LSTM_Stock_Prediction_Model.ipynb

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    "colab": {
      "name": "LSTM Stock Prediction Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMgXqkgqUi2JMlmlqrapwod",
      "include_colab_link": true
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RohanK22/trading-bot/blob/main/LSTM_Stock_Prediction_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXTXmLQdQOUT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4902971e-360b-49f9-af32-907b32463f85"
      },
      "source": [
        "# Ref: https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html\n",
        "!pip install yfinance\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import yfinance as yf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.63)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.6.3)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n"
          ],
          "name": "stdout"
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    {
      "cell_type": "code",
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          "height": 467
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        "id": "2i1Mhje2QilH",
        "outputId": "40ce6bc0-5778-467b-e7bf-6d2a1f179a08"
      },
      "source": [
        "STOCK = 'GOOGL'\n",
        "PERIOD = '5d'\n",
        "INTERVAL = '1m'\n",
        "EPOCHS = 100\n",
        "lookback_days = 60\n",
        "\n",
        "# Loading the data\n",
        "dataset_train = yf.download(tickers=STOCK, period=PERIOD, interval=INTERVAL)\n",
        "training_set = dataset_train.iloc[:, 1:2].values\n",
        "dataset_train\n",
        "# print(training_set.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
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              "                                  Open         High  ...    Adj Close  Volume\n",
              "Datetime                                             ...                     \n",
              "2021-07-12 09:30:00-04:00  2523.560059  2523.560059  ...  2518.037598   37768\n",
              "2021-07-12 09:31:00-04:00  2520.544922  2520.544922  ...  2518.125000    4233\n",
              "2021-07-12 09:32:00-04:00  2518.270996  2519.659912  ...  2517.395020    3088\n",
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              "2021-07-12 09:34:00-04:00  2517.669922  2518.399902  ...  2517.614990    4035\n",
              "...                                ...          ...  ...          ...     ...\n",
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              "2021-07-16 15:56:00-04:00  2541.215088  2541.215088  ...  2539.520020    6381\n",
              "2021-07-16 15:57:00-04:00  2539.250000  2539.870117  ...  2539.320068   10496\n",
              "2021-07-16 15:58:00-04:00  2539.540039  2540.239990  ...  2539.600098   12819\n",
              "2021-07-16 15:59:00-04:00  2540.199951  2540.500000  ...  2538.070068   17869\n",
              "\n",
              "[1766 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQNlbWifQ5cp"
      },
      "source": [
        "# Using feature scaling for better model performace\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbkmqJ8yRNXr",
        "outputId": "dc9e5c73-ec0f-4540-8c65-152c7c951de9"
      },
      "source": [
        "# Preparing Training data\n",
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(lookback_days, len(training_set_scaled)):\n",
        "    X_train.append(training_set_scaled[i - lookback_days : i , 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)\n",
        "\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
        "print(X_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1706, 60, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uRqFmzgRQse"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlXm3oSwSTGo",
        "outputId": "5729fd38-c328-4b3e-8cb7-f5792cb6a52e"
      },
      "source": [
        "# Build model and train \n",
        "regressor = Sequential()\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))\n",
        "\n",
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))\n",
        "\n",
        "regressor.add(Dense(units = 1))\n",
        "\n",
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "\n",
        "regressor.fit(X_train, y_train, epochs = EPOCHS, batch_size = 32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "54/54 [==============================] - 6s 14ms/step - loss: 0.0628\n",
            "Epoch 2/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0079\n",
            "Epoch 3/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0062\n",
            "Epoch 4/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0058\n",
            "Epoch 5/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0054\n",
            "Epoch 6/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0054\n",
            "Epoch 7/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0043\n",
            "Epoch 8/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0046\n",
            "Epoch 9/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0037\n",
            "Epoch 10/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0038\n",
            "Epoch 11/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0045\n",
            "Epoch 12/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0038\n",
            "Epoch 13/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0038\n",
            "Epoch 14/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0036\n",
            "Epoch 15/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0037\n",
            "Epoch 16/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0035\n",
            "Epoch 17/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0032\n",
            "Epoch 18/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0028\n",
            "Epoch 19/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0030\n",
            "Epoch 20/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0027\n",
            "Epoch 21/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0030\n",
            "Epoch 22/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0026\n",
            "Epoch 23/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0023\n",
            "Epoch 24/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0026\n",
