Time_Series/Part1_Time_Series_Data_BasicPlotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Part 1: Getting Time Series Data and Plotting</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code demonstrates how to view time series data with pandas and various methods of sampling, smoothing (rolling mean), and applying linear regression to the data. \n",
    "\n",
    "**if this tutorial doesn't cover what you are looking for, please leave a comment on the youtube video and I will try to cover what you are interested in.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.youtube.com/watch?v=OwnaUVt6VVE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Importing Libraries</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Getting Data and Viewing with Pandas </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-16</th>\n",
       "      <td>162.83</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-17</th>\n",
       "      <td>159.93</td>\n",
       "      <td>167.50</td>\n",
       "      <td>159.39</td>\n",
       "      <td>167.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-18</th>\n",
       "      <td>167.24</td>\n",
       "      <td>169.83</td>\n",
       "      <td>163.86</td>\n",
       "      <td>166.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-19</th>\n",
       "      <td>165.67</td>\n",
       "      <td>167.83</td>\n",
       "      <td>163.53</td>\n",
       "      <td>164.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>164.98</td>\n",
       "      <td>166.33</td>\n",
       "      <td>163.01</td>\n",
       "      <td>164.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close\n",
       "Date                                      \n",
       "2009-03-16  162.83  164.70  159.14  159.69\n",
       "2009-03-17  159.93  167.50  159.39  167.50\n",
       "2009-03-18  167.24  169.83  163.86  166.38\n",
       "2009-03-19  165.67  167.83  163.53  164.81\n",
       "2009-03-20  164.98  166.33  163.01  164.91"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://pandas-datareader.readthedocs.io/en/latest/remote_data.html\n",
    "google = web.DataReader('GOOG', data_source = 'google', start = '3/14/2009', end = '4/14/2016')\n",
    "google = google.drop('Volume', axis = 1 )\n",
    "google.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h3 align='Left'> Adding Column </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "google['Ticks'] = range(0,len(google.index.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-16</th>\n",
       "      <td>162.83</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-17</th>\n",
       "      <td>159.93</td>\n",
       "      <td>167.50</td>\n",
       "      <td>159.39</td>\n",
       "      <td>167.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-18</th>\n",
       "      <td>167.24</td>\n",
       "      <td>169.83</td>\n",
       "      <td>163.86</td>\n",
       "      <td>166.38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-19</th>\n",
       "      <td>165.67</td>\n",
       "      <td>167.83</td>\n",
       "      <td>163.53</td>\n",
       "      <td>164.81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>164.98</td>\n",
       "      <td>166.33</td>\n",
       "      <td>163.01</td>\n",
       "      <td>164.91</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Ticks\n",
       "Date                                             \n",
       "2009-03-16  162.83  164.70  159.14  159.69      0\n",
       "2009-03-17  159.93  167.50  159.39  167.50      1\n",
       "2009-03-18  167.24  169.83  163.86  166.38      2\n",
       "2009-03-19  165.67  167.83  163.53  164.81      3\n",
       "2009-03-20  164.98  166.33  163.01  164.91      4"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "google.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Plotting Ticks vs Open Price</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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i104+GT77LLlKK588kTROmSaQPH8vcc7VRbNmcPPN2d0jApdcYqWP1auTP9QP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\n4pxzLieeQJxzzuXk/wNf75GYtuIh3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12003a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#very simple plotting\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xlabel('Ticks')\n",
    "ax1.set_ylabel('Price')\n",
    "ax1.set_title('Original Plot')\n",
    "ax1.plot('Ticks', 'Open', data = google);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Sampling 1/10th of the Data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "one_tenth = google.sample(frac = .1, random_state=np.random.randint(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>647.00</td>\n",
       "      <td>648.17</td>\n",
       "      <td>622.52</td>\n",
       "      <td>623.56</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-15</th>\n",
       "      <td>296.35</td>\n",
       "      <td>297.89</td>\n",
       "      <td>295.20</td>\n",
       "      <td>296.27</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>750.00</td>\n",
       "      <td>760.59</td>\n",
       "      <td>739.44</td>\n",
       "      <td>758.09</td>\n",
       "      <td>1701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-24</th>\n",
       "      <td>562.56</td>\n",
       "      <td>574.59</td>\n",
       "      <td>561.21</td>\n",
       "      <td>570.19</td>\n",
       "      <td>1515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-23</th>\n",
       "      <td>500.00</td>\n",
       "      <td>516.86</td>\n",
       "      <td>499.81</td>\n",
       "      <td>515.19</td>\n",
       "      <td>1160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Ticks\n",
       "Date                                             \n",
       "2015-07-24  647.00  648.17  622.52  623.56   1600\n",
       "2009-12-15  296.35  297.89  295.20  296.27    191\n",
       "2015-12-16  750.00  760.59  739.44  758.09   1701\n",
       "2015-03-24  562.56  574.59  561.21  570.19   1515\n",
