hourly_date_day_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "hourly_date_day_prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "JAFWu48oXEyo",
        "colab_type": "code",
        "outputId": "ed511ab4-d837-48c1-9762-b1fedd6b7a7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "# Import required Libraries\n",
        "!pip install fastparquet\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 tensorflow.keras.models import Sequential \n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fastparquet in /usr/local/lib/python3.6/dist-packages (0.3.3)\n",
            "Requirement already satisfied: numba>=0.28 in /usr/local/lib/python3.6/dist-packages (from fastparquet) (0.48.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from fastparquet) (1.18.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from fastparquet) (1.12.0)\n",
            "Requirement already satisfied: thrift>=0.11.0 in /usr/local/lib/python3.6/dist-packages (from fastparquet) (0.13.0)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from fastparquet) (1.0.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.28->fastparquet) (46.1.3)\n",
            "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.28->fastparquet) (0.31.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->fastparquet) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->fastparquet) (2.8.1)\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "iv6b1wZvXsXT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_parquet('https://github.com/ddkpham/Stock_Prediction_NLP_ML/blob/master/wsb.hourly.joined.parquet.gz?raw=true', engine=\"fastparquet\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19UAmo8DXzrZ",
        "colab_type": "code",
        "outputId": "8037692e-0188-4b1f-8fd0-ec1b972bdd8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "dataset = data.set_index('created_utc')\n",
        "dataset = dataset.values\n",
        "data.tail(5)"
      ],
      "execution_count": 106,
      "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>created_utc</th>\n",
              "      <th>score</th>\n",
              "      <th>gilded</th>\n",
              "      <th>wordcount</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>AAPL_StockPrice</th>\n",
              "      <th>AMZN_StockPrice</th>\n",
              "      <th>BA_StockPrice</th>\n",
              "      <th>SPY_StockPrice</th>\n",
              "      <th>TSLA_StockPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17017</th>\n",
              "      <td>2019-10-31 19:00:00</td>\n",
              "      <td>9.854762</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.720238</td>\n",
              "      <td>-0.015871</td>\n",
              "      <td>0.088306</td>\n",
              "      <td>-0.011564</td>\n",
              "      <td>-0.014938</td>\n",
              "      <td>-0.038733</td>\n",
              "      <td>-0.023130</td>\n",
              "      <td>0.154235</td>\n",
              "      <td>-0.081725</td>\n",
              "      <td>-0.021721</td>\n",
              "      <td>-0.005306</td>\n",
              "      <td>-0.040358</td>\n",
              "      <td>0.062018</td>\n",
              "      <td>-0.684138</td>\n",
              "      <td>-0.025796</td>\n",
              "      <td>0.038119</td>\n",
              "      <td>0.023374</td>\n",
              "      <td>0.053640</td>\n",
              "      <td>-0.049039</td>\n",
              "      <td>-0.002019</td>\n",
              "      <td>-0.022662</td>\n",
              "      <td>0.000548</td>\n",
              "      <td>0.025454</td>\n",
              "      <td>0.035070</td>\n",
              "      <td>0.027902</td>\n",
              "      <td>-0.053098</td>\n",
              "      <td>248.56</td>\n",
              "      <td>1776.99</td>\n",
              "      <td>340.09</td>\n",
              "      <td>303.20</td>\n",
              "      <td>314.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17018</th>\n",
              "      <td>2019-10-31 20:00:00</td>\n",
              "      <td>7.543249</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.218354</td>\n",
              "      <td>-0.014352</td>\n",
              "      <td>0.084513</td>\n",
              "      <td>-0.007887</td>\n",
              "      <td>-0.016645</td>\n",
              "      <td>-0.036200</td>\n",
              "      <td>-0.021342</td>\n",
              "      <td>0.154608</td>\n",
              "      <td>-0.078857</td>\n",
              "      <td>-0.026559</td>\n",
              "      <td>-0.003333</td>\n",
              "      <td>-0.039675</td>\n",
              "      <td>0.063693</td>\n",
              "      <td>-0.675505</td>\n",
              "      <td>-0.024968</td>\n",
              "      <td>0.045586</td>\n",
              "      <td>0.027612</td>\n",
              "      <td>0.050561</td>\n",
              "      <td>-0.041637</td>\n",
              "      <td>0.000699</td>\n",
              "      <td>-0.019423</td>\n",
              "      <td>0.003253</td>\n",
              "      <td>0.018703</td>\n",
              "      <td>0.032161</td>\n",
              "      <td>0.033941</td>\n",
              "      <td>-0.055375</td>\n",
              "      <td>248.56</td>\n",
              "      <td>1776.99</td>\n",
              "      <td>340.09</td>\n",
              "      <td>303.33</td>\n",
              "      <td>314.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17019</th>\n",
              "      <td>2019-10-31 21:00:00</td>\n",
              "      <td>7.101476</td>\n",
              "      <td>0.001845</td>\n",
              "      <td>17.429889</td>\n",
              "      <td>-0.015376</td>\n",
              "      <td>0.092038</td>\n",
              "      <td>-0.022579</td>\n",
              "      <td>-0.000384</td>\n",
              "      <td>-0.039674</td>\n",
              "      <td>-0.033292</td>\n",
              "      <td>0.145958</td>\n",
              "      <td>-0.073718</td>\n",
              "      <td>-0.023163</td>\n",
              "      <td>0.003552</td>\n",
              "      <td>-0.036160</td>\n",
              "      <td>0.064825</td>\n",
              "      <td>-0.636240</td>\n",
              "      <td>-0.032800</td>\n",
              "      <td>0.044508</td>\n",
              "      <td>0.030280</td>\n",
              "      <td>0.046661</td>\n",
              "      <td>-0.032616</td>\n",
              "      <td>0.008074</td>\n",
              "      <td>-0.011401</td>\n",
              "      <td>0.011551</td>\n",
              "      <td>0.017010</td>\n",
              "      <td>0.040372</td>\n",
              "      <td>0.032420</td>\n",
              "      <td>-0.060353</td>\n",
              "      <td>248.56</td>\n",
              "      <td>1776.99</td>\n",
              "      <td>340.09</td>\n",
              "      <td>303.33</td>\n",
              "      <td>314.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17020</th>\n",
              "      <td>2019-10-31 22:00:00</td>\n",
              "      <td>6.611227</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.299376</td>\n",
              "      <td>-0.016740</td>\n",
              "      <td>0.088693</td>\n",
