machine learning regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.svm import SVR\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are using iexcloud to\n",
    "stock_name = \"AAPL\"\n",
    "token = \"pk_83d03d65e027441cbf1122bac867d84d\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = pd.read_json('https://cloud.iexapis.com/stable/stock/'+stock_name+'/chart/1m?token='+token+'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>change</th>\n",
       "      <th>changeOverTime</th>\n",
       "      <th>changePercent</th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>label</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>uClose</th>\n",
       "      <th>uHigh</th>\n",
       "      <th>uLow</th>\n",
       "      <th>uOpen</th>\n",
       "      <th>uVolume</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>219.89</td>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>220.94</td>\n",
       "      <td>Sep 26</td>\n",
       "      <td>218.83</td>\n",
       "      <td>220.00</td>\n",
       "      <td>219.89</td>\n",
       "      <td>220.94</td>\n",
       "      <td>218.83</td>\n",
       "      <td>220.00</td>\n",
       "      <td>19088312</td>\n",
       "      <td>19088312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.07</td>\n",
       "      <td>-0.004866</td>\n",
       "      <td>-0.4866</td>\n",
       "      <td>218.82</td>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>220.96</td>\n",
       "      <td>Sep 27</td>\n",
       "      <td>217.28</td>\n",
       "      <td>220.54</td>\n",
       "      <td>218.82</td>\n",
       "      <td>220.96</td>\n",
       "      <td>217.28</td>\n",
       "      <td>220.54</td>\n",
       "      <td>25361285</td>\n",
       "      <td>25361285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.15</td>\n",
       "      <td>0.018555</td>\n",
       "      <td>2.3535</td>\n",
       "      <td>223.97</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>224.58</td>\n",
       "      <td>Sep 30</td>\n",
       "      <td>220.79</td>\n",
       "      <td>220.90</td>\n",
       "      <td>223.97</td>\n",
       "      <td>224.58</td>\n",
       "      <td>220.79</td>\n",
       "      <td>220.90</td>\n",
       "      <td>26318583</td>\n",
       "      <td>26318583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.021374</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>224.59</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>228.22</td>\n",
       "      <td>Oct 1</td>\n",
       "      <td>224.20</td>\n",
       "      <td>225.07</td>\n",
       "      <td>224.59</td>\n",
       "      <td>228.22</td>\n",
       "      <td>224.20</td>\n",
       "      <td>225.07</td>\n",
       "      <td>36187163</td>\n",
       "      <td>36187163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.63</td>\n",
       "      <td>-0.004229</td>\n",
       "      <td>-2.5068</td>\n",
       "      <td>218.96</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>223.58</td>\n",
       "      <td>Oct 2</td>\n",
       "      <td>217.93</td>\n",
       "      <td>223.06</td>\n",
       "      <td>218.96</td>\n",
       "      <td>223.58</td>\n",
       "      <td>217.93</td>\n",
       "      <td>223.06</td>\n",
       "      <td>35767257</td>\n",
       "      <td>35767257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.86</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.8495</td>\n",
       "      <td>220.82</td>\n",
       "      <td>2019-10-03</td>\n",
       "      <td>220.96</td>\n",
       "      <td>Oct 3</td>\n",
       "      <td>215.13</td>\n",
       "      <td>218.43</td>\n",
       "      <td>220.82</td>\n",
       "      <td>220.96</td>\n",
       "      <td>215.13</td>\n",
       "      <td>218.43</td>\n",
       "      <td>30352686</td>\n",
       "      <td>30352686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.19</td>\n",
       "      <td>0.032380</td>\n",
       "      <td>2.8032</td>\n",
       "      <td>227.01</td>\n",
       "      <td>2019-10-04</td>\n",