       "2013-10-23  500.00  516.86  499.81  515.19   1160"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_tenth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h3 align='Left'>Reordering Data by Ticks Value</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>164.98</td>\n",
       "      <td>166.33</td>\n",
       "      <td>163.01</td>\n",
       "      <td>164.91</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-20</th>\n",
       "      <td>192.88</td>\n",
       "      <td>195.13</td>\n",
       "      <td>187.76</td>\n",
       "      <td>189.46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-28</th>\n",
       "      <td>204.14</td>\n",
       "      <td>205.60</td>\n",
       "      <td>202.10</td>\n",
       "      <td>204.99</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-03</th>\n",
       "      <td>212.79</td>\n",
       "      <td>216.02</td>\n",
       "      <td>211.79</td>\n",
       "      <td>215.61</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-04</th>\n",
       "      <td>217.43</td>\n",
       "      <td>220.40</td>\n",
       "      <td>217.03</td>\n",
       "      <td>219.92</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Ticks\n",
       "2009-03-20  164.98  166.33  163.01  164.91      4\n",
       "2009-04-20  192.88  195.13  187.76  189.46     24\n",
       "2009-05-28  204.14  205.60  202.10  204.99     51\n",
       "2009-06-03  212.79  216.02  211.79  215.61     55\n",
       "2009-06-04  217.43  220.40  217.03  219.92     56"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing index name\n",
    "one_tenth.index.name = None\n",
    "one_tenth = one_tenth.sort_values(by=['Ticks'], ascending=[True])\n",
    "one_tenth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Plotting Ticks vs Open Price on Sampled Data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w9+lmNgaYDqwHznLPd+4UkVx78EH40Y/ALHb3eued+l/rs8/g8stj68hf/CL6ITJZsSK/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\nISksJQORHLr7bvj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OVCIQEclWwfoMzGw3oC/wKtDF3RdDJAygc1JsJ2Be2mkLkmNZeeed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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fa6fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = one_tenth.plot('Ticks', 'Open', legend = False, title = 'Sampled Plot');\n",
    "axes.legend = None;\n",
    "axes.set_ylabel('Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Plotting Original Data vs Sampled Data (Subplot)</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dOmDwYOta7NPHbTELV+HdeivQrVti5QzXcrZ6deTz86UCI6LYPvoo8vCESAHY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\n43+WM/j08cdjn9u9u43dWrAgeGxaQYE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t3cSGDTZIn6iuyvkArLAQuO02209mxfHHP1rw\nVVZmy+x8950t8O2YPdu2a9e6x8rKLK9Nqh18sDXnFxfXDMAuusi6HrxlIqLs9MADNnRi0aLkdUEC\nwNNP25JGixfbUj5t27pLnEWSyu7HUBUVtk1ktRCifJHzAViqPPaYZXH+v/9zj+3YYQtbn3iiBWWA\nTQN3pnxv25a+xVy3bbMlO0IDsNdfD84lxBYwouxUWmrZ3nv0sKTKyWoBA2wm48iRbstX9+42A/Gb\nb4LPKy4GJk2y/XQGYLGCQaK6IKcDsMrK1N4/tDtR1Qbmhw5sveQS28YzKzFZYj3vtdeAjz5KT1mI\nyL/nngNOOMFa24HYyxDFq0ULC/KWL7c0PffdZzMcb74Z2LXL6rEDD7SxpYC1wjEAI0qfnA7ANmwA\n9tzTWqv+9Kfk3z9ca1a4NSLbt7eyPPpo5EXAk80ZKBtrzNkhh1hXKhFlj127gIceAv7yF+CMM6yF\nKtkvbwUF9rvvzM4+80xg5kzLx9Wrl3V9PvigDaOoqrLVRNK19M/EibXL2E+UD3I6D9jKldZs77xB\nJpszfupPf7KK7NprgW+/db8fO9ZmG335pZv7y1kEO9X+8Q9baJvJColyz1tvWX6ugw6yz0uWpOY5\nb79tg/z33NM+t21rmeY//dSWMmrTBnjhBWsFmzvXLU+qheb/IqqLcrIFbPlyCzymTk1+s72XM0D0\nkkssyet++7ljKIYOteNt2gAlJbZQ9p13WgWXDkcdlb5gj4iSR9VeoP7619Q/q0ED4De/CX5REwFO\nOsla63fbzcaNvvaaHUvXGFYiytEWsK+/tu3IkZZROVWcBbmdbsVOndw1GZ1ZPG3b2kDX5cttDEW6\ndOxo2f+JKLd88oktdTZkSKZLYjp2BD77LPLKIESUGjkXgL32mr3RATbA1Ml3kwrdurnrRgIWbP38\ns+074zXatAFmzbL9dLzRElFu+8c/bOxXNg0fSFfXIxG5cqoLct06N/hyDBqUvuc73YuXX25djoCt\nsVhVZfvJXMSWiPLP999ba/m552a6JESUaTkVgHkTjN51lyVHTWbywlicrshjj3WfyzUWiciv++4D\n/vxn64IkorotKwOwysqaubbWrgXuvtvy2ABAnz6pW3cxEqcFrFWr4OO33w68/356y0JEueXii4EP\nPwSuvDLTJSGibJCVAdiUKZZt3jv+asIE295yi22dQfDp5LSAOV2OjtGjLZcPERFg9dOuXe7nykrg\njTeAo4/mTEMiMlkZgDmB1/Tptl261H1r7NQJeOUV4OST01+url1tm65s0USUm/bbDzjiCPdFccEC\n23JVCiJyZGUAtnGjbadOte1339n2tNNs1uFvfpO5KdOqQO/emXk2EeWG5cuBxo0tV9/q1cAPPwDn\nnGPHiIiALE1D4SxRMXu2DVh1BrofcUTmykRE5Mf27UDDhkBREfD3vwMHHwy0bJnanIVElHtitoCJ\nSCcR+UJEfhKR2SJyXeB4axH5XEQWiMhnItLSc80oEVkkIvNEZGi8hVq3zpIUzphh66WNGWOzHkeM\niPdORFSXZaL+WrLEcgjuthvwt7/ZkImDDrIWMCIih58uyEoAf1bVPgAOA/AHEdkfwEgAk1S1J4Av\nAIwCABHpDeA8AL0AnATgSZH4Ug5u2ABcdZXb9QgAXbrEcwciIgAZqL/mzbNZ2o4hQ2y5n92ycsAH\nEWVKzCpBVdeq6o+B/W0A5gHoBOAMAGMDp40FMCywfzqAt1S1UlWXA1gEwFe61Dlz7O1x5UqgXz+g\ne3f3OwZgRBSvdNZfjrlzOU6UiGKL651MRPYGcCCA7wC0VdUSwCo5AG0Cp3UEsNJzWXHgWFQbNwIH\nHGDTtJcvB3r0AHbf3f2+c+d4SkpEFCyV9ZejpAQYO9bqMSKiaHwHYCLSDMA7AK4PvElqyCmhn+Pi\npJxYvdpy5tSvD3z1lS3dAVj6CSKiRKS6/gKAHTuAYcOASy7JnoW2iSh7+ZoFKSL1YJXXq6r6QeBw\niYi0VdUSEWkHYF3geDEAb3tVp8CxGkaPHv2//cmTCwEUBn3fuLHNIHr1VZtVRES5paioCEVFRRkt\nQ6rqL8Ctw1SBKVMK0bVrIWc7EuWJVNdfohr7xU9EXgGwXlX/7Dl2L4CNqnqviIwA0FpVRwYGsb4O\nYDCs6X4igH015EEiEnRov/2s1WvuXPvso1hElGNEBKoa16D2JDwz6fVX4B7/O3z77bZ82hdfMNcX\nUb5Kdv0VMwATkSMAfAVgNqyZXgHcDGAqgHGwt8WfAZynqpsD14wCcCWACliT/+dh7vu/yqtxY6C8\nHHj5Zet2fPxxVmJE+SjdAViq6q/AeaqqmDAB+MMfbNa2s14sEeWftAdgqeJUXtXVQEGBHdu+HWjS\nJCPFIaI0yEQLWKo4ddgllwCHHw787neZLhERpVKy66+MZ6Z5+GHbXnklgy8iyi2qtnLHscdmuiRE\nlGsy3gLWsiWwdavNIHKWHCKi/JRvLWCLFyuOOgooLgbiS9dKRLkmr1rA1q+34Atg8EVEucdp/WLw\nRUTxymgANnmybefNy2QpiIgSw+5HIkpURgOw1auBK64A9t8/k6UgIkoMAzAiSlRGA7A//xkoK8tk\nCYiIElevXvCatUREfmV8FuRbb2W6BEREieH4LyJKVMYDsMsuy3QJiIgSw+5HIvr/9u4vRq6yDuP4\n96lLI4L2nymNrBSjqZQbsEgxoNIogSJJ4coLvQB6YYJETWOkrV40XlkaFb2QC6MuUPmjLai9INI0\nUBMTKiVt7VoqLPQPBdIaoiHRC1O6Py/ed+04tt3dmfOeMz37fJKTnDkzc57z7p757Tsz73m3V412\nwFavhpGRJo/AzKx37oCZWa8a7YC99lqT6WZm/Vm8uOkjMLPzVaMTsR45Ei5gZjNI2yZibap+mln9\nWvW/IMfHwwNYzWYQd8DM7HzVqpnw3fkyMzOzmajxqyDNzMzMZhp3wMzMzMxq5g6YmZmZWc3cATMz\nMzOrmTtgZmZmZjVzB8zMzMysZu6AmZmZmdXMHTAzMzOzmrkDZmZmZlYzd8DMzMzMauYOmJmZmVnN\n3AEzMzMzq5k7YGZmZmY1cwfMzMzMrGbugJmZmZnVzB0wMzMzs5q5A2ZmZmZWM3fAzMzMzGo2aQdM\n0s8lnZC0v2PbPEnbJb0s6RlJczruWy9pTNJBSTeXOnAzs6lwDTOzQTSVT8BGgFu6tq0DdkTEx4Fn\ngfUAkq4EvggsBW4FHpSk6g63Pzt37nSe8wY2r4nMJtrYgFbUsLafG23PayLTeYNt0g5YRPwR+EfX\n5tuBh/P6w8AdeX0V8EREvBsRR4AxYHk1h9q/tp8czju/85rIPN8L2FS0pYa1/dxoe14Tmc4bbL2O\nAVsYEScAIuI4sDBvvxQ41vG4N/M2M7NB4hpmZo2qahB+VLQfM7MmuIaZWb0iYtIFWAzs77h9ELgk\nry8CDub1dcDajsf9HrjuLPsML168zLxlKjWn6gXXMC9evFSwVFmXhpga5WXCNuAu4H7gTuB3Hdsf\nlfQA6WP7jwEvnGmHETEQA1vNbEZwDTOzgTJpB0zSY8AKYIGk14ENwEZgi6TVwFHSVUNExEuSfg28\nBJwEvhr5raKZWRNcw8xsEMm1xczMzKxejcyEL2mlpL9KekXS2or2OSzpWUkHJI1K+nreXnTCRUmz\nJO2RtK10nqQ5krbk5x+QdF3hvDWS/iJpv6RHJc2uOq+qSTIlLcvH+YqkH00zb1Pe3z5JT0r6QMm8\njvu+KWlc0vzSeZK+lvc5KmljyTxJV0l6XtJeSS9I+mRVeYNArl+uX6ef4/pVYf06V6baWMMaGAw7\nC3iVNCj2AmAfcEUF+10EXJ3XLwZeBq4gjfG4L29fC2zM61cCe0lfw16ej0k95K4Bfglsy7eL5QEP\nAXfn9SFgTqk84EPAIWB2vv0r0liZSvOATwNX878DpKedAfwJuDavPw3cMo28m4BZeX0j8L2SeXn7\nMGmA92Fgft62tFD7VgDbgaF8+4OF854Bbs7rtwLPVfXzbHrB9cv1a/Lz3/Wrj9f3Wdq4ghbWsCY+\nAVsOjEXE0Yg4CTxBmhSxLxFxPCL25fV/kq5yGqbghIuShoEvAD/r2FwkL7+r+UxEjADk/bxTsn3A\ne4CLJA0BF5LmRKo0LyqYJFPSIuD9EbE7P+6RjudMmhcROyJiPN/cRTpviuVlDwDfOkO7S+TdQ/oj\n8G5+zNuF88ZJf1wB5pLOG6jg5zkAXL9cv/7L9ev/2t3363sm1bAmOmDdEx2+QcUTHUq6nNSj3UW6\n1LzUhIsTJ2HnQLpSeR8B3pY0ovSVwU8lva9UXkS8BfwAeD0/952I2FGwfZ2mO0nmpaTzaEI/59Rq\n0ruXYnmSVgHHImK0665S7VsCfFbSLknPSbqmcN4a4PtKA943kf/NT8G8Orl+9Zbn+uX61VNe1soa\n1sgYsJIkXQxsBb6R30l2X2VQyVUHkm4DTuR3ree6HL2qqxyGgGXATyJiGfAv0pxFpdo3l/TuYjHp\n4/yLJH25VN4karlSRNJ3gJMR8XjBjAuBb5OuxKvLEDAvIj4F3AdsKZx3D+n1dxmpkP2icF5ruH65\nfvWqxfULWlrDmuiAvQlc1nF7mNMf7/Ulf9S8FdgcERPz+pyQdEm+fxHwt47j+HAfx3EDsErSIeBx\n4HOSNgPHC+W9QXrX8WK+/SSpoJVq303AoYj4e0ScAn4DXF8wr9N0M/rOlnQX6euYL3VsLpH3UdLY\ngT9LOpyfu0fSQs7+2ui3fceApwDyR+SnJC0omHdnRPw2520Frs3bi/3+auT61Vue65frVy95E9pZ\nw6LPwaPTXUjfy08MYp1NGsS6tKJ9PwL8sGvb/eSZrTnzgMjZpI/HexrEmvd1I6cHsW4qlQf8AViS\n1zfkthVpH2n8wyjwXtI75IeAe0vkkV7Qo/38zkhf1yzPx/o0sHIaeSuBA8CCrscVyeu67zDpnV3J\n9n0F+G5eXwIcLZx3ALgxr38e2F1lXpMLrl+uX5Of/65ffb6+z9DGVtawporYStJVPmPAuor2eQNw\nilQQ9wJ7cs58YEfO2w7M7XjO+vwDPEi+4qHH7M4CViwPuArYndv4FGmQYMm8Dfm5+0mDSS+oOg94\nDHgL+DdpvMbdwLzpZgDXkAruGPDjaeaNkSbj3JOXB0vmdd1/iHwVUcH2DQGb8/NfJBeWgnnX55y9\nwPPAJ6rKG4QF1y/Xr3Of/65ffby+z9LGVtYwT8RqZmZmVrPWDcI3MzMzG3TugJmZmZnVzB0wMzMz\ns5q5A2ZmZmZWM3fAzMzMzGrmDpiZmZlZzdwBMzMzM6uZO2BmZmZmNfsP5SjcPvaw9WsAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d02d950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (10,5));\n",
    "axes[0].plot('Ticks', 'Open', data = google);\n",
    "axes[0].set_title('Original Plot');\n",
    "axes[1].plot('Ticks', 'Open', data = one_tenth);\n",
    "axes[1].set_title('Sampled Plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Change the Index From Date to Ticks</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-16</td>\n",
       "      <td>162.83</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-03-17</td>\n",
       "      <td>159.93</td>\n",
       "      <td>167.50</td>\n",
       "      <td>159.39</td>\n",
       "      <td>167.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-03-18</td>\n",
       "      <td>167.24</td>\n",
       "      <td>169.83</td>\n",
       "      <td>163.86</td>\n",
       "      <td>166.38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close  Ticks\n",
       "0 2009-03-16  162.83  164.70  159.14  159.69      0\n",
       "1 2009-03-17  159.93  167.50  159.39  167.50      1\n",
       "2 2009-03-18  167.24  169.83  163.86  166.38      2"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#google.index = google['Ticks']\n",
    "# Removing Index\n",
    "google = google.reset_index()\n",
    "google.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Plotting Original Data vs Sampled vs Rolling Mean Plot (Subplot)</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "      <th>Rolling_Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-16</td>\n",
       "      <td>162.83</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-03-17</td>\n",
       "      <td>159.93</td>\n",
       "      <td>167.50</td>\n",
       "      <td>159.39</td>\n",
       "      <td>167.50</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-03-18</td>\n",
       "      <td>167.24</td>\n",
       "      <td>169.83</td>\n",
       "      <td>163.86</td>\n",
       "      <td>166.38</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-03-19</td>\n",
       "      <td>165.67</td>\n",
       "      <td>167.83</td>\n",
       "      <td>163.53</td>\n",
       "      <td>164.81</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-03-20</td>\n",
       "      <td>164.98</td>\n",
       "      <td>166.33</td>\n",
       "      <td>163.01</td>\n",
       "      <td>164.91</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close  Ticks  Rolling_Mean\n",
       "0 2009-03-16  162.83  164.70  159.14  159.69      0           NaN\n",
       "1 2009-03-17  159.93  167.50  159.39  167.50      1           NaN\n",
       "2 2009-03-18  167.24  169.83  163.86  166.38      2           NaN\n",
       "3 2009-03-19  165.67  167.83  163.53  164.81      3           NaN\n",
       "4 2009-03-20  164.98  166.33  163.01  164.91      4           NaN"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.rolling.html\n",
    "google['Rolling_Mean'] = google['Open'].rolling(window = 80).mean()\n",
    "google.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VTM+diDTdVVfBUUfFXQoRERERqY+COxFpUPPmcZdARERERBqi4E5EMlq2DD791PcV3ImI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\nOkrLLC7lUDfFpdgaZIYMgdtvh0sugYMP9vTuzz6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4pJPcnXuuPV+3zu5/+MGmR8SO3Hnp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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c115390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (15,5));\n",
    "axes[0].plot('Ticks', 'Open', data = google);\n",
    "axes[0].set_title('Original');\n",
    "axes[1].plot('Ticks', 'Open', data = one_tenth);\n",
    "axes[1].set_title('Sampled');\n",
    "axes[2].plot('Ticks', 'Rolling_Mean', data = google);\n",