              "      <td>-0.011643</td>\n",
              "      <td>-0.014845</td>\n",
              "      <td>-0.033719</td>\n",
              "      <td>-0.025900</td>\n",
              "      <td>0.155192</td>\n",
              "      <td>-0.082462</td>\n",
              "      <td>-0.025542</td>\n",
              "      <td>-0.010772</td>\n",
              "      <td>-0.044407</td>\n",
              "      <td>0.060708</td>\n",
              "      <td>-0.658886</td>\n",
              "      <td>-0.033341</td>\n",
              "      <td>0.044305</td>\n",
              "      <td>0.028699</td>\n",
              "      <td>0.049924</td>\n",
              "      <td>-0.048469</td>\n",
              "      <td>0.002733</td>\n",
              "      <td>-0.016644</td>\n",
              "      <td>0.002730</td>\n",
              "      <td>0.023538</td>\n",
              "      <td>0.034770</td>\n",
              "      <td>0.034311</td>\n",
              "      <td>-0.053850</td>\n",
              "      <td>248.56</td>\n",
              "      <td>1776.99</td>\n",
              "      <td>340.09</td>\n",
              "      <td>303.33</td>\n",
              "      <td>314.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17021</th>\n",
              "      <td>2019-10-31 23:00:00</td>\n",
              "      <td>4.513410</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.492337</td>\n",
              "      <td>-0.005759</td>\n",
              "      <td>0.097412</td>\n",
              "      <td>-0.042368</td>\n",
              "      <td>0.008516</td>\n",
              "      <td>-0.042094</td>\n",
              "      <td>-0.027694</td>\n",
              "      <td>0.139947</td>\n",
              "      <td>-0.056377</td>\n",
              "      <td>-0.025076</td>\n",
              "      <td>0.016040</td>\n",
              "      <td>-0.034431</td>\n",
              "      <td>0.066261</td>\n",
              "      <td>-0.582691</td>\n",
              "      <td>-0.036126</td>\n",
              "      <td>0.046025</td>\n",
              "      <td>0.039163</td>\n",
              "      <td>0.035854</td>\n",
              "      <td>-0.022578</td>\n",
              "      <td>0.008534</td>\n",
              "      <td>0.010553</td>\n",
              "      <td>0.029844</td>\n",
              "      <td>-0.008701</td>\n",
              "      <td>0.045263</td>\n",
              "      <td>0.042516</td>\n",
              "      <td>-0.052322</td>\n",
              "      <td>248.56</td>\n",
              "      <td>1776.99</td>\n",
              "      <td>340.09</td>\n",
              "      <td>303.33</td>\n",
              "      <td>314.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              created_utc     score  ...  SPY_StockPrice  TSLA_StockPrice\n",
              "17017 2019-10-31 19:00:00  9.854762  ...          303.20            314.6\n",
              "17018 2019-10-31 20:00:00  7.543249  ...          303.33            314.6\n",
              "17019 2019-10-31 21:00:00  7.101476  ...          303.33            314.6\n",
              "17020 2019-10-31 22:00:00  6.611227  ...          303.33            314.6\n",
              "17021 2019-10-31 23:00:00  4.513410  ...          303.33            314.6\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3VZOkwfX1qR",
        "colab_type": "code",
        "outputId": "4297ee9f-dfb5-4e9d-dc92-5e4e6bf61bfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "opening_price = data.filter(['created_utc','AAPL_StockPrice', 'AMZN_StockPrice', \n",
        "                                              'BA_StockPrice', 'SPY_StockPrice', 'TSLA_StockPrice'])\n",
        "# Opening hour prices for stocks\n",
        "opening_dataset = opening_price.set_index('created_utc')\n",
        "opening_dataset = opening_dataset.resample('D').first().dropna(how='any')\n",
        "opening_dataset = opening_dataset.values\n",
        "print(len(opening_dataset))\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pj7VBO6YFmL",
        "colab_type": "code",
        "outputId": "85cbdb43-9316-418f-a336-b6b9ee63abd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# set training set len\n",
        "training_data_len = math.ceil(len(dataset) * 0.8)\n",
        "training_data_len\n",
        "hours = data.filter(['created_utc'])\n",
        "hours = hours['created_utc']\n",
        "print(training_data_len)\n",
        "\n",
        "# adjust training length to the end of a day since it may fall in the middle of a day\n",
        "while(hours[training_data_len].hour != 0):\n",
        "  training_data_len = training_data_len +1\n",
        "print(training_data_len)\n",
        "hours[training_data_len]"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13618\n",
            "13638\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-04-12 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AE2xD91yhbrE",
        "colab_type": "code",
        "outputId": "41f3fec5-037b-4542-afc6-6f652dfe64d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "openScaler = MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "scaled_dataset = scaler.fit_transform(dataset)\n",
        "\n",
        "scaled_open_prices = openScaler.fit_transform(opening_dataset)\n",
        "scaled_open_prices"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.51032670e-05, 3.16376784e-03, 0.00000000e+00, 0.00000000e+00,\n",
              "        1.69553372e-01],\n",
              "       [4.50619602e-04, 0.00000000e+00, 4.51017411e-03, 4.79313824e-03,\n",
              "        1.60795952e-01],\n",
              "       [0.00000000e+00, 2.41568481e-03, 1.02790015e-02, 2.16952573e-02,\n",
              "        2.29639000e-01],\n",
              "       ...,\n",
              "       [1.00000000e+00, 7.97604576e-01, 6.46877841e-01, 9.89404642e-01,\n",
              "        7.20832928e-01],\n",
              "       [9.60045062e-01, 7.86406708e-01, 6.72225718e-01, 9.88269425e-01,\n",
              "        6.57438941e-01],\n",
              "       [9.92790086e-01, 8.03098311e-01, 6.68170058e-01, 1.00000000e+00,\n",
              "        6.59871558e-01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6FfNa3hh2xZ",
        "colab_type": "code",
        "outputId": "19efd18c-a0da-42d0-f679-1fff44cb75db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# Split date into x_train, y_train \n",
        "train_xvals = scaled_dataset[0:training_data_len, :]\n",
        "\n",
        "# to check date of current row \n",
        "dates = data['created_utc']\n",
        "t_data = data[data['created_utc'] < dates[training_data_len]]\n",
        "training_days = t_data['created_utc'].dt.date.unique()\n",
        "\n",
        "print(len(training_days))\n",
        "print(training_days[-1]) # verify this is the correct date. Should be before the date in hours[training_data_len]\n",
        "\n"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "572\n",
            "2019-04-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxTWplMUEZjI",
        "colab_type": "code",
        "outputId": "34a9621b-621e-47d8-ab8c-5c8090fa444a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "i = 0\n",
        "num_hours_in_day = 24\n",
        "for idx in range(0,len(training_days)):\n",
        "  time_step = []\n",
