Robot_trading.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROBOT TRADING\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descripción del desafío\n",
    "\n",
    "Vamos a contruir un Robot Trading en Python capaz de tomar decisiones de compra y venta de Bitcoin en tiempo real, ¿interesante verdad?, aqui te proporciono el paso a paso que deberás realizar: <br>\n",
    "\n",
    "    1.- Configuración del ambiente: Para empezar, puedes utilizar un entorno virtual como Google Colaboratory, o si deseas, puedes usar el editor Python de tu preferencia, sólo asegúrate de tener Python 3.x instalado en tu computadora. También necesitarás instalar algunas librerías de Python que son esenciales para este proyecto, como Pandas, Numpy, Matplotlib, etc. \n",
    "\n",
    "    2.- Obtención de datos: Necesitarás acceder a una API que proporcione datos históricos de precios de Bitcoin en formato JSON. Por otro lado, necesitarás realizar Web Scraping en un site de noticias para obtener el precio actual y algunos indicadores de tendencias del Bitcoin.\n",
    "    \n",
    "    3.- Limpieza de datos: Una vez que tengas los datos históricos deberás cargarlos en un DataFrame de Pandas para poder manipularlos y analizarlos, deberás identificar y eliminar los outliers, además de tratar cualquier valor nulo o duplicados en la base. Finalmente, con la base limpia, calcula el precio promedio del Bitcoin.\n",
    "\n",
    "    4.- Tomar decisiones: Una vez que tengas el precio promedio, compáralo con el precio actual y tendencia del Bitcoin, que previamente obtuviste con Web Scraping. Si el precio actual es mayor/igual que la media y la tendencia es de baja, entonces se debe vender, pero si el precio actual es menor que la media y la tendencia es de alta, entonces se debe comprar, sí ninguna de las condiciones anteriores se cumple entonces se debe \"esperar\".\n",
    "\n",
    "    5.- Visualización: Utiliza la librería Matplotlib para crear un gráfico donde se muestre la evolución del precio del Bitcoin durante el periodo seleccionado, y una línea recta que pase sobre el precio medio. Por último, muestra un mensaje en el gráfico que indique “Vender”, “Comprar” o “” según sea la decisión del algoritmo.\n",
    "\n",
    "    6.- Automatización: Finalmente, ahora que tienes el algoritmo de decisión, es hora de automatizar el proceso. Utiliza la librería de Python \"time\" para ejecutar el algoritmo de decisión cada 5 minutos y actualizar el gráfico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1- Configuración del ambiente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro primer paso será validar nuestra versión de Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.11.2\n"
     ]
    }
   ],
   "source": [
    "#Comando para mostrar nuestra versión instalada de python\n",
    "!python -V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El siguiente paso será la importación de las librerías necesarias para nuestro desarrollo, entre las cuales se encuentran algunas como Pandas, Numpy, Matplotlib o DateTime, y nuestra librería para obtención de datos que será: <br>\n",
    "\n",
    "<strong>\"Yfinance\"</strong>, que es una biblioteca de Python que proporciona una interfaz conveniente para descargar datos financieros directamente de Yahoo Finance. Sobre la cual pueden aprender mucho mas en: https://pypi.org/project/yfinance/ y que será una parte importante de nuestro desarrollo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd             # Libreria para el manejo de estruturas de datos\n",
    "import numpy as np              # Libreria para el manejo de algunas operaciones matemáticas\n",
    "import matplotlib.pyplot as plt # Libreria para trabajar con las gráficas necesarias\n",
    "import yfinance as yf           # Libreria que nos dará acceso a los valores del Bitcoin\n",
    "import datetime                 # Libreria para apoyarnos con las operaciones direccionadas a fechas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1- Obtención de datos <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comprender la actualidad del valor del Bitcoin crearemos una función que obtendrá su valor durante los últimos 7 días, en intervalos de 5 minutos  para lo cual se definirán 2 variables del tipo Datetime: <br><br>\n",
    "    *end_date*, cuya función será almacenar nuestra fecha actual, y utilizarla como fecha de cierre. <br>\n",
    "    *start_date*, que será la fecha dónde comenzaremos la extracción de datos, 7 días atrás de nuestra fecha actual. \n",
    "    <br><br>\n",
    "Una vez generadas las variables de fecha, con la función \"*download*\" de la libreria **yfinance** obtendremos los valores del Bitcoin(\"BTC-USD\") en  intervalos de 5 minutos, datos que necesitaremos para nuestro análisis, y con ellos crearemos el DataFrame llamado df_bitcoin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para automatizar la tarea de extracción de valores del Bitcoin.\n",
    "def import_bitcoin_database(): \n",
    "    global df_bitcoin\n",
    "\n",
    "    # Fecha para detener la extracción de datos.\n",
    "    end_date = datetime.datetime.now() \n",
    "\n",
    "    # Fecha de inicio para la extracción de datos.\n",
    "    start_date = end_date - datetime.timedelta(days=7) \n",
    "\n",
    "    # Función para obtener los valores del Bitcoin, en el rango de fechas definido\n",
    "    # ,y el intervalo de tiempo entre cada dato.\n",
    "    df_bitcoin = yf.download(\"BTC-USD\", start_date, end_date,interval=\"5m\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez realizada nuestra función *import_bitcoin_database()*, nos aseguramos que la extracción de datos correcta ejecutando nuestra función y mostrando una pequeña muestra de los datos que se obtuvieron:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "                                   Open          High           Low  \\\n",
       "Datetime                                                              \n",
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       "\n",
       "                                  Close     Adj Close     Volume  \n",
       "Datetime                                                          \n",
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      ]
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     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejecución de función.\n",
    "import_bitcoin_database()\n",
    "\n",
    "# Ejecutamos una muestra de 15 datos para ver cómo se distribuyen los datos obtenidos.\n",
    "df_bitcoin.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Información del DataFrame (df_bitcoin)\n",
    "\n",
    "**Datetime**: Es el índice del dataframe, y contiene la fecha y hora del intervalo seleccionado, no puede estar duplicado.\n",
    "\n",
    "**Open**: Contiene el precio del Bitcoin al inicio del intervalo (USD).\n",
    "\n",
    "**High**: Contiene el precio más alto del Bitcoin durante el intervalo(USD).\n",
    "\n",
    "**Low**: Contiene el precio más bajo del Bitcoin durante el intervalo(USD).\n",
