Stock_analysis.ipynb

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      "name": "Stock analysis.ipynb",
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        "id": "zxhIknmWkl8D",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXZMaRMBkl8G",
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      "source": [
        "df = pd.read_csv(r'C:\\Users\\ADITYA\\Desktop\\CSV (10).csv')\n",
        "#This will convert the date which otherwise would hve been a string to a date time object.  We will now get a date-time ind"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "55acb22c-c51d-457f-c5b3-ab6afba5ff77"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02-12-2019</td>\n",
              "      <td>24.420000</td>\n",
              "      <td>23.889999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03-12-2019</td>\n",
              "      <td>25.690001</td>\n",
              "      <td>24.530001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>04-12-2019</td>\n",
              "      <td>26.889999</td>\n",
              "      <td>25.530001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>05-12-2019</td>\n",
              "      <td>26.660000</td>\n",
              "      <td>25.889999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>06-12-2019</td>\n",
              "      <td>26.709999</td>\n",
              "      <td>25.940001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "         Date       High        Low\n",
              "0  02-12-2019  24.420000  23.889999\n",
              "1  03-12-2019  25.690001  24.530001\n",
              "2  04-12-2019  26.889999  25.530001\n",
              "3  05-12-2019  26.660000  25.889999\n",
              "4  06-12-2019  26.709999  25.940001"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
        }
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      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 3)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
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      "cell_type": "code",
      "metadata": {
        "id": "41F7rtnrkl8P",
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      },
      "source": [
        "import datetime"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "source": [
        "li= []\n",
        "for i in range(0, 100):\n",
        "    l =df['Date'][i].split('-')\n",
        "    d = ' '\n",
        "    d = d.join(l)\n",
        "    d = datetime.datetime.strptime(d, '%d %m %Y').strftime('%m-%d-%Y')\n",
        "    date_object = datetime.datetime.strptime(d, '%m-%d-%Y').date()\n",
        "    date_object\n",
        "    li.append(date_object)\n",
        "li"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[datetime.date(2019, 12, 2),\n",
              " datetime.date(2019, 12, 3),\n",
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              " datetime.date(2019, 12, 6),\n",
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              " datetime.date(2020, 4, 24)]"
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      "source": [
        "df['Date']=li\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-12-02</td>\n",
              "      <td>24.420000</td>\n",
              "      <td>23.889999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-12-03</td>\n",
              "      <td>25.690001</td>\n",
              "      <td>24.530001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-04</td>\n",
              "      <td>26.889999</td>\n",
              "      <td>25.530001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-12-05</td>\n",
              "      <td>26.660000</td>\n",
              "      <td>25.889999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-12-06</td>\n",
              "      <td>26.709999</td>\n",
              "      <td>25.940001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-12-09</td>\n",
              "      <td>27.139999</td>\n",
              "      <td>26.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-12-10</td>\n",
              "      <td>27.530001</td>\n",
              "      <td>26.540001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-12-11</td>\n",
              "      <td>27.650000</td>\n",
              "      <td>26.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-12-12</td>\n",
              "      <td>27.799999</td>\n",
              "      <td>27.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-12-13</td>\n",
              "      <td>28.280001</td>\n",
              "      <td>27.709999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2019-12-16</td>\n",
              "      <td>28.500000</td>\n",
              "      <td>27.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2019-12-17</td>\n",
              "      <td>27.670000</td>\n",
              "      <td>26.910000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2019-12-18</td>\n",
              "      <td>27.520000</td>\n",
              "      <td>26.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2019-12-19</td>\n",
              "      <td>27.230000</td>\n",
              "      <td>26.549999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2019-12-20</td>\n",
              "      <td>26.790001</td>\n",
              "      <td>26.209999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2019-12-23</td>\n",
              "      <td>26.389999</td>\n",
              "      <td>25.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2019-12-24</td>\n",
              "      <td>26.299999</td>\n",
              "      <td>26.070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2019-12-26</td>\n",
              "      <td>26.309999</td>\n",
              "      <td>25.660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>25.850000</td>\n",
              "      <td>25.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>25.850000</td>\n",
              "      <td>25.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>25.920000</td>\n",
              "      <td>25.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>25.620001</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>25.510000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>25.730000</td>\n",
              "      <td>25.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2020-01-07</td>\n",
              "      <td>26.040001</td>\n",
              "      <td>25.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>25.320000</td>\n",
              "      <td>25.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2020-01-09</td>\n",
              "      <td>25.480000</td>\n",
              "      <td>25.049999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020-01-10</td>\n",
              "      <td>25.219999</td>\n",
              "      <td>24.700001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>25.250000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>25.360001</td>\n",
              "      <td>24.879999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>17.100000</td>\n",
              "      <td>15.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>14.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>14.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>16.670000</td>\n",
              "      <td>14.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>18.150000</td>\n",
              "      <td>14.910000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>16.740000</td>\n",
              "      <td>15.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>16.680000</td>\n",
              "      <td>14.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>15.130000</td>\n",
              "      <td>13.930000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>15.670000</td>\n",
              "      <td>14.580000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2020-03-26</td>\n",
              "      <td>15.390000</td>\n",
              "      <td>14.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2020-03-27</td>\n",
