ProyectoModulo2_EscotoH_TinajeroC_SayavedraJ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proyecto Módulo 2\n",
    "\n",
    "## Hecho por:\n",
    "- Héctor Ulises Escoto González\n",
    "- Jorge Diego Sayavedra Godinez\n",
    "- Carlos Adrian Tinajero Velazco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batalla Naval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Introducción:\n",
    "\n",
    "Las variadas aplicaciones que conllevan la utilización de métodos de resolución numérica a través de aproximaciónes matemáticas nos permiten conocer la solución de diversos problemas envueltos en la vida cotidiana, y así establecer una rigurosa metodología para crear programas que permitan resolver dichas problemáticas de una manera rápida y precisa.\n",
    "\n",
    "Es pues, el método no determinista o estadístico numérico de Montecarlo una solución que permite resolver problemas físicos y matemáticos mediante la simulación de variables aleatorias. Uno de los más claros y evidentes ejemplos que encontramos en la vida cotidiana y que a la población en general le resulta conocido es la solución del juego clásico de \"Batalla Naval\", el cual puede resolverse, evidentemente, mediante un algoritmo que incluya el método Montecarlo.\n",
    "\n",
    "La solución de dicho problema será analizada y comparada para determinar las subsecuentes conclusiones puntuales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Objetivo Principal:\n",
    "\n",
    "- Mediante los conocimientos adquiridos en la clase de Simulación Matemática desarrollar un programa que modele una situación en donde podamos apreciar la importancia del uso de métodos de solución como el de Montecarlo (en este caso se valorará su aplicación en la resolución de la estrategia naval), poner en práctica los conocimientos adquiriros y adquirir información del análisis de los resultados obtenidos del presente proyecto.\n",
    "\n",
    "## Objetivos Metodológicos:\n",
    "\n",
    "- Desarrollar y codificar el llamado \"Juego de Batalla Naval\" , considerando un espacio de acción de 10 posiciones sobre el eje \"X\" por las 10 posiciones sobre el eje \"Y\" de las que precisa el juego clásico. \n",
    "- Comparar la eficacia de las estrategias que permiten a los jugadores ganar la partida mediante la disposición \"aleatoria\" de las embarcaciones.\n",
    "- Aunado al punto anterior, nos planteamos conocer la eficacia del juego mediante una estrategia basada en la realización de disparos efectuada de manera aleatoria.\n",
    "- Graficar la información obtenida de las diferentes simulaciones con escenarios que permitan contrastar las diferentes estrategias.\n",
    "- Analizar y comparar resultados para así determinar conclusiones puntuales respecto al uso y mètodo que resulte mas eficiente en la práctica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================== \n",
      " MAPA CON COORDENADAS\n",
      "=====================\n",
      "     A  B  C  D  E  F  G  H  I\n",
      "  1  -  -  -  -  -  -  -  -  -\n",
      "  2  -  -  -  -  -  -  -  -  -\n",
      "  3  -  -  -  -  -  -  -  -  -\n",
      "  4  -  -  -  -  -  -  -  -  -\n",
      "  5  -  -  -  -  -  -  -  -  -\n",
      "  6  -  -  -  -  -  -  -  -  -\n",
      "  7  -  -  -  -  -  -  -  -  -\n",
      "  8  -  -  -  -  -  -  -  -  -\n",
      "  9  -  -  -  -  -  -  -  -  -\n",
      "intento 1 Posición(1,G) ___ Agua\n",
      "intento 2 Posición(3,C) ___ Agua\n",
      "intento 3 Posición(2,D) ___ Agua\n",
      "intento 4 Posición(5,D) ___ Barco hundido\n",
      "intento 5 Posición(1,C) ___ Barco hundido\n",
      "intento 6 Posición(2,I) ___ Agua\n",
      "intento 7 Posición(7,H) ___ Barco hundido\n",
      "intento 8 Posición(4,I) ___ Agua\n",
      "intento 9 Posición(8,A) ___ Barco hundido\n",
      "intento 10 Posición(2,E) ___ Agua\n",
      "intento 11 Posición(7,I) ___ Barco hundido\n",
      "intento 12 Posición(3,B) ___ Agua\n",
      "intento 13 Posición(1,C) ___ Agua\n",
      "intento 14 Posición(4,C) ___ Agua\n",
      "intento 15 Posición(8,E) ___ Agua\n",
      "\n",
      " SE HUNDIERON: 5 BARCOS\n",
      "     A  B  C  D  E  F  G  H  I\n",
      "  1  -  -  O  -  -  X  -  X  X\n",
      "  2  -  X  X  -  -  -  -  X  -\n",
      "  3  -  -  -  -  X  X  -  -  -\n",
      "  4  -  -  -  X  -  -  -  -  -\n",
      "  5  X  X  -  O  -  X  X  X  -\n",
      "  6  -  -  X  -  -  X  X  -  -\n",
      "  7  X  -  -  X  -  X  X  O  O\n",
      "  8  O  X  X  -  -  -  -  -  -\n",
      "  9  -  X  -  -  -  X  -  X  X\n"
     ]
    }
   ],
   "source": [
    "import random as rd\n",
    "def todojuego():\n",
    "    def game1():\n",
    "        m=['-']*10\n",
    "        abc=[chr(i) for i in range(64,91)]\n",
    "        coord={'A':1,'B':2,'C':3,'D':4, 'E':5, 'F':6, 'G':7, 'H':8, 'I':9}\n",
    "        num=0\n",
    "        for fil in range(10):\n",
    "            m[fil]=['-']*10\n",
    "            for col in range(10):\n",
    "                m[0][col]=abc[col]\n",
    "        for i in range(10):\n",
    "            m[i][0],m[0][0]=str(num),' '\n",
    "            num+=1\n",
    "        return m,coord\n",
    "    mapa,letra=game1()\n",
    "    print('=====================','\\n','MAPA CON COORDENADAS'+'\\n'+'=====================')\n",
    "    for i in mapa:\n",
    "        print(' ','  '.join(i))\n",
    " \n",
    "    def fichas():\n",
    "        con=0\n",
    "        while con<40:\n",
    "            mapa[rd.randint(1,9)][rd.randint(1,9)]='X'\n",
    "            con+=1\n",
    "        return mapa\n",
    "    fichas()\n",
    "    def jugadas():\n",
    "        intento=0\n",
    "        elim=0\n",
    "        while intento<15:\n",
    "            intento+=1\n",
    "            fila=rd.randint(1,9)\n",
    "            num=(rd.randint(65,73))\n",
    "            colu=letra[chr(num)]\n",
    "            print('intento',intento,'Posición('+str(fila)+','+chr(num)+')',end=' ___ ')\n",
    "            if mapa[fila][colu]=='X':\n",
    "                mapa[fila][colu]='O'\n",
    "                print('Barco hundido')\n",
    "                elim+=1\n",
    "            else:\n",
    "                print('Agua')\n",
