3 formas de obtener datos financieros desde Quandl con Python

quandl_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Template para obtener datos de Quandl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay varias maneras de descargar los datos desde Quandl e incorporarlos a Python \n",
    "\n",
    "1) Simplemente descargar el achivo desde Quandl en CSV y leerlo en Python<br>\n",
    "2) Utilizando la función datareader del package pandas<br>\n",
    "3) Utilizando el propio package de Quandl\n",
    "\n",
    "En estos ejemplos voy a utilizar los datos de la base de Euronext Stock Exchange https://www.quandl.com/data/EURONEXT-Euronext-Stock-Exchange\n",
    "\n",
    "Para cualquiera de las 3 opciones vamos a necesitar pandas así que lo primero es importar la librería"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Opcion leer CSV\n",
    "\n",
    "Para leer los datos de Quandl en csv trabajamos como con cualquier fichero csv con pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>52.71</td>\n",
       "      <td>52.94</td>\n",
       "      <td>52.11</td>\n",
       "      <td>52.72</td>\n",
       "      <td>7100395.0</td>\n",
       "      <td>3.737706e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>52.56</td>\n",
       "      <td>53.19</td>\n",
       "      <td>52.24</td>\n",
       "      <td>52.30</td>\n",
       "      <td>5787285.0</td>\n",
       "      <td>3.038256e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>52.99</td>\n",
       "      <td>53.16</td>\n",
       "      <td>52.57</td>\n",
       "      <td>52.64</td>\n",
       "      <td>4237939.0</td>\n",
       "      <td>2.238464e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>52.26</td>\n",
       "      <td>52.90</td>\n",
       "      <td>52.26</td>\n",
       "      <td>52.73</td>\n",
       "      <td>6205310.0</td>\n",
       "      <td>3.271056e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>51.98</td>\n",
       "      <td>52.38</td>\n",
       "      <td>51.60</td>\n",
       "      <td>52.05</td>\n",
       "      <td>8710760.0</td>\n",
       "      <td>4.533913e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low   Last     Volume      Turnover\n",
       "0  2018-06-06  52.71  52.94  52.11  52.72  7100395.0  3.737706e+08\n",
       "1  2018-06-05  52.56  53.19  52.24  52.30  5787285.0  3.038256e+08\n",
       "2  2018-06-04  52.99  53.16  52.57  52.64  4237939.0  2.238464e+08\n",
       "3  2018-06-01  52.26  52.90  52.26  52.73  6205310.0  3.271056e+08\n",
       "4  2018-05-31  51.98  52.38  51.60  52.05  8710760.0  4.533913e+08"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo: Cotizaciones acciones de TOTAL (ticker FP)\n",
    "FP = pd.read_csv('https://www.quandl.com/api/v3/datasets/EURONEXT/FP.csv?api_key=PON_AQUI_TU_API_KEY')\n",
    "FP.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Utilizar pandas datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importar librerías y paquetes\n",
    "from pandas_datareader import data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fecha_inicio= \"2015-01-01\"\n",
    "fecha_fin= \"2018-05-01\"\n",
    "\n",
    "# para poder bajar los datos debemos indicar el codigo del dataset, en este caso EURONEXT y el codigo del activo\n",
    "# en este caso VIV para las acciones de Vivendi\n",
    "Quandl_code = \"EURONEXT/VIV\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "VIV = web.DataReader(Quandl_code,\"quandl\",fecha_inicio,fecha_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>21.29</td>\n",
       "      <td>21.89</td>\n",
       "      <td>21.29</td>\n",
       "      <td>21.89</td>\n",
       "      <td>4193209.0</td>\n",
       "      <td>9.139867e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <td>21.50</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.16</td>\n",
       "      <td>21.34</td>\n",
       "      <td>2628890.0</td>\n",
       "      <td>5.614424e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26</th>\n",
       "      <td>21.15</td>\n",
       "      <td>21.51</td>\n",
       "      <td>21.03</td>\n",
       "      <td>21.50</td>\n",
       "      <td>3858488.0</td>\n",
       "      <td>8.245741e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <td>20.80</td>\n",
       "      <td>21.19</td>\n",
       "      <td>20.80</td>\n",
       "      <td>21.18</td>\n",
       "      <td>3968625.0</td>\n",
       "      <td>8.361385e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <td>21.03</td>\n",
       "      <td>21.10</td>\n",
       "      <td>20.71</td>\n",
       "      <td>20.86</td>\n",
       "      <td>17075500.0</td>\n",
       "      <td>3.559799e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low   Last      Volume      Turnover\n",
       "Date                                                            \n",
       "2018-04-30  21.29  21.89  21.29  21.89   4193209.0  9.139867e+07\n",
       "2018-04-27  21.50  21.55  21.16  21.34   2628890.0  5.614424e+07\n",
       "2018-04-26  21.15  21.51  21.03  21.50   3858488.0  8.245741e+07\n",
       "2018-04-25  20.80  21.19  20.80  21.18   3968625.0  8.361385e+07\n",