    "\n",
    "**Close**: Contiene el precio del Bitcoin al final del intervalo(USD).\n",
    "\n",
    "**Adj Close**: Contiene el precio de cierre después de ajustes por todas las divisiones y distribuciones de dividendos aplicables\n",
    "\n",
    "**Volume**: Contiene el total de transacciones hechas durante el intervalo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2- Extracción de tendencia del Bitcoin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En está sección nos enfocaremos en obtener la tendencia de los valores del Bitcoin de la siguiente página web: https://coinmarketcap.com,<br> de dónde obtendremos el valor actual del Bitcoin con su ultima actualización en dólares (USD), así como la variación de precio en la última hora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup # Libreria para trabar con la información de objetos HTML (Páginas Web)\n",
    "import requests               # Libreria para trabajar las solicitudes HTTP (Peticiones a servidores Web)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para la obtención de la última actualización del valor del BitCoin realizaremos Web Scraping mediante el uso de la libreria **BeautifulSoup de bs4**,que nos permitirá poder trabajar con objetos del tipo HTML en este caso una página web, para obtener mas información acerca de la librería, su documentación se puede encontrar en la siguiente dirección: https://beautiful-soup-4.readthedocs.io/en/latest/ <br>\n",
    "\n",
    "Ademas de utilizar el modulo **requests** para realizar peticiones HTTP, que nos permitirán obtener acceso a la información del servidor Web de una manera sencilla, para obtener mas información acerca de la librería, su documentación se puede encontrar en la siguiente dirección: <br>https://pypi.org/project/requests/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trends_extract():\n",
    "    global actual_price, trend\n",
    "\n",
    "    url = \"https://coinmarketcap.com\"\n",
    "    response = requests.get(url)\n",
    "\n",
    "    # La extracción de información será realizada por medio de un elemento BeautifulSoup \n",
    "    # el cual es un tipo de objeto que nos permite trabajar con estructuras HTML, en este caso\n",
    "    # la página web antes declarada.\n",
    "\n",
    "    # Iniciamos el proceso validando que nuestra respuesta sea exitosa dada por el servidor web,\n",
    "    # sí devuelve un 200 significa que la petición fue exitosa, de lo contrario el valor impreso será:\n",
    "    # \"Failed to retrieve the webpage\", con una respuesta exitosa el valor se almacenará en una variable,\n",
    "    # y después será convertida en un objeto \"BeautifulSoup\", que nos permitirá trabajar con información en lenguaje HTML\n",
    "    # de una manera sencilla y eficiente.\n",
    "\n",
    "    if response.status_code == 200: \n",
    "        response.encoding = 'utf-8'\n",
    "        html = response.text\n",
    "        soup = BeautifulSoup(html, \"html.parser\") \n",
    "    else:\n",
    "        print(\"Failed to retrieve the webpage.\") \n",
    "\n",
    "\n",
    "    # Continuamos obteniendo el valor actual del Bitcoin del objeto BeautifulSoap creado en el paso anterior, \n",
    "    # al cual debemos remover el signo de valor $, la coma \",\" y cualquier espacio extra que contenga \n",
    "    # para poder obtener su valor limpio con solo el valor y sus decimales, posteriormente lo convertiremos \n",
    "    # en un elemento tipo FLOAT, ya que el valor que obtenemos del BeautifulSoup es un elemento de tipo Texto.\n",
    "\n",
    "    actual_price = soup.find(\"div\", {\"class\":[\"sc-a0353bbc-0 gDrtaY\",\"sc-a0353bbc-0 gDrtaY fall\", \"sc-a0353bbc-0 gDrtaY rise\"]}).getText() \n",
    "    actual_price = float(actual_price.replace(\"$\", \"\").replace(\",\", \"\"))\n",
    "    \n",
    "\n",
    "    # Para acceder a la tendencia actual del Bitcoin accederemos al valor de 2 clases especificas, esta web \n",
    "    # al ser dinámica guarda la tendencia positiva en una clase y la negativa en otra, con lo cual\n",
    "    # nosotros podemos obtener que tipo de tendencia hay actualmente si sabemos cual de las 2 clases está siendo\n",
    "    # ejecutada por la web.\n",
    "    \n",
    "    icon_trend = soup.find(\"span\", {\"class\":[\"sc-d55c02b-0 gUnzUB\",\"sc-d55c02b-0 iwhBxy\"]})\n",
    "    icon_trend = str(icon_trend)\n",
    "\n",
    "    #Asignamos un valor en caso de la tendencia sea positiva o negativa.\n",
    "    if 'icon-Caret-down' in icon_trend:\n",
    "        trend = \"Low\"\n",
    "    elif 'icon-Caret-up' in icon_trend:\n",
    "        trend = \"High\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasamos a verificar que la función realice la extracción correcta de los valores necesarios para continuar con el programa <br>\n",
    "y verificamos que las variables tengan sus respectivos valores cargados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precio actual del Bitcoin: 34462.9\n",
      "La tendencia actual del Bitcoin es: Low\n"
     ]
    }
   ],
   "source": [
    "#Ejecución de función de WebScraping\n",
    "trends_extract()\n",
    "\n",
    "#Impresión de las variables necesarias para validación\n",
    "print(\"Precio actual del Bitcoin: \" + str(actual_price))\n",
    "print(\"La tendencia actual del Bitcoin es: \"+ trend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1- Exploración y limpieza de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta exploración será realizada para comprender el contexto de la información, familiarizarnos con la misma, y comenzar a realizar algunas ideas de como abordaremos la creación del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1995 entries, 2023-10-23 19:30:00+00:00 to 2023-10-30 19:25:00+00:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       1995 non-null   float64\n",
      " 1   High       1995 non-null   float64\n",
      " 2   Low        1995 non-null   float64\n",
      " 3   Close      1995 non-null   float64\n",
      " 4   Adj Close  1995 non-null   float64\n",
      " 5   Volume     1995 non-null   int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 109.1 KB\n"
     ]
    }
   ],
   "source": [
    "#Mostramos un resumen breve del tipo de datos que contiene nuestro DataFrame, número de datos, columnas, así como datos erroneos o nulos\n",
    "df_bitcoin.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1.995000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34166.004270</td>\n",
       "      <td>34187.399117</td>\n",
       "      <td>34145.389334</td>\n",
       "      <td>34167.793577</td>\n",
       "      <td>34167.793577</td>\n",
       "      <td>2.806397e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>476.216310</td>\n",
       "      <td>470.980549</td>\n",
       "      <td>477.405216</td>\n",
       "      <td>472.339618</td>\n",
       "      <td>472.339618</td>\n",
       "      <td>1.153924e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31145.527344</td>\n",