    "        return mapa,elim\n",
    " \n",
    "    final,remo=jugadas()\n",
    "    if remo>=5:\n",
    "        print('\\n','SE HUNDIERON:',remo,'BARCOS')\n",
    "    else:\n",
    "        print('\\n','SE HUNDIERON:',remo,'BARCOS')\n",
    "    for i in final:\n",
    "        print(' ','  '.join(i))\n",
    "#letra=[chr(i) for i in range(65,91)]\n",
    "#n='A'\n",
    "#while n in letra:\n",
    "   # print()\n",
    "   # n=input('PRESIONA UNA LETRA INICIAR EL JUEGO: ').upper()\n",
    "   # if n in letra:\n",
    "   #     todojuego()\n",
    "todojuego()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparación de Bolsa para empresas de Tarjetas de video\n",
    "\n",
    "## Hecho por:\n",
    "- Héctor Ulises Escoto González\n",
    "- Jorge Diego Sayavedra Godinez\n",
    "- Carlos Adrian Tinajero Velazco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Introducción**\n",
    "\n",
    "En la actualidad, el mundo se encuentra inmerso en un sistema económico que presenta altos e importantes impactos de manera directa y contundente en el panorama global y local, con repercusiones visibles dentro de todas las sociedades del mundo; se precisa que dicho impacto tiene causa en la imponente maquinaria que permite dar marcha y movimiento a las grandes ruedas de la economía hacia el progreso. Por lo anterior nos vemos en la necesidad y más aún, en la imperante obligación de tomar acción, apropiarnos y confrontar dicha realidad para hacer factibles los objetivos y fines planteados por ese sistema y alcanzar la prosperidad económica a través del correcto uso y la aplicación del conocimiento en materia financiera. \n",
    "\n",
    "Lo anterior justifica el presente proyecto, quedando sustentado en la necesidad de la aplicación de los medios y herramientas elementales que nos permitan analizar el escenario global económico y financiero para tomar decisiones fundadas en precedentes y evidencia que facilite la reducción del error y de las catástrofes financieras que desangren la economía de cientos de trabajadores a través de la pérdida del valor de acciones.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objetivos:\n",
    "\n",
    "Para lograr lo anterior haremos uso de las herramientas que se nos han proporcionado en la clase de simulación matemática, para estructurar un código en lenguaje de programación Python en donde resolvamos las principales problemáticas en el análsis de los escenarios del valor de las acciones de empresas. Siendo de igual valor denotar la imprescindible motivación como estudiantes de aplicar dichos conocimientos dentro del panorama real, objetivo y valorizable que nos impulse a desarrollar e inovar a través del previo conocimiento adquirido para la resolución de problemáticas como las que frecuentan el basto mundo de las altas finanzas. \n",
    "\n",
    "Así, proponemos un análisis mediante el uso de la librería Pandas a través de la codificación de funciones que faciliten el estudio del escenario financiero de las empresas Advanced Micro Devices y Nvidia Corporation.\n",
    "\n",
    "Del mismo modo nos proponemos realizar:\n",
    "\n",
    "- una comparación del valor de precio de las acciones en ambas compañias. \n",
    "\n",
    "- Calcular los rendimientos diarios \n",
    "\n",
    "- Obtener el precio de cierre de acciones.\n",
    "\n",
    "- Analizar el escenario y la realidad financiera que presentan dichas compañias \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empresa AMD: ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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ERBJs8s3PcvMTG/v9/oIMNwCb9tcD6rIeqhSQRUQSLGzBQ6v39fv9wbbMeFd1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\n0ec8Tn0il8HzzHsVpLsdLJ1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Kc7j34wu566p5fOaM4ujQwf7D7YVfAtz8xEbqmv186k+r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\nVQAXZDMzswrggmxmZlYBXJDNzMwqwD9leTWtGgT46QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x255bfe39160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "def load_adj_close_AMD(ticker, data_source, start_date, end_date):\n",
    "    panel_data = data.DataReader(ticker, data_source, start_date, end_date)\n",
    "    closes_AMD = panel_data.loc['Adj Close']\n",
    "    all_weekdays = pd.date_range(start=start_date, end=end_date)\n",
    "    closes_AMD = closes_AMD.reindex(all_weekdays)\n",
    "    closes_AMD = closes_AMD.fillna(method='ffill')\n",
    "    return closes_AMD\n",
    "ticker = ['AMD']\n",
    "data_source = 'yahoo'\n",
    "start_date = '2015-01-04'\n",
    "end_date = '2016-12-31'\n",
    "closes_AMD = load_adj_close_AMD(ticker, data_source, start_date, end_date)\n",
    "closes_AMD.plot(figsize=(8,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rendimientos diarios AMD:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0019972591949118431, 0.035755165003662784)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mu_std_daily_ret_AMD(closes_AMD):\n",
    "    daily_returns_AMD = (np.log(closes_AMD/closes_AMD.shift(1)))[1:]\n",
    "    mu_AMD = daily_returns_AMD.mean().values[0]\n",
    "    sigma_AMD = daily_returns_AMD.std().values[0]\n",
    "    return daily_returns_AMD, mu_AMD, sigma_AMD\n",
    "daily_returns_AMD, mu_AMD, sigma_AMD = mu_std_daily_ret_AMD(closes_AMD)\n",
    "mu_AMD, sigma_AMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T+K5E6LURfcn+Yi4XkPCuHPq/8Db/J7M5QRDFQmZzIqj0e+FtRZuT9K4kaAghggB3+as/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\n8CYiIrLM/wMfDrDWGvGGOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x255be167e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_returns_AMD.plot(figsize=(8,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prueba:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas_datareader import data as web\n",
    "import datetime as dt\n",
    "\n",
    "inicio = dt.datetime(2015, 1, 4)\n",
    "fin = dt.datetime(2016, 12, 31)\n",
    "\n",
    "NVDA = data.DataReader(\"NVDA\", 'yahoo', inicio, fin)  \n",
