Robot_Trading_C1_DSA.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/devjuniorflores/Robot_Trading_C1_DS_AluraLatam/blob/main/Robot_Trading_C1_DSA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwIHHDS8hQfO"
      },
      "source": [
        "#1. Creando el Ambiente"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKDF96gDJUI9"
      },
      "source": [
        "## Bibliotecas requeridas para el desarrollo del proyecto"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUwPIgvVMsnv"
      },
      "source": [
        "**Pandas**: Esta librería será utilizada para estructurar y analizar los datos financieros del Bitcoin. Ayudará en la manipulación de la información recopilada, permitiendo limpiar y preparar los datos para su análisis.\n",
        "\n",
        "**Matplotlib**: Se empleará para visualizar gráficamente los datos del Bitcoin, generando representaciones visuales de la información recopilada. Esto incluirá la creación de gráficos que representen la evolución de precios y tendencias a lo largo del tiempo.\n",
        "\n",
        "**NumPy**: Será empleado para realizar cálculos numéricos y operaciones matemáticas sobre los datos financieros del Bitcoin. Ayudará en la manipulación eficiente de arreglos y datos numéricos.\n",
        "\n",
        "**yfinance**: Esta interfaz será utilizada para obtener datos actualizados de Yahoo Finance relacionados con el Bitcoin, permitiendo acceder a información detallada sobre los precios, volúmenes y otras métricas financieras.\n",
        "\n",
        "**BeautifulSoup** (bs4): Se utilizará para analizar y extraer datos relevantes del HTML y XML de diversas fuentes, como páginas web financieras. Esto permitirá extraer información específica sobre el Bitcoin desde estas fuentes.\n",
        "\n",
        "**Time**: Este módulo se empleará para realizar operaciones que involucren temporización y programación de intervalos, como la espera entre actualizaciones de datos o ejecución de tareas periódicas.\n",
        "\n",
        "**DateTime**: Utilizaremos este módulo para manejar y manipular fechas y horas relacionadas con los datos financieros del Bitcoin, permitiendo realizar análisis de series temporales y visualizaciones por fechas.\n",
        "\n",
        "**Requests**: Esta librería será empleada para realizar solicitudes HTTP, permitiendo acceder a datos financieros desde fuentes externas como APIs o páginas web.\n",
        "\n",
        "**Urllib.request**: Se utilizará para abrir y acceder a URL, lo que permitirá la conexión con diferentes fuentes de información en la web, facilitando la obtención de datos del Bitcoin desde distintas fuentes en línea.\n",
        "\n",
        "IPython.display: Este paquete se empleará para mostrar contenido multimedia y personalizar la presentación de datos y gráficos generados, especialmente en entornos como Jupyter Notebook. Esto mejorará la presentación y comprensión de la información financiera del Bitcoin."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rWHszc7EjbEi"
      },
      "source": [
        "## Instalación de las bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jUao4dnNlPUX"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "import bs4\n",
        "import time\n",
        "import datetime\n",
        "import requests\n",
        "import urllib.request as urllib_request\n",
        "from IPython.display import clear_output\n",
        "from bs4 import BeautifulSoup\n",
        "from urllib.request import urlopen, Request"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZiW5ZTC1MWlw"
      },
      "source": [
        "## Descripcion de las bibliotecas requeridas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elxWMZ7xha7M",
        "outputId": "22e515e4-8215-4555-f10a-e1cab48f221f"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "Python 3.10.12\n",
            "------\n",
            "Name: pandas\n",
            "Version: 1.5.3\n",
            "------\n",
            "Name: numpy\n",
            "Version: 1.23.5\n",
            "------\n",
            "Name: matplotlib\n",
            "Version: 3.7.1\n",
            "------\n",
            "Name: yfinance\n",
            "Version: 0.2.31\n",
            "------\n",
            "Name: beautifulsoup4\n",
            "Version: 4.11.2\n",
            "------\n"
          ]
        }
      ],
      "source": [
        "!python -V\n",
        "print('------')\n",
        "!pip show Pandas | grep 'Name\\|Version'\n",
        "print('------')\n",
        "!pip show Numpy | grep 'Name\\|Version'\n",
        "print('------')\n",
        "!pip show Matplotlib | grep 'Name\\|Version'\n",
        "print('------')\n",
        "!pip show yfinance | grep 'Name\\|Version'\n",
        "print('------')\n",
        "!pip show beautifulsoup4 | grep 'Name\\|Version'\n",
        "print('------')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzbt4PDRjDi7"
      },
      "source": [
        "#2. Recolección de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swJ8If_8j8xR"
      },
      "source": [
        "## 2.1.Precios historicos del bitcoin\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pt_tIgzLjybp"
      },
