Stock Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP9XdakBPDhezD7qc1CsvA1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aryantandon01/Stock-Prediction/blob/main/Stock%20Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wu8E03HvEC2"
      },
      "source": [
        "#Import the libraries\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')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "6TwoMV0hva0c",
        "outputId": "b5ae3da9-3d14-48ff-f0f1-7408a04011ee"
      },
      "source": [
        "#Get the stock quote \n",
        "df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17') \n",
        "#Show the data \n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2012-01-03</th>\n",
              "      <td>14.732143</td>\n",
              "      <td>14.607143</td>\n",
              "      <td>14.621429</td>\n",
              "      <td>14.686786</td>\n",
              "      <td>302220800.0</td>\n",
              "      <td>12.669562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>14.810000</td>\n",
              "      <td>14.617143</td>\n",
              "      <td>14.642857</td>\n",
              "      <td>14.765714</td>\n",
              "      <td>260022000.0</td>\n",
              "      <td>12.737653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>14.948214</td>\n",
              "      <td>14.738214</td>\n",
              "      <td>14.819643</td>\n",
              "      <td>14.929643</td>\n",
              "      <td>271269600.0</td>\n",
              "      <td>12.879066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>15.098214</td>\n",
              "      <td>14.972143</td>\n",
              "      <td>14.991786</td>\n",
              "      <td>15.085714</td>\n",
              "      <td>318292800.0</td>\n",
              "      <td>13.013701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>15.276786</td>\n",
              "      <td>15.048214</td>\n",
              "      <td>15.196429</td>\n",
              "      <td>15.061786</td>\n",
              "      <td>394024400.0</td>\n",
              "      <td>12.993060</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>2019-12-11</th>\n",
              "      <td>67.775002</td>\n",
              "      <td>67.125000</td>\n",
              "      <td>67.202499</td>\n",
              "      <td>67.692497</td>\n",
              "      <td>78756800.0</td>\n",
              "      <td>67.112915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-12</th>\n",
              "      <td>68.139999</td>\n",
              "      <td>66.830002</td>\n",
              "      <td>66.945000</td>\n",
              "      <td>67.864998</td>\n",
              "      <td>137310400.0</td>\n",
              "      <td>67.283928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-13</th>\n",
              "      <td>68.824997</td>\n",
              "      <td>67.732498</td>\n",
              "      <td>67.864998</td>\n",
              "      <td>68.787498</td>\n",
              "      <td>133587600.0</td>\n",
              "      <td>68.198540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-16</th>\n",
              "      <td>70.197502</td>\n",
              "      <td>69.245003</td>\n",
              "      <td>69.250000</td>\n",
              "      <td>69.964996</td>\n",
              "      <td>128186000.0</td>\n",
              "      <td>69.365952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-17</th>\n",
              "      <td>70.442497</td>\n",
              "      <td>69.699997</td>\n",
              "      <td>69.892502</td>\n",
              "      <td>70.102501</td>\n",
              "      <td>114158400.0</td>\n",
              "      <td>69.502274</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2003 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low       Open      Close       Volume  Adj Close\n",
              "Date                                                                          \n",
              "2012-01-03  14.732143  14.607143  14.621429  14.686786  302220800.0  12.669562\n",
              "2012-01-04  14.810000  14.617143  14.642857  14.765714  260022000.0  12.737653\n",
              "2012-01-05  14.948214  14.738214  14.819643  14.929643  271269600.0  12.879066\n",
              "2012-01-06  15.098214  14.972143  14.991786  15.085714  318292800.0  13.013701\n",
              "2012-01-09  15.276786  15.048214  15.196429  15.061786  394024400.0  12.993060\n",
              "...               ...        ...        ...        ...          ...        ...\n",
              "2019-12-11  67.775002  67.125000  67.202499  67.692497   78756800.0  67.112915\n",
              "2019-12-12  68.139999  66.830002  66.945000  67.864998  137310400.0  67.283928\n",
              "2019-12-13  68.824997  67.732498  67.864998  68.787498  133587600.0  68.198540\n",
              "2019-12-16  70.197502  69.245003  69.250000  69.964996  128186000.0  69.365952\n",
              "2019-12-17  70.442497  69.699997  69.892502  70.102501  114158400.0  69.502274\n",
              "\n",
              "[2003 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixdh5qhnvQQH",
