Temas/Módulo 06 - Redes recurrentes/Cuadernos/rnr_Stock_Prices_Prediction_a_tres_dias.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
    "</figure>\n",
    "\n",
    "# <span style=\"color:#4361EE\"><left>Aprendizaje Profundo</left></span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\"><center>Diplomado en Inteligencia Artificial y Aprendizaje Profundo</center></span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:green\"><center>Predicción del valor de una acción. Tata Global Beverage (LSTM) a tres días<center></span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Profesores</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Alvaro  Montenegro, PhD, ammontenegrod@unal.edu.co\n",
    "1. Camilo José Torres Jiménez, Msc, cjtorresj@unal.edu.co\n",
    "1. Daniel  Montenegro, Msc, dextronomo@gmail.com "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Asesora Medios y Marketing digital</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com\n",
    "5. Jessica López Mejía, jelopezme@unal.edu.co\n",
    "6. Venus Puertas, vpuertasg@unal.edu.co"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Jefe Jurídica</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Paula Andrea Guzmán, guzmancruz.paula@gmail.com"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Coordinador Jurídico</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. David Fuentes, fuentesd065@gmail.com"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Desarrolladores Principales</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Dairo Moreno, damoralesj@unal.edu.co\n",
    "10. Joan Castro, jocastroc@unal.edu.co\n",
    "11. Bryan Riveros, briveros@unal.edu.co\n",
    "12. Rosmer Vargas, rovargasc@unal.edu.co"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Expertos en Bases de Datos</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. Giovvani Barrera, udgiovanni@gmail.com\n",
    "14. Camilo Chitivo, cchitivo@unal.edu.co"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referencias\n",
    "\n",
    "1. [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)\n",
    "2. Ralf C. Staudemeyer and Eric Rothstein Morris,[*Understanding LSTM a tutorial into Long Short-Term Memory Recurrent Neural Networks*](https://arxiv.org/pdf/1909.09586.pdf), arxiv, September 2019\n",
    "3. Karpathy, [*The Unreasonable Effectiveness of Recurrent Neural Networks*](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\n",
    "4. Anton Lucanus, [Making Automation More Efficient by Learning from Historical Trade Data](https://datafloq.com/read/making-automation-more-efficient-by-learning-from-/7444), 8:43 AM, January 7, 2020\n",
    "5. https://www.analyticsvidhya.com/blog/2018/10/predicting-stock-price-machine-learningnd-deep-learning-techniques-python/\n",
    "6. https://www.youtube.com/watch?v=2BrpKpWwT2A&list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ&index=1\n",
    "7. https://towardsdatascience.com/using-lstms-for-stock-market-predictions-tensorflow-9e83999d4653\n",
    "8. https://github.com/llSourcell/Reinforcement_Learning_for_Stock_Prediction/blob/master/README.md\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lYpNoQSeTSo1"
   },
   "source": [
    "## Importar las librería requeridas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "I-vGZMQzvnGw",
    "outputId": "329ee962-21a2-4b88-ab18-7e83330f90ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versión de Tensorflow:  2.1.0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "#setting figure size\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 20,10\n",
    "\n",
    "#importing required libraries\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#from keras.models import Sequential\n",
    "#from keras.layers import Dense, Dropout, LSTM\n",
    "print(\"Versión de Tensorflow: \", tf.__version__)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones para graficar predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funciones auxiliares\n",
    "#\n",
    "def plot_prediction(real, prediccion):\n",
    "    plt.plot(real,color='red', label='Valor real de la acción')\n",
    "    plt.plot(prediccion, color='blue', label='Predicción de la acción')\n",
    "    plt.ylim(1.1 * np.min(prediccion)/2, 1.1 * np.max(prediccion))\n",
    "    plt.xlabel('Tiempo')\n",
    "    plt.ylabel('Valor de la acción')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "#"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lectura de los datos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estos datos corresponden a la empresa *Tata Global Beverages*. Son  1235 datos que corresponden a observaciones del precio de la acción, el número de transacciones de la acción (compra-venta). Los datos son diarios (dias hábiles o comerciales). Están entre el 8 de octubre de 2013 hasta el 8 de octubre de 2018.\n",