       "      <td>227.49</td>\n",
       "      <td>Oct 4</td>\n",
       "      <td>223.89</td>\n",
       "      <td>225.64</td>\n",
       "      <td>227.01</td>\n",
       "      <td>227.49</td>\n",
       "      <td>223.89</td>\n",
       "      <td>225.64</td>\n",
       "      <td>34755553</td>\n",
       "      <td>34755553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.032607</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>227.06</td>\n",
       "      <td>2019-10-07</td>\n",
       "      <td>229.93</td>\n",
       "      <td>Oct 7</td>\n",
       "      <td>225.84</td>\n",
       "      <td>226.27</td>\n",
       "      <td>227.06</td>\n",
       "      <td>229.93</td>\n",
       "      <td>225.84</td>\n",
       "      <td>226.27</td>\n",
       "      <td>30889269</td>\n",
       "      <td>30889269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.66</td>\n",
       "      <td>0.020510</td>\n",
       "      <td>-1.1715</td>\n",
       "      <td>224.40</td>\n",
       "      <td>2019-10-08</td>\n",
       "      <td>228.06</td>\n",
       "      <td>Oct 8</td>\n",
       "      <td>224.33</td>\n",
       "      <td>225.82</td>\n",
       "      <td>224.40</td>\n",
       "      <td>228.06</td>\n",
       "      <td>224.33</td>\n",
       "      <td>225.82</td>\n",
       "      <td>29282700</td>\n",
       "      <td>29282700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.63</td>\n",
       "      <td>0.032471</td>\n",
       "      <td>1.1720</td>\n",
       "      <td>227.03</td>\n",
       "      <td>2019-10-09</td>\n",
       "      <td>227.79</td>\n",
       "      <td>Oct 9</td>\n",
       "      <td>225.64</td>\n",
       "      <td>227.03</td>\n",
       "      <td>227.03</td>\n",
       "      <td>227.79</td>\n",
       "      <td>225.64</td>\n",
       "      <td>227.03</td>\n",
       "      <td>19029424</td>\n",
       "      <td>19029424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.06</td>\n",
       "      <td>0.046387</td>\n",
       "      <td>1.3478</td>\n",
       "      <td>230.09</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>230.44</td>\n",
       "      <td>Oct 10</td>\n",
       "      <td>227.30</td>\n",
       "      <td>227.93</td>\n",
       "      <td>230.09</td>\n",
       "      <td>230.44</td>\n",
       "      <td>227.30</td>\n",
       "      <td>227.93</td>\n",
       "      <td>28962984</td>\n",
       "      <td>28962984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.12</td>\n",
       "      <td>0.074219</td>\n",
       "      <td>2.6598</td>\n",
       "      <td>236.21</td>\n",
       "      <td>2019-10-11</td>\n",
       "      <td>237.64</td>\n",
       "      <td>Oct 11</td>\n",
       "      <td>232.30</td>\n",
       "      <td>232.95</td>\n",
       "      <td>236.21</td>\n",
       "      <td>237.64</td>\n",
       "      <td>232.30</td>\n",
       "      <td>232.95</td>\n",
       "      <td>41990210</td>\n",
       "      <td>41990210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.072673</td>\n",
       "      <td>-0.1439</td>\n",
       "      <td>235.87</td>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>238.13</td>\n",
       "      <td>Oct 14</td>\n",
       "      <td>234.67</td>\n",
       "      <td>234.90</td>\n",
       "      <td>235.87</td>\n",
       "      <td>238.13</td>\n",
       "      <td>234.67</td>\n",
       "      <td>234.90</td>\n",
       "      <td>24413484</td>\n",
       "      <td>24413484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.070171</td>\n",
       "      <td>-0.2332</td>\n",
       "      <td>235.32</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>237.65</td>\n",
       "      <td>Oct 15</td>\n",
       "      <td>234.88</td>\n",
       "      <td>236.39</td>\n",
       "      <td>235.32</td>\n",
       "      <td>237.65</td>\n",
       "      <td>234.88</td>\n",
       "      <td>236.39</td>\n",
       "      <td>23040483</td>\n",
       "      <td>23040483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.065851</td>\n",
