gistfile1.txt

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import pandas.io.data\n",
      "from prettytable import PrettyTable"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "root = 'C:\\\\Users\\\\arzia_000\\\\Dropbox (Personal)\\\\iPython\\\\compellon\\\\datasets\\\\'\n",
      "output = 'C:\\\\Users\\\\arzia_000\\\\Dropbox (Personal)\\\\iPython\\\\compellon\\\\datasets\\\\out\\\\'\n",
      "data_file = 'hpq.csv'\n",
      "df = pd.read_csv(root + data_file)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pandas.io.data.DataReader('tsla','yahoo',start='2015/2/1') get data from Yahoo! finance"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>OpenHPQ</th>\n",
        "      <th>HighHPQ</th>\n",
        "      <th>LowHPQ</th>\n",
        "      <th>CloseHPQ</th>\n",
        "      <th>VolumeHPQ</th>\n",
        "      <th>Adj CloseHPQ</th>\n",
        "      <th>30DayMovAvgHPQ</th>\n",
        "      <th>%ChgHPQMovAvg</th>\n",
        "      <th>1 Day % ChgHPQ</th>\n",
        "      <th>...</th>\n",
        "      <th>LowMSH</th>\n",
        "      <th>CloseMSH</th>\n",
        "      <th>Adj CloseMSH</th>\n",
        "      <th>%ChgMSHMovAvg</th>\n",
        "      <th>1 Day % ChgMSH</th>\n",
        "      <th>2 Day % ChgMSH</th>\n",
        "      <th>3 Day % ChgMSH</th>\n",
        "      <th>4 Day % ChgMSH</th>\n",
        "      <th>5 Day % ChgMSH</th>\n",
        "      <th>MSH-HPQ</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 07/14/09</td>\n",
        "      <td> 36.92</td>\n",
        "      <td> 37.28</td>\n",
        "      <td> 36.67</td>\n",
        "      <td> 37.14</td>\n",
        "      <td> 13854100</td>\n",
        "      <td> 33.77</td>\n",
        "      <td> 16365073.33</td>\n",
        "      <td>-0.008000</td>\n",
        "      <td>-0.004146</td>\n",
        "      <td>...</td>\n",
        "      <td> 901.41</td>\n",
        "      <td> 912.96</td>\n",
        "      <td> 912.96</td>\n",
        "      <td>-0.003461</td>\n",
        "      <td>-0.003461</td>\n",
        "      <td>-0.008204</td>\n",
        "      <td>-0.015269</td>\n",
        "      <td>-0.001720</td>\n",
        "      <td>-0.002782</td>\n",
        "      <td> 27.034646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 07/15/09</td>\n",
        "      <td> 38.00</td>\n",
        "      <td> 38.85</td>\n",
        "      <td> 37.80</td>\n",
        "      <td> 38.82</td>\n",
        "      <td> 17475400</td>\n",
        "      <td> 35.29</td>\n",
        "      <td> 16395293.33</td>\n",
        "      <td> 0.001843</td>\n",
        "      <td> 0.043072</td>\n",
        "      <td>...</td>\n",
        "      <td> 886.67</td>\n",
        "      <td> 905.54</td>\n",
        "      <td> 905.54</td>\n",
        "      <td>-0.008194</td>\n",
        "      <td>-0.008194</td>\n",
        "      <td>-0.011684</td>\n",
        "      <td>-0.016465</td>\n",
        "      <td>-0.023588</td>\n",
        "      <td>-0.009928</td>\n",
        "      <td> 25.659960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 07/16/09</td>\n",
        "      <td> 38.51</td>\n",
        "      <td> 39.89</td>\n",
        "      <td> 38.40</td>\n",
        "      <td> 39.67</td>\n",
        "      <td> 20120200</td>\n",
        "      <td> 36.07</td>\n",
        "      <td> 16601160.00</td>\n",
        "      <td> 0.012401</td>\n",
        "      <td> 0.021625</td>\n",
        "      <td>...</td>\n",
        "      <td> 893.53</td>\n",
        "      <td> 901.39</td>\n",
        "      <td> 901.39</td>\n",
        "      <td>-0.004604</td>\n",
        "      <td>-0.004604</td>\n",
        "      <td>-0.012836</td>\n",
        "      <td>-0.016341</td>\n",
        "      <td>-0.021145</td>\n",
        "      <td>-0.028301</td>\n",
        "      <td> 24.990019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 07/17/09</td>\n",
        "      <td> 39.83</td>\n",
        "      <td> 40.00</td>\n",
        "      <td> 39.64</td>\n",
        "      <td> 39.98</td>\n",
        "      <td> 14567900</td>\n",
        "      <td> 36.35</td>\n",
        "      <td> 16456343.33</td>\n",
        "      <td>-0.008800</td>\n",
        "      <td> 0.007703</td>\n",
        "      <td>...</td>\n",
        "      <td> 889.63</td>\n",
        "      <td> 891.74</td>\n",
        "      <td> 891.74</td>\n",
        "      <td>-0.010822</td>\n",
        "      <td>-0.010822</td>\n",
