Untitled7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#impoer pandas library playing with data \n",
    "#import matplotlib to plot the data\n",
    "#import LogisticRegression for classification \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "open_ds = pd.read_csv(\"eopen_high_alphatext.txt\")\n",
    "close_ds = pd.read_csv(\"eclosed_high_alphatext.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#here just check moving mean \n",
    "open_ds[\"sma_10\"] = pd.stats.moments.rolling_mean(open_ds[\"0\"], 10)\n",
    "close_ds[\"sma_10\"] = pd.stats.moments.rolling_mean(close_ds[\"0\"], 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>0</th>\n",
       "      <th>sma_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30673</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>379</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11685</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2572</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7494</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2954</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4386</td>\n",
       "      <td>6874.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31710</td>\n",
       "      <td>10045.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5655</td>\n",
       "      <td>7543.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5628</td>\n",
       "      <td>8068.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2306</td>\n",
       "      <td>7130.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5697</td>\n",
       "      <td>7385.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14156</td>\n",
       "      <td>8543.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6937</td>\n",
       "      <td>8692.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14236</td>\n",
       "      <td>9366.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8016</td>\n",
       "      <td>9872.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2432</td>\n",
       "      <td>9677.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3945</td>\n",
       "      <td>6900.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>55009</td>\n",
       "      <td>11836.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3386</td>\n",
       "      <td>11612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3054</td>\n",
       "      <td>11686.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2132</td>\n",
       "      <td>11330.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7362</td>\n",
       "      <td>10650.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17543</td>\n",
       "      <td>11711.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4049</td>\n",
       "      <td>10692.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11363</td>\n",
       "      <td>11027.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3687</td>\n",
       "      <td>11153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>12804</td>\n",
       "      <td>25461.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>18336</td>\n",
       "      <td>19621.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>4296</td>\n",
       "      <td>18202.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>4182</td>\n",
       "      <td>17480.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>15654</td>\n",
       "      <td>18191.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>30100</td>\n",
       "      <td>19124.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>3009</td>\n",
       "      <td>18964.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1863</td>\n",
       "      <td>13865.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>19719</td>\n",
       "      <td>14258.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>6193</td>\n",
       "      <td>11615.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>23293</td>\n",
       "      <td>12664.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>9260</td>\n",
       "      <td>11756.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>46175</td>\n",
       "      <td>15944.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>4064</td>\n",
       "      <td>15933.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>13842</td>\n",
       "      <td>15751.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>4899</td>\n",
       "      <td>13231.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>22103</td>\n",
       "      <td>15141.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>9958</td>\n",
       "      <td>15950.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1865</td>\n",
       "      <td>14165.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>4718</td>\n",
       "      <td>14017.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>10977</td>\n",
       "      <td>12786.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>21574</td>\n",
       "      <td>14017.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>6430</td>\n",
       "      <td>10043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>7912</td>\n",
       "      <td>10427.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5433</td>\n",
       "      <td>9586.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2841</td>\n",
       "      <td>9381.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>30036</td>\n",
       "      <td>10174.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6099</td>\n",
       "      <td>9788.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>18328</td>\n",
       "      <td>11434.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5362</td>\n",
       "      <td>11499.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0   sma_10\n",
       "0       0      NaN\n",
       "1   30673      NaN\n",
       "2     379      NaN\n",
       "3   11685      NaN\n",
       "4    3150      NaN\n",
       "5    2572      NaN\n",
       "6    5450      NaN\n",
       "7    7494      NaN\n",
       "8    2954      NaN\n",
       "9    4386   6874.3\n",
       "10  31710  10045.3\n",
       "11   5655   7543.5\n",
       "12   5628   8068.4\n",
       "13   2306   7130.5\n",
       "14   5697   7385.2\n",
       "15  14156   8543.6\n",
       "16   6937   8692.3\n",
       "17  14236   9366.5\n",
       "18   8016   9872.7\n",
       "19   2432   9677.3\n",
       "20   3945   6900.8\n",
       "21  55009  11836.2\n",
       "22   3386  11612.0\n",
       "23   3054  11686.8\n",
       "24   2132  11330.3\n",
       "25   7362  10650.9\n",
       "26  17543  11711.5\n",
       "27   4049  10692.8\n",
       "28  11363  11027.5\n",
       "29   3687  11153.0\n",
       "..    ...      ...\n",
       "70  12804  25461.2\n",
       "71  18336  19621.1\n",
       "72   4296  18202.8\n",
       "73   4182  17480.1\n",
       "74  15654  18191.6\n",
       "75  30100  19124.7\n",
       "76   3009  18964.4\n",
       "77   1863  13865.2\n",
       "78  19719  14258.5\n",
       "79   6193  11615.6\n",
       "80  23293  12664.5\n",
       "81   9260  11756.9\n",
       "82  46175  15944.8\n",
       "83   4064  15933.0\n",
       "84  13842  15751.8\n",
       "85   4899  13231.7\n",