            "Epoch 25/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0026\n",
            "Epoch 26/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0025\n",
            "Epoch 27/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0025\n",
            "Epoch 28/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0022\n",
            "Epoch 29/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0023\n",
            "Epoch 30/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0021\n",
            "Epoch 31/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0021\n",
            "Epoch 32/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0023\n",
            "Epoch 33/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0021\n",
            "Epoch 34/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0019\n",
            "Epoch 35/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0019\n",
            "Epoch 36/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0021\n",
            "Epoch 37/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0017\n",
            "Epoch 38/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0017\n",
            "Epoch 39/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0017\n",
            "Epoch 40/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0019\n",
            "Epoch 41/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0021\n",
            "Epoch 42/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0016\n",
            "Epoch 43/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0013\n",
            "Epoch 44/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0017\n",
            "Epoch 45/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0016\n",
            "Epoch 46/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0017\n",
            "Epoch 47/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0015\n",
            "Epoch 48/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0014\n",
            "Epoch 49/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0013\n",
            "Epoch 50/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0018\n",
            "Epoch 51/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0016\n",
            "Epoch 52/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0014\n",
            "Epoch 53/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0014\n",
            "Epoch 54/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0014\n",
            "Epoch 55/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0013\n",
            "Epoch 56/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0013\n",
            "Epoch 57/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0012\n",
            "Epoch 58/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0014\n",
            "Epoch 59/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0013\n",
            "Epoch 60/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0012\n",
            "Epoch 61/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0017\n",
            "Epoch 62/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0014\n",
            "Epoch 63/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0016\n",
            "Epoch 64/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0012\n",
            "Epoch 65/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0013\n",
            "Epoch 66/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0011\n",
            "Epoch 67/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0011\n",
            "Epoch 68/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0013\n",
            "Epoch 69/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0012\n",
            "Epoch 70/100\n",
            "54/54 [==============================] - 1s 14ms/step - loss: 0.0010\n",
            "Epoch 71/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0012\n",
            "Epoch 72/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 9.2713e-04\n",
            "Epoch 73/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0011\n",
            "Epoch 74/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 9.7947e-04\n",
            "Epoch 75/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0010\n",
            "Epoch 76/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0012\n",
            "Epoch 77/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 9.7343e-04\n",
            "Epoch 78/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 9.5077e-04\n",
            "Epoch 79/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0010\n",
            "Epoch 80/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 8.7212e-04\n",
            "Epoch 81/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0010\n",
            "Epoch 82/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0012\n",
            "Epoch 83/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0013\n",
            "Epoch 84/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0011\n",
            "Epoch 85/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 8.3876e-04\n",
            "Epoch 86/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0010\n",
            "Epoch 87/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 9.4794e-04\n",
            "Epoch 88/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0010\n",
            "Epoch 89/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 9.1609e-04\n",
            "Epoch 90/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 9.2232e-04\n",
            "Epoch 91/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 8.5940e-04\n",
            "Epoch 92/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 9.6658e-04\n",
            "Epoch 93/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 9.8706e-04\n",
            "Epoch 94/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 7.8975e-04\n",
            "Epoch 95/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0010\n",
            "Epoch 96/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 0.0010\n",
            "Epoch 97/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 0.0010\n",
            "Epoch 98/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 8.9774e-04\n",
            "Epoch 99/100\n",
            "54/54 [==============================] - 1s 13ms/step - loss: 9.4198e-04\n",