       "      <td>-0.4037</td>\n",
       "      <td>234.37</td>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>235.24</td>\n",
       "      <td>Oct 16</td>\n",
       "      <td>233.20</td>\n",
       "      <td>233.37</td>\n",
       "      <td>234.37</td>\n",
       "      <td>235.24</td>\n",
       "      <td>233.20</td>\n",
       "      <td>233.37</td>\n",
       "      <td>19286694</td>\n",
       "      <td>19286694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.069990</td>\n",
       "      <td>0.3883</td>\n",
       "      <td>235.28</td>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>236.15</td>\n",
       "      <td>Oct 17</td>\n",
       "      <td>233.52</td>\n",
       "      <td>235.09</td>\n",
       "      <td>235.28</td>\n",
       "      <td>236.15</td>\n",
       "      <td>233.52</td>\n",
       "      <td>235.09</td>\n",
       "      <td>17272897</td>\n",
       "      <td>17272897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.13</td>\n",
       "      <td>0.075128</td>\n",
       "      <td>0.4803</td>\n",
       "      <td>236.41</td>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>237.58</td>\n",
       "      <td>Oct 18</td>\n",
       "      <td>234.29</td>\n",
       "      <td>234.59</td>\n",
       "      <td>236.41</td>\n",
       "      <td>237.58</td>\n",
       "      <td>234.29</td>\n",
       "      <td>234.59</td>\n",
       "      <td>24377166</td>\n",
       "      <td>24377166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.10</td>\n",
       "      <td>0.093774</td>\n",
       "      <td>1.7343</td>\n",
       "      <td>240.51</td>\n",
       "      <td>2019-10-21</td>\n",
       "      <td>240.99</td>\n",
       "      <td>Oct 21</td>\n",
       "      <td>237.32</td>\n",
       "      <td>237.52</td>\n",
       "      <td>240.51</td>\n",
       "      <td>240.99</td>\n",
       "      <td>237.32</td>\n",
       "      <td>237.52</td>\n",
       "      <td>22367483</td>\n",
       "      <td>22367483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.091273</td>\n",
       "      <td>-0.2287</td>\n",
       "      <td>239.96</td>\n",
       "      <td>2019-10-22</td>\n",
       "      <td>242.20</td>\n",
       "      <td>Oct 22</td>\n",
       "      <td>239.62</td>\n",
       "      <td>241.16</td>\n",
       "      <td>239.96</td>\n",
       "      <td>242.20</td>\n",
       "      <td>239.62</td>\n",
       "      <td>241.16</td>\n",
       "      <td>22684001</td>\n",
       "      <td>22684001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.22</td>\n",
       "      <td>0.105917</td>\n",
       "      <td>1.3419</td>\n",
       "      <td>243.18</td>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>243.24</td>\n",
       "      <td>Oct 23</td>\n",
       "      <td>241.22</td>\n",
       "      <td>242.10</td>\n",
       "      <td>243.18</td>\n",
       "      <td>243.24</td>\n",
       "      <td>241.22</td>\n",
       "      <td>242.10</td>\n",
       "      <td>19932545</td>\n",
       "      <td>19932545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.107736</td>\n",
       "      <td>0.1645</td>\n",
       "      <td>243.58</td>\n",
       "      <td>2019-10-24</td>\n",
       "      <td>244.80</td>\n",
       "      <td>Oct 24</td>\n",
       "      <td>241.80</td>\n",
       "      <td>244.51</td>\n",
       "      <td>243.58</td>\n",
       "      <td>244.80</td>\n",
       "      <td>241.80</td>\n",
       "      <td>244.51</td>\n",
       "      <td>17916255</td>\n",
       "      <td>17916255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.00</td>\n",
       "      <td>0.121379</td>\n",
       "      <td>1.2316</td>\n",
       "      <td>246.58</td>\n",
       "      <td>2019-10-25</td>\n",
       "      <td>246.73</td>\n",
       "      <td>Oct 25</td>\n",
       "      <td>242.88</td>\n",
       "      <td>243.16</td>\n",
       "      <td>246.58</td>\n",
       "      <td>246.73</td>\n",
       "      <td>242.88</td>\n",
       "      <td>243.16</td>\n",