       "      <td>31192.494141</td>\n",
       "      <td>31145.527344</td>\n",
       "      <td>31176.695312</td>\n",
       "      <td>31176.695312</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34023.685547</td>\n",
       "      <td>34039.101562</td>\n",
       "      <td>34006.632812</td>\n",
       "      <td>34023.150391</td>\n",
       "      <td>34023.150391</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34152.082031</td>\n",
       "      <td>34169.035156</td>\n",
       "      <td>34139.492188</td>\n",
       "      <td>34153.910156</td>\n",
       "      <td>34153.910156</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34458.753906</td>\n",
       "      <td>34482.257812</td>\n",
       "      <td>34438.160156</td>\n",
       "      <td>34461.265625</td>\n",
       "      <td>34461.265625</td>\n",
       "      <td>1.992550e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35116.218750</td>\n",
       "      <td>35150.433594</td>\n",
       "      <td>34950.511719</td>\n",
       "      <td>35044.105469</td>\n",
       "      <td>35044.105469</td>\n",
       "      <td>3.556581e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open          High           Low         Close     Adj Close  \\\n",
       "count   1995.000000   1995.000000   1995.000000   1995.000000   1995.000000   \n",
       "mean   34166.004270  34187.399117  34145.389334  34167.793577  34167.793577   \n",
       "std      476.216310    470.980549    477.405216    472.339618    472.339618   \n",
       "min    31145.527344  31192.494141  31145.527344  31176.695312  31176.695312   \n",
       "25%    34023.685547  34039.101562  34006.632812  34023.150391  34023.150391   \n",
       "50%    34152.082031  34169.035156  34139.492188  34153.910156  34153.910156   \n",
       "75%    34458.753906  34482.257812  34438.160156  34461.265625  34461.265625   \n",
       "max    35116.218750  35150.433594  34950.511719  35044.105469  35044.105469   \n",
       "\n",
       "             Volume  \n",
       "count  1.995000e+03  \n",
       "mean   2.806397e+07  \n",
       "std    1.153924e+08  \n",
       "min    0.000000e+00  \n",
       "25%    0.000000e+00  \n",
       "50%    0.000000e+00  \n",
       "75%    1.992550e+07  \n",
       "max    3.556581e+09  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejecución de algunas estadísticas descriptivas para entender númericamente la distribución de nuestros datos.\n",
    "# medidas como: \"Cuenta\", \"Media\", \"Desviación Estándar\",\"Mínimos\", \"Quartiles\"\n",
    "df_bitcoin.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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66623NHr0aB04cEC9evUy65YtWyY3N7cKx5eWlioyMlK+vr7avXu3srOzNWHCBDVu3FgLFiyQJB07dkyRkZGaNm2a3n33XSUnJ2vKlCnq0KGDIiIiJEnr169XTEyMXnvtNQ0YMEDLli1TRESEvvzyS7Vr1652fhkAGqxz585Jkvbv3+/iTqqnsLBQx48fl7+/v7y9vV3dTrUcOnTI1S0AqO+MOqZly5bGqlWrzO0DBw4Y119/vZGdnW1IMjZt2mSObd261XB3dzdycnLMfStXrjSsVqtRXFxsGIZhzJ071+jVq5fDd4wdO9aIiIgwt/v3729Mnz7d3C4tLTWuu+46Y+HChVXuOy8vz5Bk5OXlVfkYANeGv/71r4YkPi74fPXVV67+nx9AHVKdvFZn3rhXWlqqDz74QAUFBbLZbJJ+ufty7733avny5fL19a1wTFpamoKDg9W+fXtzX0REhB5++GEdPHhQffr0UVpamsLDwx2Oi4iI0KxZsyRJJSUlSk9PV2xsrDnu7u6u8PBwpaWlXbLf4uJiFRcXm9t2u/2KrhtAwxcVFSVJ6tGjh5o0aeLaZqrh0KFDuu+++/TOO++oZ8+erm6n2po3b66uXbu6ug0A9ZTLQ3JmZqZsNpuKiorUrFkzbdq0SUFBQZKk2bNna9CgQRo9enSlx+bk5DgEZEnmdk5OzmVr7Ha7CgsLdfr0aZWWllZac/jw4Uv2vXDhQs2bN696FwvgmtSmTRtNmTLF1W1csZ49e6pv376ubgMAapXLQ3L37t2VkZGhvLw8xcfHKzo6Wh9//LGOHj2qnTt36sCBA65usVKxsbGKiYkxt+12u/z8/FzYEQAAAGqKy0OyxWJRYGCgJCk0NFR79+5VXFycvL299fXXX6tFixYO9XfffbfCwsKUkpIiX1/fCqtQnDx5UpLM6Rm+vr7mvotrrFarvL295eHhIQ8Pj0prKpviUc7T01Oenp5XdM0AAACo2+rcOsllZWUqLi7Wk08+qc8//1wZGRnmR5JeeeUVrV69WpJks9mUmZmp3Nxc8/ikpCRZrVZzyobNZlNycrLDdyQlJZnzni0Wi0JDQx1qysrKlJycbNYAAADg2uLSO8mxsbEaNWqUOnXqpPz8fK1du1YpKSlKTEyUr69vpXdyO3XqpICAAEnSiBEjFBQUpPvvv18vvfSScnJy9PTTT2v69OnmXd5p06bp1Vdf1dy5czVp0iTt3LlT77//vhISEsxzxsTEKDo6Wv369VP//v21bNkyFRQUaOLEibXziwAAAECd4tKQnJubqwkTJig7O1s+Pj4KCQlRYmKihg8fXqXjPTw8tGXLFj388MOy2Wxq2rSpoqOj9fzzz5s1AQEBSkhI0OzZsxUXF6eOHTtq1apV5hrJkjR27FidOnVKzzzzjHJycnTTTTdp+/btFR7mAwAAwLXBzTAMw9VNNAR2u10+Pj7Ky8uT1Wp1dTsA8Kvt379foaGhSk9PZ3ULAA1CdfJanZuTDAAAALgaIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnLg0JK9cuVIhISGyWq2yWq2y2Wzatm2bOf7QQw+pS5cu8vb2Vtu2bTV69GgdPnzY4Rxubm4VPuvWrXOoSUlJUd++feXp6anAwECtWbOmQi/Lly+Xv7+/vLy8NGDAAO3Zs+eqXDMAAADqPpeG5I4dO2rRokVKT0/Xvn37NGzYMI0ePVoHDx6UJIWGhmr16tU6dOiQEhMTZRiGRowYodLSUofzrF69WtnZ2eYnKirKHDt27JgiIyM1dOhQZWRkaNasWZoyZYoSExPNmvXr1ysmJkbPPvus9u/fr969eysiIkK5ubm18nsAAABA3eJmGIZRnQOOHTum1NRUffvttzp37pzatm2rPn36yGazycvL61c31KpVKy1evFiTJ0+uMPb555+rd+/eOnr0qLp06fLLBbi5adOmTQ7B+GJPPPGEEhISlJWVZe77/e9/rzNnzmj79u2SpAEDBujmm2/Wq6++KkkqKyuTn5+fHn30UT355JNV6ttut8vHx0d5eXmyWq3VuWQAqJP279+v0NBQpaenq2/fvq5uBwB+terktSrfSX733XfVv39/denSRU888YQ2b96s1NRUrVq1SiNHjlT79u31yCOP6Ntvv72ipktLS7Vu3ToVFBTIZrNVGC8oKNDq1asVEBAgPz8/h7Hp06erTZs26t+/v958801dnPvT0tIUHh7uUB8REaG0tDRJUklJidLT0x1q3N3dFR4ebtZUpri4WHa73eEDAACAhqFRVYr69Okji8WiBx54QBs2bKgQUouLi5WWlqZ169apX79+WrFihX77299WqYHMzEzZbDYVFRWpWbNm2rRpk4KCgszxFStWaO7cuSooKFD37t2VlJQki8Vijj///PMaNmyYmjRpoh07duiRRx7R2bNnNXPmTElSTk6O2rdv7/Cd7du3l91uV2FhoU6fPq3S0tJKa5znP19s4cKFmjdvXpWuEQAAAPVLlULyokWLFBERcclxT09PDRkyREOGDNH8+fN1/PjxKjfQvXt3ZWRkKC8vT/Hx8YqOjtbHH39sBuXx48dr+PDhys7O1pIlS/S73/1Ou3btMqd2/OlPfzLP1adPHxUUFGjx4sVmSL5aYmNjFRMTY27b7fYK/+cBAAAA9VOVQvLlArKz1q1bq3Xr1lWut1gsCgwMlPTLg3p79+5VXFycXn/9dUmSj4+PfHx81LVrVw0cOFAtW7bUpk2bNG7cuErPN2DAAL3wwgsqLi6Wp6enfH19dfLkSYeakydPymq1ytvbWx4eHvLw8Ki0xtfX95J9e3p6ytPTs8rXCQAAgPqjSiH5Ynl5eUpKStLx48fl5uamgIAAhYeH19jDamVlZSouLq50zDAMGYZxyXFJysjIUMuWLc0Aa7PZtHXrVoeapKQkc96zxWJRaGiokpOTzYf/ysrKlJycrBkzZtTAFQEAAKC+qVZIfueddzRjxowKD6n5+Pjotdde09ixY6v15bGxsRo1apQ6deqk/Px8rV27VikpKUpMTNQ333yj9evXa8SIEWrbtq1++OEHLVq0SN7e3rrjjjskSR9++KFOnjypgQMHysvLS0lJSVqwYIEef/xx8zumTZumV199VXPnztWkSZO0c+dOvf/++0pISDBrYmJiFB0drX79+ql///5atmyZCgoKNHHixGpdDwAAABqGKofk/fv3a+LEiRo/frxmz56tHj16yDAMffHFF1q2bJnuv/9+9ejRQ717967yl+fm5mrChAnKzs6Wj4+PQkJClJiYqOHDh+vEiRNKTU3VsmXLdPr0abVv31633nqrdu/erXbt2kmSGjdurOXLl2v27NkyDEOBgYF6+eWXNXXqVPM7AgIClJCQoNmzZysuLk4dO3bUqlWrHKaQjB07VqdOndIzzzyjnJwc3XTTTdq+fXuFh/kAoK45d+7cZR8y/jUOHTrk8OfV0KNHDzVp0uSqnR8ArlSV10meOHGizp49qw8++KDS8XvuuUdWq1VvvvlmjTZYX7BOMgBXKF/LuL5iDWYAtak6ea3Kd5J37dqlFStWXHJ82rRpeuSRR6reJQDgV+vRo4fS09OvyrkLCwt1/Phx+fv7y9vb+6p8R48ePa7KeQHg16pySD5x4oS6det2yfFu3brp3//+d400BQComiZNmlzVO7GDBw++aucGgLqsyiH53Llzl33ttKenp4qKimqkKQCAa5WWlio1NVXZ2dnq0KGDwsLC5OHh4eq2AKDWVGt1i8TERPn4+FQ6dubMmZroBwDgYhs3btRjjz3m8GIof39/LV26VGPGjHFdYwBQi6oVkqOjoy877ubm9quaAQC41saNG3XPPffoN7/5jd577z3deOONysrK0oIFC3TPPfcoPj6eoAzgmlDl1S1weaxuAaC+Ky0tVWBgoIKDg7Vhwwbt2rXLnG4xePBg3X333crKytKRI0eYegGgXqpOXnOvpZ4AAHVcamqqjh8/rkGDBqlbt24aOnSo7r33Xg0dOlTdunWTzWbTsWPHlJqa6upWAeCqq3JI/uqrr7Rnzx6HfcnJyRo6dKj69++vBQsW1HhzAIDak52dLemXt6EGBwcrLS1N+fn5SktLU3BwsJ566imHOgBoyKockp944glt2bLF3D527JjuvPNOWSwW2Ww2LVy4UMuWLbsaPQIAakH520xvueUWbd68WQMHDlSzZs00cOBAbd682VwOrrwOABqyKj+4t2/fPs2dO9fcfvfdd9WtWzclJiZKkkJCQvSXv/xFs2bNqvEmAQCux8PZAK4lVQ7JP/74ozp27Ghuf/TRR7rzzjvN7SFDhuixxx6r2e4AALUmNzdX0i9vWB09erRGjhwpb29vFRYWavv27dq1a5dDHQA0ZFUOya1atVJ2drb8/PxUVlamffv2KSYmxhwvKSkRC2UAQP3VoUMHSdK9996r9evXO0yxa9SokcaNG6e1a9eadQDQkFU5JA8ZMkQvvPCCVqxYoQ8++EBlZWUaMmSIOf7FF1/I39//KrQIAKgNYWFhatu2rd59913dcccd6tq1qwoLC+Xt7a0jR45o7dq1ateuncLCwlzdKgBcdVUOyfPnz9fw4cPVuXNneXh46M9//rOaNm1qjr/99tsaNmzYVWkSAFA7yucdf/TRR9q6dau539vb21UtAYBLVDkk+/v769ChQzp48KDatm2r6667zmF83rx5DnOWAQD1S2pqqjnfuLCw0GGsqKhI0i/zkVNTUx3+JREAGqJqvZa6UaNG6t27d6Vjl9oPAKgf/v3vf5s/e3l5mcFYkjw9Pc3ti+sAoKGqckgeM2ZMpft9fHzUrVs3TZkyRW3btq2xxgAAtSsnJ8f8OTw8XH/84x914403KisrS/Pnzzcf5Lu4DgAaqiq/TMTHx6fSz5kzZ/TXv/5V3bt3V1ZW1tXsFQBwFf3444+SpJYtWyo+Pl5FRUX68MMPVVRUpPj4eLVo0cKhDgAasirfSV69evUlx8rKyjR16lTFxsbqww8/rJHGAAC164cffpAknT59Wi1btnSYl1y+XvLFdQDQkFX5TvJlT+LurpkzZyo9Pb0mTgcAcIFOnTqZP19u3fuL6wCgoarWg3uX07RpU507d66mTgcAqGW33XabFixYIEm6/fbbNWrUKPMO8rZt25SQkGDWAUBDV2MhOSkpSd26daup0wEAapmHh4f5886dO81QLElNmjSptA4AGqoqh+S///3vle7Py8tTenq6Vq1apVWrVtVYYwCA2lW+RrL0y7MmF7t4++I6AGioqhySo6KiKt3fvHlzde/eXatWrdLvf//7muoLAFDLOnToIOmX11OnpqY6jBUVFZn7y+sAoCFzMy73dAaqzG63y8fHR3l5ebJara5uBwCqrbS0VK1atZLdble7du00YcIE3XDDDfrmm2/0t7/9Tbm5ubJarfr555+ZcgGgXqpOXquR1S0AAPVfaWmpzp49K0nq16+f/P395enpKX9/f/Xr10+SdPbsWZWWlrqyTQCoFVWabrFu3boqT6X4/vvv9d1332nw4MG/qjEAQO1asWKFysrKFBERoR07dmjr1q3mWKNGjTR8+HAlJSVpxYoVmjVrlusaBYBaUKU7yStXrlTPnj310ksv6dChQxXG8/LytHXrVt17773q27evfvrppxpvFABwdX399deSpMTERI0cOVLLly/Xm2++qeXLl2vkyJFKSkpyqAOAhqxKIfnjjz/Wiy++qKSkJN14442yWq3q2rWrgoOD1bFjR7Vu3VqTJk1Sp06dlJWVpbvuuutq9w0AqGH+/v6SpJCQEG3cuFFBQUHy8vJSUFCQNm7cqJCQEIc6AGjIqry6xV133aW77rpLP/74o/75z3/q22+/VWFhodq0aaM+ffqoT58+cndnijMA1FfBwcGSpG+++UZdu3bVt99+a4517tzZ/FfC8joAaMiq/TKRNm3aXHI5OABA/VUegs+ePavi4mKNGzdON998s/bu3av4+HidP3/eoQ4AGrIae+MeAKB+a9eunSSpVatW+vnnn/Xee+/pvffeM8fL95fXAUBDRkgGADj4+eefdccdd8jb21unT59Wy5YtVVhY6LDaBQA0dIRkAIAkKScnx/x5586dKioqMre9vLwqrQOAhsqlT9qtXLlSISEhslqtslqtstls2rZtmzn+0EMPqUuXLvL29lbbtm01evRoHT582OEc3333nSIjI9WkSRO1a9dOc+bM0YULFxxqUlJS1LdvX3l6eiowMFBr1qyp0Mvy5cvl7+8vLy8vDRgwQHv27Lkq1wwAddWpU6fMn93c3BzGLt6+uA4AGqorDsklJSX68ssvKwTS6ujYsaMWLVqk9PR07du3T8OGDdPo0aN18OBBSVJoaKhWr16tQ4cOKTExUYZhaMSIEebbnkpLSxUZGamSkhLt3r1bb731ltasWaNnnnnG/I5jx44pMjJSQ4cOVUZGhmbNmqUpU6YoMTHRrFm/fr1iYmL07LPPav/+/erdu7ciIiKUm5t7xdcGAPVN69atJUlWq1Vt27Z1GGvbtq35CtfyOgBo0IxqKigoMCZNmmR4eHgYHh4extdff20YhmHMmDHDWLhwYXVPV0HLli2NVatWVTr22WefGZKMo0ePGoZhGFu3bjXc3d2NnJwcs2blypWG1Wo1iouLDcMwjLlz5xq9evVyOM/YsWONiIgIc7t///7G9OnTze3S0lLjuuuuq9b15OXlGZKMvLy8Kh8DAHXJK6+8YkgyJBnt27c33njjDePEiRPGG2+8YbRv394ce+WVV1zdKgBckerktWrfSY6NjdVnn32mlJQUhzlq4eHhWr9+/RWH9dLSUq1bt04FBQWy2WwVxgsKCrR69WoFBATIz89PkpSWlqbg4GC1b9/erIuIiJDdbjfvRqelpSk8PNzhXBEREUpLS5P0yx3x9PR0hxp3d3eFh4ebNZUpLi6W3W53+ABAfXbxnWQvLy89+OCDuu666/Tggw/K29ubO8kArinVfnBv8+bNWr9+vQYOHOgwR61Xr15X9KrSzMxM2Ww2FRUVqVmzZtq0aZOCgoLM8RUrVmju3LkqKChQ9+7dlZSUJIvFIumXh0cuDsiSzO3yB0suVWO321VYWKjTp0+rtLS00hrn+c8XW7hwoebNm1ft6wWAuqp8/eP8/Hzdcsst6tevn8PqFuXPjLBOMoBrQbVD8qlTpypdI7OgoKDCgx5V0b17d2VkZCgvL0/x8fGKjo7Wxx9/bAbl8ePHa/jw4crOztaSJUv0u9/9Trt27XK4i+0KsbGxiomJMbftdrt5hxsA6qPyecht27atdLm3du3aKTc3t8J8ZQBoiKo93aJfv35KSEgwt8uD8apVqyqdJvHfWCwWBQYGKjQ0VAsXLlTv3r0VFxdnjvv4+Khr16669dZbFR8fr8OHD2vTpk2SJF9fX508edLhfOXbvr6+l62xWq3y9vZWmzZt5OHhUWlN+Tkq4+npaa7KUf4BgPrs+uuvlyTl5ubKYrFo3LhxWrp0qcaNGyeLxWI+zFxeBwANWbXvJC9YsECjRo3SF198oQsXLiguLk5ffPGFdu/erY8//vhXN1RWVqbi4uJKxwzDkGEY5rjNZtP8+fOVm5tr3t1OSkqS1Wo170TbbLYKd0SSkpLMQG+xWBQaGqrk5GTzddtlZWVKTk7WjBkzfvX1AEB9MWDAAElSo0aN1L59e4c37nXq1EknTpzQhQsXzDoAaMiqfSf5lltuUUZGhi5cuKDg4GDt2LFD7dq1U1pamkJDQ6t1rtjYWH3yySc6fvy4MjMzFRsbq5SUFI0fP17ffPONFi5cqPT0dH333XfavXu3fvvb38rb21t33HGHJGnEiBEKCgrS/fffr88++0yJiYl6+umnNX36dHl6ekqSpk2bpm+++UZz587V4cOHtWLFCr3//vuaPXu22UdMTIz++te/6q233tKhQ4f08MMPq6CgQBMnTqzurwcA6q3XX39dknThwgX9+OOPDmOnTp0yl/wsrwOAhuyK3rjXpUsX/fWvf/3VX56bm6sJEyYoOztbPj4+CgkJUWJiooYPH64TJ04oNTVVy5Yt0+nTp9W+fXvdeuut2r17t3nX2MPDQ1u2bNHDDz8sm82mpk2bKjo6Ws8//7z5HQEBAUpISNDs2bMVFxenjh07atWqVYqIiDBrxo4dq1OnTumZZ55RTk6ObrrpJm3fvr3Cw3wA0JBd7uHri585uZKHtAGgvnEzDMOozgFbt26Vh4eHQ8iUpMTERJWVlWnUqFE12mB9Ybfb5ePjo7y8POYnA6iXli5dqscff1whISHavXu3nnjiCR05ckRdu3bViy++KJvNpszMTC1ZskSPPfaYq9sFgGqrTl6r9nSLJ5980nzj3cUMw9CTTz5Z3dMBAOqI4OBgSdJXX30lq9Wq5cuXa8eOHVq+fLmsVquOHDniUAcADVm1Q/KRI0cc1jEu16NHDx09erRGmgIA1L7y9Y+LiopkGIbuv/9+HThwQPfff78Mw1BRUZFDHQA0ZNWek+zj46NvvvlG/v7+DvuPHj2qpk2b1lRfAIBaVv4mPS8vL50/f15vv/223n77bUm/rHjRqFEjFRUV8cY9ANeEat9JHj16tGbNmuXw4MbRo0f12GOP6a677qrR5gAAtSczM1OS1K1bN509e1avvPKKZsyYoVdeeUX5+fnq1q2bQx0ANGTVDskvvfSSmjZtqh49eiggIEABAQHq2bOnWrdurSVLllyNHgEAteD48eOSpM8//1y//e1v1bhxY/Xp00eNGzfWb3/7W33++ecOdQDQkF3RdIvdu3crKSlJn332mby9vRUSEqJbb731avQHAKglXbp0kSRFRERo+/bt2rJliznWqFEjDR8+XElJSWYdADRk1V4CDpVjCTgA9V1JSYm8vb1VVlamO+64Q5GRkfL29lZhYaESEhK0detWubu7q7CwUBaLxdXtAkC1VSevVelO8p///Gc9+OCD8vLy0p///OfL1s6cObPqnQIA6gwPDw81a9ZMdrtd6enpGj16tCIiIrRlyxalp6dLkpo1ayYPDw8XdwoAV1+V7iQHBARo3759at26tQICAi59Mjc3ffPNNzXaYH3BnWQA9V1KSoqGDh2q8ePHa/369eZrqKVfplv87ne/09q1a/XRRx9pyJAhrmsUAK5Qjd9JPnbsWKU/AwAajuzsbEnSa6+9pjfffFMrVqzQ119/rS5duuiRRx5RcXGx1q5da9YBQENWrQf3zp8/rx49emjLli3q2bPn1eoJAOACHTp0kCRlZWVp4MCBmjVrlsN4+ZSL8joAaMiqtQRc48aNzTcuAQAalrCwMPn7+2vBggU6f/68UlJS9N577yklJUXnz5/XwoULFRAQoLCwMFe3CgBXXbWXgJs+fbpefPFFrVq1So0aVftwAEAd5eHhoaVLl+qee+6Rj4+PCgsLzTFvb28VFRUpPj6eB/cAXBOqnXL37t2r5ORk7dixQ8HBwRVeRb1x48Yaaw4AUPsqe57bzc2t0v0A0FBVe53kiRMnXnZ89erVv6qh+orVLQDUd6WlpQoMDFRwcLA2bNigXbt2KTs7Wx06dNDgwYN19913KysrS0eOHOFuMoB6qTp5jZeJ1BBCMoD6rnwJuLS0NA0cOLDCeFpamgYNGsQScADqrerktSo/uFdWVqYXX3xRgwcP1s0336wnn3zSYb4aAKB+K1/a7cYbb6x0vHw/S8ABuBZUOSTPnz9fTz31lJo1a6brr79ecXFxmj59+tXsDQBQiy5eAq4y5ftZAg7AtaDK0y26du2qxx9/XA899JAk6R//+IciIyNVWFgod/dqrSTXIDHdAkB9d/Gc5M2bNzv83V5WVqaoqCjmJAOo167KdIvvvvtOd9xxh7kdHh4uNzc3nThx4so7BQDUGeVLwG3ZskVRUVFKS0tTfn6+0tLSFBUVpS1btmjJkiUEZADXhCovAXfhwgV5eXk57GvcuLHOnz9f400BAFxjzJgxio+P12OPPaZBgwaZ+wMCAhQfH68xY8a4sDsAqD1Vnm7h7u6uUaNGydPT09z34YcfatiwYQ5rJV+r6yQz3QJAQ1JaWqrU1FRzCbiwsDDuIAOo96qT16p8Jzk6OrrCvvvuu6/63QEA6jwPDw+WeQNwTatySL5WXxICAACAaw/LUgAAAABOCMkAAACAE0IyAAAA4ISQDAAAADghJAMAAABOCMkAAACAE0IyAAAA4ISQDAAAADghJAMAAABOCMkAAACAE5eG5JUrVyokJERWq1VWq1U2m03btm2TJP3888969NFH1b17d3l7e6tTp06aOXOm8vLyHM7h5uZW4bNu3TqHmpSUFPXt21eenp4KDAzUmjVrKvSyfPly+fv7y8vLSwMGDNCePXuu2nUDAACgbnNpSO7YsaMWLVqk9PR07du3T8OGDdPo0aN18OBBnThxQidOnNCSJUuUlZWlNWvWaPv27Zo8eXKF86xevVrZ2dnmJyoqyhw7duyYIiMjNXToUGVkZGjWrFmaMmWKEhMTzZr169crJiZGzz77rPbv36/evXsrIiJCubm5tfFrAAAAQB3jZhiG4eomLtaqVSstXry40jD8wQcf6L777lNBQYEaNWok6Zc7yZs2bXIIxhd74oknlJCQoKysLHPf73//e505c0bbt2+XJA0YMEA333yzXn31VUlSWVmZ/Pz89Oijj+rJJ5+sUt92u10+Pj7Ky8uT1WqtziUDAACgFlQnr9WZOcmlpaVat26dCgoKZLPZKq0pv6DygFxu+vTpatOmjfr3768333xTF+f+tLQ0hYeHO9RHREQoLS1NklRSUqL09HSHGnd3d4WHh5s1lSkuLpbdbnf4AAAAoGFo9N9Lrq7MzEzZbDYVFRWpWbNm2rRpk4KCgirU/fjjj3rhhRf04IMPOux//vnnNWzYMDVp0kQ7duzQI488orNnz2rmzJmSpJycHLVv397hmPbt28tut6uwsFCnT59WaWlppTWHDx++ZN8LFy7UvHnzrvSyAQAAUIe5PCR3795dGRkZysvLU3x8vKKjo/Xxxx87BGW73a7IyEgFBQXpueeeczj+T3/6k/lznz59VFBQoMWLF5sh+WqJjY1VTEyMQ49+fn5X9TsBAABQO1w+3cJisSgwMFChoaFauHChevfurbi4OHM8Pz9fI0eOVPPmzbVp0yY1btz4sucbMGCAfvjhBxUXF0uSfH19dfLkSYeakydPymq1ytvbW23atJGHh0elNb6+vpf8Hk9PT3NVjvIPAAAAGgaXh2RnZWVlZsC12+0aMWKELBaL/v73v8vLy+u/Hp+RkaGWLVvK09NTkmSz2ZScnOxQk5SUZM57tlgsCg0NdagpKytTcnLyJedGAwAAoGFz6XSL2NhYjRo1Sp06dVJ+fr7Wrl2rlJQUJSYmmgH53Llzeueddxwejmvbtq08PDz04Ycf6