            "Epoch 100/100\n",
            "54/54 [==============================] - 1s 12ms/step - loss: 7.8237e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7feb577e9590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "PEladYJGSiNN",
        "outputId": "35c1e438-fd13-487b-b95f-72cd7a077d58"
      },
      "source": [
        "# Testing on training set\n",
        "dataset_test = dataset_train # yf.download(tickers='AAPL', period='1y', interval='1d') #pd.read_csv('tatatest.csv')\n",
        "real_stock_price = dataset_test.iloc[:, 1:2].values\n",
        "\n",
        "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - lookback_days:].values\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(lookback_days, len(inputs)):\n",
        "    X_test.append(inputs[i-lookback_days:i, 0])\n",
        "# X_test.append(inputs[len(inputs) - lookback_days:len(inputs), 0])\n",
        "# X_test.append(inputs[len(inputs) - lookback_days:len(inputs), 0])\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "\n",
        "print(X_test.shape)\n",
        "\n",
        "predicted_stock_price = regressor.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)\n",
        "\n",
        "print(str(len(real_stock_price)) + '  ' + str(len(predicted_stock_price)))\n",
        "# print(predicted_stock_price)\n",
        "\n",
        "plt.plot(real_stock_price, color = 'black', label = STOCK+' Stock Price')\n",
        "plt.plot(predicted_stock_price, color = 'green', label = 'Predicted'+STOCK+' Stock Price')\n",
        "plt.title(STOCK+' Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel(STOCK+' Stock Price')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1766, 60, 1)\n",
            "1766  1766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsRCmq1InNrf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f131ccf8-6a04-4153-d46e-29872dd9f61e"
      },
      "source": [
        "# Predicting price on a particular day beyond given bounds\n",
        "days_ahead = 50\n",
        "c = 0\n",
        "nDatapoints = len(dataset_test)\n",
        "\n",
        "print(X_train.shape[1])\n",
        "X_test = []\n",
        "X_test.append(dataset_test.iloc[nDatapoints - lookback_days + c: nDatapoints + c, 1:2].values) # Get the last few datapoints needed for future predictions\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "last_known_price = predicted_stock_price[-1][0]\n",
        "predictions = []\n",
        "predicted_price_change = []\n",
        "\n",
        "while c < days_ahead: \n",
        "  if c != 0:\n",
        "    X_test = np.expand_dims(np.expand_dims(np.append(X_test[0], predicted_price_day), axis=1), axis=0)\n",
        "    \n",
        "  # print(X_test)\n",
        "  \n",
        "  X_test = X_test[-lookback_days:]\n",
        "  temp = X_test.copy()\n",
        "  temp[0] = sc.transform(temp[0])\n",
        "  predicted_price_day = regressor.predict(temp)\n",
        "  predicted_price_day = sc.inverse_transform(predicted_price_day)\n",
        "  # print('Pred: ' + str(predicted_price_day))\n",
        "  \n",
        "  predictions.append(predicted_price_day[0][0])\n",
        "  predicted_price_change.append((predicted_price_day[0][0] - last_known_price)/(last_known_price) * 100)\n",
        "  c += 1\n",
        "\n",
        "print('Predicted Price in the coming ' + str(days_ahead) + ' days = ' + str(predictions))\n",
        "print('Predicted Price Change in the coming ' + str(days_ahead) + ' days = ' + str(predicted_price_change))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60\n",
            "Predicted Price in the coming 50 days = [2538.7659, 2537.5688, 2536.316, 2535.0835, 2533.9055, 2532.7817, 2531.705, 2530.6665, 2529.6592, 2528.6768, 2527.715, 2526.7712, 2525.8425, 2524.928, 2524.0266, 2523.1375, 2522.2607, 2521.3967, 2520.5457, 2519.709, 2518.8867, 2518.0806, 2517.2913, 2516.52, 2515.7676, 2515.035, 2514.3228, 2513.6318, 2512.9624, 2512.315, 2511.6897, 2511.0864, 2510.505, 2509.945, 2509.407, 2508.8896, 2508.393, 2507.9165, 2507.4595, 2507.0212, 2506.6013, 2506.1995, 2505.8145, 2505.4463, 2505.0938, 2504.7566, 2504.4343, 2504.126, 2503.8313, 2503.5498]\n",
            "Predicted Price Change in the coming 50 days = [0.03984744835179299, -0.007321078737732023, -0.056692661019042134, -0.10525614488869905, -0.15167428646236658, -0.19595669582486153, -0.2383823972195387, -0.2793073421344161, -0.3190008457750082, -0.35771310795098543, -0.39560762234032154, -0.4327998496592045, -0.46939561143517494, -0.5054334178566933, -0.5409517325460911, -0.5759890191257, -0.6105356384068727, -0.6445820443332195, -0.6781185045838356, -0.711087416857481, -0.743488734588027, -0.7752551231533289, -0.8063576184213161, -0.8367483504116535, -0.8663982152938843, -0.8952688425779343, -0.9233313612639904, -0.9505569003522396, -0.9769358672201633, -1.0024490766227245, -1.0270867496728897, -1.0508585721254349, -1.073774229735136, -1.0958337225019932, -1.1170369572937489, -1.1374223977327347, -1.1569902300834656, -1.1757691390812397, -1.1937784031033516, -1.2110468931496143, -1.2275938875973225, -1.2434289790689945, -1.2586002238094807, -1.2731077149510384, -1.2869995087385178, -1.3002851977944374, -1.3129839673638344, -1.3251344673335552, -1.3367462903261185, -1.3478385284543037]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NloyIfODfJ6q",
        "outputId": "80fe914c-8248-44b1-a057-0096b88755ba"
      },
      "source": [
        "# Plot predicted price \n",
        "plt.plot(real_stock_price, color = 'black', label = (STOCK + 'Stock Price'))\n",
        "plt.plot(np.append(predicted_stock_price, np.expand_dims(predictions, axis = 1)), color = 'green', label = 'Predicted ' + STOCK + ' Stock Price')\n",
        "plt.title(STOCK+' Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel(STOCK+' Stock Price')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "O1rVmqi2tQ0R",
        "outputId": "b43fa9aa-47ae-454f-ac64-d1ce23848cb2"
      },
      "source": [
        "# Plot predicted price change in upcoming days\n",
        "plt.plot(predicted_price_change, color = 'orange', label = 'Predicted Price Change (%)')\n",
        "plt.title('Price Change Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price Change')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LypJ0EaUulN-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}