        "  # last day in the list \n",
        "  if idx == len(training_days) - 1:\n",
        "    while(i < training_data_len):\n",
        "      hourly_data = train_xvals[i:i+1, :][0]\n",
        "      time_step.append(hourly_data)\n",
        "      i = i + 1\n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  else:\n",
        "    # next_date = training_days['created_utc'][idx + 1]\n",
        "    next_date = training_days[idx + 1]\n",
        "    while(next_date > dates[i]):\n",
        "    # day matches \n",
        "      hourly_data = train_xvals[i:i+1, :][0]\n",
        "      time_step.append(hourly_data)\n",
        "      i = i + 1\n",
        "    # if there isnt a full 24 hours in set \n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  time_step = np.array(time_step)\n",
        "  \n",
        "  time_step = np.reshape(time_step, (24,33))\n",
        "  x_train.append(time_step)\n",
        "  y_train.append(scaled_open_prices[idx+1].tolist())\n",
        "  test_start_index = idx + 1\n",
        "  if(idx < 2):\n",
        "    print(y_train)\n",
        "    # print('time_step',idx, time_step)\n"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.00045061960195269535, 0.0, 0.004510174113698251, 0.004793138244197692, 0.16079595212610687]]\n",
            "[[0.00045061960195269535, 0.0, 0.004510174113698251, 0.004793138244197692, 0.16079595212610687], [0.0, 0.002415684807680285, 0.01027900146842875, 0.021695257315842653, 0.2296389997080861]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sbR3dt-ni8d",
        "colab_type": "code",
        "outputId": "096d9977-39cb-4b94-94a7-ba7c03e401c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Convert both x, y training sets to np array \n",
        "x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "# Reshape the data // LSTM network expects 3 dimensional input in the form of \n",
        "# (number of samples, number of timesteps, number of features)\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 33))\n",
        "x_train.shape"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(572, 24, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwJCHUIg1jK0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Declare the LSTM model architecture \n",
        "model = Sequential()\n",
        "\n",
        "# Add layers , input shape expected is (number of timesteps, number of features)\n",
        "model.add(LSTM(75, return_sequences=True, input_shape=(x_train.shape[1], 33)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(50, return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(25))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(5))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7ACjXteIOYz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile Model \n",
        "model.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caO-SaAWIQ7h",
        "colab_type": "code",
        "outputId": "7bd58222-9765-4bbe-9109-d96fd716b8e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train the model \n",
        "model.fit(x_train, y_train, batch_size=16, epochs=100)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "36/36 [==============================] - 1s 21ms/step - loss: 0.0312\n",
            "Epoch 2/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0068\n",
            "Epoch 3/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0036\n",
            "Epoch 4/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0024\n",
            "Epoch 5/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0019\n",
            "Epoch 6/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0013\n",
            "Epoch 7/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 9.2413e-04\n",
            "Epoch 8/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 8.3363e-04\n",
            "Epoch 9/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 6.8969e-04\n",
            "Epoch 10/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 6.2224e-04\n",
            "Epoch 11/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 5.3550e-04\n",
            "Epoch 12/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 4.2802e-04\n",
            "Epoch 13/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 3.9199e-04\n",
            "Epoch 14/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 3.0657e-04\n",
            "Epoch 15/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 3.4049e-04\n",
            "Epoch 16/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 3.1981e-04\n",
            "Epoch 17/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.7502e-04\n",
            "Epoch 18/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 2.9660e-04\n",
            "Epoch 19/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 3.3282e-04\n",
            "Epoch 20/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 3.1394e-04\n",
            "Epoch 21/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.5883e-04\n",
            "Epoch 22/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 2.2391e-04\n",
            "Epoch 23/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 2.5126e-04\n",
            "Epoch 24/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.9892e-04\n",
            "Epoch 25/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.2003e-04\n",
            "Epoch 26/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.8866e-04\n",
            "Epoch 27/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.8251e-04\n",
            "Epoch 28/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.2455e-04\n",
            "Epoch 29/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 2.4219e-04\n",
            "Epoch 30/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.6886e-04\n",
            "Epoch 31/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.6802e-04\n",
            "Epoch 32/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.0485e-04\n",
            "Epoch 33/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.6334e-04\n",
            "Epoch 34/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.1724e-04\n",
            "Epoch 35/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.0777e-04\n",
            "Epoch 36/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 2.3181e-04\n",
            "Epoch 37/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.5714e-04\n",
            "Epoch 38/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.4518e-04\n",
            "Epoch 39/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.3709e-04\n",
            "Epoch 40/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.2240e-04\n",
            "Epoch 41/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.4805e-04\n",
            "Epoch 42/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.5236e-04\n",
            "Epoch 43/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.5332e-04\n",
            "Epoch 44/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.8778e-04\n",
            "Epoch 45/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.2131e-04\n",
            "Epoch 46/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.0340e-04\n",
            "Epoch 47/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.2567e-04\n",
