tensorflow_stock_prediction.ipynb

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      "name": "tensorflow-stock-prediction.ipynb",
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      "name": "python"
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/dwoolworth/tensorflow-stock-prediction/blob/main/tensorflow_stock_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Using artificial recurrent neural network (RNN) called Long Short-Term Memory\n",
        "# (LSTM) to predict the closing stock price of a corporation using the past\n",
        "# X days' stock price.\n",
        "import math\n",
        "import pandas_datareader as web\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')\n",
        "pd.set_option('max_rows', 50)\n",
        "pd.set_option('display.max_rows', 50)\n",
        "pd.set_option('min_rows', 30)\n",
        "pd.set_option('max_columns', None)\n",
        "pd.set_option('display.max_columns', None)\n",
        "\n",
        "# Analyzing big companies like IBM or ATT, etc. this model will have good,\n",
        "# predictable results. Those entities are not in the media, not typically\n",
        "# suceptible to things other than the economy or factors that impact all stocks.\n",
        "# AI is great with predictable patterns.  So we need to work on predicting/\n",
        "# foretelling smaller companies with potential more volatility given other\n",
        "# outside factors such as news, politics, etc.\n",
        "\n",
        "stock_ticker = 'STM'\n",
        "\n",
        "start_date = '2017-01-01'\n",
        "end_date = '2021-04-01'\n",
        "\n",
        "# Play with training days because this is where potentially shorter intervals\n",
        "# may yield better predictions.\n",
        "training_days = 60\n",
        "\n",
        "# Another factor to adjust given the amount of data available for the stock,\n",
        "# we'll default to 80% of data to train with, 20% of data to test with\n",
        "percent_of_data_to_train = .90\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "knC9qsvrQCai",
        "outputId": "b03c18aa-a181-4886-923d-d07093009d4f"
      },
      "source": [
        "# Get the stock quote\n",
        "df = web.DataReader(stock_ticker, data_source='yahoo', start=start_date, end=end_date)\n",
        "# Get the data\n",
        "df"
      ],
      "execution_count": 3,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>11.160000</td>\n",
              "      <td>10.850000</td>\n",
              "      <td>11.150000</td>\n",
              "      <td>10.980000</td>\n",
              "      <td>3119500.0</td>\n",
              "      <td>10.476607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>10.940000</td>\n",
              "      <td>10.770000</td>\n",
              "      <td>10.770000</td>\n",
              "      <td>10.880000</td>\n",
              "      <td>3756200.0</td>\n",
              "      <td>10.381189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>11.110000</td>\n",
              "      <td>10.910000</td>\n",
              "      <td>10.920000</td>\n",
              "      <td>10.940000</td>\n",
              "      <td>2395900.0</td>\n",
              "      <td>10.438438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06</th>\n",
              "      <td>10.820000</td>\n",
              "      <td>10.670000</td>\n",
              "      <td>10.730000</td>\n",
              "      <td>10.790000</td>\n",
              "      <td>3057800.0</td>\n",
              "      <td>10.295317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-09</th>\n",
              "      <td>11.220000</td>\n",
              "      <td>10.990000</td>\n",
              "      <td>10.990000</td>\n",
              "      <td>11.150000</td>\n",
              "      <td>3582800.0</td>\n",
              "      <td>10.638809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-10</th>\n",
              "      <td>11.380000</td>\n",
              "      <td>11.220000</td>\n",
              "      <td>11.220000</td>\n",
              "      <td>11.290000</td>\n",
              "      <td>2198000.0</td>\n",
              "      <td>10.772392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-11</th>\n",
              "      <td>11.470000</td>\n",
              "      <td>11.280000</td>\n",
              "      <td>11.320000</td>\n",
              "      <td>11.460000</td>\n",
              "      <td>2163700.0</td>\n",
              "      <td>10.934600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-12</th>\n",
              "      <td>11.410000</td>\n",
              "      <td>11.290000</td>\n",
              "      <td>11.320000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>2451900.0</td>\n",
              "      <td>10.877349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-13</th>\n",
