT5j6lmsphoMnh1GgvCCLhtYOWjs6943l4pxLhNDnMIAO6Xix/9vZFQUAvGf+JG5/dgsf+NFTXLRoGl1mNLZ1sGVPM6u31TK7ooATDisd2tC78eQwChSHe0rvaWxjUnFugqNxbuwaTM2hN6fMKeeChVN5bVcdD6zZQV5WBgU5GWSlp/H5dx0+7IkBPDmMCkdNKgTgxS21nhycSwKHmhzS08R3PpS4/gbws5VGhflTislKT+OFzXsTHYpzY1py36Hh4HhyGAWyM9I5ZmoxKzd1v9W3c24k7R9baXg7i0eCJ4dRYtFhpazeVktLe2eiQ3FuzNo/KmuCAxkCnhxGiYWHldLeaazc5E1LziVKb6OypipPDqPEosNKyUwXf3zJR2h1LtG85uCSxriCbC5cNI3fPLeFx16rTHQ4zo1J3iHtktLX3n8U4wuzue3pzYkOxbkxyWz/uKypzpPXcFB5AAATtUlEQVTDKJKTmc47jxjPMxurqU/i2w86N9qNhmYlvwhulPnI26Zzx8ot3PjERr5w9txEh+PckDEzWtq72LK3ie01zexpbKOmqZ3szDRmjsvn5Nnjhn28of5jjJ5HQW7w5DDaHDethHcdOYGf/mU9l50yk+K8zESH5FwPHZ1dtHR00dzWSUt7J60dnbR2dNHS3smexnYaWtt5s7qJN6ub2FDVwLrKBpraDnya9pzxBXznwuM4blrJAZcbCYlOUkPBk8ModPmpM3nk1V384qmNfNFrDy5BKutbePDlXbyyvY7KuhaqGlrZXtNCbXMb7Z0D67qdXJxDWUEWZx05gZnl+eRmpjOxOJuppXmUF2RTnJtJS3sny9ZW8rPH13P+T57iiIlFjC/MpjAng6LcTI6cVMSkohwKcjIoyslk7oQCMtKHtkW9pb2TDVWNPPlG8t4T+mB5chiFTppVxqlzyvnlkxs544gKFk4f/kG6nItZu7OOz/9mFa/tqscMyvKzGF+YTUVhNkdPLqIkL4vczHRyM9PJyUwjJzOd7Mx0cjLSyMpIoyQvi+LcTMYXZpOfPbBD1KUnHcb7j5nE/yzfwOu76qluaGXLniZ21bXw62feeoJGeUE2J88ex9GTi8jPCu+dmU5mmkiPe2Skpe2bbmht59G1lVTWtVLT3E5tUzuNbR00tXXS2t5JY1ytRoLCnNQ/tMoO5o7YSWTRokW2cuXKRIeRtDbubuTSXzzD7oZW/vjZ05gzviDRIblRoqvLqG/pYG9TGw2t0QGypqmNbTXRcNLLX6+iqa2TT79jNu+eP5HDxxckrJmlo7OLHbUt7Glso7G1g201zTz+WhUr39zDrrqDuwdKQXYGU0pyKc7NpCQvk4LsDHKzoiSXl53B3AkFVBRkM6U0l6mlecO0R4dO0vNmtqjf5fpLDpJuAt4PVJrZ/FBWBtwBzAA2AR8ys72SlgDXAF1AB/B5M3syrNMJrA6b3WxmHwjlNwPvAGI3Qf6Yma3qL3BPDv3bXN3E+37wBC0dnXx48XSuPOcI74MYw7q6jOb2ThpbO6hr6aC+pZ2a5nY2VjWyu6GVlvYumtujPoCmtg7aO422ji7aOrto7+yirrmdmqZ29ja10dXHYaM8HBw/edpM3n/s5JHdwYNU39JOS3vXvj6Pji6jo9Po7DI6LXqOvZZg4fRScrNS/34pQ5kcTgcagFvjksM3gT1mdp2kq4BSM/uypAKg0cxM0rHAnWY2L6zTYGY9fr6G5PBHM7vrYHbQk8PAbN3bxE8eX8+vn9nMu44czw2XLiItLfU7y1zEzKhv7aCptZOG1nYeemUX63Y1sKcx+lXf3NZJczjYVze00dHHUT09TeRlppMTfgnnZqaTlZFGZrrCcxpFOdEv5tK8LErzsygNv57zszMozMlgfGEOE4qyR0Vn7Gg20OTQb8OYmS2XNKNb8RLgjDB9C/A48GUza4hbJp/RdcFgSppamsf/O/8YJhbl8N8Pv86F/7OC3/79yZ4gUkxLeydPvbGb1dtqebO6iU3VjVQ3tLGrroXWjq63LFuSl8lh4/IpzM6gNC+T3KwM8jLTKSuIDuh5WdHBvCgnk/zsDGaU51FR4Ad191aD7TWZYGY7AMxsh6TxsRmSzgf+CxgPvC9unRxJK4mam64zs9/HzbtW0teAZcBVZtZrY6CkK4ArAKZPnz7I0Memz545h47OLn7w6Buc+/3lfPODx7EgCU75c29lZmzd28y6ynq217Swo7aZTdVNPL62cl+n55SSXKaV5XL89BIqCrIZX5RNQXYm+dnpzK4oYP6U4gTvhRsNBtQhHWoOf4xrVqoxs5K4+XvNrLTbOqcDXzOzd4XXk81su6RZwKPAWWa2XtIkYCeQBdwArDezb/QXkzcrHTwz4+4XtnHN/a9Q09TO8dNLuGjRND54wtQhP7XPHbzbnnmTb/75NWqb91/dnp4mSvOyOH1uOe8/dhInzyofFe3eLnGGrFmpD7skTQq1hklAj5HeQnPUbEnlZrbbzLaH8g2SHgeOJ0oEO8IqrZJ+CVw5yJhcPyRxwQlTOefoCdz1/FZufGIjV929muXrqvjhxQtJ96amEffilhqef3Mvz27cw59f3sn8KUV86cQjmDexiCkluYwvzPYmQJcQg00O9wFLgevC870AkuYQHfBN0kKi2kC1pFKgycxaJZUDpwDfDOvEkoyA84A1h7RHrl+FOZl8/JSZLD15BlffvZo7Vm6hue05/vP8Y5hS4vegHm61Te385rnN3PzXTeyobQGgojCby06ZyT+ddbifUeaSQr/JQdLtRJ3P5ZK2Al8nSgp3Sroc2AxcGBa/APiopHagGbgoJIojgf+R1EU02N91ZvZKWOc2SRVEw5GsAj41ZHvnDigtTVx3wTFMLM7hB4+u4/RvPsb8KcWcMnscp8wp5+1JMFZNKjEzGlo7qG+JHi3tnbR1dtHaHhsWoo0dtS38/IkNNLR2sHhGKRcsnMr5C6cwqzzfP2uXVPwiOAfApt2N/Oa5Lfx1/W5e2hpdcvKlc4/gM2fMSXBkyaOry9he20xNUztVDa1s29vMjtpmXttZz7aaFjbubqClvavf7UwsyuEbS47mnKMnjkDUzr3VkF3nkKw8OQyf6oZWLrv5OV7ZUce7jpzAx0+ZyfHTS8gcI53WbR1dPL2hmjXba9lQ1cj2mma21zSzq66V5m736E5PE5OKczh8fAGHjctnckkORTmZFORkkJuZTnZGdL1AdkYaZflZlOVnDXhICOeGgycHd0i21TTzw2XruHPlFroM8rLSeffRE7n05MM4bmrJqOy8NjO+98g6bntmM7sborOpxxdmM7U0l0nFuUwszmHO+ALG5WcxriCLScW5TCjKGZWfhRu9PDm4IbGztoVnN+3hyXVV/O6FbXR2GeMLszlxZhnzJhZyxMQi5k0sZGppbsq3mf925Rb+5a6XOGlWGR89eQanzC73zmE36nhycEOuuqGVB9bs5PHXqli7s46te5v3zSvIzuD46SW8Y24F586fmNQDj/Wmo7OLc763nK4uY9k/n+G1ATdqeXJww66+pZ3XdzWwdmcda3fUs2JDNW9URiOoTCnJ5ajJRcwYl8eUklxmVhQwc1w+5YXRcM3JVMu4+4WtfO+RdWze08T1FxzDRYv96ns3eg33RXDOUZiTyQmHlXLCYfsvjn99Vz1/fWM3z27aw9od9TyxrqrHGTwZaaI4N5NZFflMLsmlNC+LkrxM8rMyGF+UzYJpJUwtzRv2X++bdjfy7Yde448v7eCoSUXccOkJnH3UhGF9T+dShScHN6TmTihk7oRCPnbKTCDq5K1qaGVDVSObq5uobmyjvqWdqvpW3tzTxAub97K3sZ2G1o63bCczXRTnZlFekMWsinxmjMunojC6A9jsinxmHuJ1AT/7y3q+9eBrZKSJpScfxtXvPZKcTB+WwrkYTw5uWElifGEO4wtzOGnWuD6X6+wymto6eLO6iZe317KpuomapjZ21bXy6o56Hnx5F51xw03PGJfHKXPKmTuhkMMnFDBjXD5l+VkDOsC/UVnPD5etY0JhNvf8wylMKMoZkn11bjTx5OCSQnqaKMzJZP6U4l5HFe3qMvY0tbFlTxNrttdx/0vbue/F7dS3vLXGUZidwYTiHGZX5DO7ooA54wsoL8hmZ20Lm/c0UVnfwgNrdiKJH1+y0BODc33w5OBSQlqaKC/Iprwgm+Onl3LpSYdhZlTWt7JuVwOb9zSxt6mNqvpWttc0s76qkUderXxLbSNN0RhGM8vz+cLZczne763tXJ88ObiUJYkJRTl9/vpv6+ji9V31tLR3hg7wAj9F1bkB8uTgRq2sjDS/8Y1zgzQ2Bstxzjl3UDw5OOec68GTg3POuR48OTjnnOvBk4NzzrkePDk455zrwZODc865HlJ2yG5J9cBrQDFQm+BwDpXvQ3LwfUgOqb4PyR7/EWZW2N9CqXwR3GtmtkjSDWZ2RaKDORS+D8nB9yE5pPo+JHv8kgZ0I5zR0Kz0h0QHMAR8H5KD70NySPV9SPX4gdRuVlo5kLsZOeec22+gx85UrjnckOgAnHMuBQ3o2JmyNQfnnHPDJ+lqDpLOlfSapDckXRXKzpT0gqQ1km6R1GtHuqSlktaFx9K48hMkrQ7b/IGG8e72km6SVClpTVxZmaSHQ1wPS+r1RgLJEP8B9uFCSS9L6pLUZ5W0t79fKJ8p6Zmwb3dIykrAPnxL0lpJL0m6R1JJCu7DNSH+VZIekjS5j3UT/l3qLf64eVdKMknlyRp/X/sg6d8lbQt/g1WS3tvHuknxPRo0M0uaB5AOrAdmAVnAi8BRwBZgbljmG8DlvaxbBmwIz6VhujTMexY4GRDwAPCeYdyH04GFwJq4sm8CV4Xpq4DrkzX+A+zDkcARwOPAooP5+4V5dwIfDtM/Az6dgH04B8gI09f38XdI9n0oipv+HPCzZP0u9RZ/KJ8GPAi8CZQna/wH+Bv8O3BlP+slzfdosI9kqzmcCLxhZhvMrA34DXAB0Gpmr4dlHg5l3b0beNjM9pjZ3rDcuZImEf1DrbDor3ErcN5w7YCZLQf2dCteAtwSpm/p4/2TIn7ofR/M7FUze62fVXv7+y0Jv+7OBO4Ky/X1GQyZPvbhITOL3Vf0aWBqL6sm+z7Uxb3MB3prF06K71If/wsA3wW+RO+xQ5LEDwfch/4kzfdosJItOUwhqiXEbAUmAplxTRkfJPrlgaRFkm48wLpTwmNrL+UjaYKZ7QAIz+MhpeLvk6TJkv4UXva1D+OAmrgDczLsw2VEvzxTbh8kXStpC3AJ8LVQlhLfJUkfALaZ2YvdylMi/jj/GJr3boo1E6fa96g/yZYcems/7AI+DHxX0rNAPdABYGYrzewTB1jXDlCecKkeP4CZbTezWJtrSuyDpK8QfYdug9TbBzP7iplNI4r/H0NZ0n+XJOUBXyEktLcEkQLxx/kpMBtYAOwAvgOp9z3qT7Ilh62EWkEwFdgeqpGnmdmJwHJg3UDXDeVTeykfSbtClZjwXNnLMskc/0D1tQ+7gRLtP5EgYfsQOjffD1wSmia6S/p9iPNrem9iTdbv0mxgJvCipE3h/V+QNLHbcskaPwBmtsvMOs2sC/g5URNSd6n0PepVsiWH54DDQ29+FlGN4T5JsWaYbODLRJ043T0InCOpNFTzzgEeDM049ZJOCu19HwXuHYmdiXMfEDvjYmkf75/M8Q9Ur3+/cBB+jKhJEPr+DIaVpHOJvj8fMLOmPhZL9n04PO7lB4C1vSyWlN8lM1ttZuPNbIaZzSA6gC40s52pEH9M7IdecD7Q42wskvx7NCCJ7hHv/gDeC7xO1NP/lVD2LeBVooH2Ph+37CLgxrjXlwFvhMfHuy23JmzzR4TrO4Yp/tuJqprtRF/+y4naGZcR1XiWAWXJGv8B9uH8MN0K7CL6ZwWYDPzpQH+/UD6L6EyTN4DfAtkJ2Ic3iNqBV4XHz1JwH34XvgsvEQ3TMCVZv0u9xd9t/ibC2UrJGP8B/ga/AlaHv8F9wKRk/h4N9uEXwTnnnOsh2ZqVnHPOJQFPDs4553rw5OCcc64HTw7OOed68OTgnHOuB08OLmVJ6gyjYq6R9NtwBW5KkDSj+2ilko6JG+lzj6SNYfqRMDTDXX1tz7mh5qeyupQlqcHMCsL0bcDzZvbfcfNF9B3vSlSMfZE0A/ijmc3vY/7NYb4nBJcQXnNwo8UTwJzwi/xVST8BXgCmSbo43ANgjaTrYyuE8fZfkPSipGWhLD8MpvacpL9JWhLKj5b0bPgl/5Kkw8Oy94f110i6KCx7gqS/SHpe0oNxQ6ecEJZdAfzDwexcfE1D0sck/V7SH0Lt4h8lfTHE+7SksrDcbEl/DnE8IWneoX/Mbqzw5OBSXhin5j1EV61CdN+JW83seKIrW68nGiZ5AbBY0nmSKojGxbnAzI4DLgzrfgV41MwWA+8EviUpH/gU8H0zW0B0le5W4Fyisb+OCzWAP0vKBH4IfNDMTgBuAq4N2/4l8DkzO3kIdns+8BGicX2uBZrC/q4gGlYCottBfjbEcSXwkyF4XzdG9HpHNedSRK6kVWH6CeAXREMYvGlmT4fyxcDjZlYF+5qfTgc6geVmthHAzGJj9p8DfEDSleF1DjCd6KD7FUlTgbvNbJ2k1cC3Q23kj2b2hKT5RAfuh6NWLdKBHZKKgRIz+0vY7q+IEtpgPWZm9URjDdUSDaUBUYI8VlIB8Hbgt9p/s7TsQ3g/N8Z4cnCprDn8kt8nHAgb44v6WFf0PlSyiGoT3W9s9KqkZ4D3AQ9K+oSZPSrpBKIxdP5L0kPAPcDL3WsHim5JOpQdfK1x011xr7uI/q/TiO4bsKD7is4NhDcrudHuGeAdksolpQMXA38hqgm8Q9JMiO7zHZZ/EPhs6MxG0vHheRawwcx+QDTY2rGK7t/cZGb/C3yb6HaSrwEVkk4O62VKOtrMaoBaSaeG97lkOHfaojvGbZR0YYhDko4bzvd0o4vXHNyoZmY7JF1NNEyyiEbNvBdA0hXA3ZLSiO6xcTZwDfA94KWQIDYR3f/hIuDvJLUDO4nuZb6YqE+ii6hv49Nm1ibpg8APQlNSRtjey8DHgZskNREloeF2CfBTSV8FMoluVfnigVdxLuKnsjrnnOvBm5Wcc8714MnBOedcD54cnHPO9eDJwTnnXA+eHJxzzvXgycE551wPnhycc8714MnBOedcD/8fvoB1qDoTnnAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ca12f7490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampledDF.VWAP.iloc[10:].plot()\n",
