playing_with_pandas.ipynb

{
 "metadata": {
  "name": "playing_with_pandas"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "import pandas as pd"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 7
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "2+2"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 1, 
       "text": [
        "4"
       ]
      }
     ], 
     "prompt_number": 1
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "np.Serie?"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Object `np.Serie` not found."
       ]
      }
     ], 
     "prompt_number": 3
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "Series?"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Object `Series` not found."
       ]
      }
     ], 
     "prompt_number": 5
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "from pandas import Series, DataFrame"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 9
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "data = Series(np.random.randn(10),index=[['a','a','a','b','b','b','c','c','d','d'],[1,2,3,1,2,3,1,2,2,3]])"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 11
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "data"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 12, 
       "text": [
        "a  1   -0.092249", 
        "   2   -0.872634", 
        "   3   -0.352642", 
        "b  1    0.169762", 
        "   2   -0.220656", 
        "   3    0.745653", 
        "c  1   -1.213362", 
        "   2   -1.245862", 
        "d  2    0.300581", 
        "   3   -0.918258"
       ]
      }
     ], 
     "prompt_number": 12
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "data.index"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 14, 
       "text": [
        "MultiIndex", 
        "[(a, 1), (a, 2), (a, 3), (b, 1), (b, 2), (b, 3), (c, 1), (c, 2), (d, 2), (d, 3)]"
       ]
      }
     ], 
     "prompt_number": 14
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "data['b':'c']"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 16, 
       "text": [
        "b  1    0.169762", 
        "   2   -0.220656", 
        "   3    0.745653", 
        "c  1   -1.213362", 
        "   2   -1.245862"
       ]
      }
     ], 
     "prompt_number": 16
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type      Data/Info", 
        "-------------------------------", 
        "DataFrame   type      <class 'pandas.core.frame.DataFrame'>", 
        "Series      type      <class 'pandas.core.series.Series'>", 
        "data        Series    a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "pd          module    <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>"
       ]
      }
     ], 
     "prompt_number": 17
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "import pandas.io.data as web"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 18
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type      Data/Info", 
        "-------------------------------", 
        "DataFrame   type      <class 'pandas.core.frame.DataFrame'>", 
        "Series      type      <class 'pandas.core.series.Series'>", 
        "data        Series    a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "pd          module    <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "web         module    <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 19
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "pdata = pd.Panel(dict((stk,web.get_data_yahoo(stk,'1/1/2012','12/30/2012'))for stk in ['AAPL','GOOG','MSFT','DELL']))"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 20
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type      Data/Info", 
        "-------------------------------", 
        "DataFrame   type      <class 'pandas.core.frame.DataFrame'>", 
        "Series      type      <class 'pandas.core.series.Series'>", 
        "data        Series    a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "pd          module    <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata       Panel     <class 'pandas.core.panel<...>s axis: Open to Adj Close", 
        "web         module    <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 21
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "pdata=pdata.swapaxes('items','minor')"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 26
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "pdata['Adj Close']"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 30, 
       "text": [
        "<class 'pandas.core.frame.DataFrame'>", 
        "DatetimeIndex: 249 entries, 2012-01-03 00:00:00 to 2012-12-28 00:00:00", 
        "Data columns:", 
        "AAPL    249  non-null values", 
        "DELL    249  non-null values", 
        "GOOG    249  non-null values", 
        "MSFT    249  non-null values", 
        "dtypes: float64(4)"
       ]
      }
     ], 
     "prompt_number": 30
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "pdata.ix[:,'10/25/2012',:]"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>Open</th>", 
        "      <th>High</th>", 
        "      <th>Low</th>", 
        "      <th>Close</th>", 
        "      <th>Volume</th>", 
        "      <th>Adj Close</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>AAPL</th>", 
        "      <td> 620.00</td>", 
        "      <td> 622.00</td>", 
        "      <td> 605.55</td>", 
        "      <td> 609.54</td>", 
        "      <td> 23440200</td>", 
        "      <td> 603.25</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>DELL</th>", 
        "      <td>   9.31</td>", 
        "      <td>   9.34</td>", 
        "      <td>   9.22</td>", 
        "      <td>   9.24</td>", 
        "      <td> 21325500</td>", 
        "      <td>   9.12</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>GOOG</th>", 
        "      <td> 680.00</td>", 
        "      <td> 682.00</td>", 
        "      <td> 673.51</td>", 
        "      <td> 677.76</td>", 
        "      <td>  2401100</td>", 
        "      <td> 677.76</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>MSFT</th>", 
        "      <td>  28.19</td>", 
        "      <td>  28.20</td>", 
        "      <td>  27.86</td>", 
        "      <td>  27.88</td>", 
        "      <td> 54084300</td>", 
        "      <td>  27.43</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 33, 
       "text": [
        "        Open    High     Low   Close    Volume  Adj Close", 
        "AAPL  620.00  622.00  605.55  609.54  23440200     603.25", 
        "DELL    9.31    9.34    9.22    9.24  21325500       9.12", 
        "GOOG  680.00  682.00  673.51  677.76   2401100     677.76", 
        "MSFT   28.19   28.20   27.86   27.88  54084300      27.43"
       ]
      }
     ], 
     "prompt_number": 33
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type      Data/Info", 
        "-------------------------------", 
        "DataFrame   type      <class 'pandas.core.frame.DataFrame'>", 
        "Series      type      <class 'pandas.core.series.Series'>", 
        "data        Series    a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "pd          module    <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata       Panel     <class 'pandas.core.panel<...>r_axis axis: AAPL to MSFT", 
        "web         module    <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 34
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "all_data = {}", 
      "for ticker in ['AAPL','GOOG','MSFT','DELL','IBM']:", 
      "    all_data[ticker]=web.get_data_yahoo(ticker, '1/1/2012','12/30/2012')", 
      "    price = DataFrame({tic:data['Adj Close']", 
      "                       for tic, data in all_data.iteritems()})", 
      "    volume= DataFrame({tic:data['Volume']", 
      "                       for tic, data in all_data.iteritems()})"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 40
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type         Data/Info", 
        "----------------------------------", 
        "DataFrame   type         <class 'pandas.core.frame.DataFrame'>", 
        "Series      type         <class 'pandas.core.series.Series'>", 
        "all_data    dict         n=4", 
        "data        Series       a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "pd          module       <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata       Panel        <class 'pandas.core.panel<...>r_axis axis: AAPL to MSFT", 
        "price       DataFrame    <class 'pandas.core.frame<...>alues\\ndtypes: float64(4)", 
        "ticker      str          DELL", 
        "volume      DataFrame    <class 'pandas.core.frame<...> values\\ndtypes: int64(4)", 
        "web         module       <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 36
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "returns = price.pct_change()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 41
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "returns.tail()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>AAPL</th>", 
        "      <th>DELL</th>", 
        "      <th>GOOG</th>", 
        "      <th>IBM</th>", 
        "      <th>MSFT</th>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>Date</th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>2012-12-21</th>", 
        "      <td>-0.004608</td>", 
        "      <td>-0.006757</td>", 
        "      <td>-0.009317</td>", 
        "      <td>-0.006909</td>", 
        "      <td>-0.008379</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2012-12-24</th>", 
        "      <td> 0.001627</td>", 
        "      <td>-0.018465</td>", 
        "      <td>-0.008566</td>", 
        "      <td>-0.005296</td>", 
        "      <td>-0.013960</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2012-12-26</th>", 
        "      <td>-0.013787</td>", 
        "      <td> 0.003960</td>", 
        "      <td>-0.000888</td>", 
        "      <td>-0.002297</td>", 
        "      <td>-0.007452</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2012-12-27</th>", 
        "      <td> 0.004019</td>", 
        "      <td>-0.008876</td>", 
        "      <td>-0.003640</td>", 
        "      <td> 0.003924</td>", 