    "\n",
    "la columna *Date* es la fecha, *Open* es el valor de acción a la apertura del mercado, *High* el valor más alto alcanzado en el día, *Low* el valor más bajo del día, *Close* el valor al cierre, *Total_Trade_Quantity* el total de acciones transadas y *Turnover_Lacs* índice de rotación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "base_uri": "https://localhost:8080/",
     "height": 253,
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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "LWqiDRF4tmhb",
    "outputId": "cbaa88a3-85ce-4eb7-e0fe-0ccacd5bbcbc"
   },
   "outputs": [],
   "source": [
    "#Data extracted from Quandl: https://www.quandl.com/data/NSE/TATAGLOBAL-Tata-Global-Beverages-Limited\n",
    "#File downloaded from: https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2019/03/NSE-TATAGLOBAL11.csv\n",
    "#reading a local file\n",
    "#df = pd.read_csv('NSE-TATAGLOBAL11.csv')\n",
    "#df.head()\n",
    "\n",
    "#For Colab\n",
    "# pip install google-colab\n",
    "#from google.colab import files\n",
    "#uploaded = files.upload()\n",
    "#df = pd.read_csv('NSE-TATAGLOBAL11.csv')\n",
    "\n",
    "\n",
    "#reading from a local file\n",
    "df = pd.read_csv('../Datos/NSE-TATAGLOBAL11.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Shape of the data:\n",
      "(1235, 8)\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total_Trade_Quantity</th>\n",
       "      <th>Turnover_Lacs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>208.00</td>\n",
       "      <td>222.25</td>\n",
       "      <td>206.85</td>\n",
       "      <td>216.00</td>\n",
       "      <td>215.15</td>\n",
       "      <td>4642146.0</td>\n",
       "      <td>10062.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>217.00</td>\n",
       "      <td>218.60</td>\n",
       "      <td>205.90</td>\n",
       "      <td>210.25</td>\n",
       "      <td>209.20</td>\n",
       "      <td>3519515.0</td>\n",
       "      <td>7407.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>223.50</td>\n",
       "      <td>227.80</td>\n",
       "      <td>216.15</td>\n",
       "      <td>217.25</td>\n",
       "      <td>218.20</td>\n",
       "      <td>1728786.0</td>\n",
       "      <td>3815.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>230.00</td>\n",
       "      <td>237.50</td>\n",
       "      <td>225.75</td>\n",
       "      <td>226.45</td>\n",
       "      <td>227.60</td>\n",
       "      <td>1708590.0</td>\n",
       "      <td>3960.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>234.55</td>\n",
       "      <td>234.60</td>\n",
       "      <td>221.05</td>\n",
       "      <td>230.30</td>\n",
       "      <td>230.90</td>\n",
       "      <td>1534749.0</td>\n",
       "      <td>3486.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low    Last   Close  Total_Trade_Quantity  \\\n",
       "0  2018-10-08  208.00  222.25  206.85  216.00  215.15             4642146.0   \n",
       "1  2018-10-05  217.00  218.60  205.90  210.25  209.20             3519515.0   \n",
       "2  2018-10-04  223.50  227.80  216.15  217.25  218.20             1728786.0   \n",
       "3  2018-10-03  230.00  237.50  225.75  226.45  227.60             1708590.0   \n",
       "4  2018-10-01  234.55  234.60  221.05  230.30  230.90             1534749.0   \n",
       "\n",
       "   Turnover_Lacs  \n",
       "0       10062.83  \n",
       "1        7407.06  \n",
       "2        3815.79  \n",
       "3        3960.27  \n",
       "4        3486.05  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the first five rows of the data\n",
    "print('\\n Shape of the data:')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a cambiar el índice de los datos. Tomaremos la fecha como indice: *df.index*. Los datos se reordenan para invertir la tabla, debido a que los datos contienen las observaciones más recientes en la parte superior de la tabla."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extrae datos para la serie que se desea predecir-close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-10-08</th>\n",
       "      <td>155.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-09</th>\n",
       "      <td>155.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-10</th>\n",
       "      <td>160.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-11</th>\n",
