PI_DA-main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "\n",
    "Monedas a utilizar:<br>\n",
    "\n",
    "Simbolos [eth, ada, dot, link, vet, atom, xtz, neo, miota, zil]\n",
    "\n",
    "Id(nombres): [ethereum, cardano, polkadot, chainlink, vechain, cosmos, tezos, neo, iota, zilliqa]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rango de fechas en Timestamp (Cantidad de segundos desde 01 de Enero de 1970)<br>\n",
    "\n",
    "Unix Timestamp\t1661054400\n",
    "GMT\tSun Aug 21 2022 04:00:00 GMT+0000\n",
    "Your Time Zone\tSun Aug 21 2022 00:00:00 GMT-0400 (Venezuela Time)\n",
    "Relative\ta year ago\n",
    "\n",
    "Unix Timestamp\t1692590400\n",
    "GMT\tMon Aug 21 2023 04:00:00 GMT+0000\n",
    "Your Time Zone\tMon Aug 21 2023 00:00:00 GMT-0400 (Venezuela Time)\n",
    "Relative\tin 4 hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "\n",
    "Inicio de descarga y extracción de los datos a trabajar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se Cargan las librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycoingecko import CoinGeckoAPI\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import json\n",
    "import requests\n",
    "import os\n",
    "\n",
    "cg = CoinGeckoAPI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "\n",
    "## ETL (Extraer, Transformar y Cargar)\n",
    "### Es un conjunto de procesos que se utilizan para extraer, transformar y cargar datos de un origen a un destino."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Puede verificar el estado del servidor API para la API CoinGecko usando el método ping()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esta respondiendo el servicio API de coingecko\n"
     ]
    }
   ],
   "source": [
    "if(cg.ping()):\n",
    "    print('Esta respondiendo el servicio API de coingecko')\n",
    "else: print('No esta respondiendo el servicio API de coingecko')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funcion que se conecta al API de coingecko y retorna la canidad actual de monedas en su plataforma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cant_monedas():\n",
    "    all_coins_list = cg.get_coins_list()\n",
    "    all_coins_list_df = pd.DataFrame(all_coins_list)\n",
    "    cant_monedas, columnas = all_coins_list_df.shape\n",
    "    return cant_monedas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La cantidad actual de monedas:  10214\n"
     ]
    }
   ],
   "source": [
    "if(cg.ping()):\n",
    "    print('La cantidad actual de monedas: ', cant_monedas())\n",
    "else: print('Servicio API caido o sn conxion de red!!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funcion que permite cargar un archivo, se le pasa el nombre del archivo a cargar y retorna un dataframe con los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cargar_archivo(arhivo: str):\n",
    "    ruta_archivo_cardano = \"./Datasets/\"+arhivo\n",
    "    df = pd.read_csv(ruta_archivo_cardano, sep=';')\n",
    "    df.drop_duplicates(inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "Valores OHLC De CoinGecko<br>\n",
    "\n",
    "Se obtienen los valores para cada moneda por cierta cantidad de dias historicos desde la fecha actual:<br>\n",
    "- Time: La fecha y hora de la transaccion.<br>\n",
    "- Open: El precio de apertura del proceso de transaccion.<br>\n",
    "- Close: El precio al cierre del proceso de transaccion.<br>\n",
    "- Low: El precio mas bajo del proceso de transaccion.<br>\n",
    "- High: El precio mas alto del proceso de transaccion.<br>\n",
    "\n",
    "utilizando el método get_coin_ohlc_by_id().<br>\n",
    "Datos cada 4 dias desde el 22 Agosto 2022 al 22 Agosto 2023, 'Time', 'Open', 'High', 'Low', 'Close'\n",
    "- - -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se crea una funcion, para que sea facil ser llamada para cada moneda, la funcion recibe el nombre de la criptomoneda, el cambio que se desea: usd y el tiempo en dias, contados desde la fecha actual hacia atrás en dias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtener_cripto_ohlc(cripto: str, alCambio: str, tiempo: int):\n",
    "    cripto_ohlc = cg.get_coin_ohlc_by_id(id = cripto,\n",
    "        vs_currency = alCambio,\n",
    "        days = tiempo)\n",
    "    cripto_ohlc_df = pd.DataFrame(cripto_ohlc, columns = ['Time', 'Open', 'High', 'Low', 'Close'])\n",
    "    cripto_ohlc_df['Time'] = pd.to_datetime(cripto_ohlc_df['Time'], origin='unix', unit=\"ms\")\n",
    "    return cripto_ohlc_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se generan los archivos csv de cada criptomoneda en archivos individual, a cada archivo se le adiciona una columna con el Id (nombre) de la moneda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = ['ethereum', 'cardano', 'polkadot', 'chainlink', 'vechain', 'cosmos', 'tezos', 'neo', 'iota', 'zilliqa']    # Lista con los Id de las monedas\n",
    "for cripto in lista:\n",
    "    df_ohlc = obtener_cripto_ohlc(cripto, 'usd', 365)   # Se le pasa el Id de la moneda, el tipo de cambio y la cantidad historica de dias.\n",
    "    df_ohlc['Id'] = cripto                              # Se le adiciona una columna con el Id de la moneda\n",
    "    ruta_archivo = \"./Datasets/\"+cripto+'_ohlc.csv'     # Se crea un archivo con el nombre de la moneda\n",
    "    df_ohlc.to_csv(ruta_archivo, sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se genera un unico archivo csv con todas las criptomonedas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_criptomonedas = []\n",
    "lista = ['ethereum', 'cardano', 'polkadot', 'chainlink', 'vechain', 'cosmos', 'tezos', 'neo', 'iota', 'zilliqa']    # Lista con los Id de las monedas\n",
    "for cripto in lista:\n",
    "    df = obtener_cripto_ohlc(cripto, 'usd', 365)    # Se le pasa el Id de la moneda, el tipo de cambio y la cantidad historica de dias.\n",
    "    df['Id'] = cripto                               # Se le adiciona una columna con el Id de la moneda\n",
    "    df_criptomonedas.append(df)                     # Se va creando un dataset con la informacion solicitada por moneda\n",
    "\n",
    "# Se concatenan los DataFrames en uno solo\n",
    "df_criptomonedas_ohlc = pd.concat(df_criptomonedas) # Se crea un dataframe con todo el dataset\n",
    "ruta_archivo = \"./Datasets/criptomonedas_ohlc.csv\"  # Se crea un archivo con el nombre, al nombre se le adiciona ohlc para saber que informacion de las criptomonedas contiene.\n",
    "df_criptomonedas_ohlc.to_csv(ruta_archivo, sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se lee el archivo generado y se muestra el contenido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Time     Open     High      Low    Close        Id\n",
      "0  2022-08-23  1622.31  1622.31  1622.31  1622.31  ethereum\n",
      "1  2022-08-27  1665.79  1697.08  1510.19  1510.19  ethereum\n",
      "2  2022-08-31  1496.06  1555.18  1438.75  1525.40  ethereum\n",
      "3  2022-09-03  1552.56  1588.98  1552.56  1575.18  ethereum\n",
      "4  2022-09-07  1555.67  1617.94  1555.67  1564.56  ethereum\n",
      "              Open         High          Low        Close\n",
      "count   960.000000   960.000000   960.000000   960.000000\n",
      "mean    164.863462   168.437060   160.868784   164.760681\n",
      "std     489.648303   499.981373   478.390740   489.509953\n",
      "min       0.014984     0.015014     0.014720     0.014843\n",
      "25%       0.210962     0.217227     0.205961     0.212689\n",
      "50%       2.995000     3.080000     2.935000     2.990000\n",
      "75%       8.652500     8.892500     8.432500     8.570000\n",
      "max    2093.170000  2118.600000  2077.540000  2103.280000\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 960 entries, 0 to 959\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Time    960 non-null    object \n",
      " 1   Open    960 non-null    float64\n",
      " 2   High    960 non-null    float64\n",
      " 3   Low     960 non-null    float64\n",
      " 4   Close   960 non-null    float64\n",
      " 5   Id      960 non-null    object \n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 45.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df_criptomonedas_ohlc = cargar_archivo(\"criptomonedas_ohlc.csv\")\n",
    "print(df_criptomonedas_ohlc.head())\n",
    "print(df_criptomonedas_ohlc.describe())\n",
    "print(df_criptomonedas_ohlc.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "Valores del mercado (Market) De CoinGecko<br>\n",
    "\n",
    "Se obtienen los valores para cada moneda por cierta cantidad de dias historicos desde la fecha actual:<br>\n",
    "- Time: La fecha y hora de la transaccion.<br>\n",
    "      Este campo esta formateado en linux timestamp, esto significa que contiene un valor numerico el cual representa la cantidad de segundos<br> que han pasado desde el primero de Enero de 1970 (01/01/1970), por lo que se debe convertir a un dato tipo fecha luego de obtener los datos.\n",