        "      <td> 0.003754</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2012-12-28</th>", 
        "      <td>-0.010623</td>", 
        "      <td>-0.013930</td>", 
        "      <td>-0.008892</td>", 
        "      <td>-0.014956</td>", 
        "      <td>-0.015333</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 42, 
       "text": [
        "                AAPL      DELL      GOOG       IBM      MSFT", 
        "Date                                                        ", 
        "2012-12-21 -0.004608 -0.006757 -0.009317 -0.006909 -0.008379", 
        "2012-12-24  0.001627 -0.018465 -0.008566 -0.005296 -0.013960", 
        "2012-12-26 -0.013787  0.003960 -0.000888 -0.002297 -0.007452", 
        "2012-12-27  0.004019 -0.008876 -0.003640  0.003924  0.003754", 
        "2012-12-28 -0.010623 -0.013930 -0.008892 -0.014956 -0.015333"
       ]
      }
     ], 
     "prompt_number": 42
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "returns.corr()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>AAPL</th>", 
        "      <th>DELL</th>", 
        "      <th>GOOG</th>", 
        "      <th>IBM</th>", 
        "      <th>MSFT</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>AAPL</th>", 
        "      <td> 1.000000</td>", 
        "      <td> 0.208706</td>", 
        "      <td> 0.380936</td>", 
        "      <td> 0.335160</td>", 
        "      <td> 0.274678</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>DELL</th>", 
        "      <td> 0.208706</td>", 
        "      <td> 1.000000</td>", 
        "      <td> 0.064202</td>", 
        "      <td> 0.322815</td>", 
        "      <td> 0.392768</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>GOOG</th>", 
        "      <td> 0.380936</td>", 
        "      <td> 0.064202</td>", 
        "      <td> 1.000000</td>", 
        "      <td> 0.267234</td>", 
        "      <td> 0.189943</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>IBM</th>", 
        "      <td> 0.335160</td>", 
        "      <td> 0.322815</td>", 
        "      <td> 0.267234</td>", 
        "      <td> 1.000000</td>", 
        "      <td> 0.571116</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>MSFT</th>", 
        "      <td> 0.274678</td>", 
        "      <td> 0.392768</td>", 
        "      <td> 0.189943</td>", 
        "      <td> 0.571116</td>", 
        "      <td> 1.000000</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 43, 
       "text": [
        "          AAPL      DELL      GOOG       IBM      MSFT", 
        "AAPL  1.000000  0.208706  0.380936  0.335160  0.274678", 
        "DELL  0.208706  1.000000  0.064202  0.322815  0.392768", 
        "GOOG  0.380936  0.064202  1.000000  0.267234  0.189943", 
        "IBM   0.335160  0.322815  0.267234  1.000000  0.571116", 
        "MSFT  0.274678  0.392768  0.189943  0.571116  1.000000"
       ]
      }
     ], 
     "prompt_number": 43
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "print range(4)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "[0, 1, 2, 3]"
       ]
      }
     ], 
     "prompt_number": 44
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "df2 = DataFrame({'key':['a','b','d'],'da{'key':['a','b','d'],'data2':range(3)})ta2':range(3)})"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 45
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "df2"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>data2</th>", 
        "      <th>key</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0</th>", 
        "      <td> 0</td>", 
        "      <td> a</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1</th>", 
        "      <td> 1</td>", 
        "      <td> b</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2</th>", 
        "      <td> 2</td>", 
        "      <td> d</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 46, 
       "text": [
        "   data2 key", 
        "0      0   a", 
        "1      1   b", 
        "2      2   d"
       ]
      }
     ], 
     "prompt_number": 46
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "df1 = DataFrame({'key':['b','b','a','c','a','a','b'],'data1':range(7)})"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 48
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "df1"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>data1</th>", 
        "      <th>key</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0</th>", 
        "      <td> 0</td>", 
        "      <td> b</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1</th>", 
        "      <td> 1</td>", 
        "      <td> b</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2</th>", 
        "      <td> 2</td>", 
        "      <td> a</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>3</th>", 
        "      <td> 3</td>", 
        "      <td> c</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>4</th>", 
        "      <td> 4</td>", 
        "      <td> a</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>5</th>", 
        "      <td> 5</td>", 
        "      <td> a</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>6</th>", 
        "      <td> 6</td>", 
        "      <td> b</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 49, 
       "text": [
        "   data1 key", 
        "0      0   b", 
        "1      1   b", 
        "2      2   a", 
        "3      3   c", 
        "4      4   a", 
        "5      5   a", 
        "6      6   b"
       ]
      }
     ], 
     "prompt_number": 49
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "pd.merge(df1,df2,on='key')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>data1</th>", 
        "      <th>key</th>", 
        "      <th>data2</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0</th>", 
        "      <td> 0</td>", 
        "      <td> b</td>", 
        "      <td> 1</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1</th>", 
        "      <td> 1</td>", 
        "      <td> b</td>", 
        "      <td> 1</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2</th>", 
        "      <td> 6</td>", 
        "      <td> b</td>", 
        "      <td> 1</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>3</th>", 
        "      <td> 2</td>", 
        "      <td> a</td>", 
        "      <td> 0</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>4</th>", 
        "      <td> 4</td>", 
        "      <td> a</td>", 
        "      <td> 0</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>5</th>", 
        "      <td> 5</td>", 
        "      <td> a</td>", 
        "      <td> 0</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 51, 
       "text": [
        "   data1 key  data2", 
        "0      0   b      1", 
        "1      1   b      1", 
        "2      6   b      1", 
        "3      2   a      0", 
        "4      4   a      0", 
        "5      5   a      0"
       ]
      }
     ], 
     "prompt_number": 51
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "pd.concat([df1.duplicated(),df2.duplicated()])"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 56, 
       "text": [
        "0    False", 
        "1    False", 
        "2    False", 
        "3    False", 
        "4    False", 
        "5    False", 
        "6    False", 
        "0    False", 
        "1    False", 
        "2    False"
       ]
      }
     ], 
     "prompt_number": 56
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "df2.duplicated()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 55, 
       "text": [
        "0    False", 
        "1    False", 
        "2    False"
       ]
      }
     ], 
     "prompt_number": 55
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "np.random.seed(12345)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 61
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "data2 = DataFrame(np.random.randn(1000,4))"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 62
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "data2.describe()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>0</th>", 
        "      <th>1</th>", 
        "      <th>2</th>", 
        "      <th>3</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>count</th>", 
        "      <td> 1000.000000</td>", 
        "      <td> 1000.000000</td>", 
        "      <td> 1000.000000</td>", 
        "      <td> 1000.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>mean</th>", 
        "      <td>   -0.067684</td>", 
        "      <td>    0.067924</td>", 
        "      <td>    0.025598</td>", 
        "      <td>   -0.002298</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>std</th>", 
        "      <td>    0.998035</td>", 
        "      <td>    0.992106</td>", 
        "      <td>    1.006835</td>", 
        "      <td>    0.996794</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>min</th>", 
        "      <td>   -3.428254</td>", 
        "      <td>   -3.548824</td>", 
        "      <td>   -3.184377</td>", 
        "      <td>   -3.745356</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>25%</th>", 
        "      <td>   -0.774890</td>", 
        "      <td>   -0.591841</td>", 
        "      <td>   -0.641675</td>", 
        "      <td>   -0.644144</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>50%</th>", 
        "      <td>   -0.116401</td>", 
        "      <td>    0.101143</td>", 
        "      <td>    0.002073</td>", 
        "      <td>   -0.013611</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>75%</th>", 
        "      <td>    0.616366</td>", 
        "      <td>    0.780282</td>", 
        "      <td>    0.680391</td>", 
        "      <td>    0.654328</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>max</th>", 
        "      <td>    3.366626</td>", 
        "      <td>    2.653656</td>", 
        "      <td>    3.260383</td>", 
        "      <td>    3.927528</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 63, 
       "text": [
        "                 0            1            2            3", 
        "count  1000.000000  1000.000000  1000.000000  1000.000000", 
        "mean     -0.067684     0.067924     0.025598    -0.002298", 
        "std       0.998035     0.992106     1.006835     0.996794", 
        "min      -3.428254    -3.548824    -3.184377    -3.745356", 