              "      <td>15.750000</td>\n",
              "      <td>13.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>17.930000</td>\n",
              "      <td>15.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>16.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>16.379999</td>\n",
              "      <td>15.360000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>2020-04-02</td>\n",
              "      <td>15.870000</td>\n",
              "      <td>14.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>2020-04-03</td>\n",
              "      <td>15.110000</td>\n",
              "      <td>13.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>14.810000</td>\n",
              "      <td>13.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>2020-04-07</td>\n",
              "      <td>15.160000</td>\n",
              "      <td>14.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2020-04-08</td>\n",
              "      <td>15.470000</td>\n",
              "      <td>14.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2020-04-09</td>\n",
              "      <td>15.880000</td>\n",
              "      <td>15.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>2020-04-13</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>15.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>15.890000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>2020-04-15</td>\n",
              "      <td>15.410000</td>\n",
              "      <td>14.230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2020-04-16</td>\n",
              "      <td>14.590000</td>\n",
              "      <td>13.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2020-04-17</td>\n",
              "      <td>15.120000</td>\n",
              "      <td>14.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2020-04-20</td>\n",
              "      <td>15.410000</td>\n",
              "      <td>14.430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>2020-04-21</td>\n",
              "      <td>15.100000</td>\n",
              "      <td>14.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2020-04-22</td>\n",
              "      <td>15.080000</td>\n",
              "      <td>14.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>2020-04-23</td>\n",
              "      <td>15.220000</td>\n",
              "      <td>14.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>2020-04-24</td>\n",
              "      <td>15.160000</td>\n",
              "      <td>14.680000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date       High        Low\n",
              "0   2019-12-02  24.420000  23.889999\n",
              "1   2019-12-03  25.690001  24.530001\n",
              "2   2019-12-04  26.889999  25.530001\n",
              "3   2019-12-05  26.660000  25.889999\n",
              "4   2019-12-06  26.709999  25.940001\n",
              "5   2019-12-09  27.139999  26.150000\n",
              "6   2019-12-10  27.530001  26.540001\n",
              "7   2019-12-11  27.650000  26.980000\n",
              "8   2019-12-12  27.799999  27.340000\n",
              "9   2019-12-13  28.280001  27.709999\n",
              "10  2019-12-16  28.500000  27.500000\n",
              "11  2019-12-17  27.670000  26.910000\n",
              "12  2019-12-18  27.520000  26.850000\n",
              "13  2019-12-19  27.230000  26.549999\n",
              "14  2019-12-20  26.790001  26.209999\n",
              "15  2019-12-23  26.389999  25.900000\n",
              "16  2019-12-24  26.299999  26.070000\n",
              "17  2019-12-26  26.309999  25.660000\n",
              "18  2019-12-27  25.850000  25.570000\n",
              "19  2019-12-30  25.850000  25.270000\n",
              "20  2019-12-31  25.920000  25.520000\n",
              "21  2020-01-02  25.620001  25.000000\n",
              "22  2020-01-03  25.510000  25.000000\n",
              "23  2020-01-06  25.730000  25.160000\n",
              "24  2020-01-07  26.040001  25.270000\n",
              "25  2020-01-08  25.320000  25.020000\n",
              "26  2020-01-09  25.480000  25.049999\n",
              "27  2020-01-10  25.219999  24.700001\n",
              "28  2020-01-13  25.250000  25.000000\n",
              "29  2020-01-14  25.360001  24.879999\n",
              "..         ...        ...        ...\n",
              "70  2020-03-13  17.100000  15.610000\n",
              "71  2020-03-16  16.000000  14.010000\n",
              "72  2020-03-17  16.100000  14.080000\n",
              "73  2020-03-18  16.670000  14.920000\n",
              "74  2020-03-19  18.150000  14.910000\n",
              "75  2020-03-20  16.740000  15.340000\n",
              "76  2020-03-23  16.680000  14.050000\n",
              "77  2020-03-24  15.130000  13.930000\n",
              "78  2020-03-25  15.670000  14.580000\n",
              "79  2020-03-26  15.390000  14.020000\n",
              "80  2020-03-27  15.750000  13.600000\n",
              "81  2020-03-30  17.930000  15.960000\n",
              "82  2020-03-31  17.850000  16.020000\n",
              "83  2020-04-01  16.379999  15.360000\n",
              "84  2020-04-02  15.870000  14.620000\n",
              "85  2020-04-03  15.110000  13.920000\n",
              "86  2020-04-06  14.810000  13.710000\n",
              "87  2020-04-07  15.160000  14.410000\n",
              "88  2020-04-08  15.470000  14.610000\n",
              "89  2020-04-09  15.880000  15.260000\n",
              "90  2020-04-13  15.780000  15.010000\n",
              "91  2020-04-14  15.890000  15.000000\n",
              "92  2020-04-15  15.410000  14.230000\n",
              "93  2020-04-16  14.590000  13.540000\n",
              "94  2020-04-17  15.120000  14.050000\n",
              "95  2020-04-20  15.410000  14.430000\n",
              "96  2020-04-21  15.100000  14.340000\n",
              "97  2020-04-22  15.080000  14.620000\n",
              "98  2020-04-23  15.220000  14.750000\n",
              "99  2020-04-24  15.160000  14.680000\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3v5kBSokl8W",
        "colab_type": "code",
        "colab": {},
        "outputId": "6d896df8-f36e-40ea-faf9-d9e2b6c27e20"
      },
      "source": [
        "df.set_index('Date')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-12-02</th>\n",
              "      <td>24.420000</td>\n",
              "      <td>23.889999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-03</th>\n",
              "      <td>25.690001</td>\n",
              "      <td>24.530001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-04</th>\n",
              "      <td>26.889999</td>\n",
              "      <td>25.530001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-05</th>\n",
              "      <td>26.660000</td>\n",
              "      <td>25.889999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-06</th>\n",
              "      <td>26.709999</td>\n",
              "      <td>25.940001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-09</th>\n",
              "      <td>27.139999</td>\n",
              "      <td>26.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-10</th>\n",
              "      <td>27.530001</td>\n",
              "      <td>26.540001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-11</th>\n",
              "      <td>27.650000</td>\n",
              "      <td>26.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-12</th>\n",
              "      <td>27.799999</td>\n",
              "      <td>27.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-13</th>\n",
              "      <td>28.280001</td>\n",
              "      <td>27.709999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-16</th>\n",
              "      <td>28.500000</td>\n",
              "      <td>27.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-17</th>\n",
              "      <td>27.670000</td>\n",
              "      <td>26.910000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-18</th>\n",
              "      <td>27.520000</td>\n",