            "Epoch 48/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.1913e-04\n",
            "Epoch 49/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.0694e-04\n",
            "Epoch 50/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.3951e-04\n",
            "Epoch 51/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.4503e-04\n",
            "Epoch 52/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.4484e-04\n",
            "Epoch 53/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.4254e-04\n",
            "Epoch 54/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.0635e-04\n",
            "Epoch 55/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.3464e-04\n",
            "Epoch 56/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.2068e-04\n",
            "Epoch 57/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.3201e-04\n",
            "Epoch 58/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.3461e-04\n",
            "Epoch 59/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.0986e-04\n",
            "Epoch 60/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.0203e-04\n",
            "Epoch 61/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 8.4940e-05\n",
            "Epoch 62/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 9.6809e-05\n",
            "Epoch 63/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 9.9675e-05\n",
            "Epoch 64/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.0488e-04\n",
            "Epoch 65/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.1975e-04\n",
            "Epoch 66/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 9.6601e-05\n",
            "Epoch 67/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.1055e-04\n",
            "Epoch 68/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 8.3847e-05\n",
            "Epoch 69/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 8.3948e-05\n",
            "Epoch 70/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.0220e-04\n",
            "Epoch 71/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.0849e-04\n",
            "Epoch 72/100\n",
            "36/36 [==============================] - 1s 21ms/step - loss: 9.7790e-05\n",
            "Epoch 73/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.0607e-04\n",
            "Epoch 74/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.0274e-04\n",
            "Epoch 75/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 9.3628e-05\n",
            "Epoch 76/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 9.4543e-05\n",
            "Epoch 77/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.2289e-04\n",
            "Epoch 78/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.4443e-04\n",
            "Epoch 79/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.2632e-04\n",
            "Epoch 80/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 1.0691e-04\n",
            "Epoch 81/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.0200e-04\n",
            "Epoch 82/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 7.4650e-05\n",
            "Epoch 83/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 1.1874e-04\n",
            "Epoch 84/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 9.0618e-05\n",
            "Epoch 85/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 2.0427e-04\n",
            "Epoch 86/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.3712e-04\n",
            "Epoch 87/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 7.7223e-05\n",
            "Epoch 88/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 1.2172e-04\n",
            "Epoch 89/100\n",
            "36/36 [==============================] - 1s 26ms/step - loss: 7.2157e-05\n",
            "Epoch 90/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 6.9285e-05\n",
            "Epoch 91/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 6.4788e-05\n",
            "Epoch 92/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 6.1027e-05\n",
            "Epoch 93/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 5.4063e-05\n",
            "Epoch 94/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 5.3371e-05\n",
            "Epoch 95/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 7.0944e-05\n",
            "Epoch 96/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 7.6971e-05\n",
            "Epoch 97/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 6.8637e-05\n",
            "Epoch 98/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 8.4972e-05\n",
            "Epoch 99/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 6.9313e-05\n",
            "Epoch 100/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 5.7758e-05\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7eff80ebc278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BG-4nZEtIRUC",
        "colab_type": "code",
        "outputId": "adccb4c2-1a37-43d3-895f-8ac9ab2c02dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "test_x = scaled_dataset[training_data_len - 24:, :]\n",
        "scaled_open_prices_test = scaled_open_prices[len(training_days):, :]\n",
        "print(len(scaled_open_prices_test))\n",
        "print(len(training_days))\n",
        "print(len(scaled_open_prices))\n",
        "\n",
        "stock_dates = data['created_utc']\n",
        "days_after_training = data[data['created_utc'] >= stock_dates[training_data_len]]\n",
        "testing_days = days_after_training['created_utc'].dt.date.unique()\n",
        "test_days = days_after_training.reset_index(drop=True)\n"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "141\n",
            "572\n",
            "713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOxgFswok0do",
        "colab_type": "code",
        "outputId": "0e9aee5c-d276-48de-8ec2-b4e1946f37bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "num_hours_in_day = 24\n",
        "i = 0\n",
        "\n",
        "for idx in range(0, len(testing_days)):\n",
        "  time_step = []\n",
        "  if (idx == len(testing_days) -1):\n",
        "    while(i < len(days_after_training) - 24):\n",
        "      hourly_data = test_x[i:i+1,:][0]\n",
        "      time_step.append(hourly_data)\n",
        "      i = i + 1\n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  else:\n",
        "    next_date = testing_days[idx]\n",
        "    while(next_date > test_days['created_utc'][i]):\n",
        "      hourly_data = test_x[i:i+1,:][0]\n",
        "      time_step.append(hourly_data)\n",
        "      i = i + 1\n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  time_step = np.array(time_step)\n",
        "  time_step = np.reshape(time_step, (24,33))\n",
        "  x_test.append(time_step)\n",
        "  y_test.append(scaled_open_prices_test[idx].tolist())\n",
        "  print('time_step',idx, time_step.shape, len(testing_days) -1)\n",
        "  # if(idx < 2):\n",