        "25%      -0.774890    -0.591841    -0.641675    -0.644144", 
        "50%      -0.116401     0.101143     0.002073    -0.013611", 
        "75%       0.616366     0.780282     0.680391     0.654328", 
        "max       3.366626     2.653656     3.260383     3.927528"
       ]
      }
     ], 
     "prompt_number": 63
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type         Data/Info", 
        "----------------------------------", 
        "DataFrame   type         <class 'pandas.core.frame.DataFrame'>", 
        "Series      type         <class 'pandas.core.series.Series'>", 
        "all_data    dict         n=5", 
        "data        Series       a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "data2       DataFrame    <class 'pandas.core.frame<...>alues\\ndtypes: float64(4)", 
        "df1         DataFrame       data1 key\\n0      0   <...>      5   a\\n6      6   b", 
        "df2         DataFrame    "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "   data2 key\\n0      0   <...>      1   b\\n2      2   d", 
        "pd          module       <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata       Panel        "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.panel<...>r_axis axis: AAPL to MSFT", 
        "price       DataFrame    "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "returns     DataFrame    <class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "ticker      str          IBM", 
        "volume      DataFrame    <class 'pandas.core.frame<...> values\\ndtypes: int64(5)", 
        "web         module       <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 64
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "plot(np.arange(10))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 66, 
       "text": [
        "[<matplotlib.lines.Line2D at 0x4909870>]"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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HNQNwGtcXNNUMwKlcXdBUMwAnc2VBt7VJmzZRzQCczXUFffZsZzUnJ1PNAJzNNQXtX82r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\n1WIeAAAAAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 66
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "fig = plt.figure()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 68
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ax = fig.add_subplot(1,1,1)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 69
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "plot(randn(1000).cumsum())"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 75, 
       "text": [
        "[<matplotlib.lines.Line2D at 0x4b3afb0>]"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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5G0ApKwYONMzuaU4A4uIoqqm52b4+2cLFi5SPaOtWzkrqi7AAMB6LiP5paKAB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\n"
      }
     ], 
     "prompt_number": 75
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type           Data/Info", 
        "------------------------------------", 
        "DataFrame   type           <class 'pandas.core.frame.DataFrame'>", 
        "Series      type           <class 'pandas.core.series.Series'>", 
        "all_data    dict           n=5", 
        "ax          AxesSubplot    "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Axes(0.125,0.125;0.775x0.775)", 
        "data        Series         "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "data2       DataFrame      "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...>alues\\ndtypes: float64(4)", 
        "df1         DataFrame      "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "   data1 key\\n0      0   <...>      5   a\\n6      6   b", 
        "df2         DataFrame         data2 key\\n0      0   <...>      1   b\\n2      2   d", 
        "fig         Figure         "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Figure(480x320)", 
        "pd          module         <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata       Panel          "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.panel<...>r_axis axis: AAPL to MSFT", 
        "price       DataFrame      "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "returns     DataFrame      <class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "ticker      str            IBM", 
        "volume      DataFrame      "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...> values\\ndtypes: int64(5)", 
        "web         module         <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 78
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "rng = pd.date_range('1/1/2000',periods=100,freq='D')"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 79
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "rng"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 80, 
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>", 
        "[2000-01-01 00:00:00, ..., 2000-04-09 00:00:00]", 
        "Length: 100, Freq: D, Timezone: None"
       ]
      }
     ], 
     "prompt_number": 80
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ts = Series(randn(len(rng)), index=rng)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 81
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ts.describe()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 85, 
       "text": [
        "count    100.000000", 
        "mean      -0.126396", 
        "std        0.986838", 
        "min       -2.939965", 
        "25%       -0.850419", 
        "50%       -0.008369", 
        "75%        0.610584", 
        "max        1.521939"
       ]
      }
     ], 
     "prompt_number": 85
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ts.plot()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 90, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x49d1170>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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680g5V7MVtOboFV3yB9AB8emn9P9+/ex/3otMWyna+/drX+qrM221aPMDQku0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\nkSQRUrQlEokkifh//Yhhlx+EtpcAAAAASUVORK5CYII=\n"
      }
     ], 
     "prompt_number": 90
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ts2 = ts.resample('M',how='mean')"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 88
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ts2.describe();ts2.plot()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 91, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x56bd270>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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E0LEjvPUWdOpUvs/4o7p9ly4QFwc33qi6vUh1qPAIIDAwkN27d1O3bl1OnjxJ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\niHgoJQAREQ/1/12L1mBTBG1nAAAAAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 91
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "a = arange(9).reshape(3,3)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 92
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "a"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 93, 
       "text": [
        "array([[0, 1, 2],", 
        "       [3, 4, 5],", 
        "       [6, 7, 8]])"
       ]
      }
     ], 
     "prompt_number": 93
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "eig(a)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 105, 
       "text": [
        "(array([  1.33484692e+01,  -1.34846923e+00,  -6.22650894e-16]),", 
        " array([[ 0.16476382,  0.79969966,  0.40824829],", 
        "       [ 0.50577448,  0.10420579, -0.81649658],", 
        "       [ 0.84678513, -0.59128809,  0.40824829]]))"
       ]
      }
     ], 
     "prompt_number": 105
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "transpose(a)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 107, 
       "text": [
        "array([[0, 3, 6],", 
        "       [1, 4, 7],", 
        "       [2, 5, 8]])"
       ]
      }
     ], 
     "prompt_number": 107
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "a.transpose()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 108, 
       "text": [
        "array([[0, 3, 6],", 
        "       [1, 4, 7],", 
        "       [2, 5, 8]])"
       ]
      }
     ], 
     "prompt_number": 108
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [], 
     "language": "python", 
     "outputs": [
      {
       "ename": "AttributeError", 
       "evalue": "'numpy.ndarray' object has no attribute 'eig'", 
       "output_type": "pyerr", 
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)", 
        "\u001b[1;32mC:\\Users\\Suso\\<ipython-input-109-428510d12418>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 
        "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'eig'"
       ]
      }
     ], 
     "prompt_number": 109
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "import pandas.io.data as web"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 110
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable    Type             Data/Info", 
        "--------------------------------------", 
        "DataFrame   type             <class 'pandas.core.frame.DataFrame'>", 
        "Series      type             <class 'pandas.core.series.Series'>", 
        "a           ndarray          3x3: 9 elems, type `int32`, 36 bytes", 
        "all_data    dict             n=5", 
        "ax          AxesSubplot      Axes(0.125,0.125;0.775x0.775)", 
        "data        Series           a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "data2       DataFrame        "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...>alues\\ndtypes: float64(4)", 
        "df1         DataFrame           data1 key\\n0      0   <...>      5   a\\n6      6   b", 
        "df2         DataFrame           data2 key\\n0      0   <...>      1   b\\n2      2   d", 
        "fig         Figure           Figure(480x320)", 
        "pd          module           <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata       Panel            <class 'pandas.core.panel<...>r_axis axis: AAPL to MSFT", 
        "price       DataFrame        "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "returns     DataFrame        <class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "rng         DatetimeIndex    <class 'pandas.tseries.in<...>, Freq: D, Timezone: None", 