        "      <td>-0.015475</td>\n",
        "      <td>-0.023796</td>\n",
        "      <td>-0.027340</td>\n",
        "      <td>-0.032195</td>\n",
        "      <td> 24.532050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 07/20/09</td>\n",
        "      <td> 40.16</td>\n",
        "      <td> 40.50</td>\n",
        "      <td> 39.84</td>\n",
        "      <td> 40.43</td>\n",
        "      <td> 11944200</td>\n",
        "      <td> 36.76</td>\n",
        "      <td> 15989310.00</td>\n",
        "      <td>-0.029209</td>\n",
        "      <td> 0.011153</td>\n",
        "      <td>...</td>\n",
        "      <td> 903.08</td>\n",
        "      <td> 904.39</td>\n",
        "      <td> 904.39</td>\n",
        "      <td> 0.013987</td>\n",
        "      <td> 0.013987</td>\n",
        "      <td> 0.003317</td>\n",
        "      <td>-0.001272</td>\n",
        "      <td>-0.009476</td>\n",
        "      <td>-0.012970</td>\n",
        "      <td> 24.602557</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 54 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "       Date  OpenHPQ  HighHPQ  LowHPQ  CloseHPQ  VolumeHPQ  Adj CloseHPQ  \\\n",
        "0  07/14/09    36.92    37.28   36.67     37.14   13854100         33.77   \n",
        "1  07/15/09    38.00    38.85   37.80     38.82   17475400         35.29   \n",
        "2  07/16/09    38.51    39.89   38.40     39.67   20120200         36.07   \n",
        "3  07/17/09    39.83    40.00   39.64     39.98   14567900         36.35   \n",
        "4  07/20/09    40.16    40.50   39.84     40.43   11944200         36.76   \n",
        "\n",
        "   30DayMovAvgHPQ  %ChgHPQMovAvg  1 Day % ChgHPQ       ...        LowMSH  \\\n",
        "0     16365073.33      -0.008000       -0.004146       ...        901.41   \n",
        "1     16395293.33       0.001843        0.043072       ...        886.67   \n",
        "2     16601160.00       0.012401        0.021625       ...        893.53   \n",
        "3     16456343.33      -0.008800        0.007703       ...        889.63   \n",
        "4     15989310.00      -0.029209        0.011153       ...        903.08   \n",
        "\n",
        "   CloseMSH  Adj CloseMSH  %ChgMSHMovAvg  1 Day % ChgMSH  2 Day % ChgMSH  \\\n",
        "0    912.96        912.96      -0.003461       -0.003461       -0.008204   \n",
        "1    905.54        905.54      -0.008194       -0.008194       -0.011684   \n",
        "2    901.39        901.39      -0.004604       -0.004604       -0.012836   \n",
        "3    891.74        891.74      -0.010822       -0.010822       -0.015475   \n",
        "4    904.39        904.39       0.013987        0.013987        0.003317   \n",
        "\n",
        "   3 Day % ChgMSH  4 Day % ChgMSH  5 Day % ChgMSH    MSH-HPQ  \n",
        "0       -0.015269       -0.001720       -0.002782  27.034646  \n",
        "1       -0.016465       -0.023588       -0.009928  25.659960  \n",
        "2       -0.016341       -0.021145       -0.028301  24.990019  \n",
        "3       -0.023796       -0.027340       -0.032195  24.532050  \n",
        "4       -0.001272       -0.009476       -0.012970  24.602557  \n",
        "\n",
        "[5 rows x 54 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Add more data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Price"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Percentage Price Oscillator (PPO)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['PPO'] = ((pd.stats.moments.rolling_mean(df['Adj CloseHPQ'], window = 12) - pd.stats.moments.rolling_mean(df['Adj CloseHPQ'], window = 26)) / pd.stats.moments.rolling_mean(df['Adj CloseHPQ'], window = 26))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.PPO.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10aacf28>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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i1VHo6ZHetXMxGpuTT5bEpJ9IuPWGw46MMteskp6E3dsPSxiRMD3l3bvlPb2u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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe334fd0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Daily Stock Activity (DSA)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['DSA'] = (df.HighHPQ-df.LowHPQ)/df.OpenHPQ"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.DSA.