    "plt.title('VWAP of Resampled Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the next indicator, we need TurnOver value for that \n",
    "data['TurnOver'] = data['VWAP']*data['TTQ']\n",
    "resampledDF['TurnOver'] = resampledDF['VWAP']*resampledDF['TTQ']\n",
    "\n",
    "resampledDF['TPt'] = 0\n",
    "data['TPt'] = 0\n",
    "resampledDF.loc[resampledDF.index[0] ,'TPt'] = resampledDF['Mt'].iloc[0]\n",
    "\n",
    "#Have to use for loop, not Happy!\n",
    "for i in range(1, len(resampledDF)):\n",
    "    if resampledDF.loc[resampledDF.index[i], 'TTQ'] == resampledDF.loc[resampledDF.index[i-1], 'TTQ']:\n",
    "        resampledDF.loc[resampledDF.index[i], 'TPt'] = resampledDF.loc[resampledDF.index[i-1], 'TPt']\n",
    "    else:\n",
    "        resampledDF.loc[resampledDF.index[i],'TPt'] = (resampledDF.loc[resampledDF.index[i],'TTV']-resampledDF.loc[resampledDF.index[i-1],'TTV'])/\\\n",
    "                                                (resampledDF.loc[resampledDF.index[i],'TTQ']-resampledDF.loc[resampledDF.index[i-1],'TTQ'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c31d42750>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Difference between TPt metric and LTP')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c31d49dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = resampledDF['TPt'] - resampledDF['LTP']\n",
    "temp.plot()\n",
    "plt.title('Difference between TPt metric and LTP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3e460110>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Plot of Rt')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2ed1a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now calculate the Rt metric\n",
    "resampledDF['Rt'] = 0\n",
    "resampledDF['Rt'] = resampledDF.TPt - (resampledDF.Mt +resampledDF.Mt.shift())/2.\n",
    "\n",
    "# There is an error in the data value of VWAP at 15:00 , we don't want to trade the last 30 mins and the \n",
    "# first 15 mins\n",
    "openTime  = datetime.time(9,30,00)\n",
    "closeTime = datetime.time(15,00,00)\n",
    "resampledDF = resampledDF[(resampledDF.TempTime >= openTime)&(resampledDF.TempTime < closeTime)]\n",
    "\n",
    "resampledDF.Rt.plot()\n",
    "plt.title('Plot of Rt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have to check whether the Rt process is stationary- we perform the same check as has been performed in the paper (Fig 3.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c3e7f6190>]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Variance Ratio Test for different values of k')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Ratio of Variances')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'Value of k')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3243e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ratio = []\n",
    "for i in range(1,101):\n",
    "    num = np.var(resampledDF['Rt'].iloc[::i].diff().dropna().tolist())\n",
    "    den =i*np.var(resampledDF['Rt'].iloc[::1].diff().dropna().tolist())\n",
    "    ratio.append(num/den)\n",
    "\n",
    "plt.plot(ratio)\n",
    "plt.title('Variance Ratio Test for different values of k')\n",
    "plt.ylabel('Ratio of Variances')\n",
    "plt.xlabel('Value of k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Declining values of ratio test confirm the fact that the proces Rt is mean reverting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>AveragePriceChange</td> <th>  R-squared:         </th> <td>   0.002</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.002</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   70.13</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Tue, 08 May 2018</td>  <th>  Prob (F-statistic):</th> <td>5.72e-17</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>18:10:24</td>      <th>  Log-Likelihood:    </th> <td> -35789.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td> 39579</td>       <th>  AIC:               </th> <td>7.158e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td> 39577</td>       <th>  BIC:               </th> <td>7.160e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     1</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0023</td> <td>    0.003</td> <td>   -0.775</td> <td> 0.438</td> <td>   -0.008</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rt</th>    <td>    0.0543</td> <td>    0.006</td> <td>    8.375</td> <td> 0.000</td> <td>    0.042</td> <td>    0.067</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4487.136</td> <th>  Durbin-Watson:     </th> <td>   0.143</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>38243.490</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.211</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 7.797</td>  <th>  Cond. No.          </th> <td>    2.16</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     AveragePriceChange   R-squared:                       0.002\n",
       "Model:                            OLS   Adj. R-squared:                  0.002\n",
       "Method:                 Least Squares   F-statistic:                     70.13\n",
       "Date:                Tue, 08 May 2018   Prob (F-statistic):           5.72e-17\n",
       "Time:                        18:10:24   Log-Likelihood:                -35789.\n",
       "No. Observations:               39579   AIC:                         7.158e+04\n",
       "Df Residuals:                   39577   BIC:                         7.160e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0023      0.003     -0.775      0.438      -0.008       0.004\n",
       "Rt             0.0543      0.006      8.375      0.000       0.042       0.067\n",
       "==============================================================================\n",
       "Omnibus:                     4487.136   Durbin-Watson:                   0.143\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            38243.490\n",
       "Skew:                          -0.211   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.797   Cond. No.                         2.16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c4243c350>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c29c5fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build Average Price Change metric that corresponds to return over the next 20 periods\n",
    "# 20 periods\n",
    "resampledDF['AveragePriceChange'] = resampledDF['Mt'].rolling(20).mean().shift(-20) - resampledDF['Mt']\n",
    "\n",
    "#Regression Analysis\n",
    "resampledDF.dropna(inplace=True)\n",
    "X = pd.DataFrame(resampledDF.Rt, index = resampledDF.index)\n",
    "X = sm.add_constant(X) ## add an intercept (beta_0) to our model\n",
    "y = resampledDF[\"AveragePriceChange\"] \n",
    "\n",
    "# Calibrate\n",
    "model = sm.OLS(y, X).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()\n",
    "\n",
    "#Plot the regression line as well alongside the data\n",