        "ticker      str              IBM", 
        "ts          TimeSeries       2000-01-01   -1.166530\\n2<...>199\\nFreq: D, Length: 100", 
        "ts2         TimeSeries       2000-01-31    0.000120\\n2<...>4-30    0.347948\\nFreq: M", 
        "volume      DataFrame        <class 'pandas.core.frame<...> values\\ndtypes: int64(5)", 
        "web         module           <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 111
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "price2 = web.get_data_yahoo('AAPL','2011-01-01')['Adj Close']"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 112
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "price2[-5:]"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 113, 
       "text": [
        "Date", 
        "2013-03-22    461.91", 
        "2013-03-25    463.58", 
        "2013-03-26    461.14", 
        "2013-03-27    452.08", 
        "2013-03-28    442.66", 
        "Name: Adj Close"
       ]
      }
     ], 
     "prompt_number": 113
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "price2.plot()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 114, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x49c0310>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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ezt97/foc5y93hDFHzfYhGho5agJM4zMHeKJQ2eo12jCELin0rqrZESuiJn+H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\nRK5CYII=\n"
      }
     ], 
     "prompt_number": 114
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "returns2 = price2.pct_change()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 115
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ret2_index = (1+returns2).cumprod() "
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 116
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ret2_index.plot()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 118, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x5767730>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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RLllSd6z31Km8NGZqtsJgt24TW/G1yYHiqOvpU/Ukv6akOF4rcyJHXYZokvze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\n"
      }
     ], 
     "prompt_number": 118
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable     Type             Data/Info", 
        "---------------------------------------", 
        "DataFrame    type             <class 'pandas.core.frame.DataFrame'>", 
        "Series       type             <class 'pandas.core.series.Series'>", 
        "a            ndarray          3x3: 9 elems, type `int32`, 36 bytes", 
        "all_data     dict             n=5", 
        "ax           AxesSubplot      Axes(0.125,0.125;0.775x0.775)", 
        "data         Series           a  1   -0.092249\\n   2   <...>.300581\\n   3   -0.918258", 
        "data2        DataFrame        <class 'pandas.core.frame<...>alues\\ndtypes: float64(4)", 
        "df1          DataFrame           data1 key\\n0      0   <...>      5   a\\n6      6   b", 
        "df2          DataFrame           data2 key\\n0      0   <...>      1   b\\n2      2   d", 
        "fig          Figure           Figure(480x320)", 
        "pd           module           <module 'pandas' from 'C:<...>ges\\pandas\\__init__.pyc'>", 
        "pdata        Panel            <class 'pandas.core.panel<...>r_axis axis: AAPL to MSFT", 
        "price        DataFrame        <class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "price2       TimeSeries       Date\\n2011-01-03    324.7<...>e: Adj Close, Length: 562", 
        "ret2_index   TimeSeries       Date\\n2011-01-03         <...>    1.362953\\nLength: 562", 
        "returns      DataFrame        <class 'pandas.core.frame<...>alues\\ndtypes: float64(5)", 
        "returns2     TimeSeries       Date\\n2011-01-03         <...>e: Adj Close, Length: 562", 
        "rng          DatetimeIndex    <class 'pandas.tseries.in<...>, Freq: D, Timezone: None", 
        "ticker       str              IBM", 
        "ts           TimeSeries       2000-01-01   -1.166530\\n2<...>199\\nFreq: D, Length: 100", 
        "ts2          TimeSeries       2000-01-31    0.000120\\n2<...>4-30    0.347948\\nFreq: M", 
        "volume       DataFrame        "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "<class 'pandas.core.frame<...> values\\ndtypes: int64(5)", 
        "web          module           <module 'pandas.io.data' <...>ages\\pandas\\io\\data.pyc'>"
       ]
      }
     ], 
     "prompt_number": 119
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ret2_index[0]=1"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 122
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ret2_index"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 123, 
       "text": [
        "Date", 
        "2011-01-03    1.000000", 
        "2011-01-04    1.005204", 
        "2011-01-05    1.013424", 
        "2011-01-06    1.012624", 
        "2011-01-07    1.019860", 
        "2011-01-10    1.039073", 
        "2011-01-11    1.036609", 
        "2011-01-12    1.045046", 
        "2011-01-13    1.048864", 
        "2011-01-14    1.057362", 
        "2011-01-18    1.033623", 
        "2011-01-19    1.028111", 
        "2011-01-20    1.009422", 
        "2011-01-21    0.991348", 
        "2011-01-24    1.023893", 
        "...", 
        "2013-03-08    1.329269", 
        "2013-03-11    1.348205", 
        "2013-03-12    1.319139", 
        "2013-03-13    1.318893", 
        "2013-03-14    1.331671", 
        "2013-03-15    1.366032", 
        "2013-03-18    1.403165", 
        "2013-03-19    1.399378", 
        "2013-03-20    1.391958", 
        "2013-03-21    1.393959", 
        "2013-03-22    1.422224", 
        "2013-03-25    1.427366", 
        "2013-03-26    1.419853", 
        "2013-03-27    1.391958", 
        "2013-03-28    1.362953", 
        "Length: 562"
       ]
      }
     ], 
     "prompt_number": 123
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ma60 = pd.rolling_mean(ret2_index, 60, min_periods = 50)", 
      "ma30 = pd.rolling_mean(ret2_index, 30, min_periods = 10)", 
      "ma10 = pd.rolling_mean(ret2_index, 10, min_periods = 5)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 140
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ewma60 = pd.ewma(ret2_index, span=60)", 
      "ewma30 = pd.ewma(ret2_index, span=10)", 
      "ewma10 = pd.ewma(ret2_index, span=5)"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 141
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "fig, axes = plt.subplots(nrows = 2, ncols = 1, sharex = True, sharey = True, figsize = (12,7))", 
      "ret2_index.plot(style='k-',ax=axes[0])", 
      "ma60.plot(style='r--',ax=axes[0])", 
      "ma30.plot(style='b--',ax=axes[0])", 
      "ma10.plot(style='k--',ax=axes[0])", 
      "ret2_index.plot(style='k-',ax=axes[1])", 
      "ewma60.plot(style='r--',ax=axes[1])", 
      "ewma30.plot(style='b--',ax=axes[1])", 
      "ewma10.plot(style='k--',ax=axes[1])", 
      "axes[0].set_title('Simple MA and Exponentially-weighted MA')", 
      "axes[1].set_title('Exponentially-weighted MA')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 143, 
       "text": [
        "<matplotlib.text.Text at 0x71b62d0>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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0KAwNDZGQkCBoS0pKgqurK6SkpLB8+XKJD62vD0REANevA5068f8vkTdvAPbJ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\n/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yGnvX9f2dnZiSFDhghLS0vh6uoq2rRpIzZs2JDs2pcvX4rw8HDh4OAgFAqF8PT0THPs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\nhC8AABGELwAAET4EAajDR1PYQQAAAABJRU5ErkJggg==\n"
      }
     ], 
     "prompt_number": 143
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ret2_index.plot(style='k-')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 132, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x594d1b0>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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3QtPXCi8vL4FPKD5///03IiMj8eOPPwIA5s6dC39/f0ydOlX4QByOxDebKBQ2\nQAjBihUrUFRUhNOnTytahyIHnJ2d8dZbbyEuLg4xMTGwsrLC+++/j759++KXX37B5cuXATS9qMNP\nWykydomLne1Km4waNQo3b95ETU0NSkpKEBsbi7Fjx7anSgpFoYSGhuLmzZtSTXRA6Vzo6OggOjoa\nDg4OOHnyJObOnYvTp08jOzub6X4IQOAhNRt7n0gM3r6+vhgzZgxSUlJgbW2NQ4cOYd++fdi3bx+A\npq45fn5+GDZsGGbPno2goCDo6Oh0iHh7afm1jQ2w0QlQLq+YmBjMnTsXenp6r7W/Mp0rWaAILx0d\nHdy4cQNeXl7w8PDAypUrcefOHaxevRq9evVinDgcDq5duwYXFxekpqYK1MGG1+cl5rz/+OOPVitY\nvnx5mx/mUChsJzk5Ge+8846iNShyREdHB4aGhkyWgP+qfX19vUDLGwAmT54MR0dHpKSkYPjw4QCa\nxlOZMWOGwtPASvuGJf9BAZtgoxOgPF5hYWH466+/4Ozs/Np1KMu5khWK8NLR0cGbb74pMMgYvyVt\nYmIi5NSvXz+kpKQwy8nJyR3i2Rp0DksK5f+ZPHkyAMDR0VHBJhR54uPjg549ewqsMzU1hY+PD1xd\nXYXK9+vXD+fPn2eW2ZAyAZS45c3GHCAbnYCu7VVVVYWKigq8ePEChoaGePnyZZvGMJGnkzygXv9j\n5syZGDp0qND606dPw8zMTMjJwcEBiYmJmDVrFgghrAnetOVNUWrGjh2L+vp6rF27FlOnTu3wMUwo\n7IcfvBMTE3H37l38/fffilYC0IZ+3jI7EO3nTWEhHA4H6urqmD59OubMmYN3331X0UoUFsL/NmZq\naso8wGzrnJiyOLbM+3lTKF0BVVVVhIWFYdq0aYpWobAUfvDu27cvjh07xsx1qUiUNnizMQfIRieg\n63rV1tYCaJrb0NbWVuKQoh3lJC+oV9sR5aSiogIAuHv3LgwMDNDY2NjBVsIobfCmUG7cuAFLS0sA\nwIULFxRsQ2EzzbsVqqqqsiJ405w3RWlZunQpevTogW+++QY8Hq9dvUwoXRsdHR1UV1eDEIK6ujoY\nGBigrq6uQ44tLnbS4E1RSurq6tCzZ0/ExMSgb9++itahsBx9fX1UVFSAEILGxkZoamoys/HIG/rA\nsgWdJdfGBrqi1w8//AA7OzuZB+6ueK7kCRu9JOW8+b83NjYqvDGqtMGbotzk5eUhICBA0RqUTsKw\nYcOYsbX3NIb3AAAgAElEQVQ5HA66desGHo+nUCeaNqEoJXPnzsXs2bMxb948RatQOgGvXr0Cl8tl\nBrHS0NBARUUFNDQ0hMoWFRWhsLBQZrMgiYud9A1LilJSXl7+2sO+UpSPlkFaVVVVbF/vZcuWAYDc\nJ2RX2rRJZ8m1sYGu6FVRUcFM8itLuuK5kids9GqLEz/v3ZKoqCicO3cOxcXFcjATRGmDN0W5oS1v\nSnsQ19f74MGD8Pf3R1FRkdwdaM6bopRYW1vjzp07QoPvUyhtwczMDAkJCTAzMxNYP3bsWKxatQr/\n+c9/kJeXJ5Nj0a6CFEozysrK5JI2oSgH4lrez549w8iRI1FcXCz3xqrSBu/OmmtTBF3Nq6ysDADk\nkjbpaudK3rDRq60575YPLOvr61FSUoJevXqhR48e2LFjh5wMm1Da4E1RXrKzs9GrVy/6OjzltRHV\n8i4tLYWhoSG6deuGixcv4osvvnjt1jchBCUlJRLLSAze/v7+MDc3x6BBgyRW8uDBA6iqquKvv/6S\n3lJBsHFOPzY6AV3PKysrS2657q52ruQNG73a4iQqeJeUlMDIyAgA4OTkBC0trdca87uwsBDTp09H\n7969UV1dLbacxODt5+eHK1euSDwQl8vFunXrMG3aNPpAktIpePLkCRwcHBStQenESGp583FwcEBg\nYCB++OEHqeq+cOECunXrBldXV9y4cUNsOYnB283NTUBGFLt27YKPj0+nmz6qs+baFEFX84qNjRU5\n0aws6GrnSt6w0et1+3k3b3kDwJ49e+Dl5YWvv/5aqobtixcv4Orqin79+uH58+diy7Ur552bm4vz\n589