              "      <td>11.670000</td>\n",
              "      <td>11.530000</td>\n",
              "      <td>11.540000</td>\n",
              "      <td>11.630000</td>\n",
              "      <td>1899100.0</td>\n",
              "      <td>11.096806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-17</th>\n",
              "      <td>11.570000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>11.550000</td>\n",
              "      <td>11.410000</td>\n",
              "      <td>1945000.0</td>\n",
              "      <td>10.886892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-18</th>\n",
              "      <td>11.550000</td>\n",
              "      <td>11.380000</td>\n",
              "      <td>11.380000</td>\n",
              "      <td>11.450000</td>\n",
              "      <td>2615800.0</td>\n",
              "      <td>10.925057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-19</th>\n",
              "      <td>11.630000</td>\n",
              "      <td>11.450000</td>\n",
              "      <td>11.460000</td>\n",
              "      <td>11.590000</td>\n",
              "      <td>2834600.0</td>\n",
              "      <td>11.058639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-20</th>\n",
              "      <td>11.650000</td>\n",
              "      <td>11.540000</td>\n",
              "      <td>11.540000</td>\n",
              "      <td>11.610000</td>\n",
              "      <td>2370100.0</td>\n",
              "      <td>11.077721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-23</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>11.540000</td>\n",
              "      <td>11.630000</td>\n",
              "      <td>11.680000</td>\n",
              "      <td>3698300.0</td>\n",
              "      <td>11.144513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-24</th>\n",
              "      <td>12.040000</td>\n",
              "      <td>11.810000</td>\n",
              "      <td>11.820000</td>\n",
              "      <td>11.990000</td>\n",
              "      <td>8494300.0</td>\n",
              "      <td>11.440300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-12</th>\n",
              "      <td>35.810001</td>\n",
              "      <td>35.049999</td>\n",
              "      <td>35.290001</td>\n",
              "      <td>35.750000</td>\n",
              "      <td>1760500.0</td>\n",
              "      <td>35.709538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-15</th>\n",
              "      <td>36.509998</td>\n",
              "      <td>35.689999</td>\n",
              "      <td>35.970001</td>\n",
              "      <td>36.500000</td>\n",
              "      <td>2083400.0</td>\n",
              "      <td>36.458691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-16</th>\n",
              "      <td>36.919998</td>\n",
              "      <td>36.310001</td>\n",
              "      <td>36.430000</td>\n",
              "      <td>36.400002</td>\n",
              "      <td>2436700.0</td>\n",
              "      <td>36.358807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-17</th>\n",
              "      <td>37.160000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.200001</td>\n",
              "      <td>36.990002</td>\n",
              "      <td>2142700.0</td>\n",
              "      <td>36.948139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-18</th>\n",
              "      <td>36.480000</td>\n",
              "      <td>35.630001</td>\n",
              "      <td>36.330002</td>\n",
              "      <td>35.630001</td>\n",
              "      <td>1949200.0</td>\n",
              "      <td>35.589676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-19</th>\n",
              "      <td>37.320000</td>\n",
              "      <td>35.959999</td>\n",
              "      <td>36.259998</td>\n",
              "      <td>37.110001</td>\n",
              "      <td>4509100.0</td>\n",
              "      <td>37.068001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-22</th>\n",
              "      <td>38.520000</td>\n",
              "      <td>37.430000</td>\n",
              "      <td>37.529999</td>\n",
              "      <td>38.230000</td>\n",
              "      <td>3252700.0</td>\n",
              "      <td>38.230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-23</th>\n",
              "      <td>38.020000</td>\n",
              "      <td>36.910000</td>\n",
              "      <td>37.980000</td>\n",
              "      <td>37.009998</td>\n",
              "      <td>1889000.0</td>\n",
              "      <td>37.009998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-24</th>\n",
              "      <td>37.330002</td>\n",
              "      <td>36.680000</td>\n",