    "axes[2].set_title('Smoothed (Rolling_Mean)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Plotting Original Data and Smoothed Data on Same Plot</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+17GOVSAWi6X4eF6yLuLj433s/T6zj7x1MTyxFmAUyCmnAHCCE1gX7cfJ3KxZ\nM958802ioqL8y+SZgeT1XaxdC8Att9ySr1zsp59+yptvvpn7TLVqwd69po5Ju3ZwxhnQqVPu0l1H\ngbw7YQJXXXUVU6dOZe3atezfv58tW7bwzjvvMHz4cDq3aUNKHkf9HXfc4V/uSoBVIBaLpWh27zbK\nIDER6tb1OeVJ1eGhe/futPQ42vPiNh3t3++NIwkZPhwmTeKHH37gzjvvpGHDhsTGxtKnTx8++uij\nwpMOVqtmlt16Av88BZRefhk+/5yQkBBeeOGFfLOX8ePHc/DgQZOsEUzhKrdyrFUL4uM5dO21LF64\nEIB9RcSCRGRmkuZSmm3atKnU1VKtArFYLEXjcTxv3GhWKjlkZ2fz8ccf+3Tt665CmHcG4q4Psm+f\nSR0yezacdx7cey+xsbE88sgjrFmzhvXr1/PSSy/lc4TnIyTEjLNypTl++2149FGjGK64AtatIzo6\nmjfeeINq1ap5L+uamMiUp54yUecenHvt27ePOV98AenpVH/vPXavWMHukBB6pafz8oED7Nq9m127\nd7M5IYFLDx/miz17GJWcTJSqz3LjO++8M99qsMqEVSAWi6Vo3E5tz5JWYMWKFT7pSKKjo+nRo0fB\n4+RVILVrm7oZBfg+io17VtSoEbjNRs7S4iZNmjBs2DDTpsqrBw/yx+uv8+20ad6un69dy6BBgzjj\njDN48Omnve03pqWREBpKo5wcermW8kap8saBA7TPyuLq9HQiVb0zkKZNm3L11Vcf3XNVcKwCsVgs\nhZOZaX7V+yGv+eryyy8n0kmqCOSfgfTpY9r++1+TtNBTAvdoqVfPBDV6/BqeCPYOHeCxx+DBByE9\nndtUadyoER4JT8vKYs/Spbzp+FduGzmSxYsXk5WVRU4e2ae7FGdeNCaG2hER1I2MJE2EqKgoJk2a\nRFhYWRR9LT+sArFYLIVTrx589pl5GY8d623OyMhgoeMb8NCnTx//Y7z1lu/xI48Yx3dx04MURZ06\nxkHvmYmIwAUXwOjRxpQ1YQJUq0b43Xfz9tVXExsRAcDIQ4foc/gwm5wXfXYepXFTbCwpTts5rhkJ\nL70EP/7oPZQ6dYg4coQ3Jkyg7YUXEh8fz3nnnReYZ6vAWAVisVgKx7P09sMPfQpILV26lGRXmvba\ntWtzwQUX+F7reSG3bg2vv55/7MaNAyPj4sVm665Y+M030KtX7rHju2lZvTqv5wli/CAyknb16/u0\ntWjRgo4hwbf0AAAgAElEQVRjxpD12GMA3Pjvf5sTl1xiTGSdOsGtt5q2556DnBzC162j+skn+87C\nKjGVe35lsViODs+S1HPO8cZsePgwTz2Qq6++umCTjQjceCPkzVPlSlFyVHiSFuYNMnRTrRpcdhkc\nOMDpeaonpoaFERMTQ7PYWLp3706/fv1o166dcYBnZ5trRUxOLE8qe4Bp08zHw/jxMGlSYJ7pGMAq\nEIvFUjAJCcZP8f33Pv6M5ORkFnt+9Tv4LdHqNgn5Uy4xMYGS1ODPT/H88zB0KPz8M3TpYtKRuJYT\nJ2zaxJ9NmlClShX/Y4aG5iZX3LfPJ4GkD1OnmpnJWWcd5UMcO1gTlsViyUXVLH3t1Mks2e3YEVq1\nyucM//zzzzniic7GrDjq2LFjweP6W8o6bx4U9NIuKZ5oc3eOLA93320yB4MJXNyzJzdyHWjQokXB\nyiMv4eEFrxgbOBDmzjUR98cJVoFYLJZcliyBzz83UdwzZpgZiJ/8T3nNV3369PEf75C3LT7eW5qW\n664LlNTQr5//+3nwmOJatzYKpLQ10wujWjW4/vrAj1uBCboCEZGtIvKziKwTkVVOW6yILBKR30Xk\nSxGp4eo/UkT+EJGNInJJsOWzWCwuXAkPmTgRLrwQnKp6HhISEvjuu+982gpcfeXB82Jv186smAo0\nRRWL8iQ3PPFEk/LkyScDL8NxSFnMQHKAOFU9Q1U9xsGHgCWq2gpYCowEEJG2wLVAG+ByYKpU5jBO\ni6WikbfexVVXGb+Bi/fee8+nul+7du1oUdByXH//fT1lcANJy5besrp+8ZidYmONw91Ty6Sg1O6W\nYlEWCkT83KcX4PlZMxPo7exfDcxT1SxV3Qr8ARw/HimLpbzxOML/9z+zzWPvV1Xmzp3r03ZdYaYo\njwJxK5LBg4ueMZSUGjW8ZXX94nmOWrVMxUGA224zUeuWUlMWCkSBxSKyWkRucdrqq2oigKomAJ5k\nN42B7a5rdzptFoulLDnnHDj1VONEd7Fq1Sq2unJHhYeH+199VRRlbVjwKBB3fIaTCdhSespiGW8X\nVd0tInWBRSLyO0apuCmkULF/Ro8e7d2Pi4ur1BkvLZYy5cYbISrKm0PKTd7Zx2WXXUZsbGzBY/mb\ngZQHnqW3bjl69iwfWcqQZcuWsWzZsqCNL4UWmQ/0zURGASnALRi/SKKINAC+UtU2IvIQoKo63un/\nBTBKVX/IM46WpdwWy3FDq1YmrXnr1vlOJScn06FDBw67/CSzZ8+mW7duBY+3a5eJNv/pJ3DXRy9r\n5syB9HQTyOhRIsfhO0REUNWAafOgmrBEJEpEqjn70cAlQDywABjsdBsEzHf2FwDXi0iEiDQDmgO+\ntSEtFkvwyMw0sQ5++OCDD3yUR+PGjenqFIIqkIoyA/m//8uNgt+61Td63FJqgm3Cqg98JCLq3Ott\nVV0kIj8C74rIzcA2zMorVHWDiLwLbAAygTvsVMNiKUMKUSDzPOViHfr3709oQVHZFZmmTXNzWFmO\niqAqEFXdAuQrBqyq+4HuBVzzNPC0v3MWiyXIFKBANm7cSHx8vPdYRLi+OEFzFWUGYgkKNhLdYrHk\nUoACmTNnjs/xhRdeSCO7BPa4p0QKREQKqGpvsVgqBX4USFJSUsliP9zYGUilplgKRETOE5ENwG/O\n8ekiMjWoklkslrLHjwKZNWsWaa7cUfXr1+eKK64oa8ksFZDizkCmAJcC+wBU9WfgwmAJZbFYyok8\nCiQlJYVXXnnFp8uQIUOIcCr6FYmdgVRqim3CUtXteZqyAyyLxWIpT3JyTPEkV92O119/nQMHDniP\nq1evzsCBA4s/plUclZrirsLaLiLnASoi4cAwYGPwxLJYLGWOZ/bhvPQTExOZOtXXUn3bbbdRozRV\nBK0iqZQUdwbyH+BOTF6qnZiluXcGSyiLxRIANm+GdeuK399lvlJVHnroIQ4dOuQ9XaNGDW4tafyE\nVRyVmmLNQFR1L3BDkGWxWCwlZf9+k2HWH+eeC3v3Fj9lh0uBTJs2jS+//NLn9P33309MaUvQWkVS\nKSnuKqyZIlLTdRwrIjOCJ5bFYimS/ftNvfLsAtyRycklG89RIN999x1jx471OdWxY0cGDx5cchmt\n4qjUFNeE1V5VD3oOVPUAcEZwRLJYLMUiKclsv/oq/7lXXgFPzfKHHy54jIyM3P3MTLJDQ/n3v/9N\ntkspxcTE8MILLxxd2hKrSColxVUgISLizdksIrUom1TwFoulIDwKxF2PY80aozhmuAwETz9t6mAs\nWpR/jKpV4eefAcg+fJh9ycns37/fe1pEePHFF2nWrFnpZLSKo1JTXCUwCVghIu9hKgz2A54KmlQW\ni6VoPCaqCy7IbevcGZ59Fjp1MkWhXnjBtB8+DDNmsLVlS5YvX46qcuXEidQClo0bx+qOHflx7lzG\neWYtDvfffz/du/tNW1cyrCKplBTXiT7LyaDrSfz/L1XdEDyxLBZLkSQlQXQ0pKT4tm/aZExYH33k\nVSAqQsZHH3H+t9+Sk5NDVE4ONyYmAvDnggW8u3QpNXJyOOx60cfFxTFs2LCjk9EqjkpNoSYsEYlx\ntrWABGCO80lw2iwWS3mRnGxqen/zDRw4YFZdAbz9ttmeeKK364zmzcnIzOSC9HQALnL5PoakpbH6\nn39olpXFDsfPUbt2bZ577jlCQgKUb9UqkkpJUTOQOcCVwBp8y86Kc3xykOSyWCxF8eKLcNBZ23L/\n/bBypdn3+EYaN+ZIRgbD/vUvPl+zhpDISLpkZNAsK4vWWVk8Wb06r0RHszMhAYCzjhzh79BQQkJC\nePnll6lbt+7Ry2hTmVRqCv15oapXiogAXVX1ZNenmapa5WGxlCerVsHzz5v9P/7wPTdwIDn16jF0\n6FDmr13LERF2h4ZyV2oq/01O5oy6dQlv146zzjnHe8nlUVFUa9+et99+m/PPPz8wMlrFUakp0gei\nqioiC4HTykAei8VSHPbtM7mrrrsOGjSAiy4y7c8/D927o61bM3rUKBYsWOC9ZL/LHHVaeDinvfCC\ncbaPHQtPPcUJe/dy3axZUFSZ2tJgFUmlpLgGzrUicmZQJbFYLMXHE9QnkrsKa948uPtuaNOGyZMn\n89prr/lccqBpU7NTv75Jc9K4sTl+9FG46y6zf+mlgZXTKo5KTXGX8Z4NDBCRrUAqjg9EVdsHSzCL\nxVII7riM0FBvupKMjAwmTJiQLwlivXr1GDV/PsyfD8OHm8b69XM7/OtfsGULBMppnherSColxVUg\nAf5ZYrFYjoqwMJgwwafp119/5d///jdbtmzxaY+JiWHOnDk0adoUhg41s48DB3xf6ueeC++/H3g5\nreKo1BSqQESkKiYTb3MgHnhdVbPKQjCL5bhm8WITOZ5HSXjZswfa5xoAVq9ezYABA3yy5wJUrVqV\n2bNn07Zt29xGj+O9LLGKpFJS1Hx1JtAZozwux0SkWyyWYPP66zBxookg98feveAss121ahXXX399\nPuXRsGFD5syZQ+fOnYMtbcFYxVGpKUqBtFXVAar6KiZ9yQVF9LdYLIHAM2N4qoCMQXv2QN26/Pzz\nzwwYMIB0J0DQQ//+/fnqq684x7VMt1yxiqRSUpQCyfTsHI3pSkRCRGStiCxwjmNFZJGI/C4iX4pI\nDVffkSLyh4hsFJFLSntPi+WYJjkZevUC1zJcLzt2wJo1ZFSvzu23305KnlQmI0aMYOLEiaWv3RFI\nrOKo1BSlQE4XkWTncwho79kXkZIUGxgGuHNnPQQsUdVWwFJgJICItAWuBdpgTGZTnUBGi+X44sAB\nOPNM+OUX8xL+9tvcuh9OipLnP/6YrVu3+lw2cuRIhg0bRoX7b1PR5LEEhKIi0UNVNcb5VFfVMNd+\nsX7eiMgJwBWAe1F6L4x/BWfb29m/GpinqlmquhX4AzirBM9jsRz7ZGebFCUtW+a2XXCBmY1kGUNA\nTlQUL77yis9lAwcO5O677y5LSYvGKo5KTZAWffswBXgA31xa9VU1EUBVE4B6TntjYLur306nzWI5\nPvjsM7NE98MPoU4d09anj9mmpMBvvwGwMzSUzEyvhZn69evzyCOPlLW0RWNzYVVqgloUSkR6Aomq\n+pOIxBXStZhFm3MZPXq0dz8uLo64uMKGt1iOETZvzt1v1coECE6caFKzJyTA11+zs3Vrehw44HPZ\nmDFjKobPw1KhWLZsGcuWLQva+KJa4nd38QcX+S8wAMgCIoHqwEeYpcFxqpooIg2Ar1S1jYg8hIlw\nH+9c/wUwSlV/yDOuBlNui6XceOwxiI+Hq66CIUNMW1ISnH8+/PorAK9Uq8YT1at7L4mLi+Ptt9+u\neH4PMMuQIyNh2zZo0qS8pTnuERFUNWB/KEE1Yanqw6raxMncez2wVFUHAp8Ag51ug4D5zv4C4HoR\niRCRZpgAxlXBlNFiqTBkZ8Mnn0C/frnKA6BGDbj3Xu9hw6zcBZExMTFMnDixYioPS6WnLHwg/hgH\n9BCR34GLnWOcKofvYlZsfQbcYacaluOGq64y9cnb+6aYU1U+XL7ce1w3JweAkJAQpkyZQqNGjcpU\nzBJhfSCVmqD6QNyo6tfA187+fsBvoWVVfRp4uqzkslgCRloavPceDBpU8mtVISrKlKDNo0DGjBnD\n2599xj/R0fwnNZWqzm+qSZMmcfnllwdCcoulVATVBxIsrA/EUiE54wz46SdvZtwSMXs2DBwIX30F\nrgUhM2bM4NFHH/Ue35iayt+RkVzz8sv08azOqsgcOQJVqsD27XDCCeUtzXFPoH0gZTYDsVgqNd9/\nb5QHwMaNcPLJ5sVZXP7802xdjuaNGzcyatQon26fnHACs2bNolOnTkcrscVy1NgZiMUSCGJiwJ3M\nsGNHWLOmeNcmJEDDhmbm8dVXgPF7XHPNNXz//ffebtHR0Xz88ceceuqpARQ8yGRmQkSESb/S2IZ0\nlTfH1Cosi+W44dAhuPji3OP4+KKvUTXLXD0R5ePGeU99+umnPsoDYMqUKceW8rBUeqwCsVgCQffu\n8OCD0Lq1OXbyVRXKd9+ZGIk9e0yhp7PPBiA9PZ0xY8b4dI2Li6Nnz56Bljr42FVYlRqrQCyWo0UV\nliyB6tWN/2PkSBP4VxSeWuaffAJXXultfvHFF9m1a5f3OCwsjCeeeMLGelgqHFaBWCxHy/79Ztui\nhdm2bw8ZGYVfk5qau799O9SsCcDKlSt5Pk/FwFtuuYXmzZsHStqyxc5AKjVWgVgsR8sff0DnzrnJ\nD6tWLbiSIECDBnDnnehZuYmm/9y9m+XLl3PbbbeR7UnbDtStW5d77rknWJIHH6s4KjV2Ga/FcrT8\n8Ufu7AMKVSB6001IYiLMnMnHNWtStUoVLs/I4P9uuYXtYfn/O06cOJHqrrxXxyxWkVRK7AzEYjka\nrrwSbrwRmjbNbfOjQDIzM3nnnXeQN9/0tu09coSnnAy6h/28YIcPH06PHj2CInaZYRVHpcYqEIvl\naFi40GzdyQ9dCiQ5OZmpU6fS9eyzSbn5Zp9LT8zOZktoKAApeV60PXr04L777gue3GWNVSSVEmvC\nslgCgdvJ7SiQn595hkGvv84/hw7RMjOTIWlpAKwND2d2VBSbw8KoXqMGV3XuTNPUVFJSUqhTpw59\n+vThpptuItRRLsc0VnFUaqwCsVhKS0HZEKpW5ciWLZw+YgSXxcQwKzqaE12O8UG1a9Pxkku48//+\nj65du1K1atUyEthiCSxWgVgspeWqq8zWkwPLYfnq1VyQnAzA4LQ0ZkVH08ZVw+OzVas4sTiBhhZL\nBcf6QCyWknLgAMyZY/wfNWvC6ad7T61du5Y7R470Hu92zFBxp59O+uOPg6pVHpZKg1UgFktJOekk\nuOEGs793r7dZVXn88cfZ65irnouOpk52NtPuuIPzfvmFyAYNykFYiyV4WAViObb5739h2bKyvadj\nngLA5ej+8ssvWbt2LYjQsn59ZkRHc1pWFlfOmmWuqVGjbOW0WIKMVSCWY5tHHjGFmMqKjAyTnvyv\nv+Cjj7zN2dnZjHNl000JCaGTp1rgH3+Y7bGajsRiKQCrQCzHPu68UoXRp09u3EZp2bnT1O5o1gx6\n9/Y2f/jhh2zatMl7LCI8+NBDudetXQuu1CUWS2XAKhBLLtnZpSvHWl4cOWK2xYk1eP99+PhjEzku\nAr//Xrp7LlhgvicXmZmZTJw40aetb9++tG7dGg4eNA0NG5bufhZLBcYqEEsuYWEm0d+xwoQJZpuS\nYlKp//prwX3nz/c9Tkws+f3efhvuucdU13Mxe/Zstm/f7j0ODw/n/vvvNwc1asCGDcfW92qxFBOr\nQI4X0tLAFYtQIP/8E3xZAsUPP8Azz5iZSEoKuExI+di0ydTscDjkfs6FC8FtbvLHP//AgAFm31Up\nMDk5mSlTpvh0HTBgAE1ctc1p06bIR7FYjkWsAgkUU6fCY4+VtxT+UYXoaBg+vPjXHDhg4hs8DuCK\nwiefQKtWxgz1ySemEqAHJzFhXnL27SMrPp6rn3mG352Mt/ffeis9e/bknXfeIXvKFBg/nrlz57J0\n6VIyMzPzD+Kp+QFw7rlm3JwcRowYwV7XUt7IyEiGl+R7tliOYYIaiS4iVYBvgAjnXu+r6hgRiQXe\nAZoCW4FrVTXJuWYkcDOQBQxT1UXBlDEg7NgBd95p9seMgZAKpJcPH86NmC7M7r91a+7+okXmV/0v\nv5g63xs2QLVqRy/LwYOQkwO1apV+jLlzc2caixbBGWdA48bGuR0aSnZ2Nr/99hvr1q0j86236LRy\nJVOrVOG6nBx+jI/n4jp1mJCURIwq69atY926ddTav58e4E1eWLduXa655hr69+/PKaecYu514IDZ\n/vgjAKmpqdx3330sWLDAR7w77riDunXrlv75LJZjCNEgO01FJEpV00QkFPgOGAr0Bfap6jMiMgKI\nVdWHRKQt8DZwJnACsARooXmEFJG8TeXLokVw6aVm/+DBirXe/8QTjYJr0wbS02HLlvx9srON/6MA\ntlarxmvDhtGhQweaNGlCTk4OzZo1o379+iWT5YorjPnnwIGSJ9lbvhxOOw3GjoVp04xT/JJLcs9f\neik/Xngh//noI2K3baNedjYD0tK4PCODBVWr8mNEBK9FRwPweHIye0NC6JCZyeMxMSzbs4cYVU5p\n0ID0PHKdddZZDBgwgH7XXGOKRq1ezdatW7n55pv57bfffPo2b96cxYsXU6VKlZI9W2VHBBISoKR/\nL5aAIyKoauAyXKpqmXyAKOBHjHL4DajvtDcAfnP2HwJGuK75HDjbz1haoXjlFdUhQ1RPPFF18+by\nliaXTz5RBdX581WzslQjIlQPH/bts2eP6qpVqqCJy5eb/s5nUGysd/+kBg20YcOGPp/zzjtP3333\nXc3JySmePFWqmPFmzCjZc1x6qbnulltUBw5UfeMNn9NJSUm6sXlzvTE2VrvXqePzDJ7PeXXreuWe\nXK+e/hoWlq/PrpCQfM/YsGFDPatuXfP9NG+uEyZM0BYtWuTr0759e92yZUvJnut4AVQTEspbCouq\nOu/OgL3Xg25rEZEQEVkHJACLVXW1ozwSHU2QANRzujcGtrsu3+m0VVwyMmD1ahMktn27qYddUVi6\nFK67Dq6+2kRMN2pkZPRw5AjUreuNT7jqnnsY5VS/a9agAYuqVuV551d7Cz8O+C1btjBs2DCGDx9O\nmpOqvEA2bsytE/7XX0XL/scf5rWelgZffmnadu0yzux69bzdEhMTufTSS/lzxw7CVDk/I4OtTnT4\n1xER3udJjIqid+/efPXVV9yzfj2n+nmehjk5VPNjqmvv+ES6JSczefJkUlJSfM63adOG+fPnc9JJ\nJxX9XBZLJSLo2XhVNQc4Q0RigI9E5FQgr/2pxPao0aNHe/fj4uKIi4s7CilLyZ9/5kYX//UXjBxp\nXnjFJSPDvNgLMR8dFStX+jr2RUzpVVVjCnrsMYiKgrQ05terx/adO5lerRrTo6O9JqZ3oqIYmppK\n86wsfg0PB+CErCx2uGR+7733iI+PZ86cOTQoaLlq27Zm+9//wuefFy17y5YwdCg8/7w5vuceWLwY\n/v4bpk8HICUlhQEDBrBt2zYyMY62+jk5zK1Wjb+uuIJ2Z5/NlR06kLZoEb8/8UT++hrnnw/ffkvW\nzTcTuncvsnw5P//8MwsXLmTOnDmsXLmSGjk5XrPWfj/1OXr16sWkSZOIiooq+pksljJm2bJlLAtm\nqp9ATmeK+gCPAfcBG/E1YW1U/yasL6ioJqwpU3LNHx9+aNo8x3Pn+r/mm298p/IREaoPPxw8Gc84\nQ/XHH3OPPeadvn1zZR09Wh8cMkQb5TFRXXPNNbp48WLduHGj/n7LLbquUycdNHCgDj/7bFXQpn5M\nWmeffbZu27bNvyxg7v/ZZ6o1ahQte14z1I4dZhsbq6qqmZmZOmDAAO+950VG6lPVq+vK6tV185Qp\nRY9/553GdPfVV8a8l5Vlxp8zRzU9XVVVt65YoQo6JypK51et6vOszZo10xkzZhTffHc8Y01YFQYC\nbMIKtsKoA9Rw9iMxK7KuAMZ7FAUwAhjn7LcF1mF+TDYDNuM4+vOMG+jvtWRs2pT7Yrvlltz2tWu9\n7S+99JLef//9OmbMGH322Wf1tddeUwX9u3t3/euvvzQ7O9v0ve664MnZrp3qL7/kHg8Zkit3+/aq\n2dn6wfvv51MEt99+u2ZmZvo+16mnqr77rs9LvWvXrvmuPfPMM/0rkRYtVH/7TfXAAdXq1YuW3a08\nTjtNNSfH7Ddvrjk5OXr//ff73PexmJjc/nv3lu77atAgd4xdu1SfekoVNCckRFd16KDXXnut3n77\n7frss8/q33//Xbp7HI9YBVJhONYUyGnAWuAn4BfgEae9FmaF1e/AIqCm65qRjuLYCFxSwLgB/lpL\nQEKC6sknm6/u1VdVDx3ynjqyZo33BeTPGaugb0ZFacOGDbVVq1aqoBs7ddKEYP3natlSdeNG37bk\nZCPj5Zfrtm3btGXLlj4yduvWTQ/ndbRnZKhGRqrWru3zYk/7/XcdPHhwvufs3LlzfofySSep/vmn\nUQShoapHjhQuO6heeaXqXXepLlyY29aggU6aNCnfPfvGxZnzQ4eW/vsaNMhXcbk/d99d+nGPd6wC\nqTAEWoEE1YmuqvGq2lFVO6hqe1V9ymnfr6rdVbWVql6iqgdd1zytqs1VtY1WxBiQ887LdQJ36eKN\nj0hPT+eGp59mQGws+wtZotosK4uqqjyzbRsAP2/YQJcuXZg9e3bgZc3MBMdv4cWRN6dGDe666y4O\nHTrkPVWlShWmTp2afxlqRIRxXO/bB4MGedNyRM6bx/Tp0+nTp49P9507d9K3b1+2uJcMe2QRMTJM\nm1aw3NnZpt+CBfDCC+w96yy2Od9X1t69+fJONW7cmJfmzTMHp55a1LdSMK7vwkuvXmb75JOlH9di\nqaRUoIi3Y4CUFKM8pk83W9fLatSoUXz73Xd8U6UKXmOKiz7p6QBceOQIfyUkcPXhwwCEqZKWlsaD\nDz7I8x6HcaA4csS8/N04yu37pCR+dILiPDz++OO0atXK/1jOC5xp08xqqPHj4dAhwsLCeO655/Ip\nkd27d9OvXz8SPTmn3MosKQnuuqtguTMzISKCr5Yto1u3brRv355zzz2X+LAw1uZRzjVr1mTOnDkm\nJmXrVrjppoLHLYoZM8zKNTc33WTiTSpSbM+xiI2NqZTYmujF4fBhswxV1QSz3XKLz+mvv/7aO4PI\nEiFDhJPr1eOam28mJCSEQ4cOcdfTT/tc89/q1VHg9pQUwlXJFGHcuHGkp6fzwAMPEBKIaHZ/MxDg\nt7FjuWnqVJ+27t27M3jw4MLHO+GEXIVUvbo3KDEsLIznn3+e0NBQ3n//fW/33bt3c/PNN/PBBx9Q\n1S1LjRpGifhTcIBmZHAEuMFT9c+hV506Psv1IiIiePPNN2nRooVpaNq0cPmLokYNuPBC88Ng4ECT\nyuWyy3JnIZbSkZYGkZHlLYUlCFgFUhQHD0JsrNl/5BFo3drndHJyMvfee69PW2p4OJ/Onk3N007L\nbfz+e/jPf+Czz+Dll+m7ahW/T55MrenTufLwYT5y/oM999xzxMfH89xzz1G7du2jk93PC3rz5s30\nnTOHVNcMqU6dOkyePBkpLDp840aoUyf3uFo1MyNzCA0NZcqUKYSGhvLOO+9429etW8c999zD1MxM\nJDycrKws9jzxBA2HDeOH6dNp2rcvYWFhHE5NZe/q1eytWZMv33qLkZ5U7S4Ou+SLjo5m+vTpnBXo\nGhuhoYVn9bWUHKs8Ki3WhFUUbjPPU0/ls7GPHj2a3bt3e49DQkKoWaMGNT/7zDS89ZbJ/ZSYaF7A\nTvK/Vq1bc/WIEQB0y1NfYunSpZx77rmMGzeO/e4kfiVhyxaj/FwzkLVr13LllVdywJPXyeHZZ5+l\njls5+KN16/wK5NAhk+L8zTcBo0QmTJhAt27dfC6dP38+mWlp3DhkCG3atKHTuHEsqFqVWY8+ykNt\n29KzbVuWt2lDh2uuYdDAgez49FO25ImNqVevHlWqVEFEOOOMM5g/f375xP5YLJZcAumRL6sPZbkK\na9Ys1RtuUO3Xz3g2Vq/2nlqyZEm+1UBjx47NXbnjWRkEqlFRqklJqtnZZjmrh6+/1kOnn65t2rTx\nu3LrlFNO0bFjx+qBAweKL3N6eu59nTiFn376SVu1apVv/CnFiZnwx6JFqt265d6nWzfvqaSkJL3w\nwgvNPVxLY09wxY7MiIrSR52lt0s9KU6cz901auh7kZHasGFDbdOmjf7oxLIcOXJEjxS1estisRQI\nx9IqrErBhg2mmpzHh+HUdkhKSsotGuTQsmVL07Z8uWlwR4CGhZl04yEhJh25h9q1qfbzzyybPJnO\nnTvnu31aWhovvfQS3bp1Y6s7Y25B/PmnyU4LJtpchISEBPr3709ycrJP1/79+zNs2LCix/RH9eq+\ns7xFjDEAABrESURBVLOlS6FvXzhyhJiYGGbOnEnNmjWp5TKVZbtMUPtCQmjppBO5yJPixKFZdjb7\nQkKoXbs277//Pp06dQJMoaZwPz4di8VSPlgFUhTffmvMTieeCB07mroawMMPP5y7wghjvnn22WfN\nEtjzzwfnpccPP5hrzznH//iNGgFQ78kn+eCDD3j00Uep5SfdeUJCAjfccAMHDx7Md86HJUtya1fc\ncw8Ajz32WL7rhg8fzsSJEwv3exRGRAR4FFJKijFpffgh9O8PdepwUtOmvPXWW7Rz/EffuHwxtWvX\nJuaUUxhYQNqXbtHRNLngAhYvXkxbTwoUi8VS8QjkdKasPpSVCeuHH4xZZf9+n+b58+fnMwWNGzeu\n4HF27lRNTS34/Jo1Js2HE/2dmpqqL7/8srZv3z7ffXr37q1JSUkFj/XggyYA76uvVNW/me3JJ588\n+hQc2dkmaO+jj8xx3iy4zveR8emnurd9e31+8mSdO3eubt682dx73jzf/u5PbKzqggVHJ5/FYskH\nx1IkerA+ZaZAPJHJLnbv3q2tW7f2eSFffPHFmpGRUfr7JCTkvjx/+snbnJqaqrfeems+BdC1a1fd\nvn27/7G6djVp3FU1LS1Nzz77bJ9rL7/8cs3Kyiq9rAUxYEB+RaCqOnu2av/++fsvXKjeNCWgOnmy\naufOudf+/HPgZbRYjnMCrUCsCaswmjeHhx/2Hubk5DB06FCSkpK8beHh4bz44otE+IlnKDbuFOId\nOnh3o6KiePHFF/MtVd30+++MvfBC1q1d6zvO7t2miqCz0uv555/n77//9p4OCQlh/Pjx+bPSBoKZ\nM03NdVVTPAjgq6/M6jN/hYQ8zzx0KDRpYsxtq1fn1h0/2pgOi8USdKwC8cenn5qYjx9/9Kmz/dpr\nr/Htt9/6dB05ciSt88SGlBj3OvnYWPMS/uILmDWLiFateOONN7yOZDB1K17ZsoUPe/Rg2rRpZHlq\nW+zebV68VauyadMmpuYJFrzppps4zR2bEkhCQkwMBeQqjG7dID7ed9GAB0/f887LjXKH3EqFNvLb\nYqn4BHI6U1YfgmnCysz0NcM41fPWr1+vTZo08TEH9evXz2TVDQRPPKH6+eeqzZoZ+79bhvR0TU9P\n19uHDNHT6tXTD6tWVQX9xEkxfskll+jPP/+sumSJ5lx0kS5fvlzPPPNMH1k7dOigycnJgZG1OHz5\npWqXLqrnnWfS2Ofl4EHVESPyt/func9saKl4NG3aVAH7qaCfpk2b+v13I8AmrKDXRA8GQa2J/tdf\ncMopucerVpHerh2XXnopmzdv9jbHxMSwdOlSGjmrqALCoUNmyXBqqm97fLyRKSqKDaedRtv4eLaH\nhnJidjYLqlblP7GxhISE8FCTJrTdvJkBfgpUvfLKK1x99dWBk7UoVq/2Vjrk77/NSrTiMGyYybfl\n5A6zVEyc2trlLYalAAr69wl0TXRrwsrLn3+a7erVZtuxI0888YSP8gAYP358YJUHGL9AXuUhAt98\n480A3DY+HoBPHROPJylj1aws/vXDD6z3szS2d+/eXHXVVYGVtSjclQlL8j1NmgR5IuUtFkvFxCoQ\nD07da+Lj4Y47oHNnOHSIxUuXMnPmTJ+u119/Pb2CkWDPHZMxa5Zx4E+dahSIR7EBnH4613zxBeN6\n92aH40u4PyWFRjk53pxaHrp27cr48eNLH+9RWk48ER580OyXxGkfFgZVqwZHJovFElBsMkUwgXe1\na5ugv5kzjQMd2JOeni9R4kknncSTZVEbYuBAs92+HUaN8s1D9dNP1AEeeu89sqOiOLtOHf6zezeD\nY2P5LTyc0NBQrrjiCgYPHsw555xT9srDwx13+M22a7FYKgdWgfzwQ26akm7djAmpXz+ysrK4++67\n2bdvn7draGgoL730EtFONHpQmDDBpAnxcMIJxjeyaVP+vmFhhDZrxnu9e0N8PL2GDaP/OefQsWNH\n6tatGzwZi0vTprYQk8VSiTm+negffmjyN4FZTvr99wDs+ecf7r33Xv73v//5dH/ggQe4x0kPUqa0\naQN79hhZmzb1iU3hvPNgxQqIizNxFxZLGWCd6CVn5syZvPbaayz35Mo7CrZt20azZs3IysryWzvI\nOtGDzd69ucoDYMQIeOopvvv4Yy6++OJ8yuPMM8/k7rvvLmMhHZo1M+Vk77jDV3mAUR7gP9bCYjlO\n+fbbb+nSpQs1a9akTp06XHDBBaxZs6bM7r9t2zZCQkLIycnxaQ+kObncTNMuKrcJ6/XXzbLYK67w\nbU9JAY+JZ9MmaNmS7Pr1mbJpE1PuuCOf5m7UqBHTp08nzM/y2DLBI6unsJWbX3/l/9u79+io6muB\n4989JESwhBAghEASXmLBFihSkEcpgoi2gnZdCaCigBSXAreVR4FSFbgI0iVgsVcKV4QEEaWIolZ5\nlaLVQhAFpEUwJJIiYGx4JgiYwL5/nJMwefBIzMyZwf1Za1bO/M6cOXsmyew5vycrV4JXyc2YEJOX\nl0ffvn1ZsGAB/fv355tvvuHvf/+7M9FpkKjqd+Iq7eq+Ahk+HH7+85JlR4/CX/7ibI8aVTzmY9T0\n6cyZM6fML7xDhw689tprxMXFBSPi8t12m/OznFl6ueEGp5G9vH3GBFlCQkKV3yrqs88+Q0RISUlB\nRIiKiuKWW27hBz/4AampqXTr1o0xY8ZQp04dWrRowebNm0lNTSUpKYn4+HjS0tKKn+vkyZPcf//9\nxMXF0bRpU5588snifarK9OnTadKkCfHx8QwZMoS8vDzA6f0IEBMTQ3R0NOnp6cXHjB8/ntjYWJo3\nb86aNWtKnGv48OEkJCSQmJjIY489Vvx5dP78ecaNG0f9+vVp0aIFfyn6DPPY1Z1AiqYC91sxkIkT\nYeDA4u0PP/qIjm3asLrU5a3P52Ps2LGsWrWKxCsdBBcogwY5VVX+c2YZY8rVsmVLqlWrxpAhQ1iz\nZk2ZpQy2bt1Ku3btOHr0KIMGDWLgwIFs27aNzMxMli5dyqhRo/jaHU81atQo8vLy2L9/P5s2bSIt\nLY3FixcDsHjxYtLS0nj33XfJysoiLy+PkSNHAvDee+8BTlI4efIknTp1AiA9PZ1WrVpx5MgRxo8f\nz4MPPlgc1wMPPED16tXJyspi+/btrF+/nueffx6AhQsX8vbbb7Nz5062bdvGypUrA/smXqmqHNYe\nrBtXMtXF8ePOlBjduqkuWuTMULt8uTMzLOj5ceP0T3/6kzZu3LjMbLc//OEP9f3337/8OYz5jrrY\n/2B5q2p+21tl7NmzR4cOHaqJiYkaERGhd955p+bk5OiSJUu0ZcuWxY/btWuX+nw+/c9//lNcVrdu\nXd25c6eeO3dOq1evrnv8VhBdsGCB3nzzzaqq2qtXL50/f37xvr1792pkZKSeO3dOP//8c/X5fCWm\nOlqyZIled911xfe//vprFRHNycnRnJwcjYqK0jNnzhTvX758ufZ0V/rs2bOnLliwoHjfunXryjy/\nv4v9fqjiqUwCWqkvIo2BNKABcB74P1WdJyJ1gFeAZGA/kKKqJ9xjJgHDgELgV6q6rlIn/+gjZzDb\n8OHOuI6DB53yG26gID2dSStW8NLUqWUOu+mmm5g/fz4NyptB1hgTFq6//npeeOEFwKnSuvfee/n1\nr39Nnz59Svxv13AH3tbzG2dVo0YN8vPzyc3NpbCwkKSkpOJ9ycnJHHQ/Sw4dOkSy36zRycnJFBYW\nkpOTc9EG7ni/GRqKzp2fn8+RI0coKCigYcOGwIUv9kXnPnToUImakOQQma060K3ChcAYVd0hIt8D\nPhKRdcBQYIOq/l5EJgCTgIki0hpIAVoBjYENInKdmzmvzJkzEBXlLEX7s585tyFDindrZibDZs/m\nr+V0pRs5ciQTJkzwrrHcmDB36NAhr0Moo2XLlgwZMoSFCxfSp0+fKz6uXr16REZGkp2dXTzjdnZ2\nNo3cJaMTEhLI9ptJOjs7m8jISBo0aMAXX3xRoRgTExO55pprOHLkSLnJp2HDhhw4cKDEuUJBQNtA\nVPVLVd3hbucDn+IkhjuBovlBUoG73O1+wMuqWqiq+4EMoORiGJeybZszNfpLL8HatdC1q9ODqWig\nICBnzpRJHkVreE+ePNmShzFhbu/evcyZM6f4SuHAgQMsX76cmy6yrPTFvp/6fD5SUlKYPHky+fn5\nZGdnM3fuXAa7s0QMGjSIuXPnsn//fvLz85k8eTIDBw7E5/NRv359fD4fmf5TEF1CfHw8t956K48+\n+ih5eXmoKllZWcVtKSkpKcybN4+DBw9y7NgxZs2aVdG3JSCC1oguIk2AdsAWoIGq5oCTZICiLk6N\ngAN+hx10y67M2LHOz/vuc9b0cBdW4pFHODZ7drmHJCYm8tZbb9G7d+8rPo0xJnTVqlWL9PR0OnXq\nRK1atejSpQtt2rRh9kU+A0p/4/e/P2/ePGrWrEmzZs3o3r079913H0OHDgVg2LBhDB48mO7du9O8\neXNq1qzJvHnzAKd6avLkyXTt2pXY2Fi2bt162XOnpaXxzTff0Lp1a2JjY+nfvz9fuouz/fKXv6RP\nnz60bduWDh068F/+Y9g8FJSR6G711Sbgf1R1tYgcVdVYv/1HVLWuiDwLbFbVl9zy54G3VXVVqefT\nJ554ovh+jx496JGY6KwgePPNF0ZkFxRARAT79u1jwIAB1DxwgAhV9kZGAtCuXTtSU1NDY9oPY8LI\nd2GMQzgr+v1s2rSJTZs2FZdPnTq1SkeiB7y+RkQigJXAUlVd7RbniEgDVc0RkXjgK7f8IODfZ7ax\nW1bGlClTLtxJT4cRI5wxER07OgnkJz9xZnYFXn/9dQ4fPlx8H6B79+4sWrQosPNaGWOMh3r06EGP\nHj2K708tp+PQtxHwKxARSQNyVXWMX9ks4KiqznIb0euoalEj+jKgE07V1XqgTCN6ibmwzp69MP33\n44/D734HJ06UmL1WVRkzZgyvvPIKAHfccce3X8fcmO8wuwIJbcGaCyugCUREugLvAbu4sNzib4Gt\nwAqcq41snG68x91jJgEPAgVcpBtviQTyyCMwfz60bAl79pRcU8NPYWEhI0aMIDY2llmzZlGtImtU\nGGNKsAQS2q6KBBIoxQnE/+rj2DGIibnkcQUFBURERITEJGTGhDNLIKEtWAkkvPus9uzp/Hzwwcsm\nD4BIt/HcGGPMtxe+c2GdP+9cdUybBu58McYYY4InfKuwYmLg+HHIyHC67xpjgsaqsEKbLSh1OUUz\nbEZHexuHMcZ8R4VvAgFnmnMbBGiMqQIzZ85kxIgRVf7Yy/H5fGRlZVXJcwVb+FZhAYRh7MZcDcKh\nCmvJkiXMmTOHzMxMateuzV133cXMmTOpXbu216GVUK1aNTIyMmjWrFmVPadVYV3Oiy96HYExJkTN\nnj2bSZMmMXv2bE6ePMmWLVvIzs6md+/eFBYWlnn8uXPnPIjSEeqJ+FLCN4HcfbfXERhjQlBeXh5T\npkzhj3/8I71796ZatWokJSWxYsUKsrOzefHFF5k6dSr9+/dn8ODBxMTEkJqaytSpU4tn2gVncsMm\nTZpQv359pk+fTtOmTdm4cSNAicdmZ2fj8/lIS0sjOTmZuLg4ZsyYUfw8H374IV26dKFOnTo0atSI\n0aNHl5vEwlH4JpCoKK8jMMaEoH/84x+cPXuWX/ziFyXKr732Wm6//XbWr18PwBtvvEFKSgrHjx/n\nnnvuAS7Mjrt7925GjhzJ8uXLOXz4MCdOnCiz1knpAckffPABGRkZbNiwgWnTprF3717AqaJ65pln\nOHr0KJs3b2bjxo0899xzAXntwRa+CcQYE7pEquZWCbm5udSrVw+fr+zHW8OGDcnNzQWgc+fO9O3b\nF4Brima0cL366qv069ePzp07ExERwbRp0y7zcoUpU6ZQvXp12rRpQ9u2bdm5cycA7du3p