    "      El rango de datos a descargar sera desde el 22 de Agosto 2022 hasta el 22 de Agosto de 2023.<br>\n",
    "- Prices: El precio de la moneda para la fecha de la transaccion.<br>\n",
    "- market_caps: La capacidad del mercado a la fecha de la transaccion.<br>\n",
    "- total_volumes: La cantidad total del volumen que se movio a la fecha de la transaccion.<br>\n",
    "- - -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se crea una funcion, para que sea facil ser llamada para cada moneda, la funcion recibe el nombre de la criptomoneda, el cambio que se desea: usd, la fecha inicial y la fecha final en formato timestamp, y retorna un dataframe con los datos solicitados.\n",
    "\n",
    "- Hasta, Fecha Final<br>\n",
    "Unix Timestamp\t1692676800<br>\n",
    "GMT\tTue Aug 22 2023 04:00:00 GMT+0000<br>\n",
    "Your Time Zone\tTue Aug 22 2023 00:00:00 GMT-0400 (hora de Venezuela)<br>\n",
    "Relative\t16 hours ago<br>\n",
    "\n",
    "- Desde, Fecha Inicial<br>\n",
    "Unix Timestamp\t1661140800<br>\n",
    "GMT\tMon Aug 22 2022 04:00:00 GMT+0000<br>\n",
    "Your Time Zone\tMon Aug 22 2022 00:00:00 GMT-0400 (hora de Venezuela)<br>\n",
    "Relative\ta year ago<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se crea una funcion\n",
    "\n",
    "def obtener_cripto_market(cripto: str, alCambio: str, desde: int, hasta: int):\n",
    "    coin_history = cg.get_coin_market_chart_range_by_id(id = cripto,\n",
    "        vs_currency = alCambio ,\n",
    "        from_timestamp = desde,  # timestamp\n",
    "        to_timestamp = hasta,    # timestamp\n",
    "        localization = False)\n",
    "    coin_history_df = pd.DataFrame(coin_history)\n",
    "    return coin_history_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se generan los archivos csv de cada criptomoneda en archivos individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se generan los archivos csv de cada criptomoneda\n",
    "\n",
    "lista = ['ethereum', 'cardano', 'polkadot', 'chainlink', 'vechain', 'cosmos', 'tezos', 'neo', 'iota', 'zilliqa']\n",
    "for cripto in lista:\n",
    "    df_market = obtener_cripto_market(cripto, 'usd', 1661140800, 1692676800)    # 1 año de informacion\n",
    "    df_market['Id'] = cripto                                                     # Se le adiciona el Id de la moneda \n",
    "    df_1 = pd.DataFrame(df_market['prices'].to_list(), columns=['time', 'prices'], index=df_market.index)   # Se recibe el tiempo y el precio en una lista, se procede a separarlas.\n",
    "    df_1['fecha'] = pd.to_datetime(df_1['time'], unit=\"ms\")                      # Se cambia el timestamp por un campo con la fecha MM/DD/AAAA\n",
    "    df_1 = df_1.drop(columns=['time'])                                           # Se borra la columna original\n",
    "    df_1[['time_market_caps','market_caps']] = pd.DataFrame(df_market['market_caps'].to_list(), columns=['time', 'market_caps'], index=df_market.index) # Se recibe el tiempo y el market_caps en una lista, se procede a separarlas.\n",
    "    df_1 = df_1.drop(columns=['time_market_caps'])                               # Se borra la columna original\n",
    "    df_1[['time_total_volumes','total_volumes']] = pd.DataFrame(df_market['total_volumes'].to_list(), columns=['time', 'total_volumes'], index=df_market.index) # Se recibe el tiempo y el total_volumes en una lista, se procede a separarlas.\n",
    "    df_1 = df_1.drop(columns=['time_total_volumes'])                             # Se borra la columna original\n",
    "    df_1['Id'] = cripto                                                          # Se le adiciona el Id de la moneda  \n",
    "        \n",
    "    ruta_archivo = \"./Datasets/\"+cripto+'_market.csv'                            # Se crea un archivo con el nombre de cada criptomoneda\n",
    "    df_1.to_csv(ruta_archivo, sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se generan los archivos csv de todas las criptomoneda en un solo archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se generan los archivos csv de cada criptomoneda\n",
    "\n",
    "df_criptomonedas = []\n",
    "lista = ['ethereum', 'cardano', 'polkadot', 'chainlink', 'vechain', 'cosmos', 'tezos', 'neo', 'iota', 'zilliqa']\n",
    "for cripto in lista:\n",
    "    df_market = obtener_cripto_market(cripto, 'usd', 1661140800, 1692676800)     # 1 año de informacion           \n",
    "    \n",
    "    df_1 = pd.DataFrame(df_market['prices'].to_list(), columns=['time', 'prices'], index=df_market.index)   # Se recibe el tiempo y el precio en una lista, se procede a separarlas.\n",
    "    df_1['fecha'] = pd.to_datetime(df_1['time'], unit=\"ms\")                     # Se cambia el timestamp por un campo con la fecha MM/DD/AAAA\n",
    "    df_1 = df_1.drop(columns=['time'])                                          # Se borra la columna original\n",
    "    df_1[['time_market_caps','market_caps']] = pd.DataFrame(df_market['market_caps'].to_list(), columns=['time', 'market_caps'], index=df_market.index) # Se recibe el tiempo y el market_caps en una lista, se procede a separarlas.\n",
    "    df_1 = df_1.drop(columns=['time_market_caps'])                              # Se borra la columna original\n",
    "    df_1[['time_total_volumes','total_volumes']] = pd.DataFrame(df_market['total_volumes'].to_list(), columns=['time', 'total_volumes'], index=df_market.index) # Se recibe el tiempo y el total_volumes en una lista, se procede a separarlas.\n",
    "    df_1 = df_1.drop(columns=['time_total_volumes'])                            # Se borra la columna original\n",
    "    df_1['Id'] = cripto                                                         # Se le adiciona el Id de la moneda\n",
    "    \n",
    "    df_criptomonedas.append(df_1)                                               # Se va creando un dataset con cada una de la informacion de las criptomonedas\n",
    "\n",
    "df_criptomonedas_market = pd.concat(df_criptomonedas)                           # Se concatenan los DataFrames en uno solo\n",
    "\n",
    "ruta_archivo = './Datasets/criptomonedas_market.csv'                            # Se crea un archivo con la informacion de las criptomonedas y se le adiciona la palabra market\n",
    "df_criptomonedas_market.to_csv(ruta_archivo, sep=';', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        prices       fecha   market_caps  total_volumes        Id\n",
      "0  1622.307035  2022-08-23  1.937082e+11   1.664864e+10  ethereum\n",
      "1  1665.791907  2022-08-24  1.998892e+11   1.718234e+10  ethereum\n",
      "2  1657.793642  2022-08-25  1.991176e+11   1.532291e+10  ethereum\n",
      "3  1697.076972  2022-08-26  2.042408e+11   1.290583e+10  ethereum\n",
      "4  1510.189111  2022-08-27  1.812385e+11   2.317004e+10  ethereum\n",
      "            prices   market_caps  total_volumes\n",
      "count  3650.000000  3.650000e+03   3.650000e+03\n",
      "mean    164.777153  2.244664e+10   1.132464e+09\n",
      "std     489.433792  5.816531e+10   3.667901e+09\n",
      "min       0.014720  2.693585e+08   3.597693e+06\n",
      "25%       0.211904  6.777457e+08   2.885303e+07\n",
      "50%       2.980622  2.193934e+09   9.367775e+07\n",
      "75%       8.625538  6.942037e+09   2.763469e+08\n",
      "max    2118.598789  2.535078e+11   6.521171e+10\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3650 entries, 0 to 3649\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   prices         3650 non-null   float64\n",
      " 1   fecha          3650 non-null   object \n",
      " 2   market_caps    3650 non-null   float64\n",
      " 3   total_volumes  3650 non-null   float64\n",
      " 4   Id             3650 non-null   object \n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 142.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df_criptomonedas_market = cargar_archivo(\"criptomonedas_market.csv\")\n",
    "print(df_criptomonedas_market.head())\n",
    "print(df_criptomonedas_market.describe())\n",
    "print(df_criptomonedas_market.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando Informacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prices</th>\n",
       "      <th>fecha</th>\n",
       "      <th>market_caps</th>\n",
       "      <th>total_volumes</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>8.491968</td>\n",
       "      <td>2023-08-13</td>\n",
       "      <td>5.990570e+08</td>\n",
       "      <td>1.345129e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>8.466605</td>\n",
       "      <td>2023-08-14</td>\n",
       "      <td>5.967716e+08</td>\n",
       "      <td>1.526738e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>8.531993</td>\n",
       "      <td>2023-08-15</td>\n",
       "      <td>6.018523e+08</td>\n",
       "      <td>1.746101e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>8.066459</td>\n",
       "      <td>2023-08-16</td>\n",
       "      <td>5.695575e+08</td>\n",
       "      <td>2.204732e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>7.722567</td>\n",