              "      <td>26.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-19</th>\n",
              "      <td>27.230000</td>\n",
              "      <td>26.549999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-20</th>\n",
              "      <td>26.790001</td>\n",
              "      <td>26.209999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-23</th>\n",
              "      <td>26.389999</td>\n",
              "      <td>25.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>26.299999</td>\n",
              "      <td>26.070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-26</th>\n",
              "      <td>26.309999</td>\n",
              "      <td>25.660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-27</th>\n",
              "      <td>25.850000</td>\n",
              "      <td>25.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-30</th>\n",
              "      <td>25.850000</td>\n",
              "      <td>25.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>25.920000</td>\n",
              "      <td>25.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>25.620001</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>25.510000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>25.730000</td>\n",
              "      <td>25.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>26.040001</td>\n",
              "      <td>25.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>25.320000</td>\n",
              "      <td>25.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-09</th>\n",
              "      <td>25.480000</td>\n",
              "      <td>25.049999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-10</th>\n",
              "      <td>25.219999</td>\n",
              "      <td>24.700001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-13</th>\n",
              "      <td>25.250000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-14</th>\n",
              "      <td>25.360001</td>\n",
              "      <td>24.879999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>17.100000</td>\n",
              "      <td>15.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>14.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>16.100000</td>\n",
              "      <td>14.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>16.670000</td>\n",
              "      <td>14.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>18.150000</td>\n",
              "      <td>14.910000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-20</th>\n",
              "      <td>16.740000</td>\n",
              "      <td>15.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-23</th>\n",
              "      <td>16.680000</td>\n",
              "      <td>14.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-24</th>\n",
              "      <td>15.130000</td>\n",
              "      <td>13.930000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>15.670000</td>\n",
              "      <td>14.580000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>15.390000</td>\n",
              "      <td>14.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>15.750000</td>\n",
              "      <td>13.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>17.930000</td>\n",
              "      <td>15.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>17.850000</td>\n",
              "      <td>16.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>16.379999</td>\n",
              "      <td>15.360000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-02</th>\n",
              "      <td>15.870000</td>\n",
              "      <td>14.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-03</th>\n",
              "      <td>15.110000</td>\n",
              "      <td>13.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>14.810000</td>\n",
              "      <td>13.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-07</th>\n",
              "      <td>15.160000</td>\n",
              "      <td>14.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-08</th>\n",
              "      <td>15.470000</td>\n",
              "      <td>14.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-09</th>\n",
              "      <td>15.880000</td>\n",
              "      <td>15.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-13</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>15.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-14</th>\n",
              "      <td>15.890000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-15</th>\n",
              "      <td>15.410000</td>\n",
              "      <td>14.230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-16</th>\n",
              "      <td>14.590000</td>\n",
              "      <td>13.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-17</th>\n",
              "      <td>15.120000</td>\n",
              "      <td>14.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-20</th>\n",
              "      <td>15.410000</td>\n",
              "      <td>14.430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-21</th>\n",
              "      <td>15.100000</td>\n",
              "      <td>14.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-22</th>\n",
              "      <td>15.080000</td>\n",
              "      <td>14.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-23</th>\n",
              "      <td>15.220000</td>\n",
              "      <td>14.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-24</th>\n",
              "      <td>15.160000</td>\n",
              "      <td>14.680000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low\n",
              "Date                            \n",
              "2019-12-02  24.420000  23.889999\n",
              "2019-12-03  25.690001  24.530001\n",
              "2019-12-04  26.889999  25.530001\n",
              "2019-12-05  26.660000  25.889999\n",
              "2019-12-06  26.709999  25.940001\n",
              "2019-12-09  27.139999  26.150000\n",
              "2019-12-10  27.530001  26.540001\n",
              "2019-12-11  27.650000  26.980000\n",
              "2019-12-12  27.799999  27.340000\n",
              "2019-12-13  28.280001  27.709999\n",
              "2019-12-16  28.500000  27.500000\n",
              "2019-12-17  27.670000  26.910000\n",
              "2019-12-18  27.520000  26.850000\n",
              "2019-12-19  27.230000  26.549999\n",
              "2019-12-20  26.790001  26.209999\n",
              "2019-12-23  26.389999  25.900000\n",
              "2019-12-24  26.299999  26.070000\n",
              "2019-12-26  26.309999  25.660000\n",
              "2019-12-27  25.850000  25.570000\n",
              "2019-12-30  25.850000  25.270000\n",
              "2019-12-31  25.920000  25.520000\n",
              "2020-01-02  25.620001  25.000000\n",
              "2020-01-03  25.510000  25.000000\n",
              "2020-01-06  25.730000  25.160000\n",
              "2020-01-07  26.040001  25.270000\n",
              "2020-01-08  25.320000  25.020000\n",
              "2020-01-09  25.480000  25.049999\n",
              "2020-01-10  25.219999  24.700001\n",
              "2020-01-13  25.250000  25.000000\n",
              "2020-01-14  25.360001  24.879999\n",
              "...               ...        ...\n",
              "2020-03-13  17.100000  15.610000\n",
              "2020-03-16  16.000000  14.010000\n",
              "2020-03-17  16.100000  14.080000\n",
              "2020-03-18  16.670000  14.920000\n",
              "2020-03-19  18.150000  14.910000\n",
              "2020-03-20  16.740000  15.340000\n",
              "2020-03-23  16.680000  14.050000\n",
              "2020-03-24  15.130000  13.930000\n",
              "2020-03-25  15.670000  14.580000\n",
              "2020-03-26  15.390000  14.020000\n",
              "2020-03-27  15.750000  13.600000\n",
              "2020-03-30  17.930000  15.960000\n",
              "2020-03-31  17.850000  16.020000\n",