       "      <td>18369296</td>\n",
       "      <td>18369296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    change  changeOverTime  changePercent   close       date    high   label  \\\n",
       "0     0.00        0.000000         0.0000  219.89 2019-09-26  220.94  Sep 26   \n",
       "1    -1.07       -0.004866        -0.4866  218.82 2019-09-27  220.96  Sep 27   \n",
       "2     5.15        0.018555         2.3535  223.97 2019-09-30  224.58  Sep 30   \n",
       "3     0.62        0.021374         0.2768  224.59 2019-10-01  228.22   Oct 1   \n",
       "4    -5.63       -0.004229        -2.5068  218.96 2019-10-02  223.58   Oct 2   \n",
       "5     1.86        0.004229         0.8495  220.82 2019-10-03  220.96   Oct 3   \n",
       "6     6.19        0.032380         2.8032  227.01 2019-10-04  227.49   Oct 4   \n",
       "7     0.05        0.032607         0.0220  227.06 2019-10-07  229.93   Oct 7   \n",
       "8    -2.66        0.020510        -1.1715  224.40 2019-10-08  228.06   Oct 8   \n",
       "9     2.63        0.032471         1.1720  227.03 2019-10-09  227.79   Oct 9   \n",
       "10    3.06        0.046387         1.3478  230.09 2019-10-10  230.44  Oct 10   \n",
       "11    6.12        0.074219         2.6598  236.21 2019-10-11  237.64  Oct 11   \n",
       "12   -0.34        0.072673        -0.1439  235.87 2019-10-14  238.13  Oct 14   \n",
       "13   -0.55        0.070171        -0.2332  235.32 2019-10-15  237.65  Oct 15   \n",
       "14   -0.95        0.065851        -0.4037  234.37 2019-10-16  235.24  Oct 16   \n",
       "15    0.91        0.069990         0.3883  235.28 2019-10-17  236.15  Oct 17   \n",
       "16    1.13        0.075128         0.4803  236.41 2019-10-18  237.58  Oct 18   \n",
       "17    4.10        0.093774         1.7343  240.51 2019-10-21  240.99  Oct 21   \n",
       "18   -0.55        0.091273        -0.2287  239.96 2019-10-22  242.20  Oct 22   \n",
       "19    3.22        0.105917         1.3419  243.18 2019-10-23  243.24  Oct 23   \n",
       "20    0.40        0.107736         0.1645  243.58 2019-10-24  244.80  Oct 24   \n",
       "21    3.00        0.121379         1.2316  246.58 2019-10-25  246.73  Oct 25   \n",
       "\n",
       "       low    open  uClose   uHigh    uLow   uOpen   uVolume    volume  \n",
       "0   218.83  220.00  219.89  220.94  218.83  220.00  19088312  19088312  \n",
       "1   217.28  220.54  218.82  220.96  217.28  220.54  25361285  25361285  \n",
       "2   220.79  220.90  223.97  224.58  220.79  220.90  26318583  26318583  \n",
       "3   224.20  225.07  224.59  228.22  224.20  225.07  36187163  36187163  \n",
       "4   217.93  223.06  218.96  223.58  217.93  223.06  35767257  35767257  \n",
       "5   215.13  218.43  220.82  220.96  215.13  218.43  30352686  30352686  \n",
       "6   223.89  225.64  227.01  227.49  223.89  225.64  34755553  34755553  \n",
       "7   225.84  226.27  227.06  229.93  225.84  226.27  30889269  30889269  \n",
       "8   224.33  225.82  224.40  228.06  224.33  225.82  29282700  29282700  \n",
       "9   225.64  227.03  227.03  227.79  225.64  227.03  19029424  19029424  \n",
       "10  227.30  227.93  230.09  230.44  227.30  227.93  28962984  28962984  \n",
       "11  232.30  232.95  236.21  237.64  232.30  232.95  41990210  41990210  \n",
       "12  234.67  234.90  235.87  238.13  234.67  234.90  24413484  24413484  \n",
       "13  234.88  236.39  235.32  237.65  234.88  236.39  23040483  23040483  \n",
       "14  233.20  233.37  234.37  235.24  233.20  233.37  19286694  19286694  \n",
       "15  233.52  235.09  235.28  236.15  233.52  235.09  17272897  17272897  \n",
       "16  234.29  234.59  236.41  237.58  234.29  234.59  24377166  24377166  \n",
       "17  237.32  237.52  240.51  240.99  237.32  237.52  22367483  22367483  \n",
       "18  239.62  241.16  239.96  242.20  239.62  241.16  22684001  22684001  \n",
       "19  241.22  242.10  243.18  243.24  241.22  242.10  19932545  19932545  \n",
       "20  241.80  244.51  243.58  244.80  241.80  244.51  17916255  17916255  \n",