      "source": [
        "### Obtener los datos del Bitcoin de los ultimos 7 dias en intervalos de cada 5 minutos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sDAEFrcFqKvR"
      },
      "outputs": [],
      "source": [
        "def importar_base_bitcoin(): #Creando funcion para la recoleccion de los datos historicos del Bitcoin\n",
        "  global df_bitcoin #Creando la variable global df_bitcoin\n",
        "  btc = yf.Ticker(\"BTC-USD\") #Indicando el ticket a utilizar (BTC-USD: Bitcoin)\n",
        "  df_bitcoin = btc.history(period=\"8d\", interval=\"5m\") #Alojando en una variable global \"df_bitcoin\" los datos requeridos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdL7FVYa31A9"
      },
      "source": [
        "## Precio y tendencia actual del Bitcoin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIZGJUrk38tN"
      },
      "source": [
        "### Extrayendo desde ***CoinMarketCap.com***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "55cZtrQvmmdV"
      },
      "outputs": [],
      "source": [
        "def extraer_tendencias(): #Definiendo funcion para la extraccion del precio actual y tendencia del Bitcoin\n",
        "  global precio_actual, tendencia  # Declaración de variables globales para precio y tendencia\n",
        "  url = \"https://coinmarketcap.com/\"  # URL de la página a analizar\n",
        "\n",
        "  req = requests.get(url)  # Realizar una solicitud GET para obtener la página\n",
        "  response = req.text  # Obtener el contenido HTML de la página\n",
        "  soup = BeautifulSoup(response, \"html.parser\")  # Crear un objeto BeautifulSoup para analizar el HTML\n",
        "\n",
        "  tbody = soup.find(\"tbody\")  # Encontrar la sección (tbody) que contiene los datos relevantes\n",
        "  # Extraer el precio actual de la criptomoneda (BTC)\n",
        "  td_precio_actual = tbody.tr.findAll(\"td\")[3]\n",
        "  precio_actual = float(td_precio_actual.find(\"span\").getText().replace(\"$\", '').replace(',', '').replace('USD', ''))\n",
        "\n",
        "  # Determinar la tendencia actual (Alza o Baja)\n",
        "  td_tendencia_actual = tbody.tr.findAll(\"td\")[4]\n",
        "  indicador = td_tendencia_actual.find('span', class_=\"icon-Caret-down\")  # Buscar el indicador de tendencia\n",
        "  if indicador is None:  # Si no se encuentra el indicador de baja\n",
        "      tendencia = \"Alta\"  # La tendencia es \"Alta\" (alza)\n",
        "  else:\n",
        "      tendencia = \"Baja\"  # Si se encuentra el indicador de baja, la tendencia es \"Baja\" (bajada)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wT_lnVoeV4Fq"
      },
      "source": [
        "### Extrayendo desde _**Cripto.com**_ (Respaldo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        "outputId": "c3c446c6-b05b-4789-bbaa-f1bf04c213ac"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34537.96"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "url_crypto = \"https://crypto.com/price/bitcoin\"  # URL de la página de la criptomoneda Bitcoin en crypto.com\n",
        "\n",
        "# Encabezados simulando ser un navegador web para evitar posibles restricciones de acceso\n",
        "headers = {\n",
        "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36'\n",
        "}\n",
        "\n",
        "# Creación de una solicitud con los encabezados definidos\n",
        "req = Request(url_crypto, headers=headers)\n",
        "response_crypto = urlopen(req)  # Realización de la solicitud y obtención de la respuesta\n",
        "html_crypto = response_crypto.read()  # Lectura del contenido HTML de la página\n",
        "\n",
        "soup_crypto = BeautifulSoup(html_crypto, \"html.parser\")  # Creación de un objeto BeautifulSoup para analizar el HTML\n",
        "\n",
        "# Extracción del precio actual de Bitcoin desde el HTML\n",
        "div_precio_actual = soup_crypto.find(\"h2\", class_=\"chakra-heading css-fophx6\")\n",
        "precio_actual = float(div_precio_actual.find(\"span\").getText().replace(\"$\", '').replace(',', '').replace('USD', ''))\n",
        "\n",
        "precio_actual  # Devuelve el precio actual de Bitcoin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYBs3z3S4fYt"
      },
      "source": [
        "#3. Limpieza de Datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vCHzfouNbFf"
      },
      "source": [
        "## 3.1.Entendiendo el dataframe y sus campos mas importantes a tomar en cuenta"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rfr1C7cjQPLJ"
      },
      "source": [
        "**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.\n",
        "\n",
        "**High**: Contiene el precio más alto del Bitcoin durante el intervalo.\n",
        "\n",
        "**Low**: Contiene el precio más bajo del Bitcoin durante el intervalo.\n",
        "\n",
        "**Close**: Contiene el precio del Bitcoin al final del intervalo.\n",
        "\n",
        "**Volume**: Contiene el total de transacciones hechas durante el intervalo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dV63EIdQQrn_"
      },
      "source": [
        "### Mostrar datos estadísticos del dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "fHshqlaYs_XZ",