       "      <td>2023-08-17</td>\n",
       "      <td>5.445099e+08</td>\n",
       "      <td>3.200573e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>6.895905</td>\n",
       "      <td>2023-08-18</td>\n",
       "      <td>4.864329e+08</td>\n",
       "      <td>3.513797e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>6.995337</td>\n",
       "      <td>2023-08-19</td>\n",
       "      <td>4.930101e+08</td>\n",
       "      <td>2.792332e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>7.164443</td>\n",
       "      <td>2023-08-20</td>\n",
       "      <td>5.052173e+08</td>\n",
       "      <td>1.694791e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>7.197331</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>5.077098e+08</td>\n",
       "      <td>1.263874e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>7.099019</td>\n",
       "      <td>2023-08-22</td>\n",
       "      <td>5.012212e+08</td>\n",
       "      <td>1.731781e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prices       fecha   market_caps  total_volumes   Id\n",
       "355  8.491968  2023-08-13  5.990570e+08   1.345129e+07  neo\n",
       "356  8.466605  2023-08-14  5.967716e+08   1.526738e+07  neo\n",
       "357  8.531993  2023-08-15  6.018523e+08   1.746101e+07  neo\n",
       "358  8.066459  2023-08-16  5.695575e+08   2.204732e+07  neo\n",
       "359  7.722567  2023-08-17  5.445099e+08   3.200573e+07  neo\n",
       "360  6.895905  2023-08-18  4.864329e+08   3.513797e+07  neo\n",
       "361  6.995337  2023-08-19  4.930101e+08   2.792332e+07  neo\n",
       "362  7.164443  2023-08-20  5.052173e+08   1.694791e+07  neo\n",
       "363  7.197331  2023-08-21  5.077098e+08   1.263874e+07  neo\n",
       "364  7.099019  2023-08-22  5.012212e+08   1.731781e+07  neo"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neo_marker = cargar_archivo(\"neo_market.csv\")\n",
    "df_neo_marker.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de nulos:  prices           0\n",
      "fecha            0\n",
      "market_caps      0\n",
      "total_volumes    0\n",
      "Id               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Cantidad de nulos: ', df_neo_marker.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando los datos del mercado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>prices</th>\n",
       "      <th>fecha</th>\n",
       "      <th>market_caps</th>\n",
       "      <th>total_volumes</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.704574</td>\n",
       "      <td>2022-08-23</td>\n",
       "      <td>6.831695e+08</td>\n",
       "      <td>4.774113e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.963653</td>\n",
       "      <td>2022-08-24</td>\n",
       "      <td>7.039974e+08</td>\n",
       "      <td>7.109397e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.944398</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>6.999845e+08</td>\n",
       "      <td>7.181430e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.016593</td>\n",
       "      <td>2022-08-26</td>\n",
       "      <td>7.060113e+08</td>\n",
       "      <td>5.143740e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.025246</td>\n",
       "      <td>2022-08-27</td>\n",
       "      <td>6.350392e+08</td>\n",
       "      <td>6.734565e+07</td>\n",
       "      <td>neo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      prices       fecha   market_caps  total_volumes   Id\n",
       "0   9.704574  2022-08-23  6.831695e+08   4.774113e+07  neo\n",
       "1   9.963653  2022-08-24  7.039974e+08   7.109397e+07  neo\n",
       "2   9.944398  2022-08-25  6.999845e+08   7.181430e+07  neo\n",
       "3  10.016593  2022-08-26  7.060113e+08   5.143740e+07  neo\n",
       "4   9.025246  2022-08-27  6.350392e+08   6.734565e+07  neo"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neo_marker = cargar_archivo(\"neo_market.csv\")\n",
    "df_neo_marker.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "\n",
    "## El Análisis Exploratorio de Datos (EDA)\n",
    "### Es un paso crítico en cualquier proyecto de ciencia de datos. Este proceso implica comprender los datos, identificar patrones y hacer observaciones iniciales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando un dataframe de market para la moneda neo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_neo_marker = cargar_archivo(\"neo_market.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>prices</th>\n",
       "      <th>market_caps</th>\n",
       "      <th>total_volumes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>364.000000</td>\n",
       "      <td>3.640000e+02</td>\n",
       "      <td>3.640000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.072605</td>\n",
       "      <td>6.399279e+08</td>\n",
       "      <td>4.593051e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.797252</td>\n",
       "      <td>1.268238e+08</td>\n",
       "      <td>6.183791e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.052090</td>\n",
       "      <td>4.268815e+08</td>\n",
       "      <td>8.886490e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.850235</td>\n",
       "      <td>5.535704e+08</td>\n",
       "      <td>2.207722e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.886332</td>\n",
       "      <td>6.279229e+08</td>\n",
       "      <td>3.298409e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.916738</td>\n",
       "      <td>6.989996e+08</td>\n",
       "      <td>4.913695e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.461398</td>\n",
       "      <td>1.020367e+09</td>\n",
       "      <td>8.768999e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           prices   market_caps  total_volumes\n",
       "count  364.000000  3.640000e+02   3.640000e+02\n",
       "mean     9.072605  6.399279e+08   4.593051e+07\n",
       "std      1.797252  1.268238e+08   6.183791e+07\n",
       "min      6.052090  4.268815e+08   8.886490e+06\n",
       "25%      7.850235  5.535704e+08   2.207722e+07\n",
       "50%      8.886332  6.279229e+08   3.298409e+07\n",
       "75%      9.916738  6.989996e+08   4.913695e+07\n",
       "max     14.461398  1.020367e+09   8.768999e+08"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neo_marker.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificar si hay outlies o valores extremos\n",
    "\n",
    "columnas_numericas = ['prices', 'market_caps', 'total_volumes']\n",
    "fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(8,10))\n",
    "fig.subplots_adjust(hspace=1)\n",
    "for i, col in enumerate(columnas_numericas):\n",
    "    sns.boxplot(x=col, data=df_neo_marker, ax=ax[i])\n",
    "    ax[i].set_title(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando un dataframe de ohlc para la moneda neo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Open       High        Low      Close\n",
      "count  96.000000  96.000000  96.000000  96.000000\n",
      "mean    9.091563   9.341667   8.756875   9.016875\n",
      "std     1.777338   1.897772   1.675661   1.788277\n",
      "min     6.130000   6.200000   6.050000   6.090000\n",
      "25%     7.865000   8.045000   7.677500   7.727500\n",
      "50%     8.940000   9.035000   8.595000   8.720000\n",
      "75%     9.960000  10.132500   9.535000   9.775000\n",
      "max    13.580000  14.460000  12.940000  13.560000\n"
     ]
    }
   ],
   "source": [
    "df_neo_ohlc = cargar_archivo(\"neo_ohlc.csv\")\n",
    "print(df_neo_ohlc.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificar si hay outlies o valores extremos\n",
    "df_neo_ohlc = cargar_archivo(\"neo_ohlc.csv\")\n",
    "\n",
    "columnas_numericas = ['Open', 'High', 'Low','Close']\n",
    "fig, ax = plt.subplots(nrows=4, ncols=1, figsize=(8,10))\n",
    "fig.subplots_adjust(hspace=1)\n",
    "for i, col in enumerate(columnas_numericas):\n",
    "    sns.boxplot(x=col, data=df_neo_ohlc, ax=ax[i])\n",
    "    ax[i].set_title(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando el archivo con todo el contenido de market para todas las monedas para graficar los outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificar si hay outlies o valores extremos\n",
    "df_criptomonedas_market = cargar_archivo(\"criptomonedas_market.csv\")\n",
    "\n",
    "columnas_numericas = ['prices', 'market_caps','total_volumes']\n",
    "fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(8,10))\n",
    "fig.subplots_adjust(hspace=1)\n",
    "for i, col in enumerate(columnas_numericas):\n",
    "    sns.boxplot(x=col, data=df_criptomonedas_market, ax=ax[i])\n",
    "    ax[i].set_title(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando el archivo con todo el contenido de ohlc para todas las monedas para graficar los outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificar si hay outlies o valores extremos\n",
    "df_criptomonedas_ohlc = cargar_archivo(\"criptomonedas_ohlc.csv\")\n",
    "\n",
