naicigam/python_stocks_01.ipynb

## python_stocks_01.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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>2009-01-02</th>\n",
       "      <td>308.600525</td>\n",
       "      <td>352.330597</td>\n",
       "      <td>282.750488</td>\n",
       "      <td>338.530579</td>\n",
       "      <td>11498100</td>\n",
       "      <td>169.096191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-02</th>\n",
       "      <td>334.290588</td>\n",
       "      <td>381.000641</td>\n",
       "      <td>329.550568</td>\n",
       "      <td>337.990601</td>\n",
       "      <td>12362400</td>\n",
       "      <td>168.826477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-02</th>\n",
       "      <td>333.330566</td>\n",
       "      <td>359.160614</td>\n",
       "      <td>289.450470</td>\n",
       "      <td>348.060608</td>\n",
       "      <td>10733600</td>\n",
       "      <td>173.856445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>343.780609</td>\n",
       "      <td>403.750702</td>\n",
       "      <td>340.610565</td>\n",
       "      <td>395.970703</td>\n",
       "      <td>8812600</td>\n",
       "      <td>197.787552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-01</th>\n",
       "      <td>395.030701</td>\n",
       "      <td>417.230713</td>\n",
       "      <td>384.690674</td>\n",
       "      <td>417.230713</td>\n",
       "      <td>5990600</td>\n",
       "      <td>208.406952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close    Volume  \\\n",
       "Date                                                                   \n",
       "2009-01-02  308.600525  352.330597  282.750488  338.530579  11498100   \n",
       "2009-02-02  334.290588  381.000641  329.550568  337.990601  12362400   \n",
       "2009-03-02  333.330566  359.160614  289.450470  348.060608  10733600   \n",
       "2009-04-01  343.780609  403.750702  340.610565  395.970703   8812600   \n",
       "2009-05-01  395.030701  417.230713  384.690674  417.230713   5990600   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2009-01-02  169.096191  \n",
       "2009-02-02  168.826477  \n",
       "2009-03-02  173.856445  \n",
       "2009-04-01  197.787552  \n",
       "2009-05-01  208.406952  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline",
    "\n",
    "import pandas_datareader.data as reader\n",
    "import datetime\n",
    "\n",
    "# descargo los datos de Yahoo! Finance\n",
    "symbol = 'GOOG'\n",
    "start = datetime.datetime(2009, 1, 1)\n",
    "end = datetime.datetime(2017, 1, 1)\n",
    "data = reader.get_data_yahoo(symbol, start, end, interval='m')\n",
    "\n",
    "# muestro los primeros valores\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21586d872e8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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W9m7uWD+TjJQoAGZNNXs+i8oDt1+998ai6MkzBt0THg1p1Fp/e4DN6wc4bjOw+WJDCSE8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\n1IUQIoD8f05jm3LfFGA6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21586dcaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico el precio de cierre\n",
    "data['Adj Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Return</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-02</th>\n",
       "      <td>308.600525</td>\n",
       "      <td>352.330597</td>\n",
       "      <td>282.750488</td>\n",
       "      <td>338.530579</td>\n",
       "      <td>11498100</td>\n",
       "      <td>169.096191</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-02</th>\n",
       "      <td>334.290588</td>\n",
       "      <td>381.000641</td>\n",
       "      <td>329.550568</td>\n",
       "      <td>337.990601</td>\n",
       "      <td>12362400</td>\n",
       "      <td>168.826477</td>\n",
       "      <td>-0.001595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-02</th>\n",
       "      <td>333.330566</td>\n",
       "      <td>359.160614</td>\n",
       "      <td>289.450470</td>\n",
       "      <td>348.060608</td>\n",
       "      <td>10733600</td>\n",
       "      <td>173.856445</td>\n",
       "      <td>0.029794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>343.780609</td>\n",
       "      <td>403.750702</td>\n",
       "      <td>340.610565</td>\n",
       "      <td>395.970703</td>\n",
       "      <td>8812600</td>\n",
       "      <td>197.787552</td>\n",
       "      <td>0.137649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-01</th>\n",
       "      <td>395.030701</td>\n",
       "      <td>417.230713</td>\n",
       "      <td>384.690674</td>\n",
       "      <td>417.230713</td>\n",
       "      <td>5990600</td>\n",
       "      <td>208.406952</td>\n",
       "      <td>0.053691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close    Volume  \\\n",
       "Date                                                                   \n",
       "2009-01-02  308.600525  352.330597  282.750488  338.530579  11498100   \n",
       "2009-02-02  334.290588  381.000641  329.550568  337.990601  12362400   \n",
       "2009-03-02  333.330566  359.160614  289.450470  348.060608  10733600   \n",
       "2009-04-01  343.780609  403.750702  340.610565  395.970703   8812600   \n",
       "2009-05-01  395.030701  417.230713  384.690674  417.230713   5990600   \n",
       "\n",
       "             Adj Close    Return  \n",
       "Date                              \n",
       "2009-01-02  169.096191       NaN  \n",
       "2009-02-02  168.826477 -0.001595  \n",
       "2009-03-02  173.856445  0.029794  \n",
       "2009-04-01  197.787552  0.137649  \n",
       "2009-05-01  208.406952  0.053691  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculo el retorno diario\n",
    "data = data.assign( Return=data['Adj Close'].pct_change() )\n",
    "\n",
    "# muestro los primeros valores\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21585cce3c8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RoczWotBOnf7adgMCz2JkSMBqXcWoE/S3Wpx7jz1n23LMJ/RwotFS3nGef/nq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\niIu+mVmN/B+n11xsjXu7AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21586e380b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico los retornos.\n",
    "data['Return'].plot()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}