        "outputId": "2368adc1-b4ad-4ba4-ed16-827574785429"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1979 entries, 2023-10-25 00:00:00+00:00 to 2023-11-01 01:00:00+00:00\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Open          1979 non-null   float64\n",
            " 1   High          1979 non-null   float64\n",
            " 2   Low           1979 non-null   float64\n",
            " 3   Close         1979 non-null   float64\n",
            " 4   Volume        1979 non-null   int64  \n",
            " 5   Dividends     1979 non-null   float64\n",
            " 6   Stock Splits  1979 non-null   float64\n",
            "dtypes: float64(6), int64(1)\n",
            "memory usage: 123.7 KB\n"
          ]
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              "          Open     High      Low    Close       Volume  Dividends  \\\n",
              "count   1979.0   1979.0   1979.0   1979.0       1979.0     1979.0   \n",
              "mean   34268.0  34284.0  34253.0  34269.0   13203143.0        0.0   \n",
              "std      264.0    265.0    261.0    263.0   39513643.0        0.0   \n",
              "min    33451.0  33474.0  33417.0  33455.0          0.0        0.0   \n",
              "25%    34084.0  34092.0  34074.0  34085.0          0.0        0.0   \n",
              "50%    34234.0  34254.0  34214.0  34232.0          0.0        0.0   \n",
              "75%    34479.0  34493.0  34467.0  34480.0   12368384.0        0.0   \n",
              "max    35116.0  35134.0  34951.0  35044.0  718720000.0        0.0   \n",
              "\n",
              "       Stock Splits  \n",
              "count        1979.0  \n",
              "mean            0.0  \n",
              "std             0.0  \n",
              "min             0.0  \n",
              "25%             0.0  \n",
              "50%             0.0  \n",
              "75%             0.0  \n",
              "max             0.0  "
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              "    <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",
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              "      <th>Dividends</th>\n",
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              "      <th>count</th>\n",
              "      <td>1979.0</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>1979.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>34268.0</td>\n",
              "      <td>34284.0</td>\n",
              "      <td>34253.0</td>\n",
              "      <td>34269.0</td>\n",
              "      <td>13203143.0</td>\n",
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              "      <th>std</th>\n",
              "      <td>264.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>39513643.0</td>\n",
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              "      <th>min</th>\n",
              "      <td>33451.0</td>\n",
              "      <td>33474.0</td>\n",
              "      <td>33417.0</td>\n",
              "      <td>33455.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>25%</th>\n",
              "      <td>34084.0</td>\n",
              "      <td>34092.0</td>\n",
              "      <td>34074.0</td>\n",
              "      <td>34085.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>34234.0</td>\n",
              "      <td>34254.0</td>\n",
              "      <td>34214.0</td>\n",
              "      <td>34232.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>75%</th>\n",
              "      <td>34479.0</td>\n",
              "      <td>34493.0</td>\n",
              "      <td>34467.0</td>\n",
              "      <td>34480.0</td>\n",
              "      <td>12368384.0</td>\n",
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              "      <td>35116.0</td>\n",
              "      <td>35134.0</td>\n",
              "      <td>34951.0</td>\n",
              "      <td>35044.0</td>\n",
              "      <td>718720000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-839f4e62-e43f-4aaf-8e1d-2d1bc5c9f2e7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9acd72aa-b253-47cc-b151-5b1629492d0b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9acd72aa-b253-47cc-b151-5b1629492d0b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9acd72aa-b253-47cc-b151-5b1629492d0b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "importar_base_bitcoin()\n",
        "df_bitcoin.info()\n",
        "df_bitcoin.describe().round(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VB32NxYtWjY"
      },
      "source": [
        "###Mostrar un boxplot sin tratar aún los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "V3EKcp1otWIL",
        "outputId": "d5cc73ef-112b-471b-8f15-c1e903eca999"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df_bitcoin.boxplot(['Close'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMPncKhTKcCp"
      },
      "source": [
        "## 3.2.Limpieza de Datos paso a paso"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sykq9DlXQSkq"
      },
      "source": [
