LSTM_IA_60days_forecast.ipynb

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    "colab": {
      "name": "LSTM_IA_60days_forecast.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM8h5jqWgiMaKrCcA8RTKNe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "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/ferranfont/Generacion_de_nombres_con_Redes_Recurrentes/blob/master/LSTM_IA_60days_forecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**# AI Recurrent Neural Network LSTM, to precit closing price of a stock using 60 past 60 days stock price.**\n"
      ],
      "metadata": {
        "id": "CRhrkBzYeIc-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install yfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdmEFkeHjoiD",
        "outputId": "ce5ba4d8-e0e9-4c0a-a813-74a72d0db49d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.67)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.6.4)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.2)\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",
            "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: 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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXKDUcjDeGwH"
      },
      "outputs": [],
      "source": [
        "#Import Libraries\n",
        "import math\n",
        "import pandas_datareader as web\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf\n",
        "# Import IA Libraries\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zsXI9gt9FgZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "bffcc216-6222-4eaa-d2de-42e8ba3fe675"
      },
      "source": [
        "df_aapl= yf.Ticker('AAPL')\n",
        "df = df_aapl.history(start = '2012-01-01', end = '2019-12-17')\n",
        "#show AAPL data\n",
        "#df.info\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Dividends</th>\n",
              "      <th>Stock Splits</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",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>12.535905</td>\n",
              "      <td>12.630827</td>\n",
              "      <td>12.523656</td>\n",
              "      <td>12.591939</td>\n",
              "      <td>302220800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>12.554275</td>\n",
              "      <td>12.697578</td>\n",
              "      <td>12.532228</td>\n",
              "      <td>12.659608</td>\n",
              "      <td>260022000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>12.705845</td>\n",
              "      <td>12.816077</td>\n",
              "      <td>12.636030</td>\n",
              "      <td>12.800155</td>\n",
              "      <td>271269600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>12.853436</td>\n",
              "      <td>12.944684</td>\n",
              "      <td>12.836595</td>\n",
              "      <td>12.933967</td>\n",
              "      <td>318292800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>13.028889</td>\n",
              "      <td>13.097784</td>\n",
              "      <td>12.901814</td>\n",
              "      <td>12.913450</td>\n",
              "      <td>394024400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-10</th>\n",
              "      <td>66.167148</td>\n",
              "      <td>66.529270</td>\n",
              "      <td>65.492169</td>\n",
              "      <td>66.137589</td>\n",
              "      <td>90420400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-11</th>\n",
              "      <td>66.218888</td>\n",
              "      <td>66.783010</td>\n",
              "      <td>66.142523</td>\n",
              "      <td>66.701714</td>\n",
              "      <td>78756800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-12</th>\n",
              "      <td>65.965141</td>\n",
              "      <td>67.142650</td>\n",
              "      <td>65.851827</td>\n",
              "      <td>66.871674</td>\n",
              "      <td>137310400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-13</th>\n",
              "      <td>66.871680</td>\n",
              "      <td>67.817627</td>\n",
              "      <td>66.741119</td>\n",
              "      <td>67.780678</td>\n",
              "      <td>133587600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-16</th>\n",
              "      <td>68.236410</td>\n",
              "      <td>69.170044</td>\n",
              "      <td>68.231486</td>\n",
              "      <td>68.940941</td>\n",
              "      <td>128186000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2002 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Open       High        Low  ...     Volume  Dividends  Stock Splits\n",
              "Date                                         ...                                    \n",