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xe4f2b00>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hSFoSzILLLgveFlU9BuRYREHsvLNs4ZRX9Pu3DZT2ox/J/7fd5q3r0qU8eBwH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\n+lrTBCl2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe4fccf8>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Volume"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "1 day % change in volume"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['VolumeHPQ_1d_pct_change'] = df.VolumeHPQ.pct_change(periods=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.VolumeHPQ_1d_pct_change.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xe5f9b70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xMsAJI9d0dgaPiRNkv15eWJOXZO7kV6yQUXzYSjnVC9T98OhyRtD53Oy3n11L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\nkaA8vLoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe7dbb70>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Percentage Volume Oscillator (PVO)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['PVO'] = ((pd.stats.moments.rolling_mean(df.VolumeHPQ, window = 12) - pd.stats.moments.rolling_mean(df.VolumeHPQ, window = 26)) / pd.stats.moments.rolling_mean(df.VolumeHPQ, window = 26))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.PVO.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xef790f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M19WVw5AhwLx584q2z+fz2LGDf5fl118HDjssh1tvBe64I48nngDe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\nHA6HwwHg/wNR24tWtglHKgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10854048>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>OpenHPQ</th>\n",
        "      <th>HighHPQ</th>\n",
        "      <th>LowHPQ</th>\n",
        "      <th>CloseHPQ</th>\n",
        "      <th>VolumeHPQ</th>\n",
        "      <th>Adj CloseHPQ</th>\n",
        "      <th>30DayMovAvgHPQ</th>\n",
        "      <th>%ChgHPQMovAvg</th>\n",
        "      <th>1 Day % ChgHPQ</th>\n",
        "      <th>...</th>\n",
        "      <th>1 Day % ChgMSH</th>\n",
        "      <th>2 Day % ChgMSH</th>\n",
        "      <th>3 Day % ChgMSH</th>\n",
        "      <th>4 Day % ChgMSH</th>\n",
        "      <th>5 Day % ChgMSH</th>\n",
        "      <th>MSH-HPQ</th>\n",
        "      <th>DSA</th>\n",
        "      <th>VolumeHPQ_1d_pct_change</th>\n",
        "      <th>PVO</th>\n",
        "      <th>PPO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 07/14/09</td>\n",
        "      <td> 36.92</td>\n",
        "      <td> 37.28</td>\n",
        "      <td> 36.67</td>\n",
        "      <td> 37.14</td>\n",
        "      <td> 13854100</td>\n",
        "      <td> 33.77</td>\n",
        "      <td> 16365073.33</td>\n",
        "      <td>-0.008000</td>\n",
        "      <td>-0.004146</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.003461</td>\n",
        "      <td>-0.008204</td>\n",
        "      <td>-0.015269</td>\n",
        "      <td>-0.001720</td>\n",
        "      <td>-0.002782</td>\n",
        "      <td> 27.034646</td>\n",
        "      <td> 0.016522</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 07/15/09</td>\n",
        "      <td> 38.00</td>\n",
        "      <td> 38.85</td>\n",
        "      <td> 37.80</td>\n",
        "      <td> 38.82</td>\n",
        "      <td> 17475400</td>\n",
        "      <td> 35.29</td>\n",
        "      <td> 16395293.33</td>\n",
        "      <td> 0.001843</td>\n",
        "      <td> 0.043072</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.008194</td>\n",
        "      <td>-0.011684</td>\n",
        "      <td>-0.016465</td>\n",
        "      <td>-0.023588</td>\n",
        "      <td>-0.009928</td>\n",
        "      <td> 25.659960</td>\n",
        "      <td> 0.027632</td>\n",
        "      <td> 0.261388</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 07/16/09</td>\n",
        "      <td> 38.51</td>\n",
        "      <td> 39.89</td>\n",
        "      <td> 38.40</td>\n",
        "      <td> 39.67</td>\n",
        "      <td> 20120200</td>\n",
        "      <td> 36.07</td>\n",
        "      <td> 16601160.00</td>\n",
        "      <td> 0.012401</td>\n",
        "      <td> 0.021625</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.004604</td>\n",
        "      <td>-0.012836</td>\n",
        "      <td>-0.016341</td>\n",
        "      <td>-0.021145</td>\n",
        "      <td>-0.028301</td>\n",
        "      <td> 24.990019</td>\n",
        "      <td> 0.038691</td>\n",
        "      <td> 0.151344</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 07/17/09</td>\n",