uTJkxo4cKC8vLyUlJSkBQsW6PHHHze/Y9q0aXr11Vc1d+5cTZo0STt37tT777+vhIQEsyYmJkbR0dHq16+f+vfvr2XLlqmgoEATJ06s9d8JAAAAXM+lITk3N1cTJkxQdna2fHx8FBISosTERA0fPlwpKSn69NNPJUmBgYEOxx07dkz+/v5q3Lixli9frtmzZ8swDAUGBurll1/W1KlTzdqAgAAlJCRo9uzZiouLU8eOHbVq1SpFRESYNWPHjtWpU6f0zDPPKCcnRzfddJO2b99e4WE+AAAAXBvq3DrJ9RXrJAMAANRt9XKdZAAAAKCuICQDAAAATgjJAAAAgBNCMgAAAOCEkAwAAAA4ISQDAAAATgjJAAAAgBNCMgAAAOCEkAwAAAA4ISQDAAAATgjJAAAAgBNCMgAAAOCEkAwAAAA4ISQDAAAAThq5ugEAQN1TWlqq1NRUZWdnq0OHDgoLC5OHh4er2wKAWsOdZACAg40bNyowMFBDhw7Vvffeq6FDhyowMFAbN250dWsAUGsIyQAA08aNG3XPPfcoODhYaWlpys/PV1pamoKDg3XPPfcQlAFcM9wMwzBc3URDYLfb5ePjo7y8PFmtVle3AwDVVlpaqsDAQAUHB2vz5s1yd//PfZSysjJFRUUpKytLR44cYeoFgHqpOnmNO8kAAElSamqqjh8/rqeeesohIEuSu7u7YmNjdezYMaWmprqoQwCoPYRkAIAkKTs7W5J04403Vjpevr+8DgAaMkIyAECS1KFDB0lSVlZWpePl+8vrAKAhIyQDACRJYWFh8vf314IFC1RWVuYwVlZWpoULFyogIEBhYWEu6hAAag8hGQAgSfLw8NDSpUu1ZcsWRUVFOaxuERUVpS1btmjJkiU8tAfgmsDLRAAApjFjxig+Pl6PPfaYBg0aZO4PCAhQfHy8xowZ48LuAKD2sARcDWEJOAANCW/cA9AQsQQcAAAA8CsQkgEADngtNQAQkgEAF+G11ADwC+Yk1xDmJAOo7y5+LfWGDRu0a9cuc07y4MGDdffdd/NaagD1GnOSAQDVVv5a6kGDBqlbt24O0y26desmm83Ga6kBXDMIyQAASf953XRsbGyl0y2eeuophzoAaMhYJxkAIElq166dJOmWW27R5s2b5e7+y32UgQMHavPmzbr11lu1a9cusw4AGjLuJAMAqsTNzc3VLQBArSEkAwAkSbm5uZKkXbt2Vfpa6l27djnUAUBD5tKQvHLlSoWEhMhqtcpqtcpms2nbtm2SpJ9//lmPPvqounfvLm9vb3Xq1EkzZ85UXl6ewzm+++47RUZGqkmTJmrXrp3mzJmjCxcuONSkpKSob9++8vT0VGBgoNasWVOhl+XLl8vf319eXl4aMGCA9uzZc9WuGwDqog4dOkiSFixYoMzMTA0aNEhWq1WDBg1SVlaW5s+f71AHAA2ZS+ckd+zYUYsWLVLXrl1lGIbeeustjR49WgcOHJBhGDpx4oSWLFmioKAgffvtt5o2bZpOnDih+Ph4Sb8sVxQZGSlfX1/t3r1b2dnZmjBhgho3bqwFCxZIko4dO6bIyEhNmzZN7777rpKTkzVlyhR16NBBERERkqT169crJiZGr732mgYMGKBly5YpIiJCX375JXPvAFwzwsLC5O/vr927d+urr76qdAm4gIAAhYWFubpVALj6jDqmZcuWxqpVqyode//99w2LxWKcP3/eMAzD2Lp1q+Hu7m7k5OSYNStXrjSsVqtRXFxsGIZhzJ071+jVq5fDecaOHWtERESY2/379zemT59ubpeWlhrXXXedsXDhwir3nZeXZ0gy8vLyqnwMANQ1GzZsMNzc3Iw777zT2L17t2G3243du3cbd955p+Hm5mZs2LDB1S0CwBWrTl6rM3OSS0tLtW7dOhUUFMhms1VaU77wc6NGv9wAL1+WqH379mZNRESE7Ha7Dh48aNaEh4c7nCciIkJpaWmSpJKSEqWnpzvUuLu7Kzw83KypTHFxsex2u8MHAOq7MWPGKD4+vtLpFvHx8RozZoyrWwSAWuHyJeAyMzNls9lUVFSkZs2aadOmTQoKCqpQ9+OPP+qFF17Qgw8+aO7LyclxCMiSzO2cnJzL1tjtdhUWFur06dMqLS2ttObw4cOX7HvhwoWaN29e9S4WAOqBMWPGaPTo0UpNTTWnW4SFhfGWPQDXFJffSe7evbsyMjL06aef6uGHH1Z0dLS++OILhxq73a7IyEgFBQXpueeec02jTmJjY5WXl2d+vv/+e1e3BAAAgBri8jvJFotFgYGBkqTQ0FDt3btXcXFxev311yVJ+fn5GjlypJo3b65NmzapcePG5rG+vr4VVqE4efKkOVb+Z/m+i2usVqu8vb3l4eEhDw+PSmvKz1EZT09PeXp6XuFVA0DdtXHjRj322GM6fvy4uc/f319Lly5lugWAa4bL7yQ7KysrU3FxsaRf7iCPGDFCFotFf//73+Xl5eVQa7PZlJmZ6bBmZ1JSkqxWqzllw2azKTk52eG4pKQkc96zxWJRaGioQ01ZWZmSk5MvOTcaABqqjRs36p577qn0xsE999yjjRs3uqgzAKhdLg3JsbGx+uSTT3T8+HFlZmYqNjZWKSkpGj9+vBmQCwoK9L//+7+y2+3KyclRTk6OSktLJUkjRoxQUFCQ7r//fn322WdKTEzU008/renTp5t3eadNm6ZvvvlGc+fO1eHDh7VixQq9//77mj17ttlHTEyM/vrXv+qtt97SoUOH9PDDD6ugoEATJ050ye8FAFyhtLRUDz/8sAzD0O233+7wMpHbb79dhmHo4YcfNv8OBoAG7aqvtXEZkyZNMjp37mxYLBajbdu2xu23327s2LHDMAzD+OijjwxJlX6OHTtmnuP48ePGqFGjDG9vb6NNmzbGY489Zi4RV+6jjz4ybrrpJsNisRg33HCDsXr16gq9/OUvfzE6depkWCwWo3///sa//vWval0LS8ABqO/+8Y9/GJKMW265xSgtLXUYKy0tNQYPHmxIMv7xj3+4qEMA+HWqk9fcDMMwXBXQGxK73S4fHx9zmToAqG/+9Kc/6f/9v/+n5ORkDRs2rML4P/7xDw0fPlxPP/20XnjhBRd0CAC/TnXyWp2bkwwAAAC4GiEZACBJGjJkiCTp2WefVVlZmcNYWVmZuTZ8eR0ANGSEZACApF/Cb9u2bfXPf/5To0ePdnhwb/To0frnP/+pdu3aEZIBXBNcvk4yAKBu8PDw0Guvvaa7775bycnJ2rJliznWpEkTSdLKlSt58x6AawJ3kgEApjFjxmjOnDkqKSlx2F9cXKw5c+bwMhEA1wzuJAMATBs3btSSJUsUGRmpUaNGydvbW4WFhdq2bZuWLFmigQMHEpQBXBNYAq6GsAQcgPqutLRUgYGBCg4O1ubNm+Xu/p9/bCwrK1NUVJSysrJ05MgRplwAqJdYAg4AUG2pqak6fvy4nnrqKYeALEnu7u6KjY3VsWPHlJqa6qIOAaD2EJIBAJKk7OxsSdKNN95Y6Xj5/vI6AGjICMkAAElShw4dJElZWVmVjpfvL68DgIaMkAwAkCSFhYXJ399fCxYsqPRlIgsXLlRAQIDCwsJc1CEA1B5WtwAASPplneSlS5fqnnvu0ejRozVy5EhzdYvt27crISFB8fHxPLQH4JrA6hY1hNUtADQUc+fO1SuvvKILFy6Y+xo1aqTZs2frpZdecmFnAPDrVCevcScZAGBinWQA+AV3kmsId5IB1HcXr5O8YcMG7dq1S9nZ2erQoYMGDx6su+++m3WSAdRrrJMMAKi28nWSBw0apG7dumno0KG69957NXToUHXr1k02m411kgFcMwjJAABJ/1n/+KmnnlJwcLDS0tKUn5+vtLQ0BQcH649//KNDHQA0ZIRkAIAkqV27dpKkwYMHa8OGDSoqKtKHH36ooqIibdiwQYMHD3aoA4CGjAf3AAAOfvzxR3Xt2lXffvutua9z587y9vZ2YVcAULsIyQAASVJubq4k6fDhwxXGLg7M5XUA0JAx3QIAIMlxGoW7u+N/Hi7eZroFgGsBIRkAIEkqKSmRJDVu3Fg5OTkaPHiw/Pz8NHjwYOXk5Khx48YOdQDQkDHdAgAgSVq7dq0k6fz58w53i7///nuH7bVr12rUqFG13h8A1CbuJAMAJElnz56t0ToAqM8IyQAASVJoaGiN1gFAfUZIBgBIkj799FOH7ZtvvlnPPfecbr755svWAUBDxJxkAIAkKTMz02F779692rt373+tA4CGiDvJAABJ0unTp2u0DgDqM0IyAECS1KJFC/PnRo0c/6Hx4u2L6wCgoSIkAwAkSdddd53584ULFxzGLt6+uA4AGipCMgBAkjRo0KAarQOA+oyQDACQJOXk5NRoHQDUZ4RkAIAk6eTJkzVaBwD1GUvAAQAkSUVFRebPbdq0kY+PjwoLC+Xt7a28vDz9+OOPFeoAoKEiJAMAJEmdOnXSrl27JEk//fSTGYolyc3NzaEOABo6l063WLlypUJCQmS1WmW1WmWz2bRt2zZz/I033tCQIUNktVrl5uamM2fOVDiHv7+/3NzcHD6LFi1yqPn8888VFhYmLy8v+fn56aWXXqpwng8++EA9evSQl5eXgoODtXXr1hq/XgCoy/r27VujdQBQn7k0JHfs2FGLFi1Senq69u3bp2HDhmn06NE6ePCgJOncuXMaOXKknnrqqcue5/nnn1d2drb5efTRR80xu92uESNGqHPnzkpPT9fixYv13HPP6Y033jBrdu/erXHjxmny5Mk6cOCAoqKiFBUVpaysrKtz4QBQB7Vv39782TAMh7GLty+uA4CGyqXTLe68806H7fnz52vlypX617/+pV69emnWrFmSpJSUlMuep3nz5vL19a107N1331VJSYnefPNNWSwW9erVSxkZGXr55Zf14IMPSpLi4uI0cuRIzZkzR5L0wgsvKCkpSa+++qpee+21X3eRAFBP/PTTTzVaBwD1WZ1Z3aK0tFTr1q1TQUGBbDZbtY5dtGiRWrdurT59+mjx4sUOi96npaXp1ltvlcViMfdFREToyy+/NF+tmpaWpvDwcIdzRkREKC0t7ZLfWVxcLLvd7vABgPqsdevW5s8Xz0F23r64DgAaKpc/uJeZmSmbzaaioiI1a9ZMmzZtUlBQUJWPnzlzpvr27atWrVpp9+7dio2NVXZ2tl5++WVJv6znGRAQ4HBM+T8V5uTkqGXLlsrJyanwz4ft27e/7FqgCxcu1Lx586rcJwDUdRf/nXfHHXdo1KhR8vb2VmFhobZt26aEhIQKdQDQULk8JHfv3l0ZGRnKy8tTfHy8oqOj9fHHH1c5KMfExJg/h4SEyGKx6KGHHtLChQvl6el5tdpWbGysw3fb7Xb5+fldte8DgKstIyND0i+rV2RlZZmhWPrlIWk/Pz99//33Zh0ANGQuD8kWi0WBgYGSpNDQUO3du1dxcXF6/fXXr+h8AwYM0IULF3T8+HF1795dvr6+FRa+L98un8d8qZpLzXOWJE9Pz6sawgGgtp07d06S9P333ysyMlJz5swx7yRv377dDM3ldQDQkNWZOcnlysrKVFxcfMXHZ2RkyN3dXe3atZMk2Ww2ffLJJzp//rxZk5SUpO7du6tly5ZmTXJyssN5kpKSqj03GgDqs1tuuUXSL3eSMzMzNWPGDE2ePFkzZsxQVlaWuT5yeR0ANGRuhvM6P7UoNjZWo0aNUqdOnZSfn6+1a9fqxRdfVGJiooYPH66cnBzl5ORo3759mjp1qj755BM1b95cnTp1UqtWrZSWlqZPP/1UQ4cOVfPmzZWWlqbZs2dr1KhReuuttyRJeXl56t69u0aMGKEnnnhCWVlZmjRpkl555RVzdYvdu3frtttu06JFixQZGal169ZpwYIF2r9/v2688cYqXYvdbpePj4/y8vJktVqv2u8MAK6WkpISeXt7q6ys7JI17u7uKiwsdHgYGgDqi2rlNcOFJk2aZHTu3NmwWCxG27Ztjdtvv93YsWOHOf7ss88akip8Vq9ebRiGYaSnpxsDBgwwfHx8DC8vL6Nnz57GggULjKKiIofv+eyzz4xbbrnF8PT0NK6//npj0aJFFXp5//33jW7duhkWi8Xo1auXkZCQUK1rycvLMyQZeXl51f9FAEAdcfPNN1f692755+abb3Z1iwBwxaqT11x6J7kh4U4ygPqupKRETZs2lcViUXFxsUpLS82xRo0ayWKxqKSkRAUFBdxJBlAvVSev1bk5yQAA11ixYoUuXLiguLg45efna/r06RoxYoSmT58uu92uV155RRcuXNCKFStc3SoAXHUuX90CAFA3fP3115J+eXFIUFCQjh8/LknasWOHEhIS9Mc//tGhDgAaMu4kAwAkSV26dJEkTZkyRcHBwUpLS1N+fr7S0tIUHBysqVOnOtQBQEPGnOQawpxkAPVdYWGhmjRpIovFovz8fId5xyUlJWrevLlKSkp07tw5eXt7u7BTALgy1clrTLcAAEiSPv30U0m/BOJOnTpp/PjxuuGGG/TNN9/o3XffVUlJiVk3ZMgQF3YKAFcfIRkAIEnKzs6WJEVGRiohIUEvv/yyw3j5/vI6AGjICMkAAElShw4dJEkJCQmKjIxUYGCgCgsL5e3traNHj5qvpS6vA4CGjDnJNYQ5yQDqu/J1klu3bq0ffvhBjRr95z7KhQsX1LFjR/3000+skwyg3mJOMgCg2nbv3q0LFy4oNzdX//M//6ORI0fK29tbhYWF2r59u3Jzc2UYhnbv3s2cZAANHiEZACDpP3OSZ86cqeXLl2vLli3mWKNGjTRz5kzFxcUxJxnANYGQDACQ9J+5xnFxcfrNb36jUaNGmXeSt23bpri4OIc6AGjImJNcQ5iTDKC+Y04ygIauOnmNN+4BACQ5zkkeM2aMwxv3xowZo9zcXF24cEG7d+92dasAcNURkgEAkv4zJ/ntt99WZmamBg0aJKvVqkGDBikrK0tvv/22Qx0ANGSEZACApP/MNf7+++/lPBOvrKxM3333nUMdADRkhGQAgCQpLCxM7dq1U2xsrIKDgx2mWwQHB+upp55Su3btFBYW5upWAeCqIyQDAEwX30E2DMP8AMC1hpAMAJAkpaam6tSpU1q4cKGysrIc5iQfPHhQCxYsUG5urlJTU13dKgBcdYRkAICk/zyQ5+fnV+mc5E6dOjnUAUBDRkgGAEj6zwN59913n0JCQhzmJIeEhOi+++5zqAOAhoyXidQQXiYCoL7jZSIAGjpeJgIAqDZeJgIA/0FIBgBI4mUiAHCxRv+9BABwLSifa9ylSxcdPXpUqampys7OVocOHRQWFqY9e/Y41AFAQ8ac5BrCnGQA9V1paakCAwMVHByszZs3y939P//YWFZWpqioKGVlZenIkSPy8PBwYacAcGWYkwwAqDYPDw8tXbpUW7ZsUVRUlMOc5KioKG3ZskVLliwhIAO4JjDdAgBgGjNmjOLj4/XYY49p0KBB5v6AgADFx8drzJgxLuwOAGoP0y1qCNMtADQkpaWlFeYkcwcZQH1XnbzGnWQAQAUeHh4aMmSIq9sAAJdhTjIAAADghJAMAAAAOCEkAwAAAE4IyQAAAIATQjIAAADgxKUheeXKlQoJCZHVapXVapXNZtO2bdvM8TfeeENDhgyR1WqVm5ubzpw5U+EcP//8s8aPHy+r1aoWLVpo8uTJOnv2rEPN559/rrCwMHl5ecnPz08vvfRShfN88MEH6tGjh7y8vBQcHKytW7fW+PUCAACgfnBpSO7YsaMWLVqk9PR07du3T8OGDdPo0aN18OBBSdK5c+c0cuRIPfXUU5c8x/jx43Xw4EElJSVpy5Yt+uSTT/Tggw+a43a7XSNGjFDnzp2Vnp6uxYsX67nnntMbb7xh1uzevVvjxo3T5MmTdeDAAUVFRZmvXwUAAMC1p869TKRVq1ZavHixJk+ebO5LSUnR0KFDdfr0abVo0cLcf+jQIQUFBWnv3r3q16+fJGn79u2644479MMPP+i6667TypUr9cc//lE5OTmyWCySpCeffFKbN2/W4cOHJUljx45VQUGBtmzZYp574MCBuummm/Taa69VqW9eJgIAAFC3VSev1Zk5yaWlpVq3bp0KCgpks9mqdExaWppatGhhBmRJCg8Pl7u7uz799FOz5tZbbzUDsiRFREToyy+/1OnTp82a8PBwh3NHREQoLS3tkt9dXFwsu93u8AEAAEDD4PI37mVmZspms6moqEjNmjXTpk2bFBQUVKVjc3Jy1K5dO4d9jRo1UqtWrZSTk2PWBAQEONS0b9/eHGvZsqVycnLMfRfXlJ+jMgsXLtS8efMq7CcsAwAA1E3lOa0qEylcHpK7d++ujIwM5eXlKT4+XtHR0fr444+rHJRdJTY2VjExMeb2v//9bwUFBcnPz8+FXQEAAOC/yc/Pl4+Pz2VrXB6SLRaLAgMDJUmhoaHau3ev4uLi9Prrr//XY319fZWbm+uw78KFC/r555/l6+tr1pw8edKhpnz7v9WUj1fG09NTnp6e5nazZs30/fffq3nz5nJzc/uvvQMAAKB2GYah/Px8XXfddf+11uUh2VlZWZmKi4urVGuz2XTmzBmlp6crNDRUkrRz506VlZVpwIABZs0f//hHnT9/Xo0bN5YkJSUlqXv37mrZsqVZk5ycrFmzZpnnTkpKqvLcaElyd3dXx44dq1wPAACA2vff7iCXc+mDe7Gxsfrkk090/PhxZWZmKjY2VikpKRo/frykX+YMZ2Rk6OjRo5J+mb+ckZGhn3/+WZLUs2dPjRw5UlOnTtWePXu0a9cuzZgxQ7///e/N/4dw7733ymKxaPLkyTp48KDWr1+vuLg4h6kSf/jDH7R9+3YtXbpUhw8f1nPPPad9+/ZpxowZtfwbAQAAQJ1guNCkSZOMzp07GxaLxWjbtq1x++23Gzt27DDHn332WUNShc/q1avNmp9++skYN26c0axZM8NqtRoTJ0408vPzHb7ns88+M2655RbD09PTuP76641FixZV6OX99983unXrZlgsFqNXr15GQkLCVbtuAAAA1G11bp1kAAAAwNXqzDrJAAAAQF1BSAaAOsjNzU2bN292dRtXpDZ7f+CBB2rlewBcewjJAFDLcnJy9Oijj+qGG26Qp6en/Pz8dOeddyo5OdnVrdWI7OxsjRo1ytVtAMCvQkgGgFp0/PhxhYaGaufOnVq8eLEyMzO1fft2DR06VNOnT3d1e79KSUmJpF/Wnr94HfmaVlhYqJkzZ+qGG27Q2rVr5e/vrzvvvPOyb0kFgOoiJANALXrkkUfk5uamPXv26O6771a3bt3Uq1cvxcTE6F//+tclj8vMzNSwYcPk7e2t1q1b68EHH9TZs2fN8ZSUFPXv319NmzZVixYtNHjwYH377bfm+P/93/+pb9++8vLy0g033KB58+bpwoULl/y+Bx54QFFRUZo3b57atm0rq9WqadOmmUFYkoYMGaIZM2Zo1qxZatOmjSIiIiRVnG7xww8/aNy4cWrVqpWaNm2qfv366dNPP73i3hYsWKD169frL3/5i37zm9/onXfeUf/+/R16A4Bfq869TAQAGqqff/5Z27dv1/z589W0adMK4y1atKj0uIKCAkVERMhms2nv3r3Kzc3VlClTNGPGDK1Zs0YXLlxQVFSUpk6dqvfee08lJSXas2eP+fbP1NRUTZgwQX/+858VFhamr7/+Wg8++KAk6dlnn71kv8nJyfLy8lJKSoqOHz+uiRMnqnXr1po/f75Z89Zbb+nhhx/Wrl27Kj3H2bNnddttt+n666/X3//+d/n6+mr//v0qKyu74t4OHDigu+66S5GRkfrggw90yy236JZbbrnkdQDAFXH1GnQAcK349NNPDUnGxo0b/2utJGPTpk2GYRjGG2+8YbRs2dI4e/asOZ6QkGC4u7sbOTk5xk8//WRIMlJSUio91+23324sWLDAYd/bb79tdOjQ4ZLfHx0dbbRq1cooKCgw961cudJo1qyZUVpaahiGYdx2221Gnz59Ltv766+/bjRv3tz46aefaqy3BQsWGG3atDHee+89Y9y4cZesA4BfgzvJAFBLjCtclv7QoUPq3bu3w93nwYMHq6ysTF9++aVuvfVWPfDAA4qIiNDw4cMVHh6u3/3ud+rQoYMk6bPPPtOuXbsc7gCXlpaqqKhI586dU5MmTSr93t69ezuM2Ww2nT17Vt9//706d+4sSQoNDb1s7xkZGerTp49atWpV6fiV9DZnzhw1atRI8+fP18GDB7V//35NmDBBc+bMUePGjS/bDwBUFSEZAGpJ165d5ebmpsOHD9f4uVevXq2ZM2dq+/btWr9+vZ5++mklJSVp4MCBOnv2rObNm6cxY8ZUOM7Ly+tXfW9l00Yu5u3tfdnxK+mtUaNGmjNnjubMmaPf/e53+p//+R/94Q9/0NmzZ7VgwYKqNw8Al0FIBoBa0qpVK0VERGj58uWaOXNmhYB55syZSucl9+zZU2vWrFFBQYF5zK5du+Tu7q7u3bubdX369FGfPn0UGxsrm82mtWvXauDAgerbt6++/PJLBQYGVqvfzz77TIWFhWbQ/de//qVmzZrJz8+vyucICQnRqlWr9PPPP1d6N/lKeyvXpEkTjRs3Tvv27VNqauoVnQMAKsPqFgBQi5YvX67S0lL1799fGzZs0JEjR3To0CH9+c9/ls1mq/SY8ePHy8vLS9HR0crKytJHH32kRx99VPfff7/at2+vY8eOKTY2Vmlpafr222+1Y8cOHTlyRD179pQkPfPMM/rb3/6mefPm6eDBgzp06JDWrVunp59++rK9lpSUaPLkyfriiy+0detWPfvss5oxY4bc3av+n45x48bJ19dXUVFR2rVrl7755htt2LBBaWlpV9zbs88+q61bt+qnn36SYRjat2+f/u///u+/Tv0AgGpx9aRoALjWnDhxwpg+fbrRuXNnw2KxGNdff71x1113GR999JFZo4sefjMMw/j888+NoUOHGl5eXkarVq2MqVOnGvn5+YZhGEZOTo4RFRVldOjQwbBYLEbnzp2NZ555xnzAzjAMY/v27cagQYMMb29vw2q1Gv379zfeeOONS/YYHR1tjB492njmmWeM1q1bG82aNTOmTp1qFBUVmTW33Xab8Yc//KHCsc69Hz9+3Lj77rsNq9VqNGnSxOjXr5/x6aefXnFvf/vb34xBgwYZLVq0MNzc3Iz27dsbkydPNn8fAFAT3AzjCp8kAQA0WA888IDOnDlT51+N/cADD2jNmjWubgNAA8R0CwAAAMAJIRkAUG9xFxnA1cJ0CwAAAMAJd5IBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACc/H+nrRTanQf1wQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostraremos una gráfica de caja para una rápida y gráfica comprensión de los datos\n",
    "\n",
    "plt.figure(\"figure\", figsize=(8,5))\n",
    "plt.boxplot(df_bitcoin[\"Close\"])\n",
    "plt.xlabel(\"Close price $\")\n",
    "plt.ylabel(\"Price (USD)\")\n",
    "plt.xticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2- Limpieza de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "En esta sección nos enfocaremos en desarrollar una función que se encargará de la limpieza de los datos obtenidos anteriormente. <br>\n",
    "La limpieza se abordará de la siguiente manera, en lo posible: <br>\n",
    "\n",
    "-Generar una copia de la base df_bitcoin para realizar la limpieza en esta nueva base(df_clean_bitcoin), sin modificar la base original. <br>\n",
    "\n",
    "-Para la limpieza, se utilizarán los atributos(Columnas): Datetime, Close y Volume. <br>\n",
    "<blockquote>\n",
    "        -<strong>Datetime</strong>: Es el índice del dataframe, y contiene la fecha y hora del intervalo seleccionado, no puede estar duplicado.<br>\n",
    "        -<strong>Close</strong>: Contiene el precio del Bitcoin al final del intervalo.<br>\n",
    "        -<strong>Volume</strong>: Contiene el total de transacciones hechas durante el intervalo.<br>\n",
    "</blockquote>\n",
    "\n",
    "-Identificar duplicados en el atributo \"Datetime\" y tratarlos para almacenar sólo índices únicos. <br>\n",
    "\n",
    "-Buscar valores nulos en la columna \"Close\" y trátarlos.<br>\n",
    "\n",
    "-Verificar que todos los registros de la base tengan un Volumen(Volume) de transacción mayor a 0, caso contrário, elimínarlos.<br>\n",
    "\n",
    "-identificar y eliminar los outliers en el precio del Bitcoin(\"Close\"), utilizando un gráfico de boxplot para identificarlos.<br>\n",
    "\n",
    "-Filtrar o seleccionar únicamente los registros cuyo precio(Close) se encuentre entre el 1er cuartil(Q1) y el 3er cuartil(Q3) del boxplot generado.<br>\n",
    "\n",
    "-Finalmente, calcular el precio promedio de la columna \"Close\" y guardaremos el valor en la variable bitcoin_mean.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_cleaning():\n",
    "    global df_clean_bitcoin, bitcoin_mean\n",
    "    \n",
    "    # Creamos un DF con la copia de los valores obtenidos con la función copy().\n",
    "    df_clean_bitcoin = df_bitcoin.copy()\n",
    "    df_clean_bitcoin = df_clean_bitcoin.reset_index()\n",
    "\n",
    "    # Limpieza de datos duplicados en el atributo \"Datetime\"\n",
    "    df_clean_bitcoin = df_clean_bitcoin.drop_duplicates(subset=[\"Datetime\"])\n",
    "\n",
    "    # Eliminación de valores nulos del atributo \"Close\"\n",