        "###Creando una copia del dataframe para no trabajar sobre esta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0sDQKXAt4htp",
        "outputId": "92bf0281-bf3a-40e3-c756-b42fdc3741c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Open          High           Low  \\\n",
              "Datetime                                                              \n",
              "2023-10-25 00:00:00+00:00  33916.042969  33941.207031  33916.042969   \n",
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              "2023-11-01 01:00:00+00:00  34524.902344  34534.761719  34524.902344   \n",
              "\n",
              "                                  Close     Volume  Dividends  Stock Splits  \n",
              "Datetime                                                                     \n",
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              "\n",
              "[1979 rows x 7 columns]"
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              "      <th>2023-11-01 00:50:00+00:00</th>\n",
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              "<p>1979 rows × 7 columns</p>\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-54036fb8-d6d1-4636-89c1-312b4613e6af button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-54036fb8-d6d1-4636-89c1-312b4613e6af');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9565dc66-0ade-4680-b7a5-40b69fcd3677 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df_bitcoin_limpio = df_bitcoin\n",
        "df_bitcoin_limpio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JqaYojSxN0Ac"
      },
      "source": [
        "###Eliminando campos no relevantes para el análisis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zR-fTlSnNiZv",
        "outputId": "f3c64b04-7efd-402b-d33b-30a66ae4bd96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Open          High           Low  \\\n",
              "Datetime                                                              \n",
              "2023-10-25 00:00:00+00:00  33916.042969  33941.207031  33916.042969   \n",
              "2023-10-25 00:05:00+00:00  33938.441406  33942.093750  33914.734375   \n",
              "2023-10-25 00:10:00+00:00  33935.175781  33950.281250  33886.140625   \n",
              "2023-10-25 00:15:00+00:00  33852.980469  33910.554688  33852.980469   \n",
              "2023-10-25 00:20:00+00:00  33933.507812  33934.132812  33913.386719   \n",
              "...                                 ...           ...           ...   \n",
              "2023-11-01 00:40:00+00:00  34599.437500  34599.437500  34532.910156   \n",
              "2023-11-01 00:45:00+00:00  34528.011719  34560.507812  34528.011719   \n",
              "2023-11-01 00:50:00+00:00  34552.320312  34555.968750  34527.500000   \n",
              "2023-11-01 00:55:00+00:00  34524.503906  34533.667969  34524.503906   \n",
              "2023-11-01 01:00:00+00:00  34524.902344  34534.761719  34524.902344   \n",
              "\n",
              "                                  Close     Volume  \n",
              "Datetime                                            \n",
              "2023-10-25 00:00:00+00:00  33941.207031          0  \n",
              "2023-10-25 00:05:00+00:00  33936.296875          0  \n",
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              "2023-10-25 00:20:00+00:00  33913.386719          0  \n",
              "...                                 ...        ...  \n",
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              "2023-11-01 01:00:00+00:00  34534.761719          0  \n",
              "\n",
              "[1979 rows x 5 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df_bitcoin_limpio = df_bitcoin_limpio.drop(['Dividends','Stock Splits'], axis=1)\n",
        "df_bitcoin_limpio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwp59dZ9rq3z"
      },
      "source": [
        "###Eliminando los duplicados en el indice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9RlvPudorxbI",
        "outputId": "7718146f-9743-46c6-9f78-698afc2e882f"
      },
      "outputs": [
        {
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              "                                   Open          High           Low  \\\n",
              "Datetime                                                              \n",
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              "      <th>2023-11-01 00:50:00+00:00</th>\n",
              "      <td>34552.320312</td>\n",
              "      <td>34555.968750</td>\n",
              "      <td>34527.500000</td>\n",
              "      <td>34527.500000</td>\n",
              "      <td>5731328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-11-01 00:55:00+00:00</th>\n",
              "      <td>34524.503906</td>\n",
              "      <td>34533.667969</td>\n",
              "      <td>34524.503906</td>\n",
              "      <td>34526.164062</td>\n",
              "      <td>2027520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-11-01 01:00:00+00:00</th>\n",
              "      <td>34524.902344</td>\n",
              "      <td>34534.761719</td>\n",
              "      <td>34524.902344</td>\n",