    "sns.lmplot(x='Rt',y='AveragePriceChange',data=resampledDF,fit_reg=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Figure 3.3] Though it's difficult to spot here, the regression points to a significant positive relationship between the average price change over the next specified(20) periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3ea385d0>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3ea50890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the relationships with the bid-ask spread\n",
    "resampledDF['Spread'] = resampledDF['Ask Price'] - resampledDF['Bid Price']\n",
    "resampledDF.plot(x=['Spread'], y=['Rt'], kind='scatter', alpha = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Figure 3.4] Can't say much based on this relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c376d4590>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c426f60d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampledDF.plot(x=['Spread'], y=['VOI'], kind='scatter', alpha = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Figure 3.5] Again, here we can't say anything based on the graph. Though it does seem that larger volume imbalances are accompanies by smaller spreads and vice-versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'EsteeId', u'LTP', u'LTQ', u'LTT', u'Bid Price', u'Bid Quantity',\n",
       "       u'Ask Price', u'Ask Quantity', u'Bid Price 2', u'Bid Quantity 2',\n",
       "       u'Ask Price 2', u'Ask Quantity 2', u'Bid Price 3',\n",
       "       u'Bid Quantity 3', u'Ask Price 3', u'Ask Quantity 3',\n",
       "       u'Bid Price 4', u'Bid Quantity 4', u'Ask Price 4',\n",
       "       u'Ask Quantity 4', u'Bid Price 5', u'Bid Quantity 5',\n",
       "       u'Ask Price 5', u'Ask Quantity 5', u'VWAP', u'System Time', u'TTQ',\n",
       "       u'ATP', u'TTV', u'TBQ', u'TSQ', u'OI', u'Processed Time',\n",
       "       u'TickType', u'MsgType', u'BCSequence Number', u'Receive Counter',\n",
       "       u'Publish Counter', u'Receive by DL Counter', u'BidAskChange Flag',\n",
       "       'TempTime', 'deltaVtB', 'deltaVtA', 'VOI', 'DeltaVOI', 'Mt',\n",
       "       'DeltaMt', 'TurnOver', 'TPt', 'Rt', 'AveragePriceChange', 'Spread'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run the final Regression model on which trading is based on\n",
    "resampledDF.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>AveragePriceChange</td> <th>  R-squared:         </th> <td>   0.011</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.010</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   32.50</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Wed, 09 May 2018</td>  <th>  Prob (F-statistic):</th> <td>1.17e-81</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>22:57:59</td>      <th>  Log-Likelihood:    </th> <td> -35607.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td> 39564</td>       <th>  AIC:               </th> <td>7.124e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td> 39550</td>       <th>  BIC:               </th> <td>7.136e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>    13</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0034</td> <td>    0.003</td> <td>   -1.147</td> <td> 0.251</td> <td>   -0.009</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI0</th>  <td> 1.118e-05</td> <td> 1.33e-06</td> <td>    8.410</td> <td> 0.000</td> <td> 8.58e-06</td> <td> 1.38e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI1</th>  <td> 1.024e-05</td> <td> 1.42e-06</td> <td>    7.213</td> <td> 0.000</td> <td> 7.46e-06</td> <td>  1.3e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI2</th>  <td> 8.083e-06</td> <td> 1.41e-06</td> <td>    5.730</td> <td> 0.000</td> <td> 5.32e-06</td> <td> 1.08e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI3</th>  <td> 7.346e-06</td> <td> 1.37e-06</td> <td>    5.356</td> <td> 0.000</td> <td> 4.66e-06</td> <td>    1e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI4</th>  <td> 6.771e-06</td> <td> 1.64e-06</td> <td>    4.138</td> <td> 0.000</td> <td> 3.56e-06</td> <td> 9.98e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI5</th>  <td> 7.986e-06</td> <td> 1.59e-06</td> <td>    5.012</td> <td> 0.000</td> <td> 4.86e-06</td> <td> 1.11e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR0</th>  <td>   -0.0013</td> <td>    0.002</td> <td>   -0.621</td> <td> 0.535</td> <td>   -0.006</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR1</th>  <td>   -0.0053</td> <td>    0.003</td> <td>   -2.056</td> <td> 0.040</td> <td>   -0.010</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR2</th>  <td>   -0.0041</td> <td>    0.003</td> <td>   -1.535</td> <td> 0.125</td> <td>   -0.009</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR3</th>  <td>   -0.0043</td> <td>    0.003</td> <td>   -1.620</td> <td> 0.105</td> <td>   -0.010</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR4</th>  <td>   -0.0016</td> <td>    0.003</td> <td>   -0.615</td> <td> 0.539</td> <td>   -0.007</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR5</th>  <td>   -0.0012</td> <td>    0.002</td> <td>   -0.576</td> <td> 0.565</td> <td>   -0.005</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R0</th>    <td>    0.0111</td> <td>    0.003</td> <td>    4.335</td> <td> 0.000</td> <td>    0.006</td> <td>    0.016</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4278.089</td> <th>  Durbin-Watson:     </th> <td>   0.155</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>34229.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.201</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 7.539</td>  <th>  Cond. No.          </th> <td>2.82e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     AveragePriceChange   R-squared:                       0.011\n",
       "Model:                            OLS   Adj. R-squared:                  0.010\n",
       "Method:                 Least Squares   F-statistic:                     32.50\n",
       "Date:                Wed, 09 May 2018   Prob (F-statistic):           1.17e-81\n",
       "Time:                        22:57:59   Log-Likelihood:                -35607.\n",
       "No. Observations:               39564   AIC:                         7.124e+04\n",
       "Df Residuals:                   39550   BIC:                         7.136e+04\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0034      0.003     -1.147      0.251      -0.009       0.002\n",
       "VOI0        1.118e-05   1.33e-06      8.410      0.000    8.58e-06    1.38e-05\n",
       "VOI1        1.024e-05   1.42e-06      7.213      0.000    7.46e-06     1.3e-05\n",
       "VOI2        8.083e-06   1.41e-06      5.730      0.000    5.32e-06    1.08e-05\n",
       "VOI3        7.346e-06   1.37e-06      5.356      0.000    4.66e-06       1e-05\n",
       "VOI4        6.771e-06   1.64e-06      4.138      0.000    3.56e-06    9.98e-06\n",
       "VOI5        7.986e-06   1.59e-06      5.012      0.000    4.86e-06    1.11e-05\n",
       "OIR0          -0.0013      0.002     -0.621      0.535      -0.006       0.003\n",