    "    df_clean_bitcoin = df_clean_bitcoin[df_clean_bitcoin.Close.notnull()]\n",
    "\n",
    "    # Obtenemos solo aquellos valores del atributo \"Volume\" que sean mayores a \"0\"\n",
    "    df_clean_bitcoin = df_clean_bitcoin[df_clean_bitcoin[\"Volume\"]>0]\n",
    "    \n",
    "\n",
    "    # Filtrar o seleccionar únicamente los registros cuyo precio(\"Close\") se \n",
    "    # encuentren entre el 1er cuartil(Q1) y el 3er cuartil(Q3)\n",
    "    valor = df_clean_bitcoin[\"Close\"]\n",
    "    Q1 = valor.quantile(.25)\n",
    "    Q3 = valor.quantile(.75)\n",
    "    seleccion = (valor >= Q1) & (valor <= Q3)\n",
    "    df_clean_bitcoin = df_clean_bitcoin[seleccion]\n",
    "    df_clean_bitcoin = df_clean_bitcoin.reset_index(drop=True)\n",
    "\n",
    "    # Calculo de la media del atributo \"Close\"\n",
    "    bitcoin_mean = df_clean_bitcoin[\"Close\"].mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez que hayamos desarrollado la función *data_cleaning()*, que obtendrá nuestros valores limpios y tratados, procedemos a validar la información. <br>\n",
    "<br>\n",
    "Ejecutamos nuestra función, y mostramos el valor de nuestras variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor promedio de cierre es: 34300.27028245192\n"
     ]
    }
   ],
   "source": [
    "# Ejecución de la función de limpieza de datos\n",
    "data_cleaning()\n",
    "\n",
    "# Impresión de variables resultantes\n",
    "print(\"El valor promedio de cierre es: \"+str(bitcoin_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez validadas nuestras variables, mostramos nuestro DataFrame con los valores limpios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Datetime</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-10-23 22:40:00+00:00</td>\n",
       "      <td>32379.609375</td>\n",
       "      <td>34370.437500</td>\n",
       "      <td>32379.609375</td>\n",
       "      <td>34321.128906</td>\n",
       "      <td>34321.128906</td>\n",
       "      <td>1552650240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-10-24 01:55:00+00:00</td>\n",
       "      <td>33989.128906</td>\n",
       "      <td>34147.531250</td>\n",
       "      <td>33987.808594</td>\n",
       "      <td>34147.531250</td>\n",
       "      <td>34147.531250</td>\n",
       "      <td>167407616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-24 02:00:00+00:00</td>\n",
       "      <td>34166.652344</td>\n",
       "      <td>34354.492188</td>\n",
       "      <td>34166.652344</td>\n",
       "      <td>34354.492188</td>\n",
       "      <td>34354.492188</td>\n",
       "      <td>254320640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-24 02:05:00+00:00</td>\n",
       "      <td>34437.867188</td>\n",
       "      <td>34437.867188</td>\n",
       "      <td>34160.183594</td>\n",
       "      <td>34371.335938</td>\n",
       "      <td>34371.335938</td>\n",
       "      <td>242442240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-24 02:10:00+00:00</td>\n",
       "      <td>34424.980469</td>\n",
       "      <td>34486.449219</td>\n",
       "      <td>34388.628906</td>\n",
       "      <td>34486.449219</td>\n",
       "      <td>34486.449219</td>\n",
       "      <td>314904576</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>437</th>\n",
       "      <td>2023-10-30 17:25:00+00:00</td>\n",
       "      <td>34204.214844</td>\n",
       "      <td>34247.484375</td>\n",
       "      <td>34170.261719</td>\n",
       "      <td>34247.484375</td>\n",
       "      <td>34247.484375</td>\n",
       "      <td>181274624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2023-10-30 17:30:00+00:00</td>\n",
       "      <td>34271.574219</td>\n",
       "      <td>34272.652344</td>\n",
       "      <td>34255.394531</td>\n",
       "      <td>34255.394531</td>\n",
       "      <td>34255.394531</td>\n",
       "      <td>95356928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2023-10-30 19:15:00+00:00</td>\n",
       "      <td>34342.714844</td>\n",
       "      <td>34356.984375</td>\n",
       "      <td>34340.988281</td>\n",
       "      <td>34340.988281</td>\n",
       "      <td>34340.988281</td>\n",
       "      <td>23675904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>2023-10-30 19:20:00+00:00</td>\n",
       "      <td>34339.535156</td>\n",
       "      <td>34353.960938</td>\n",
       "      <td>34339.535156</td>\n",
       "      <td>34353.308594</td>\n",
       "      <td>34353.308594</td>\n",
       "      <td>3212288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>2023-10-30 19:25:00+00:00</td>\n",
       "      <td>34364.179688</td>\n",
       "      <td>34418.546875</td>\n",
       "      <td>34364.179688</td>\n",
       "      <td>34418.546875</td>\n",
       "      <td>34418.546875</td>\n",
       "      <td>34274304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Datetime          Open          High           Low  \\\n",
       "0   2023-10-23 22:40:00+00:00  32379.609375  34370.437500  32379.609375   \n",
       "1   2023-10-24 01:55:00+00:00  33989.128906  34147.531250  33987.808594   \n",
       "2   2023-10-24 02:00:00+00:00  34166.652344  34354.492188  34166.652344   \n",
       "3   2023-10-24 02:05:00+00:00  34437.867188  34437.867188  34160.183594   \n",
       "4   2023-10-24 02:10:00+00:00  34424.980469  34486.449219  34388.628906   \n",
       "..                        ...           ...           ...           ...   \n",
       "437 2023-10-30 17:25:00+00:00  34204.214844  34247.484375  34170.261719   \n",
       "438 2023-10-30 17:30:00+00:00  34271.574219  34272.652344  34255.394531   \n",
       "439 2023-10-30 19:15:00+00:00  34342.714844  34356.984375  34340.988281   \n",
       "440 2023-10-30 19:20:00+00:00  34339.535156  34353.960938  34339.535156   \n",
       "441 2023-10-30 19:25:00+00:00  34364.179688  34418.546875  34364.179688   \n",
       "\n",
       "            Close     Adj Close      Volume  \n",
       "0    34321.128906  34321.128906  1552650240  \n",
       "1    34147.531250  34147.531250   167407616  \n",
       "2    34354.492188  34354.492188   254320640  \n",
       "3    34371.335938  34371.335938   242442240  \n",
       "4    34486.449219  34486.449219   314904576  \n",
       "..            ...           ...         ...  \n",
       "437  34247.484375  34247.484375   181274624  \n",
       "438  34255.394531  34255.394531    95356928  \n",
       "439  34340.988281  34340.988281    23675904  \n",
       "440  34353.308594  34353.308594     3212288  \n",
       "441  34418.546875  34418.546875    34274304  \n",
       "\n",
       "[442 rows x 7 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impresión de DataFrame resultante de la limpieza de datos\n",
    "df_clean_bitcoin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1- Toma de decisiones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Con la obtención de datos confiables por medio de la limpieza, podremos continuar para generar la decisión acerca de que hacer con respecto al precio del Bitcoin y su tendencia basados en los 3 escenarios siguientes:\n",