       "86  22103  15141.1\n",
       "87   9958  15950.6\n",
       "88   1865  14165.2\n",
       "89   4718  14017.7\n",
       "90  10977  12786.1\n",
       "91  21574  14017.5\n",
       "92   6430  10043.0\n",
       "93   7912  10427.8\n",
       "94   5433   9586.9\n",
       "95   2841   9381.1\n",
       "96  30036  10174.4\n",
       "97   6099   9788.5\n",
       "98  18328  11434.8\n",
       "99   5362  11499.2\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4987732810>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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a9TBzBFx6ba5du15hcPBholZRK+mEezUMr65oSaJPOT0osxZItuT5kubmgvMh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49878a7210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This is to plot the points\n",
    "plt.scatter(open_ds.index, open_ds[\"sma_10\"], c=\"red\")\n",
    "plt.scatter(close_ds.index, close_ds[\"sma_10\"], c=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f49876600d0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IPgCQAaAZwJ3MzFpTdwJYASAcwH+YeZ1WvhzAW0SUBaAMwA1aW+VE9E8AP2j1FmlJcr8R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\njIGp4xkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4987700510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(open_ds[\"0\"])\n",
    "plt.plot(close_ds[\"0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>sma_10</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21320.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20052.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20410.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8016.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11983.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14160.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13043.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20050.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20610.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21016.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28358.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28892.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30392.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>37282.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>34759.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>33411.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>33666.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28566.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28370.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29673.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23277.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27216.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25609.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27699.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26779.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26959.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>32754.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30891.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>33955.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31987.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32309.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>30892.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33954.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>23584.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>23177.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35042.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>54007.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>56318.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>53549.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>58836.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>59506.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>69118.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>69033.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>70636.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>70599.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>58872.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>39663.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>37897.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sma_10  label\n",
       "0   21320.9      0\n",
       "1   20052.5      0\n",
       "2   20410.0      0\n",
       "3    8016.9      0\n",
       "4   11983.3      0\n",
       "5   14160.8      0\n",
       "6   13043.7      0\n",
       "7   20050.3      0\n",
       "8   20610.2      0\n",
       "9   21016.7      0\n",
       "10  28358.6      0\n",
       "11  28892.3      0\n",
       "12  30392.5      0\n",
       "13  37282.9      0\n",
       "14  34759.7      0\n",
       "15  33411.3      0\n",
       "16  33666.6      0\n",
       "17  28566.0      0\n",
       "18  28370.7      0\n",
       "19  29673.7      0\n",
       "20  23277.3      0\n",
       "21  27216.3      0\n",
       "22  25609.0      0\n",
       "23  27699.4      0\n",
       "24  26779.7      0\n",
       "25  26959.8      0\n",
       "26  32754.1      0\n",
       "27  30891.0      0\n",
       "28  33955.3      0\n",
       "29  31987.5      0\n",
       "30  32309.7      0\n",
       "31  30892.9      0\n",
       "32  33954.0      0\n",
       "33  23584.9      0\n",
       "34  23177.2      0\n",
       "35  35042.2      0\n",
       "36  54007.7      0\n",
       "37  56318.9      0\n",
       "38  53549.9      0\n",
       "39  58836.5      0\n",
       "40  59506.0      0\n",
       "41  69118.3      0\n",
       "42  69033.9      0\n",
       "43  70636.6      0\n",
       "44  70599.8      0\n",
       "45  58872.8      0\n",
       "46  39663.6      0\n",
       "47  37897.1      0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here label the open with 0 \n",
    "ds = pd.DataFrame()\n",
    "ds[\"sma_10\"] = open_ds[\"sma_10\"][10:].values\n",
    "ds[\"label\"] = 0\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>sma_10</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10045.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7543.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8068.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7130.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7385.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8543.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8692.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9366.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9872.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9677.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6900.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11836.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11612.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11686.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11330.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10650.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11711.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10692.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11027.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11153.