       "OIR1          -0.0053      0.003     -2.056      0.040      -0.010      -0.000\n",
       "OIR2          -0.0041      0.003     -1.535      0.125      -0.009       0.001\n",
       "OIR3          -0.0043      0.003     -1.620      0.105      -0.010       0.001\n",
       "OIR4          -0.0016      0.003     -0.615      0.539      -0.007       0.004\n",
       "OIR5          -0.0012      0.002     -0.576      0.565      -0.005       0.003\n",
       "R0             0.0111      0.003      4.335      0.000       0.006       0.016\n",
       "==============================================================================\n",
       "Omnibus:                     4278.089   Durbin-Watson:                   0.155\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            34229.516\n",
       "Skew:                          -0.201   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.539   Cond. No.                     2.82e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.82e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run the final Regression model on which trading is based on\n",
    "resampledDF['VOI0'] = resampledDF['VOI']/resampledDF['Spread']\n",
    "resampledDF['OIR0'] = resampledDF['OIR']/resampledDF['Spread']\n",
    "resampledDF['R0']   = resampledDF['Rt']/resampledDF['Spread']\n",
    "VOIFeatureList = ['VOI0']\n",
    "OIRFeatureList = ['OIR0']\n",
    "for i in range(1,6):\n",
    "    VOIString = 'VOI' + str(i)\n",
    "    OIRString = 'OIR' + str(i)\n",
    "    VOIFeatureList.append(VOIString)\n",
    "    OIRFeatureList.append(OIRString)\n",
    "    resampledDF[VOIString] = resampledDF['VOI'].shift(i)/resampledDF['Spread']\n",
    "    resampledDF[OIRString] = resampledDF['OIR'].shift(i)/resampledDF['Spread']\n",
    "\n",
    "#Run the regression now\n",
    "#Regression Analysis\n",
    "featureList = VOIFeatureList\n",
    "featureList.extend(OIRFeatureList)\n",
    "featureList.append('R0')\n",
    "resampledDF.dropna(inplace=True)\n",
    "X = resampledDF[featureList]\n",
    "X = sm.add_constant(X) ## add an intercept (beta_0) to our model\n",
    "y = resampledDF[\"AveragePriceChange\"] \n",
    "\n",
    "# Calibrate\n",
    "model = sm.OLS(y, X).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R^2 value is extremely small. VOI coefficients are significant across all lags considered, same can't be said for OIR coefficients. Rt/S feature is statistically significant as well.\n",
    "\n",
    "Exporing the state of residuals below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c880dfed0>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c8956b3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = resampledDF[\"AveragePriceChange\"] -  predictions\n",
    "residuals.hist(bins=51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c87d9a950>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c87d9a950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##ACF function for the OIR-ResampledData\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plt.figure(1, figsize=(10,10))\n",
    "\n",
    "ax1 = plt.subplot(111)\n",
    "a=plot_acf(residuals, ax = ax1, lags = np.arange(1,11), \\\n",
    "        title = 'Residual Autocorrelation')\n",
    "\n",
    "a=plt.suptitle('Residual Autocorrelation', fontsize = 24)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>AveragePriceChange</td> <th>  R-squared:         </th> <td>   0.016</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.015</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   24.58</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Wed, 09 May 2018</td>  <th>  Prob (F-statistic):</th> <td>6.55e-60</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>01:07:03</td>      <th>  Log-Likelihood:    </th> <td> -19582.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td> 19787</td>       <th>  AIC:               </th> <td>3.919e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td> 19773</td>       <th>  BIC:               </th> <td>3.930e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>    13</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0044</td> <td>    0.005</td> <td>   -0.955</td> <td> 0.340</td> <td>   -0.014</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI0</th>  <td>  1.19e-05</td> <td> 1.68e-06</td> <td>    7.091</td> <td> 0.000</td> <td> 8.61e-06</td> <td> 1.52e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI1</th>  <td> 1.084e-05</td> <td> 1.79e-06</td> <td>    6.044</td> <td> 0.000</td> <td> 7.32e-06</td> <td> 1.44e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI2</th>  <td> 8.416e-06</td> <td>  1.8e-06</td> <td>    4.670</td> <td> 0.000</td> <td> 4.88e-06</td> <td> 1.19e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI3</th>  <td>  7.92e-06</td> <td> 1.76e-06</td> <td>    4.492</td> <td> 0.000</td> <td> 4.46e-06</td> <td> 1.14e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI4</th>  <td>  1.18e-05</td> <td> 2.31e-06</td> <td>    5.116</td> <td> 0.000</td> <td> 7.28e-06</td> <td> 1.63e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI5</th>  <td> 1.199e-05</td> <td> 2.07e-06</td> <td>    5.784</td> <td> 0.000</td> <td> 7.93e-06</td> <td> 1.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR0</th>  <td>   -0.0053</td> <td>    0.003</td> <td>   -1.924</td> <td> 0.054</td> <td>   -0.011</td> <td> 9.96e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR1</th>  <td>   -0.0026</td> <td>    0.003</td> <td>   -0.796</td> <td> 0.426</td> <td>   -0.009</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR2</th>  <td>   -0.0042</td> <td>    0.003</td> <td>   -1.236</td> <td> 0.216</td> <td>   -0.011</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR3</th>  <td>   -0.0060</td> <td>    0.003</td> <td>   -1.762</td> <td> 0.078</td> <td>   -0.013</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR4</th>  <td>   -0.0035</td> <td>    0.003</td> <td>   -1.029</td> <td> 0.303</td> <td>   -0.010</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR5</th>  <td>   -0.0008</td> <td>    0.003</td> <td>   -0.277</td> <td> 0.782</td> <td>   -0.006</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R0</th>    <td>    0.0111</td> <td>    0.004</td> <td>    2.834</td> <td> 0.005</td> <td>    0.003</td> <td>    0.019</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2414.431</td> <th>  Durbin-Watson:     </th> <td>   0.159</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>11437.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.511</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.582</td>  <th>  Cond. No.          </th> <td>2.95e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     AveragePriceChange   R-squared:                       0.016\n",
       "Model:                            OLS   Adj. R-squared:                  0.015\n",
       "Method:                 Least Squares   F-statistic:                     24.58\n",
       "Date:                Wed, 09 May 2018   Prob (F-statistic):           6.55e-60\n",
       "Time:                        01:07:03   Log-Likelihood:                -19582.\n",