        "outputId": "ff851aaf-ec04-4c10-e147-670bfb5f04ca"
      },
      "source": [
        "# Rows and columns\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2003, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "WrIGgfvLxAUH",
        "outputId": "76c87a8c-19c2-46c5-bb8a-296926b41df7"
      },
      "source": [
        "#Visualisation of Closing Prices\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Close Price History')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Close Price USD ($)',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": {
        "id": "1ZFhgVJKxsuz"
      },
      "source": [
        "#Create a new dataframe with only the 'Close' column\n",
        "data = df.filter(['Close'])\n",
        "#Converting the dataframe to a numpy array\n",
        "dataset = data.values\n",
        "#Get /Compute the number of rows to train the model on\n",
        "training_data_len = math.ceil( len(dataset) *.8) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z17-9E-ezJXW"
      },
      "source": [
        "#Scale all of the data to be values between 0 and 1\n",
        "scaler = MinMaxScaler(feature_range=(0, 1)) \n",
        "scaled_data = scaler.fit_transform(dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i58DmphTjD9j"
      },
      "source": [
        "#Create the scaled training data set\n",
        "train_data = scaled_data[0:training_data_len  , : ]\n",
        "\n",
        "#Split the data into x_train and y_train data sets\n",
        "x_train=[]\n",
        "y_train = []\n",
        "for i in range(60,len(train_data)):\n",
        "    x_train.append(train_data[i-60:i,0])\n",
        "    y_train.append(train_data[i,0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6GQbRSYpgC5"
      },
      "source": [
        "#Convert x_train and y_train to numpy arrays\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jk2QmKFpMvy1"
      },
      "source": [
        "#Reshape the data into the shape accepted by the LSTM\n",
        "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n",
        "#x_train.shape[0] is 1543 and x_train.shape[1] is 60, so we can also replace those two indices with these numbers."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd1tOEgfNH9k"
      },
      "source": [
        "#Build the LSTM model\n",
        "model = Sequential()\n",
        "#1st LSTM layer\n",
        "model.add(LSTM(units=50, return_sequences=True,input_shape=(x_train.shape[1],1)))\n",
        "#50 indicates there are 50 neurons\n",
        "#2nd LSTM layer\n",
        "model.add(LSTM(units=50, return_sequences=False))\n",
        "#It's false because we're not going to use anymore LSTM layers for our model's architecture\n",
        "model.add(Dense(units=25))\n",
        "model.add(Dense(units=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So0XzUi2OHKu"
      },
      "source": [
        "#Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "#Optimizer is used to improve upon the loss function\n",
        "#Loss function tells how the model did on training"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXtXdDuPSp3b",
        "outputId": "0a882b42-4d97-404f-8c50-2894788fc1ff"
      },
      "source": [
        "#Train the model\n",
        "model.fit(x_train, y_train, batch_size=1, epochs=1)\n",
        "#Fit is another name for train\n",
        "#Batch size is the total number of training examples present in a single batch\n",
        "#epochs = Number of iterations when the entire dataset has passed forward and backwar through a neural network"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1543/1543 [==============================] - 39s 23ms/step - loss: 0.0018\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f99f8f1e7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OT6kV0R_TfQ0"
      },
      "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 - 60: , : ]\n",
        "#The ':' after the comma means we want back all of the columns\n",
        "\n",
        "#Create the datat sets x_test and y_test\n",
        "x_test = []\n",
        "y_test =  dataset[training_data_len : , : ] \n",
        "#Get all of the rows from index 1603 to the rest and all of the columns (in this case it's only column 'Close'), so 2003 - 1603 = 400 rows of data\n",
        "for i in range(60,len(test_data)):\n",
        "    x_test.append(test_data[i-60:i,0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dw6y3fVCEigC"
      },
      "source": [
        "#Convert the data to a numpy array\n",
        "x_test = np.array(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhraH3eeEtX_"
      },
      "source": [
        "#Reshape the model cos the LSTM model is expecting a 3-D Data\n",
        "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n",
        "#1 is the closed price"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYGS4bJwFZvx"
      },
      "source": [
        "#Get the model's predicted price value \n",
        "predictions = model.predict(x_test) \n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "#Undo Scaling\n",
        "#We want the predictions dataset to contain the same values as y_test dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQnQ3eLoF8JM",