j+fLlAEBziBTWk5aWhuvXr2PChAmKVqF0YvhvWJ46dQqpqakAgFOnTgkMYDV48GAEBARAQ0MD\nlZWVrdZJCMG6deuQnJyMHj16oEePHnjx4oV4h/b8AatXr8bmzZuZfoitfbosWrSIGdzFwMAAgwcP\nZvJL/E+7jlrmr1PU8cUtN3djg4+Hhwc8PDxY5dN8mU9byhNCEBQUhM8//xzPnj3Ds2fPFO6v7Pc7\nG5fbcr/X1tYiJiYGR44cgaqqKoKCgvD7778jKChIqLypqSlCQ0PRs2dPicfPzs7Gli1bADQNnFZX\nVyd5zHDSChkZGcTJyUnktt69exNbW1tia2tLdHR0iJmZGTl//rzIsm04FIUiV9LT04mpqSlpbGxU\ntAqlkzNy5Ehy7949YmVlRWxtbUlISAgxMTEhBQUFIsuuWbOGVFdXS6zz4MGDxNXVlfTo0YPk5uaS\nixcvkmnTpomNne1Km6SnpyMjIwMZGRnw8fHB3r17MXPmzPZU2WG0bLmxATY6AV3DKyIiAnZ2dtDT\n0xP4aqtIp46EerWdtjipqKigrq4OBQUFCAoKwsmTJ1FeXi7yxS8TExNs27YNu3btwtq1a8XWeefO\nHQQEBCA3N7dNaROJwdvX1xdjxoxBSkoKrK2tcejQIezbtw/79u1r9Y+jUNjExYsX8c477yAkJETR\nKpQuQPfu3ZGSkgJTU1O88cYbiIiIgIqKisghYk1MTAAA69evx7Zt28SORnjnzh2MHTuWWba0tJT4\nij0d24TS5amoqECfPn1w584d2kWQIhN8fX1RUFAACwsLHD9+HP369UN5eTny8/OFyubl5eH8+fNY\ns2YNamtr8fjxYzg5OQmUKSwshL29PYqLi5lvhlwuF927dxc7aw99w5LS5dmzZw+mTJlCAzdFZpiZ\nmSEiIgLTpk0DALi7u4sdK8fS0hLLli3DnDlzoK2tjatXrwqVSUxMhJOTk9B0a+bm5mIdlDZ4d9Zc\nmyLozF4ffvghPvvsM3z++efyF0LnPleKgI1ebXHiB9UpU6YAaOo1YmBgIHGfI0eO4Ndff8XDhw+F\nthUWFooM1KLK8qEz6VC6NLt27QIADBw4UMEmlK6EmZkZhgwZAgsLCwDA22+/DXt7e4n7cDgcmJub\n4+XLl8w6QgiWLVsGGxsboeFlATD1i6yP5rwpXRk3Nzd88803mDhxoqJVKF2I/Px8pKenY8yYMVLt\nFx8fjwULFuDRo0cAgPDwcEyaNAlTp07FiBEjmH7izaHjeVOUkpqaGjpjDkXmWFhYSB24gaYWe/OW\n99atW6GhoYFHjx5JPcSI0gbvzpprUwSd2au2thaampryl/l/OvO5UgRs9JKnk4mJCYqLi3Hjxg3Y\n2dnh0aNHmDdvHvLy8tC7d2+p6qI5b0qXpqODN4UiCTU1Ndja2mLDhg3IyMjA7t27UVhYCAAYNWqU\nVHXRnDelS2NhYYHY2FhYWloqWoVCAQAsX74cwcHBqKqqwu3bt2FlZYWoqCi89957IsuLi500eFO6\nHA0NDVBVVQWHw4G+vj6ys7PpfJUU1vDnn3/Cx8cHmpqaKC4ubvWbIX1g2QJly7W1h87mNWPGDLi5\nuSEzMxMVFRU05w3qJQ3ydpo4cSI4HA727dvXrnuT5rwpXY5//vkHpaWlCAwMBNCUZ6RQ2IKhoSFG\njRoFKyurdtVD0yaU14bH46GhoUHkYDwdRXV1Naqrq3Hu3DlMnz4dSUlJ8PPzQ0FBAXg8HgDQ+47C\nOmpra9G9e/c2TWBDc94UmdHY2Ah7e3vU1tZCV1cXT58+VZjLxx9/jB07doDL5UJLSwtqamo4f/48\nli5disbGRqSnp9P7jtKpoTnvFihjru11aelVWlqKoqIiFBQUKDQwRkZGIjo6GleuXEF9fT0mTZqE\nTz75BO7u7vD398epU6ewePHiDndiI9Sr7bDRSRQ0502RmpKSElhaWmLnzp3YsWOHwjy4XC7i4uIw\nfPhwqKmp4cKFC8y2devWAQAOHjyoKD0KRa4obcu7+dx+bIENTunp6UyumE9LL/4s2VpaWqipqelA\nO0FMTExgbW3Nqm6AbLiGoqBebYeNTqJQ2uBNESY3Nxd9+vTBgQMHJJYrKSmBoaGhwoN3aGhop/lH\no1BkjdIGbzbmtRTtdO7cOQDAgwcPBNaLynkruuVdVFSErVu3YtWqVQo5vjgUfQ3FQb3aDhudRKG0\nwZsiTEREBHx8fERO5dScly9fwsTEpE3Bu7a2VqYPNQsKClBbW4v169dj8uTJGDBggMzqplA6E7Sr\nIAVAU59tMzMz7Nu3DytXrkR8fLzIweGBprEZBg4ciLlz52LAgAF4+fIl3njjDaxevRq3bt2Cra0t\nPvjgAwBN1/3YsWNYsGBBux3Dw8Ph7e2N+vp66OrqIiUlhVX5bgpFHrxWV0F/f3+Ym5tj0KBBIreH\nhITAxcUFLi4umD9/PlJTU2VjS5GIlpYWXrx4IdM6ExISYGhoiKFDhyI/Px+9e/fGL7/8IrJsSkoK\nHBwcmJZ3Tk4OwsPDMXPmTJSXl2PNmjVYuHAhcz/IwpU/dOacOXNQU1ODCxcu0MBNUWokBm8/Pz9c\nuXJF7HY7OzvcunUL8fHxmDp1Kr799luZC8oLNua12uJUXl6O2tpa5OTkyPzYEyZMQI8ePTBw4ED8\n/fffOHLkiJBXfn4+4uLiMHToUGhqaqK2thb37t1jtv/888+wsrLCsWPHsGTJEgBAt27tz87xUzq7\ndu3CrVu3MGLEiE57DRUB9Wo7bHQShcT/Kjc3NxgaGordPnr0aKb14+npiZs3b8rWjiJETEwMgKYH\ndrIkKSkJgwcPhrq6OhISEjB69GikpaUJtZr//fdfDB8+HMbGxujWrRs0NTUFZsPW1NRkXpd3cXEB\nh8MRmDnkdYmOjoarqys0NDTg5ubW7voolM6OzB5Y7t+/H15eXrKqTu6wsYtZa06EEHz33XcAmh4a\n7tmzB1988YVMjp2ZmQlbW1tmWUNDA97e3jhz5oyAV2FhoUAufNy4cQgODsaWLVtw584dAIC1tTWG\nDBmCnTt34tChQygoKGiXW1RUFC5duoS33npLYH1nvIaKgnq1HTY6iUImb1iGhYUhODgYd+/elVhu\n0aJFTIAwMDDA4MGDmRPF/6pCl8UvR0dHo6SkBGvWrMHt27cRGhqKAQMGoKGhgQmcr1t/UlKSQAs5\nMjISWlpaSElJEShfWFgIU1NTZtnHxwdhYWFwcnJCfX09gKb+13fu3EFkZCTMzc1RUFDQJp+cnBx4\neXlh165dcHBwgLm5OTw8PPD333/D09MTCQkJrLoedJkuy2M5MjKSSVk2b1AJQVohIyODODk5id0e\nHx9P+vTpQ54+fSqxnjYcqkOJiIhQtIIQrTktXbqU7Ny5k5w4cYIAILq6ugQAWbFiRZvqb2xsJDwe\nT2h9bW0t0dLSIjU1NQLrL126RCZPnizg9emnn5JNmzYxy8XFxeTzzz8Xe8x//vmHuLi4tOqWmJhI\n9PX1iaurKzE2NibGxsYkISGBEELIkiVLyK+//iq0T2e8hoqCerUdtjmJi53tSptkZ2fD29sbISEh\n6Nu3b3uqorSB5ORkpoteUFAQTpw4AaCpL3Vr1NTUQFVVFZs2bRLaFhsbi379+gkNDG9nZ4f09HSB\ndS3TJkZGRvj+++/FHpff8gaAU6dO4dWrV0JlXr16hVmzZuGLL77Av//+i7Vr18Lf35/5+0pLSyU+\ne6FQlBJJEX/evHnE0tKSqKmpESsrK/Lbb7+RX3/9lWkFLV68mBgZGZHBgweTwYMHk+HDh0v96UFp\nG0+ePCGqqqokLy9PYL27uztZs2ZNq/v/+uuvBACZOXOm0Lbt27eT5cuXC62vqKggmpqaAq31SZMm\nkStXrrTZu76+nqiqqhIul0sAkJCQEKEyZ8+eJe7u7oSQppb6q1evyNmzZ4mXlxchhJCJEyeS69ev\nt/mYFEpXQlzslJjz/uOPPyQG/oMHD9JR2zqIyMhIvP3227CwsBBY/+677zI9UPjU1dVBQ0ODGeid\nx+Nhx44d2L9/P9PybmhowNGjR+Hr64vo6GhMnz5d6Ji6urpQVVVFeXk5DAwMAACpqano169fm73V\n1NRgZmaGkJAQAGBy6M1JSEhgZs52dXUF0PTQ8/nz5wD+N5YKhUL5H0r7ejz/AQGbkOSUn58vMmga\nGBigrKxMYJ2mpqZAn/uUlBTU1dXB398fL1++RGVlJS5duoQlS5bgzp07iI6OZoJnS3r06MGMeVJT\nU4PCwkJYW1tL9XedOHGCGYMkLS1NaHtSUpLQa+5WVlZ4/vw5Hj58iJycHBgZGQnt19muoSKhXm2H\njU6iUNrg3dnIz88XanUDgL6+PsrLy5llfit848aNyMvLA9D0hqOtrS1UVFQwYMAAJCQkICoqCgBw\n/vx5lJeXw97eXuRxBwwYgMTERABNgbdPnz5QUVGRyt3NzQ3u7u4AgIyMDKHtWVlZsLOzE1hnamqK\nwsJCDB8+HF9++aXkp+4UijKi6LwNpW3MmjWL/Pnnn0LrY2JiyNChQ5nlefPmkW3btpH+/fuTu3fv\nEkIICQ4OJvPmzSOEEOLn50fWrVtHjI2NyZIlS4iKigo5dOiQ2ONevnyZuLi4kEWLFpHRo0eT2bNn\nv5b/06dPyaZNm4i5uTkhhJCqqipmW69evcizZ8+E9tm0aRM5ceLEax2PQukqiIudNHh3Ang8Hund\nuzd59OiR0Lbi4mKip6dH6uvrSVVVFdHR0SGlpaVk2rRp5NKlS6SqqooAIL6+voSQpoeTAMiWLVtI\nXl4eiY6OlnhsLpdL7O3tCQACgKxbt+61/w4ul0tsbW3JvXv3CABSW1tL/Pz8mN8pFIow4mKn0qZN\n2JjXEuf0+PFjEELg5OQktM3IyAi9evXCn3/+CR0dHQwZMgQGBgYwMDBAaWkpMzYNP2fs7u6ON998\nE2vWrIGFhQVGjhwp0albt26YOnUqs+zg4PCaf11TXYGBgVi7di0AIC8vD4cPHwYAdO/eXer6OtM1\nVDTUq+2w0UkUShu8OxNnz57F7Nmzmd4jLRk4cCCio6MBAH369AEAGBoaorS0FHl5eQgMDMSuXbsA\nAEOGDEFoaKhUeevp06dj69atAIBevXq150/B8uXLkZCQAKDpPQFtbe1W38ylUCjCKG3w5r+WyibE\nOfGDtzj69OnDdMHjt2D5wbuiogKGhoZiA39bmDFjBtauXYvhw4fD2dn5tesBmh6wfvTRRwCAK1eu\noHfv3hg9evRr1dWZrqGioV5th41OoqCzx7OcjIwMvHjxAmPGjBFbpm/fvjh06BAAMEHawMAA+fn5\nUFNTk9m41/fv35dJPd988w2Ki4sREhIisn85hUJpnU7V8n7x4gUqKipkUhcb81qinK5fv47p06dL\nTHP06dOH6Raorq4OoKmrXVFRESoqKqCnpydzr/bA4XCwZs0a5Ofnv3arG+g815ANUK+2w0YnUXSq\n4N2zZ08EBgYqWqNDSUtLQ//+/SWW4Y8r07NnT2zcuBEAYGFhgfz8fJkEb3nQp0+fVtNBFApFPKwP\n3g0NDaivr2cmeigpKREqExERAXt7exQWFra5XjbmtfhOsbGxWLp0KYCm4N27d2+J+1lYWEBLSwvT\np09nXiOXZfCW17ny9PRsV0qHzdeQbVCvtsNGJ1GwPnjPnDkTGhoaeOedd7B161aR039dvnwZaWlp\n+PTTT8HlchVgKVt27tyJAwcOYP/+/Th79myrM6RzOBzY2dlBW1ubWWdhYYHnz58jKyuLlS1vCoXS\nPlgfvPn9lC9fvowlS5YgKytLaCblzMxM7Nu3D0eOHMHbb78NACKHHm0OG/NafKe4uDjMmzcPgYGB\nWLlypdgJoJvTt29f6OjoMMumpqZwdXWFo6MjBg4cKBMvtsFGLzY6AdRLGtjoJApWB+9nz57B1NQU\nly9fhqurK/T09KChoYHi4mKmzPz583H69Gk4OzsjLS0NERERWLFiBfT19QXSKLW1tVLNYp6UlARP\nT0+EhYXJ9G9qjcbGRqSkpGDnzp2YNm0a82HUGi4uLrC0tGSWVVRUcP36dZw8eVJg/G0KhdJFUPQr\nnpJYvXo1Wb16tcC6IUOGMK90X758mXltu6KigjmOuro66d+/P4mJiWH227FjB+FwOARAqzNlREdH\nE2NjY+Lt7U0cHBzIhg0byKtXr8j9+/cJj8cjL1++JIWFhVL/PW3hxo0bbZp5hkKhKAfiYqdCWt5p\naWlwdHSUmJ/Ozs7GsWPHsH79eoH1Hh4eTCrl888/x/fff49Vq1ZBV1eXKWNsbAxHR0dkZ2cz65KS\nkvDjjz/i888/x4wZM4SGUW3OnTt38O677+LkyZNIS0vDt99+Cy8vL4wYMQLBwcGwsrLCiBEjhNI3\nzdm3bx+qqqpaPRfN4fF42LBhAz788EOp9qNQKMqHQoL34cOHkZKSgr/++kvk9ri4OAQFBSEgIADm\n5uYC22bPno2zZ8+isrISKSkpWLduHXbu3MlsDwgIwEcffQRra2uBh5spKSlwcXHB999/j6FDh+LX\nX38V65eSkgIHBweoqKjg559/xoIFC5CamgovLy9s3LgRAQEBaGhoQGhoqMj909PTsWzZMhgYGKCx\nsbHN52X9+vVobGzEokWL2rxPR8DWHCAbvdjoBFAvaWCjkyg6/A3LuLg47Ny5E7/88gu+++47eHt7\no1u3bkhKSsLChQsRFhaGd999F0lJSYiPjxfaf8yYMXjx4gX27NmDAQMGCL28cuDAAQDA999/L5Dz\nTklJYSYz8Pb2xqFDh7BmzRrmpZbmpKamwsfHBwCwcuVKAACXyxU4lp6eHuLj4zFz5kyh/UNCQvDm\nm2/i2rVruHbtGmbMmNHqeamsrMSBAwdw48YNdOvG6kcRFAqFDXRk3oY/POno0aMJj8cjQ4YMIefO\nnSOEEBIUFEQAkEuXLhENDQ1y//59sXUtXryYACB79+4VW+aXX35h5mX86aefCADC5XIJIU1Dk771\n1lvkww8/FLlvjx49SFZWlsS/58iRI2T+/PmEx+ORy5cvk1evXhFCmoZvtbe3J9HR0SQkJISMGjVK\n5IztLbl7967EOUApFIpyIi5MSwzefn5+xMzMjDg5OYkts379etK7d2/i6upKnjx5IlFgz549ZObM\nmaS+vp4QQsiff/5JRo0aRQghxM3NjfTs2ZO89dZbxNHRUeIfc/r0aQKAqUcUwcHBxNfXl5SVlTEP\nNZtTWFhI9PX1ycWLFwXWV1ZWEk1NTSbQiyMlJYVYWFiQq1evEgDMpLwxMTHE3t6e8Hg8wuVyiZOT\nE7lw4YLEuggh5Pjx4+Sdd95ptRyFQlEuxAVvid/P/fz8mIeDorh//z6ioqLw8OFDfPzxx/j4448l\ntvJ37NiBtWvXQk1NDQDw5ptvIjY2FkVFRYiNjcXChQtx6dIl7Nu3T2I9s2bNwvXr15l6RMGfHiw0\nNBReXl6orq4W2J6QkIBff/0Vb775Jl6+fIno6Gikp6cjNTUVffv2bTV14eDggL59+2L16tUAmsbc\njomJwciRI/Hee++Bw+GgW7du+Oijj3DixAmJdRFCkJaWxtp0CVtzgGz0YqMTQL2kgY1OopAYLdzc\n3CTO2h0TEwMfHx8YGRnB19cXT548kXgwNTU1uLm5Mcvq6uqwsbHBwYMHMWrUKAQEBODKlSsYP358\nq/W88cYbEssYGBjg0qVLCAgIgI+PD7S0tITKzJs3D76+vti/fz9Gjx6NefPmSTU7ekBAAJ48eYL1\n69fjm2++YSbxbf7AcdCgQUhOTpZYz48//oivvvqq1TFMKBQKhaG1JntGRobYtMl7771Hrl69yiyP\nHDmSpKWliW36h4WFCa338vIiVlZW5Mcff2xNRSoePXrEpEtKSkrElsvIyGDK2dvbky+++IJ89tln\nbTpGdXU1ef/99wmXyyWlpaUEAPnjjz8EyvDTNrt37ybZ2dnE2NiY2VZYWEgyMzPJhAkTyJIlS9qU\nG6dQKMqFuDDdrt4mpClnLrBO0qD/kyZNElrn6OiI0NBQTJkypT0qQhgYGAAARo4cKfHbg62tLX77\n7TckJiZi79692L9/P8LDw9t0DC0tLRw9epQ5XnFxsdCx9PX1cfbsWSxbtgy9evVCcXExamtroamp\niblz5yI8PBw9e/bEpUuX2jVhAoVCUS7aFbxHjhyJpKQkZo7DwsJC2NnZiS2/aNEi2NraAmgKdoMH\nD4ajoyPMzc1RXFyMyMhIZkQvft7pdZefPXuG8+fPM135Wm7fsWMHBg8eDA8PD/j7+yMyMhJnzpzB\nwoUL4eTk1O7jN1+eNWsW3n33XRw8eBAAkJycjPLycqYr47p16xAdHY24uDgmhy7L47d3uXkOkA0+\n/GU2ni/+Orb4iLrf2eDD5vOl6Ps9MjISR44cAQAmXoqktSa7pLRJTEwMGTt2LCkqKiIhISHE09NT\n6qZ/dnY2OXbsWGsaMkfUK/JZWVmkoaFBLsfz8vIirq6uZPTo0WTVqlXkyZMnBAAxMTEhNTU1Yp3Y\nAPVqO2x0IoR6SQPbnMTFTs7/bxSJr68vbt68iaKiIpibm+Obb75BQ0MDADCTIqxfvx4nT56EkZER\ngoODxT5043A4El8nVxYePXqEuXPn4tNPP4W/vz+ioqIwbtw4RWtRKBSWIi52SgzeHSGgbDQ0NEBf\nXx8bNmxAcHAw4uPjoapKpxKlUCiiERc72dmxuANontfqSNTU1PDWW29h69atmDdvnkDgVpRTa1Cv\ntsNGJ4B6SQMbnUShtMFbkWzevBk1NTWwsLBQtAqFQumk0LSJgvjrr78wbNgw2NjYKFqFQqGwGJrz\nplAolE4IzXm3gI15LTY6AdRLGtjoBFAvaWCjkyiUNnhTKBRKZ4amTSgUCoXF0LQJhUKhdCGUNniz\nMa/FRieAekkDG50A6iUNbHQShdIGbwqFQunM0Jw3hUKhsBia86ZQKJQuhNIGbzbmtdjoBFAvaWCj\nE0C9pIGNTqJQ2uBNoVAonRma86ZQKBQWQ3PeFAqF0oVQ2uDNxrwWG50A6iUNbHQCqJc0sNFJFEob\nvCkUCqUzQ3PeFAqFwmJozptCoVC6EK0G71u3bqF///6wt7fHrl27hLbX1tZi4cKFGDJkCNzd3XH+\n/Hm5iMoaNua12OgEUC9pYKMTQL2kgY1Oomg1eH/00UfYt28fwsLCsHv3bhQVFQlsP3r0KLS1tREb\nG4tjx45hzZo1nSI9EhcXp2gFIdjoBFAvaWCjE0C9pIGNTqKQGLzLy8sBAOPHj0evXr0wZcoUxMTE\nCJTR19dHZWUlGhoaUFJSAi0tLXA4HPkZy4iysjJFKwjBRieAekkDG50A6iUNbHQShcTg/eDBAzg6\nOjLLAwYMQHR0tEAZX19fcLlcmJiYYNy4cQgJCZGPKYVCoVAY2v3A8pdffoGqqiry8vIQHh4OT09P\n8Hg8WbjJlczMTEUrCMFGJ4B6SQMbnQDqJQ1sdBIJkUBZWRkZPHgws7xy5Upy8eJFgTJz5swhV65c\nYZZHjBhBnjx5IlQXAPpDf+gP/aE/r/EjClVIQF9fH0BTjxMbGxtcv34dGzduFCgzadIkhIaGYvLk\nycjMzERJSYlAqoVPZ3iISaFQKJ0FicEbAHbs2IHAwEA0NDTgww8/hImJCfbt2wcACAwMxLx585CU\nlIRhw4bB1NQUP//8s9ylKRQKRdnpsDcsFUFFRQX09PQUrUHpYjx58gT29vZQVW217UOhyA2Vr7/+\n+mtFS8iav/76CzNmzICWlhacnJygrq6uaCVUVlYiNDQUffr0YeU/fWZmJlRUVKChoaFoFYb4+Hh0\n69YN3bt3R7duin8Z+MyZM1i6dCliYmLw+PFjqKuro1evXgp1qqqqwu7du5GVlQVjY2Po6uoq1Kcl\nT58+xYMHD2BpacmK/0MAiI6ORmNjI7S1taGioqJondemywXv7OxsHD16FAYGBmhsbISlpSV69uyp\nUKeoqCi4ubnhjz/+wIQJE2BjY8OavvCNjY1Yu3Yt5s+fD1tbW7i