        "  #   print('time_step',idx, time_step)\n"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-153-b3ec40160d0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m       \u001b[0mtime_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiller\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m   \u001b[0mtime_step\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[0mtime_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m   \u001b[0mtime_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m24\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m33\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m   \u001b[0mx_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m   \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_open_prices_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    299\u001b[0m            [5, 6]])\n\u001b[1;32m    300\u001b[0m     \"\"\"\n\u001b[0;32m--> 301\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\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    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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     62\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 120 into shape (24,33)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfIgJlSll9b4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the data into a np array\n",
        "x_test = np.array(x_test)\n",
        "\n",
        "# Reshape data into 3 dimensions ( num samples, timesteps, num features )\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 33))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTK6EuL2tHvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the models predicted price values \n",
        "predictions = model.predict(x_test)\n",
        "\n",
        "# Inverse transform \n",
        "predictions = openScaler.inverse_transform(predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcEEjti0tMSU",
        "colab_type": "code",
        "outputId": "493d11aa-f078-440a-f5a9-54471c888015",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Evaluate model with root mean square error (RMSE)\n",
        "rmse = np.sqrt(np.mean((predictions-y_test)**2))\n",
        "rmse"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "857.4320505849481"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxXmF_3itNqk",
        "colab_type": "code",
        "outputId": "1a459825-2660-4428-d36f-2bd477294e92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        }
      },
      "source": [
        "plotData = data.set_index('created_utc')\n",
        "plotData = plotData.resample('D').first().dropna(how='any')\n",
        "plotData\n",
        "# This is so it graphs nicely with dates \n",
        "AAPL = plotData[\"AAPL_StockPrice\"]\n",
        "AAPL = AAPL.to_frame()\n",
        "AMZN = plotData[\"AMZN_StockPrice\"]\n",
        "AMZN = AMZN.to_frame()\n",
        "BA = plotData[\"BA_StockPrice\"]\n",
        "BA = BA.to_frame();\n",
        "SPY = plotData[\"SPY_StockPrice\"]\n",
        "SPY = SPY.to_frame();\n",
        "TSLA = plotData[\"TSLA_StockPrice\"]\n",
        "TSLA = TSLA.to_frame()\n",
        "\n",
        "appleTest = AAPL[0:len(training_days)]\n",
        "amazonTest = AMZN[0:len(training_days)]\n",
        "boeTest = BA[0:len(training_days)]\n",
        "spyTest = SPY[0:len(training_days)]\n",
        "teslaTest = TSLA[0:len(training_days)]\n",
        "\n",
        "appleValid = AAPL[len(training_days):]\n",
        "amazonValid = AMZN[len(training_days):]\n",
        "boeValid = BA[len(training_days):]\n",
        "spyValid = SPY[len(training_days):]\n",
        "teslaValid = TSLA[len(training_days):]\n",
        "# print(appleValid)\n",
        "\n",
        "print(len(appleValid), len(predictions))\n",
        "appleValid['Predictions'] = predictions[:,:1]\n",
        "amazonValid[\"Predictions\"] = predictions[:,1:2]\n",
        "boeValid['Predictions'] = predictions[:, 2:3]\n",
        "spyValid[\"Predictions\"] = predictions[:, 3:4]\n",
        "teslaValid[\"Predictions\"] = predictions[:, 4:5]\n"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "141 141\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:30: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:33: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:34: 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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-2PTuVjtbbG",
        "colab_type": "code",
        "outputId": "9afecf63-dd9e-4582-be96-7ac729f5607f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        }
      },
      "source": [
        "# Visualize data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Next Day Prediction with hourly NLP data \\n (SPY)')\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('Close Price USD ($)', fontsize=18)\n",
        "# plt.plot(appleTest)\n",
        "# plt.plot(appleValid)\n",
        "# plt.plot(appleValid['Predictions'])\n",
        "# plt.plot(amazonTest)\n",
        "# plt.plot(amazonValid)\n",
        "# plt.plot(amazonValid['Predictions'])\n",
        "# plt.plot(boeTest)\n",
        "# plt.plot(boeValid)\n",
        "# plt.plot(boeValid['Predictions'])\n",
        "# plt.plot(spyTest)\n",
        "plt.plot(spyValid)\n",
        "plt.plot(spyValid['Predictions'], color='orange')\n",
        "# plt.plot(teslaTest)\n",
        "# plt.plot(teslaValid)\n",
        "# plt.plot(teslaValid['Predictions'])\n",
        "plt.legend([ 'Validation', 'Prediction'])\n",
        "plt.show()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "px9sr1fC0q6l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "49aa98c5-f56c-45f8-f48f-07e895d0c703"
      },
      "source": [
        "# create dataset with hourly prices and no nlp data \n",
        "hour_data = data.filter(['created_utc','AAPL_StockPrice', 'AMZN_StockPrice', \n",
        "                                              'BA_StockPrice', 'SPY_StockPrice', 'TSLA_StockPrice'])\n",
        "hour_data = hour_data.set_index('created_utc')\n",
        "hour_data.head(5)"
      ],
      "execution_count": 126,
      "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>AAPL_StockPrice</th>\n",
              "      <th>AMZN_StockPrice</th>\n",
              "      <th>BA_StockPrice</th>\n",
              "      <th>SPY_StockPrice</th>\n",
              "      <th>TSLA_StockPrice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>created_utc</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 00:00:00</th>\n",
              "      <td>116.01</td>\n",
              "      <td>757.76</td>\n",
              "      <td>155.68</td>\n",
              "      <td>224.86</td>\n",
              "      <td>214.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03 01:00:00</th>\n",
              "      <td>116.01</td>\n",
              "      <td>757.76</td>\n",
              "      <td>155.68</td>\n",
              "      <td>224.86</td>\n",
              "      <td>214.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03 02:00:00</th>\n",
              "      <td>116.01</td>\n",
              "      <td>757.76</td>\n",
              "      <td>155.68</td>\n",
              "      <td>224.86</td>\n",
              "      <td>214.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03 03:00:00</th>\n",
              "      <td>116.01</td>\n",
              "      <td>757.76</td>\n",
              "      <td>155.68</td>\n",
              "      <td>224.86</td>\n",
              "      <td>214.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03 04:00:00</th>\n",
              "      <td>116.01</td>\n",
