Stock Closing Price Prediction.ipynb

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    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ah9WXQNgRRnU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gatzt3TQSpz2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "88888b63-5d90-4316-b19f-7379a56a689b"
      },
      "source": [
        "#imports\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": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  from pandas.util.testing import assert_frame_equal\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjtcrHabTMNz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "07876684-01af-4d52-b019-f9e8958ba476"
      },
      "source": [
        "#generate stock quote\n",
        "df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-08-10')\n",
        "df"
      ],
      "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>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>58.928570</td>\n",
              "      <td>58.428570</td>\n",
              "      <td>58.485714</td>\n",
              "      <td>58.747143</td>\n",
              "      <td>75555200.0</td>\n",
              "      <td>50.765709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>59.240002</td>\n",
              "      <td>58.468571</td>\n",
              "      <td>58.571430</td>\n",
              "      <td>59.062859</td>\n",
              "      <td>65005500.0</td>\n",
              "      <td>51.038536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>59.792858</td>\n",
              "      <td>58.952858</td>\n",
              "      <td>59.278572</td>\n",
              "      <td>59.718571</td>\n",
              "      <td>67817400.0</td>\n",
              "      <td>51.605175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>60.392857</td>\n",
              "      <td>59.888573</td>\n",
              "      <td>59.967144</td>\n",
              "      <td>60.342857</td>\n",
              "      <td>79573200.0</td>\n",
              "      <td>52.144630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>61.107143</td>\n",
              "      <td>60.192856</td>\n",
              "      <td>60.785713</td>\n",
              "      <td>60.247143</td>\n",
              "      <td>98506100.0</td>\n",
              "      <td>52.061932</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>2020-08-04</th>\n",
              "      <td>443.160004</td>\n",
              "      <td>433.549988</td>\n",
              "      <td>436.529999</td>\n",
              "      <td>438.660004</td>\n",
              "      <td>43267900.0</td>\n",
              "      <td>437.870514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>441.570007</td>\n",
              "      <td>435.589996</td>\n",
              "      <td>437.510010</td>\n",
              "      <td>440.250000</td>\n",
              "      <td>30498000.0</td>\n",
              "      <td>439.457642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-06</th>\n",
              "      <td>457.649994</td>\n",
              "      <td>439.190002</td>\n",
              "      <td>441.619995</td>\n",
              "      <td>455.609985</td>\n",
              "      <td>50607200.0</td>\n",
              "      <td>454.790009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-07</th>\n",
              "      <td>454.700012</td>\n",
              "      <td>441.170013</td>\n",
              "      <td>452.820007</td>\n",
              "      <td>444.450012</td>\n",
              "      <td>49453300.0</td>\n",
              "      <td>444.450012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-10</th>\n",
              "      <td>455.100006</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>450.399994</td>\n",
              "      <td>450.910004</td>\n",
              "      <td>53100900.0</td>\n",
              "      <td>450.910004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2165 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low  ...      Volume   Adj Close\n",
              "Date                                ...                        \n",
              "2012-01-03   58.928570   58.428570  ...  75555200.0   50.765709\n",
              "2012-01-04   59.240002   58.468571  ...  65005500.0   51.038536\n",
              "2012-01-05   59.792858   58.952858  ...  67817400.0   51.605175\n",
              "2012-01-06   60.392857   59.888573  ...  79573200.0   52.144630\n",
              "2012-01-09   61.107143   60.192856  ...  98506100.0   52.061932\n",
              "...                ...         ...  ...         ...         ...\n",
              "2020-08-04  443.160004  433.549988  ...  43267900.0  437.870514\n",
              "2020-08-05  441.570007  435.589996  ...  30498000.0  439.457642\n",
              "2020-08-06  457.649994  439.190002  ...  50607200.0  454.790009\n",
              "2020-08-07  454.700012  441.170013  ...  49453300.0  444.450012\n",
              "2020-08-10  455.100006  440.000000  ...  53100900.0  450.910004\n",
              "\n",
              "[2165 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ET1YM9O5T4oa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9562eb60-2db6-4bac-db61-52a259c2c9b4"
      },
      "source": [
        "#get number of rows and columns in the data set\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2165, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQFZyRJWT9pG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "488abc13-749f-4233-e0dc-bb81d9e99a64"
      },
      "source": [
        "#Plot closing price graph\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Close Price History')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel('Date', fontsize=20)\n",
        "plt.ylabel('Closing Price (USD)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Closing Price (USD)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xx-UMs_2XqV5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4586a39c-eba5-4570-e1f9-9b33bf0bd1ca"
      },
      "source": [
        "#Create a dataframe with only the 'Close' column\n",
        "data = df.filter(['Close'])\n",
        "#Convert the dataframe to a numpy array\n",
        "dataset = data.values\n",
        "#Get the number of rows to train the model on\n",
        "training_data_len = math.ceil(len(dataset) * .8)\n",
        "\n",
        "training_data_len"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1732"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u60_A7SLYRYN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "060696a0-2100-4ceb-984e-a53e65314ca0"
      },
      "source": [
        "#Scale the data\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "\n",
        "scaled_data\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00739618],\n",
              "       [0.00818583],\n",
              "       [0.00982585],\n",
              "       ...,\n",
              "       [1.        ],\n",
              "       [0.97208751],\n",
              "       [0.98824476]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBWdRP4rY9lC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create the scaled training data set\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "#Split the data into x_train and y_train data sets\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(60, len(train_data)):\n",
        "  x_train.append(train_data[i-60:i])\n",
        "  y_train.append(train_data[i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2LGxKe-bDHb",
        "colab_type": "code",
        "colab": {}
      },
      "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": "If3TwKHkKNL8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d4882e7e-9131-4e36-a228-1e009d7b68da"
      },
      "source": [
        "#Reshape the data\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1672, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNy6ZAIWKq1k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Build the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(LSTM(50, return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fiXRR-0LSjw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUDurEjWLlSn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "37e8dead-ca4b-4c81-b4c4-84547480548b"
      },
      "source": [
        "#Train the model\n",
        "model.fit(x_train, y_train, batch_size=1, epochs=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1672/1672 [==============================] - 38s 23ms/step - loss: 3.4622e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f11c841c5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNhtsQltL7nK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create the testing data set\n",
        "#Create a new array containing scaled values from index 1543 to 2020\n",
        "test_data = scaled_data[training_data_len - 60: , :]\n",
        "#Create the data sets x_test and y_test\n",
        "x_test = []\n",
        "y_test = dataset[training_data_len:, :]\n",
        "for i in range(60, len(test_data)):\n",
        "  x_test.append(test_data[i-60:i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3v7HxDtjNnlP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Convert the data to a numpy array\n",
        "x_test = np.array(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVskj-K7Nu8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Reshape the data\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SX60cBtsOOX9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get the models predicted price values\n",
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tr0fy-QQcJGS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get the root mean squared error (RMSE)\n",
        "rmse = np.sqrt(np.mean(predictions - y_test)**2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYEu2L7ZM5bn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "4ed6b405-3268-4e7d-fc68-79a7290fe56e"
      },
      "source": [
        "#Plotting data\n",
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        "valid['Predictions'] = predictions\n",
        "#style\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Predicted Close')\n",
        "plt.xlabel('Date', fontsize=16)\n",
        "plt.ylabel('Closing Price (USD)', fontsize=16)\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": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}