4uCg8UCYlJWHRokW4fPkyEhMT\nERcXh/Hjx4MQorDzFhERgT179iAoKAh+fn7IycnBsWPH4OvrqxAfoOnD5J133oGZmRlSU1Nx8+ZN\nWDlyYycAABfWSURBVFhYKPx+B4BXr15h9erV2LZtG3JychATE4OGhgb069dPYU6JiYlYvHgxrl27\nhoSEBISHh2P69OkK82kvim/OyAB+5ocQAlNTU2zYsAF//PEHXr16haioKKG3QjuaxsZG/PXXXwgK\nCkJwcDBKSkoU6tOc5ORkWFpaYsuWLQgLC0N2drZCfV6+fIldu3Zh8uTJuH79OlauXIlDhw7h8ePH\nHR64m2cU+/Xrhw0bNmDs2LEwMTGBo6MjevbsCS6Xq7CH8ZmZmdi7dy927NiB7777DoaGhsjIyFCI\nS0tCQ0PR0NDAvHk9aNAgREZGor6+XiE+RUVFOHToECZNmoSwsDCsWrUK6enpqK2tVYiPTJDUVbAz\n8P333xN3d3eBdY2NjYQQQsLDw8mCBQtIWFgYs64jyMzMJFlZWcxyZWUlIYSQ6upqMmnSJHLy5EnC\n5XI7zKclL1++ZH5/9eoVs+zr60t++OEHUldXpyg10tjYSKKiophlLpdLAgICyIMHDzrUQ9R9xePx\nCI/HI4QQEhoaSjw8PDrUqeV9VVhYSKqrq5l7aebMmeT06dOMa0fT/L6qqKggycnJzPLu3bvJ8uXL\nFebG4/FIWVkZs7xs2TLi5+dHoqOjO9xFVnTaljePx8P27dtx+/ZtpKWl4YcffgDQ1Mrl57EmTJgA\nKysrREREoKamRu5OhBBs3LgRDg4O8PPzY9br6OgAALS0tLBw4UIcP35cIS2krKwsTJ06FW5ubsz5\nUFNTg4mJCQBg7dq1uHbtGh4/fozGxsYOcbp06RLs7e1x7949AICKigpGjRrFbK+rq8Pt27eZXC6R\ncytX0n3VvOWfmJiI8ePHy9WFj7j7ysTEBFpaWiCEgBACbW1tmJqaAkCHfksRdV/p6uqiX79+zAt7\nzZ+ldIRby/uKw+FAX18fDQ0NOHz4MFJSUjBgwAD4+vril19+AdD5ujN3uuDd0NAAAOjWrRs8PDxw\n+vRphIWF4b///S8qKyuhqqoKHo/H3DSBgYEoLi7Gb7/9hqlTp+Lx48dyc6usrERFRQUiIiKgrq6O\n33//HQAEvlovWLAAampquHnzJgAgNjZWbj4t2b9/PxwdHTFy5EjwH3WQ/88j83g8DB06FEOGDEFw\ncDBUVVXlfjM/ePAAR44cgbm5OTZt2sSsb/5ANzs7GxYWFkyuVF7/+G29r/iUlZXB09MTaWlpWLJk\nCZ4+fSoXL0D8fcX/gFVRUUFJSQlSU1MxevRoAE3psI5C1H3FP1f8e+ju3btwcnIC0PT/IE/E3VdA\nU2Nl9uzZCA8Px8cff4wDBw4gODgYQMd+4MkERTX5paWxsZEEBgaS+fPnk23btjHr+V8Z582bR959\n911CCCH19fUC+w4cOJCYmZmRDRs2yNzr4cOHJCsri1RXVxNCCMnNzSWEEHLmzBkydOhQ0tDQwHjy\nUzepqamkX79+xMHBgcyZM4fU1tbK7atkQUEBU3dOTg4pKysjSUlJxMnJiSQlJRFCms5t8zTOlClT\nyOLFi4mLiwuJjY2VqQ+PxyOvXr0ihDR97ec7ODs7k5CQEKYMn3v37pF169aR+vp6snLlSnLgwAGZ\n+rzufTVo0CAyffp0MmzYMPLjjz/K1IkQ6e4rQgi5f/8+8fX1JYmJieSNN94ga9euZc6zPGjrfUUI\nIXV1dSQgIIAUFBSQo0ePklmzZpGUlBSZ+rTlvhKVqnz48CH5/PPPmfPcmegUwZvL5ZKffvqJeHt7\nk8jISDJ8+HCyd+9egTLl5eVET0+PPHz4kBDyvwBw+PBhMmfOHFJcXMyUlUWgrKurI6tWrSKWlpbk\nvffeI35+fgLbGxsbydy5c8mXX34pcMyamhqyYsUKYmZmxtxU8iA2Npa4uLgQT09PsnbtWqG/ecOG\nDcTHx4cQInhTp6WlEU1NTeLu7k5iYmJk6rRr1y4ybtw4snLlSvLixQtCyP8C4pkzZ4izszOpra0l\nhPzvfH3++eekd+/exM3NjSxdupSUlJTIzOd176vnz5+TXr16kbVr1wrcV7Lgde+rkydPEg6HQ8aM\nGcO6+6qsrIz06NGDDBw4kEyfPp38+++/MnWS5r4ipOk5T01NDTl8+DAZO3YsOXbsmEx9OopOEbwJ\nIcTf359s2rSJENJ0A82cOZOcP3+e1NfXM5/wW7duJe7u7iQ+Pp7s3LmT1NfXMy0UQghpaGiQWQs3\nMzOTeHh4kLKyMlJVVUVGjhxJ9u/fL1AmOjqaODk5MS2C0tJSUlFRQc6fPy9QTtYPU3k8HgkMDCRb\ntmwhubm55O233yZr1qwRKJOfn0+GDx9Orl69Sghp+lBpbGwkhw4dIkeOHJGpDyGEPH78mHh4eJCw\nsDCyatUq8v7775O0tDSBMlOmTCEbN24UWLd06VLi6ekp8A1Alg97pb2vdu3aRQghJCMjg6mjsbFR\nofcVl8slp0+fJl988YVAOVk/FH+d+6qxsZGkpaWRvn37ktDQUJn6EPJ691V9fT3ZvHkz8fLy6vAH\n4bKElcH7+fPnZPPmzeT8+fPMJ+n58+fJZ599xvTc+Pnnn8lHH30kkI4oLi4mHA6H9OzZk5w7d06g\nTlkEyOfPnzO/v3z5knh7e5O7d+8SQgi5ffs2cXd3J6mpqQLH++qrr8iAAQPI6NGjyY0bNwTqa/7B\nImuWLVtGdu/eTQhpaj2OGjWKGUCM73bu3DkyduxY8uWXX5Jt27bJ3Kd5QLtw4QIZMWIEs7x+/Xqy\nceNG8vTpU2ZdcnIyGTBgALl9+zZZt24dyc3NZdIFhDQFo/YEJFncV2fPnhXwUfR9NWrUKBIeHi5Q\nH5vuq59++knmDu29r7Kyskh5eTmzvb33laJg3QPLvXv3wt3dHWVlZTh37hy++eYb5ObmwtTUFHV1\ndXj06BEAYNGiRbh58ybS09OhoqKC+Ph4zJ07F59++imeP3+OWbNmCdTbnjepHjx4AE9PTyxfvhxb\ntmxBeno69PT04ODggPLycnC5XIwdOxYODg44cuQIc7ykpCRcvHgR2tra+O677zBx4kSBemX1pmVI\nSAj8/Pywfft25OXlAQBcXV3xf+2dfVBU1f/H3ws6CAoTYuqQ4diYgWi6PhYjaqChE6M24BOKmJKo\niIiliCmaSiiaT1lmPo4WzDT4gExiDIVgqYgpEiqogagYtiCECzLusu/fH/zuGVb4lsYuCJ7XP7B7\nH/Zz733fzznnns/ncy0tLUWJAD8/P3z99dfCNqA29vXMmTPIzs6Gn5+fSTM/t2/fji1btuD8+fMA\nAG9vb7z66qti9n/SpEl48OABioqKxDZvvPEGtFotRo8ejbZt28LR0RGOjo4AaifnLCws/nMCkal0\nNWHCBLFPCwuLZtdVVFQU3nnnHbFPg8Hw3OnKlJhCV05OTqJsRk1NTaN01Zw8VxbrdDrcv38fx44d\nQ3R0NCIjI2FhYYHCwkK4urqKKI2bN2/Czs4OQ4YMQWJiIgDA1dUV8fHxWL9+PQCYLNQtLS0NQUFB\nmDFjBrZv3w6NRoM9e/bAysoKNjY2SE1NFfV/w8PDkZCQIJJwMjMzMX/+fJw/fx4eHh4ipMtUPHz4\nEDNmzMCePXvg5+eH33//HRs2bEBxcTG6du2KjIwM3L17FwAQEhKC69ev49dffwVQO/ufkJCA1NRU\nJCQkoEuXLiaxKTMzE/3790dWVhaqq6uxadMm7Nu3DyqVCv369RO/r1arYWlpKaJtysvLsXLlSgwY\nMAA3b97E2rVrjfbbGIf0IunKFE7IlLrq2rVro+0BzKerlpwe/9w8NlGGLUVFRUaz+sOGDRNDyJyc\nHEZHR3PMmDE8ceIE1Wq1WFZ3P6YYAilDs4cPHxo9o46NjaWPjw/J2ln20NBQxsTEUKPR8Pbt2wwI\nCDBKBlAw11B2y5YtLCsrI0leuXKF48aN4/3790mS8+bN4+eff86srCySZHBwME+cOGEWOxSOHTvG\nuLg48fnQoUMMDQ0lWTt5FBISIibUkpKSjBJdSkpKxP86nc4k11Hq6r8hdfX806wVkmpqakTLp/QY\nlGEySWi1WnTs2BEvvfQSgNpeUO/evaHVavHNN98gICBAxLUqNLbn8ejRI1hbW0OlUoEkOnTogPfe\ne08sd3R0hEqlQnV1Nbp164agoCAcOHAA06dPR35+Pjw8PERSjnIcKpXK5MWoDAYDLCwsMGfOHFhb\nW0Ov16N3797QaDQoKipC586dERYWhoSEBHz88ccYMGAAfvjhByxcuNCkdigox+np6QmdTic+3759\nG23btgUAjBkzBp06dcLs2bPRpk0bfPvtt3B3d4der0ebNm3g4OAAoFYXjTlfUlf/HamrlkOzHIly\nAZQbLCsrC3369BEnVln+559/4u7du3BxcQFQm3jg7OyMVatWoU2bNiKoXlm/sURHR0On0yE8PBxW\nVlZin5aWlkLUZ8+eRbdu3dCuXTsAgIuLC6Kjo5GYmAh7e/t6WXemCvyvW96WpHAmNjY2AGofK+Tm\n5sLa2hqurq4AgNdffx2hoaFwcHBAXl4efvzxR/Tq1csk9ih2KMen/FUcjMFgENdYyeBs3749RowY\ngTVr1uDMmTNwcnLCypUr691Q/3UoK3X17EhdtWCatqNvTGZmJufOncvw8PAGEwqOHz/O+fPnMyMj\ng8OGDWN0dLRRfQlThWgpQ8/09HR6enqK4WBdlN8JCwtjeno6dTodN2/ezN9++63eeqYO/SspKWFE\nRARJ8saNG0Y1I+radvLkSc6cOZMkefXqVaMaIabEYDDUG3o+eR2Uz6NGjRKPIJRYadJ4uG/qYazU\n1dMhddWyabIJyydTYnNycjBkyBDY2tpi/fr1Ddb6zc3Nxc6dOxEREYFPPvkEy5Ytg0qlMuq5mKIH\norTQ7u7uGDRoEPbv34+HDx/WW48kCgoK8NVXX2Ho0KEoLi4WvRFled2eX2NRzpmDgwNu3bqFXr16\nwcfHB1evXm1w/du3b0Ov12PdunWYPn06tFqtSex40iaVSgULCwtcv34dJ06cEL2huqhUKpSWlsLG\nxgbW1taYNGkSVqxYgQcPHoCkSL9Xep6NsacuUlf/jtRVK8HcrUPd1q+yspIXLlxgRUUFSdLHx0dM\nLDRUyS4mJoZbt279n/szlX3FxcVcvXo1z549S41GwxEjRvDkyZP1Wv179+5RpVJx6tSpzMnJMakd\n/8SNGzcYFRXFjh07Mi0t7X+u5+3tzXbt2hnFLZuDR48e8dChQ1Sr1fTw8OCKFSvE+ah7zgoKCqhS\nqejq6sodO3aY1Aapq8YjddWyabLHJkePHuXAgQM5btw4Tpw4kbm5uSwpKWG7du1EQL0yLGzoRjLV\nkDEsLIxr164lSTF7Xl1dzblz54pMu507d3LKlCksLi6u9/t1U8bNEdxfd39paWl0c3NjTEwM9Xo9\nN27cSG9vb5LGdTYU244cOVJvuN1Ynjzver2es2fPpouLC8vLy1leXs6IiAhGRkaKxAflGC5dulSv\nboSph/5SV0+H1FXrw+TOOyUlhfn5+eJzVVUV9+7dSycnJ9G6R0VFMTg4mGVlZVy9ejU9PT1JNlxz\npO6zSFOQlpZGe3t75ubm0tfXl8nJySTJ1NRUzpo1i0lJSTQYDBw/fjz37t0rnqE9aYMpQ7Tu3LnD\ntLQ0o9rMBQUF9PPzq5dS7Orqyvj4eJIUojZnRp1CYWGh6HUlJyfTyspKXOekpCQuWrRI2NXQ9Wps\naQKpq2dH6qp1Y1LnXVpaSkdHR3p6enLXrl0ka094RkYG7e3tRQGYvLw8LliwgGfPniVJqlSqeqnj\n5kC5yJMnT+aECRMYFxdHf39/sTw8PJyBgYF8/Pgxjx8/Tnd3d9GLMpc94eHhdHJyopeXF6dMmSLO\nW1ZWFt99913Ro1AK68THx3PMmDEMDg6mm5tbg7G/jSUsLIxr1qwhWXutpkyZwqlTpzIoKIiXL18m\nSQYEBDAwMJAkqdVqGRMTwzlz5hileis0thcpdfXs9khdtX5M6rzLysro7e3NgwcP0s3Njfv27RMi\niYqKopeXl1jX19dXtKjKhTM3yk1WWlpKOzs7fv/99wwODub+/ftJ1rb0Dg4OohBQ3eJD5uDQoUP0\n8vKiRqNhaWkpT506xb59+zIlJYUXL17kwoULjd6cogwT09PT+eWXXxq9ucSUpKen097enhUVFZw3\nb56otDd8+HCOHDmSer2ef/zxBwcNGiRm/DMyMurV2DAVUlfPhtTVi4HJH5v4+/tz8+bNzMzM5Icf\nfsh169bx8ePHvHfvHvv168epU6cyNjaWQ4cOFZXilBa0KVpS5aZfvXo1Bw4cyNOnT7NHjx7MzMxk\naGgoZ82axezsbLG+uYZkOp2Oc+fOFQJWQtp27NjBkSNHUqPRMCAggJGRkczLy+OFCxcYGBho8nKa\nT6Ic7/vvv8+goCCStb0kDw8PLl68mM7OzoyOjiZJrlq1iu7u7ma1R0Hq6umQunpxMLnzPnLkiLgI\n27dvp52dHRcvXsyqqirGxcVRrVZz3rx5TdYr+ideeeUVnjp1iocPH+bEiRPrldQ0N4GBgQwPDydp\nPOHi5ubG8+fPs6ioiJ999hmnTZvGt956y6x1mhWUm6ykpIS2trbMz8/nd999J0p/fvrpp7Szs+Ot\nW7dYWVnJa9euGW1nLqSunh6pqxcDk2dYarVaXLx4EZMnT0ZOTg62bt2KY8eOYcGCBZgwYQK8vb1h\nZWWFN998U1T0aurXDykxoBs3bkRISAiys7Mxfvx4EUdbN73anPj7+2PXrl24d+8eHB0dRQq1Wq3G\nlStXMHjwYERERIiCQE2BSqVCTU0NHBwcEBISAn9/fyxduhSlpaUoKCjA33//jbfffhtarRbdu3eH\ns7Nzk8TUSl09PVJXLwimbg3Ky8tpb2/P4OBg8V1eXh5TU1Op1+uZlJTEsWPHinrKzYXSovv6+opJ\nraau61tYWMiFCxdyyZIl4judTsexY8eKSbfm5rXXXuNHH33EL774gj179uSGDRuaxQ6pq6dH6urF\nwCxx3osWLTJ6k0ZdKioqRDJFc/PXX39x2rRpvHLlSrPZUFxczD59+jA4OJi7du2iu7u7UQW35kJx\nNvHx8XR1dSVJowL2zRFXK3X19EhdtX7MMibJz89HdXU1DAZDvWGira0tbG1tzfGzz0x2djb69+8P\nZ2fnZrOhS5cuOHjwIPr27Yv09HRMmzYNCQkJ6Ny5c7PZBNRW0SMJHx8fuLi4ICUlBXZ2dqipqQHJ\nZinyI3X19EhdtX5UpAnfDvD/lJWVwd7e3tS7lTQDGo0GYWFhWL58OXr37t2stkhdtR6eJ121VMzS\n81ZuMIPBYI7dS5qQ56EXqSB11Xp4nnTVUjFLz1sikUgk5kXG4UgkEkkLRDpviUQiaYFI5y2RSCQt\nEOm8JRKJpAUinbekVWJpaQm1Wo1evXph8ODB2LdvH/5tbr6wsBBxcXFNZKFE0jik85a0SmxsbHDp\n0iVcu3YN69atw+7du7Ft27Z/3KagoACxsbFNZKFE0jik85a0aiwtLeHl5YWlS5ciJiYGAHDr1i0M\nHz4cAwYMgK+vLy5fvgwAWLZsGU6fPg21Wo1t27aBJHbv3o3Ro0dj1KhROHLkSHMeikRihIzzlrRK\nbG1tjd7UrtVq8fLLL0Oj0cDS0hIWFhawsrJCRkYGtm3bhtjYWKSlpWHTpk1ITEwEAJw6dQqJiYnY\ntGkTqqqq4O7ujnPnzjX4RnqJpKkxeUlYieR5hLVF2ESZ2MjISPz000+oqanBnTt3xDp1OXz4MJKT\nk/Hzzz8DACoqKnDu3DkMHz68aY2XSBpAOm/JC0FycjI6deqE9u3b48CBAygpKcEvv/yCyspKdOnS\npcFtDAYDli9fjoCAgCa2ViL5d+Qzb0mrpqamBikpKdi8eTOWLFkCACgqKkL37t1hZWWF3bt3i1op\n3bt3h0ajEdv6+fnh4MGD4rvr16+jqqqq6Q9CImkA2fOWtEoePXoEtVqNyspK2NnZYf78+fjggw8A\nAAEBAZg1axb69u2LSZMmoUOHDgCAHj16oGfPnlCr1Zg5cyZCQ0Ph5+eHiRMnorS0FJ07d8bRo0eb\n87AkEoGcsJRIJJIWiHxsIpFIJC0Q6bwlEomkBSKdt0QikbRApPOWSCSSFoh03hKJRNICkc5bIpFI\nWiDSeUskEkkL5P8Ab0nrdRA4xgUAAAAASUVORK5CYII=\n"
      }
     ], 
     "prompt_number": 132
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "pwd"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 144, 
       "text": [
        "u'C:\\\\Users\\\\Suso'"
       ]
      }
     ], 
     "prompt_number": 144
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ls"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        " Volume in drive C is Windows7_OS", 
        " Volume Serial Number is 46D8-2FED", 
        "", 
        " Directory of C:\\Users\\Suso", 
        "", 
        "03/30/2013  06:21 PM    <DIR>          .", 
        "03/30/2013  06:21 PM    <DIR>          ..", 
        "03/30/2013  01:35 PM    <DIR>          .designer", 
        "03/26/2013  09:30 PM    <DIR>          .eclipse", 
        "12/27/2010  07:51 PM    <DIR>          .idlerc", 
        "03/26/2013  10:57 PM    <DIR>          .ipython", 
        "03/30/2013  05:30 PM    <DIR>          .matplotlib", 
        "01/08/2013  08:08 PM    <DIR>          .oces2", 
        "07/14/2012  09:15 PM    <DIR>          Contacts", 
        "02/06/2013  08:12 AM            66,579 danid.log", 
        "03/28/2013  04:39 PM    <DIR>          Desktop", 
        "03/30/2013  02:03 PM    <DIR>          Documents", 
        "04/01/2013  07:54 PM    <DIR>          Downloads", 
        "03/29/2013  09:12 PM    <DIR>          Dropbox", 
        "03/27/2013  11:37 AM    <DIR>          Favorites", 
        "07/14/2012  09:15 PM    <DIR>          Links", 
        "07/14/2012  09:15 PM    <DIR>          Music", 
        "07/20/2012  03:40 PM    <DIR>          Pictures", 
        "04/01/2013  08:28 PM           306,502 playing_with_pandas.ipynb", 
        "09/04/2010  11:06 PM    <DIR>          Roaming", 
        "07/14/2012  09:15 PM    <DIR>          Saved Games", 