              "      <td>757.76</td>\n",
              "      <td>155.68</td>\n",
              "      <td>224.86</td>\n",
              "      <td>214.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     AAPL_StockPrice  ...  TSLA_StockPrice\n",
              "created_utc                           ...                 \n",
              "2017-01-03 00:00:00           116.01  ...            214.3\n",
              "2017-01-03 01:00:00           116.01  ...            214.3\n",
              "2017-01-03 02:00:00           116.01  ...            214.3\n",
              "2017-01-03 03:00:00           116.01  ...            214.3\n",
              "2017-01-03 04:00:00           116.01  ...            214.3\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnQdo7zRBBoX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hour_dataset = hour_data.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wc2q2X0yBhAh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1354b6b6-f27a-4b72-a5c1-dd85829a866a"
      },
      "source": [
        "train_len = math.ceil(len(hour_dataset) * 0.8)\n",
        "train_len\n",
        "hours = data.filter(['created_utc'])\n",
        "hours = hours['created_utc']\n",
        "\n",
        "# adjust training length to the end of a day since it may fall in the middle of a day\n",
        "while(hours[train_len].hour != 0):\n",
        "  train_len = train_len + 1\n",
        "hours[train_len]"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-04-12 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEdat36PB-fL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hourScaler = MinMaxScaler(feature_range=(0,1))\n",
        "priceScaler = MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "scaled_hour_dataset = hourScaler.fit_transform(hour_dataset)\n",
        "scaled_price_dataset = priceScaler.fit_transform(opening_dataset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKLyZe0ICu6C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "dea5139f-2dcb-4be6-aea2-b0879074139f"
      },
      "source": [
        "train_xvals2 = scaled_hour_dataset[0: train_len, :]\n",
        "\n",
        "# to check date of current row \n",
        "dates = data['created_utc']\n",
        "h_data = data[data['created_utc'] < dates[train_len]]\n",
        "train_days = h_data['created_utc'].dt.date.unique()\n",
        "\n",
        "print(len(train_days))\n",
        "print(train_days[-1])"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "572\n",
            "2019-04-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KM6hCKRPDBZ7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "01177f71-88a6-4abb-aa9c-40b4c5f01a89"
      },
      "source": [
        "x_train2 = []\n",
        "y_train2 = []\n",
        "j = 0\n",
        "num_hours_in_day = 24\n",
        "for idx in range(0,len(train_days)):\n",
        "  time_step = []\n",
        "  # last day in the list \n",
        "  if idx == len(train_days) - 1:\n",
        "    while(j < train_len):\n",
        "      hourly_data = train_xvals2[j:j+1, :][0]\n",
        "      time_step.append(hourly_data)\n",
        "      j = j + 1\n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  else:\n",
        "    next_date = train_days[idx + 1]\n",
        "    while(next_date > dates[j]):\n",
        "    # day matches \n",
        "      hourly_data = train_xvals2[j:j+1, :][0]\n",
        "      time_step.append(hourly_data)\n",
        "      j = j + 1\n",
        "    # if there isnt a full 24 hours in set \n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  time_step = np.array(time_step)\n",
        "  # print('time_step',idx, time_step)\n",
        "  time_step = np.reshape(time_step, (24,5))\n",
        "  x_train2.append(time_step)\n",
        "  y_train2.append(scaled_open_prices[idx+1].tolist())\n",
        "  test_start_index = idx + 1\n",
        "  if(idx < 2):\n",
        "    print(y_train)\n",
        "    # print('time_step',idx, time_step)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4.50619602e-04 0.00000000e+00 4.51017411e-03 4.79313824e-03\n",
            "  1.60795952e-01]\n",
            " [0.00000000e+00 2.41568481e-03 1.02790015e-02 2.16952573e-02\n",
            "  2.29639000e-01]\n",
            " [4.80660909e-03 2.18190886e-02 1.29011957e-02 1.94248234e-02\n",
            "  2.31098570e-01]\n",
            " ...\n",
            " [6.25084491e-01 8.42606446e-01 7.44108804e-01 7.87714430e-01\n",
            "  4.54656028e-01]\n",
            " [6.37626737e-01 8.54560190e-01 7.30438431e-01 8.00075681e-01\n",
            "  4.72657390e-01]\n",
            " [6.22230567e-01 8.45637741e-01 7.59282568e-01 7.99066599e-01\n",
            "  4.35195096e-01]]\n",
            "[[4.50619602e-04 0.00000000e+00 4.51017411e-03 4.79313824e-03\n",
            "  1.60795952e-01]\n",
            " [0.00000000e+00 2.41568481e-03 1.02790015e-02 2.16952573e-02\n",
            "  2.29639000e-01]\n",
            " [4.80660909e-03 2.18190886e-02 1.29011957e-02 1.94248234e-02\n",
            "  2.31098570e-01]\n",
            " ...\n",
            " [6.25084491e-01 8.42606446e-01 7.44108804e-01 7.87714430e-01\n",
            "  4.54656028e-01]\n",
            " [6.37626737e-01 8.54560190e-01 7.30438431e-01 8.00075681e-01\n",
            "  4.72657390e-01]\n",
            " [6.22230567e-01 8.45637741e-01 7.59282568e-01 7.99066599e-01\n",
            "  4.35195096e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNJ0UGaVEZuH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0f7bc707-4c84-41be-eacb-eba4caaaed60"
      },
      "source": [
        "# Convert both x, y training sets to np array \n",
        "x_train2, y_train2 = np.array(x_train2), np.array(y_train2)\n",
        "# Reshape the data // LSTM network expects 3 dimensional input in the form of \n",
        "# (number of samples, number of timesteps, number of features)\n",
        "x_train2 = np.reshape(x_train2, (x_train2.shape[0], x_train2.shape[1], 5))\n",
        "x_train2.shape"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(572, 24, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rqnw1GdjDptv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Declare the LSTM model architecture \n",
        "model2 = Sequential()\n",
        "\n",
        "# Add layers , input shape expected is (number of timesteps, number of features)\n",
        "model2.add(LSTM(75, return_sequences=True, input_shape=(x_train2.shape[1], 5)))\n",
        "model2.add(Dropout(0.2))\n",
        "\n",
        "model2.add(LSTM(50, return_sequences=False))\n",
        "model2.add(Dropout(0.2))\n",
        "\n",
        "model2.add(Dense(25))\n",
        "model2.add(Dropout(0.2))\n",
        "\n",
        "model2.add(Dense(5))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEhDIZDFEj9W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile Model \n",
        "model2.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WgNfP04ElqH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5f07526c-51a6-4ae1-c8f1-e2acbb81174b"
      },
      "source": [
        "# Train the model \n",
        "model2.fit(x_train2, y_train2, batch_size=16, epochs=100)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0602\n",
            "Epoch 2/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0250\n",