    "AMD = data.DataReader(\"AMD\", 'yahoo', inicio, fin)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>19.220278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>18.637550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>18.588987</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>19.288261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>19.365957</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>19.123156</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>19.094017</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>19.171713</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>19.035746</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>19.385382</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>19.443657</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>19.715591</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>20.055517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>20.113789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>20.026384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>19.064880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>18.754093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>19.210564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>18.647261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>19.055170</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>19.531063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>19.579624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>19.900122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-06</th>\n",
       "      <td>19.812712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-09</th>\n",
       "      <td>19.793291</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-10</th>\n",
       "      <td>20.346882</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-11</th>\n",
       "      <td>20.210909</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>21.658016</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-13</th>\n",
       "      <td>21.667727</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-17</th>\n",
       "      <td>21.735714</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-18</th>\n",
       "      <td>30.636536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-19</th>\n",
       "      <td>30.725395</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-20</th>\n",
       "      <td>30.991972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-23</th>\n",
       "      <td>30.527931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-24</th>\n",
       "      <td>30.774765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-25</th>\n",
       "      <td>30.735270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>30.991972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>31.317789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>32.334724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>32.087898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>32.018784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>33.322044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>32.690166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>33.134464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>32.295235</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>32.561806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>32.078026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>32.157013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>32.551941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>32.749397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>32.255741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>31.732464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>32.482830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>32.512447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>32.640797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>32.749397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>32.719784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>33.252930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>32.966610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>32.542061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close\n",
       "Date                 \n",
       "2015-01-05  19.220278\n",
       "2015-01-06  18.637550\n",
       "2015-01-07  18.588987\n",
       "2015-01-08  19.288261\n",
       "2015-01-09  19.365957\n",
       "2015-01-12  19.123156\n",
       "2015-01-13  19.094017\n",
       "2015-01-14  19.171713\n",
       "2015-01-15  19.035746\n",
       "2015-01-16  19.385382\n",
       "2015-01-20  19.443657\n",
       "2015-01-21  19.715591\n",
       "2015-01-22  20.055517\n",
       "2015-01-23  20.113789\n",
       "2015-01-26  20.026384\n",
       "2015-01-27  19.064880\n",
       "2015-01-28  18.754093\n",
       "2015-01-29  19.210564\n",
       "2015-01-30  18.647261\n",
       "2015-02-02  19.055170\n",
       "2015-02-03  19.531063\n",
       "2015-02-04  19.579624\n",
       "2015-02-05  19.900122\n",
       "2015-02-06  19.812712\n",
       "2015-02-09  19.793291\n",
       "2015-02-10  20.346882\n",
       "2015-02-11  20.210909\n",
       "2015-02-12  21.658016\n",
       "2015-02-13  21.667727\n",
       "2015-02-17  21.735714\n",
       "...               ...\n",
       "2015-11-18  30.636536\n",
       "2015-11-19  30.725395\n",
       "2015-11-20  30.991972\n",
       "2015-11-23  30.527931\n",
       "2015-11-24  30.774765\n",
       "2015-11-25  30.735270\n",
       "2015-11-27  30.991972\n",
       "2015-11-30  31.317789\n",
       "2015-12-01  32.334724\n",
       "2015-12-02  32.087898\n",
       "2015-12-03  32.018784\n",
       "2015-12-04  33.322044\n",
       "2015-12-07  32.690166\n",
       "2015-12-08  33.134464\n",
       "2015-12-09  32.295235\n",
       "2015-12-10  32.561806\n",
       "2015-12-11  32.078026\n",
       "2015-12-14  32.157013\n",
       "2015-12-15  32.551941\n",
       "2015-12-16  32.749397\n",
       "2015-12-17  32.255741\n",
       "2015-12-18  31.732464\n",
       "2015-12-21  32.482830\n",
       "2015-12-22  32.512447\n",
       "2015-12-23  32.640797\n",
       "2015-12-24  32.749397\n",
       "2015-12-28  32.719784\n",
       "2015-12-29  33.252930\n",
       "2015-12-30  32.966610\n",
       "2015-12-31  32.542061\n",