              "      <td>34534.761719</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1979 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e41d5e26-34ac-428b-9887-eefffdd0c0a9')\"\n",
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              "    </button>\n",
              "\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-e41d5e26-34ac-428b-9887-eefffdd0c0a9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e41d5e26-34ac-428b-9887-eefffdd0c0a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2f087808-7294-4980-906b-b405e0306d68 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df_bitcoin_limpio.drop_duplicates(inplace=True)\n",
        "df_bitcoin_limpio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YaVlfojxS2l"
      },
      "source": [
        "###Buscando valores nulos en la columna 'Close' para posteriormente tratarlos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "alVxw6h-xYG8",
        "outputId": "a317f431-6c50-413c-a6af-02a1c6fd64c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Open, High, Low, Close, Volume]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-24051c75-ab24-47aa-9de7-7fb5cc993e9b\" class=\"colab-df-container\">\n",
              "    <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",
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              "        text-align: right;\n",
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              "</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>Volume</th>\n",
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              "    <tr>\n",
              "      <th>Datetime</th>\n",
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              "      <th></th>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-24051c75-ab24-47aa-9de7-7fb5cc993e9b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-24051c75-ab24-47aa-9de7-7fb5cc993e9b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df_bitcoin_limpio[df_bitcoin_limpio['Close'].isnull()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE3bPEEbQ9Vp"
      },
      "source": [
        "###Eliminando valores nulos del dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adnnXOGXx8dF",
        "outputId": "81639899-18ac-40f0-de54-5581a02320bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Se han eliminado 0 filas\n"
          ]
        }
      ],
      "source": [
        "a = df_bitcoin_limpio.shape[0]\n",
        "df_bitcoin_limpio.dropna(subset = ['Close'], inplace=True)\n",
        "b = df_bitcoin_limpio.shape[0]\n",
        "print(f\"Se han eliminado {a-b} filas\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KHFLEXKNyt2H"
      },
      "source": [
        "###Verificando que todos los registros de la base tengan un volumen de transacción mayor a 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "bEnNQxbf4RJD"
      },
      "outputs": [],
      "source": [
        "seleccion = (df_bitcoin_limpio['Volume']==0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xtoFGJ_I5tAA",
        "outputId": "0e5dae58-d78e-47aa-f7f3-efa90f3215db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Open          High           Low  \\\n",
              "Datetime                                                              \n",
              "2023-10-25 00:15:00+00:00  33852.980469  33910.554688  33852.980469   \n",
              "2023-10-25 07:45:00+00:00  33816.480469  33822.468750  33709.109375   \n",
              "2023-10-25 07:50:00+00:00  33803.085938  33868.492188  33803.085938   \n",
              "2023-10-25 09:15:00+00:00  34063.812500  34071.816406  34047.765625   \n",
              "2023-10-25 09:20:00+00:00  34054.421875  34088.605469  34054.421875   \n",
              "...                                 ...           ...           ...   \n",
              "2023-11-01 00:35:00+00:00  34584.746094  34605.472656  34584.746094   \n",
              "2023-11-01 00:40:00+00:00  34599.437500  34599.437500  34532.910156   \n",
              "2023-11-01 00:45:00+00:00  34528.011719  34560.507812  34528.011719   \n",
              "2023-11-01 00:50:00+00:00  34552.320312  34555.968750  34527.500000   \n",
              "2023-11-01 00:55:00+00:00  34524.503906  34533.667969  34524.503906   \n",
              "\n",
              "                                  Close     Volume  \n",
              "Datetime                                            \n",
              "2023-10-25 00:15:00+00:00  33910.554688   16486400  \n",
              "2023-10-25 07:45:00+00:00  33822.468750   53430272  \n",
              "2023-10-25 07:50:00+00:00  33868.492188    8282112  \n",
              "2023-10-25 09:15:00+00:00  34047.765625    3244032  \n",
              "2023-10-25 09:20:00+00:00  34088.605469    2584576  \n",
              "...                                 ...        ...  \n",
              "2023-11-01 00:35:00+00:00  34603.691406   29455360  \n",
              "2023-11-01 00:40:00+00:00  34532.910156    5244928  \n",
              "2023-11-01 00:45:00+00:00  34560.507812  306705408  \n",
              "2023-11-01 00:50:00+00:00  34527.500000    5731328  \n",
              "2023-11-01 00:55:00+00:00  34526.164062    2027520  \n",