2PHjogI\nSUlJjBgxgnfffbdSry3UhPdIdGNMaPKwXr9evXrk5uZy/vz5Mknk8OHDxcvX+i8RW1rpJWRr1KhB\n3bp1L3le/6Vya9asSX5+PgAZGRmMGTOGbdu2cfr0aQoLC7nxxhsr/LpCkV2BGGOuKp07dyYqKopV\nq0osI0R+fj7vvPMOvXr1AspWQflr2LBhiWVpT58+zZEjRyoVz8MPP0yrVq3IzMzk+PHjPPnkk2Hd\ncO7PEogx5qoSHR3N448/zujRo1m7di2FhYXs37+fAQMGkJSUVKKh/GLuvvtu3nzzTbZs2UJBQUHJ\n9YfKcamEkJeXR3R0NDVr1mTPnj3Mnz+/oi8pZFkCMcZcdcaPH8+MGTMYN24ctWvXpnPnziQnJ7Nh\nw4YrmlS1devWPPvsswwYMICEhASio6OJi4sj6iKddy61LO7TTz/NsmXLiI6O5qGHHmLgwIGXPDac\nhO9AwjCM25irRTgMJKxKp06dIiYmhn379pGcnOx1OJdlAwmNMcZDb731FqdPn+bUqVOMHTuWNm3a\nhEXyCCZLIMYYU47Vq1eTkJBA48aNyczM5OWXX/Y6pJBjVVjGmAr7rlVhhRurwjLGGBPSLIEYY4yp\nFEsgxhhjKsWmMjHGVFhycnJYj1+42gWrt1hAG9FFZBFwB5Cjqm3csjrAK0AysB9IUdUT7r5JwDCg\nEPiVqq67yPNaI7oxxlRQuDWiLwb6lCqbCGxQ1euBjcAkABFpDaQArYDbgeckzL7ibNq0yesQyrCY\nrozFdOVCMS6LyRsBTSCq+j5wrFTxnUCqu50K3OVu9wNeVtVCVd0PZAAdAxlfVQvFPxiL6cpYTFcu\nFOOymLzhRSN6nKrmAKjql0CcW94IOOD3uINumTHGmBAUCr2wrDHDGGPCUMBHootIMvCmXyP6p0AP\nVc0RkXjgb6raSkQmAqqqs9zHrQGeUNX0cp7Tko4xxlRCVTaiB6Mbr7i3Im8AQ4BZwAPAar/yZSIy\nF6fqqgWwtbwnrMo3wBhjTOUENIGIyEtAD6CuiPwbeAJ4CviziAwDsnF6XqGqu0VkBbAbKAAesb66\nxhgTusJyMkVjjDHeC4VG9AoRkdtEZI+IfCYiE4J43sYislFE/iUiu0Tkv93yOiKyTkT2ishaEant\nd8wkEckQkU9F5NYAxuYTkY9F5I1QiElEaovIn91z/EtEOoVATI+KyD9F5BMRWSYi1b2ISUQWiUiO\niHziV1bhOESkvftaPhORZwIQ0+/dc+4QkVdFJNrrmPz2jRWR8yISGwoxicho97y7ROSpYMZ0sbhE\npK2IbBaR7SKyVUQ6BCQuVQ2bG07C24czij0S2AF8P0jnjgfaudvfA/YC38dpy/mNWz4BeMrdbg1s\nx6kmbOLGLQGK7VHgReAN976nMQFLgKHudgRQ28uYgAQgC6ju3n8Fp/0t6DEB3YB2wCd+ZRWOA0gH\nfuxuvw30qeKYbgF87vZTwEyvY3LLGwNrgM+BWLeslYfvUw9gHRDh3q8XzJguEdda4FZ3+3aczkpV\n/vsLtyuQjkCGqmaragHwMs7AxIBT1S9VdYe7nQ98ivPH7OnASBFpDPwMeN6v2LOY3G+qP1HVxQDu\nuU54GZOrGnCtiEQANXDGGQU9Jq2CwbXi9F6spaofuo9L8zumSmJS1Q2qet69uwXnb93TmFxzgfGl\nyu70MKaHcRJ+ofuY3GDGdIm4zuN8cQOIwfl7hyr+/YVbAik92PALPBhsKCJNcDL+FqCBejswsugf\nyr8xy8uYmgK5IrLYrVZbKCI1vYxJVQ8Bs4F/u89/QlU3eBlTKRUdXNsI52+/SKD/D4bhfCP1NCYR\n6QccUNVdpXZ5+T61BLqLyBYR+ZuI3BgCMYFTK/G0OJ2Xfo87ZVRVxxVuCcRzIvI9YCXOZI/5lB0I\nGbReCSLyc5yJKndQsqt0acHsKREBtAf+V1XbA6dw5j/z8n2KwflGmIxTnXWtiNzrZUyXESpxICKT\ngQJVXe5xHDWA3+L05AwlEUAdVb0J+A3wZ4/jKfIwzmdUEk4yeSEQJwm3BHIQSPK735gLl2YB51Z/\nrASWqmrR+JUcEWng7o8HvvKLNTHAsXYF+olIFrAc6CkiS4EvPYzpC5xvidvc+6/iJBQv36dbgCxV\nPaqq54DXgC4ex+SvonEEJT4RGYJTPXqPX7FXMTXHqbPfKSKfu8//sYjEcfHPhWC8TweAVQBu9c85\nEanrcUwAD6jq625cK4Efu+VV+vsLtwTyIdBCRJJFpDowEGcAYrC8AOxW1T/4lRUNjISyAyMHitPb\npymXGBhZWar6W1VNUtVmOO/FRlUdDLzpYUw5wAERaekW9QL+hYfvE07V1U0ico2IiBvTbg9jutjg\n2iuKw63mOiEiHd3Xc7/fMVUSk4jchlM12k9Vz5aKNegxqeo/VTVeVZupalOcLyo/UtWv3JgGePE+\nAa8DPQHcv/nqqnokyDGVF9dBEfmpG1cvnLYOqOrf37dp/ffiBtyG0wMqA5gYxPN2Bc7h9PzaDnzs\nxhILbHBjWgfE+B0zCaeXw6e4PSICGN9PudALy9OYgLY4yX4Hzrez2iEQ0xPu83+C01Ad6UVMwEvA\nIeAsTmIbCtSpaBzAjcAu9//gDwGIKQNnoO/H7u05r2MqtT8LtxeWx+9TBLDUPcc24KfBjOkScXVx\n49kObMZJtlUelw0kNMYYUynhVoVljDEmRFgCMcYYUymWQIwxxlSKJRBjjDGVYgnEGGNMpVgCMcYY\nUynBWJHQmLDlThn+V5zpRRrijAX6CmfQ1ilV7XaJY/8GjFXVj4MRqzHBZgnEmEtQ1aPAjwBE5HEg\nX1XneBuVMaHBqrCMuXIlJqwUkTy/7QnuYjzbRWRGqceJOzvxNHEW/1rsPnaniPwqWMEbU9XsCsSY\nylMAEbkd6IuzGM9Zd/bfIpHAMmCXqs4UkfZAI1Vt4x4bXfpJjQkXdgVizLfXC1is7qSDqnrcb98C\n3OTh3s8CmorIH0SkD5CHMWHKEogxgfUBcLOIREFxcmkLbAIeouRKksaEFUsgxlReUZvIemCou+gR\nIlLH7zGLgHeAFSJSzV0ropqqvgY8httAb0w4sjYQYypPAVR1rYi0BbaJyFmc5V9/57d/rojUxlln\nehawWER87v6JnkRuTBWw6dyNMcZUilVhGWOMqRRLIMYYYyrFEogxxphKsQRijDGmUiyBGGOMqRRL\nIMYYYyrFEogxxphKsQRijDGmUv4fcZELSg20OQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120462310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure();\n",
    "ax = fig.add_subplot(111);\n",
    "ax.plot(google['Ticks'], google['Rolling_Mean'], color = (0,0,0), linewidth = 4, alpha = .9, label = 'Smoothed');\n",
    "ax.plot(google['Ticks'], google['Open'], color = (1,0,0), label = 'Original');\n",
    "ax.set_title('Original and Smoothed Price')\n",
    "ax.set_xlabel('Ticks')\n",
    "ax.set_ylabel('Price')\n",
    "ax.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Plotting Original Data and Smoothed Data on Same Plot, but with Date as x axis</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ItftXqFCBKVOm+K0hvPfee6xfv95ubNlig83i4yEqCurVs2m4q1Z1u5M6M4Oh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\negAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120775250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Almost exactly the code as above but with datetime\n",
    "fig = plt.figure();\n",
    "ax = fig.add_subplot(111);\n",
    "ax.plot(google['Date'], google['Rolling_Mean'], color = (0,0,0), linewidth = 4, alpha = .9, label = 'Smoothed');\n",
    "ax.plot(google['Date'], google['Open'], color = (1,0,0), label = 'Original');\n",
    "ax.set_title('Original and Smoothed Price')\n",
    "ax.set_xlabel('Ticks')\n",
    "ax.set_ylabel('Price')\n",
    "ax.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting Date works best with datetime as the type. \n",
    "type(google['Date'].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Getting Data Only from Tick 800 to 1200</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filt_google = google[(google['Ticks'] >= 800) & (google['Ticks'] <= 1200)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "      <th>Rolling_Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>2012-05-17</td>\n",
       "      <td>316.60</td>\n",
       "      <td>318.61</td>\n",
       "      <td>310.30</td>\n",
       "      <td>311.21</td>\n",
       "      <td>800</td>\n",
       "      <td>306.568375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>312.24</td>\n",
       "      <td>315.89</td>\n",
       "      <td>298.05</td>\n",
       "      <td>299.90</td>\n",
       "      <td>801</td>\n",
       "      <td>306.865500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>299.96</td>\n",
       "      <td>307.54</td>\n",
       "      <td>299.70</td>\n",
       "      <td>306.75</td>\n",
       "      <td>802</td>\n",
       "      <td>307.043750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>306.41</td>\n",
       "      <td>306.60</td>\n",
       "      <td>297.70</td>\n",
       "      <td>300.10</td>\n",
       "      <td>803</td>\n",
       "      <td>307.310125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>2012-05-23</td>\n",