        "07/14/2012  09:15 PM    <DIR>          Searches", 
        "12/25/2011  09:38 PM    <DIR>          Tracing", 
        "03/28/2013  04:44 PM                86 Untitled0.ipynb", 
        "01/06/2013  07:45 PM    <DIR>          Videos", 
        "03/26/2013  09:31 PM    <DIR>          workspace", 
        "               3 File(s)        373,167 bytes", 
        "              23 Dir(s)  100,563,881,984 bytes free"
       ]
      }
     ], 
     "prompt_number": 146
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "X = np.array([0,1,2,3,4])", 
      "Y = np.array([3,5,4,6,7])", 
      "pylab.scatter(X, Y)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 148, 
       "text": [
        "<matplotlib.collections.PathCollection at 0x74e34f0>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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aWmvs2LF2j9IjTp06ZdXV1VmWZVktLS3WPffcY7W3t9s8VXh1dnZalmVZXq/XysjIsBob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\nnvUerIEAAAAASUVORK5CYII=\n"
      }
     ], 
     "prompt_number": 148
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "Xr = X - X.mean(); Yr = Y - Y.mean()", 
      "slope = (Xr*Yr).sum() / (Xr**2).sum()", 
      "intercept = Y.mean() - X.mean() * slope", 
      "(intercept, slope)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 149, 
       "text": [
        "(3.2000000000000002, 0.90000000000000002)"
       ]
      }
     ], 
     "prompt_number": 149
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%pylab inline", 
      "%load_ext rmagic "
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "", 
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].", 
        "For more information, type 'help(pylab)'."
       ]
      }, 
      {
       "ename": "ImportError", 
       "evalue": "No module named rmagic", 
       "output_type": "pyerr", 
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)", 
        "\u001b[1;32mC:\\Users\\Suso\\<ipython-input-1-02c94a7bc609>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'pylab inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'load_ext rmagic'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[1;34m(self, arg_s, next_input)\u001b[0m\n\u001b[0;32m   1983\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_magic_locals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1984\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1985\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1986\u001b[0m             \u001b[1;31m# Ensure we're not keeping object references around:\u001b[0m\r\n\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1987\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_magic_locals\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[1;32mC:\\Python27\\lib\\site-packages\\IPython\\core\\magic.pyc\u001b[0m in \u001b[0;36mmagic_load_ext\u001b[1;34m(self, module_str)\u001b[0m\n\u001b[0;32m   3373\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_load_ext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3374\u001b[0m         \u001b[1;34m\"\"\"Load an IPython extension by its module name.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3375\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3377\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_unload_ext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\IPython\\core\\extensions.pyc\u001b[0m in \u001b[0;36mload_extension\u001b[1;34m(self, module_str)\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m                 \u001b[0m__import__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m         \u001b[0mmod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 
        "\u001b[1;31mImportError\u001b[0m: No module named rmagic"
       ]
      }
     ], 
     "prompt_number": 1
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "import datetime", 
      "import numpy as np", 
      "import matplotlib.colors as colors", 
      "import matplotlib.finance as finance", 
      "import matplotlib.dates as mdates", 
      "import matplotlib.ticker as mticker", 
      "import matplotlib.mlab as mlab", 
      "import matplotlib.pyplot as plt", 
      "import matplotlib.font_manager as font_manager", 
      "", 
      "", 
      "startdate = datetime.date(2006,1,1)", 
      "today = enddate = datetime.date.today()", 
      "ticker = 'SPY'", 
      "", 
      "", 
      "fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)", 
      "# a numpy record array with fields: date, open, high, low, close, volume, adj_close)", 
      "", 
      "r = mlab.csv2rec(fh); fh.close()", 
      "r.sort()", 
      "", 
      "", 
      "def moving_average(x, n, type='simple'):", 
      "    \"\"\"", 
      "    compute an n period moving average.", 
      "", 
      "    type is 'simple' | 'exponential'", 
      "", 
      "    \"\"\"", 
      "    x = np.asarray(x)", 
      "    if type=='simple':", 
      "        weights = np.ones(n)", 
      "    else:", 
      "        weights = np.exp(np.linspace(-1., 0., n))", 
      "", 
      "    weights /= weights.sum()", 
      "", 
      "", 
      "    a =  np.convolve(x, weights, mode='full')[:len(x)]", 
      "    a[:n] = a[n]", 
      "    return a", 
      "", 
      "def relative_strength(prices, n=14):", 
      "    \"\"\"", 
      "    compute the n period relative strength indicator", 
      "    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex", 
      "    http://www.investopedia.com/terms/r/rsi.asp", 
      "    \"\"\"", 
      "", 
      "    deltas = np.diff(prices)", 
      "    seed = deltas[:n+1]", 
      "    up = seed[seed>=0].sum()/n", 
      "    down = -seed[seed<0].sum()/n", 
      "    rs = up/down", 
      "    rsi = np.zeros_like(prices)", 
      "    rsi[:n] = 100. - 100./(1.+rs)", 
      "", 
      "    for i in range(n, len(prices)):", 
      "        delta = deltas[i-1] # cause the diff is 1 shorter", 
      "", 
      "        if delta>0:", 
      "            upval = delta", 
      "            downval = 0.", 
      "        else:", 
      "            upval = 0.", 
      "            downval = -delta", 
      "", 
      "        up = (up*(n-1) + upval)/n", 
      "        down = (down*(n-1) + downval)/n", 
      "", 
      "        rs = up/down", 
      "        rsi[i] = 100. - 100./(1.+rs)", 
      "", 
      "    return rsi", 
      "", 
      "def moving_average_convergence(x, nslow=26, nfast=12):", 
      "    \"\"\"", 
      "    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'", 
      "    return value is emaslow, emafast, macd which are len(x) arrays", 
      "    \"\"\"", 
      "    emaslow = moving_average(x, nslow, type='exponential')", 
      "    emafast = moving_average(x, nfast, type='exponential')", 
      "    return emaslow, emafast, emafast - emaslow", 
      "", 
      "", 
      "plt.rc('axes', grid=True)", 
      "plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)", 
      "", 
      "textsize = 9", 
      "left, width = 0.1, 0.8", 
      "rect1 = [left, 0.7, width, 0.2]", 
      "rect2 = [left, 0.3, width, 0.4]", 
      "rect3 = [left, 0.1, width, 0.2]", 
      "", 
      "", 
      "fig = plt.figure(facecolor='white')", 
      "axescolor  = '#f6f6f6'  # the axes background color", 
      "", 
      "ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height", 
      "ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)", 
      "ax2t = ax2.twinx()", 
      "ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)", 
      "", 
      "", 
      "", 
      "### plot the relative strength indicator", 
      "prices = r.adj_close", 
      "rsi = relative_strength(prices)", 
      "fillcolor = 'darkgoldenrod'", 
      "", 
      "ax1.plot(r.date, rsi, color=fillcolor)", 
      "ax1.axhline(70, color=fillcolor)", 
      "ax1.axhline(30, color=fillcolor)", 
      "ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)", 
      "ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)", 
      "ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)", 
      "ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)", 
      "ax1.set_ylim(0, 100)", 
      "ax1.set_yticks([30,70])", 
      "ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)", 
      "ax1.set_title('%s daily'%ticker)", 
      "", 
      "### plot the price and volume data", 
      "dx = r.adj_close - r.close", 
      "low = r.low + dx", 
      "high = r.high + dx", 
      "", 
      "deltas = np.zeros_like(prices)", 
      "deltas[1:] = np.diff(prices)", 
      "up = deltas>0", 
      "ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')", 
      "ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')", 
      "ma20 = moving_average(prices, 20, type='simple')", 
      "ma200 = moving_average(prices, 200, type='simple')", 
      "", 
      "linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')", 
      "linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')", 
      "", 
      "", 
      "last = r[-1]", 
      "s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (", 
      "    today.strftime('%d-%b-%Y'),", 
      "    last.open, last.high,", 
      "    last.low, last.close,", 
      "    last.volume*1e-6,", 
      "    last.close-last.open )", 
      "t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)", 
      "", 
      "props = font_manager.FontProperties(size=10)", 
      "leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)", 
      "leg.get_frame().set_alpha(0.5)", 
      "", 
      "", 
      "volume = (r.close*r.volume)/1e6  # dollar volume in millions", 
      "vmax = volume.max()", 
      "poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)", 
      "ax2t.set_ylim(0, 5*vmax)", 
      "ax2t.set_yticks([])", 
      "", 
      "", 
      "### compute the MACD indicator", 
      "fillcolor = 'darkslategrey'", 
      "nslow = 26", 
      "nfast = 12", 
      "nema = 9", 
      "emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)", 
      "ema9 = moving_average(macd, nema, type='exponential')", 
      "ax3.plot(r.date, macd, color='black', lw=2)", 
      "ax3.plot(r.date, ema9, color='blue', lw=1)", 
      "ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)", 
      "", 
      "", 
      "ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',", 