              "      <td>37.209999</td>\n",
              "      <td>36.689999</td>\n",
              "      <td>1840600.0</td>\n",
              "      <td>36.689999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-25</th>\n",
              "      <td>37.279999</td>\n",
              "      <td>36.369999</td>\n",
              "      <td>36.590000</td>\n",
              "      <td>37.090000</td>\n",
              "      <td>2208000.0</td>\n",
              "      <td>37.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-26</th>\n",
              "      <td>38.590000</td>\n",
              "      <td>37.439999</td>\n",
              "      <td>37.439999</td>\n",
              "      <td>38.590000</td>\n",
              "      <td>1872300.0</td>\n",
              "      <td>38.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-29</th>\n",
              "      <td>38.060001</td>\n",
              "      <td>37.369999</td>\n",
              "      <td>37.950001</td>\n",
              "      <td>37.770000</td>\n",
              "      <td>1896800.0</td>\n",
              "      <td>37.770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-30</th>\n",
              "      <td>37.639999</td>\n",
              "      <td>37.040001</td>\n",
              "      <td>37.209999</td>\n",
              "      <td>37.439999</td>\n",
              "      <td>1552800.0</td>\n",
              "      <td>37.439999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-31</th>\n",
              "      <td>38.599998</td>\n",
              "      <td>37.759998</td>\n",
              "      <td>37.770000</td>\n",
              "      <td>38.330002</td>\n",
              "      <td>1539100.0</td>\n",
              "      <td>38.330002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-01</th>\n",
              "      <td>39.360001</td>\n",
              "      <td>38.740002</td>\n",
              "      <td>38.750000</td>\n",
              "      <td>39.349998</td>\n",
              "      <td>1604400.0</td>\n",
              "      <td>39.349998</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1069 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low       Open      Close     Volume  Adj Close\n",
              "Date                                                                        \n",
              "2017-01-03  11.160000  10.850000  11.150000  10.980000  3119500.0  10.476607\n",
              "2017-01-04  10.940000  10.770000  10.770000  10.880000  3756200.0  10.381189\n",
              "2017-01-05  11.110000  10.910000  10.920000  10.940000  2395900.0  10.438438\n",
              "2017-01-06  10.820000  10.670000  10.730000  10.790000  3057800.0  10.295317\n",
              "2017-01-09  11.220000  10.990000  10.990000  11.150000  3582800.0  10.638809\n",
              "2017-01-10  11.380000  11.220000  11.220000  11.290000  2198000.0  10.772392\n",
              "2017-01-11  11.470000  11.280000  11.320000  11.460000  2163700.0  10.934600\n",
              "2017-01-12  11.410000  11.290000  11.320000  11.400000  2451900.0  10.877349\n",
              "2017-01-13  11.670000  11.530000  11.540000  11.630000  1899100.0  11.096806\n",
              "2017-01-17  11.570000  11.360000  11.550000  11.410000  1945000.0  10.886892\n",
              "2017-01-18  11.550000  11.380000  11.380000  11.450000  2615800.0  10.925057\n",
              "2017-01-19  11.630000  11.450000  11.460000  11.590000  2834600.0  11.058639\n",
              "2017-01-20  11.650000  11.540000  11.540000  11.610000  2370100.0  11.077721\n",
              "2017-01-23  11.700000  11.540000  11.630000  11.680000  3698300.0  11.144513\n",
              "2017-01-24  12.040000  11.810000  11.820000  11.990000  8494300.0  11.440300\n",
              "...               ...        ...        ...        ...        ...        ...\n",
              "2021-03-12  35.810001  35.049999  35.290001  35.750000  1760500.0  35.709538\n",
              "2021-03-15  36.509998  35.689999  35.970001  36.500000  2083400.0  36.458691\n",
              "2021-03-16  36.919998  36.310001  36.430000  36.400002  2436700.0  36.358807\n",
              "2021-03-17  37.160000  36.000000  36.200001  36.990002  2142700.0  36.948139\n",
              "2021-03-18  36.480000  35.630001  36.330002  35.630001  1949200.0  35.589676\n",
              "2021-03-19  37.320000  35.959999  36.259998  37.110001  4509100.0  37.068001\n",
              "2021-03-22  38.520000  37.430000  37.529999  38.230000  3252700.0  38.230000\n",
              "2021-03-23  38.020000  36.910000  37.980000  37.009998  1889000.0  37.009998\n",
              "2021-03-24  37.330002  36.680000  37.209999  36.689999  1840600.0  36.689999\n",
              "2021-03-25  37.279999  36.369999  36.590000  37.090000  2208000.0  37.090000\n",