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11249.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6127.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6436.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7118.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7135.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6438.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5682.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5513.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5205.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5056.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>25461.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>19621.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>18202.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>17480.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>18191.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>19124.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>18964.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>13865.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>14258.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>11615.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>12664.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>11756.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>15944.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>15933.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>15751.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>13231.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>15141.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>15950.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>14165.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>14017.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>12786.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>14017.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10043.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10427.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>9586.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>9381.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>10174.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>9788.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>11434.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>11499.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sma_10  label\n",
       "0   10045.3      1\n",
       "1    7543.5      1\n",
       "2    8068.4      1\n",
       "3    7130.5      1\n",
       "4    7385.2      1\n",
       "5    8543.6      1\n",
       "6    8692.3      1\n",
       "7    9366.5      1\n",
       "8    9872.7      1\n",
       "9    9677.3      1\n",
       "10   6900.8      1\n",
       "11  11836.2      1\n",
       "12  11612.0      1\n",
       "13  11686.8      1\n",
       "14  11330.3      1\n",
       "15  10650.9      1\n",
       "16  11711.5      1\n",
       "17  10692.8      1\n",
       "18  11027.5      1\n",
       "19  11153.0      1\n",
       "20  11249.3      1\n",
       "21   6127.9      1\n",
       "22   6436.9      1\n",
       "23   7118.0      1\n",
       "24   7135.2      1\n",
       "25   6438.9      1\n",
       "26   5682.8      1\n",
       "27   5513.4      1\n",
       "28   5205.3      1\n",
       "29   5056.4      1\n",
       "..      ...    ...\n",
       "60  25461.2      1\n",
       "61  19621.1      1\n",
       "62  18202.8      1\n",
       "63  17480.1      1\n",
       "64  18191.6      1\n",
       "65  19124.7      1\n",
       "66  18964.4      1\n",
       "67  13865.2      1\n",
       "68  14258.5      1\n",
       "69  11615.6      1\n",
       "70  12664.5      1\n",
       "71  11756.9      1\n",
       "72  15944.8      1\n",
       "73  15933.0      1\n",
       "74  15751.8      1\n",
       "75  13231.7      1\n",
       "76  15141.1      1\n",
       "77  15950.6      1\n",
       "78  14165.2      1\n",
       "79  14017.7      1\n",
       "80  12786.1      1\n",
       "81  14017.5      1\n",
       "82  10043.0      1\n",
       "83  10427.8      1\n",
       "84   9586.9      1\n",
       "85   9381.1      1\n",
       "86  10174.4      1\n",
       "87   9788.5      1\n",
       "88  11434.8      1\n",
       "89  11499.2      1\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here label the close with 0 \n",
    "ds_c = pd.DataFrame()\n",
    "ds_c[\"sma_10\"] = close_ds[\"sma_10\"][10:].values\n",
    "ds_c[\"label\"] = 1\n",
    "ds_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>sma_10</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21320.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20052.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20410.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8016.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11983.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14160.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13043.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20050.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20610.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21016.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28358.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28892.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30392.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>37282.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>34759.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>33411.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>33666.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28566.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28370.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29673.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23277.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27216.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25609.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27699.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26779.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26959.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>32754.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30891.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>33955.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31987.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>25461.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>19621.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>18202.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>17480.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>18191.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>19124.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>18964.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>13865.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>14258.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>11615.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>12664.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>11756.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>15944.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>15933.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>15751.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>13231.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>15141.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>15950.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>14165.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>14017.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>12786.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>14017.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>10043.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>10427.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>9586.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>9381.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>10174.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>9788.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>11434.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>11499.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sma_10  label\n",