    "\n",
    "1.- Sí el precio actual es mayor/igual que el precio promedio y la tendencia es de baja, entonces guardamos la instrucción: ‘Vender’.<br>\n",
    "2.- Sí el precio actual es menor que el precio promedio y la tendencia es de alta, entonces guardamos la instrucción: ‘Comprar’.<br>\n",
    "3.- Sí ninguna de las 2 condiciones anteriores se cumple, entonces guardamos la instrucción: 'Esperar'.<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decision():\n",
    "    global decision_algorithm, marker, marker_color\n",
    "\n",
    "    # Ciclo para asignar la decisión con base en 2 resultados:\n",
    "    # la media del cierre(Close) de la moneda y su tendencia(trend).\n",
    "    \n",
    "    if actual_price >= bitcoin_mean and trend == \"Low\":\n",
    "        decision_algorithm = \"Sell\"\n",
    "        marker = \"v\"\n",
    "        marker_color = \"Red\"\n",
    "    elif actual_price < bitcoin_mean and trend == \"High\":\n",
    "        decision_algorithm = \"Buy\"\n",
    "        marker = \"^\"\n",
    "        marker_color = \"Black\"\n",
    "    else:\n",
    "        decision_algorithm = \"Wait\"\n",
    "        marker = \">\"\n",
    "        marker_color = \"Purple\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuamos con la prueba de nuestra función *decision()*, validando que resulte entre alguno de los 3 posibles escenarios: **Sell**, **Buy** y **Wait**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La decisión resultante fue: Sell\n",
      "Nuestra tendencia tiene dirección: v\n",
      "El color decidido fue: Red\n"
     ]
    }
   ],
   "source": [
    "# Ejecución de nuestra función de decision de tendencia\n",
    "decision()\n",
    "\n",
    "# Se realiza la impresión de las variables esperadas, para validar su información\n",
    "print(\"La decisión resultante fue: \"+decision_algorithm)  # Decisión resultante\n",
    "print(\"Nuestra tendencia tiene dirección: \"+marker)       # Tendencia\n",
    "print(\"El color decidido fue: \"+marker_color)             # Color para representar la tendencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1- Visualización"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez hayamos realizado todos nuestros procesos de limpieza, así como los procesos de decisión, utilizaremos la información obtenida para desarrollar un gráfico en el cual se muestre la tendencía de los valores del Bitcoin durante el transcurso de los ultimos 7 días."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_visualization():\n",
    "    global df_bitGraph\n",
    "\n",
    "    # Generamos una DataFrame para trabajar la representación gráfica.\n",
    "    df_bitGraph = df_bitcoin.copy()\n",
    "\n",
    "    # Añadimos una columna que contenga el valor promedio.\n",
    "    df_bitGraph[\"Average\"] = bitcoin_mean\n",
    "\n",
    "    # Creamos el lienzo de nuestra gráfica.\n",
    "    plt.rc(\"figure\", figsize = (16,5))\n",
    "\n",
    "    # Graficamos el valor de cierre, con relación al índice.\n",
    "    plt.plot(df_bitGraph.index, df_bitcoin[\"Close\"], label=\"Close\", color = \"green\")\n",
    "\n",
    "    # Ahora graficamos el promedio, igualmente versus el índice.\n",
    "    plt.plot(df_bitGraph.index, df_bitGraph[\"Average\"], label=\"Average\", color=\"black\", linestyle = \"--\")\n",
    "\n",
    "    # Agregamos una representación distinta al último valor obtenido.\n",
    "    # Obtenemos el último valor del índice\n",
    "    last_index = df_bitGraph.index[-1]\n",
    "\n",
    "    # Último valor en la columna de cierre de precio.\n",
    "    last_close = df_bitGraph['Close'].iloc[-1]\n",
    "\n",
    "    # Graficamos el último punto, con una forma y orientación distinta así como su color según rendencia.\n",
    "    plt.scatter(last_index, last_close, color=marker_color, marker=marker, s=70)\n",
    "\n",
    "\n",
    "    # PARTE VISUAL DEL GRÁFICO.\n",
    "\n",
    "    # Aquí eliminamos las líneas del borde del gráfico\n",
    "    ax = plt.gca()\n",
    "    ax.spines[\"right\"].set_color(\"None\")\n",
    "    ax.spines[\"top\"].set_color(\"None\")\n",
    "    ax.spines[\"bottom\"].set_color(\"None\")\n",
    "    ax.spines[\"left\"].set_color(\"None\")\n",
    "\n",
    "    #Visualizamos la cuadrícula del gráfico.\n",
    "    plt.grid(visible=True)\n",
    "    #Actualizamos el título.\n",
    "    plt.title(\"Bitcoin Price Analysis and Trading Trends\")\n",
    "    #Actualizamos el nombre del eje X.\n",
    "    plt.xlabel('Date (YYYY-MM-DD)', fontdict={\"weight\":\"bold\"})\n",
    "    #Actualizamos el nombre del eje y. \n",
    "    plt.ylabel('Closing price (USD)', fontdict={\"weight\":\"bold\"})\n",
    "    #Genramos la leyenda y ajustamos su posición.\n",
    "    plt.legend(loc=\"lower right\") \n",
    "\n",
    "    #Ahora mostramos en el gráfico cual fue la decisión resultante de nuestro algoritmo para esta muestra de datos.\n",
    "    plt.annotate(\"Decision: \"+decision_algorithm, xy=(.85, 0.5), xycoords='axes fraction', fontsize=16, color='purple', weight = \"bold\")\n",
    "\n",
    "    #Y graficamos.\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez tengamos nuestra función *data_visualization()* procedemos a realizar la ejecución de la misma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Realizamos la ejecución de la función de visualización\n",
    "data_visualization()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1- Automatización"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para finalizar con nuestro análisis, desarrollaremos la solución para que se ejecute de manera automática con ayuda de algunas librerias como lo son: <br>\n",
    "**IPython.display** y **Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output #Libreria necesaria para actualizar limpiar los resultados de pantalla\n",
    "import time                             #Utilizado para funciones referente a tiempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez que tenemos todas nuestras funciones estructuradas y validadas, procederemos a crear un ciclo infinito, en el cual se harán intervalos de 5 minutos para volver a correr la instrucción que obtendrá toda la información necesaria a traves de las funciones y generá un gráfico actualizado en cada ciclo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "while(True):\n",
    "    clear_output()            #Función para limpiar la gráfica y generar una nueva.\n",
    "    import_bitcoin_database() #Importamos los datos desde yfinance.\n",
    "    trends_extract()          #WebScraping de página web.\n",
    "    data_cleaning()           #Limpieza de datos\n",
    "    decision()                #Algoritmo de decisión\n",
    "    data_visualization()      #Representación gráfica de la información\n",
    "    time.sleep(300)           #Tiempo de espera antes de volver a iniciar el ciclo\n",
    "  "
   ]
  }
 ],
 "metadata": {
  "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}