              "2021-03-26  38.590000  37.439999  37.439999  38.590000  1872300.0  38.590000\n",
              "2021-03-29  38.060001  37.369999  37.950001  37.770000  1896800.0  37.770000\n",
              "2021-03-30  37.639999  37.040001  37.209999  37.439999  1552800.0  37.439999\n",
              "2021-03-31  38.599998  37.759998  37.770000  38.330002  1539100.0  38.330002\n",
              "2021-04-01  39.360001  38.740002  38.750000  39.349998  1604400.0  39.349998\n",
              "\n",
              "[1069 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
        }
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    },
    {
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      "metadata": {
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        },
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        "outputId": "b5561843-0bf3-4e67-9b9d-22ef2c5b85b7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1069, 6)"
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          "metadata": {
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          "execution_count": 4
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "riUH-DCQQgtF",
        "outputId": "a3092281-967b-4513-9300-ca199750dab0"
      },
      "source": [
        "# Visualize the closing price history\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Closing Price History')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel('Date', fontsize=14)\n",
        "plt.ylabel('Closing Price USD ($)', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dx8qOM5sRUGN",
        "outputId": "c4371da6-b33e-4bef-d220-e0c02ca300de"
      },
      "source": [
        "# Create a new dataframe with only the 'Close' column\n",
        "data = df.filter(['Close'])\n",
        "dataset = data.values\n",
        "# Get the number of rows to train the model on\n",
        "training_data_len = math.ceil(len(dataset) * percent_of_data_to_train)\n",
        "\n",
        "training_data_len"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "963"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBo0d8s9Rw2G",
        "outputId": "217ba7b4-f129-4857-caf5-a62d2715056b"
      },
      "source": [
        "# Scale the data so that each data point is a value between 0 and 1\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "\n",
        "scaled_data"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00593749],\n",
              "       [0.0028125 ],\n",
              "       [0.00468749],\n",
              "       ...,\n",
              "       [0.83281243],\n",
              "       [0.86062503],\n",
              "       [0.89249993]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfEz0u_GSpDo",
        "outputId": "cd8a168a-8298-4970-b5aa-c64dde036d04"
      },
      "source": [
        "# Create the training data set\n",
        "# Create the scaled training data set\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "days_training = training_days\n",
        "# Split the data into x_train and y_train data sets\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(days_training, len(train_data)):\n",
        "  x_train.append(train_data[i-days_training:i, 0])\n",
        "  y_train.append(train_data[i, 0])\n",
        "  if i <= (days_training + 1):\n",
        "    print(x_train)\n",
        "    print(y_train)\n",
        "    print()\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.00593749, 0.0028125 , 0.00468749, 0.        , 0.01124999,\n",
            "       0.015625  , 0.0209375 , 0.01906249, 0.02625   , 0.019375  ,\n",
            "       0.02062499, 0.02500001, 0.02562499, 0.02781251, 0.03749999,\n",
            "       0.04343751, 0.06687501, 0.0875    , 0.0834375 , 0.07656249,\n",
            "       0.09062499, 0.10781249, 0.11187499, 0.10843751, 0.1109375 ,\n",
            "       0.09843749, 0.08968749, 0.08906251, 0.09281251, 0.09343749,\n",
            "       0.09999999, 0.11531248, 0.10843751, 0.12031251, 0.13499999,\n",
            "       0.13718751, 0.1315625 , 0.14812499, 0.1396875 , 0.15312498,\n",
            "       0.14968749, 0.15281251, 0.14500001, 0.14437499, 0.15      ,\n",
            "       0.138125  , 0.1396875 , 0.139375  , 0.139375  , 0.14968749,\n",
            "       0.14906251, 0.15093749, 0.1446875 , 0.12      , 0.136875  ,\n",
            "       0.139375  , 0.1590625 , 0.15437498, 0.15218749, 0.14812499])]\n",
            "[0.1453124837484216]\n",
            "\n",
            "[array([0.00593749, 0.0028125 , 0.00468749, 0.        , 0.01124999,\n",
            "       0.015625  , 0.0209375 , 0.01906249, 0.02625   , 0.019375  ,\n",
            "       0.02062499, 0.02500001, 0.02562499, 0.02781251, 0.03749999,\n",
            "       0.04343751, 0.06687501, 0.0875    , 0.0834375 , 0.07656249,\n",
            "       0.09062499, 0.10781249, 0.11187499, 0.10843751, 0.1109375 ,\n",