       "No. Observations:               19787   AIC:                         3.919e+04\n",
       "Df Residuals:                   19773   BIC:                         3.930e+04\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0044      0.005     -0.955      0.340      -0.014       0.005\n",
       "VOI0         1.19e-05   1.68e-06      7.091      0.000    8.61e-06    1.52e-05\n",
       "VOI1        1.084e-05   1.79e-06      6.044      0.000    7.32e-06    1.44e-05\n",
       "VOI2        8.416e-06    1.8e-06      4.670      0.000    4.88e-06    1.19e-05\n",
       "VOI3         7.92e-06   1.76e-06      4.492      0.000    4.46e-06    1.14e-05\n",
       "VOI4         1.18e-05   2.31e-06      5.116      0.000    7.28e-06    1.63e-05\n",
       "VOI5        1.199e-05   2.07e-06      5.784      0.000    7.93e-06    1.61e-05\n",
       "OIR0          -0.0053      0.003     -1.924      0.054      -0.011    9.96e-05\n",
       "OIR1          -0.0026      0.003     -0.796      0.426      -0.009       0.004\n",
       "OIR2          -0.0042      0.003     -1.236      0.216      -0.011       0.002\n",
       "OIR3          -0.0060      0.003     -1.762      0.078      -0.013       0.001\n",
       "OIR4          -0.0035      0.003     -1.029      0.303      -0.010       0.003\n",
       "OIR5          -0.0008      0.003     -0.277      0.782      -0.006       0.005\n",
       "R0             0.0111      0.004      2.834      0.005       0.003       0.019\n",
       "==============================================================================\n",
       "Omnibus:                     2414.431   Durbin-Watson:                   0.159\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            11437.217\n",
       "Skew:                          -0.511   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.582   Cond. No.                     2.95e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.95e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Try to simulate a trading run \n",
    "inSample  = resampledDF.iloc[:int(len(resampledDF)/2)]\n",
    "outSample = resampledDF.iloc[int(len(resampledDF)/2):]\n",
    "\n",
    "#The idea is to train the model on the inSample and then use the model to trade on the outSample\n",
    "XinSample = resampledDF[featureList].iloc[:int(len(resampledDF)/2)]\n",
    "XinSample = sm.add_constant(XinSample) ## add an intercept (beta_0) to our model\n",
    "y = resampledDF[\"AveragePriceChange\"] .iloc[:int(len(resampledDF)/2)]\n",
    "\n",
    "# Calibrate\n",
    "model = sm.OLS(y, XinSample).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(XinSample)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3e73df50>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3cca36d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampledDF.AveragePriceChange.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c72950750>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7e51bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predictions on outSampledData\n",
    "#The idea is to train the model on the inSample and then use the model to trade on the outSample\n",
    "XoutSample = resampledDF[featureList].iloc[int(len(resampledDF)/2):]\n",
    "XoutSample = sm.add_constant(XoutSample) ## add an intercept (beta_0) to our model\n",
    "#Get the predictions\n",
    "predictions = model.predict(XoutSample)\n",
    "predictions.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c374d0590>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7e38e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions.hist(bins=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7e38e1d0>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7e3ad190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outSample.LTP.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "tradeSimulation = outSample[['Mt', 'Bid Price', 'Ask Price']]\n",
    "tradeSimulation['Prediction'] =predictions\n",
    "tradeSimulation['PositionOn'] = False\n",
    "tradeColumnValues = ['Time', 'Side', 'Entry Price', 'Exit Price']\n",
    "trades = pd.DataFrame([], columns = tradeColumnValues)\n",
    "for i in range(len(outSample) -20):\n",
    "    if tradeSimulation.iloc[i]['Prediction'] > 0.2 and tradeSimulation.iloc[i]['PositionOn'] == False:\n",
    "        for j in range(20):\n",
    "            tradeSimulation.loc[tradeSimulation.index[i+j], 'PositionOn'] = True\n",
    "        entryPrice = tradeSimulation.iloc[i]['Ask Price']\n",
    "        exitPrice  = tradeSimulation.iloc[i+20]['Bid Price']\n",
    "        tradeTime  = tradeSimulation.index[i]\n",
    "        tempDF = pd.DataFrame([[tradeTime, 'Long', entryPrice, exitPrice]], columns = tradeColumnValues)\n",
    "        trades = trades.append(tempDF)\n",
    "    if tradeSimulation.iloc[i]['Prediction'] < -0.2 and tradeSimulation.iloc[i]['PositionOn'] == False:\n",
    "        for j in range(20):\n",
    "            tradeSimulation.loc[tradeSimulation.index[i+j], 'PositionOn'] = True\n",
    "        entryPrice = tradeSimulation.iloc[i]['Bid Price']\n",
    "        exitPrice  = tradeSimulation.iloc[i+20]['Ask Price']\n",
    "        tradeTime  = tradeSimulation.index[i]\n",
    "        tempDF = pd.DataFrame([[tradeTime, 'Short', entryPrice, exitPrice]], columns = tradeColumnValues)\n",
    "        trades = trades.append(tempDF)\n",
    "\n",
    "trades['Return'] = 0\n",
    "trades['Return'] = ( trades['Exit Price'] -trades['Entry Price'])/ trades['Entry Price']\n",
    "trades['Return'] = -trades['Return']*(trades['Side'] == 'Short') + trades['Return']*(trades['Side'] == 'Long')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "trades.to_csv('Trades1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "397"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(predictions<-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying the simulation for a look ahead of 5 periods, i.e. 2.5 seconds. Remains to be seen if this fairs well than the attempt to predict over 20 steps ahead(10 seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>AveragePriceChange5</td> <th>  R-squared:         </th> <td>   0.007</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.007</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>    <th>  F-statistic:       </th> <td>   22.26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Wed, 09 May 2018</td>   <th>  Prob (F-statistic):</th> <td>6.52e-54</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>21:36:56</td>       <th>  Log-Likelihood:    </th> <td> -11918.