              "2020-04-01  16.379999  15.360000\n",
              "2020-04-02  15.870000  14.620000\n",
              "2020-04-03  15.110000  13.920000\n",
              "2020-04-06  14.810000  13.710000\n",
              "2020-04-07  15.160000  14.410000\n",
              "2020-04-08  15.470000  14.610000\n",
              "2020-04-09  15.880000  15.260000\n",
              "2020-04-13  15.780000  15.010000\n",
              "2020-04-14  15.890000  15.000000\n",
              "2020-04-15  15.410000  14.230000\n",
              "2020-04-16  14.590000  13.540000\n",
              "2020-04-17  15.120000  14.050000\n",
              "2020-04-20  15.410000  14.430000\n",
              "2020-04-21  15.100000  14.340000\n",
              "2020-04-22  15.080000  14.620000\n",
              "2020-04-23  15.220000  14.750000\n",
              "2020-04-24  15.160000  14.680000\n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Oi9deLTkl8Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['new']= ((df['High']+df['Low'])/2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5IkTRJwkl8a",
        "colab_type": "code",
        "colab": {},
        "outputId": "f69f1e03-0a26-4c23-df3d-b505503f6040"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>new</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-12-02</td>\n",
              "      <td>24.420000</td>\n",
              "      <td>23.889999</td>\n",
              "      <td>24.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-12-03</td>\n",
              "      <td>25.690001</td>\n",
              "      <td>24.530001</td>\n",
              "      <td>25.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-04</td>\n",
              "      <td>26.889999</td>\n",
              "      <td>25.530001</td>\n",
              "      <td>26.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-12-05</td>\n",
              "      <td>26.660000</td>\n",
              "      <td>25.889999</td>\n",
              "      <td>26.274999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-12-06</td>\n",
              "      <td>26.709999</td>\n",
              "      <td>25.940001</td>\n",
              "      <td>26.325000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date       High        Low        new\n",
              "0  2019-12-02  24.420000  23.889999  24.155000\n",
              "1  2019-12-03  25.690001  24.530001  25.110001\n",
              "2  2019-12-04  26.889999  25.530001  26.210000\n",
              "3  2019-12-05  26.660000  25.889999  26.274999\n",
              "4  2019-12-06  26.709999  25.940001  26.325000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMfDPwVGkl8c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop('High',axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7B6D1x0kl8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop('Low',axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dcrk9ao1kl8i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=df.set_index('Date')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVfDOyplkl8k",
        "colab_type": "code",
        "colab": {},
        "outputId": "4c86d968-a21f-4f4b-c372-95034ec18577"
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x16e321002b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1agVa_nWkl8n",
        "colab_type": "code",
        "colab": {},
        "outputId": "8ca0fec3-1054-4e0a-9c6e-cfcd27cffa3a"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WJJI4fBkl8p",
        "colab_type": "code",
        "colab": {},
        "outputId": "102c29a1-0fbc-44c2-c6b1-33774968f100"
      },
      "source": [
        "len(df)- 20"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5i8pQCNKkl8r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_size = 20"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMJnIJ4Gkl8u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_ind = len(df)- test_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd6P-tfdkl8y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = df.iloc[:test_ind]\n",
        "test = df.iloc[test_ind:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6oERYFYkl8z",
        "colab_type": "code",
        "colab": {},
        "outputId": "3ce08ca2-9972-498f-b2ac-710fb60ce477"
      },
      "source": [
        "train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>new</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-12-02</th>\n",
              "      <td>24.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-03</th>\n",
              "      <td>25.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-04</th>\n",
              "      <td>26.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-05</th>\n",
              "      <td>26.274999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-06</th>\n",
              "      <td>26.325000</td>\n",
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              "    <tr>\n",
              "      <th>2019-12-09</th>\n",
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              "    <tr>\n",
              "      <th>2019-12-10</th>\n",
              "      <td>27.035001</td>\n",
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              "    <tr>\n",
              "      <th>2019-12-11</th>\n",
              "      <td>27.315000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-12</th>\n",
              "      <td>27.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-13</th>\n",
              "      <td>27.995000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-16</th>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-17</th>\n",
              "      <td>27.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-18</th>\n",
              "      <td>27.185000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-19</th>\n",
              "      <td>26.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-20</th>\n",
              "      <td>26.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-23</th>\n",
              "      <td>26.144999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>26.184999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-26</th>\n",
              "      <td>25.985000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-27</th>\n",
              "      <td>25.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-30</th>\n",
              "      <td>25.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>25.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>25.310001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>25.255000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>25.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>25.655000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>25.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-09</th>\n",
              "      <td>25.265000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-10</th>\n",
              "      <td>24.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-13</th>\n",
              "      <td>25.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-14</th>\n",
              "      <td>25.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>24.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>24.635001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>24.615000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>24.959999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-20</th>\n",
              "      <td>23.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-21</th>\n",
              "      <td>21.675000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>20.965000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>20.445001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>20.775000</td>\n",
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              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>21.070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>20.845001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>20.729999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>19.865000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>19.910001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-05</th>\n",