       "0    21320.9      0\n",
       "1    20052.5      0\n",
       "2    20410.0      0\n",
       "3     8016.9      0\n",
       "4    11983.3      0\n",
       "5    14160.8      0\n",
       "6    13043.7      0\n",
       "7    20050.3      0\n",
       "8    20610.2      0\n",
       "9    21016.7      0\n",
       "10   28358.6      0\n",
       "11   28892.3      0\n",
       "12   30392.5      0\n",
       "13   37282.9      0\n",
       "14   34759.7      0\n",
       "15   33411.3      0\n",
       "16   33666.6      0\n",
       "17   28566.0      0\n",
       "18   28370.7      0\n",
       "19   29673.7      0\n",
       "20   23277.3      0\n",
       "21   27216.3      0\n",
       "22   25609.0      0\n",
       "23   27699.4      0\n",
       "24   26779.7      0\n",
       "25   26959.8      0\n",
       "26   32754.1      0\n",
       "27   30891.0      0\n",
       "28   33955.3      0\n",
       "29   31987.5      0\n",
       "..       ...    ...\n",
       "108  25461.2      1\n",
       "109  19621.1      1\n",
       "110  18202.8      1\n",
       "111  17480.1      1\n",
       "112  18191.6      1\n",
       "113  19124.7      1\n",
       "114  18964.4      1\n",
       "115  13865.2      1\n",
       "116  14258.5      1\n",
       "117  11615.6      1\n",
       "118  12664.5      1\n",
       "119  11756.9      1\n",
       "120  15944.8      1\n",
       "121  15933.0      1\n",
       "122  15751.8      1\n",
       "123  13231.7      1\n",
       "124  15141.1      1\n",
       "125  15950.6      1\n",
       "126  14165.2      1\n",
       "127  14017.7      1\n",
       "128  12786.1      1\n",
       "129  14017.5      1\n",
       "130  10043.0      1\n",
       "131  10427.8      1\n",
       "132   9586.9      1\n",
       "133   9381.1      1\n",
       "134  10174.4      1\n",
       "135   9788.5      1\n",
       "136  11434.8      1\n",
       "137  11499.2      1\n",
       "\n",
       "[138 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concat both \n",
    "ds = pd.concat([ds, ds_c], ignore_index=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#the classifier \n",
    "cls = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      21320.9\n",
       "1      20052.5\n",
       "2      20410.0\n",
       "3       8016.9\n",
       "4      11983.3\n",
       "5      14160.8\n",
       "6      13043.7\n",
       "7      20050.3\n",
       "8      20610.2\n",
       "9      21016.7\n",
       "10     28358.6\n",
       "11     28892.3\n",
       "12     30392.5\n",
       "13     37282.9\n",
       "14     34759.7\n",
       "15     33411.3\n",
       "16     33666.6\n",
       "17     28566.0\n",
       "18     28370.7\n",
       "19     29673.7\n",
       "20     23277.3\n",
       "21     27216.3\n",
       "22     25609.0\n",
       "23     27699.4\n",
       "24     26779.7\n",
       "25     26959.8\n",
       "26     32754.1\n",
       "27     30891.0\n",
       "28     33955.3\n",
       "29     31987.5\n",
       "        ...   \n",
       "108    25461.2\n",
       "109    19621.1\n",
       "110    18202.8\n",
       "111    17480.1\n",
       "112    18191.6\n",
       "113    19124.7\n",
       "114    18964.4\n",
       "115    13865.2\n",
       "116    14258.5\n",
       "117    11615.6\n",
       "118    12664.5\n",
       "119    11756.9\n",
       "120    15944.8\n",
       "121    15933.0\n",
       "122    15751.8\n",
       "123    13231.7\n",
       "124    15141.1\n",
       "125    15950.6\n",
       "126    14165.2\n",
       "127    14017.7\n",
       "128    12786.1\n",
       "129    14017.5\n",
       "130    10043.0\n",
       "131    10427.8\n",
       "132     9586.9\n",
       "133     9381.1\n",
       "134    10174.4\n",
       "135     9788.5\n",
       "136    11434.8\n",
       "137    11499.2\n",
       "Name: sma_10, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sma_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "5      0\n",
       "6      0\n",
       "7      0\n",
       "8      0\n",
       "9      0\n",
       "10     0\n",
       "11     0\n",
       "12     0\n",
       "13     0\n",
       "14     0\n",
       "15     0\n",
       "16     0\n",
       "17     0\n",
       "18     0\n",
       "19     0\n",
       "20     0\n",
       "21     0\n",
       "22     0\n",
       "23     0\n",
       "24     0\n",
       "25     0\n",
       "26     0\n",
       "27     0\n",
       "28     0\n",
       "29     0\n",
       "      ..\n",
       "108    1\n",
       "109    1\n",
       "110    1\n",
       "111    1\n",
       "112    1\n",
       "113    1\n",
       "114    1\n",
       "115    1\n",
       "116    1\n",
       "117    1\n",
       "118    1\n",
       "119    1\n",
       "120    1\n",
       "121    1\n",
       "122    1\n",
       "123    1\n",
       "124    1\n",
       "125    1\n",
       "126    1\n",
       "127    1\n",
       "128    1\n",
       "129    1\n",
       "130    1\n",
       "131    1\n",
       "132    1\n",
       "133    1\n",
       "134    1\n",
       "135    1\n",
       "136    1\n",
       "137    1\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#learning the classifer \n",
    "cls.fit(ds[[\"sma_10\"]], ds.label)\n",
    "cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8188405797101449"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the accuracy of the classifer \n",
    "cls.score(ds[[\"sma_10\"]], ds.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#split data to train and test \n",
    "x_train,x_test,y_train,y_test = train_test_split(ds[[\"sma_10\"]], ds.label, train_size=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#do the same traning \n",
    "cls.fit(x_train, y_train)\n",
    "cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83495145631067957"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of train \n",
    "cls.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77142857142857146"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of the test\n",
    "cls.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython2",
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