              "\n",
              "[815 rows x 5 columns]"
            ],
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2023-10-25 00:15:00+00:00</th>\n",
              "      <td>33852.980469</td>\n",
              "      <td>33910.554688</td>\n",
              "      <td>33852.980469</td>\n",
              "      <td>33910.554688</td>\n",
              "      <td>16486400</td>\n",
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              "    <tr>\n",
              "      <th>2023-10-25 07:45:00+00:00</th>\n",
              "      <td>33816.480469</td>\n",
              "      <td>33822.468750</td>\n",
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              "      <td>33822.468750</td>\n",
              "      <td>53430272</td>\n",
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              "    <tr>\n",
              "      <th>2023-10-25 07:50:00+00:00</th>\n",
              "      <td>33803.085938</td>\n",
              "      <td>33868.492188</td>\n",
              "      <td>33803.085938</td>\n",
              "      <td>33868.492188</td>\n",
              "      <td>8282112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-25 09:15:00+00:00</th>\n",
              "      <td>34063.812500</td>\n",
              "      <td>34071.816406</td>\n",
              "      <td>34047.765625</td>\n",
              "      <td>34047.765625</td>\n",
              "      <td>3244032</td>\n",
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              "    <tr>\n",
              "      <th>2023-10-25 09:20:00+00:00</th>\n",
              "      <td>34054.421875</td>\n",
              "      <td>34088.605469</td>\n",
              "      <td>34054.421875</td>\n",
              "      <td>34088.605469</td>\n",
              "      <td>2584576</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2023-11-01 00:35:00+00:00</th>\n",
              "      <td>34584.746094</td>\n",
              "      <td>34605.472656</td>\n",
              "      <td>34584.746094</td>\n",
              "      <td>34603.691406</td>\n",
              "      <td>29455360</td>\n",
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              "    <tr>\n",
              "      <th>2023-11-01 00:40:00+00:00</th>\n",
              "      <td>34599.437500</td>\n",
              "      <td>34599.437500</td>\n",
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              "      <td>34532.910156</td>\n",
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              "    <tr>\n",
              "      <th>2023-11-01 00:45:00+00:00</th>\n",
              "      <td>34528.011719</td>\n",
              "      <td>34560.507812</td>\n",
              "      <td>34528.011719</td>\n",
              "      <td>34560.507812</td>\n",
              "      <td>306705408</td>\n",
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              "    <tr>\n",
              "      <th>2023-11-01 00:50:00+00:00</th>\n",
              "      <td>34552.320312</td>\n",
              "      <td>34555.968750</td>\n",
              "      <td>34527.500000</td>\n",
              "      <td>34527.500000</td>\n",
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              "      <th>2023-11-01 00:55:00+00:00</th>\n",
              "      <td>34524.503906</td>\n",
              "      <td>34533.667969</td>\n",
              "      <td>34524.503906</td>\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df_bitcoin_limpio = df_bitcoin_limpio[~seleccion]\n",
        "df_bitcoin_limpio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFk6gVOaHN_i"
      },
      "source": [
        "###Filtrando o seleccionando únicamente los registros cuyo precio(Close) se encuentren entre el 1er cuartil(Q1) y el 3er cuartil(Q3) del boxplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxgogNRjE0y-",
        "outputId": "e34b19a8-5e65-4abc-cc87-db3f8cc73a16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34157.478515625 34559.36328125\n"
          ]
        }
      ],
      "source": [
        "close = df_bitcoin_limpio['Close']\n",
        "Q1 = close.quantile(.25)\n",
        "Q3 = close.quantile(.75)\n",
        "print(Q1,Q3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_kxP-7Q9SZd"
      },
      "source": [
        "###Identificando y eliminando los outliers en el precio del Bitcoin de la columna \"Close\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "emtMcMk3FVFv",
        "outputId": "36f2b486-fbe7-435c-8bc6-f5acc7b31c81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Open          High           Low  \\\n",
              "Datetime                                                              \n",
              "2023-10-25 00:15:00+00:00  33852.980469  33910.554688  33852.980469   \n",
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              "2023-11-01 00:55:00+00:00  34524.503906  34533.667969  34524.503906   \n",
              "\n",
              "                                  Close     Volume  \n",
              "Datetime                                            \n",
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              "2023-11-01 00:55:00+00:00  34526.164062    2027520  \n",