       "2018-04-24  21.03  21.10  20.71  20.86  17075500.0  3.559799e+08"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>20.025</td>\n",
       "      <td>20.355</td>\n",
       "      <td>19.965</td>\n",
       "      <td>20.300</td>\n",
       "      <td>5124585.0</td>\n",
       "      <td>1.035987e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>19.875</td>\n",
       "      <td>19.955</td>\n",
       "      <td>19.730</td>\n",
       "      <td>19.840</td>\n",
       "      <td>4498851.0</td>\n",
       "      <td>8.942174e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>20.125</td>\n",
       "      <td>20.190</td>\n",
       "      <td>19.785</td>\n",
       "      <td>19.800</td>\n",
       "      <td>7089694.0</td>\n",
       "      <td>1.412263e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>20.415</td>\n",
       "      <td>20.685</td>\n",
       "      <td>20.105</td>\n",
       "      <td>20.135</td>\n",
       "      <td>6215784.0</td>\n",
       "      <td>1.259081e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>20.830</td>\n",
       "      <td>20.900</td>\n",
       "      <td>20.475</td>\n",
       "      <td>20.555</td>\n",
       "      <td>3073138.0</td>\n",
       "      <td>6.327595e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last     Volume      Turnover\n",
       "Date                                                               \n",
       "2015-01-08  20.025  20.355  19.965  20.300  5124585.0  1.035987e+08\n",
       "2015-01-07  19.875  19.955  19.730  19.840  4498851.0  8.942174e+07\n",
       "2015-01-06  20.125  20.190  19.785  19.800  7089694.0  1.412263e+08\n",
       "2015-01-05  20.415  20.685  20.105  20.135  6215784.0  1.259081e+08\n",
       "2015-01-02  20.830  20.900  20.475  20.555  3073138.0  6.327595e+07"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIV.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Utilizar la librería de Quandl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Primero tenemos que instalar el package: pip install quandl<br>\n",
    "3.2 Necesitas un código API (para conseguirlo hay que registrarse gratuitamente en la web quandl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "quandl.ApiConfig.api_key = \"TU_CODIGO_API_AQUI\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "VIV_2 = quandl.get(Quandl_code, start_date= fecha_inicio, end_date= fecha_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>20.830</td>\n",
       "      <td>20.900</td>\n",
       "      <td>20.475</td>\n",
       "      <td>20.555</td>\n",
       "      <td>3073138.0</td>\n",
       "      <td>6.327595e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>20.415</td>\n",
       "      <td>20.685</td>\n",
       "      <td>20.105</td>\n",
       "      <td>20.135</td>\n",
       "      <td>6215784.0</td>\n",
       "      <td>1.259081e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>20.125</td>\n",
       "      <td>20.190</td>\n",
       "      <td>19.785</td>\n",
       "      <td>19.800</td>\n",
       "      <td>7089694.0</td>\n",
       "      <td>1.412263e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>19.875</td>\n",
       "      <td>19.955</td>\n",
       "      <td>19.730</td>\n",
       "      <td>19.840</td>\n",
       "      <td>4498851.0</td>\n",
       "      <td>8.942174e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>20.025</td>\n",
       "      <td>20.355</td>\n",
       "      <td>19.965</td>\n",
       "      <td>20.300</td>\n",
       "      <td>5124585.0</td>\n",
       "      <td>1.035987e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last     Volume      Turnover\n",
       "Date                                                               \n",
       "2015-01-02  20.830  20.900  20.475  20.555  3073138.0  6.327595e+07\n",
       "2015-01-05  20.415  20.685  20.105  20.135  6215784.0  1.259081e+08\n",
       "2015-01-06  20.125  20.190  19.785  19.800  7089694.0  1.412263e+08\n",
       "2015-01-07  19.875  19.955  19.730  19.840  4498851.0  8.942174e+07\n",
       "2015-01-08  20.025  20.355  19.965  20.300  5124585.0  1.035987e+08"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIV_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***nota***: habrás notado que cuando utilizamos el datareader de pandas o el csv la serie esta ordenada de forma decreciente y cuando utilizamos el package em forma creciente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Rsdh//ztX3tu7l8e0Kiv5OLs7RaMt7cxMcyYXwZ+wlrZSagGAzCC7xzo5ySef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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201c67f3be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "VIV_2['Last'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**notas**: el único campo obligatorio es el código quandl, después como opciones podemos especificar las fechas, qué columna queremos, seleccionar solo algunas filas, agrupar los datos por mes,etc <br>\n",
    "\n",
    "Este notebook es el soporte del artículo http://estrategiastrading.com/datos-financieros-python-quandl/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}