       "      <td>160.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-14</th>\n",
       "      <td>159.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close\n",
       "Date              \n",
       "2013-10-08  155.80\n",
       "2013-10-09  155.55\n",
       "2013-10-10  160.15\n",
       "2013-10-11  160.05\n",
       "2013-10-14  159.45"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe with date and the target variable\n",
    "\n",
    "df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d')\n",
    "df.index = df['Date']\n",
    "df = df.sort_index(ascending=True, axis=0)\n",
    "data = pd.DataFrame(df[['Date', 'Close']])\n",
    "#\n",
    "#setting index\n",
    "data.index = data.Date\n",
    "data.drop('Date', axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3Vqv_JU4Td2F"
   },
   "source": [
    "## Visualización de la serie precio al cierre\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(data[:987], label='Conjunto de entrenamiento (Training set): 987 puntos (80%)')\n",
    "plt.plot(data['Close'][987:], label='Conjunto de Validación (Validation set): 248 puntos (20%)') #248 data\n",
    "plt.title(\"Tata Global Beverages: Historia del precio la acción al cierre (Close)\", size = 20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparación de los datos para el entrenamiento"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para evitar problemas con las tendencias y para mejorar la estimación (entrenamiento) los datos se van a transformar a la escala $[0,1]$. Para las predicciones se utiliza la transformación inversa."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primero extrae los valores y se crea el  objeto MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating train and test sets\n",
    "dataset = data.values\n",
    "\n",
    "# create the scaler object and scale the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(dataset)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Crea datos de entrenamiento "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " La red LSTM tendrá como entrada \"time_step\" datos consecutivos, y como salida 5 datos (la predicción a\n",
    " partir de esos \"time_step\" datos se hace para los siguentes 5 días). Se conformará de esta forma el set de entrenamiento\n",
    "\n",
    "1. Número de datos consecutivos para entrenamiento: time_step = 60. \n",
    "2. Días a predecir: days = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(927, 60, 1)\n",
      "(927, 1)\n"
     ]
    }
   ],
   "source": [
    "# tamaños para entrenamiento y validación\n",
    "len_train = 987 # 80%\n",
    "len_valid = 248 # 20%\n",
    "len_data = len_train + len_valid\n",
    "# \n",
    "# Longitud de las tiras de datos para hacer las predicciones\n",
    "time_step = 60\n",
    "# días hasta predecir\n",
    "days = 3\n",
    "#\n",
    "# crea datos de entrenamiento\n",
    "x_train, y_train = [], []\n",
    "#\n",
    "for i in range(time_step ,len_train ):\n",
    "    # x: bloques de \"time_step\" datos: 0-time_step, 1-time_step+1, 2-time_step+2, etc\n",
    "    x_train.append(scaled_data[i-time_step :i,0])\n",
    "    # y: el siguiente dato\n",
    "    y_train.append(scaled_data[i+days,0])\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "#\n",
    "#\n",
    "# Arregla el tamaño, porque la capa LSTM espera tensores de tres dimensiones: \n",
    "# (batch, longitud de la tira de datos, número de features)\n",
    "# Como es una serie univariada solamente hay 1 feature.\n",
    "#\n",
    "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n",
    "y_train = np.reshape(y_train, (y_train.shape[0],1))\n",
    "#\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crea el modelo LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 60, 70)            20160     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 70)                39480     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 71        \n",
      "=================================================================\n",
      "Total params: 59,711\n",
      "Trainable params: 59,711\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# unidades de salida de las capas LSTM\n",
    "#\n",
    "units = 70\n",
    "#\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.LSTM(units=units, return_sequences=True, input_shape=(x_train.shape[1],1)),\n",
    "    tf.keras.layers.LSTM(units=units),\n",
    "    tf.keras.layers.Dense(1)])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Compila\n",
    "\n",