       "\n",
       "[251 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NVDA01 = NVDA['2015'][['Adj Close']]\n",
    "AMD01 = AMD['2015'][['Adj Close']]\n",
    "NVDA01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>19.035746</td>\n",
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       "      <td>19.443657</td>\n",
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       "      <td>19.715591</td>\n",
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       "      <th>2015-01-22</th>\n",
       "      <td>20.055517</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>20.113789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>20.026384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>19.064880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>18.754093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>19.210564</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>18.647261</td>\n",
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       "      <th>2015-02-02</th>\n",
       "      <td>19.055170</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>19.531063</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>19.579624</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>19.900122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-06</th>\n",
       "      <td>19.812712</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-09</th>\n",
       "      <td>19.793291</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-10</th>\n",
       "      <td>20.346882</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-11</th>\n",
       "      <td>20.210909</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>21.658016</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-13</th>\n",
       "      <td>21.667727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-17</th>\n",
       "      <td>21.735714</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-18</th>\n",
       "      <td>30.636536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-19</th>\n",
       "      <td>30.725395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-20</th>\n",
       "      <td>30.991972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-23</th>\n",
       "      <td>30.527931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-24</th>\n",
       "      <td>30.774765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-25</th>\n",
       "      <td>30.735270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>30.991972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>31.317789</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>32.334724</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>32.087898</td>\n",
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       "      <th>2015-12-03</th>\n",
       "      <td>32.018784</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>33.322044</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>32.690166</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>33.134464</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>32.295235</td>\n",
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       "      <th>2015-12-10</th>\n",
       "      <td>32.561806</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>32.078026</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>32.157013</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>32.551941</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>32.749397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>32.255741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>31.732464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>32.482830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>32.512447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>32.640797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>32.749397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>32.719784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>33.252930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>32.966610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>32.542061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close\n",
       "Date                 \n",
       "2015-01-05  19.220278\n",
       "2015-01-06  18.637550\n",
       "2015-01-07  18.588987\n",
       "2015-01-08  19.288261\n",
       "2015-01-09  19.365957\n",
       "2015-01-12  19.123156\n",
       "2015-01-13  19.094017\n",
       "2015-01-14  19.171713\n",
       "2015-01-15  19.035746\n",
       "2015-01-16  19.385382\n",
       "2015-01-20  19.443657\n",
       "2015-01-21  19.715591\n",
       "2015-01-22  20.055517\n",
       "2015-01-23  20.113789\n",
       "2015-01-26  20.026384\n",
       "2015-01-27  19.064880\n",
       "2015-01-28  18.754093\n",
       "2015-01-29  19.210564\n",
       "2015-01-30  18.647261\n",
       "2015-02-02  19.055170\n",
       "2015-02-03  19.531063\n",
       "2015-02-04  19.579624\n",
       "2015-02-05  19.900122\n",
       "2015-02-06  19.812712\n",
       "2015-02-09  19.793291\n",
       "2015-02-10  20.346882\n",
       "2015-02-11  20.210909\n",
       "2015-02-12  21.658016\n",
       "2015-02-13  21.667727\n",
       "2015-02-17  21.735714\n",
       "...               ...\n",
       "2015-11-18  30.636536\n",
       "2015-11-19  30.725395\n",
       "2015-11-20  30.991972\n",
       "2015-11-23  30.527931\n",
       "2015-11-24  30.774765\n",
       "2015-11-25  30.735270\n",
       "2015-11-27  30.991972\n",
       "2015-11-30  31.317789\n",
       "2015-12-01  32.334724\n",
       "2015-12-02  32.087898\n",
       "2015-12-03  32.018784\n",
       "2015-12-04  33.322044\n",
       "2015-12-07  32.690166\n",
       "2015-12-08  33.134464\n",
       "2015-12-09  32.295235\n",
       "2015-12-10  32.561806\n",
       "2015-12-11  32.078026\n",
       "2015-12-14  32.157013\n",
       "2015-12-15  32.551941\n",
       "2015-12-16  32.749397\n",
       "2015-12-17  32.255741\n",
       "2015-12-18  31.732464\n",
       "2015-12-21  32.482830\n",
       "2015-12-22  32.512447\n",
       "2015-12-23  32.640797\n",
       "2015-12-24  32.749397\n",
       "2015-12-28  32.719784\n",
       "2015-12-29  33.252930\n",