              "      <td>20.680000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-06</th>\n",
              "      <td>20.555001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-09</th>\n",
              "      <td>19.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-10</th>\n",
              "      <td>19.085000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-11</th>\n",
              "      <td>17.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>16.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>16.355000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>15.005000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>15.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>15.795000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>16.530000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-20</th>\n",
              "      <td>16.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-23</th>\n",
              "      <td>15.365000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-24</th>\n",
              "      <td>14.530000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>15.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>14.705000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  new\n",
              "Date                 \n",
              "2019-12-02  24.155000\n",
              "2019-12-03  25.110001\n",
              "2019-12-04  26.210000\n",
              "2019-12-05  26.274999\n",
              "2019-12-06  26.325000\n",
              "2019-12-09  26.644999\n",
              "2019-12-10  27.035001\n",
              "2019-12-11  27.315000\n",
              "2019-12-12  27.570000\n",
              "2019-12-13  27.995000\n",
              "2019-12-16  28.000000\n",
              "2019-12-17  27.290000\n",
              "2019-12-18  27.185000\n",
              "2019-12-19  26.890000\n",
              "2019-12-20  26.500000\n",
              "2019-12-23  26.144999\n",
              "2019-12-24  26.184999\n",
              "2019-12-26  25.985000\n",
              "2019-12-27  25.710000\n",
              "2019-12-30  25.560000\n",
              "2019-12-31  25.720000\n",
              "2020-01-02  25.310001\n",
              "2020-01-03  25.255000\n",
              "2020-01-06  25.445000\n",
              "2020-01-07  25.655000\n",
              "2020-01-08  25.170000\n",
              "2020-01-09  25.265000\n",
              "2020-01-10  24.960000\n",
              "2020-01-13  25.125000\n",
              "2020-01-14  25.120000\n",
              "...               ...\n",
              "2020-02-13  24.570000\n",
              "2020-02-14  24.635001\n",
              "2020-02-18  24.615000\n",
              "2020-02-19  24.959999\n",
              "2020-02-20  23.125000\n",
              "2020-02-21  21.675000\n",
              "2020-02-24  20.965000\n",
              "2020-02-25  20.445001\n",
              "2020-02-26  20.775000\n",
              "2020-02-27  21.070000\n",
              "2020-02-28  20.845001\n",
              "2020-03-02  20.729999\n",
              "2020-03-03  19.865000\n",
              "2020-03-04  19.910001\n",
              "2020-03-05  20.680000\n",
              "2020-03-06  20.555001\n",
              "2020-03-09  19.710000\n",
              "2020-03-10  19.085000\n",
              "2020-03-11  17.710000\n",
              "2020-03-12  16.610000\n",
              "2020-03-13  16.355000\n",
              "2020-03-16  15.005000\n",
              "2020-03-17  15.090000\n",
              "2020-03-18  15.795000\n",
              "2020-03-19  16.530000\n",
              "2020-03-20  16.040000\n",
              "2020-03-23  15.365000\n",
              "2020-03-24  14.530000\n",
              "2020-03-25  15.125000\n",
              "2020-03-26  14.705000\n",
              "\n",
              "[80 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBAgKvK2kl81",
        "colab_type": "code",
        "colab": {},
        "outputId": "c10eb3f8-a73d-46b9-a8b5-a20545fe5f39"
      },
      "source": [
        "test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>new</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>14.675000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>16.945000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>16.935000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>15.869999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-02</th>\n",
              "      <td>15.245000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-03</th>\n",
              "      <td>14.515000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>14.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-07</th>\n",
              "      <td>14.785000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-08</th>\n",
              "      <td>15.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-09</th>\n",
              "      <td>15.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-13</th>\n",
              "      <td>15.395000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-14</th>\n",
              "      <td>15.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-15</th>\n",
              "      <td>14.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-16</th>\n",
              "      <td>14.065000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-17</th>\n",
              "      <td>14.585000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-20</th>\n",
              "      <td>14.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-21</th>\n",
              "      <td>14.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-22</th>\n",
              "      <td>14.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-23</th>\n",
              "      <td>14.985000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-24</th>\n",
              "      <td>14.920000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  new\n",
              "Date                 \n",
              "2020-03-27  14.675000\n",
              "2020-03-30  16.945000\n",
              "2020-03-31  16.935000\n",
              "2020-04-01  15.869999\n",
              "2020-04-02  15.245000\n",
              "2020-04-03  14.515000\n",
              "2020-04-06  14.260000\n",
              "2020-04-07  14.785000\n",
              "2020-04-08  15.040000\n",
              "2020-04-09  15.570000\n",
              "2020-04-13  15.395000\n",
              "2020-04-14  15.445000\n",
              "2020-04-15  14.820000\n",
              "2020-04-16  14.065000\n",
              "2020-04-17  14.585000\n",
              "2020-04-20  14.920000\n",
              "2020-04-21  14.720000\n",
              "2020-04-22  14.850000\n",
              "2020-04-23  14.985000\n",
              "2020-04-24  14.920000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx2OprKYkl83",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spjlQ1Rlkl85",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6okSIzYikl86",
        "colab_type": "code",
        "colab": {},
        "outputId": "73168d5b-1318-4a0b-bf09-07150b88084c"
      },
      "source": [
        "scaler.fit(train)\n",
        "#We will only fit the training the data and not the test data#"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler(copy=True, feature_range=(0, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4KthPPSkl88",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaled_train = scaler.transform(train)\n",
        "scaled_test = scaler.transform(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3Os7pe7kl8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJkHO9iqkl9C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's redefine to get 12 days  back and then predict the next days out\n",
        "#length of batch should be less than 20 days #\n",