              "\n",
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              "      <td>34532.910156</td>\n",
              "      <td>34532.910156</td>\n",
              "      <td>5244928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-11-01 00:45:00+00:00</th>\n",
              "      <td>34528.011719</td>\n",
              "      <td>34560.507812</td>\n",
              "      <td>34528.011719</td>\n",
              "      <td>34560.507812</td>\n",
              "      <td>306705408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-11-01 00:50:00+00:00</th>\n",
              "      <td>34552.320312</td>\n",
              "      <td>34555.968750</td>\n",
              "      <td>34527.500000</td>\n",
              "      <td>34527.500000</td>\n",
              "      <td>5731328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-11-01 00:55:00+00:00</th>\n",
              "      <td>34524.503906</td>\n",
              "      <td>34533.667969</td>\n",
              "      <td>34524.503906</td>\n",
              "      <td>34526.164062</td>\n",
              "      <td>2027520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>815 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a55f5db4-2a56-4a9e-b5fd-8d98760bf264')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a55f5db4-2a56-4a9e-b5fd-8d98760bf264 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a55f5db4-2a56-4a9e-b5fd-8d98760bf264');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7761e2a4-3966-4f78-a1e6-f6d38fdeaada\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7761e2a4-3966-4f78-a1e6-f6d38fdeaada')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7761e2a4-3966-4f78-a1e6-f6d38fdeaada button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "seleccion = ((close>=Q1)&(close<=Q3))\n",
        "df_bitcoin_limpio_ = df_bitcoin_limpio[seleccion]\n",
        "df_bitcoin_limpio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxGW3I1QHxNt"
      },
      "source": [
        "###Calculando el precio promedio(Close) de esta selección y guárdandola en la variable \"media_bitcoin\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XC_75pO1H1sw",
        "outputId": "ba251946-146f-49e8-bf7f-850a25866693"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34342.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "media_bitcoin = df_bitcoin_limpio.Close.mean().round()\n",
        "media_bitcoin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_s3OjcNO2-E"
      },
      "source": [
        "## 3.3.Funcion Limpieza de Datos (Resumen pasos anteriores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "IsrdTrpUO5GX"
      },
      "outputs": [],
      "source": [
        "def limpieza_datos():\n",
        "  global media_bitcoin  # Declaración de variables globales para media_bitcoin y df_bitcoin_limpio\n",
        "  global df_bitcoin_limpio\n",
        "\n",
        "  df_bitcoin_limpio = df_bitcoin  # Asignación del DataFrame original a df_bitcoin_limpio\n",
        "\n",
        "  # Eliminación de columnas 'Dividends' y 'Stock Splits' del DataFrame df_bitcoin_limpio\n",
        "  df_bitcoin_limpio = df_bitcoin_limpio.drop(['Dividends', 'Stock Splits'], axis=1)\n",
        "\n",
        "  # Eliminar filas duplicadas en el DataFrame df_bitcoin_limpio\n",
        "  df_bitcoin_limpio.drop_duplicates(inplace=True)\n",
        "\n",
        "  # Eliminar filas con valores NaN en la columna 'Close' del DataFrame df_bitcoin_limpio\n",
        "  df_bitcoin_limpio.dropna(subset=['Close'], inplace=True)\n",
        "\n",
        "  # Crear una selección booleana para identificar las filas con volumen igual a cero en el DataFrame\n",
        "  seleccion_volume = (df_bitcoin_limpio['Volume'] == 0)\n",
        "\n",
        "  # Eliminar filas donde el volumen es igual a cero en el DataFrame df_bitcoin_limpio\n",
        "  df_bitcoin_limpio = df_bitcoin_limpio[~seleccion_volume]\n",
        "\n",
        "  # Calcular los percentiles (Q1 y Q3) de la columna 'Close' en el DataFrame df_bitcoin_limpio\n",
        "  close = df_bitcoin_limpio['Close']\n",
        "  Q1 = close.quantile(.25)\n",
        "  Q3 = close.quantile(.75)\n",
        "\n",
        "  # Crear una selección booleana para identificar valores dentro del rango intercuartílico (IQR)\n",
        "  seleccion = ((close >= Q1) & (close <= Q3))\n",
        "\n",
        "  # Filtrar el DataFrame df_bitcoin_limpio para mantener solo valores dentro del rango IQR\n",
        "  df_bitcoin_limpio = df_bitcoin_limpio[seleccion]\n",
        "\n",
        "  # Calcular la media de la columna 'Close' en el DataFrame df_bitcoin_limpio y redondear el resultado\n",
        "  media_bitcoin = df_bitcoin_limpio['Close'].mean().round()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8fZ7JmE_j_j"
      },
      "source": [
        "#4. Toma de Decisiones"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LP1cmu24R_VC"
      },
      "source": [
        "## 4.1.Generando la lógica de la toma de decisiones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "mafbf8bS_om5"
      },
      "outputs": [],