    "columnas_numericas = ['Open', 'High', 'Low','Close']\n",
    "fig, ax = plt.subplots(nrows=4, ncols=1, figsize=(8,10))\n",
    "fig.subplots_adjust(hspace=1)\n",
    "for i, col in enumerate(columnas_numericas):\n",
    "    sns.boxplot(x=col, data=df_criptomonedas_ohlc, ax=ax[i])\n",
    "    ax[i].set_title(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se realizan algunos graficos diferentes para verificar los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from mpl_finance import candlestick_ohlc\n",
    "#import mplfinance as candlestickohlc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets de ohlc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se hace un grafico scatter plot y se grafica Time, Close, Low, High para la moneda neo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_neo_ohlc = cargar_archivo(\"neo_ohlc.csv\")\n",
    "\n",
    "# Crear el gráfico de dispersión\n",
    "xTime = df_neo_ohlc['Time']\n",
    "yLow = df_neo_ohlc['Low']\n",
    "yClose = df_neo_ohlc['Close']\n",
    "yOpen = df_neo_ohlc['Open']\n",
    "yHigh = df_neo_ohlc['High']\n",
    "\n",
    "plt.scatter(xTime, yOpen, c = \"green\", label = \"Open\", s=10)\n",
    "plt.scatter(xTime, yClose, c = \"red\", label = \"Close\", s=10)\n",
    "plt.scatter(xTime, yLow, c = \"blue\", label = \"Low\", s=10)\n",
    "plt.scatter(xTime, yHigh, c = \"orange\", label = \"High\", s=10)\n",
    "# Personalizar el gráfico\n",
    "plt.title('Scatter Plot de Time vs Close')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close, Low, High')\n",
    "plt.legend()\n",
    "plt.title('Scatter plot Close, Low y High para la criptomoneda neo')\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se realiza graficos de Histograma con el campo Open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_neo_ohlc = cargar_archivo(\"neo_ohlc.csv\")\n",
    "\n",
    "plt.hist(df_neo_ohlc['Open'], bins=20)  # Puedes ajustar el número de bins según tus datos\n",
    "plt.title('Histograma de Open')\n",
    "plt.xlabel('Open')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 96 artists>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, sharey = True)\n",
    "\n",
    "ax[0, 0].bar(df_neo_ohlc['Time'], df_neo_ohlc['Low'], color = 'tab:blue')\n",
    "ax[0, 1].bar(df_neo_ohlc['Time'], df_neo_ohlc['Close'], color = 'tab:red')\n",
    "ax[1, 0].bar(df_neo_ohlc['Time'], df_neo_ohlc['Open'], color = 'tab:green')\n",
    "ax[1, 1].bar(df_neo_ohlc['Time'], df_neo_ohlc['High'], color = 'tab:orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se realiza graficos de velas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Opcion 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mplfinance as mpf\n",
    "df_neo_ohlc = pd.read_csv('./Datasets/neo_ohlc.csv',index_col=0,parse_dates=True,sep=';')\n",
    "\n",
    "df_neo_ohlc.index.name = 'Time'\n",
    "mpf.plot(df_neo_ohlc, type='ohlc', mav=(3,6,9))\n",
    "#mpf.plot(data,type='candle',mav=(3,6,9),show_nontrading=True)\n",
    "#mpf.plot(data, type='ohlc', mav=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Opcion 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from mpl_finance import candlestick_ohlc\n",
    "\n",
    "df_neo_ohlc = cargar_archivo(\"neo_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_neo_ohlc['Time'] = pd.to_datetime(df_neo_ohlc['Time'])\n",
    "df_neo_ohlc['Time'] = df_neo_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_neo_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zbujm5eWliIgIXb58WT/99JPCw8P14Ycf6uuvv1br1q2VK1cuWSwWffrpp+rdu7cKFCjg4h4B98dkMsnDw0MtW7bUa6+9JqvV6lzz98yZM4qJidFjjz3mPL58+fIKDg7WsGHDNH36dJUrV06lSpVSs2bNlC9fPnl5ebmwN8BfFxoaqvfff1+S9NZbb8lqtWrp0qW6fPmy88pHx0Txpk2bVKxYMZlMJn3++eeKiIjQhQsXtHDhQpUtW9aV3QDum+PHerFixWQ2m/Xjjz9K+mPyt3LlynrqqaecGzlL0tKlS9W1a1ctX75cW7Zs0VdffaX9+/frxRdfdE4eA9lRRr9LCxUqpDZt2qh27dqyWq3Oc/z58+cl3RzzODiWN3n33Xe1efNm7d27V19//bUuXryoNm3aGGryC+6DsQ+MKDAwUMOHD9ebb77pDLGbNWsmm82mAwcO6K233lLXrl21du1aSenv+qtatapq166tX3/9Vb/99psLe5H1THZmeOEGHJvz2O1255ec2WzW6tWr9emnn+qxxx7TF1984Tw+NjZWn332mbZs2SKLxSKz2ayePXuqadOmLuwFcDur1Sqz2Xxfx7722mu6cuWKPv30U1WsWNG5mZskPffccypVqpRGjBghDw8PfuQgx3Oc92+tc8djU6ZM0YQJEzRnzhyVKlXK+ZrY2FgFBASkO9YRlgA5jaP2HbWcmpoqLy8vjR07Vt9884169uyp9u3bS/qj3jt27KiDBw86lzsZMGCAmjdv7uKeALdLSUmRxWLJcANOx2NnzpzRG2+8oVKlSmnQoEEKCAhwfg6OHTumt956S0888YQGDBigTZs2aeTIkTpz5oz8/Pzk5eWlfv366emnn3ZRD4E7O3nypE6cOOG8Qv1WjnN/Rr8RIiMjtX37dv3www/y9vZ2Pj537lxNmjTJebWwl5eX+vfv71waFMguTpw4odmzZ6tfv34ZhtKMfYD0vv32W9WpU0cFCxbU4cOH1bdvXz366KMaOXKk845Wx+fmwoULOnfunKpWreriVmet+5tNA1wkOTlZ8+bNU2BgoIKCgvToo48qd+7ct01UOX4QmUwm5z+S1KBBA/32229avHix1q1bpwYNGshqtSogIECffPKJUlJSdO7cOYWGhrqkf8DdnDlzRh988IHatm2r5s2bp5vgvZVjcqBfv35q06aNli5dqpIlS8rPz0/Jycny9vZW06ZNtXjx4vsOUQBXSk5O1syZM2W1WhUSEqIqVaooODg43bn/1v2ZHByP/fjjj6pYsaJKlCjhfM5utztDj1uPJfRAdpOcnKw5c+bIYrGoePHieuyxx5Q3b97bxj6O2v9zLTs2t122bJlq1aqlsLAwSdL169cVFxenpKQk9ejRQ2+88UYW9wy4P59//rkSExP19ttvK0+ePLeFH47QOyQkRE2bNtX8+fO1fPlyvfLKK/Ly8lJaWppKlSqlsLAw7du3T5L0xBNPqEaNGrp27Zqio6NVqVIlV3UPuKPk5GT9+9//1qJFi9SnTx898cQTt03+Os79jjG94/Nx7do1rV69Wi1atHCGHo7nIiIiFB4erkuXLunSpUuqVatW1nYMuIdba1+SXnjhhQzvxmDsA3eUkpKiZcuWqUiRIipWrJiKFi0qSRle/OHg+F3w4osvOj8HVapUUYMGDbRx40bduHHDGXzcuux/4cKFs6BH2QszYMi2Fi9erCFDhqhYsWLO27hCQ0M1ceLEdJuwOdZxz+gHkaenp1q2bKnt27fryy+/VIMGDZxJvyRZLBZCD2Q7ji+4c+fOadu2bfL399fTTz9925WPjrubHD+IKlSooM6dO2vRokUqVqyYXn31VXl7eys5OVk7d+5U0aJFZbVaueMD2dqyZcs0ePBgFSxYUB4eHjp16pSKFy+uefPmpVuOKqNzf1pamo4cOaL9+/crMjJSnp6estlsOnnypL7++mu98sorKlOmjCu6BdyXJUuWaOjQocqXL59SU1N15coVPfHEExo7dqw8PT3T3en05/r38PBQWlqaLBaLunTpoiFDhmj58uXONdz9/f3Vu3dvVa1aVf7+/i7sJZAxxwUeq1evVlJSkmrXrq3GjRvf9qPfZrM5j+3SpYvWr1+vhQsXqnLlyipfvrzz+Pz58+vQoUPOyQGLxaKCBQuqYMGCrugecFdTp07VmDFjVKJECY0fP1716tW74xXvt57/Hf/evn27rl27poYNGzqPPXHihNavX69mzZqpSJEi8vf3Zz9LZDtTp07V6NGjVbZsWUVERGjDhg0qXbr0bccx9oE7WrRokYYPHy5fX1/FxsYqV65c6t+/v1q2bJmu1v9c/3++eM9xF6Cfn5/i4uLk4+OTpf3Izpj5QrZ0/PhxjR07Vq1atdL48eP1zTffaNiwYTp9+rQiIyN16dIlSXJO4ppMJq1cuVLffPON8z0cE7sVK1bUM888o927dzs3OWTiF9nNrasOOr7M9u/fL7vdrn379mnOnDnpjnd88Xl6eurkyZN69dVX9d///lfvvPOOHn/8cY0bN07Tpk3Tb7/9puXLl+vEiRNq0qSJzGYztY9s69SpU5o0aZJeeuklTZ48WbNnz9bQoUN17tw5jRw5UpKce3k4zv2Oq7ukmwPA7du3y2Qy6amnnlJcXJwmTJigF198UVu3bk237AOQ3Rw4cEATJ07Uq6++qilTpmjhwoXq2bOn1q1bp8mTJ0u6Ge7dWv9r167VunXrnO/h+BHUqlUrVapUSQsWLNCGDRuczzdo0IAf/si2PDw8lJCQoGvXrunMmTNasWKFLly4IEnO5Wwd9W82m/Xzzz/rxIkTioyM1PXr1zVkyBClpKQoKSlJp0+f1tatW9W8eXPu7EO2lpycrE8//VTDhw/Xhx9+qClTpqhhw4bpQo8/17/JZNJvv/2m//3vf85jNm3apMDAQNWuXVuxsbGaMGGCWrVqpV9++YXPALKlzZs368knn9TUqVPVp08fffXVVypWrJg8PT0VFxfnPO7Ptc/YB+7i4MGDmjBhgtq3b6+pU6dqwYIFKlKkiCZOnKgjR45Iyrj+HXt4REVFac2aNZJujqEcYXfbtm3TXSxudMx+IVtaunSpzp8/rxYtWigoKEi5c+dW7dq11alTJ/3888/66aeflJKSIrPZrGPHjqlz587q1auXzp8/r7S0NOf7OO7saNasmZo3b87mVci2br2Lw26369KlS1q4cKHCw8MVFBSkJUuWKDo6Ot2eBlarVR999JHCw8OVK1cutWjRQhaLRf3791fr1q312WefqW/fvho0aJBatWqldu3aubiXQHp/3mZs7dq1io6OVtOmTVW4cGH5+vqqTp06qlSpklavXq3k5GSZzWZ5enrqxIkT6ty5s3r37q1Dhw4532PLli165JFHtGnTJkVERGjGjBmKjIzU8uXL0y19Bbjan+t/2bJlSkhIUIsWLRQcHCx/f3+1bNlSJUqU0ObNm5Wamuqs/5MnT+q1115T9+7ddfTo0XTv4xgHdevWTXny5Em3xBuQXWS0zaTdbldycrLKli2rRo0a6YcfftDGjRsl/bGc7a31/9Zbb2n79u2qUaOGevbsqdOnT6t+/fp644031LVrV5lMJjVr1iyruwbc0631bzab5e/vrxIlSqhs2bIKDAyUh4eHYmNjFR0drdTU1Nvqv0OHDnrjjTd08uRJ5/v873//02OPPaZ169YpIiJC06dP18cff6yZM2eqUKFCLuglcGdXr17V7Nmz1bRpU82ZM0cdOnRwLtMs3dzE2eHP537GPsip/jz2Wb16tWJiYtSoUSMVL15cjzzyiLp27apjx46lW975z/V//PhxSTd/O/Tu3Vtt27bVp59+qrfffltpaWl65plnsrxv2RlLXSFbSkhIUP78+VW+fHlJcm5aFR8fL5vNpnnz5ql27dq6cuWKOnXqpHr16mnRokW3BRuOK9uLFy+uESNGZHk/gHu5ePGiPvjgAzVu3Fht27Z1LtG2efNmXb58WZMmTdK6des0fvx4zZ49W++9956zrkeOHKk9e/Zo7NixatCggXMZoJIlS6p///5q3769fv/9d5UsWTLd4BHILhyBn2MZkqtXr8rHx8e5DIPValXevHnl5eWlPHnyOIO/n376SX379lX9+vWd53673S6r1Sqr1aqTJ09q0KBB6tChgyIjI13ZReCO/rx8z9WrV+Xv76+QkBBJN8c+hQsXltlslsVikZeXl2w2m7777jsNHjxYTz75pBYuXKhy5cqlex/HD6UaNWpoyZIlWdMZIJP+vHyD40rGhIQE7dy5UytXrlSHDh00Z84cVapUSaVLl86w/h3LF7Zs2VKVKlXSDz/8oLNnz6pu3brq0aOHq7oH3NWt9e/p6amKFStq6dKlOnLkiMqXL69PPvlEy5Ytk5+fnwoXLqznnntOERERGjt2rMaOHauGDRvqu+++c/5WjomJ0fnz53Xo0CFt2rSJ8Q+yvXz58unjjz9Wvnz50q1GYLVa5enpqcuXL6tAgQLOx2fPns3YBzleRvt1eHp6Kk+ePM4/nz9/XtWqVZOvr69zfPTNN99o8ODBatCgQbqxz2uvvaawsDBt3LhRJ0+eVHh4uN56660s609OYbJndLkNkEXmzp2r+fPnKyAgQKGhoeratasCAgK0cOFCRUZGauzYsXr66aedxw8YMEClS5fWqFGj9Pbbb6tp06a6dOmSKlSokOEaqEB25Vinfd++fXr55ZdVrVo1ffHFF84vvQ0bNuj69etq3ry5UlJS1KtXL506dUpDhw5V5cqVJUnXrl2Tp6en/Pz8XNgTIPP+HPg5go89e/Zo6tSp6tu3r4oVKybp5mZv7dq1U758+ZzL/ezatUspKSmqXLnybef+fv36KSUlRQMGDGAdd2RLc+fO1U8//aT8+fPr0UcfVefOneXh4aGVK1dqyZIl+vjjj50/9uPj4/Xcc8+pXr16GjRokCRpzZo18vb2VvXq1Rn7IMe5U/1LNwOQ48ePq1evXlqwYIG2bt2qbt26qXfv3s4QY+3atTKbzapZs+ZtSwE5JhQcEwVAdvPn+u/UqZNzwrZFixYqXry4cuXKpZMnTzp/A6xatUr79+/XrFmzdOXKFXl6eqa74MnhhRdeUFBQkD7++GPGP8h2bq39sLAwvf766+nO047fApMmTdLXX3+tX375Jd0eBatXr5aPjw9jH+RIdzr3Hzt2TG3atFHlypX17LPPKjk5WUOGDFFgYKBsNpvq1KmjPn366Pz580pMTFStWrXuWP8pKSl8Nu6A4ANZzm63KzU1VcOHD9fy5cvVrFkzxcTEaM2aNapQoYLef/99hYaGqlevXtqxY4datmypRx55RN9//728vLw0ZcoUffbZZ9q7d69WrFjh6u4Af8u8efM0YMAAFSpUSK+88oq6d+/ufC4tLU12u11ms1nr1q3ToEGDVKVKFf33v/+VlP5HPpAT3CvwS05OVlJSkvLmzet8zcmTJ9W6dWv16tVLHTt2vON7O34wMehDdmS325WUlKRBgwZp/fr1atiwoU6fPq3t27eradOmevvtt1WqVCldunQp3YTVjh079Nprr2nIkCF67rnnXNgD4K+7W/2Hh4erW7duzru2o6Ki1KVLF61evVp+fn7q1q2bjh8/rpdeekl79+7VkCFDuOADOcq96r9z586qUKGCvv32Ww0cOFBly5bV0KFDVaZMGXl4eOjo0aMaOHCg4uLitHjx4gzf32QyKS4uLt1Vw4Cr3e+53zGGl6QffvhBgwcP1vTp01W+fPl0zwE5yd3qv1mzZurevbvKlCmjX375RUuWLNGOHTuUkJCg7t276/HHH9eePXv0/fffK1euXFq8eDEXdPwN/J9DljOZTIqNjdUvv/yi7t2768MPP9SoUaM0efJkJSYm6t///rd8fX2dd3vs3btXs2bNUtOmTTV37lwFBwerevXq+v333xUVFeXq7gB/id1uV1xcnObMmaNGjRrpkUce0fLly53rlTqWvDKbb65I2KBBA9WtW1c7duzQ8uXLne8B5CSOoO7AgQNKSUnRsWPHNHv2bOfz3t7eztAjNTVVkvTzzz8rJSVFNWvWvOt7O34UEXogOzKZTIqOjtamTZvUr18/DR48WDNnztTnn3+unTt3auTIkbJarSpYsKDS0tKc9b9q1Sr5+PioYsWKt70n3wHIKe5W/zt27NDo0aNltVolSefOnVNwcLDzavbu3bvr7Nmz+uKLL2S322WxWKh95Cj3qv/x48fLarWqVq1aKlOmjBo3bqxy5cqlW7I5PDxcp06d0s6dOzN8f0mEHsh27vfc7+np6dyjw8/PT6mpqTp//rwkEXogx7pX/X/xxRdKTU1V48aNNXDgQOXJk0cdO3ZUp06dVKdOHXXr1k19+vTRkSNHtHLlSkmM/f8qgg+4xK5du3T9+nXVr1/fOVirWbOmOnTooLNnz2rYsGHy8/PTsGHDNHfuXK1bt07vv/++8wqvwMBAmUwm5cuXz4W9AP46k8mk7du36/Tp03rvvffUtm1bXbt2Td98840kpUv0bTabJKldu3bKnTu35s2bp/j4eHl4eDifA3KC+wn8HByTXqtXr1aFChXS7eF06NAhXblyJWsbD/xNW7Zs0Y0bN9SsWTPnOb5Zs2Zq06aNdu/eralTp0q6+f1wa/3XqVNHJUqUcL7PoUOHnMcBOcWd6v+FF15QVFSUpkyZIunmJJePj49MJpNGjBih9u3bKygoSGazWS1btpTFYqH2kePcrf53796tmTNnqmTJkho5cqQ6deok6eaYKS0tTRaLRY888ohMJlO6O2KBnOBe537H2McxoVu/fn2ZzWadPHlS0h+blgM50d3G/lFRUZo2bZqkm/Ojx44d0/PPPy8vLy/nxSAlS5aUj4+Pbty4IYmx/19F8IEs5fhCK1asmOLj450TVykpKZKkxo0bq2XLlvr222916tQpWSwWJSYm6uDBg4qPj3ce++uvv6pYsWLc6o4czd/fX/3791epUqX07LPPqkqVKtq4caM2bdok6Y+BnuNLsmzZsnrmmWe0Z88eTZ8+Pd1zQE6QmcBPko4cOaK9e/cqPDxcHh4eio6O1n/+8x+1atVKP/zwgyu6AGSaY+xTqFAh3bhxQ6dPn5b0x9jnxRdfVIUKFTR37lydP3/e+TnYuXOnTp06pfDwcElSdHS0hg8frlatWmnu3Lku6AmQefdb//PmzdOVK1d08eJF7d27V/Xq1dPy5cs1atQo/fjjj/Lx8dGMGTN09uxZl/UFyKz7qf/HH39cs2fP1vnz51WqVCmlpaXp2LFjMplMziU8f/31VxUsWJC7OpBjZHbsYzabZbPZlJKSojp16jg3KOeOD+REman/ixcvysvLS7ly5dIvv/wiSTKbzbJardq6dasCAgKce7zir2HGDFnKkVD6+voqLCxMs2bNkiTnbet58+ZVeHi4ihQponHjxkmSkpKS1Lt3b/Xv31/ff/+9FixYoDVr1qh169YKDAx0WV+Av6t69ep67rnnnAHHK6+8Ig8PD82ePdu5nqnjS9NxJXxERIQef/xxlSpVymXtBv6OewV+t971sXPnTtlsNoWEhGjhwoVq06aNVqxYobFjx951vw8gO3GMffLmzauiRYs6f8xbLBbZbDYVLlxYzZs3V1pamr799lvn6zZt2iSLxaJChQpp7ty5ioiI0E8//aSxY8cqIiLCJX0BMut+6z81NVXz589XxYoVlS9fPnXr1k3z5s1TkyZNlDt3bnXv3l2xsbG3begMZGd/5fy/ceNGdevWTV999ZVWr16tuXPnatGiRXr++edVoEABl/UFyIy/UvseHh7y9fVVxYoVlZiYqFWrVklieR/kPJkZ+yxcuFBVqlTRY489psmTJ2vRokXauHGj5syZo2nTpumZZ55RSEiIK7uT4xF8wCVKlCihKlWqaM+ePdqyZYskOW/nqlKliurUqaNDhw7p1KlTCgoKUufOnXXlyhV99dVXmjRpkjp27KjXX3/dlV0AMjR37lyNGzdOc+fO1eHDhyVlfIuuYwBnNpudV7LUqFFDjRo10t69e7VgwYJ0xzmWtSpYsKAmTZqkZ555Jiu6Azxw9wr8PDw8nN8H169fl9Vq1cCBA/Xhhx/q1Vdf1erVq/X000+7sgvAX1K1alUVLVpUW7du1cGDByX98f3QpEkTFS9eXPv27dPFixclSQkJCbLb7frggw80cOBAdejQgfpHjnWv+i9RooS2bNmiIkWKaMmSJercubNzaVtJ6tixo5YuXarChQu7rA/AX3U/5/+9e/cqISFBpUqVUvny5TV69GgNGjRIkydP1uuvv64333zTlV0A/pL7HfvcuoTtE088oQsXLmj9+vVKS0tjeR/kWPc79vHy8lLfvn1VrVo1DRgwQB9++KHGjRunDh066P3333fu+4q/huADD9T9TPrabDaZTCY1b95cXl5eziVOvLy8lJKSIovFopo1a+r33393bu7Ztm1bffPNN5o4caKWL1+uzp07Z23HgHuIiorSs88+q/Hjx2vz5s365JNP1LlzZx05ciTdnRuO9XpvHcDZbLZ0+3gUKFBAixYt0uXLl9Pt4+FY/oRbfpHdPOjAz1HrqampMplMatSokbZs2aLu3btnRXeATFm+fLnWrVun/fv3Ox/789WJjrv4XnrpJZ09e1aLFy+W9MfYJ1euXGrYsKH27dsnLy8v2Ww2xcbGKjU1VbVq1aL+kW09qPpv0KCB9u/fLy8vLwUGBt629CFX/CI7etDn/7S0NJUtW1ZjxozRzz//rNGjR2vFihVc8Ids50HXvuOcb7fbVa5cOf3nP//RBx98wO9eZEsPuv5v3LihKlWqOM/9n3/+uVavXs285wNCbIQHIioqSpGRkUpMTFSxYsUUFRUlf39/TZs2TWFhYbLb7TKZTLLb7bLZbPLw8FDt2rXVuHFjLVy4UDNnzlSHDh1ksVgkSQUKFFBcXJxz4sxut8vT01MlS5Z0ZTeBDCUlJWnMmDEqW7as3n77bQUFBWnv3r0aOHCgPv74Y2e4Z7VanRO++/fv1/r169W9e/d0A72QkBC1atVK48aN0+TJkxUZGck+Hsi2Mnvuv/XHy62BXrt27bRt2zYtWrRIjRo1ci7l0KhRIz377LPc3otsafv27YqMjFRSUpI8PT11/vx59e7dW+3bt5efn1+6+nf8GGrWrJmWL1+uVatW6fHHH3deBCJJ+fPn1/Xr13X9+nUFBASobdu26t69e7qNzYHs4kHXf4ECBXT9+nXFx8crX758t/19XPGL7ORhnP/j4+MVFxfn3MejUKFCKlSokMv6CGTkYY19EhISFBAQ4Px7HPubAdnJw6j/hISEdOf+oKAgBQUFuayP7ojZNPxtt076Tps2TZMnT9aUKVMUEBCgjz/+2Hmc1WqVyWSS2WxWVFSUZs2apbfffltVq1bVf//7X23btk2xsbG6du2a5s+fr5o1azqDDn7sIDvbtWuXtm3bpkaNGqlEiRLy8fFRlSpV9OSTTyoqKkonTpxw1v6NGzf0wQcfqHXr1tqxY4eSk5Nve7/WrVurUqVKKlOmjAt6A9yfzJ77HYHfxIkTJd0MPDw8PNIFfkePHtXkyZOdry1TpgyhB7KlhIQEjRo1StWqVdOsWbP07bffKiIiQpMnT9aOHTsk3Ry7OO7wM5vN2r9/v5YtW6YBAwYof/78+vzzz3Xq1CmlpKTo2rVr+vHHH1WvXj0VL15cklS5cmVCD2RLD7P+ixUr5uLeAXdH/cOosqL2mfdBdsW5P+ci+MDf9lcmfV988UWtXbtW3t7eeuedd1S3bl117txZL7/8sjp16qT169frpZdeYgNDZEtXr1517kEg3ZwATkpKUrly5STdXJ7H09NTvr6+8vX1dd7JFBUVpSZNmmj37t366quvNH78eHl7ezvfx2QyyWazyc/PT+PHj1fr1q2ztmNAJhD4wch27NihPXv2ONenDgoKUs+ePZWQkJDuR7unp6du3Lihf/3rX2rdurXWrl2rAgUKqHfv3goICFDLli3VoUMHde3aVbt27VKbNm1c2Cvg/lD/MDLqH0ZF7cPIqP+ci6WukGlXr16Vn5+fc4OdjCZ9vby8Mpz0ffPNN5UvXz599dVXqlmzpiSpVKlSGj9+vBYtWqTjx4/Lbrfr9ddfT3erI5AdbN68WWPGjJHNZpPdbtdrr72mxo0bq2rVqmrevLliYmJUqlQpZ2B34cIF5c6d23nbor+/v4YMGaJq1arJz88vw7/DsawVG1ghu3lQ5/7atWunq+8/B37UPrKjP9f/1atXlZSUpPLlyzuP2b59uwoUKCCTyaSYmBgFBgZq8+bN6tWrl4KCgjRp0iTVrl1bklS3bl1VqVJF33zzjc6ePSsvLy9NnDhRgYGBLukfcDfUP4yM+odRUfswMurffZjs7BSH+3SnSd+kpCR9/PHHevnll1WjRg3n8R999JE2btyoxYsXy9/fXydOnNDp06dvm/R1bPoDZFc2m02TJ0/WjBkz1LhxYxUpUkRr1qzRmTNn1L59e/Xs2VOXL1927ksgScnJyYqIiFDJkiU1atQo53qPQE7zsM79QE5wp/pPTEzUxo0b1ahRI+XOnVtRUVHq2rWrrFarvL295efnpz59+ujxxx/X7t279eSTTzL2QY5D/cPIqH8YFbUPI6P+3Q/BB+6JSV8YXUxMjF577TU99dRTevPNN+Xt7a0bN26od+/eOnXqlGbMmKHChQtL+mMD802bNqlLly4aMmSIWrduzWcAOQ7nfhjZ3eq/Xbt2euutt9LV96xZsxQdHa3GjRsrOjpaixYt0pYtW7R8+XKFhITwWUCOQv3DyKh/GBW1DyOj/t2YHbiHK1eu2Fu0aGEfOXKkPSkpyW632+2JiYn2rl272ps2bWo/f/6889jU1FS73W63b9y40V62bFn7/Pnz7Xa73W6z2bK+4cDf5Kjb9evX28uUKWM/ffq03W6329PS0ux2u93+xRdf2GvWrGmPjo52vsbx3ODBg+116tRJ99zp06ftR48ezarmA38L534Y2f3Wv9VqdT4XHx/vfP327dvtNWvWtH/22WdZ33jgb6L+YWTUP4yK2oeRUf/ui83NcUf2/78ZaN++fTpy5IjatGkjb29v2Ww25cqVSxUqVNDVq1dls9mcr3HsT7BmzRoFBASoTp06km6u4X7mzBkdO3Ys6zsCZMLx48cVFRWlmJgYZ0JvNpsVEBCgkydPSpKz5tPS0mS32511L938DCQlJWnNmjWqV6+eihQpomvXrmnmzJlq2rSpxowZ4/xsAdkR534Y2f3Uf2xsrLP+Hbes58qVS7lz51ZaWpokqUyZMvLy8lKuXLlc0xHgL6D+YWTUP4yK2oeRUf/uj+AD6TDpC6OKj49X37591bZtW73xxhtq1aqVpk6dKrvdrnLlyqlv374qWrSopD++HHfu3KmSJUsqICAgXV0fPnxYFy5cUFhYmLZs2aJ27dpp5MiR6t+/v0aNGsUtj8h2OPfDyDJb/5LS1X9KSooOHjwo6eaPIbvdri1btig1NVUVKlTIwp4AmUf9w8iofxgVtQ8jo/6NxezqBiB7iI+P17///W9t2LBBnp6eMpvN6tSpkzp16nTfk76OE8afJ30HDx6sc+fOqX///urYsaOrugjc1ZgxY3T48GGNHDlSSUlJ2rNnj4YPH65r167pjTfeUJs2bZzHenl56erVqzp48KA6dOggi8WS7r0SExNltVo1c+ZMXbp0Sf/4xz+0YMGC244DXI1zP4zsQdX/uXPn9PHHHyskJER169aVj4+PxowZoypVqqhSpUqu7CJwR9Q/jIz6h1FR+zAy6t+YCD4giUlfGJfNZtOlS5e0fPlytWvXTnXr1pWHh4eefvppxcXFaeHChQoICFDHjh1ls9mcSf/q1at1/fp11atX77b3TE1NlYeHh8qWLasZM2aoRIkSWdwr4P5w7oeR/d36d/wgyp8/v8LDwzVlyhTt3LlTNptN9evX14ABA+Tl5eWq7gF3Rf3DyKh/GBW1DyOj/o2J4MPgmPSFEUVHR+vy5csKDg5WgQIF5Ovrq7i4OIWGhsrDw0PJycny9vbWO++8o8OHD2vJkiV64oknFBYWppSUFFksFi1dulShoaEKCwuTdPPqgcWLFys5OVnt27fXnDlz9Pjjj7u4p0DGOPfDyB5U/TvudvLz81PHjh31/PPPKy4uTr6+vipQoIDL+gfcDfUPI6P+YVTUPoyM+jc29vgwoOjoaEVFReny5cvy8PDIcNJXkt555x2FhIRoyZIlOnLkiDw8PJSSkiJJGU76fvPNN5o6dapq1aqlOXPmaNKkSUx8IVtJSkrSBx98oFdeeUW9evVSy5YtNXHiREVHR6tUqVJav369JMnb21tWq1V58+bVyy+/rNjYWC1YsECSZLFYdP36de3atUtPP/20/Pz8tHHjRnXu3FlDhw5VWlqaLBYLoQeyHc79MLKHVf/ffvutxo0bJ0nKly+fihcvzg8fZDvUP4yM+odRUfswMuofDgQfBsKkL4wsNjZWffv21YEDBzRgwABFRkbqhRde0KhRo3TgwAEVLFhQ+/fv1969eyX9kea3aNFCpUuX1vbt23X8+HFJ0qFDh5SYmCh/f3/169dPXbp0UUhIiDZv3qyuXbu6rI9ARjj3w8gedv0PGTJEuXLlcmUXgTui/mFk1D+MitqHkVH/+DOWujKI2NhYDRgwQL///rsGDBigtLQ07dmzR6NGjVJQUFC6Sd8KFSqkm/RdvHixc9K3ZMmSt036/vDDD3r22Wc1depU+fn5ubinQMYOHjyobdu2acSIEWrQoIEkqUmTJtqxY4dWr16tLl26qH379lq7dq0effRRWSwWpaamysvLSy+99JJ69+6t2NhYSdLVq1clSZ999pnKly+vefPmqUKFCq7qGnBHnPthZNQ/jIz6h5FR/zAqah9GRv0jIwQfBsGkL4xuz549slgsztsUJclut+uRRx7Rhg0bNHr0aDVu3Fjz589XpUqVVL9+fefajk8++aT8/Px0/PhxVatWTXnz5lXx4sXVv39/NW7c2FVdAu6Jcz+MjPqHkVH/MDLqH0ZF7cPIqH9khKWuDOJuk747duxQ9erVnZO+W7ZskaQMJ30lOSd9x40bpwULFvDhR44QEhKivHnzKi4uTna7XXa7XZ6enrp+/bry588vSYqMjFRSUpJmz56t06dPy9PTU5L022+/6caNGypUqJAkqUaNGlq5ciWhB7I9zv0wMuofRkb9w8iofxgVtQ8jo/6REYIPg2DSF0YXHh6uAQMGqFSpUjKZTDKZTLp69ap27dqlSpUqSZKKFSum3r176+jRo/rnP/+p9evXa+vWrVq4cKHKlSvH/gXIcTj3w8iofxgZ9Q8jo/5hVNQ+jIz6R0ZY6sogwsPDlS9fPuekryTnpO9TTz0l6Y9J3ylTpuif//ynunfvLh8fHyZ94RZMJpPq1Kkj6WbqbzKZdOTIEV2+fFn16tVzHte2bVsVLlxYw4YN07vvvisPDw8FBwdr2LBhCgwMdFXzgb+Ecz+MjPqHkVH/MDLqH0ZF7cPIqH9khODDIJj0Bf7g+BJcvXq18uTJ41z/UZKsVqsaNGigmjVr6vz587p69aqqVavmqqYCfwvnfhgZ9Q8jo/5hZNQ/jIrah5FR/8gIwYcBMekLSJcuXdKKFStUv359eXt7KyUlRQcPHtSYMWNUp04dtW/fXqVKlXJ1M4EHhnM/jIz6h5FR/zAy6h9GRe3DyKh/OLDHh0FlNOkbFRWlHj16aPr06fLy8lKpUqX48MNtnTx5UtHR0WrQoIEuXryoYcOGqX379oqLi9Ozzz4ri8Xi6iYCDxznfhgZ9Q8jo/5hZNQ/jIrah5FR/5AIPgyLSV8Y3bFjx2QymbRjxw69+OKLWrduncaOHas5c+aocOHCrm4e8FBw7oeRUf8wMuofRkb9w6iofRgZ9Q+Jpa4M69ZJ35EjR8rT01Njx47Vk08+6eqmAVnC09NTdrtdy5YtU7du3dSlSxdXNwl46Dj3w8iofxgZ9Q8jo/5hVNQ+jIz6h0TwYVhM+sLoQkND1adPH3Xs2JGkH4bBuR9GRv3DyKh/GBn1D6Oi9mFk1D8kyWS32+2ubgSy3v/+9z/t2LGDSV8Ylt1ud254BRgF534YGfUPI6P+YWTUP4yK2oeRUf+QCD4Mi0lfADAezv0wMuofRkb9w8iofxgVtQ8jo/4hEXwAAAAAAAAAAAA34uHqBgAAAAAAAAAAADwoBB8AAAAAAAAAAMBtEHwAAAAAAAAAAAC3QfABAAAAAAAAAADcBsEHAAAAAAAAAABwGwQfAAAAAAAAAADAbRB8AAAAAAAAAAAAt0HwAQAAAAAAAAAA3AbBBwAAAAAAAAAAcBsEHwAAAAAAAAAAwG38H2WbQb6KmtcpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_chainlink_ohlc = cargar_archivo(\"chainlink_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_chainlink_ohlc['Time'] = pd.to_datetime(df_chainlink_ohlc['Time'])\n",