            "       0.09843749, 0.08968749, 0.08906251, 0.09281251, 0.09343749,\n",
            "       0.09999999, 0.11531248, 0.10843751, 0.12031251, 0.13499999,\n",
            "       0.13718751, 0.1315625 , 0.14812499, 0.1396875 , 0.15312498,\n",
            "       0.14968749, 0.15281251, 0.14500001, 0.14437499, 0.15      ,\n",
            "       0.138125  , 0.1396875 , 0.139375  , 0.139375  , 0.14968749,\n",
            "       0.14906251, 0.15093749, 0.1446875 , 0.12      , 0.136875  ,\n",
            "       0.139375  , 0.1590625 , 0.15437498, 0.15218749, 0.14812499]), array([0.0028125 , 0.00468749, 0.        , 0.01124999, 0.015625  ,\n",
            "       0.0209375 , 0.01906249, 0.02625   , 0.019375  , 0.02062499,\n",
            "       0.02500001, 0.02562499, 0.02781251, 0.03749999, 0.04343751,\n",
            "       0.06687501, 0.0875    , 0.0834375 , 0.07656249, 0.09062499,\n",
            "       0.10781249, 0.11187499, 0.10843751, 0.1109375 , 0.09843749,\n",
            "       0.08968749, 0.08906251, 0.09281251, 0.09343749, 0.09999999,\n",
            "       0.11531248, 0.10843751, 0.12031251, 0.13499999, 0.13718751,\n",
            "       0.1315625 , 0.14812499, 0.1396875 , 0.15312498, 0.14968749,\n",
            "       0.15281251, 0.14500001, 0.14437499, 0.15      , 0.138125  ,\n",
            "       0.1396875 , 0.139375  , 0.139375  , 0.14968749, 0.14906251,\n",
            "       0.15093749, 0.1446875 , 0.12      , 0.136875  , 0.139375  ,\n",
            "       0.1590625 , 0.15437498, 0.15218749, 0.14812499, 0.14531248])]\n",
            "[0.1453124837484216, 0.14593749803490946]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ki1xP1Q8TitS"
      },
      "source": [
        "# Convert the x_train and y_train to numpy array\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07r03NuJUNCC",
        "outputId": "b0949089-6d49-48b2-bff5-089f9febf0d0"
      },
      "source": [
        "# Reshape the data (LSTM model is expecting 3 dimensional data)\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
        "x_train.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(903, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OR7Rqq3UmLq"
      },
      "source": [
        "# Build the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(LSTM(50, return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chkYzv45VIXr"
      },
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7ATPF9bVTur",
        "outputId": "d86f1e36-43c1-471f-e3a6-5dcceb1a0e7c"
      },
      "source": [
        "# Train the model\n",
        "model.fit(x_train, y_train, batch_size=1, epochs=1)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "903/903 [==============================] - 27s 26ms/step - loss: 0.0049\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fcb889afe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzevWtz8VqNC"
      },
      "source": [
        "# Create the testing data set\n",
        "# Create a new array containing scaled values from index 1543 to 2003\n",
        "test_data = scaled_data[training_data_len - days_training: , :]\n",
        "# Create the data sets x_test and y_test\n",
        "x_test = []\n",
        "y_test = dataset[training_data_len:, :]\n",
        "for i in range(days_training, len(test_data)):\n",
        "  x_test.append(test_data[i-days_training:i, 0])\n",
        "  "
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4qdibaOWbEk"
      },
      "source": [
        "# Convert the data to a numpy array\n",
        "x_test = np.array(x_test)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJwPbrBeWkuX"
      },
      "source": [
        "# Reshape the data (need it to be 3 dimensional)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yCc2jGjW66z"
      },
      "source": [
        "# Get the models predicted price values\n",
        "predictions = model.predict(x_test)\n",
        "# Unscale the values\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "# So we're wanting predictions to contain the same values as y_test dataset"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXcV4BhEXXce",
        "outputId": "31af6f6f-f7ef-477f-c940-f1becc49d7c6"
      },
      "source": [
        "# Get the root mean squared error (RMSE)\n",
        "# ...evaluating performance of model based on standard deviation\n",
        "rmse = np.sqrt(np.mean(((predictions - y_test )**2)))\n",
        "# A value of 0.0 means that the values matched up perfectly.\n",
        "# A value between 1.0 and 5.0 is still 'ok', but obviously, a more\n",
        "# accurate value is better here.  Our results are as follows:\n",
        "rmse"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7018494522766556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "wHJG4Rk9YQoH",