        "length = 12\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRw0Bv5Wkl9E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# What does the first batch look like?\n",
        "X,y = generator[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFSNL6cwkl9F",
        "colab_type": "code",
        "colab": {},
        "outputId": "ab0e8fa7-d81d-434b-d2a0-a36134ee305f"
      },
      "source": [
        "print(f'Given the Array: \\n{X.flatten()}')\n",
        "print(f'Predict this y: \\n {y}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Given the Array: \n",
            "[0.71455082 0.78544922 0.8671121  0.8719376  0.87564959 0.89940605\n",
            " 0.92835939 0.94914625 0.96807717 0.9996288  1.         0.94729027]\n",
            "Predict this y: \n",
            " [[0.93949517]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WL9qzyJPkl9H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diBY1fVNkl9J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We're only using one feature in our time series\n",
        "n_features = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nDhSDATkl9L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(20, activation='relu', input_shape=(length, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMPjfmiIkl9N",
        "colab_type": "code",
        "colab": {},
        "outputId": "aca25f83-0492-4ea2-9fc4-e0c44fa103cc"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_2 (LSTM)                (None, 20)                1760      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 21        \n",
            "=================================================================\n",
            "Total params: 1,781\n",
            "Trainable params: 1,781\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NImkSqR9kl9P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YmskdWGkl9T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jjgaEmkkl9V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "validation_generator = TimeseriesGenerator(scaled_test,scaled_test, length=length, batch_size=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXIGkfzVkl9X",
        "colab_type": "code",
        "colab": {},
        "outputId": "9e3631fe-40ef-4ccd-b969-6b7ee82f52a6"
      },
      "source": [
        "# fit model\n",
        "model.fit_generator(generator,epochs=20,\n",
        "                    validation_data=validation_generator,\n",
        "                   callbacks=[early_stop])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 68 steps, validate for 8 steps\n",
            "Epoch 1/20\n",
            "68/68 [==============================] - 3s 37ms/step - loss: 0.1223 - val_loss: 0.0316\n",
            "Epoch 2/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0151 - val_loss: 0.0205\n",
            "Epoch 3/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0122 - val_loss: 0.0095\n",
            "Epoch 4/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0087 - val_loss: 0.0050\n",
            "Epoch 5/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0075 - val_loss: 0.0032\n",
            "Epoch 6/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0063 - val_loss: 0.0017\n",
            "Epoch 7/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0056 - val_loss: 0.0013\n",
            "Epoch 8/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0054 - val_loss: 8.3020e-04\n",
            "Epoch 9/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0069 - val_loss: 7.8285e-04\n",
            "Epoch 10/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0055 - val_loss: 6.7499e-04\n",
            "Epoch 11/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0053 - val_loss: 6.0432e-04\n",
            "Epoch 12/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0049 - val_loss: 6.2418e-04\n",
            "Epoch 13/20\n",
            "68/68 [==============================] - 1s 11ms/step - loss: 0.0065 - val_loss: 6.2976e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x16e523d8828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeJIyF4vkl9Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "losses = pd.DataFrame(model.history.history)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQFEdqqCkl9b",
        "colab_type": "code",
        "colab": {},
        "outputId": "3c341fa7-0668-4cc9-b198-01ae66c32681"
      },
      "source": [
        "losses.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x16e54685128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfXkGvdnkl9d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_predictions = []\n",
        "\n",
        "first_eval_batch = scaled_train[-length:]\n",
        "current_batch = first_eval_batch.reshape((1, length, n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "    \n",
        "    # get prediction 1 time stamp ahead ([0] is for grabbing just the number instead of [array])\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "    \n",
        "    # store prediction\n",
        "    test_predictions.append(current_pred) \n",
        "    \n",
        "    # update batch to now include prediction and drop first value\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQuPd-VEkl9e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVrpWVbKkl9g",
        "colab_type": "code",
        "colab": {},
        "outputId": "deb9566a-a8cb-4b23-deda-920ad65d7abe"
      },
      "source": [
        "test['Predictions'] = true_predictions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Users\\ADITYA\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oy1gzHnkl9k",
        "colab_type": "code",
        "colab": {},
        "outputId": "88937e0e-9be4-4aef-f5c8-810286f265bf"
      },
      "source": [
        "test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>new</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>14.675000</td>\n",
              "      <td>14.804444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>16.945000</td>\n",
              "      <td>14.722748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>16.935000</td>\n",
              "      <td>14.668863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>15.869999</td>\n",
              "      <td>14.628432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-02</th>\n",
              "      <td>15.245000</td>\n",
              "      <td>14.605696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-03</th>\n",
              "      <td>14.515000</td>\n",
              "      <td>14.587604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>14.260000</td>\n",
              "      <td>14.569541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-07</th>\n",
              "      <td>14.785000</td>\n",
              "      <td>14.551187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-08</th>\n",
              "      <td>15.040000</td>\n",
              "      <td>14.539926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-09</th>\n",
              "      <td>15.570000</td>\n",
              "      <td>14.534507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-13</th>\n",
              "      <td>15.395000</td>\n",
              "      <td>14.532599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-14</th>\n",
              "      <td>15.445000</td>\n",
              "      <td>14.529691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-15</th>\n",
              "      <td>14.820000</td>\n",
              "      <td>14.528485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-16</th>\n",
              "      <td>14.065000</td>\n",
              "      <td>14.527317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-17</th>\n",
              "      <td>14.585000</td>\n",
              "      <td>14.526507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-20</th>\n",
              "      <td>14.920000</td>\n",
              "      <td>14.525936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-21</th>\n",
              "      <td>14.720000</td>\n",