       "21  242.88  243.16  246.58  246.73  242.88  243.16  18369296  18369296  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_temp.set_index('date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_list = []\n",
    "open_list=[]\n",
    "\n",
    "for ct in range(0,len((df_temp))):\n",
    "    date_list.append([\n",
    "    round(((time.mktime(list(df_temp.date)[ct].timetuple())/100)-\\\n",
    "    (time.mktime(list(df_temp.date)[0].timetuple())/100))/864)\n",
    "    ])\n",
    "    #date_list.append([list(range(1,len(df_temp)+1))[ct]])\n",
    "    open_list.append([float(df_temp.open[ct])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0],\n",
       " [1],\n",
       " [4],\n",
       " [5],\n",
       " [6],\n",
       " [7],\n",
       " [8],\n",
       " [11],\n",
       " [12],\n",
       " [13],\n",
       " [14],\n",
       " [15],\n",
       " [18],\n",
       " [19],\n",
       " [20],\n",
       " [21],\n",
       " [22],\n",
       " [25],\n",
       " [26],\n",
       " [27],\n",
       " [28],\n",
       " [29]]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.1,\n",
       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_lin = SVR(kernel= 'linear',C=1e3)\n",
    "svr_poly = SVR(kernel= 'poly',C=1e3, degree = 2)\n",
    "svr_rbf = SVR(kernel = 'rbf',C=1e3,gamma=0.1)\n",
    "svr_lin.fit(date_list,open_list)\n",
    "svr_poly.fit(date_list,open_list)\n",
    "svr_rbf.fit(date_list,open_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(date_list,open_list, color='black',label='Data')\n",
    "plt.plot(date_list,svr_rbf.predict(date_list),color='red',label='RBF Model')\n",
    "plt.plot(date_list,svr_lin.predict(date_list),color='green',label='Linear Model')\n",
    "plt.plot(date_list,svr_poly.predict(date_list),color='blue',label='Polynomial Model')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Open Price')\n",
    "plt.title('Support Vector Regression')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[205.59395847]\n",
      "[243.16]\n"
     ]
    }
   ],
   "source": [
    "print(svr_rbf.predict([[367]]))\n",
    "print(open_list[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_poly.predict([[len(df_temp)+10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_lin.predict([[len(df_temp)+10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(time.mktime(list(df_temp.date)[0].timetuple())/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time.mktime(list(df_temp.date)[1].timetuple())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "3.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "3.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "3.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "3.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-2757f37a0fa4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     print(((time.mktime(list(df_temp.date)[x].timetuple())/100)-\\\n\u001b[0m\u001b[1;32m      3\u001b[0m     (time.mktime(list(df_temp.date)[x-1].timetuple())/100))/864)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "\n",
    "(((time.mktime(list(df_temp.date)[1].timetuple())/100)-\\\n",
    "(time.mktime(list(df_temp.date)[0].timetuple())/100))/864)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_date_list=[]\n",
    "for i in list(range(round(len(df_temp)/2),round(len(df_temp)/2)*3)):\n",
    "    future_date_list.append([i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(date_list,open_list, color='black',label='Data')\n",
    "plt.plot(date_list,svr_rbf.predict(date_list),color='red',label='RBF Model')\n",
    "plt.plot(future_date_list,svr_rbf.predict(future_date_list),color='blue',label='RBF Model Future')\n",
    "plt.plot(date_list,svr_lin.predict(date_list),color='green',label='Linear Model')\n",
    "#plt.plot(date_list,svr_poly.predict(date_list),color='blue',label='Polynomial Model')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Open Price')\n",
    "plt.title('Support Vector Regression')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}