        "outputId": "fd7641f1-c044-4b08-a5e3-3f40b663092e"
      },
      "source": [
        "# Plot the data\n",
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        "valid['Predictions'] = predictions\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('Closing Price USD ($)', fontsize=14)\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid[['Close', 'Predictions']])\n",
        "plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Iep5wJmlaZnf",
        "outputId": "fd339a5b-3c05-411c-9c5c-dcc6e84a88b9"
      },
      "source": [
        "# Show the valid and predicted prices\n",
        "valid"
      ],
      "execution_count": 20,
      "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>Close</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>2020-10-29</th>\n",
              "      <td>31.139999</td>\n",
              "      <td>33.247456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-30</th>\n",
              "      <td>30.540001</td>\n",
              "      <td>32.647324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-02</th>\n",
              "      <td>31.100000</td>\n",
              "      <td>32.087536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-03</th>\n",
              "      <td>32.150002</td>\n",
              "      <td>31.726259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-04</th>\n",
              "      <td>33.040001</td>\n",
              "      <td>31.684692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-05</th>\n",
              "      <td>34.070000</td>\n",
              "      <td>31.954290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-06</th>\n",
              "      <td>34.590000</td>\n",
              "      <td>32.509232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-09</th>\n",
              "      <td>35.080002</td>\n",
              "      <td>33.213009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-10</th>\n",
              "      <td>33.509998</td>\n",
              "      <td>33.966908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-11</th>\n",
              "      <td>34.580002</td>\n",
              "      <td>34.379051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-12</th>\n",
              "      <td>33.549999</td>\n",
              "      <td>34.723934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-13</th>\n",
              "      <td>34.349998</td>\n",
              "      <td>34.803322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-16</th>\n",
              "      <td>35.549999</td>\n",
              "      <td>34.861286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-17</th>\n",
              "      <td>35.259998</td>\n",
              "      <td>35.082157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-18</th>\n",
              "      <td>35.410000</td>\n",
              "      <td>35.300518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-12</th>\n",
              "      <td>35.750000</td>\n",
              "      <td>35.660660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-15</th>\n",
              "      <td>36.500000</td>\n",
              "      <td>35.876053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-16</th>\n",
              "      <td>36.400002</td>\n",
              "      <td>36.233986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-17</th>\n",
              "      <td>36.990002</td>\n",
              "      <td>36.589485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-18</th>\n",
              "      <td>35.630001</td>\n",
              "      <td>36.982979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-19</th>\n",
              "      <td>37.110001</td>\n",
              "      <td>37.105751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-22</th>\n",
              "      <td>38.230000</td>\n",
              "      <td>37.312645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-23</th>\n",
              "      <td>37.009998</td>\n",
              "      <td>37.697689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-24</th>\n",
              "      <td>36.689999</td>\n",
              "      <td>37.905777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-25</th>\n",
              "      <td>37.090000</td>\n",
              "      <td>37.941811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-26</th>\n",
              "      <td>38.590000</td>\n",
              "      <td>37.944347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-29</th>\n",
              "      <td>37.770000</td>\n",
              "      <td>38.167591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-30</th>\n",
              "      <td>37.439999</td>\n",
              "      <td>38.324314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-31</th>\n",