              "      <td>14.525531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-22</th>\n",
              "      <td>14.850000</td>\n",
              "      <td>14.525236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-23</th>\n",
              "      <td>14.985000</td>\n",
              "      <td>14.525022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-24</th>\n",
              "      <td>14.920000</td>\n",
              "      <td>14.524874</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  new  Predictions\n",
              "Date                              \n",
              "2020-03-27  14.675000    14.804444\n",
              "2020-03-30  16.945000    14.722748\n",
              "2020-03-31  16.935000    14.668863\n",
              "2020-04-01  15.869999    14.628432\n",
              "2020-04-02  15.245000    14.605696\n",
              "2020-04-03  14.515000    14.587604\n",
              "2020-04-06  14.260000    14.569541\n",
              "2020-04-07  14.785000    14.551187\n",
              "2020-04-08  15.040000    14.539926\n",
              "2020-04-09  15.570000    14.534507\n",
              "2020-04-13  15.395000    14.532599\n",
              "2020-04-14  15.445000    14.529691\n",
              "2020-04-15  14.820000    14.528485\n",
              "2020-04-16  14.065000    14.527317\n",
              "2020-04-17  14.585000    14.526507\n",
              "2020-04-20  14.920000    14.525936\n",
              "2020-04-21  14.720000    14.525531\n",
              "2020-04-22  14.850000    14.525236\n",
              "2020-04-23  14.985000    14.525022\n",
              "2020-04-24  14.920000    14.524874"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xgF9vUKkl9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "full_scaler = MinMaxScaler()\n",
        "scaled_full_data = full_scaler.fit_transform(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2r1M3tykl9p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "length = 12 # Length of the output sequences (in number of timesteps)\n",
        "generator = TimeseriesGenerator(scaled_full_data, scaled_full_data, length=length, batch_size=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWUuCvEGkl9s",
        "colab_type": "code",
        "colab": {},
        "outputId": "c5e11812-127d-43bd-d8f8-e10ce6097bdc"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(20, activation='relu', input_shape=(length, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "\n",
        "# fit model\n",
        "model.fit_generator(generator,epochs=8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 88 steps\n",
            "Epoch 1/8\n",
            "88/88 [==============================] - 2s 27ms/step - loss: 0.0981\n",
            "Epoch 2/8\n",
            "88/88 [==============================] - 1s 11ms/step - loss: 0.0106\n",
            "Epoch 3/8\n",
            "88/88 [==============================] - 1s 11ms/step - loss: 0.0060\n",
            "Epoch 4/8\n",
            "88/88 [==============================] - 1s 11ms/step - loss: 0.0046\n",
            "Epoch 5/8\n",
            "88/88 [==============================] - 1s 11ms/step - loss: 0.0049\n",
            "Epoch 6/8\n",
            "88/88 [==============================] - 1s 11ms/step - loss: 0.0048\n",
            "Epoch 7/8\n",
            "88/88 [==============================] - 1s 10ms/step - loss: 0.0045\n",
            "Epoch 8/8\n",
            "88/88 [==============================] - 1s 10ms/step - loss: 0.0041\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x16e521e55c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGj4uABckl9u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "forecast = []\n",
        "# Replace periods with whatever forecast length you want\n",
        "periods = 15\n",
        "\n",
        "first_eval_batch = scaled_full_data[-length:]\n",
        "current_batch = first_eval_batch.reshape((1, length, n_features))\n",
        "\n",
        "for i in range(periods):\n",
        "    \n",
        "    # get prediction 1 time stamp ahead ([0] is for grabbing just the number instead of [array])\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "    \n",
        "    # store prediction\n",
        "    forecast.append(current_pred) \n",
        "    \n",
        "    # update batch to now include prediction and drop first value\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDc7XBSYkl9v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "forecast = scaler.inverse_transform(forecast)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UM1uMoFNkl9w",
        "colab_type": "code",
        "colab": {},
        "outputId": "c2183f51-b4b9-4554-e384-a9e560e5fb46"
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>new</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-12-02</th>\n",
              "      <td>24.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-03</th>\n",
              "      <td>25.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-04</th>\n",
              "      <td>26.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-05</th>\n",
              "      <td>26.274999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-06</th>\n",
              "      <td>26.325000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-09</th>\n",
              "      <td>26.644999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-10</th>\n",
              "      <td>27.035001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-11</th>\n",
              "      <td>27.315000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-12</th>\n",
              "      <td>27.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-13</th>\n",
              "      <td>27.995000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-16</th>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-17</th>\n",
              "      <td>27.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-18</th>\n",
              "      <td>27.185000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-19</th>\n",
              "      <td>26.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-20</th>\n",
              "      <td>26.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-23</th>\n",
              "      <td>26.144999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>26.184999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-26</th>\n",
              "      <td>25.985000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-27</th>\n",
              "      <td>25.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-30</th>\n",
              "      <td>25.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>25.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>25.310001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>25.255000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>25.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>25.655000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>25.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-09</th>\n",
              "      <td>25.265000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-10</th>\n",
              "      <td>24.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-13</th>\n",
              "      <td>25.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-14</th>\n",
              "      <td>25.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>16.355000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>15.005000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>15.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>15.795000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>16.530000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-20</th>\n",
              "      <td>16.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-23</th>\n",
              "      <td>15.365000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-24</th>\n",
