s_n_p_autocorrelation.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:7d38208f53ae99910b8b3d549e40ad206296c82b945ee7118ead36e5e24e411f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import Quandl\n",
      "import numpy as np\n",
      "\n",
      "snp_data = Quandl.get('YAHOO/INDEX_GSPC')\n",
      "snp_data.columns = ['Open','High','Low','Close','Volume','AdjustedClose']\n",
      "percentage_change = snp_data.AdjustedClose.pct_change()\n",
      "percentage_change.fillna(0)\n",
      "percentage_change.autocorr()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "0.027558097594043013"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "percentage_change.autocorr?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import autocorrelation_plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "autocorrelation_plot(percentage_change)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x108020a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l5s52eSewajfbJXBjRNwcEb8+mWj9qH3+s/Z8YMZSzFjGUDP21tOIiBuAQxZ46v2jK5mZ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1080205d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function\n",
      "import numpy as np\n",
      "from scipy import stats\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import statsmodels.api as sm\n",
      "from statsmodels.graphics.api import qqplot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.graphics.tsa.plot_acf(percentage_change.values.squeeze(), lags=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 33,
       "text": [
        "<matplotlib.figure.Figure at 0x10ebd0d50>"
       ]
      },
      {
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       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10ebd0d50>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.graphics.tsa.plot_pacf(percentage_change.values.squeeze(), lags=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 37,
       "text": [
        "<matplotlib.figure.Figure at 0x110a566d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x110a566d0>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}