    "df_chainlink_ohlc['Time'] = df_chainlink_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_chainlink_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAIDtCDkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAAICelPcdrboEAACADjNVFwAAAFWYWW5GrSg6jtVWr8S+C8/cuN3O82jVG8MubWS9+uiZqksAAADoIOQAAGDizCw3o3HieNTKctN9jQdP3vhzO8uiubgk6AAAABhRtqsCAGDi1Ipiy4Bj07iy3LTaAwAAgNEh5AAAYOK08zzaWdZ9XJZFO8+HUBEAAAA7YbsqAAAmTqveiObi0qZVGocfOxOXzp67cVtPDgAAgNEm5AAAYCK16o2IWwKM9oGDcfXosYoqgp1bWWtGsd59a7V8No/5OcEdAADjQ8gBAACQsJW1Zpw6fzzKje59ZrLpLBZOLwk6AAAYG3pyAAAAJKxYL3oKOCIiyo2ypxUfAACQCiEHAAD0odem5REalwMAAOw121UBAEAfbte0fCsalwMAAOwtIQcAAPRpq6blAAAADJ+QA+gws9wc6JWpTzx9Lh65/8wgSgMAAAAA6CDkmDB3Pn4uXn3UF85sbfqVl6P+oz8YtbJ748p2lkVzcalr0PF8cWFQ5QEAAAAAdBByTJjsOV84c3u1K1/vKeCIiKiV5fUVHzeFHCtrzSjWO1eBrF69Es9eeqbjWD6bx/ycLT4AAAAAgN0RcgADsbLWjFPnj0e5sTkkeejJkx23s+ksFk4vCToAAAAAgF2pVV0AMB6K9WLLgGMr5Ua5acUHAAAAAEC/hBwkLZ/NI5vOuo7LprPIZ/MhVAQAAAAAwLDYroqkzc81YuH0UtdVAXpAAAAAAACMHyEHyZufawgwAACIiIh2nkc7y6JWbr+NZjvLop1b6QsAAKkTcjCS7nz8XLz66JmqywAAIDGteiOai0tRK7650vfwY2fi0tlzHePaeR6tugtlAAAgdUIORlL23IWqSwAAerSy1rR1JCOlVW9E3BRgtA8cjKtHj1VYEQAAsFeEHAAA7NjKWjNOnT8e5cb2WwNl01ksnF4SdAAAADBQtaoLAAAgXcV60TXgiIgoN8quqz0AAACgX0IOAAAAAAAgSUIOYCDy2Tyy6aynsdl0FvlsvscVAQAAAADjTk8OYCDm5xqxcHqpp61INJ8FAAAAAAZByAEMzPxcY1fhxcxyM2rFN0OS2uqV2HfhmY4x7TyPVl1AAgAAAAAIOYARMbPcjMaJ41ErO5vXNh482XG7nWXRXFwSdAAAAAAAenIAo6FWFJsCji3HlWXHag8AAAAAYHIJOQAAAAAAgCQJOQAAAAAAgCQJOQAAgLFW3ne06hIAAIA9IuQAAADG2quPnqm6BAAAYI8IOWDMtfM82lnWfVyWxbWDB4dQEQAAAADAYMxUXQCwt1r1RjQXl6JWFNuOa+d51zEAMGgzy82efv608zxa9cYQKmLcmGMAADDehBwwAVr1RkQPv7TvE3IAMEQzy81onDgetbLsOradZdFcXPIlNH0xxwAAYPzZrgoAgErUiqKnL58jImplacUhfTPHAABg/Ak5AAAAAACAJAk52DNPPH2u6hIAAAAAABhjQg72zPPFhapLAAAAAABgjAk5AAAAAACAJAk5AAAAAACAJAk5AAAAAACAJAk5ABKUz+aRTWc9jc2ms8hn8z2uCAAAAACGb6bqAgDo3/xcIxZOL0WxXnQdm8/mMT/XGEJVAAAAADBcQg6ARM3PNYQXAAD05Ymnz8Uj95+pugwAgIGxXRUAAABMiOeLC1WXAAAwUEIOAAAAAAAgSUIOAAAAAAAgSXpyMBAra81NDZBXr16JZy8903FMA2QAAAAAAAZFyMGuraw149T541FulJvue+jJkx23s+ksFk4vdQQdM8vNqBWdAUlt9Ursu9AZkLTzPFp1AQkAAAAAANcJOdi1Yr3YMuDYSrlRRrFe3Ag5Zpab0ThxPGrl5sc3HjzZcbudZdFcXBJ0AAAAAAAQEXpyULFaUWwZcGw5tiw3rfgAAAAAAGByCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkzVRdAEDVVtaaUax3b2qfz+YxP9cYQkUAAAAAQC+EHMBEW1lrxqnzx6PcKLuOzaazWDi9JOgAACAJW13Ms3r1Sjx76ZmOYy7mAQBSJuQAJlqxXvQUcERElBtlFOuFXwABABh5213M89CTJztuu5gHAEiZnhwAAAAwZnZyMQ8AQIqEHAAAAAAAQJKEHAAAAAAAQJKEHAAAAAAAQJI0HgcAAJhgM8vNqBWd/Rhqq1di34VnOo618zxadY2pAQAYLUIOAACACTWz3IzGieNRKzc3qG48eLLjdjvLorm4JOgAAGCk2K4KGAntPI92lnUfl2XRzvMhVAQAMP5qRbFlwLHl2LLctOIDAACqZiUHMBJa9UY0F5e6/uJsmwQAgNGy1XZXt/IZDgCAvSLkAEZGq96I8MsvABUq7ztadQkM0JsrRXtZqWC16M5st93VzWx1BQDAXhFyAADAN7z66JmqS2CAbrdS9PBjZ+LS2XMdx6w02Jlet7u6sdWV5xgAgAETcgAAAGNrq5Wi7QMH4+rRYxVVBAAADJLG4wAAAAAAQJKEHAAAAAAAQJKEHAAAAAAAQJKEHAAAADBm8tk8sumsp7HZdBb5bL7HFQEA7A2NxwEAAGDMzM81YuH0UhTrRcfxTzx1Jj72wLmOY/lsHvNzjWGWBwAwMEIOAAAAGEPzc41N4cWBfQfjXYePVVQRAMDg2a4KAAAAAABIkpADAAAAJsQ786NVlwAAMFBCDgAAAJgQj9x/puoSAAAGSsgBAAAAAAAkScgBAAAAAAAkScgBAAAAAAAkaabqAgAm2cpaM4r1ouu4fDaP+bnGECoCAAAAgHQIOQAqsrLWjFPnj0e5UXYdm01nsXB6SdABAAxUO8+jnWVRK7t/HmlnWbTzfAhVAQBA74QcABUp1oueAo6IiHKjjGK9EHIAwACU9x2tuoSR0ao3orm4FLWi+8rSdp5Hq+6zCAAAo0XIAQAATJRXHz1TdQkjpVVvRAgvAABIlMbjAAAAAABAkqzkAAAAgBG1staMYn377cTy2dy2pgDAxBJyAAAAwAhaWWvGqfPHu/Zxy6azWDi9JOgAACaS7aoAAABgBBXrRdeAIyKi3Ci7rvYAABhXQg4AAAAAACBJQg4AAAAAACBJQg4AAAAAACBJQg4AAAAAACBJQg4A9lQ7z6OdZd3HZVm083wIFQEAAAAwLmaqLgBgEGaWm1Eriq7j2nkerXpjCBXxpla9Ec3FpY7/PocfOxOXzp7rGOe/DQAAAAD9EnIAyZtZbkbjxPGolWXXse0si+biki/Th6xVb0Tc9Jy3DxyMq0ePVVgRAAAAAOPAdlVA8mpF0VPAERFRK8ueVnwAAAAAAKPPSo4xttX2PbXVK7HvwjMdx2wRAwAAAABAioQcY2q77XsaD57suG37HgAAAAAAUmS7qjFl+x4AAAAAAMadkAOgIvlsHtl01tPYbDqLfDbf44oAAAAAIC22qwKoyPxcIxZOL0Wx3rmS6hNPnYmPPXCu41g+m8f8nC3lAAAAAOBmQg6ACs3PNTaFFwf2HYx3HT5WUUUAAAAAkA7bVQEAAAAAAEkScgAAAAAAAEkScgAAAAAAAEnaVcjxta99Ld7znvfEU0891XH8P/yH/xA//uM/HsePH4/3ve998ZGPfCS++tWv7qpQAAAAAACAm+045HjppZfigx/8YKyurnYcf/rpp+NDH/pQ3HnnnfH444/Hz//8z8d//a//NX7sx34siqLYdcHA3mnnebSzrLexWRbtPN/jigAAAAAAbm+m3we02+344he/GJ/85Ce3vP9Xf/VX4zu+4zviV37lV6JWu56hvPvd746TJ0/Gb/3Wb8WHPvSh3VUM7JlWvRHNxaWo9RBItvM8WvXGEKoCAAAAANha3yHHxYsX4+zZs/GBD3wg3vve98bDDz/ccf+xY8fiz//5P38j4IiIOHLkSBw4cMCWVZCAVr0RIbwAABiIOx8/F68+eqbqMgAAYGz1HXLcc889sbCwEHffffemXhwREX/jb/yNTce+/OUvx9e//vX4zu/8zp1VCQAAkKDsuQtVlwAAAGOt75Dj0KFDfY0viiJ+4Rd+Ie6666546KGHbjtuaur6/xiMfp/L3Tz/uznXMOvs9e+/+f9JwzjNwYiIqahmDg51/nvPZ4R479+dfp63W99Dd/Me/K3fkkc2nUW5UXZ9XDadxbd+S+6/8RbM/8GaXm7G9C3bftZWr0T27DMdxzbyPDYGuHK2ys8zu3kPqFoK8z/l55fRlcLch71i/jPJUpr//dbYd8jRj1deeSU+9KEPxSuvvBKf+9znYm5u7rZj3z5/KO644469LGeyvHygr+H33nUgon5oR6e6XOv/XO+45xvnGmKd/Xj7/N6fgwHaxTza1fzdI/tn3xLvGMI8v52hzP+K/42wFe/9O9PP++im99BdvH+/Iw7FH37kD+PS65e6Pu7w/sPxbW/9tr7ONWnM/wH46lcjTnxvxPr6prvu/aGTnQdmZyMuXoz4tsHMy0o/z/TxOh7WZ/l+jfL839V7LHQxynMf9pr5zyRLYf6/9tpb+hq/ZyHHxYsX48Mf/nC89tpr8ZnPfCaOHTu27fgXVi7H/v1v7FU5E2ffK6txbx/jX3xlNa4uX97RuV68tNrf+FdW41D7+rmGWWcvpqauv9BfWLkc167t2WkYsN3Mo93M373y+vob8ZU9nOe3M8z5f2T9jXi5gn8jbMV7/+708z5663vo7j8HHIxDcbDr41prEV9Zu9x13CQy/wdn+mtfj8a1a1HrPjTa165F82tfj43pywM5d5WfZ/p5He/1Z/l+pTD/d/MeC7eTwtyHvWL+M8lSmv+vv/5aX+P3JOT4gz/4g/jpn/7pOHDgQHzhC1/oqRfHtWsx8k9uSvp9Lnfz/O/mXMOscxTPw2CM2xy8FtXOv6HMf68xRpD3/p3p5zm79TkexffgSeW53b3WfCOai0tRu2W7qsOPnYlLZ891HGvnebTmG9d/6A9Ala+l3bwHjIpRrStiPJ5fRpc5wyQz/5lkKcz/fusbeMjx/PPPx4c//OG4995747Of/WwcOXJk0KcAAAAYOa16I+KWXhvtAwfj6tHtV7UDAAA7N/CQ42Mf+1i0Wq34yEc+Ei+99FK89NJLN+7L8zy+bUD7zgIAAAAAAJNtoCFHs9mM559/PiIi/ubf/Jub7v/RH/3R+KVf+qVBnhIAAAAAAJhQuwo5Hnjggbh48eKN241Go+M2AAAAAADAXtmTxuOkb2WtGcV60XVcPpsPoRoAAAAAANhMyMEmK2vNOHX+eJQbZdex2XQWn/pznx9CVQAAAAAA0KlWdQGMnmK96CngiIgoN8q4cvXKHlcEAAAAAACbCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAAAAAAIAkCTkAANixfDaPbDrrOi6bziKfzYdQEQAAAJNkpuoCAABI1/xcIxZOL0WxXmw7Lp/NY36uMaSqAAAAmBRCDirVzvNoZ1nUyrL72CyLdu4KUAAYNfNzDQEG3EZ539GqSwAAgLEm5KBSrXojmotLUSu2v/oz4nog0qr7AgUAgHS8+uiZqksAAICxJuSgcq16I0J4AQAAAABAnzQeZ9cO7ntrTw1HIzQdBQAAAABgcKzkYNfetv9ITw1HIzQdZTzNLDe7brlmuzUAAAAAGDwhBwOh4SiTama5GY0Tx6NWltuOa2dZNBeXBB0AAAAAMEBCDoBdqBVF14AjIqJWltdXewg5AIDbsDoUAAD6J+QAAAComNWhAACwM0IOAEaaq1oBmASTujq0l5/zEX7WAwBwe0IOAEaWq1oBYHz1+nM+ws96AABur1Z1AQBwO31f1QoAJKPXn/MRftYDAHB7VnIA7EI7z6OdZT2tNGjn+ZCqAgCAdKysNaNY7x5i5bN5zM9ZzQMAdBJyAOxCq96I5uJSx5WFhx87E5fOnusYZx9pAABSM4zeaCtrzTh1/niUG91X9WTTWSycXhJ0AAAdhBwAu9SqNzqaf7YPHIyrR49VWBEAAOzOsHqjFetFTwFHRES5UUaxXgg5AIAOenIAAAAAHfRGAwBSIeQAAAAAAACSJOQAAAAAAACSJOQAAAAAAACSJOQAktfO82hnWW9jsyzaeb7HFe3OO/OjVZcAAAAAAEmYqboAgN1q1RvRXFza1PDw8GNn4tLZcx3H2nkerXpjmOX17ZH7z1RdAgAAAAAkQcgBjIVWvRFxS3jRPnAwrh49VlFFAAAAAMBeE3IAAAAAyZhZbm5axX2rFFZwAwCDIeQAAAAAkvAtL70cjR/9waiV5bbj2lkWzcUlQQcATACNxwEAAIAk7Lv89a4BR0RErSy7rvYAAMaDkAMAAAAAAEiSkAMAgEq08zzaWdbb2CyLdp7vcUUAAACkRk8OAAAq0ao3orm41NN2IhrIAgAAsBUhBwAAlWnVGxHCCwAAAHbIdlUAAAAAAECShBzARMtn88ime9sPPpvOIp+1HzwAABFPPH2u6hIAAAjbVY2tNxt51sqy+1iNPJlg83ONWDi9FMV69/3g89k85udsqQIAQMTzxYWqSxgLb150VG50/901m87i4L6DQ6gKAEiJkGNMaeQJvZufawgvAACgAv1edPS2F7qPAwAmi5BjjGnkCQAAwKjr76IjIQcA0ElPDgAAAAAAIElCDgAAAAAAIElCDgAAAAAAIElCDgAAAAAAIElCDgAAAAAAIElCDgAAAAAAIElCDgAAAMbCE0+fq7oEAACGTMgBAADAWHi+uFB1CQAADJmQAwAAAAAASJKQAwAAAAAASJKQAxhb5X1Hqy4BAGCstfM82lnWfVyWRTvPh1BRnz7+8aorAABgl2aqLgBgr7z66JmqSwAAGGuteiOai0tRK4ptx7XzPFr1xpCq6sN//s9VVwAAwC4JOQAGzAoSAGCStOqNiFEMMAAAmAi2qwIYMCtIAAAAAGA4hBwAAAAAAECShBwAAAAAAECS9OQAAJhwK2vNKNa3bxocEZHP5jE/Z999YDDaeR7tLItaWXYfm2XRzvMhVLW1rd4nV69eiWcvPdNxzPskAMDwCTkAACbYylozTp0/HuVG9y8Zs+ksFk4v+QIPGIhWvRHNxaWoFZ3hweHHzsSls+c6jrXz/HqD8wps9z750JMnO257nwQAGD4hBwDABCvWi54CjoiIcqOMYr3w5R0wMK16I+KW8KJ94GBcPXqsooo28z4JADDa9OQAAAAAAACSJOQAAAAAAACSJOQAAAAAAACSJOQAAACAEZTP5pFNZ13HZdNZ5LP5ECoCABg9Go8DAADACJqfa8TC6aUo1ottx+WzuWbnAMDEEnKwyZtXC5UbZdexrhgCAADYO/NzDQEGAMA2hBxscrurhT7x1Jn42APnOo65YggAAAAAgKoIOdjSVlcLHdh3MN51+FhFFQEAAAAAQCeNxwEAAICJdefj57oPAgBGlpADAAAAmFjZcxeqLgEA2AUhBwAAAAAAkCQ9OQAAABh7M8vNqBXFjdtTUxHx9a/HvgvPxLVr14+18zxa9c7ehPlsHtl0FuVG2fUc2XQW+Wy+6fjKWjOK9WKLR3Se59a+iAAAdCfkAAAAYKzNLDejceJ41MrNQcW9v3vyxp/bWRbNxaWOoGN+rhELp5e6hhQRWwcVK2vNOHX+eNeQJJvOYuH0kqADAKBPQg4AAADGWq0otgw4No0ry+urPW5ZzTE/19hx+FCsFz2tAik3yijWCyEHAECf9OQAAABgZJT3Ha26BBiaOx8/V3UJAJA8IQcAAEDF2nke7SzrPi7Lop1v7vkwTl599EzVJcDQZM9dqLoEAEie7aoAAAAq1qo3orm41NEY+/BjZ+LS2c6rvLdqjD2ptmrmvXr1Sjx76ZmOY/lsHt8+xLoAABguIQcAAMAIaNUbHb0g2gcOxtWjxyqsaHRt18z7oSdPdtzOprP43ff+dtSzrGtfjklYKQMAMG6EHAAAACSl12beEdcber+UZ5tWykxNRdx714F48ZXVuHbt+jErZQAA0iPkAAAAYOzdulJmaioi6ofi6vLlGyEHAADp0XgcAAAAAABIkpUcAAAAwESYWW52bFsWEVFbvRL7LnQ2rLd1GQCkQ8gBAAAAjL2Z5WY0ThzfsgF948GTHbfb2fU+LoIOABh9tqsCAAAAxl6tKLYMOLYcW5abVnwAAKPJSo4R98rrX4tXXn952zF37T8Sd+2/e0gVAQAAQP+eePpcPHL/marLAADGjJBjxP36//O5+N//0ye3HfORP/2z8b+8+38dUkUAAAD0Kp/NI5vOotzYfgVBNp1FPpsPqapqPF9cqLqESvXSD0QvEADon5BjxP3Yd/9kfP+3/dC2Y+7af2RI1QAAAKOm1y/RIybji/RRMz/XiIXTS1Gsf/PL7U88dSY+9sC5jnH5bB7zc6Pz5XY7z6OdZV23d2pnWbTzyZlTWwUVt9oqqOi1H4heIADQPyHHiLtr/922ogIAAG5rqy/Rb2fUvkifFPNzjY7n/cC+g/Guw8cqrKi7Vr0RzcWlji/0Dz92Ji6d7Qxnhr3yoMrwZbug4tZz3xpU9NoP5EYvECEHAPRMyAEAAJC4W79Eh0Fo1RsdX7a3DxyMq0erDWe2Cl8iNgcwexG+CCoAYDQJOQAAAIBk3Bq+RIxGAAMAVKNWdQEAAAAAu1Hed3TPz/HmVlldx01YnxIAqJqVHAAAAEDSXn30zJ6fY1T7lADApBNyAAAAAPRgFPuUAMCks10VAAAAAACQJCEHAADACBpGjwEAAEidkAMAAGAEDaPHANV4Zy7AAgAYFCEHAAAADNEj9wuwAAAGRcgBAAAAwFA88fS5qksAYMwIOQAAAAASc+fjaYYFzxcXqi4BgDEj5AAAAABITPacsAAAIoQcAAAAAABAomaqLgAAAAAYLytrzSjWi45jq1evxLOXnrlxO5/NY36uMezSKtHO82hnWdTKcvtxWRbtPB9SVQAwHoQcAAAAwMCsrDXj1PnjUW5s/kL/oSdP3vhzNp3FwumliQg6WvVGNBeXolZ0Bj8Hf+3/jCs/8cEbt9t5Hq36+D8fADBIQg4AAABgYIr1YsuA41blRhnFejERIUfE9aAjbgkwLv3yr1RUDQCMDz05AAAAAACAJAk5AAAASEo+m0c2nfU0NpvOIp/V4wAAYFzZrgoAAICkzM81YuH00qbG1luZpObWbK/X5t8RGoAPSi8N6CO8TgHYHSEHAAAAyZmfa/hSlL7crvn3VjQA371eG9BHTFYTegAGT8gBAAAADMyb24l1az5exVZiWzX/TsHMcnNTOFNbvRL7LnSuiBilcKbXBvQRk9eEHoDBEnIAAAAAA9PrdmK2KOrNzHIzGieOb7nNVuPBkx2321kWzcWlkQk6AGAYhBwAAADAQNlObHBqRdFTH5GIiFpZXl/xIeQAYILUqi4AAAAAAABgJ4QcAAAAAABAkoQcAAAAQFflfUerLgEAYBMhBwAAANDVq4+eqboE6Mudj5+rugQAhkDIAQAAADCi2nke7SzrbWyWRTvP97iidGTPXai6BACGYKbqAgAAAADYWqveiObiUtSKouvYdp5Hq94YQlUAMDqEHAAAAAAjrFVvRAgvAGBLtqsCAAAAAACSJOQAAAAAAACSZLsqAAAAAJI2s9zc1Lektnol9l14puOYviUA40fIAQAAAECyZpab0ThxPGpluem+xoMnO263syyai0uCDoAxYrsqAAAAAJJVK4otA44tx5blphUfAKRNyAEAAAAAACRJyAEAAAAAACRJTw4AAAAAKrey1oxiffutpPLZPObn9NMA4JuEHAAAAABU6v99/eX4n/+vH4xyY/veGtl0FgunlwQdANxguyoAAAAAKnXl6te7BhwREeVG2XW1BwCTRchBz96ZH626BAAAAAAAuMF2VfTskfvPVF0CAAAA0KeZ5WbUiuurH6amIuLlA7HvldW4du2bY9p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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cosmos_ohlc = cargar_archivo(\"cosmos_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_cosmos_ohlc['Time'] = pd.to_datetime(df_cosmos_ohlc['Time'])\n",
    "df_cosmos_ohlc['Time'] = df_cosmos_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_cosmos_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ethereum_ohlc = cargar_archivo(\"ethereum_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_ethereum_ohlc['Time'] = pd.to_datetime(df_ethereum_ohlc['Time'])\n",
    "df_ethereum_ohlc['Time'] = df_ethereum_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_ethereum_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_iota_ohlc = cargar_archivo(\"iota_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_iota_ohlc['Time'] = pd.to_datetime(df_iota_ohlc['Time'])\n",
    "df_iota_ohlc['Time'] = df_iota_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_iota_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lyxb98MMPslqt7sdnz56tyZMnu69c9vX11dChQ93TnQIFydGjRzVz5ky9/PLLuQbc9H2A7L755hs1btxYZcuW1e+//64hQ4botttu09ixY9132br+bk6fPq2TJ0/qzjvv9HKr88/1jaYBhVx6errmzJkjm82mkJAQ3XbbbSpRokSOwS7XFyuTyeT+J0nNmzfXb7/9pgULFmjNmjVq3ry57Ha7SpcurXfeeUcZGRk6efKkIiMjvXJ8wNXExsbq9ddfV/fu3dWhQ4dsg8SXcw0yvPzyy+ratasWLVqkypUrKyAgQOnp6bJarWrbtq0WLFhw3WEM4G3p6emaMWOG7Ha7wsLCVLduXVWoUCHb+f/yNaxcXI/9+OOPqlWrliIiItzPOZ1Od3hy+baEJyho0tPTNWvWLPn5+Sk8PFy33367SpUqlaP/46p9z1p2LSS8ePFiNWzYUFWqVJEkXbhwQUlJSUpLS9Mzzzyjp59+Op+PDLi2Dz/8UKmpqXr++edVsmTJHCGKKzgPCwtT27Zt9f3332vJkiV67LHH5Ovrq6ysLEVFRalKlSras2ePJKlJkyaqX7++zp8/r7i4ONWuXdtbhwdcUXp6ut58803Nnz9fgwcPVpMmTXIMILvO+64+vevv4/z581q5cqU6duzoDk9cz3Xr1k3t27fXmTNndObMGTVs2DB/Dwy4DpfXvyQ99NBDud4dQt8HRVFGRoYWL16s8uXLKzQ0VBUrVpSkXC8kcXF9L3j44Yfdfwd169ZV8+bNtW7dOl28eNEdoFy+pEFwcHA+HFHBwQgYirwFCxZo5MiRCg0Ndd+aFhkZqUmTJmVb7M41x31uX6x8fHzUqVMnbdmyRf/973/VvHlz91UHkuTn50d4ggLH9SF58uRJbd68WYGBgbr33ntzXInputvK9cWqZs2a6tu3r+bPn6/Q0FD16tVLVqtV6enp2rZtmypWrCi73c4dKCjwFi9erBEjRqhs2bIym806duyYwsPDNWfOnGzTbOV2/s/KytLBgwe1d+9eRUdHy8fHRw6HQzExMfryyy/12GOPqWrVqt44LOC6LFy4UKNGjVJQUJAyMzN17tw5NWnSRBMmTJCPj0+2O688699sNisrK0t+fn566qmnNHLkSC1ZssQ9z31gYKAGDRqkO++8U4GBgV48SiAn14UiK1euVFpamho1aqTWrVvnGDhwOBzubZ966imtXbtW8+bNU506dVSjRg339rfccosOHDjgHmDw8/NT2bJlVbZsWW8cHnBV06ZN0/jx4xUREaGJEyeqadOmV7z6/vJzv+t/t2zZovPnz6tFixbubY8ePaq1a9eqXbt2Kl++vAIDA1nvEwXStGnT9PHHH6tatWrq1q2bfvnlF9166605tqPvg6Jo/vz5GjNmjIoXL66EhAQVK1ZMQ4cOVadOnbLVumf9e14E6LozMSAgQElJSfL398/X4yioGPlCkXbkyBFNmDBBnTt31sSJE/X1119r9OjROn78uKKjo3XmzBlJcg8Gm0wmLVu2TF9//bX7NVwDxLVq1dJ9992nHTt2uBeTZAAZBc3lszK6PhD37t0rp9OpPXv2aNasWdm2d314+vj4KCYmRr169dJ//vMfvfjii7rjjjv0ySef6PPPP9dvv/2mJUuW6OjRo2rTpo0sFgu1jwLt2LFjmjx5sh555BFNmTJFM2fO1KhRo3Ty5EmNHTtWktxrnbjO/66rzaRLHcktW7bIZDKpVatWSkpK0qeffqqHH35YmzZtyjalBVDQ7Nu3T5MmTVKvXr00depUzZs3TwMHDtSaNWs0ZcoUSZdCwsvrf/Xq1VqzZo37NVxfpjp37qzatWtr7ty5+uWXX9zPN2/enAEEFEhms1kpKSk6f/68YmNjtXTpUp0+fVqS3NP0umrfYrFoxYoVOnr0qKKjo3XhwgWNHDlSGRkZSktL0/Hjx7Vp0yZ16NCBuwxRoKWnp+vdd9/VmDFj9MYbb2jq1Klq0aJFtvDEs/5NJpN+++03/e9//3Nvs379etlsNjVq1EgJCQn69NNP1blzZ/3888/8DaDA2rBhg+655x5NmzZNgwcP1meffabQ0FD5+PgoKSnJvZ1n/dP3QVGxf/9+ffrpp+rRo4emTZumuXPnqnz58po0aZIOHjwoKff6d61xsnPnTq1atUrSpX6UKzjv3r17tgvPjYzRLxRpixYt0qlTp9SxY0eFhISoRIkSatSokfr06aMVK1bop59+UkZGhiwWiw4fPqy+ffvqhRde0KlTp5SVleV+HdedJu3atVOHDh1YIAwF1uV3lTidTp05c0bz5s1T+/btFRISooULFyouLi7beg92u13/93//p/bt26tYsWLq2LGj/Pz8NHToUHXp0kUffPCBhgwZouHDh6tz5856/PHHvXyUQE6eS7qtXr1acXFxatu2rYKDg1W8eHE1btxYtWvX1sqVK5Weni6LxSIfHx8dPXpUffv21aBBg3TgwAH3a2zcuFGVKlXS+vXr1a1bN02fPl3R0dFasmRJtim9AG/zrP/FixcrJSVFHTt2VIUKFRQYGKhOnTopIiJCGzZsUGZmprv+Y2Ji9MQTT2jAgAE6dOhQttdx9YX69++vkiVLZpu6DigIclvO0+l0Kj09XdWqVVPLli31ww8/aN26dZL+mqb38tp/7rnntGXLFtWvX18DBw7U8ePH1axZMz399NPq16+fTCaT2rVrl9+HBlzT5fVvsVgUGBioiIgIVatWTTabTWazWQkJCYqLi1NmZmaO+u/Zs6eefvppxcTEuF/nf//7n26//XatWbNG3bp10xdffKG33npLM2bMULly5bxwlMDVJSYmaubMmWrbtq1mzZqlnj17uqegli4tlu3ief6n74PCyrP/s3LlSsXHx6tly5YKDw9XpUqV1K9fPx0+fDjbtNWe9X/kyBFJl747DBo0SN27d9e7776r559/XllZWbrvvvvy/dgKKqbwQpGWkpKiW265RTVq1JAk98JgycnJcjgcmjNnjho1aqRz586pT58+atq0qebPn58jIHFdaR8eHq73338/348DuJY///xTr7/+ulq3bq3u3bu7p57bsGGDzp49q8mTJ2vNmjWaOHGiZs6cqZdeesld12PHjtWuXbs0YcIENW/e3D21UeXKlTV06FD16NFDf/zxhypXrpytAwoUJK7w0DXFSmJiovz9/d1TTNjtdpUqVUq+vr4qWbKkO0T86aefNGTIEDVr1sx9/nc6nbLb7bLb7YqJidHw4cPVs2dPRUdHe/MQgSvynJooMTFRgYGBCgsLk3Sp/xMcHCyLxSI/Pz/5+vrK4XDo22+/1YgRI3TPPfdo3rx5ql69erbXcX3hql+/vhYuXJg/BwPkgeeUFK6rKlNSUrRt2zYtW7ZMPXv21KxZs1S7dm3deuutuda+a0rGTp06qXbt2vrhhx904sQJ3X333XrmmWe8dXjAVV1e/z4+PqpVq5YWLVqkgwcPqkaNGnrnnXe0ePFiBQQEKDg4WA888IC6deumCRMmaMKECWrRooW+/fZb93fl+Ph4nTp1SgcOHND69evp+6BQCAoK0ltvvaWgoKBsMyTY7Xb5+Pjo7NmzKlOmjPvxmTNn0vdBoZfbeiY+Pj4qWbKk++dTp06pXr16Kl68uLuP9PXXX2vEiBFq3rx5tv7PE088oSpVqmjdunWKiYlR+/bt9dxzz+Xb8RQGJmdul+0Ahczs2bP1/fffq3Tp0oqMjFS/fv1UunRpzZs3T9HR0ZowYYLuvfde9/bDhg3TrbfeqnHjxun5559X27ZtdebMGdWsWTPXOWKBgso1h/2ePXv06KOPql69evroo4/cH5y//PKLLly4oA4dOigjI0MvvPCCjh07plGjRqlOnTqSpPPnz8vHx0cBAQFePBLgxniGh64AZdeuXZo2bZqGDBmi0NBQSZcW1Xv88ccVFBTknsZo+/btysjIUJ06dXKc/19++WVlZGRo2LBhzHWPAmn27Nn66aefdMstt+i2225T3759ZTabtWzZMi1cuFBvvfWWe9AgOTlZDzzwgJo2barhw4dLklatWiWr1aq77rqL/g8KlSvVvnQpSDly5IheeOEFzZ07V5s2bVL//v01aNAgdxiyevVqWSwWNWjQIMcUR65BCddgA1DQeNZ/nz593IO+HTt2VHh4uIoVK6aYmBj3d4Dly5dr7969+uqrr3Tu3Dn5+Phku3DK5aGHHlJISIjeeust+j4okC6v/ypVqujJJ5/Mdq52fReYPHmyvvzyS/3888/Z1nBYuXKl/P396fugULrS+f/w4cPq2rWr6tSpo/vvv1/p6ekaOXKkbDabHA6HGjdurMGDB+vUqVNKTU1Vw4YNr1j/GRkZ/G3kggAFhZbT6VRmZqbGjBmjJUuWqF27doqPj9eqVatUs2ZNvfLKK4qMjNQLL7ygrVu3qlOnTqpUqZK+++47+fr6aurUqfrggw+0e/duLV261NuHA/wtc+bM0bBhw1SuXDk99thjGjBggPu5rKwsOZ1OWSwWrVmzRsOHD1fdunX1n//8R1L2wQKgsLhWeJienq60tDSVKlXKvU9MTIy6dOmiF154Qb17977ia7u+eNF5REHkdDqVlpam4cOHa+3atWrRooWOHz+uLVu2qG3btnr++ecVFRWlM2fOZBv82rp1q5544gmNHDlSDzzwgBePALgxV6v99u3bq3///u67yHfu3KmnnnpKK1euVEBAgPr3768jR47okUce0e7duzVy5EguHEGhcq3679u3r2rWrKlvvvlGb7/9tqpVq6ZRo0apatWqMpvNOnTokN5++20lJSVpwYIFub6+yWRSUlJStiuYgYLges//rj68JP3www8aMWKEvvjiC9WoUSPbc0BhcrX6b9eunQYMGKCqVavq559/1sKFC7V161alpKRowIABuuOOO7Rr1y599913KlasmBYsWMDFITeI3xoKLZPJpISEBP38888aMGCA3njjDY0bN05TpkxRamqq3nzzTRUvXtx998nu3bv11VdfqW3btpo9e7YqVKigu+66S3/88Yd27tzp7cMBbojT6VRSUpJmzZqlli1bqlKlSlqyZIl7LlfXVF4Wy6UZG5s3b667775bW7du1ZIlS9yvARQ2rtBv3759ysjI0OHDhzVz5kz381ar1R2eZGZmSpJWrFihjIwMNWjQ4Kqv7fpyRXiCgshkMikuLk7r16/Xyy+/rBEjRmjGjBn68MMPtW3bNo0dO1Z2u11ly5ZVVlaWu/6XL18uf39/1apVK8dr8jmAwuBqtb9161Z9/PHHstvtkqSTJ0+qQoUK7qvrBwwYoBMnTuijjz6S0+mUn58fdY9C5Vr1P3HiRNntdjVs2FBVq1ZV69atVb169WxTUbdv317Hjh3Ttm3bcn19SYQnKJCu9/zv4+PjXsMkICBAmZmZOnXqlCQRnqDQulb9f/TRR8rMzFTr1q319ttvq2TJkurdu7f69Omjxo0bq3///ho8eLAOHjyoZcuWSaLvfyMIUFCobd++XRcuXFCzZs3cnb4GDRqoZ8+eOnHihEaPHq2AgACNHj1as2fP1po1a/TKK6+4rziz2WwymUwKCgry4lEAN85kMmnLli06fvy4XnrpJXXv3l3nz5/X119/LUnZri5wOBySpMcff1wlSpTQnDlzlJycLLPZ7H4OKCyuJzx0cQ2grVy5UjVr1sy2ztWBAwd07ty5/G088Ddt3LhRFy9eVLt27dzn+Xbt2qlr167asWOHpk2bJunSZ8Tl9d+4cWNFRES4X+fAgQPu7YDC4Eq1/9BDD2nnzp2aOnWqpEsDZf7+/jKZTHr//ffVo0cPhYSEyGKxqFOnTvLz86PuUehcrf537NihGTNmqHLlyho7dqz69Okj6VJ/KSsrS35+fqpUqZJMJlO2u3OBwuJa539X38c1MNysWTNZLBbFxMRI+mtxeKAwulrff+fOnfr8888lXRojPXz4sB588EH5+vq6LyypXLmy/P39dfHiRUn0/W8EAQoKJdeHYmhoqJKTk92DXxkZGZKk1q1bq1OnTvrmm2907Ngx+fn5KTU1Vfv371dycrJ7219//VWhoaHcwo9CLTAwUEOHDlVUVJTuv/9+1a1bV+vWrdP69esl/dVZdH3QVqtWTffdd5927dqlL774IttzQGGRl/BQkg4ePKjdu3erffv2MpvNiouL03vvvafOnTvrhx9+8MYhAHnm6v+UK1dOFy9e1PHjxyX91f95+OGHVbNmTc2ePVunTp1y/x1s27ZNx44dU/v27SVJcXFxGjNmjDp37qzZs2d74UiAvLne2p8zZ47OnTunP//8U7t371bTpk21ZMkSjRs3Tj/++KP8/f01ffp0nThxwmvHAuTV9dT/HXfcoZkzZ+rUqVOKiopSVlaWDh8+LJPJ5J6W9Ndff1XZsmW5ywSFSl77PhaLRQ6HQxkZGWrcuLF7IXjuQEFhlJf6//PPP+Xr66tixYrp559/liRZLBbZ7XZt2rRJpUuXdq+Di7xjxAyFkistLV68uKpUqaKvvvpKkty345cqVUrt27dX+fLl9cknn0iS0tLSNGjQIA0dOlTfffed5s6dq1WrVqlLly6y2WxeOxbg77rrrrv0wAMPuIOSxx57TGazWTNnznTP9er64HVdld+tWzfdcccdioqK8lq7gb/rWuHh5XehbNu2TQ6HQ2FhYZo3b566du2qpUuXasKECVddDwUoSFz9n1KlSqlixYruQQE/Pz85HA4FBwerQ4cOysrK0jfffOPeb/369fLz81