            "Epoch 3/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0218\n",
            "Epoch 4/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0187\n",
            "Epoch 5/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0170\n",
            "Epoch 6/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0170\n",
            "Epoch 7/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0143\n",
            "Epoch 8/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0143\n",
            "Epoch 9/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0124\n",
            "Epoch 10/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0114\n",
            "Epoch 11/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0108\n",
            "Epoch 12/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0103\n",
            "Epoch 13/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0102\n",
            "Epoch 14/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0094\n",
            "Epoch 15/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0092\n",
            "Epoch 16/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0097\n",
            "Epoch 17/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0088\n",
            "Epoch 18/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0080\n",
            "Epoch 19/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0075\n",
            "Epoch 20/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0074\n",
            "Epoch 21/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0068\n",
            "Epoch 22/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0069\n",
            "Epoch 23/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0067\n",
            "Epoch 24/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0069\n",
            "Epoch 25/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0068\n",
            "Epoch 26/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0066\n",
            "Epoch 27/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0062\n",
            "Epoch 28/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0061\n",
            "Epoch 29/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0063\n",
            "Epoch 30/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0061\n",
            "Epoch 31/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0059\n",
            "Epoch 32/100\n",
            "36/36 [==============================] - 1s 26ms/step - loss: 0.0058\n",
            "Epoch 33/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0060\n",
            "Epoch 34/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0054\n",
            "Epoch 35/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0046\n",
            "Epoch 36/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0046\n",
            "Epoch 37/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0050\n",
            "Epoch 38/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0048\n",
            "Epoch 39/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0047\n",
            "Epoch 40/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0049\n",
            "Epoch 41/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0047\n",
            "Epoch 42/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0049\n",
            "Epoch 43/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0042\n",
            "Epoch 44/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0046\n",
            "Epoch 45/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0047\n",
            "Epoch 46/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0047\n",
            "Epoch 47/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0041\n",
            "Epoch 48/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0041\n",
            "Epoch 49/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0046\n",
            "Epoch 50/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0041\n",
            "Epoch 51/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0040\n",
            "Epoch 52/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0043\n",
            "Epoch 53/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0040\n",
            "Epoch 54/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0038\n",
            "Epoch 55/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0039\n",
            "Epoch 56/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0037\n",
            "Epoch 57/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0041\n",
            "Epoch 58/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0034\n",
            "Epoch 59/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0039\n",
            "Epoch 60/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0042\n",
            "Epoch 61/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0041\n",
            "Epoch 62/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0038\n",
            "Epoch 63/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0035\n",
            "Epoch 64/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0037\n",
            "Epoch 65/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0039\n",
            "Epoch 66/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0039\n",
            "Epoch 67/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0040\n",
            "Epoch 68/100\n",
            "36/36 [==============================] - 1s 22ms/step - loss: 0.0034\n",
            "Epoch 69/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0035\n",
            "Epoch 70/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0037\n",
            "Epoch 71/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0036\n",
            "Epoch 72/100\n",
            "36/36 [==============================] - 1s 35ms/step - loss: 0.0030\n",
            "Epoch 73/100\n",
            "36/36 [==============================] - 1s 27ms/step - loss: 0.0032\n",
            "Epoch 74/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0031\n",
            "Epoch 75/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0029\n",
            "Epoch 76/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0034\n",
            "Epoch 77/100\n",
            "36/36 [==============================] - 1s 26ms/step - loss: 0.0032\n",
            "Epoch 78/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0034\n",
            "Epoch 79/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0036\n",
            "Epoch 80/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0033\n",
            "Epoch 81/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0031\n",
            "Epoch 82/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0030\n",
            "Epoch 83/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0033\n",
            "Epoch 84/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0030\n",
            "Epoch 85/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0029\n",
            "Epoch 86/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0030\n",
            "Epoch 87/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0028\n",
            "Epoch 88/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0029\n",