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td> 39569</td>        <th>  AIC:               </th> <td>2.386e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td> 39555</td>        <th>  BIC:               </th> <td>2.398e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>    13</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0009</td> <td>    0.002</td> <td>   -0.547</td> <td> 0.584</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI0</th>  <td> 5.572e-06</td> <td> 7.31e-07</td> <td>    7.626</td> <td> 0.000</td> <td> 4.14e-06</td> <td>    7e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI1</th>  <td>     4e-06</td> <td>  7.8e-07</td> <td>    5.127</td> <td> 0.000</td> <td> 2.47e-06</td> <td> 5.53e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI2</th>  <td> 3.652e-06</td> <td> 7.75e-07</td> <td>    4.711</td> <td> 0.000</td> <td> 2.13e-06</td> <td> 5.17e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI3</th>  <td>  2.24e-06</td> <td> 7.54e-07</td> <td>    2.972</td> <td> 0.003</td> <td> 7.63e-07</td> <td> 3.72e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI4</th>  <td> 2.488e-06</td> <td> 8.99e-07</td> <td>    2.767</td> <td> 0.006</td> <td> 7.25e-07</td> <td> 4.25e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOI5</th>  <td> 3.832e-06</td> <td> 8.75e-07</td> <td>    4.377</td> <td> 0.000</td> <td> 2.12e-06</td> <td> 5.55e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR0</th>  <td>    0.0063</td> <td>    0.001</td> <td>    5.400</td> <td> 0.000</td> <td>    0.004</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR1</th>  <td>   -0.0043</td> <td>    0.001</td> <td>   -3.049</td> <td> 0.002</td> <td>   -0.007</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR2</th>  <td>   -0.0020</td> <td>    0.001</td> <td>   -1.366</td> <td> 0.172</td> <td>   -0.005</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR3</th>  <td>   -0.0011</td> <td>    0.001</td> <td>   -0.754</td> <td> 0.451</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR4</th>  <td>   -0.0009</td> <td>    0.001</td> <td>   -0.616</td> <td> 0.538</td> <td>   -0.004</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OIR5</th>  <td>   -0.0026</td> <td>    0.001</td> <td>   -2.157</td> <td> 0.031</td> <td>   -0.005</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R0</th>    <td>    0.0060</td> <td>    0.001</td> <td>    4.222</td> <td> 0.000</td> <td>    0.003</td> <td>    0.009</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>8676.634</td> <th>  Durbin-Watson:     </th>  <td>   0.554</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>252338.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.399</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>15.346</td>  <th>  Cond. No.          </th>  <td>2.82e+03</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                            \n",
       "===============================================================================\n",
       "Dep. Variable:     AveragePriceChange5   R-squared:                       0.007\n",
       "Model:                             OLS   Adj. R-squared:                  0.007\n",
       "Method:                  Least Squares   F-statistic:                     22.26\n",
       "Date:                 Wed, 09 May 2018   Prob (F-statistic):           6.52e-54\n",
       "Time:                         21:36:56   Log-Likelihood:                -11918.\n",
       "No. Observations:                39569   AIC:                         2.386e+04\n",
       "Df Residuals:                    39555   BIC:                         2.398e+04\n",
       "Df Model:                           13                                         \n",
       "Covariance Type:             nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0009      0.002     -0.547      0.584      -0.004       0.002\n",
       "VOI0        5.572e-06   7.31e-07      7.626      0.000    4.14e-06       7e-06\n",
       "VOI1            4e-06    7.8e-07      5.127      0.000    2.47e-06    5.53e-06\n",
       "VOI2        3.652e-06   7.75e-07      4.711      0.000    2.13e-06    5.17e-06\n",
       "VOI3         2.24e-06   7.54e-07      2.972      0.003    7.63e-07    3.72e-06\n",
       "VOI4        2.488e-06   8.99e-07      2.767      0.006    7.25e-07    4.25e-06\n",
       "VOI5        3.832e-06   8.75e-07      4.377      0.000    2.12e-06    5.55e-06\n",
       "OIR0           0.0063      0.001      5.400      0.000       0.004       0.009\n",
       "OIR1          -0.0043      0.001     -3.049      0.002      -0.007      -0.002\n",
       "OIR2          -0.0020      0.001     -1.366      0.172      -0.005       0.001\n",
       "OIR3          -0.0011      0.001     -0.754      0.451      -0.004       0.002\n",
       "OIR4          -0.0009      0.001     -0.616      0.538      -0.004       0.002\n",
       "OIR5          -0.0026      0.001     -2.157      0.031      -0.005      -0.000\n",
       "R0             0.0060      0.001      4.222      0.000       0.003       0.009\n",
       "==============================================================================\n",
       "Omnibus:                     8676.634   Durbin-Watson:                   0.554\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           252338.787\n",
       "Skew:                          -0.399   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.346   Cond. No.                     2.82e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.82e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build Average Price Change metric that corresponds to return over the next 20 periods\n",
    "# 20 periods\n",
    "resampledDF['AveragePriceChange5'] = resampledDF['Mt'].rolling(5).mean().shift(-5) - resampledDF['Mt']\n",
    "#Run the regression now\n",
    "#Regression Analysis\n",
    "resampledDF.dropna(inplace=True)\n",
    "X = resampledDF[featureList]\n",
    "X = sm.add_constant(X) ## add an intercept (beta_0) to our model\n",
    "y = resampledDF[\"AveragePriceChange5\"] \n",
    "\n",
    "# Calibrate\n",
    "model = sm.OLS(y, X).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the predictions\n",
    "predictions = model.predict(XoutSample)\n",
    "\n",
    "#Simulate the trades over the outSample data\n",
    "tradeSimulation = outSample[['Mt', 'Bid Price', 'Ask Price']]\n",
    "tradeSimulation['Prediction'] =predictions\n",
    "tradeSimulation['PositionOn'] = False\n",
    "tradeColumnValues = ['Time', 'Side', 'Entry Price', 'Exit Price']\n",
    "trades = pd.DataFrame([], columns = tradeColumnValues)\n",
    "for i in range(len(outSample) -5):\n",
    "    if tradeSimulation.iloc[i]['Prediction'] > 0.2 and tradeSimulation.iloc[i]['PositionOn'] == False:\n",
    "        for j in range(20):\n",
    "            tradeSimulation.loc[tradeSimulation.index[i+j], 'PositionOn'] = True\n",
    "        entryPrice = tradeSimulation.iloc[i]['Ask Price']\n",
    "        exitPrice  = tradeSimulation.iloc[i+20]['Bid Price']\n",
    "        tradeTime  = tradeSimulation.index[i]\n",
    "        tempDF = pd.DataFrame([[tradeTime, 'Long', entryPrice, exitPrice]], columns = tradeColumnValues)\n",
    "        trades = trades.append(tempDF)\n",
    "    if tradeSimulation.iloc[i]['Prediction'] < -0.2 and tradeSimulation.iloc[i]['PositionOn'] == False:\n",
    "        for j in range(20):\n",
    "            tradeSimulation.loc[tradeSimulation.index[i+j], 'PositionOn'] = True\n",
    "        entryPrice = tradeSimulation.iloc[i]['Bid Price']\n",
    "        exitPrice  = tradeSimulation.iloc[i+20]['Ask Price']\n",
    "        tradeTime  = tradeSimulation.index[i]\n",