              "      <td>14.530000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>15.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>14.705000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>14.675000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>16.945000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>16.935000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>15.869999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-02</th>\n",
              "      <td>15.245000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-03</th>\n",
              "      <td>14.515000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>14.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-07</th>\n",
              "      <td>14.785000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-08</th>\n",
              "      <td>15.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-09</th>\n",
              "      <td>15.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-13</th>\n",
              "      <td>15.395000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-14</th>\n",
              "      <td>15.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-15</th>\n",
              "      <td>14.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-16</th>\n",
              "      <td>14.065000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-17</th>\n",
              "      <td>14.585000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-20</th>\n",
              "      <td>14.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-21</th>\n",
              "      <td>14.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-22</th>\n",
              "      <td>14.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-23</th>\n",
              "      <td>14.985000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-24</th>\n",
              "      <td>14.920000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  new\n",
              "Date                 \n",
              "2019-12-02  24.155000\n",
              "2019-12-03  25.110001\n",
              "2019-12-04  26.210000\n",
              "2019-12-05  26.274999\n",
              "2019-12-06  26.325000\n",
              "2019-12-09  26.644999\n",
              "2019-12-10  27.035001\n",
              "2019-12-11  27.315000\n",
              "2019-12-12  27.570000\n",
              "2019-12-13  27.995000\n",
              "2019-12-16  28.000000\n",
              "2019-12-17  27.290000\n",
              "2019-12-18  27.185000\n",
              "2019-12-19  26.890000\n",
              "2019-12-20  26.500000\n",
              "2019-12-23  26.144999\n",
              "2019-12-24  26.184999\n",
              "2019-12-26  25.985000\n",
              "2019-12-27  25.710000\n",
              "2019-12-30  25.560000\n",
              "2019-12-31  25.720000\n",
              "2020-01-02  25.310001\n",
              "2020-01-03  25.255000\n",
              "2020-01-06  25.445000\n",
              "2020-01-07  25.655000\n",
              "2020-01-08  25.170000\n",
              "2020-01-09  25.265000\n",
              "2020-01-10  24.960000\n",
              "2020-01-13  25.125000\n",
              "2020-01-14  25.120000\n",
              "...               ...\n",
              "2020-03-13  16.355000\n",
              "2020-03-16  15.005000\n",
              "2020-03-17  15.090000\n",
              "2020-03-18  15.795000\n",
              "2020-03-19  16.530000\n",
              "2020-03-20  16.040000\n",
              "2020-03-23  15.365000\n",
              "2020-03-24  14.530000\n",
              "2020-03-25  15.125000\n",
              "2020-03-26  14.705000\n",
              "2020-03-27  14.675000\n",
              "2020-03-30  16.945000\n",
              "2020-03-31  16.935000\n",
              "2020-04-01  15.869999\n",
              "2020-04-02  15.245000\n",
              "2020-04-03  14.515000\n",
              "2020-04-06  14.260000\n",
              "2020-04-07  14.785000\n",
              "2020-04-08  15.040000\n",
              "2020-04-09  15.570000\n",
              "2020-04-13  15.395000\n",
              "2020-04-14  15.445000\n",
              "2020-04-15  14.820000\n",
              "2020-04-16  14.065000\n",
              "2020-04-17  14.585000\n",
              "2020-04-20  14.920000\n",
              "2020-04-21  14.720000\n",
              "2020-04-22  14.850000\n",
              "2020-04-23  14.985000\n",
              "2020-04-24  14.920000\n",
              "\n",
              "[100 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrzSbdspkl9z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "forecast_index = pd.date_range(start='2020-04-24',periods=15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_Zf7LTdkl91",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "forecast_df = pd.DataFrame(data=forecast,index=forecast_index,\n",
        "                           columns=['Forecast'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSrG0nPrkl93",
        "colab_type": "code",
        "colab": {},
        "outputId": "221aa617-7a18-46f3-d916-8653071b9673"
      },
      "source": [
        "forecast_df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Forecast</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-04-24</th>\n",
              "      <td>15.055151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-25</th>\n",
              "      <td>15.019964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-26</th>\n",
              "      <td>14.987403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-27</th>\n",
              "      <td>14.958627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-28</th>\n",
              "      <td>14.932724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-29</th>\n",
              "      <td>14.911245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>14.893737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>14.876313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-02</th>\n",
              "      <td>14.859885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-03</th>\n",
              "      <td>14.845917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-04</th>\n",
              "      <td>14.833884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-05</th>\n",
              "      <td>14.823720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-06</th>\n",
              "      <td>14.815334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-07</th>\n",
              "      <td>14.809205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-08</th>\n",
              "      <td>14.804014</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Forecast\n",
              "2020-04-24  15.055151\n",
              "2020-04-25  15.019964\n",
              "2020-04-26  14.987403\n",
              "2020-04-27  14.958627\n",
              "2020-04-28  14.932724\n",
              "2020-04-29  14.911245\n",
              "2020-04-30  14.893737\n",
              "2020-05-01  14.876313\n",
              "2020-05-02  14.859885\n",
              "2020-05-03  14.845917\n",
              "2020-05-04  14.833884\n",
              "2020-05-05  14.823720\n",
              "2020-05-06  14.815334\n",
              "2020-05-07  14.809205\n",
              "2020-05-08  14.804014"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8NyX6U1kl97",
        "colab_type": "code",
        "colab": {},
        "outputId": "04d413f6-8f00-4df2-aef3-ff9086cb1f80"
      },
      "source": [
        "df.plot()\n",
        "forecast_df.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x16e55fd1a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkboAjZ0kl99",
        "colab_type": "code",
        "colab": {},
        "outputId": "e98340b7-1f3d-4c5f-e44c-40270fa23289"
      },
      "source": [
        "ax = df.plot()\n",
        "forecast_df.plot(ax=ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x16e55fcda90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1WAG79Fkl9-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}