            "Epoch 89/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0034\n",
            "Epoch 90/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0031\n",
            "Epoch 91/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0029\n",
            "Epoch 92/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0034\n",
            "Epoch 93/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0031\n",
            "Epoch 94/100\n",
            "36/36 [==============================] - 1s 26ms/step - loss: 0.0030\n",
            "Epoch 95/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0030\n",
            "Epoch 96/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0030\n",
            "Epoch 97/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0028\n",
            "Epoch 98/100\n",
            "36/36 [==============================] - 1s 24ms/step - loss: 0.0029\n",
            "Epoch 99/100\n",
            "36/36 [==============================] - 1s 25ms/step - loss: 0.0030\n",
            "Epoch 100/100\n",
            "36/36 [==============================] - 1s 23ms/step - loss: 0.0028\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7eff7b438be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmgKg7siEm8X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "0ccdff8d-1eb4-4365-aea4-0ef1b63e1760"
      },
      "source": [
        "test_x2 = scaled_hour_dataset[train_len - 24:, :]\n",
        "scaled_prices_test = scaled_price_dataset[len(train_days):, :]\n",
        "print(len(scaled_prices_test))\n",
        "print(len(train_days))\n",
        "print(len(scaled_price_dataset))\n",
        "\n",
        "stock_dates = data['created_utc']\n",
        "after_training_data = data[data['created_utc'] >= stock_dates[train_len]]\n",
        "testing_days2 = after_training_data['created_utc'].dt.date.unique()\n",
        "test_days2 = after_training_data.reset_index(drop=True)"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "141\n",
            "572\n",
            "713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phQ4NYGyF-Vt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b6b57ce7-fd23-42b0-d777-e8ec09057f18"
      },
      "source": [
        "x_test2 = []\n",
        "y_test2 = []\n",
        "num_hours_in_day = 24\n",
        "i = 0\n",
        "\n",
        "for idx in range(0, len(testing_days2)):\n",
        "  time_step = []\n",
        "  if (idx == len(testing_days2) -1):\n",
        "    while(i < len(after_training_data) - 24):\n",
        "      hourly_data = test_x2[i:i+1,:][0]\n",
        "      time_step.append(hourly_data)\n",
        "      i = i + 1\n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  else:\n",
        "    next_date = testing_days2[idx]\n",
        "    while(next_date > test_days2['created_utc'][i]):\n",
        "      hourly_data = test_x2[i:i+1,:][0]\n",
        "      time_step.append(hourly_data)\n",
        "      i = i + 1\n",
        "    while(len(time_step) < num_hours_in_day):\n",
        "      filler = np.zeros(len(hourly_data))\n",
        "      time_step.append(filler)\n",
        "  time_step = np.array(time_step)\n",
        "  time_step = np.reshape(time_step, (24,5))\n",
        "  x_test2.append(time_step)\n",
        "  y_test2.append(scaled_prices_test[idx].tolist())\n",
        "  print('time_step',idx, time_step.shape, len(testing_days) -1)\n",
        "  # if(idx < 2):\n",
        "  #   print('time_step',idx, time_step)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADokgLKkGXae",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the data into a np array\n",
        "x_test2 = np.array(x_test2)\n",
        "\n",
        "# Reshape data into 3 dimensions ( num samples, timesteps, num features )\n",
        "x_test2 = np.reshape(x_test2, (x_test2.shape[0], x_test2.shape[1], 5))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwShjENCGfV1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the models predicted price values \n",
        "predictions2 = model2.predict(x_test2)\n",
        "\n",
        "# Inverse transform \n",
        "predictions2 = priceScaler.inverse_transform(predictions2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slrqfbDNKDta",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "528fefe9-9bce-41cd-9eb2-b0d7731e32ce"
      },
      "source": [
        "# Evaluate model with root mean square error (RMSE)\n",
        "rmse2 = np.sqrt(np.mean((predictions2-y_test2)**2))\n",
        "rmse2"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "852.9317932274272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJlE5v6DKFLB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "b666ef1e-703c-47c1-d2fa-5b54e9d3e91d"
      },
      "source": [
        "appleValid['Predictions2'] = predictions2[:,:1]\n",
        "amazonValid[\"Predictions2\"] = predictions2[:,1:2]\n",
        "boeValid['Predictions2'] = predictions2[:, 2:3]\n",
        "spyValid[\"Predictions2\"] = predictions2[:, 3:4]\n",
        "teslaValid[\"Predictions2\"] = predictions2[:, 4:5]"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: 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",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: 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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: 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",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBXwEAQ1KQSq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "ac54efbb-4d9f-43bc-e0ab-845e44b8571f"
      },
      "source": [
        "# Visualize data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Stock Prediction with and without Reddit word vector data \\n (SPY)')\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('Close Price USD ($)', fontsize=18)\n",
        "# plt.plot(appleTest)\n",
        "# plt.plot(appleValid)\n",
        "# plt.plot(appleValid['Predictions'], color='red')\n",
        "# plt.plot(appleValid['Predictions2'], color='green')\n",
        "# plt.plot(amazonTest)\n",
        "# plt.plot(amazonValid)\n",
        "# plt.plot(amazonValid['Predictions'], color='red')\n",
        "# plt.plot(amazonValid['Predictions2'], color='green')\n",
        "# plt.plot(boeTest)\n",
        "# plt.plot(boeValid)\n",
        "# plt.plot(boeValid['Predictions'], color='red')\n",
        "# plt.plot(boeValid['Predictions2'], color='green')\n",
        "# plt.plot(spyTest, label='Train')\n",
        "plt.plot(spyValid[1:], label='Actual')\n",
        "# plt.plot(spyValid['Predictions'][1:], label='Prediction with vector', color='orange')\n",
        "plt.plot(spyValid['Predictions2'][1:], label='Prediction without vector', color='green')\n",
        "\n",
        "# plt.plot(teslaTest, label='Train')\n",
        "# plt.plot(teslaValid, label=\"Actual\")\n",
        "# plt.plot(teslaValid['Predictions'], label=\"vector prediction\", color='red')\n",
        "# plt.plot(teslaValid['Predictions2'], label=\"without vector prediction\", color=\"green\")\n",
        "plt.legend(('Validation', 'Prediction with vector', 'Prediction without'), title=\"Legend\",)\n",
        "plt.show()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtF9-qHOKSzZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}