        "      <td> 39.83</td>\n",
        "      <td> 40.00</td>\n",
        "      <td> 39.64</td>\n",
        "      <td> 39.98</td>\n",
        "      <td> 14567900</td>\n",
        "      <td> 36.35</td>\n",
        "      <td> 16456343.33</td>\n",
        "      <td>-0.008800</td>\n",
        "      <td> 0.007703</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.010822</td>\n",
        "      <td>-0.015475</td>\n",
        "      <td>-0.023796</td>\n",
        "      <td>-0.027340</td>\n",
        "      <td>-0.032195</td>\n",
        "      <td> 24.532050</td>\n",
        "      <td> 0.009038</td>\n",
        "      <td>-0.275957</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 07/20/09</td>\n",
        "      <td> 40.16</td>\n",
        "      <td> 40.50</td>\n",
        "      <td> 39.84</td>\n",
        "      <td> 40.43</td>\n",
        "      <td> 11944200</td>\n",
        "      <td> 36.76</td>\n",
        "      <td> 15989310.00</td>\n",
        "      <td>-0.029209</td>\n",
        "      <td> 0.011153</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.013987</td>\n",
        "      <td> 0.003317</td>\n",
        "      <td>-0.001272</td>\n",
        "      <td>-0.009476</td>\n",
        "      <td>-0.012970</td>\n",
        "      <td> 24.602557</td>\n",
        "      <td> 0.016434</td>\n",
        "      <td>-0.180101</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 58 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "       Date  OpenHPQ  HighHPQ  LowHPQ  CloseHPQ  VolumeHPQ  Adj CloseHPQ  \\\n",
        "0  07/14/09    36.92    37.28   36.67     37.14   13854100         33.77   \n",
        "1  07/15/09    38.00    38.85   37.80     38.82   17475400         35.29   \n",
        "2  07/16/09    38.51    39.89   38.40     39.67   20120200         36.07   \n",
        "3  07/17/09    39.83    40.00   39.64     39.98   14567900         36.35   \n",
        "4  07/20/09    40.16    40.50   39.84     40.43   11944200         36.76   \n",
        "\n",
        "   30DayMovAvgHPQ  %ChgHPQMovAvg  1 Day % ChgHPQ       ...        \\\n",
        "0     16365073.33      -0.008000       -0.004146       ...         \n",
        "1     16395293.33       0.001843        0.043072       ...         \n",
        "2     16601160.00       0.012401        0.021625       ...         \n",
        "3     16456343.33      -0.008800        0.007703       ...         \n",
        "4     15989310.00      -0.029209        0.011153       ...         \n",
        "\n",
        "   1 Day % ChgMSH  2 Day % ChgMSH  3 Day % ChgMSH  4 Day % ChgMSH  \\\n",
        "0       -0.003461       -0.008204       -0.015269       -0.001720   \n",
        "1       -0.008194       -0.011684       -0.016465       -0.023588   \n",
        "2       -0.004604       -0.012836       -0.016341       -0.021145   \n",
        "3       -0.010822       -0.015475       -0.023796       -0.027340   \n",
        "4        0.013987        0.003317       -0.001272       -0.009476   \n",
        "\n",
        "   5 Day % ChgMSH    MSH-HPQ       DSA  VolumeHPQ_1d_pct_change  PVO  PPO  \n",
        "0       -0.002782  27.034646  0.016522                      NaN  NaN  NaN  \n",
        "1       -0.009928  25.659960  0.027632                 0.261388  NaN  NaN  \n",
        "2       -0.028301  24.990019  0.038691                 0.151344  NaN  NaN  \n",
        "3       -0.032195  24.532050  0.009038                -0.275957  NaN  NaN  \n",
        "4       -0.012970  24.602557  0.016434                -0.180101  NaN  NaN  \n",
        "\n",
        "[5 rows x 58 columns]"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cleanup"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Remove the first 25 records to get rid of NANs generated by rolling means"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_HPQ_new = df.ix[25:,:]\n",
      "df_HPQ_new.to_csv('hpq_new.csv',index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Some data exploration"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "A list of variables in the dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = PrettyTable(['#','Field/column','Count (not null)','%'])\n",
      "t.add_row(['','Total records',len(df),'100%'])\n",
      "for i in range(len(df.columns)):\n",
      "    c = len(df[df.ix[:,i].notnull()])\n",
      "    t.add_row([i, df.columns.values[i][:20], c, str(100 *  c / len(df)) + '%' ])\n",
