Proyecto2_EscotoH_TinajeroC_SayavedraJ.ipynb

{
 "cells": [
  {
   "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": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAF0CAYAAADyyEq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAF0CAYAAAAKKLFNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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",
<<<<<<< Updated upstream
   "execution_count": 4,
=======
<<<<<<< Updated upstream
   "execution_count": 4,
=======
   "execution_count": 39,
<<<<<<< Updated upstream
>>>>>>> Stashed changes
=======
>>>>>>> Stashed changes
>>>>>>> Stashed changes
   "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",
       "<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></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",
       "    </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",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>19.123156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>19.094017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>19.171713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>19.035746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>19.385382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>19.443657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>19.715591</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-10</th>\n",
       "      <td>20.346882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-11</th>\n",
       "      <td>20.210909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>21.658016</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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",
       "    </tr>\n",
       "    <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": {
      "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></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",
       "    </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",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>19.123156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>19.094017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>19.171713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>19.035746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>19.385382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>19.443657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>19.715591</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-10</th>\n",
       "      <td>20.346882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-11</th>\n",
       "      <td>20.210909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>21.658016</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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",
       "    </tr>\n",
       "    <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": 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",
       "    .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></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": "iVBORw0KGgoAAAANSUhEUgAAAskAAAHMCAYAAAAnEDXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAF0CAYAAADsAXoJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAF0CAYAAADRkxtvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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",
    "A partir de los objetivos principales podemos recpitular 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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}