       "2015-12-30  32.966610\n",
       "2015-12-31  32.542061\n",
       "\n",
       "[251 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NVDA01['2015-01-04':'2016-12-31'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">NVDA</th>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>19.220278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>18.637550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>18.588987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>19.288261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>19.365957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Adj Close\n",
       "     Date                 \n",
       "NVDA 2015-01-05  19.220278\n",
       "     2015-01-06  18.637550\n",
       "     2015-01-07  18.588987\n",
       "     2015-01-08  19.288261\n",
       "     2015-01-09  19.365957"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close = pd.concat([NVDA01, AMD01], keys=['NVDA', 'AMD'])\n",
    "close[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "RemoteDataError",
     "evalue": "Unable to read URL: https://query1.finance.yahoo.com/v7/finance/download/AMD?period1=1420351200&period2=1483250399&interval=1d&events=history&crumb=kKt3uOmOc%5Cu002FY",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRemoteDataError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-dacd4efad266>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0msimbolos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'NVDA'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'AMD'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mall_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_stocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimbolos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minicio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfin\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-dacd4efad266>\u001b[0m in \u001b[0;36mall_stocks\u001b[1;34m(symbols, start, end)\u001b[0m\n\u001b[0;32m      3\u001b[0m            \u001b[1;32mreturn\u001b[0m \u001b[0mweb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'yahoo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m        \u001b[0mdatas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msymbols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m        \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatas\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbols'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0msimbolos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'NVDA'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'AMD'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[0;32m    204\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m                        copy=copy)\n\u001b[0m\u001b[0;32m    207\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[0;32m    234\u001b[0m             \u001b[0mobjs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 236\u001b[1;33m             \u001b[0mobjs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    237\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-dacd4efad266>\u001b[0m in \u001b[0;36mdata\u001b[1;34m(symbols)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mall_stocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m        \u001b[1;32mdef\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m            \u001b[1;32mreturn\u001b[0m \u001b[0mweb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'yahoo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m        \u001b[0mdatas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msymbols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m        \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatas\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbols'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas_datareader\\data.py\u001b[0m in \u001b[0;36mDataReader\u001b[1;34m(name, data_source, start, end, retry_count, pause, session, access_key)\u001b[0m\n\u001b[0;32m    119\u001b[0m                                 \u001b[0madjust_price\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m                                 \u001b[0mretry_count\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mretry_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpause\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpause\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m                                 session=session).read()\n\u001b[0m\u001b[0;32m    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mdata_source\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"yahoo-actions\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas_datareader\\yahoo\\daily.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    113\u001b[0m         \u001b[1;34m\"\"\" read one data from specified URL \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 115\u001b[1;33m             \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mYahooDailyReader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mret_index\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m                 \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ret_Index'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_calc_return_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Adj Close'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas_datareader\\base.