      "source": [
        "def tomar_decisiones():\n",
        "  global algoritmo_decision  # Declaración de variables globales para algoritmo_decision y color\n",
        "  global color\n",
        "\n",
        "  if (precio_actual >= media_bitcoin) and (tendencia == \"Baja\"):\n",
        "      # Si el precio actual es mayor o igual a la media y la tendencia es a la baja\n",
        "      color = \"red\"  # Se asigna el color rojo para representar una decisión de \"Vender\"\n",
        "      algoritmo_decision = \"vender\"  # Se establece la decisión del algoritmo como \"Vender\"\n",
        "\n",
        "  elif (precio_actual < media_bitcoin) and (tendencia == \"Alta\"):\n",
        "      # Si el precio actual es menor a la media y la tendencia es al alza\n",
        "      color = \"green\"  # Se asigna el color verde para representar una decisión de \"Comprar\"\n",
        "      algoritmo_decision = \"comprar\"  # Se establece la decisión del algoritmo como \"Comprar\"\n",
        "\n",
        "  else:\n",
        "      # En caso contrario (ninguna de las condiciones anteriores)\n",
        "      color = \"orange\"  # Se asigna el color naranja para representar una decisión de \"Esperar\"\n",
        "      algoritmo_decision = \"esperar\"  # Se establece la decisión del algoritmo como \"Esperar\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rK7KuNNPCNZY"
      },
      "source": [
        "#5. Visualización"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfqkDXQ4SgkT"
      },
      "source": [
        "## 5.1.Generando la visualización"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "TiCS_dQwChuN"
      },
      "outputs": [],
      "source": [
        "def visualizacion():\n",
        "  # Imprime la hora de ejecución del script\n",
        "  print(\"Hora de ejecución: \", datetime.datetime.now())\n",
        "\n",
        "  # Imprime el precio actual del Bitcoin, el precio medio, la tendencia y la decisión del algoritmo\n",
        "  print(\"Precio Actual Bitcoin: \", precio_actual)\n",
        "  print(\"Precio Medio Bitcoin: \", media_bitcoin)\n",
        "  print(\"Tendencia: \", tendencia)\n",
        "  print(\"Decisión: \", algoritmo_decision)\n",
        "\n",
        "  # Se agrega una nueva columna 'Promedio' en el DataFrame df_bitcoin con el valor de media_bitcoin\n",
        "  df_bitcoin['Promedio'] = media_bitcoin\n",
        "\n",
        "  # Se configuran los detalles del gráfico\n",
        "  plt.figure(figsize=(16, 5))\n",
        "  plt.title(\"Bitcoin Price ($)\", fontsize=18, fontweight='bold', color=\"#00DA59\")\n",
        "\n",
        "  # Se trazan las series de tiempo para 'Close' y 'Promedio' en el DataFrame df_bitcoin\n",
        "  plt.plot(df_bitcoin.index, df_bitcoin['Close'])  # Gráfico de precios 'Close'\n",
        "  plt.plot(df_bitcoin.index, df_bitcoin['Promedio'])  # Gráfico del promedio\n",
        "\n",
        "  # Se agrega un texto en el gráfico para representar la decisión con colores basados en la variable color\n",
        "  plt.annotate(str(\"Bitbot te recomienda \" + algoritmo_decision), xy=(df_bitcoin.index[-550], round(max(df_bitcoin['Close']))-100),\n",
        "                xytext=(df_bitcoin.index[-550], round(max(df_bitcoin['Close']))-100), fontsize=15, color=color, fontweight='bold',\n",
        "                style='italic', backgroundcolor='yellow')\n",
        "\n",
        "  # Se muestra el gráfico\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FcihHpjtRz3J"
      },
      "source": [
        "# 6. Automatización"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzpW8f-3SwLW"
      },
      "source": [
        "## 6.1.Creando la automatización del bot y su ejecución"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "d_R4KdBCR24i",
        "outputId": "ac397d98-623c-4809-e404-610bae0e3e59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hora de ejecución:  2023-11-01 01:15:13.562196\n",
            "Precio Actual Bitcoin:  34528.0\n",
            "Precio Medio Bitcoin:  34368.0\n",
            "Tendencia:  Baja\n",
            "Decisión:  vender\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "while True:\n",
        "    clear_output()  # Borra la salida anterior en un entorno\n",
        "\n",
        "    # Llama a las funciones necesarias para importar, procesar, analizar y visualizar los datos\n",
        "    importar_base_bitcoin()  # Importa datos actualizados del Bitcoin\n",
        "    extraer_tendencias()  # Extrae tendencias actuales\n",
        "    limpieza_datos()  # Realiza la limpieza y preparación de los datos\n",
        "    tomar_decisiones()  # Toma decisiones basadas en los datos procesados\n",
        "    visualizacion()  # Genera y muestra gráficos con los datos procesados\n",
        "\n",
        "    time.sleep(300)  # Espera 5 minutos antes de volver a ejecutar el ciclo"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "dV63EIdQQrn_",
        "3VB32NxYtWjY",
        "sykq9DlXQSkq",
        "JqaYojSxN0Ac",
        "hwp59dZ9rq3z",
        "8YaVlfojxS2l",
        "vE3bPEEbQ9Vp",
        "KHFLEXKNyt2H"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}