       "      <td>300.52</td>\n",
       "      <td>304.50</td>\n",
       "      <td>298.26</td>\n",
       "      <td>304.43</td>\n",
       "      <td>804</td>\n",
       "      <td>307.457375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date    Open    High     Low   Close  Ticks  Rolling_Mean\n",
       "800 2012-05-17  316.60  318.61  310.30  311.21    800    306.568375\n",
       "801 2012-05-18  312.24  315.89  298.05  299.90    801    306.865500\n",
       "802 2012-05-21  299.96  307.54  299.70  306.75    802    307.043750\n",
       "803 2012-05-22  306.41  306.60  297.70  300.10    803    307.310125\n",
       "804 2012-05-23  300.52  304.50  298.26  304.43    804    307.457375"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt_google.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Linear Regression</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y =  0.48 x +  -100.16\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(filt_google[['Ticks']], filt_google[['Rolling_Mean']])\n",
    "m = model.coef_[0]\n",
    "b = model.intercept_\n",
    "#equation of the line\n",
    "print 'y = ', round(m[0],2), 'x + ', round(b[0],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 280.99658148],\n",
       "       [ 281.47302422],\n",
       "       [ 281.94946697],\n",
       "       [ 282.42590971],\n",
       "       [ 282.90235246]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the equation of the line to get y values\n",
    "predictions = model.predict(filt_google[['Ticks']])\n",
    "predictions[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>280.996581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>281.473024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>281.949467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>282.425910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>282.902352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pred\n",
       "800  280.996581\n",
       "801  281.473024\n",
       "802  281.949467\n",
       "803  282.425910\n",
       "804  282.902352"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a DataFrame for the predictions\n",
    "predictions = pd.DataFrame(data = predictions, index = filt_google.index.values, columns = ['Pred'])\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Joining the Two DataFrames</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Ticks</th>\n",
       "      <th>Rolling_Mean</th>\n",
       "      <th>Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>2012-05-17</td>\n",
       "      <td>316.60</td>\n",
       "      <td>318.61</td>\n",
       "      <td>310.30</td>\n",
       "      <td>311.21</td>\n",
       "      <td>800</td>\n",
       "      <td>306.568375</td>\n",
       "      <td>280.996581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>312.24</td>\n",
       "      <td>315.89</td>\n",
       "      <td>298.05</td>\n",
       "      <td>299.90</td>\n",
       "      <td>801</td>\n",
       "      <td>306.865500</td>\n",
       "      <td>281.473024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>299.96</td>\n",
       "      <td>307.54</td>\n",
       "      <td>299.70</td>\n",
       "      <td>306.75</td>\n",
       "      <td>802</td>\n",
       "      <td>307.043750</td>\n",
       "      <td>281.949467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>306.41</td>\n",
       "      <td>306.60</td>\n",
       "      <td>297.70</td>\n",
       "      <td>300.10</td>\n",
       "      <td>803</td>\n",
       "      <td>307.310125</td>\n",
       "      <td>282.425910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>2012-05-23</td>\n",
       "      <td>300.52</td>\n",
       "      <td>304.50</td>\n",
       "      <td>298.26</td>\n",
       "      <td>304.43</td>\n",
       "      <td>804</td>\n",
       "      <td>307.457375</td>\n",
       "      <td>282.902352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date    Open    High     Low   Close  Ticks  Rolling_Mean  \\\n",
       "800 2012-05-17  316.60  318.61  310.30  311.21    800    306.568375   \n",
       "801 2012-05-18  312.24  315.89  298.05  299.90    801    306.865500   \n",
       "802 2012-05-21  299.96  307.54  299.70  306.75    802    307.043750   \n",
       "803 2012-05-22  306.41  306.60  297.70  300.10    803    307.310125   \n",
       "804 2012-05-23  300.52  304.50  298.26  304.43    804    307.457375   \n",
       "\n",
       "           Pred  \n",
       "800  280.996581  \n",
       "801  281.473024  \n",
       "802  281.949467  \n",
       "803  282.425910  \n",
       "804  282.902352  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join and concat documentation\n",
    "# http://pandas.pydata.org/pandas-docs/stable/merging.html\n",
    "\n",
    "joined_df = filt_google.join(predictions, how = 'inner')\n",
    "joined_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Plotting the Values Based on the Equation vs the Rolling Mean</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GqLr9drhwAXbs4MLYsQyaPJmOHTsSERGRZvPChQvzzjvv8MYbb+Dj452/9XMrpfUHdgPB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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1200b9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure();\n",
    "ax = fig.add_subplot(111);\n",
    "ax.plot(joined_df['Ticks'], joined_df['Rolling_Mean'], color = (0,0,0), linewidth = 4, alpha = .9, label = 'Smoothed');\n",
    "ax.plot(joined_df['Ticks'], joined_df['Pred'], color = (1,0,0), label = 'Prediction');\n",
    "ax.set_title('Rolling Mean vs Linear Regression')\n",
    "ax.set_xlabel('Ticks')\n",
    "ax.set_ylabel('Price')\n",
    "ax.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97617654301753054"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# documentation \n",
    "# http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html+\n",
    "import sklearn\n",
    "\n",
    "r_squared = sklearn.metrics.r2_score(joined_df['Rolling_Mean'],joined_df['Pred'],multioutput='uniform_average')\n",
    "r_squared"
   ]
  }
 ],
 "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}