    "Se usará el optimizador Adam y la función de pérdida MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mse', optimizer='adam')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrena\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 927 samples\n",
      "Epoch 1/20\n",
      "927/927 [==============================] - 9s 10ms/sample - loss: 0.0047\n",
      "Epoch 2/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 0.0013\n",
      "Epoch 3/20\n",
      "927/927 [==============================] - 3s 4ms/sample - loss: 0.0012\n",
      "Epoch 4/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 0.0011\n",
      "Epoch 5/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 0.0010\n",
      "Epoch 6/20\n",
      "927/927 [==============================] - 3s 4ms/sample - loss: 0.0011\n",
      "Epoch 7/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 0.0010\n",
      "Epoch 8/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 9.7597e-04\n",
      "Epoch 9/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 9.7361e-04\n",
      "Epoch 10/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 9.2738e-04\n",
      "Epoch 11/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 9.3117e-04\n",
      "Epoch 12/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 9.4974e-04\n",
      "Epoch 13/20\n",
      "927/927 [==============================] - 5s 5ms/sample - loss: 9.1089e-04\n",
      "Epoch 14/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 8.6969e-04\n",
      "Epoch 15/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 8.9655e-04\n",
      "Epoch 16/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 9.0081e-04\n",
      "Epoch 17/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 8.7000e-04\n",
      "Epoch 18/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 8.2125e-04\n",
      "Epoch 19/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 8.3076e-04\n",
      "Epoch 20/20\n",
      "927/927 [==============================] - 4s 4ms/sample - loss: 8.3306e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train,y_train,epochs=20,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicciones"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepara los datos de validación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = data[len_data - len_valid - time_step:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs  = scaler.transform(inputs)\n",
    "\n",
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "# aqui vamos\n",
    "\n",
    "for i in range(time_step,inputs.shape[0]):\n",
    "    x_test.append(inputs[i-time_step:i,0])\n",
    "x_test = np.array(x_test)\n",
    "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 60, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcula predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions\n",
    "prediction = model.predict(x_test)\n",
    "prediction = scaler.inverse_transform(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de las predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar resultadosdata[len_data - len_valid - time_step:].valuesdata['Close'][987:]\n",
    "set_validation = data[len_data - len_valid :].values\n",
    "plot_prediction(set_validation ,prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.DataFrame(data[:len_train])\n",
    "valid = pd.DataFrame(data[len_train:])\n",
    "valid['Predictions'] = prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.295141957871067"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.mean(np.power(valid['Close'].values-valid['Predictions'].values,2)))\n",
    "rmsLSTM = np.sqrt(np.mean(np.power(valid['Close'].values-valid['Predictions'].values,2)))\n",
    "rmsLSTM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 771
    },
    "colab_type": "code",
    "id": "CtUpsaUhfhVV",
    "outputId": "65e7d286-0130-432c-f729-84dd0bae0f37"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for plotting\n",
    "rmsLSTM = np.sqrt(np.mean(np.power(valid['Close'].values-valid['Predictions'].values,2)))\n",
    "\n",
    "train = pd.DataFrame(data[:len_train])\n",
    "valid = pd.DataFrame(data[len_train:])\n",
    "\n",
    "valid['Predictions'] = prediction\n",
    "plt.plot(train['Close'])\n",
    "plot = plt.plot(valid[['Close','Predictions']])\n",
    "plt.annotate(\"rms = \"+str(rmsLSTM) , xy=(data.index[1],300), size = 15)\n",
    "plt.legend(plot, (\"Observado\",\"Predicho\"))\n",
    "plt.title(\"Historia del precio de cierre y predicciones\", size = 30)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_9aZ3BspEzzr"
   },
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Project_Stock_Prices_Prediction.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}