              "      <td>38.330002</td>\n",
              "      <td>38.373631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-01</th>\n",
              "      <td>39.349998</td>\n",
              "      <td>38.506195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Close  Predictions\n",
              "Date                              \n",
              "2020-10-29  31.139999    33.247456\n",
              "2020-10-30  30.540001    32.647324\n",
              "2020-11-02  31.100000    32.087536\n",
              "2020-11-03  32.150002    31.726259\n",
              "2020-11-04  33.040001    31.684692\n",
              "2020-11-05  34.070000    31.954290\n",
              "2020-11-06  34.590000    32.509232\n",
              "2020-11-09  35.080002    33.213009\n",
              "2020-11-10  33.509998    33.966908\n",
              "2020-11-11  34.580002    34.379051\n",
              "2020-11-12  33.549999    34.723934\n",
              "2020-11-13  34.349998    34.803322\n",
              "2020-11-16  35.549999    34.861286\n",
              "2020-11-17  35.259998    35.082157\n",
              "2020-11-18  35.410000    35.300518\n",
              "...               ...          ...\n",
              "2021-03-12  35.750000    35.660660\n",
              "2021-03-15  36.500000    35.876053\n",
              "2021-03-16  36.400002    36.233986\n",
              "2021-03-17  36.990002    36.589485\n",
              "2021-03-18  35.630001    36.982979\n",
              "2021-03-19  37.110001    37.105751\n",
              "2021-03-22  38.230000    37.312645\n",
              "2021-03-23  37.009998    37.697689\n",
              "2021-03-24  36.689999    37.905777\n",
              "2021-03-25  37.090000    37.941811\n",
              "2021-03-26  38.590000    37.944347\n",
              "2021-03-29  37.770000    38.167591\n",
              "2021-03-30  37.439999    38.324314\n",
              "2021-03-31  38.330002    38.373631\n",
              "2021-04-01  39.349998    38.506195\n",
              "\n",
              "[106 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkW-AOgvarF6"
      },
      "source": [
        "# Get the quote\n",
        "ticker_quote = web.DataReader(stock_ticker, data_source='yahoo', start=start_date, end=end_date)\n",
        "new_df = ticker_quote.filter(['Close'])\n",
        "# Get the last X day closing price values and convert the dataframe to an array\n",
        "last_X_days = new_df[-training_days:].values\n",
        "# Scale the data to be values bewteen 0 and 1\n",
        "last_X_days_scaled = scaler.transform(last_X_days)\n",
        "# Create an empty list\n",
        "X_test = []\n",
        "# Append the past X days\n",
        "X_test.append(last_X_days_scaled)\n",
        "X_test1 = np.array(X_test)\n",
        "# Reshape the data to be 3 dimensional\n",
        "X_test1 = np.reshape(X_test1, (X_test1.shape[0], X_test1.shape[1], 1))"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "EPfvDQPVVZzZ",
        "outputId": "90a6e676-2d9a-486c-984d-b06a0830deeb"
      },
      "source": [
        "#X_predictions = []\n",
        "#for i in range(30):\n",
        "  # Get the predicted price (scaled) from numpy.array()\n",
        "#  pred_price = model.predict(X_test1)\n",
        "#  print(X_test1.shape)\n",
        "  # Append the predicted price onto the end\n",
        "  # X_test = []\n",
        "  # Append the past X days\n",
        "#  last_X_days_scaled\n",
        "#  X_test.append(pred_price)\n",
        "#  print(X_test)\n",
        "#  X_test1 = np.array(X_test)\n",
        "#  X_test1 = np.reshape(X_test1, (X_test1.shape[0], X_test1.shape[1], 1))\n",
        "  # Undo the scaling\n",
        "#  X_predictions.append(scaler.inverse_transform(pred_price))\n",
        "\n",
        "#print(X_predictions)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 60, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-137fbe368c5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0;31m# Append the past X days\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0;31m# X_test.append(last_X_days_scaled)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m   \u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_price\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0mX_test1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'append'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pj_-VQibkeQn",
        "outputId": "3f626b42-3dab-47c7-ded8-bf1d532a3480"
      },
      "source": [
        "ticker_quote2 = web.DataReader(stock_ticker, data_source='yahoo', start=end_date, end=end_date)\n",
        "ticker_quote2['Close']"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2021-04-01    39.349998\n",
              "2021-04-01    39.349998\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    }
  ]
}