        "outputId": "4c2dfece-9669-4bcd-ec17-030f1e84ae20"
      },
      "source": [
        "#Get the root mean squared error (RMSE)\n",
        "rmse=np.sqrt(np.mean(((predictions- y_test)**2)))\n",
        "rmse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5836753927838316"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "4-IhJ66LTzlI",
        "outputId": "3cc93054-07da-4fc2-f43a-503c26d6166e"
      },
      "source": [
        "#Plot the data\n",
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        "valid['Predictions'] = predictions\n",
        "\n",
        "#Visualise the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('Close Price USD ($)', fontsize=18)\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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/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"
          ],
          "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": 431
        },
        "id": "zTT2J6zdUy0C",
        "outputId": "80fabaae-66ea-441c-8a87-3108201bf7cc"
      },
      "source": [
        "#Show the valid and predicted prices\n",
        "valid"
      ],
      "execution_count": null,
      "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>2018-05-17</th>\n",
              "      <td>46.747501</td>\n",
              "      <td>49.313587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-05-18</th>\n",
              "      <td>46.577499</td>\n",
              "      <td>49.299488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-05-21</th>\n",
              "      <td>46.907501</td>\n",
              "      <td>49.225281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-05-22</th>\n",
              "      <td>46.790001</td>\n",
              "      <td>49.182030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-05-23</th>\n",
              "      <td>47.090000</td>\n",
              "      <td>49.141914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-11</th>\n",
              "      <td>67.692497</td>\n",
              "      <td>68.822250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-12</th>\n",
              "      <td>67.864998</td>\n",
              "      <td>69.083717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-13</th>\n",
              "      <td>68.787498</td>\n",
              "      <td>69.353027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-16</th>\n",
              "      <td>69.964996</td>\n",
              "      <td>69.723145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-17</th>\n",
              "      <td>70.102501</td>\n",
              "      <td>70.252594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Close  Predictions\n",
              "Date                              \n",
              "2018-05-17  46.747501    49.313587\n",
              "2018-05-18  46.577499    49.299488\n",
              "2018-05-21  46.907501    49.225281\n",
              "2018-05-22  46.790001    49.182030\n",
              "2018-05-23  47.090000    49.141914\n",
              "...               ...          ...\n",
              "2019-12-11  67.692497    68.822250\n",
              "2019-12-12  67.864998    69.083717\n",
              "2019-12-13  68.787498    69.353027\n",
              "2019-12-16  69.964996    69.723145\n",
              "2019-12-17  70.102501    70.252594\n",
              "\n",
              "[400 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UF2BfOIy56CB",
        "outputId": "505fafae-39f9-459c-aa3d-ca015f0e5168"
      },
      "source": [
        "#Get the quote\n",
        "apple_quote = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17')\n",
        "#Create a new dataframe\n",
        "new_df = apple_quote.filter(['Close'])\n",
        "#Get the last 60 day closing price values and convert the dataframe to an array\n",
        "last_60_days = new_df[-60:].values\n",
        "#Scale the data to be values between 0 and 1\n",
        "last_60_days_scaled = scaler.transform(last_60_days)\n",
        "#Create an empty list\n",
        "X_test = []\n",
        "#Append the past 60 days into X_test list\n",
        "X_test.append(last_60_days_scaled)\n",
        "#Convert the X_test data set to a numpy array\n",
        "X_test = np.array(X_test)\n",
        "#Reshape the data\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "#Get the predicted scale price\n",
        "pred_price = model.predict(X_test)\n",
        "#Undo the scaling\n",
        "pred_price = scaler.inverse_transform(pred_price)\n",
        "print(pred_price)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[70.78463]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3Fh2UsM7evD",
        "outputId": "9988e7f3-e707-42f1-b283-29bb208866fe"
      },
      "source": [
        "#Get the quote\n",
        "apple_quote2 = web.DataReader('AAPL', data_source='yahoo', start='2019-12-18', end='2019-12-18')\n",
        "print(apple_quote2['Close'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date\n",
            "2019-12-18    69.934998\n",
            "Name: Close, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTPYXRN_78bU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}