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m             df = self._read_one_data(self.url,\n\u001b[1;32m--> 181\u001b[1;33m                                      params=self._get_params(self.symbols))\n\u001b[0m\u001b[0;32m    182\u001b[0m         \u001b[1;31m# Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas_datareader\\base.py\u001b[0m in \u001b[0;36m_read_one_data\u001b[1;34m(self, url, params)\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[1;34m\"\"\" read one data from specified URL \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_format\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'string'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_url_as_StringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_format\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'json'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas_datareader\\base.py\u001b[0m in \u001b[0;36m_read_url_as_StringIO\u001b[1;34m(self, url, params)\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[0mOpen\u001b[0m \u001b[0murl\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mand\u001b[0m \u001b[0mretry\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \"\"\"\n\u001b[1;32m---> 90\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m         \u001b[0mtext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sanitize_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\JORGE DIEGO SAYAVEDR\\Anaconda3\\lib\\site-packages\\pandas_datareader\\base.py\u001b[0m in \u001b[0;36m_get_response\u001b[1;34m(self, url, params, headers)\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m             \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murl\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"?\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0murlencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mRemoteDataError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unable to read URL: {0}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_crumb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRemoteDataError\u001b[0m: Unable to read URL: https://query1.finance.yahoo.com/v7/finance/download/AMD?period1=1420351200&period2=1483250399&interval=1d&events=history&crumb=kKt3uOmOc%5Cu002FY"
     ]
    }
   ],
   "source": [
    "def all_stocks(symbols, start, end):\n",
    "       def data(symbols):\n",
    "           return web.DataReader(symbols, 'yahoo', start, end)\n",
    "       datas = map(data, symbols)\n",
    "       return pd.concat(datas, keys=symbols, names=['symbols','Date'])\n",
    "\n",
    "simbolos = ['NVDA','AMD']\n",
    "\n",
    "all_data = all_stocks(simbolos, inicio, fin)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>symbols</th>\n",
       "      <th>AMD</th>\n",
       "      <th>NVDA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2.66</td>\n",
       "      <td>19.220278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>2.63</td>\n",
       "      <td>18.637550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>2.58</td>\n",
       "      <td>18.588987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>2.61</td>\n",
       "      <td>19.288261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>2.63</td>\n",
       "      <td>19.365957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbols      AMD       NVDA\n",
       "Date                       \n",
       "2015-01-05  2.66  19.220278\n",
       "2015-01-06  2.63  18.637550\n",
       "2015-01-07  2.58  18.588987\n",
       "2015-01-08  2.61  19.288261\n",
       "2015-01-09  2.63  19.365957"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solo_cierre = all_data[['Adj Close']].reset_index()\n",
    "pivot_cierre = solo_cierre.pivot('Date', 'symbols', 'Adj Close')\n",
    "\n",
    "pivot_cierre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a8ce447518>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3PRxyBmxeHWe1U7kyJEuSJO1PiybAc8Ng3YI4PNuumna0ZKKCcAg4SZKkolgz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\nqlk/lk10H5L/n8emlXF66U6nQOdTiv1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+Ku855+gSxOlyoCfBX9+6kUQmx1vWNrCoOkrn\nQJpUNgfA20+fxyUr6ye8v1ze8eOn93HhiloW1sysFuFjpSRZREREZryBVJYbb3+GbN5xwfJaBlJZ\nFtfEOGdpNQ0VE1tquLknwe9f7uTJ3Z209Ca5+txF3LlpPw7HN685c9Tl/pbeBD4z5pSPXs2tqSvO\nU7u7KC8Jcv6yGvZ1xdnbOch3H9/DF+7ZzqWr6w95zbFKZXP8+/07ee5AL4OpLIOpLNm843XLatje\n0s+15y8m4Dfa+pIsqysd7uLQNZimP5mhtjRMNOSnpTfJPVtaeOSlDr5y1Tq2t/Rx8y+34XB84OKl\nfPex3TgHNaUhIkE/A8ksd21u5qbLV/EX5y8mHPDx+5c7+e7je/jjvm7KIgH+4S1r2LCqnvb+FM29\nCX67tZXm3iT/cMWaonz2mcCcc9NdBtavX+82btw43cUQERGRGagnnuba7z3N1gO9VMdCtPWnRj2/\nrC7GN953JnWlEb792C4eebGdrnia7sEMjZURLl09h93tg9y77SDOQWU0SCwU4EBPAoDPvGU11124\n9LjKuKdjkMv+7WHeffZCbr7ylKN67QsH+/nVlhbWNJRxzpIatjb38ZNNTTy9u4vm3iRr51dQFgkQ\nCwXoHEyzaW83ZeEA/ans8D7M4NJVcygJ+fndtoMkM3kAAj4jm38l16srC9Pen2JeZQk3X3kyl66e\nQ9dgmqDfhruLZHJ5PvqjZ/j18weJhfzMqyrhxdYBamIhLlszhyd3dxFPZ8k7aB/xf3HF2gb+7ap1\nh/Q7nmnMbJNzbv0Rt1OSLCIiIjNVx0CKq7/9JLvaB7nlvWfwhtX19CWyREI+drYN8OSuLm55cCd+\nn5HI5OhPZjm30LpcURJkx8E+ntrdRWU0xLvOWsDb1jVyUn0ZOee4f3srvYkMf75+AWbHPzjvpjuf\n46d/PMCDn7iEeZWHtm7n847OwTStfUna+pPs7ojzq+ea2dzUw1AeawbOQW1pmLMWV/GnZ8znsjVz\nhvfhnGNPZ5y55RE27e2mMhqkMhrkxts3s7N9gHQ2z4o5Zbz3nIV0DqTpSaSZX1nCmsYKntnXzdcf\nepm/uXgp15y3mEjQf9jPksnluX97G795voW9XXHeddYCrlw3j0jQzwM7WvmrWzdSFQ3yt29cybzK\nCAurYyyvLz3uGE4FJckiIiIyq7X2JXn3t/5AS0+Sb12zngtW1I673cY9XfzrvS/QWFnC+y9cyprG\n8lHPD6ayRIJ+/JM8S0VTV5w/+fIjNFREOGNhFfFMjupoiO54mt9uayWfd6NadQFWzS3jTafM5T3n\nLKSpK84TOzsxg+suXPqqSexYubzDOUcm5wj6jcAktuY65/jS717kkpV1nLmoetLeZ7IoSRYREZGi\n6o1nqIhO3gwO/ckMT+3uYnVDOTWlIW68fTMPv9jObX91Nmctnh3J2KMvtXPTnVvIO0dJyM/+7gTp\nbJ4/Xz+furIwc8oj1JdFqC8PM7c8QkNFpCit2DJxE02SNXBPRERExvXTP+5nb2ec1Q1ldA1m+Mxd\nW/jI61ewrL6Uy0+ZO9z3NJPL83L7AKlMnrkVkXEHrjnn6E1kKI8E8fmMjoEUfYkMHQNpBtNZ7tvW\nyl3PHGAwnRv1ug9vWD5rEmSAC1fU8fhNrx++v6t9gOae5GFbwWXmUpIsIiIih9jW3MfHf/IsI3sH\nhPw+vnL/SwC87bRGrj1/EUtqS7nutqf5476e4e1iIT91ZWHqysJEgn52tg2QzOTojmcI+o260jDt\nAykyuVd2Hg74uGJtI1ec1sDejkGe2tPFM/t6+KsLlkzZZ54MS+tKWVo3O/rqymhKkkVERE4QXYNp\nbn1iD+lsnlDAR8hvNFaW8OZTG0b1b23rS7KrY5CKkiD9ySxnLKwc1Yc1ns7yyTufo6IkyK9vuIi2\n/iT7uxOcOq+CW5/YQy7vuPWJPdz9bPPwa/7+8lUsqytlX1ec/d0J2gdSdPSn6BpMc86SasIBP8vq\nY3THM7T2JqkpDXHSnDIqSoKUlwRZOaeMqljI29lK+IvXLcE5p64IMm3UJ1lERGQWc87xx33dNHUl\n+L+/e4ED3QkCfh/pbH54m8aKCH/7xpUMprLc/Wwzm/Z2j9rH8vpSYiH/cNeHdDZPMpPjG+9bP2pm\nhZGaexJsb+ljx8F+aktDXHWUq6yJTBcN3BMREZlim/Z288COVv76gqVUD7WKFoFzjnjam94skcmR\ny+d5cncX25r7+OO+Hra39AGwqCbKl69ax+kLq3DOm0nhyV1d3PzLrbzYOgB4sylcsbaBtfMr6Ulk\nGExluXPTfqLhALWlIWKhAAG/ccnKei4u0nLFIjOJkmQREZEp9oHvb+Tera1URoN8/I0raetL8uAL\n7Syti/G6ZbXs746zeX8vLT0J3rBmDs55i1Bk83mioQCxcIDSsJ9YOEDQ72Nrcy+7O+I0dcUZGLFw\nxJCySIA1DeVcsbaBU+ZVsKaxnHDg0GnDcnnHtuY+SkI+lteXTUUoRGYsJcmv4nfbWgkWzpLHyucd\n/ckswYARDkx8TsV83tExkKK5N0k05Gd5XSm+SZ6PUUREZg7nHGd/4X6W15WSd44nd3cBcPaSana0\n9NGXzOL3GSfNKaMsHOCpPV0E/caimhghv4/BtLfs8EAqO7xa2vyqElbOKWNBdZS5FRFiIT8Bv49c\n3nHRijoWVJeoz67IUdIUcIex42AfH/rBJrJ5x/nLasjmHOlcnnQ2TyKdY39PYlQ/Lr/PCAd8hAM+\nQgEf4YCfUMCHz6ClN0muMOw3nc2PmiC8MhrkrMXVnL24mprSVy65hQI+zlxU9arrzDvnSGbyxNNZ\ncnlHbWm46Al3U1eceZUlSuRFZph83vFCaz9bm/voT2YYSGYZSGcZKCRYp86r4PSFlVRFQ1RGQ5O+\nOMJsks87HtvZQSqb56Q5pSyoih62jusaTNPWnyTg8xHwGX6fEfR7dX1lNDhu4pnPOxwcNubNvUna\n+1N8eMNyrjlvEfduPUh5JMj5y2vJ5vLs64oztyJCNOT99KayOfw2/qIP2VyeVDZPLPya+5kWmTFm\nREvyvBWnuC/+1y8pLwnSOZAmmR09R6LfjAtX1B2ygs5IiXSOFws/LBv3dtFYUcLC6ijlJQHa+1O0\n9CbZcqCXP+zqpDwS5PWr6tnZPkDI7yuMAPYRCXrrk9eXhcnlHalsnlQ2RzrrVVapTOF+Lk8m52is\niBAKeJVb0O+joSLC3IoSehMZntrdyVO7u9jTGR+3vI0VEcJBP+lsnkxu6PZKwj5SdSzEn54+j55E\npjAgw6vQAz4j4PPh9xl558jlC7fC39mcoy+ZoXswTTrnyObzrJ5bjsPxh11dvH1dI+87bxH1Zd6c\nlkOf5UThnONgX5IXDvbTn/QGoqQLsTZgWX0p5yypOeQHr60vOfzjFAv7x710KXK8euJpdrYNsKgm\nRm1piN5Ehkdf6uCzv9hKx0B61LahgI/ScIBMLk9/8pVL7mZQWRJkXlUJ//TWk1k/i+aSPV4vHOxn\n415virCSoJ/1i6v4j4deZsfB/uFtIkEfy+tLqSwJ8cy+bvIOfAY+M/rH6bowpDwSwMw4f1kNezrj\npLM5MjlHe3+KRCbH4pooi2tjpDJ5ehIZsrk85y2r4c5N+xlM57j7w69j7fzKqQiDiByDWdXdItKw\nws299stH3O7UeRWsaShnflUJ86tLePiFdu7b3uYNaMjkGPoo1bEQPfH0qLkdAz5jcW2MDSvruOqs\nBVPWJ6tjIMXgiMq4P5nlD7s62dbcRybvLR0ZDvgI+l+5hfxGJOQnFgpgBg/saOOhF9qpiYVYUhsb\nToizhUQ4m8/jMy9xHkqefYV/Y+EAtaVhgn4fZt7SnQAr6sv41ZaW4XJFQ37ecfo85ldFSaSzhIN+\nBlPeAJFYyBvMURYJcrAvWVj6Erw2FQp/e384IF+YML43kaUsEqA8EiST804woqEANYXBLBUlQUIB\nX6EPXoCAz9jX5Z1UhAI+r4XHb/QVBpb0JrLEM9nhk4Og3ygJBUhmcqSyefxmJLM5khnv9siLHRzo\nSbzq/09pOMCS2hiLa2PEU1n2dyd4obV/1Da1pSFu/cuzOWVexXEeDYeXzXk/tkOx0eXTw+saTNM1\nmKa0cBITDQWIp7PEQgF8PsM5x2A6V5jAP8GKwhRTNbHQUcW1pTdBe38K8E7CH3ihjb0dcWLhAJWF\nFcfyzvsulIYDrGrw6pTmngQdA2na+1N0x9MEfD52tvWP6iuayzse2NHGQCo7nLQNXYlaO7+Ca89b\nPNxaHAsHhk9gnXNsOdDLns44XQPe1Fpd8TQPv9hOS0+Smy5fxTvPXMD2g30829TDlgO9LKqJctma\nuZzSWH7Uy9S29SUxs+Hv6lRIZnI8uKONbz26i8FUjprSEC29SToGUrxh9RwuPqmOJ17u4I6N+wGv\nvu9LZMjmHUvrYnx4w3IW1cR4qbWfF1sHeKmtn9a+JGctriYWDpDPO/IOqmNBltaVks07cvl8oS51\nDKay7OoYpKM/xca93Zw2v4JoOEDQZ1THwpRGAmxr7qO1L0kk6KOiJMhAKstTu7soDQeIhgI88ncb\nTrhGB5ETyaxKktevX+9+9cBj9CczVMfCREOjW+4GUlnu3tzMPVta2NsVH/7hCviMPztzPtFQgLJI\ngNUNZaycW87imiipbJ72/hS9iQz1ZWFqSsOz+rJkPu+K3jViV/sA+7ritPWl+P2uTn79fAvJTB4z\nL/EN+IySoJ94JjfcreRIzMBgeN7LgWSWvmSGkN9HOOhnIJklncsfcT+H23fI7yv8qI1fnqDfiAT8\nhIM+VjeU84bVc1g1t4zqWMi7YlC4apDLOzbt7eb3uzrZ3THIns5BIgE/C6ujnLGoirqyMIMpr3/g\nD57cRy7veOtpjdSVhSkNeycv+bxjW4s39dGGVfWcNr9yQsdYJpfnd9taSWZy5J23hOlDL7TTm/Di\nZAZLamMsqytlaV2MpXUxamJhykuCnFy4muI3m/DxMDQqPhzwjZskOeddNelLZoinckTDfpq64jz2\nUicr55aybkEVldEgLb1JMrk8kYCf0kiA8kjgqJOuIelsnn1dg2xv6SeVzVNfFqa+PEwq412S7o6n\niYYCpLK5wglXhqauOBv3dNNW+P6PFQl6ra19ifGPsVDAR7Rw8rlqbhkOWFBVQlkkOHyC6fcZm5t6\neHpPFz3xzOjX+30srImSSOfoiacxM6yQ4A6ksqOOyXDAR21pmMpokHg6x5LaGLvaB0aduK+aW8ZV\nZy1gy4FeUtk8taXeErWXrq4fNZ/tRPQmMnzsjs3ct71t1OONFRFa+pI4ByvqS3nd8loqSoJUlARp\nrIzQE89wz/MHqY56J8AHe5NEgn76C107hk5awTtZvGhFHU3dccoiQc5fVkNlNDS8v8W1UQI+H2WR\nwPAqbADt/Sn2dcXJ5vL0JjK09CY52JckGvTTWejy0DmQHp5Tt6/QUr6oJsqCqijxdJaGyhIiAT+/\neK55+ErbBy5ayrvPXsiimijP7e+lpTfBZWvmTls9n8s7fIW6U93YRGa2WZckH83AvWQmx4GeBCVB\nP42Vh+/bK0cnk8uTyOSIBv3knBtu9crnHd3xNL2JDHMrIoVEzjC8xBUm3vKZyuaGB6QMprKksvnh\ngSqpbJ6F1VH8ZmQKLTuZnNftoSwSIBYKUFI4gXLOkck5EukckZCX+Obd4fsKHo/nD/Ty2V9s5bn9\nXjIzUlk4wGA6S955l2jLIsFRz4cDPpbUxjhtQSVlEa+1/HtP7GFX++DwNtWxEBtW1nPSnFI6B9Pk\n845dHYPDJzEjE6tI0Dccv4DPhhP/BVVRIkEfvYkM4YCfkqCfVQ1ltPQmebapZzixDAV8zK8soaEy\nQktPkr5k5rBJ5USURwKctqCShorI8BWFcMDruhQO+GjtS/FCax8vtQ6QLxxTPmM4EZqoUMBHfVmY\n9YuqOLmxgrqyMPF0zjuRSWcpCfpp708Rz+QojwSpjgWZW1HCvMoI+7ridA1maOtPkihMobW5qYeQ\n30dTd3z4ZGVIVTTIm05pYFldjMU1Me/Ez+D0BVWvLHQwRjKTY2fbAGawsDpaOJGa2kTJOcdDL7Sz\nraWPNY3lrJ1XQU1pmAM9CZ7c1ck3H9nFge7EId0MGisimBkNFRHmVESIp7KURoKkMrnh47Y3nuGZ\nph427uliVUM5B7oTR7xK82oCPq/lvDQcoL48TE0s5K3OVhqmtjTMyrllXLp6ziHf50Taq/sjQR/z\nq6LH/P4i8tp2QifJItNhqEV2ZPeZ6liIwVSOR15q5/e7OkllRiebyUyOLQd6R7XILa2NcdPlqzhp\nThmZXJ6ldaWHTe5T2RxNXXF64hla+1I8ubuT6lgI57yTmqH+8lube8k5L+FJZnL0JbNs2d/L/KoS\nTltQyYo5pWRzXvm3tfTRE08X+uwHKY8EKS/xEvxYyOtm4/MZbz6lgT2dg2xu6mEg6bXmlQT9JDI5\nBpJed5qDfV4S3jmYwl9ICtO5PMmMtxBBZTTE6oYyTppTRijgI1m4KlETCzOvqoSTG8u9BHcgRWtf\ncri1tjoWIp7KURLyU1ESPOqW1aPlnHcJPpvPD/fzP1FlC/2an2nqZntLP9dftHRUy+9EOOfoS2SH\nW/m742lebvfm4O1Pjm5VL4sEWFZfSsjvtTI3VJRQEwuRyuaJBH3qWiQiU05JssgMkh7RYt5QETnm\nbgpHQ8u5ioiIHGqiSfKk/VKb2ZvM7AUz22lmN03W+4jMBqGAj6pYiAXV0SlJkEGD/0RERI7HpPxa\nm5kfuAW4HFgDvNvM1kzGe4mIiIiIFNtkNWmdDex0zu1yzqWB24ErJ+m9RERERESKarKS5HlA04j7\n+wuPDTOz681so5ltbG9vn6RiiIiIiIgcvWmb7dw5903n3Hrn3Pq6urrpKoaIiIiIyCEmK0k+ACwY\ncX9+4TERERERkRlvspLkp4EVZrbEzELAu4C7J+m9RERERESKKjAZO3XOZc3sw8C9gB/4rnNu62S8\nl4iIiIhIsU1KkgzgnLsHuGey9i8iIiIiMlmmbeCeiIiIiMhMpSRZRERERGQMJckiIiIiImMoSRYR\nERERGUNJsoiIiIjIGEqSRURERETGUJIsIiIiIjKGOeemuwyYWTuwd7rLMYvVAh3TXYgTiOJZfIpp\n8SmmxaV4FpfiWXyKafEscs7VHWmjGZEky/Exs43OufXTXY4TheJZfIpp8SmmxaV4FpfiWXyK6dRT\ndwsRERERkTGUJIuIiIiIjKEk+cTwzekuwAlG8Sw+xbT4FNPiUjyLS/EsPsV0iqlPsoiIiIjIGGpJ\nFhEREREZQ0myiIiIiMgYSpJnATNbPd1lOJGY2cfM7I2Fv226y3MiMLOKEX8rpkWgOBaX6tHiUj1a\nfKpHZx4lyTOcmX0V+LWZLZ7mosx6ZvZGM7sX+CRwDYBTp/zjYmavN7PNwH+Y2adAMT1eZnalmd0G\nnDbdZTlRqB4tHtWjxad6dOYKTHcBZDQzszFfjhqgC7jMzP7LOZeapqLNSoWz8SDwv4CLgf8NhICz\nzCwIZFUZHRszKwU+BXwOeAq4zcyizrnPTG/JZp+h772ZbcCLZwY4z8z2Oue6p7l4s47q0eJSPTp5\nVI/ObGpJnkFGVuxm5i88/Afg68B7gBXTVbbZaCiezrk08HPn3IXOuXuAbuBdzrmMKvZjY2Y+oBRo\nAp5xzjUB1wFX6bL20RmT0O0G/gT4BHAOsHbaCjZLqR4tLtWjk0f16MynJHmGMLMPAz81sxvNrNE5\nlzOzEPAm4C7gQeBdZvanZnbE9cZf60bE83+aWYNz7unC40Hn3MPALjO7fHpLObuY2YfM7H8AOOfy\ngAPq8Cp5nHO7gJ8BNxe2V5+6IxhznM51zu1xzrU45x4AWoGLzWzeNBdz1lA9WlyqR4tP9ejsoiR5\nBjCzdwDXAl/F64f4KTM7s3DmvtE51wG8BHwU+GdAX5pXMSaea4FPm9lQ/86smVUDe4HcNBVxVjGz\nMjP7T7xLrbeZWQDAOdcKbAduHLH5TcDZZnayWpde3TjH6WfMbN2ITX4AnITXojzydfr+j0P1aHGp\nHi0u1aOzk5LkmeEc4OvOuQeBf8K75PrBwnNvNrNH8QZJ3IV32bBvOgo5i4wXzxvAGwzhnOsCSoAN\nMHzJSw7DOdcPPOycmwv8ErhlxNOfBdaZ2ZvNLFxoGfklXv9FeXXjHacfHXrSOfcc8DRwSmFgzycL\nj+tHc3yqR4tL9WgRqR6dnXRQT6MRLUK7gPcCOOf24n05qszsPLyz+Cecc+ucc9cAcwH1VRrHEeIZ\nM7MrR2z+A7wz9UihQpJxjIjp3YV/bwTebWYrAJxzA8D/Ad6F13J3M3Ah0DLVZZ0tjvI4/RFeH8Uf\nA7VjXi+oHi021aPFp3p09lKSPIVszPRDI1qE/huIj6h8DgL3431JfuCc++SIl73DOffMJBd1VjjK\neD4ErB5RWUWA29GlwlEOF1Pn3KCZ+ZxzB/EGQH17xDa3A1/Au3xdB1xeuIQogJmtN7P6ofsTza30\nTQAAB45JREFUPU7NG/X+FWALsNY594kxr39NOsp4qh49gmM9PguPqR4dx+Fiqnp09lGSPAXM7Awz\nuw+42V4ZbT18dlm4bPVT4ENmZs65XiAGlDjn8mbmH7qU5ZxLTsNHmFGOMZ6lQHjED8DPnXPfcs5l\nprr8M9GrxXTsZVTn3E3AEjM7z8zmmtk5zrkdwD865z7onDswxcWfkczsZDN7AvhHoHLE40c6TiOF\n4zQJ3OCce4tz7jXfonSM8VQ9ehjHcXyqHj2MV4up6tHZSUnyJCp8MT6Nd8n0dufcNc65XOE531BF\nY2ZR4LdAM/BNM2sETgeyAM65nC5lFS+e4MV0yj/ADDSRmBYSjFKgYsRL/wV4