              "2012-01-03  12.535905  12.630827  12.523656  ...  302220800        0.0           0.0\n",
              "2012-01-04  12.554275  12.697578  12.532228  ...  260022000        0.0           0.0\n",
              "2012-01-05  12.705845  12.816077  12.636030  ...  271269600        0.0           0.0\n",
              "2012-01-06  12.853436  12.944684  12.836595  ...  318292800        0.0           0.0\n",
              "2012-01-09  13.028889  13.097784  12.901814  ...  394024400        0.0           0.0\n",
              "...               ...        ...        ...  ...        ...        ...           ...\n",
              "2019-12-10  66.167148  66.529270  65.492169  ...   90420400        0.0           0.0\n",
              "2019-12-11  66.218888  66.783010  66.142523  ...   78756800        0.0           0.0\n",
              "2019-12-12  65.965141  67.142650  65.851827  ...  137310400        0.0           0.0\n",
              "2019-12-13  66.871680  67.817627  66.741119  ...  133587600        0.0           0.0\n",
              "2019-12-16  68.236410  69.170044  68.231486  ...  128186000        0.0           0.0\n",
              "\n",
              "[2002 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get number of rows and columns\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f6611yGkWE2",
        "outputId": "0ecf5663-6e60-41e8-d05c-6d0a756ce604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2002, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize data in a chart\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Precios de Apple al cierre')\n",
        "plt.xlabel('Fecha',fontsize=16)\n",
        "plt.ylabel('Precios de cierre',fontsize=16)\n",
        "plt.plot(df['Close'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "xsNtYVnpkeFv",
        "outputId": "3ad95f79-e812-4d64-d718-1f0a443a5ab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Crate a new dataframe with only the closing column\n",
        "data = df.filter(['Close'])\n",
        "#Convert the dataframe to a numpy array\n",
        "dataset = data.values\n",
        "#get the numbers of rows to train the model on 80 % and round it with math.ceil\n",
        "training_data_len = math.ceil( len(dataset) * .8 )\n",
        "training_data_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltRYfC4cmW1G",
        "outputId": "fe50c133-a5a0-4a61-fd0f-5358551abbbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1602"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Scale de data\n",
        "scaler= MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "scaled_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJx4mp2_xa97",
        "outputId": "262540f0-eb40-4d32-dd3c-e4109d55ae07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00805073],\n",
              "       [0.00924195],\n",
              "       [0.01171609],\n",
              "       ...,\n",
              "       [0.9635733 ],\n",
              "       [0.97957511],\n",
              "       [1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the training dataset\n",
        "#Create the scaled training dataset\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "#Split the data into x_train and y_train data sets\n",
        "#x_Variables independientes\n",
        "#Y_Variables dependientes o targets\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,0])\n",
        "  y_train.append(train_data[i,0])\n",
        "  if i<=60:\n",
        "    print(x_train)\n",
        "    print(y_train)\n",
        "    print()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uV59mv_m1ZR8",
        "outputId": "7010964c-00e2-4962-c7e1-3b4d9e9aa0e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.00805073, 0.00924195, 0.01171609, 0.01407167, 0.01371051,\n",
            "       0.01452444, 0.01415249, 0.01352723, 0.0126756 , 0.01531141,\n",
            "       0.0176885 , 0.01695545, 0.01293967, 0.01677219, 0.01299898,\n",
            "       0.02714845, 0.02605425, 0.02748265, 0.03057127, 0.03244173,\n",
            "       0.03228541, 0.03170867, 0.03416659, 0.03647904, 0.03909877,\n",
            "       0.04333008, 0.05221862, 0.05235338, 0.05730161, 0.06099942,\n",
            "       0.0546443 , 0.05709144, 0.05704292, 0.06390477, 0.06292914,\n",
            "       0.06473484, 0.06797981, 0.06978551, 0.07498716, 0.07877648,\n",
            "       0.07987077, 0.08025351, 0.07377438, 0.07221123, 0.07244301,\n",
            "       0.07853396, 0.08024813, 0.08392968, 0.09260805, 0.10418632,\n",
            "       0.1020195 , 0.10202484, 0.11039586, 0.11301552, 0.11115048,\n",
            "       0.10944726, 0.10767379, 0.11356533, 0.11760799, 0.11930057])]\n",
            "[0.11511762991217822]\n",
            "\n"
          ]
        }
      ]
    }
  ]
}