O5cuU0e/ZsdevWTT/99JMmTJigbt26eeVYgLy43trPzMzU999/r1q1aikoKEj9+/fXnDlz1KZNG5UoUUIDBgxQQkJCjoWzgYLsRs7969atU//+/fXZZ59p5cqVmj17tubPn68HH3xQZcqU8dqxAHl1I/VvNptVvHhx1apVS6mpqVq+fLkkpi1C4ZOX/s+8efNUt25d3X777ZoyZYrmz5+vdevWadasWfr888913333KSwszJuHU6gRoKBQi4iIUN26dbVr1y5t3LhRkty3qNWtW1eNGzfWgQMHdOzYMYWEhKhv3746d+6cPvvsM02ePFm9e/fWk08+6c1DAHI1e/ZsffLJJ5o9e7Z+//13SbnfduzqBFosFvdVNfXr11fLli21e/duzZ07N9t2rum6ypYtq8mTJ+u+++7Lj8MBboprhYdms9n9mXDhwgXZ7Xa9/fbbeuONN9SrVy+tXLlS9957rzcPAbghd955pypWrKhNmzZp//79kv76jGjTpo3Cw8O1Z88e/fnnn5KklJQUOZ1Ovf7663r77bfVs2dP6h+F0rVqPyIiQhs3blT58uW1cOFC9e3b1z1lryT17t1bixYtUnBwsNeOAbhR13Pu3717t1JSUhQVFaUaNWro448/1vDhwzVlyhQ9+eSTevbZZ715CMANu96+z+VT8zZp0kSnT5/W2rVrlZWVxbRFKLSut//j6+urIUOGqF69eho2bJjeeOMNffLJJ+rZs6deeeUV99q4yDsCFBRI1zN47HA4ZDKZ1KFDB/n6+rqnbfH19VVGRob8/PzUoEED/fHHH+4FVLt3766vv/5akyZN0pIlS9S3b9/8PTDgGnbu3Kn7779fEydO1IYNG/TOO++ob9++OnjwYLY7SVzzGV/eCXQ4HNnWOSlTpozmz5+vs2fPZlvnxDWlC7cxoyD6p8NDV71nZmbKZDKpZcuW2rhxowYMGJAfhwPkyZIlS7RmzRrt3bvX/Zjn1ZKuOwsfeeQRnThxQgsWLJD0V/+nWLFiatGihfbs2SNfX185HA4lJCQoMzNTDRs2pP5RIP1Ttd+8eXPt3btXvr6+stlsOaZz5OpjFET/9Lk/KytL1apV0/jx47VixQp9/PHHWrp0KRcOokD6p+vfdd53Op2qXr263nvvPb3++ut890WB9E/X/8WLF1W3bl33+f/DDz/UypUrGfv8BxA9oUDZuXOnoqOjlZqaqtDQUO3cuVOBgYH6/PPPVaVKFTmdTplMJjmdTjkcDpnNZjVq1EitW7fWvHnzNGPGDPXs2VN+fn6SpDJlyigpKck9+OZ0OuXj46PKlSt78zCBXKWlpWn8+PGqVq2ann/+eYWEhGj37t16++239dZbb7lDQrvd7h403rt3r9auXasBAwZk6yyGhYWpc+fO+uSTTzRlyhRFR0ezzgkKtLye/y//EnR5OPj4449r8+bNmj9/vlq2bOmepqJly5a6//77uW0ZBdKWLVsUHR2ttLQ0+fj46NSpUxo0aJB69OihgICAbPXv+lLVrl07LVmyRMuXL9cdd9zhvqBEkm655RZduHBBFy5cUOnSpdW9e3cNGDAg2wLyQEHwT9d+mTJldOHCBSUnJysoKCjH+3H1MQqSm3HuT05OVlJSknudk3LlyqlcuXJeO0bgSm5W3yclJUWlS5d2v49r/TegILkZ9Z+SkpLt/B8SEqKQkBCvHWNRw2gaCozLB48///xzTZkyRVOnTlXp0qX11ltvubez2+0ymUyyWCzauXOnvvrqKz3//PO688479Z///EebN29WQkKCzp8/r++//14NGjRwByZ8aUJBtn37dm3evFktW7ZURESE/P39VbduXd1zzz3auXOnjh496q79ixcv6vXXX1eXLl20detWpaen53i9Ll26qHbt2qpataoXjga4fnk9/7vCw0mTJkm6FJyYzeZs4eGhQ4c0ZcoU975Vq1YlPEGBlJKSonHjxqlevXr66quv9M0336hbt26aMmWKtm7dKulS/8V116HFYtHevXu1ePFiDRs2TLfccos+/PBDHTt2TBkZGTp//rx+/PFHNW3aVOHh4ZKkOnXqEJ6gwLmZtR8aGurlowOujvqHkeVH/TP2g4KK83/hRICCAuNGBo8ffvhhrV69WlarVS+++KLuvvtu9e3bV48++qj69OmjtWvX6pFHHmGhSBRIiYmJ7vUZpEuDyGlpaapevbqkS1MO+fj4qHjx4ipevLj7zqqdO3eqTZs22rFjhz777DNNnDhRVqvV/Tomk0kOh0MBAQGaOHGiunTpkr8HBuQR4SGMbOvWrdq1a5d7/u6QkBANHDhQKSkp2b78+/j46OLFi3rttdfUpUsXrV69WmXKlNGgQYNUunRpderUST179lS/fv20fft2de3a1YtHBVwbtQ8jo/5hZNQ/jIz6L5yYwgtek5iYqICAAPciRrkNHvv6+uY6ePzss88qKChIn332mRo0aCBJioqK0sSJEzV//nwdOXJETqdTTz75ZLbbN4GCYMOGDRo/frwcDoecTqeeeOIJtW7dWnfeeac6dOig+Ph4RUVFuYO/06dPq0SJEu5bMQMDAzVy5EjVq1dPAQEBub6Ha7ouFglDQfRPnf8bNWqUrcY9w0PqHwWRZ/0nJiYqLS1NNWrUcG+zZcsWlSlTRiaTSfHx8bLZbNqwYYNeeOEFhYSEaPLkyWrUqJEk6e6771bdunX19ddf68SJE/L19dWkSZNks9m8cnzAlVD7MDLqH0ZG/cPIqP+iweRkJT3ksysNHqelpemtt97So48+qvr167u3/7//+z+tW7dOCxYsUGBgoI4eParjx4/nGDx2LawEFFQOh0NTpkzR9OnT1bp1a5UvX16rVq1SbGysevTooYEDB+rs2bPuNRskKT09Xd26dVPlypU1btw491yYQGF0s87/QGFwpfpPTU3VunXr1LJlS5UoUUI7d+5Uv379ZLfbZbVaFRAQoMGDB+uOO+7Qjh07dM8999D/QaFC7cPIqH8YGfUPI6P+ixYCFOQbBo9hdPHx8XriiSfUqlUrPfvss7Jarbp48aIGDRqkY8eOafr06QoODpb010Lx69ev11NPPaWRI0eqS5cu/A2gUOL8DyO7Wv0//vjjeu6557LV91dffaW4uDi1bt1acXFxmj9/vjZu3KglS5YoLCyMvwUUGtQ+jIz6h5FR/zAy6r+IcgL55Ny5c86OHTs6x44d60xLS3M6nU5namqqs1+/fs62bds6T5065d42MzPT6XQ6nevWrXNWq1bN+f333zudTqfT4XDkf8OBv8lVt2vXrnVWrVrVefz4cafT6XRmZWU5nU6n86OPPnI2aNDAGRcX597H9dyIESOcjRs3zvbc8ePHnYcOHcqv5gN/G+d/GNn11r/dbnc/l5yc7N5/y5YtzgYNGjg/+OCD/G888DdQ+zAy6h9GRv3DyKj/oolF5HHTOf//TU579uzRwYMH1bVrV1mtVjkcDhUrVkw1a9ZUYmKiHA6Hex/X+g2rVq1S6dKl1bhxY0mX5rePjY3V4cOH8/9AgDw4cuSIdu7cqfj4ePfVAhaLRaVLl1ZMTIwkuWs+KytLTqfTXffSpb+BtLQ0rVq1Sk2bNlX58uV1/vx5zZgxQ23bttX48ePdf1tAQcX5H0Z2PfWfkJDgrn/XrfjFihVTiRIllJWVJUmqWrWqfH19VaxYMe8cCJBH1D6MjPqHkVH/MDLqv2gjQMFNweAxjCo5OVlDhgxR9+7d9fTTT6tz586aNm2anE6nqlevriFDhqhixYqS/vqA3bZtmypXrqzSpUtnq+vff/9dp0+fVpUqVbRx40Y9/vjjGjt2rIYOHapx48ZxGycKJM7/MLK81r+kbPWfkZGh/fv3S7r0pcrpdGrjxo3KzMxUzZo18/FIgLyh9mFk1D+MjPqHkVH/xmHxdgNQtCQnJ+vNN9/UL7/8Ih8fH1ksFvXp00d9+vS57sFj10nHc/B4xIgROnnypIYOHarevXt76xCBqxo/frx+//13jR07Vmlpadq1a5fGjBmj8+fP6+mnn1bXrl3d2/r6+ioxMVH79+9Xz5495efnl+21UlNTZbfbNWPGDJ05c0b/+te/NHfu3BzbAQUB538Y2T9V/ydPntRbb72lsLAw3X333fL399f48eNVt25d1a5d25uHCOSK2oeRUf8wMuofRkb9Gw8BCv5RDB7DqBwOh86cOaMlS5bo8ccf19133y2z2ax7771XSUlJmjdvnkqXLq3evXvL4XC4rzpYuXKlLly4oKZNm+Z4zczMTJnNZlWrVk3Tp09XREREPh8VcP04/8PI/m79u75Y3XLLLWrfvr2mTp2qbdu2yeFwqFmzZho2bJh8fX29dXjAFVH7MDLqH0ZG/cPIqH/jIUDBP4LBYxhRXFyczp49qwoVKqhMmTIqXry4kpKSFBkZKbPZrPT0dFmtVr344ov6/ffftXDhQjVp0kRVqlRRRkaG/Pz8tGjRIkVGRqpKlSqSLl3JsGDBAqWnp6tHjx6aNWuW7rjjDi8fKXBlnP9hZP9U/bvuvgoICFDv3r314IMPKikpScWLF1eZMmW8dnzAlVD7MDLqH0ZG/cPIqH/jYg0U3LC4uDjt3LlTZ8+eldlsznXwWJJefPFFhYWFaeHChTp48KDMZrMyMjIkKdfB46+//lrTpk1Tw4YNNWvWLE2ePJnBMxQoaWlpev311/XYY4/phRdeUKdOnTRp0iTFxcUpKipKa9eulSRZrVbZ7XaVKlVKjz76qBISEjR37lxJkp+fny5cuKDt27fr3nvvVUBAgNatW6e+fftq1KhRysrKkp+fH+EJCiTO/zCym1X/33zzjT755BNJUlBQkMLDw/kChQKF2oeRUf8wMuofRkb9QyJAwQ1g8BhGlpCQoCFDhmjfvn0aNmyYoqOj9dBDD2ncuHHat2+fypYtq71792r37t2S/rqyoGPHjrr11lu1ZcsWHTlyRJJ04MABpaamKjAwUC+//LKeeuophYWFacOGDerXr5/XjhG4Es7/MLKbXf8jR45UsWLFvHmIQK6ofRgZ9Q8jo/5hZNQ/LscUXsiThIQEDRs2TH/88YeGDRumrKws7dq1S+PGjVNISEi2weOaNWtmGzxesGCBe/C4cuXKOQaPf/jhB91///2aNm2aAgICvHykQO7279+vzZs36/3331fz5s0lSW3atNHWrVu1cuVKPfXUU+rRo4dWr16t2267TX5+fsrMzJSvr68eeeQRDRo0SAkJCZKkxMRESdIHH3ygGjVqaM6cOapZs6a3Dg24Ks7/MDLqH0ZF7cPIqH8YGfUPI6P+4YkABXnC4DGMbteuXfLz83PfeilJTqdTlSpV0i+//KKPP/5YrVu31vfff6/atWurWbNm7nkv77nnHgUEBOjIkSOqV6+eSpUqpfDwcA0dOlStW7f21iEB14XzP4yM+odRUfswMuofRkb9w8iof3hiCi/kydUGj7du3aq77rrLPXi8ceNGScp18FiSe/D4k08+0dy5czmBoFAICwtTqVKllJSUJKfTKafTKR8fH124cEG33HKLJCk6OlppaWmaOXOmjh8/Lh8fH0nSb7/9posXL6pcuXKSpPr162vZsmWEJygUOP/DyKh/GBW1DyOj/mFk1D+MjPqHJwIU5AmDxzC69u3ba9iwYYqKipLJZJLJZFJiYqK2b9+u2rVrS5JCQ0M1aNAgHTp0SK+++qrWrl2rTZs2ad68eapevTprO6BQ4vwPI6P+YVTUPoyM+oeRUf8wMuofnpjCC3nSvn17BQUFuQePJbkHj1u1aiXpr8HjqVOn6tVXX9WAAQPk7+/P4DGKBJPJpMaNG0u6dAWCyWTSwYMHdfbsWTVt2tS9Xffu3RUcHKzRo0fr3//+t8xmsypUqKDRo0fLZrN5q/nADeP8DyOj/mFU1D6MjPqHkVH/MDLqH54IUJAnDB4Df3F9kK5cuVIlS5Z0z40pSXa7Xc2bN1eDBg106tQpJSYmql69et5qKvC3cf6HkVH/MCpqH0ZG/cPIqH8YGfUPTwQouGEMHgPSmTNntHTpUjVr1kxWq1UZGRnav3+/xo8fr8aNG6tHjx6KiorydjOBfxTnfxgZ9Q+jovZhZNQ/jIz6h5FR/5BYAwV/U26Dxzt37tQzzzyjL774Qr6+voqKiuIEgiIrJiZGcXFxat68uf7880+NHj1aPXr0UFJSku6//375+fl5u4nATcH5H0ZG/cOoqH0YGfUPI6P+YWTUPwhQ8LcweAyjO3z4sEwmk7Zu3aqHH35Ya9as0YQJEzRr1iwFBwd7u3nATcP5H0ZG/cOoqH0YGfUPI6P+YWTUP5jCC3/L5YPHY8eOlY+PjyZMmKB77rnH200D8oWPj4+cTqcWL16s/v3766mnnvJ2k4B8wfkfRkb9w6iofRgZ9Q8jo/5hZNQ/CFDwtzB4DKOLjIzU4MGD1bt3b646gKFw/oeRUf8wKmofRkb9w8iofxgZ9Q+T0+l0ersRKLz+97//aevWrQwew7CcTqd7UTHASDj/w8iofxgVtQ8jo/5hZNQ/jIz6BwEK/hYGjwHAmDj/w8iofxgVtQ8jo/5hZNQ/jIz6BwEKAAAAAAAAAACAB7O3GwAAAAAAAAAAAFDQEKAAAAAAAAAAAAB4IEABAAAAAAAAAADwQIACAAAAAAAAAADggQAFAAAAAAAAAADAAwEKAAAAAAAAAACABwIUAAAAAAAAAAAADwQoAAAAAAAAAAAAHghQAAAAAAAAAAAAPBCgAAAAAAAAAAAAePh/L6qtVh0uIXwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_zilliqa_ohlc = cargar_archivo(\"zilliqa_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_zilliqa_ohlc['Time'] = pd.to_datetime(df_zilliqa_ohlc['Time'])\n",
    "df_zilliqa_ohlc['Time'] = df_zilliqa_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_zilliqa_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_polkadot_ohlc = cargar_archivo(\"polkadot_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_polkadot_ohlc['Time'] = pd.to_datetime(df_polkadot_ohlc['Time'])\n",
    "df_polkadot_ohlc['Time'] = df_polkadot_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_polkadot_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cardano_ohlc = cargar_archivo(\"cardano_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_cardano_ohlc['Time'] = pd.to_datetime(df_cardano_ohlc['Time'])\n",
    "df_cardano_ohlc['Time'] = df_cardano_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_cardano_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_vechain_ohlc = cargar_archivo(\"vechain_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_vechain_ohlc['Time'] = pd.to_datetime(df_vechain_ohlc['Time'])\n",
    "df_vechain_ohlc['Time'] = df_vechain_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_vechain_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tezos_ohlc = cargar_archivo(\"tezos_ohlc.csv\")\n",
    "\n",
    "# Convertir la columna de fechas a un formato de fecha reconocido por matplotlib\n",
    "df_tezos_ohlc['Time'] = pd.to_datetime(df_tezos_ohlc['Time'])\n",
    "df_tezos_ohlc['Time'] = df_tezos_ohlc['Time'].apply(mdates.date2num)\n",
    "\n",
    "# Crear una figura y un subplot\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "\n",
    "# Configurar el formato de las fechas en el eje x\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Plot de las velas\n",
    "candlestick_ohlc(ax, \n",
    "                df_tezos_ohlc[['Time', 'Open', 'High', 'Low', 'Close']].values, \n",
    "                width = 2.5, \n",
    "                colorup = 'green', \n",
    "                colordown = 'red')\n",
    "\n",
    "# Configurar las etiquetas del eje x para que se muestren correctamente\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}