HHgErzXpNd/COY4b\ngJ85597qnHsRJnycZgCcc1nnXNv0FH1GOtZ4qh4d33HFE1SPjuOwMVU9OjspSZ5EhYM9BDzmnPs2\ngJmdbt6o1qGK6HN4073MAT6GN+3TD4Ee4IvTUe6ZSvEsvgnG9Ga8S6+nFO5fDnwE+BJwsvOmgpKC\nQotlNV78vlZ47B1mNh+IFu5/Hh2nE6J4FpfiWXwTjOnnUD0665hOWorLzC4Gks65Jwv3Y8CdwDa8\nvnGtQC/wE+A+4JvA/3LO7Ryxj6hzLj7VZZ+JFM/iO96YmtkaoN95E98L48Y0AjwDfBx4N96gu4NA\nAm8Gi9vQcXpYimdxKZ7Fd7wxVT06SzjndCvCDSjD67/VBXwXqBrx3HvwBpBcXLj/AeB7wKIR2/im\n+zPMpJviOSNj6p/uzzDTbkeI6d8Be4BrCvfnAU8Cl47YRsep4ql4zqJbEWKqenQW3dTdonjSwAPA\n1Xh9uN459IRz7ofAO90rl1PuA6op9D0s9FlSX7nRFM/iO96Yqv/hoQ4bU7zR6xG8keo4byDOwxTm\nPtVxOi7Fs7gUz+I73piqHp1FlCQfBzO7xswuNrNK51wKbzqX+4AXgfVmdlJhO3PeSOEhl+H1XRqA\n4aUpX/MUz+JTTItvojF13tynHwWuMbN1ZvZB4A14izIopgWKZ3EpnsWnmL52qU/yUTIzw5uI/odA\nHngZb5qhG5y37CnmTRB+LV5/pc8XHvMBF+DNe7oP+KTzpnt5TVM8i08xLb5jjWnh8avwlkk+GfiU\nc27rFBd/xlE8i0vxLD7FVEAtyUfFzPzOO6soAw445y7FW/a0C29wEwDOuZeATUCjmS0vdOh3wAG8\nORCvVPKheE4GxbT4jiOmMTMLOud+DHy6ENPX/I+l4llcimfxKaYyJDDdBZgNzFtc4XOA38zuAcop\nrDDknMuZ2Q1As5ldPNSn0zn3MzNbDfwGbwL21zvntuGdjb6mKZ7Fp5gWX5FiugHY7nTJTvEsMsWz\n+BRTGUstyUdg3jQvm4AqYCfeFygDbDCzs2G4n9E/FW5Dr3sn8GngQbwlZbdNacFnKMWz+BTT4iti\nTLdPacFnKMWzuBTP4lNMZTzqk3wEZnYhsNg59/3C/a8DW/DmPvyIc+7MQl/OeuCreP04dxdeh3Pu\n0Wkq+oykeBafYlp8imlxKZ7FpXgWn2Iq41FL8pFtAu4oXIYBbwnJhc65W/EuyXykcHY5H8g654ZG\nsT6qL824FM/iU0yLTzEtLsWzuBTP4lNM5RBKko/AORd3zqXcK3MbXga0F/7+S2C1mf0S+BHeajvy\nKhTP4lNMi08xLS7Fs7gUz+JTTGU8Grg3QYWzS4e3lv3dhYf7gU/hrcW+23kTh8sEKJ7Fp5gWn2Ja\nXIpncSmexaeYykhqSZ64PN6qOR3A2sIZ5T8AeefcY/rSHDXFs/gU0+JTTItL8SwuxbP4FFMZpoF7\nR8HMzgWeKNy+55z7zjQXaVZTPItPMS0+xbS4FM/iUjyLTzGVIUqSj4KZzQfeB3zJeUtTynFQPItP\nMS0+xbS4FM/iUjyLTzGVIUqSRURERETGUJ9kEREREZExlCSLiIiIiIyhJFlEREREZAwlySIiIiIi\nYyhJFhEREREZQ0myiMgMYmY5M9tsZlvN7Fkz+5iZvWpdbWaLzew9U1VGEZHXAiXJIiIzS8I5t845\ndzJwGXA58I9HeM1iQEmyiEgRaZ5kEZEZxMwGnHOlI+4vBZ4GaoFFwPeBWOHpDzvnnjCzPwCrgd3A\nbcBXgS8ClwBh4Bbn3Dem7EOIiJwAlCSLiMwgY5PkwmM9wEqgH8g755JmtgL4kXNuvZldAnzcOXdF\nYfvrgXrn3OfNLAw8DrzTObd7Sj+MiMgsFpjuAoiIyIQFga+Z2TogB5x0mO3eCKw1sz8r3K8AVuC1\nNIuIyAQoSRYRmcEK3S1yQBte3+RW4DS8MSXJw70M+Ihz7t4pKaSIyAlIA/dERGYoM6sD/hP4mvP6\nxlUALc65PPA+wF/YtB8oG/HSe4EPmlmwsJ+TzCyGiIhMmFqSRURmlhIz24zXtSKLN1DvS4Xnvg7c\naWbXAL8BBguPPwfkzOxZ4FbgK3gzXvzRzAxoB94+VR9AROREoIF7IiIiIiJjqLuFiIiIiMgYSpJF\nRERERMZQkiwiIiIiMoaSZBERERGRMZQki4iIiIiMoSRZRERERGQMJckiIiIiImMoSRYRERERGeP/\nA1Sf5cPPoAX8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8ce144898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot_cierre.plot(figsize=(12,8)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empresa Nvidia: ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oRUS6ucJqBwB//PpkZgzPCHJtJNDUohYR6eaKfEHdL1n3pnsiBbWISDdXWOUN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\nClJJohwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8cc822fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "def load_adj_close_NVDA(ticker, data_source, start_date, end_date):\n",
    "    panel_data = data.DataReader(ticker, data_source, start_date, end_date)\n",
    "    closes_NVDA = panel_data.loc['Adj Close']\n",
    "    all_weekdays = pd.date_range(start=start_date, end=end_date)\n",
    "    closes_NVDA = closes_NVDA.reindex(all_weekdays)\n",
    "    closes_NVDA = closes_NVDA.fillna(method='ffill')\n",
    "    return closes_NVDA\n",
    "ticker = ['NVDA']\n",
    "data_source = 'yahoo'\n",
    "start_date = '2015-01-04'\n",
    "end_date = '2016-12-31'\n",
    "closes_NVDA = load_adj_close_NVDA(ticker, data_source, start_date, end_date)\n",
    "closes_NVDA.plot(figsize=(8,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rendimientos diarios Nvidia:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0023570745701586185, 0.020787806283585199)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mu_std_daily_ret(closes_NVDA):\n",
    "    daily_returns_NVDA = (np.log(closes_NVDA/closes_NVDA.shift(1)))[1:]\n",
    "    mu_NVDA = daily_returns_NVDA.mean().values[0]\n",
    "    sigma_NVDA = daily_returns_NVDA.std().values[0]\n",
    "    return daily_returns_NVDA, mu_NVDA, sigma_NVDA\n",
    "daily_returns_NVDA, mu_NVDA, sigma_NVDA = mu_std_daily_ret(closes_NVDA)\n",
    "mu_NVDA, sigma_NVDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xQmjsER8au4z1rnNaR7j0f+E8tGBLO688uhjvP5zznLBiinffIxhtVKarDCvy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\n90s7yljv6OUl4jwO7Ey+WqCntTLCido69nTL4YX64Dm/pnhnTSj9QT0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/BDACwG4An1FK\nvZpcbQghGgp2QgghpEDQFE8IIYQUCAp2QgghpEBQsBNCCCEFgoKdEEIIKRAU7IQQQkiBoGAnhBBC\nCgQFOyGEEFIgKNgJIYSQAvH/J1qUE+oL8E0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8ce3c4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_returns_NVDA.plot(figsize=(7,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusiones\n",
    "\n",
    "Nuestras conclusiones giran en torno a varios ejes, tanto desprendidos de los objetivos generales, como de otros sucesos que fueron sucediendo.\n",
    "\n",
    "Respecto a Batalla Naval, pudimos lograr un código funcional que es una buena aproximación del popular juego y vislumbramos más herramientas de Python que, con algo más de tiempo, nos hubieran permitido desarrollar un código más complejo que aprendiera de sus intentos para tener una estrategia más apta. Sin embargo, notamos también que el rumbo que estaba empezando a tomar el programa se alejaba un poco de lo que habíamos planteado en clase y por ello decidimos usar también la proyección de datos de bolsa.\n",
    "\n",
    "Respecto a la comparación de bolsas de empresas podemos recapitular las funciones escenciales utilizadas para el análisis de los datos de ambas compañias, es preciso dar constancia del valor de dichas funciones en su papel analítico que nos permite realizar una comparativa precisa y eficiente de las acciones de las compañias. Concluimos en dicha necesidad y en la del cumplimiento de nuestros objetivos establecidos en la planeación de nuestro proyecto. En este caso de igual manera fuimos capaces de encontrar variedad de nuevos parámetros y funciones relacionadas al tema, aunque la mayoría estaban pensadas a otro tipo de problemas, tal como el tiempo de recuperación de una inversión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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