      "\n",
      "print t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+----+--------------------+------------------+------+\n",
        "| #  |    Field/column    | Count (not null) |  %   |\n",
        "+----+--------------------+------------------+------+\n",
        "|    |   Total records    |       1217       | 100% |\n",
        "| 0  |        Date        |       1217       | 100% |\n",
        "| 1  |      OpenHPQ       |       1217       | 100% |\n",
        "| 2  |      HighHPQ       |       1217       | 100% |\n",
        "| 3  |       LowHPQ       |       1217       | 100% |\n",
        "| 4  |      CloseHPQ      |       1217       | 100% |\n",
        "| 5  |     VolumeHPQ      |       1217       | 100% |\n",
        "| 6  |    Adj CloseHPQ    |       1217       | 100% |\n",
        "| 7  |   30DayMovAvgHPQ   |       1217       | 100% |\n",
        "| 8  |   %ChgHPQMovAvg    |       1217       | 100% |\n",
        "| 9  |   1 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 10 |   2 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 11 |   3 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 12 |   4 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 13 |   5 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 14 |   6 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 15 |   7 Day % ChgHPQ   |       1217       | 100% |\n",
        "| 16 | Fwd1DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 17 | Fwd2DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 18 | Fwd3DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 19 | Fwd4DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 20 | Fwd5DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 21 | Fwd6DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 22 | Fwd7DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 23 | Fwd8DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 24 | Fwd9DayPrcChg%HPQ  |       1217       | 100% |\n",
        "| 25 | Fwd10DayPrcChg%HPQ |       1217       | 100% |\n",
        "| 26 | Fwd11DayPrcChg%HPQ |       1217       | 100% |\n",
        "| 27 | Fwd12DayPrcChg%HPQ |       1217       | 100% |\n",
        "| 28 |      OpenNAS       |       1217       | 100% |\n",
        "| 29 |      HighNAS       |       1217       | 100% |\n",
        "| 30 |       LowNAS       |       1217       | 100% |\n",
        "| 31 |      CloseNAS      |       1217       | 100% |\n",
        "| 32 |     VolumeNAS      |       1217       | 100% |\n",
        "| 33 |      NAS-HPQ       |       1217       | 100% |\n",
        "| 34 |    Adj CloseNAS    |       1217       | 100% |\n",
        "| 35 |   30DayMovAvgNAS   |       1217       | 100% |\n",
        "| 36 |   %ChgNASMovAvg    |       1217       | 100% |\n",
        "| 37 |   1 Day % ChgNAS   |       1217       | 100% |\n",
        "| 38 |   2 Day % ChgNAS   |       1217       | 100% |\n",
        "| 39 |   3 Day % ChgNAS   |       1217       | 100% |\n",
        "| 40 |   4 Day % ChgNAS   |       1217       | 100% |\n",
        "| 41 |   5 Day % ChgNAS   |       1217       | 100% |\n",
        "| 42 |      OpenMSH       |       1217       | 100% |\n",
        "| 43 |      HighMSH       |       1217       | 100% |\n",
        "| 44 |       LowMSH       |       1217       | 100% |\n",
        "| 45 |      CloseMSH      |       1217       | 100% |\n",
        "| 46 |    Adj CloseMSH    |       1217       | 100% |\n",
        "| 47 |   %ChgMSHMovAvg    |       1217       | 100% |\n",
        "| 48 |   1 Day % ChgMSH   |       1217       | 100% |\n",
        "| 49 |   2 Day % ChgMSH   |       1217       | 100% |\n",
        "| 50 |   3 Day % ChgMSH   |       1217       | 100% |\n",
        "| 51 |   4 Day % ChgMSH   |       1217       | 100% |\n",
        "| 52 |   5 Day % ChgMSH   |       1217       | 100% |\n",
        "| 53 |      MSH-HPQ       |       1217       | 100% |\n",
        "+----+--------------------+------------------+------+\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Split the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_size = 2./3\n",
      "data_train = df_x.ix[0:int(math.floor(train_size * len(df_x)))-1,:]\n",
      "data_test  = df_x.ix[int(math.floor(train_size * len(df_x))):len(df_x)-1,:]\n",
      "data_train.to_csv(output + 'hpq_train.csv', index=False)\n",
      "data_test.to_csv(output + 'hpq_test.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}