    "        tempDF = pd.DataFrame([[tradeTime, 'Short', entryPrice, exitPrice]], columns = tradeColumnValues)\n",
    "        trades = trades.append(tempDF)\n",
    "\n",
    "trades['Return'] = 0\n",
    "trades['Return'] = ( trades['Exit Price'] -trades['Entry Price'])/ trades['Entry Price']\n",
    "trades['Return'] = -trades['Return']*(trades['Side'] == 'Short') + trades['Return']*(trades['Side'] == 'Long')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "preditions = model.predict(resampledDF[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c29dc0710>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i5/OJNc4fYN32g/S6d2JSz/CWN7L7NVBtHbv99q1FTF22lTO6tWRw10LrYILlYfOeQ2xeFH3SxuGyci57YVZwv9wY1xEvX3+7J1jpwpkSZqpwLibvVtBe+zJySOjqbZEdfbsPHqFmQQ0a1kksa1/5Yj33vbOEj5dtDR4rK6+gZkFlu8Bp/y2w1f+FT9fSv2Nz+3jqVJjI62989avg9p7vSyktr6BFwzox7zPymc8AqFMzsfaMM86Ji7/j+ijDAg+XlfPCjLVcd2onajvufdx9k6hbqwbLHjg3ZjyvfbmBGwcfk1CaYrFl7yHq1iygicMpYTLzLwJiuNelIQOWaQJg3bgRce+1YMNubnr1q+CIrcCXRzTf9oG+joUbd7uKcrRW86Y9hzjvGasRFRgSHZ6+aF89iWj/U1NX0qVlQ849ro3reWMSb9G/MWcD+w+XMWFBdNv+R8u2cGa3VsH992NoTFVQ7Vr+ZeUVrNiyj+0HrJbW6PFfAvDXaasZ9uR0T+K4IUwonOXvk+WVIvqDpz/lR3+J7OgtrzARnXTOcGWOkltaXsG/527ktS/dW7Wb94R+zve5fzIDH54aEe7z1TtCnG3932drmbl6O7vsfpCpDvEP74Nwttic3hgDFTnRhr9z9EOiHVy9fzeJkgenRHx9bdx1MLKjHLjljQURx9zGW6/dvj+4PXVp9LSM/3Qdj05cztlPTIsQ2UOlFew7VMrBI2XMXR/d9v2T8bOinluwYTcjnpoR8rVzw8tzeW9hqNljwMNTOWlcaL4GWszOVB04XEZZeUXI6DWoFMkCx9dv4Jl2GRs5iCBWnv5pyoqQZxooB0Mej12/Nu05xNMfRZppkpmLEGE+i5LOeKaw3QeP8PjkFdz46lchJkcnAx6xnveSTXuZuTrSMZ4zijL788vZaArn6hetOn+otJy3Yvhaen766piz072i2rX8d39fytlPhBbCP3+8Kmha8IKPl4dmjLM1868Y46d3HzzC7oOlQU+LidBl7Acxzw965CP+c+Mg+h3dPNjPsP9wGdv3H2arY6m7S//2BQDjPljGoxccz+/eWRJyH2dBfvqjVdx69rHB/UDdXLv9AB2a1w8en7ve+lpx2tSfnLKSPd+XcsvZXWlYpyZ7Ha4ldh08QqvGdQF4zTG5LTDq5LFJy7l/VE/X39n7d5NCWn3nPDGdAy7mIbeW5Vl/nMa020/n6KMaBI/9+NnPg9tvzIleEbfbwyHX7TjI4k17Ofqo+pSWV8bhnD09+denud5jfQxTwAPvLmHxpr0s+nYPOw8coXeHJny4+Ds+dLgLCJguDxwp53BZOXVqFgDuNv+e905kSPeWTFm6FSeBxW2cI45++JeZ/HpI15DfE2BrjCHMBWHm0573unvAdGPZ5sivgxdnruO+ke75Hs6pf/g4ZH+NyzDXmau3h+T1tn2H+fDr77jhlbk88qPjuLR/EUvD0nHwSDlN6oUK97Z9h3l88gqeSmBARmBZyfCh3+E+e175Yj3jP13rmu4/f7yKKwcdzcPvLwMS+wpLB0/EX0SGAU8CBcALxphxYefrAP8A+gE7gIuNMeu8iDucWi5vXi+Ef8/3pTSpV8vVplfuqHwffB39U+7cJ2eEDL+Lx3VxhjAGeHfhZo4+qkFIP8PZT0xn5wF3O/ObMcQunEOl5UyxW8bLvtvHsu8iK++h0nJ27D/MUQ3r8ITtcG78Z2s5tUuLEFfCpfZ8BwN8viZyRM/LX6xn9EnFUdPiHFniJvyxGPzoJ0y5ZXBS1wAhnkzj9eM8kYSzvT0HS9l18Ahz7BdoWUUFN7zibnoqcQwnPvbuD1n7yHBEJNj/smXvYd6e/y3nHd8WIEL4wRLJn736VURndniaRzw1g3ZN68UcQeV2/0SJ1k/lNHumy2V/C/3S2nWwNPhs739nCUs37+XkY1qEhPnFa/OYtmIb7/78lJDjiQi/k3DPt8P+FLqo0N3/iz6j/dGJyz1tpMZD0u0VF5ECYAUwFNgIzAYuNcYscYS5CTjeGHODiFwC/NAYc3Gs+5aUlJg5c5JzxQpWZ2H3e2J0WqbIr4d0Zf2OA7w1L/Z43WxRv3YBBxMUxGNaNmTV1v0xw1x1UnHSfo9m/fYsBriYnMI5v09b/pcFl7+3Du3KH11MRdmib1HTmEOAozG0Ryueu6If/5v3Lbe+GWnmUqoHqbb8RWSuMaYkbjgPxH8QcJ8x5hx7/y4AY8wjjjAT7TCfi0hN4Dug0MSIPFXxLy2viGsqUfxJq8Z1gp/nipLrZFr8vejwbQc4Dd0b7WOuYYwxZcAe4KjwG4nIGBGZIyJztm1LrcMj0bH8iv9Q4VeUSrwQfze1jfAEkEAYjDHPG2NKjDElhYWFqSVG12fMKZ7/Sb9sJ0FRFBe8EP+NQAfHfnsg3KAbDGObfZoAqa0Pl4PcdnbXbCchJ/nszjM5u2frbCcjI3Rq0SB+oBzk76PjWgMUn+CF+M8GuohIRxGpDVwCTAgLMwEYbW9fAHwUy96fLh2a13M9/rMzOnse10tX9+fmM7sE9/sXN48R2juGdG/F364s4Z/XDsjI/U/t0oIHRvUk3Ir28A+PC9kf1rM1T1/a1/Ue7Zpa+bDwvrM9SdP/XXUit59zLCd1jrAYxuXr352T9DX/vHaAqxlx3bgRfHTb6fztSktIzzi2kKm3DuaaUzpy0+md+dEJ4VbP5Lj9nGMjji24N7Fn2KNNYy45sUPU84WNYk+Wqy6E28sn3HwynQsbUL+2NUy2fTN3jQjgdIE99+4hnqQpmUbisa0aeRJnLNIWf9uGfzMwEVgKvGGMWSwi94vISDvY34GjRGQVcAtwZ7rxxmLGHWcGK2aAoT1aMbhry4iwF/Zrn9S9Hzi/FxN/dRonH2MJUEAbBtizXX81tEu0S1OibZO6rHoochapiPWb+hY1c73utesGckJRU+rVKgge++zOMxOK87zebXn5mgH8ZFAxax4ZwbgfVQr+ZQOKgtsz7jiD537Sj/N6t2Xir0LHuDuHzDWuW4sHz+8FQN1akUVu9KCjg9tf3HVW1HSd1rWQn51xDP+8bmDUMEvujxT5O8/tRsM6NVlwT3Ivocb1avHQD3uFHDunZ+UMzTZNrDkL3ds0pnNhQ/7fD3pwx7BuPH5RH+75QY+k4vrgl6cGt53j6Kfcchp3ntuNJvVqsW7cCK50PKtwrh/cibduOolxPz6eMad1Cjl3Xu+23PODHsG5AYWN6jD11tChr+f3aRtRb9woOdq9zCXKzDvPZM3Dw5lxxxm8cGUJ/ez79enQNCLsP69LvnEzwp6x++gFxweP9WjTmKm3nk6xPf7/uSvczZHn9mrNuz8/hVuGWi/gJvVq0aRe5azqWM8n6EkAuKikPb//cWW9ad24Ljef2YUTiiJ/Y1+XY6+NiV7GvcKTGb7GmPeNMV2NMZ2NMQ/Zx+4xxkywtw8ZYy40xhxjjOlvjFnjRbyxCJ/sI7hPg//RCdHFP7yAHNeuCT8ZeDTHtm4U9KdSI8ytsXO2YufC1E0DgQomIiGzBh/5UWjLu17tAtY+Mpwzu4W+2AZ1Poq3bjqZBrabhx5tGtPanmDlxo2nV34VhTucOrN75EsTCJnw1dFhBjmnZyt6tWsSEvaKgUezbtwI5rsI8M/OrHR/0LpJ9DQm0ptTv3bl1JVrTukIVK5j63SN8C9H5frFme7uF9xmif6wb2V56dWuCf+58SRuGRrZokvUvcaoPm2pV6uA7m0aB48N7lrIfef1oH/H5hzTshE3DK7MG6df/gAnFDVlzt1DuOvc7tS1X/a/Hd49JMxjFx7P1ad0DE7YO6pBbToXNgyeX/XQuTx+UR+G9mhFPJ6+rC9DuscP58Zr1w2kbdN61KghdGhenyE9WgWf81qXiU8ndW7BJ7edzuyxQ5g9dggvX9M/bhyPXdgbCP3KCdShkX2suRDRytmzV/SjV7smwRm73ds0omZBDfp3bE7foqYM7dGKFQ+6u/R46er+3GTXo6Lm9bn4xCI62RrwyrVWut+66eSI69668aSIY80b1I77O9Ol2s3wDTCoU6hp4LSu7h3INQtS6yAOFNhwv+cCNKhdwIEj5RzbupGr/51E6Nm2cXACkJNm9WsH4wkgIoy/6kSK73wv6v3+76cnhrQo7z2vR8gs31F92vLsJ5b7h3BTR2BGaSwS7WevW6uAgZ2a88Uaq8vnlGNaUNiwDh1bNODE4tgtymT78gP+gI5qGFmRBjjKR+sm7iaACgM1a1S+eI9t1YhhvUL7MPpFaQWf5XhhvnBlCcUtGjDk8WkR4W48vTNPXmKZzZymiu5tGnPVyR0jwrs1YP58+QlxfR8FCHelMP32MxAJdUtw7SkdeSFsspKTNk3qpezMr0urhhHHArdyuvB4/if9gjO/ix0Ni8JGsQeCnNqlBfVqRy+v15/WidGDimOGAauxBARfvG9cPyh4rnYM/1FHH2U1iNoEylTwMUUvvNkapFJtxd/ZygO4fEARs9ZG9jHHeuzh5l5nHoW39AP7IsL0O85g36Ey/jBxWUJpfeWaAVzx99BZie8u9MatcCDNgaS/+/NTqCFC55YNIlw8BEhl3VfnFT3bNokaDiL9xogIH992evw4kkzXdad2pG3Tuozs3TZ2eqI4iGnTpC492zZm6ea9zF63M2EXBBAqpkMSaE0nSnhKLxtQVCk0MQj4Zwp6ArUzoeio+hFhR/ZpG1P8ndcD3D2iOw++tzRuGqx0ROL2Iok1UKBb60auM80BXr4mtplIROIKP0DT+rVTGmd/UUkH2jSpx6ldrC+0wC/LxRHo1c6xWzSiCUcsPQkXQefe+X2tTr2AuSPotlbgqIZ1Qlor8ejaumHQRBFguG23DCTh3vN6cOe53YjqySoK4Quu9GrXhB5tG0cLDkT6bknWbefPzojtxdJ5u0w2emoW1GBUn3ZxXxpO7bl+sGUrv+TEDrRqXJdaBTW4+wc9ePvmU6L2r7gR8Qw9IuLFmeB1gUdQ6Q8o8TjcaN+s8qXh9KMTPx2RKU7W+7LT3JgrBOz2IsJpXQuDv/PntkkxljkzW/hC/AP2cPdCHb36RFRgR8G9tH8HVj88POioLNB6SaXK1xCJ6KMIjEYIRPnTkzuG2H4TFc1AB1cyk99SmSjnrNRxhc/xUzPxyXvT6Z2TGgUVSM5lA4po0cAynyRqs49Gplp6iSzc4kYgOYksAJNIDGNHWH0K0fq1fjOsGwAXl4SOPHJ7LMk+qywsYx3Bq9cOCOlQdvMpBlaf4rpxI0L6onKF3EtRBhh/1YlAlBWyYhS88HPhdnZnd4Gz5Z8s0TqjrXOhN0y24P/1J/34ct1OjkrQJgypCXIyVziFJ1WNHDu8O2/M2cBK20fRuB8dx4KNllfTO2zhSRYv9ToV01kihIt2otEE8jTQCXpRSfThoIlQt1ZB0Czitpzpjad35upTiqlVowb/irLIToBnLjuBF2eui3CKFo2fnlwcdDbo5LfDQ/M9marym2HdaN6gVvyANgHHcIE1oH+XhEkwV/BFyz8WxTE+WcNbsLEqWmWdFJdjkYy/qnLImIhwQpSOw2hxhr8UoLKzyUmzBrU5J8mJVqm0Wr3UukTmSlx3WqeQTvxL+hdFjIRKmAw0JaO1BLNNk3q1WP3wcK4PGwrqJNnO3ED4nmHmxDo1CxJaPa9D8/r8vySGxp58TAvm3zM05FjXVg0Zc1rq83huPL0zF59YFD9gFLq1zvy4fK/JzRJahcQaUtWyUaidLlYxTrbl37R+ZbwCjOoTOjEoWvWLVS0L0lBg58vkvLAO0oZ1vf1AdP4GN21444ZBkQfd7pOCZr8+ZmDIyA1nekQqBwo0dxkhlAyJ2vyd8zASIaLln+D3ijNUQQ2J+XXnjCFgy461QlogfCIjjrxqJET2fWSnRzXQUEnlazkwCTJbVGuzz99Hl4SMZU624/JYx9t8VJ+2/OKsGBO4XGz+MU1KKYSLh1cVK3xYbEENoXZBjahLUlpxJx55iNknweucX0rpMLBT5Ozgvh2sr67Tu7YM9g/9sG96s3QTJZnOUqj8Uu1c2CCpYcSplI1urRvx5vWD2LrvMPVrF9Dn/slpx9EgRl9KMqOGcsDsD8D4n54YubpYgrz/y1MjltX8zbDI4cDRAAAV00lEQVRu/HX66ihXeEu1Fv+zwiaipFNgAmOxo1HZekywJeYI59ZqcQ4dTZRk7MzJtpS+umeoJ4tiA1zYr0PQj32iqXCufRog2hDNZDmufROWPzgsOJ8hXXt4Julb1IyPbzudacu3ct87S5K2+SdDnVoF1CyoQds4LVS3CY7RiGUO65jECLkMeodJioZ1aoZM0EuG8NnDYJmfnBMuM4mvzD6Bz6zwCWBeEBTrFK4VOxfcfA9Fu59bPaufwPjlVGlYp2ZEQU2VywYUBafJx9OLoT1aMeHmyFmR4K2pPpGJbLlCxxYNKLBF1DkJzStSLcvpjnBK5v0U4TbYdQJBOqmp/vhK/ItbNOCLu85K6M36l8tPSCkOZyG8tH9iHUiBS24/p1vktPnwIfcxCnRglEuPFFsiVUml6Sd2jf/1kK4c3z7S94nfubBfe356cnFCvqQuKknOf1VANRMV4yJ73H1/279VoxT7iLJlt/cr1drs40brJnXjLmH45CV9gpOsEsXNBHFql0LWjRvh6nahS8vKvgi3Tr94Jg23ihkYm56qywovaJPgZJZKs1bqceXKp382qFurgHvPS2x44R8u6J1SHIlmTY+2jZl2++kUNa9PnZoFnNIl0v+QpxGSG2P98x3fiT9ECuvbPzuZUX/+LL17Bj+VY5fg9s3qMbJ3WxrUqRll+nikQ7qE05BE2Eww/qqSuK4dAgzqfBRFzevzy1id6HHI9u9NFZHcFq9U0hbouB59UnHK8ZaXJx6xV/09fsZXZp9o9HZxJZssibZkX712QMxJSG5+b0LOxyj06cwyBnjuihPS8kV/ZrdWwRnP8WhSrxbT7zgjwvunH1j7SGprs1YVyQ5eiMflAxIzf0ab6+KKan/a+LLlnwnbYqJlMV7cAQ+UAVNQ1A7fWPdJsdIO69WGYb2SM3dlk8CLMh9nV+YDXtWSdnEWTgmQjBvjyA5flxFz+oaIiS/FPxMEWt3pTuu/97ye9C1q5nAUFR5P9GsDzrbO7xPbi6Ub+bj0sUmyY1KpPuSy2SweU245jY27vs92MlT83UilYCVq9ol3vkGdmlzav4iDR8qoU7MGt50duaRfNAob1WHlQ+em5Jgtn/HXr808+SCsTcNctudTGTimZSOOaRnpDiKZeQ5e4Eubv9vn4Gd3npny6kQALRtbU9tjTYNPhvq1a7L8wXOTXgC9VkGNlGy18Sby5CLpitSMO85gjkfrs1Yngn1HOayoTsdy0RjQ0fv5PJni9TEDeTNBtyZeoS1/m3ZN66U1SeqpS/oyZekWOhVGrlTkxLMK5eGEmtoFNdJ2YZwNKn3ppfYwctEvfC5QOQPDm8KarfH7sVxJ5BpubkcyjS9b/tFIR5ibNajNhQm4BUh3BEUefJFXGenMqs42s8cOiblYvRekbUbIowebSrX6TYquv6sLvhR/j1zUpETz+t4szOxpvczhSh5rxMbgrtZkouPb599w0cJGdTK+utOHvzqVJfefk/R1JxQ1Y8TxbRiXqovsMNo2zb1VrIAq86GTq+TPd1E1IJU1QcPxalZrOre55pSOHDhc5kk60mFYrzYs/t05efV5X5Wk6q+ods0a/Pmy1NybuDGyd1t++fp8z+6neIPWGhfyYXxw+uaj1FfTSmbhjXSJZy9W4c99MrFUZ6Jcf1on/jp9Tdbiz2W05sRhws0nc+BwebaT4Vvy4UUcjXt+0CNkTQjFe965+RTOe+bTqOfvGt6du4Z3r8IU5Q++FP9kTCe56lEy3baU8xHk8pC+fObqUzpmOwm+Qctw8viywzea9OdT+fGyPayudJV8JZ+/DLONL1v+Wl5CyeVWk76YqgcvXd2f749kbpCAlpPkSavlLyLNRWSyiKy0/0e45RORPiLyuYgsFpGFInJxOnF6QbzWQj5Mb0+3qNepWYOR9kLtuVxttGVXPRjctTAjTgPzoa7mKumafe4EphpjugBT7f1wDgJXGmN6AsOAP4lIThrSszkqIVG8Kuwiwr3nVd2oHUXJJHlQdXOOdMV/FPCSvf0ScH54AGPMCmPMSnt7E7AVKEwzXt/jZWHPh5eeoijekq7Nv5UxZjOAMWaziLSMFVhE+gO1gdVRzo8BxgAUFSW2AEQq5POnopdmEBV9Jdf49w2DEl4QCLT7Lh3iir+ITAHcXEuOTSYiEWkDvAyMNsZUuIUxxjwPPA9QUlKSsXyNJ/758HLwUrb1FaDkCiXFzVO6Tstw8sQVf2NMVJ+3IrJFRNrYrf42WCYdt3CNgfeAu40xX6ScWo9oVDd/Bzl5+WLSCqPkO165O/Ej6dr8JwCj7e3RwNvhAUSkNvBf4B/GmDfTjM8TahZYshdYLSsf8dRko28BJd9RE2bSpCv+44ChIrISGGrvIyIlIvKCHeYi4DTgKhGZb//1STNeTwgvLvlQfOraa/s2qJP62gMBtL4oin9Jy/5hjNkBRDglN8bMAa61t18BXkknHqWSc3q25jfDuvGTQUd7dk99Byj5ihp9Uid/jd8+paCG+N4PuaKEow2Y5PGlb594+K01kctDPrU/T4mFlo/UUfFXdMSEkvfkcPslZ1HxV3IardSKkhl8Kf5RG7oqNIqSZ+hXa6r4usM33NY9rGdr3vrqW3rn4YLgqZAPbnDVIqUkQqolecotg1m/44CnackXfC3+4bbus3u2Zu0jw3O6A1TJXyb9+jTq105/foZSSbqNg2NaNuSYlg29SUye4Uvxj6XtKvxKpujaStfzzRRab5NHbf5KTltNtU4rSmbwpfgH0NZC7qMvaiUWHVs0AODyAZlzAV9d8aXZRwlFX4FKvnJUwzqsGzci28nIS3zd8tfJTRb6FBTFf/ha/BVFUfyKr8Vfbf6KovgVX4u/79F3n6L4Fl+Kf/3aVj936yaJLxStKIpSnfDlaJ8ebRvz5CV9OLNby2wnRYlDh+b1s50ERamW+FL8AUb1aVdlcdUqEErLc3hMTQ4nrUm9WtlOgqJUS3wr/lXJnLFDOVxWnu1kRKD93YriX1T8q4Am9WsB2oJVFCV38GWHr6Ioit9R8VcURfEhavbxMQ1q16RV4zr8dnj3bCdFUZQqRsXfxxTUEGb9dki2k6EoShZQs4+iKIoPUfFXFEXxIWmJv4g0F5HJIrLS/t8sRtjGIvKtiDyTTpyKoihK+qTb8r8TmGqM6QJMtfej8QAwLc34FEVRFA9IV/xHAS/Z2y8B57sFEpF+QCtgUprxKYqiKB6Qrvi3MsZsBrD/R3hKE5EawB+B29OMS1EURfGIuEM9RWQK0Nrl1NgE47gJeN8YsyHe4ikiMgYYA1BUpAsyK4qiZIq44m+MiToQXES2iEgbY8xmEWkDbHUJNgg4VURuAhoCtUVkvzEmon/AGPM88DxASUlJDvuaVBT/cVFJe7q2apTtZCgeke4krwnAaGCc/f/t8ADGmMsD2yJyFVDiJvyKouQ2f7igd7aToHhIujb/ccBQEVkJDLX3EZESEXkh3cQpiqIomSGtlr8xZgdwlsvxOcC1LsdfBF5MJ05FURQlfXSGr6Ioig9R8VcURfEhKv6Koig+RMVfURTFh6j4K4qi+BAVf0VRFB+i4q8oiuJDVPwVRVF8iIq/oiiKD1HxVxRF8SEq/oqiKD5ExV9RFMWHqPgriqL4EBV/RVEUH6LiryiK4kNU/BVFUXyIir+iKIoPUfFXFEXxISr+iqIoPkTFX1EUxYeo+CuKovgQFX9FURQfouKvKIriQ1T8FUVRfIiKv6Ioig9R8VcURfEhKv6Koig+JC3xF5HmIjJZRFba/5tFCVckIpNEZKmILBGR4nTiVRRFUdIj3Zb/ncBUY0wXYKq978Y/gEeNMd2B/sDWNONVFEVR0iBd8R8FvGRvvwScHx5ARHoANY0xkwGMMfuNMQfTjFdRFEVJg3TFv5UxZjOA/b+lS5iuwG4ReUtE5onIoyJSkGa8iqIoShrUjBdARKYArV1OjU0ijlOBvsA3wL+Aq4C/u8Q1BhgDUFRUlODtFUVRlGSJK/7GmCHRzonIFhFpY4zZLCJtcLflbwTmGWPW2Nf8DxiIi/gbY54HngcoKSkxif0ERVEUJVnSNftMAEbb26OBt13CzAaaiUihvX8msCTNeBVFUZQ0SFf8xwFDRWQlMNTeR0RKROQFAGNMOXAbMFVEFgEC/C3NeBVFUZQ0iGv2iYUxZgdwlsvxOcC1jv3JwPHpxKUoiqJ4h87wVRRF8SEq/oqiKD5ExV9RFMWHqPgriqL4EBV/RVEUH6LiryiK4kNU/BVFUXyIir+iKIoPUfFXFEXxISr+iqIoPkTFX1EUxYek5dtHUTLFg+f34rh2TbKdDEWptqj4KznJFQOPznYSFKVao2YfRVEUH6LiryiK4kNU/BVFUXyIir+iKIoPUfFXFEXxISr+iqIoPkTFX1EUxYeo+CuKovgQMcZkOw2uiMg+YHm205EgTYA92U5EBikCvsl2IjKE5l3+Up3zLp18O9YY0yheoFwW/znGmJJspyMRROR5Y8yYbKcjU4jINmNMYbbTkQk07/KX6px36eRbotqpZh9veCfbCcgwu7OdgAyieZe/VOe8y3i+qfh7gDGmOhdCqL6f1pp3eUw1z7uM51sui//z2U6AEkTzIn/RvMtP0sm3hK7NWZu/oiiKkjlyueWfFURkvIhsFZGvHcceFZFlIrJQRP4rIk1drqsrIl+KyAIRWSwiv3Oc6ygis0RkpYj8S0RqV9Xv8RNR8u4BO9/mi8gkEWnrct3RIjLXDrNYRG5wnOsnIotEZJWIPCUiUlW/x0+45Z3j3G0iYkSkhcs5zbtUMcbon+MPOA04AfjacexsoKa9/Xvg9y7XCdDQ3q4FzAIG2vtvAJfY288BN2bx940Htob9vgeAhcB8YBLQNsq1o4GV9t9ox/F+wCJgFfAU9hdljuRdY8f2L4DnXK6rDdSxtxsC6wLPAPgSGGTn7wfAubmUd45ztwEGaFFd8s4+3gGYCKx3+235kHdR6tx9wLd2nZsPDI9y7TCsIe+rgDsdxzvaGrMS+BdQO+l0ZeNh5PofUOxWwexzPwRejXN9feArYIBd8LZT+fIYBEzM4m9LVSCbA2vs/83s7Wb2uZyoZAnk3V3As3GuPwprfHVboA2wzHHuUuCvuZR39vF4Apm3eQf8G+hti7rriy3X8y5KnbsPuC3OdQXAaqAT1ktuAdDDPpd2g1LNPslzNVYlQUTaisj7gRMiUiAi87He8pONMbOwCuRuY0yZHWwj0K6K0xzEGDMd2Bl2bK9jtwFWCzKcc7B+005jzC5gMjBMRNpgvTw+N1ZJ/AdwfmZSnxoi8pCIbAAuB+6xj5WIyAuOMB1EZCGwAevLbhNWPm103Crn8s7mCeAO3PMN8jTvRGQk8K0xZkHY8bzKuxj5Fo/+wCpjzBpjzBHgdWCUbb46E+vFCPASKeSbin8SiMhYoAx4FcAYs8kYMzxw3hhTbozpA7QH+otIL6wWVTg518uegEC2w6pcAQKVKWcqWTSMMWONMR2w8u1m+9gcY8y1jjAbjDHHA8cAo0WkFXmQdwkKZN7lnYjUB8Zil0Un1SXvgJvt/qjxItIMIhqU0fLNkwalin+CiMho4AfA5XYrKSrGmN3AJ1j2uu1AUxEJrJfcHtiUwaSmRAICGa0y5UMlC/BP4MexAtitxsXAqViVqr3jdE7lXRICmY951xnLrr1ARNZhPfuvRKR1tAvyKe+AZ7F+Yx9gM/BHiGhQZjTfVPwTQESGAb8BRhpjDkYJUxgYBSQi9YAhWDZHA3wMXGAHHQ28nflUp0w0gdyIZVsOEKhMOV3JRKSLY3cksMwlTHs7z7BbYCcDy40xm4F9IjLQ/tS+ktzKu0QFMu/yzhizyBjT0hhTbIwpxkrrCcaY75zh8jXvjDFbbEtBBfA3LBNPONHyzZsGZTY6QHL5D3gN601caj/8a7B62jdQ2TP/nB22LfC+vX08MA9r1MzXwD2Oe3bC6lhbBbyJPTohi7+xmNDOpy6O7Z8D/3a5pjmwFqvDsJm93dw+NxsYSGWnoevIhSzl3X/s/FiI5Q6gnR22BHjB3h5qn19g/x/juGeJff1q4BmyNBomWt6FnVtH9A7fvMu7aL8tH/POpc61cWz/Gnjd5ZqaWJ3zHans8O1pn3uT0A7fm5JOU7Yehv5l5y9VgbT3r8Z6ga0Cfuo4njOVrDr/pSqQmne5l2/Ay1hDbBcCEwIvAxwNSnt/OLDCzp+xjuNpNyh1hq+iKIoPUZu/oiiKD1HxVxRF8SEq/oqiKD5ExV9RFMWHqPgriqL4EBV/JacRkXLbXe/XIvKmPas1LxCR4nAXxSJynP175ovIThFZa29Psaf2/zva/RTFS3Sop5LTiMh+Y0xDe/tVYK4x5nHHecEqxxXZSmM0RKQYeNcY0yvK+Rft8yr4SpWjLX8ln5gBHGO3qJeKyF+wXGd3EJFL7YU7vhaR3wcuEJFhIvKVWIvsTLWPNbCdac0WkXkiMso+3lOsBXnm2w63uthh37Ov/1pELrbD9hORafZCIhNtD5mB4wtE5HPgZ8n8OOeXgohcJSL/E5F37K+Dm0XkFju9X4hIcztcZxH50E7HDBHplv5jVnxBtme/6Z/+xfoD9tv/a2L5ZrkRa6p8BZWL5bTF8uNeaIf7CMvFbSGWW46OdriAS4OHgSvs7aZYMygbAE9jOe4Dazp9PSw/R39zpKcJ1mI9M4FC+9jFwHh7eyEw2N5+lCiuGOzzLwIXOPaLA+GBq7Bmbzayf8ce4Ab73BPAr+ztqdjuObDWj/go23mmf/nxF3AMpCi5Sj2x1kgAq+X/dyyxX2+M+cI+fiLwiTFmGwTNQ6cB5cB0Y8xaAGNMwKf62cBIEbnN3q8LFAGfA2NFpD3wljFmpYgsAh6zvybeNcbMsF119wImW1YnCoDNItIEaGqMmWbf92Xg3DR++8fGmH1YDsr2YLneAMstwPEi0hA4CXhTKlcorJNGfIqPUPFXcp3vjbVGQhBb6A44D0W5VnB3dSvAj40xy8OOLxWRWcAIYKKIXGuM+UhE+mH5WHlERCYB/wUWG2MGhaWraZT4UuWwY7vCsV+BVXdrYPl17xN+oaLEQ23+SnVgFjBYRFqISAHWkn3TsFryg0WkI0DATo615OHP7c5iRKSv/b8TsMYY8xSWs63jxVrw/aAx5hXgMazl+JYDhSIyyL6uloj0NNY6DntE5BQ7nssz+aONtQLbWhG50E6HiEjvTMapVB+05a/kPcaYzSJyF9a6CYLlFfFtABEZA7wlIjWwltccirVg/Z+AhfYLYB3WQj0XA1eISCnwHXA/lknpURGpwPLKeKMx5oiIXAA8ZZt6atr3Wwz8FBgvIgexXjKZ5nLgWRG5G6sv4nUs17+KEhMd6qkoiuJD1OyjKIriQ1T8FUVRfIiKv6Ioig9R8VcURfEhKv6Koig+RMVfURTFh6j4K4qi+BAVf0VRFB/y/wGXB7yMXnjuigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7fb6af50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}