      "         transform=ax3.transAxes, fontsize=textsize)", 
      "", 
      "#ax3.set_yticks([])", 
      "# turn off upper axis tick labels, rotate the lower ones, etc", 
      "for ax in ax1, ax2, ax2t, ax3:", 
      "    if ax!=ax3:", 
      "        for label in ax.get_xticklabels():", 
      "            label.set_visible(False)", 
      "    else:", 
      "        for label in ax.get_xticklabels():", 
      "            label.set_rotation(30)", 
      "            label.set_horizontalalignment('right')", 
      "", 
      "    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')", 
      "", 
      "", 
      "", 
      "class MyLocator(mticker.MaxNLocator):", 
      "    def __init__(self, *args, **kwargs):", 
      "        mticker.MaxNLocator.__init__(self, *args, **kwargs)", 
      "", 
      "    def __call__(self, *args, **kwargs):", 
      "        return mticker.MaxNLocator.__call__(self, *args, **kwargs)", 
      "", 
      "# at most 5 ticks, pruning the upper and lower so they don't overlap", 
      "# with other ticks", 
      "#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))", 
      "#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))", 
      "", 
      "ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))", 
      "ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))", 
      "", 
      "plt.show()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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WAZVag0eNRhhrNJDacGz1cLYvqO22ruunBXNSabOvWqNH6xANrVJrZPWLGQVK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7+W9P1R5FGP518V5E8Aani6UK9RI7Zs2YKzszN5eXk4Oztz8OBBUlNTASWwA/CxXhXNY4+Fs2nT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\nYII=\n"
      }
     ], 
     "prompt_number": 2
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "whos"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Variable                     Type              Data/Info", 
        "--------------------------------------------------------", 
        "MyLocator                    classobj          __main__.MyLocator", 
        "ax                           Axes              Axes(0.1,0.1;0.8x0.2)", 
        "ax1                          Axes              Axes(0.1,0.7;0.8x0.2)", 
        "ax2                          Axes              Axes(0.1,0.3;0.8x0.4)", 
        "ax2t                         Axes              Axes(0.1,0.3;0.8x0.4)", 
        "ax3                          Axes              Axes(0.1,0.1;0.8x0.2)", 
        "axescolor                    str               #f6f6f6", 
        "deltas                       ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "dx                           ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "ema9                         ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "emafast                      ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "emaslow                      ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "enddate                      date              2013-04-02", 
        "fh                           file              <closed file 'C:\\Users\\Su<...>, mode 'r' at 0x0496CF98>", 
        "fig                          Figure            Figure(480x320)", 
        "fillcolor                    str               darkslategrey", 
        "finance                      module            <module 'matplotlib.finan<...>\\matplotlib\\finance.pyc'>", 
        "font_manager                 module            <module 'matplotlib.font_<...>lotlib\\font_manager.pyc'>", 
        "high                         ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "label                        Text              Text(734869,0,u'2013')", 
        "last                         record            (datetime.date(2013, 4, 1<...>156.05, 99168600, 156.05)", 
        "left                         float             0.1", 
        "leg                          Legend            Legend", 
        "linema20                     Line2D            Line2D(MA (20))", 
        "linema200                    Line2D            Line2D(MA (200))", 
        "low                          ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "ma20                         ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "ma200                        ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "macd                         ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "mdates                       module            <module 'matplotlib.dates<...>es\\matplotlib\\dates.pyc'>", 
        "moving_average               function          <function moving_average at 0x047F8B70>", 
        "moving_average_convergence   function          <function moving_average_<...>onvergence at 0x04891070>", 
        "mticker                      module            <module 'matplotlib.ticke<...>s\\matplotlib\\ticker.pyc'>", 
        "nema                         int               9", 
        "nfast                        int               12", 
        "nslow                        int               26", 
        "prices                       ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "props                        FontProperties    :family=sans-serif:style=<...>:stretch=normal:size=10.0", 
        "r                            recarray          [ (datetime.date(2006, 1,<...>56.05, 99168600, 156.05)]", 
        "rect1                        list              n=4", 
        "rect2                        list              n=4", 
        "rect3                        list              n=4", 
        "relative_strength            function          <function relative_strength at 0x048B9770>", 
        "rsi                          ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "s                            str               02-Apr-2013 O:156.59 H:15<...>156.05, V:99.2M Chg:-0.54", 
        "startdate                    date              2006-01-01", 
        "t4                           Text              Text(0.3,0.9,'02-Apr-2013<...>6.05, V:99.2M Chg:-0.54')", 
        "textsize                     int               9", 
        "ticker                       str               SPY", 
        "today                        date              2013-04-02", 
        "up                           ndarray           1822: 1822 elems, type `bool`, 1822 bytes", 
        "vmax                         float64           93188.092029", 
        "volume                       ndarray           1822: 1822 elems, type `float64`, 14576 bytes", 
        "width                        float             0.8"
       ]
      }
     ], 
     "prompt_number": 3
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "cd C:\\Python27\\Lib\\PyAlgotrade\\PyAlgoTrade-0.11"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "C:\\Python27\\Lib\\PyAlgotrade\\PyAlgoTrade-0.11"
       ]
      }
     ], 
     "prompt_number": 4
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "ls"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        " Volume in drive C is Windows7_OS", 
        " Volume Serial Number is 46D8-2FED", 
        "", 
        " Directory of C:\\Python27\\Lib\\PyAlgotrade\\PyAlgoTrade-0.11", 
        "", 
        "04/02/2013  03:10 PM    <DIR>          .", 
        "04/02/2013  03:10 PM    <DIR>          ..", 
        "04/02/2013  03:10 PM    <DIR>          googleappengine", 
        "03/17/2013  05:26 AM               421 PKG-INFO", 
        "04/02/2013  03:10 PM    <DIR>          pyalgotrade", 
        "03/28/2012  03:34 AM                 0 README", 
        "03/17/2013  04:57 AM             1,329 setup.py", 
        "               3 File(s)          1,750 bytes", 
        "               4 Dir(s)  99,858,501,632 bytes free"
       ]
      }
     ], 
     "prompt_number": 5
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "from pyalgotrade import strategy", 
      "from pyalgotrade.barfeed import yahoofeed", 
      "from pyalgotrade import plotter", 
      "from pyalgotrade.tools import yahoofinance", 
      "from pyalgotrade.technical import vwap"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 9
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "import os", 
      "", 
      "class MyStrategy(strategy.Strategy):", 
      "\tdef __init__(self, feed, instrument, vwapWindowSize):", 
      "\t\tstrategy.Strategy.__init__(self, feed)", 
      "\t\tself.__instrument = instrument", 
      "\t\tself.__vwap = vwap.VWAP(feed[instrument], vwapWindowSize)", 
      "", 
      "\tdef getVWAPDS(self):", 
      "\t\treturn self.__vwap", 
      "", 
      "\tdef onBars(self, bars):", 
      "\t\tvwap = self.__vwap[-1]", 
      "\t\tif vwap == None:", 
      "\t\t\treturn", 
      "", 
      "\t\tshares = self.getBroker().getShares(self.__instrument)", 
      "\t\tprice = bars[self.__instrument].getClose()", 
      "\t\tnotional = shares * price", 
      "\t\tif price < vwap * 0.995 and notional > 0:", 
      "\t\t\tself.order(self.__instrument, -100)", 
      "\t\telif price > vwap * 1.005 and notional < 1000000:", 
      "\t\t\tself.order(self.__instrument, 100) ", 
      "", 
      "def build_feed(instruments, fromYear, toYear):", 
      "\tfeed = yahoofeed.Feed()", 
      "", 
      "\tfor year in range(fromYear, toYear+1):", 
      "\t\tfor symbol in instruments:", 
      "\t\t\tfileName = \"%s-%d-yahoofinance.csv\" % (symbol, year)", 
      "\t\t\tif not os.path.exists(fileName):", 
      "\t\t\t\tprint \"Downloading %s %d\" % (symbol, year)", 
      "\t\t\t\tcsv = yahoofinance.get_daily_csv(symbol, year)", 
      "\t\t\t\tf = open(fileName, \"w\")", 
      "\t\t\t\tf.write(csv)", 
      "\t\t\t\tf.close()", 
      "\t\t\tfeed.addBarsFromCSV(symbol, fileName)", 
      "\treturn feed", 
      "", 
      "def main(plot):", 
      "\tinstrument = \"aapl\"", 
      "\tvwapWindowSize = 5", 
      "", 
      "\t# Download the bars.", 
      "\tfeed = build_feed([instrument], 2011, 2012)", 
      "", 
      "\tmyStrategy = MyStrategy(feed, instrument, vwapWindowSize)", 
      "", 
      "\tif plot:", 
      "\t\tplt = plotter.StrategyPlotter(myStrategy, True, False, True)", 
      "\t\tplt.getInstrumentSubplot(instrument).addDataSeries(\"vwap\", myStrategy.getVWAPDS())", 
      "", 
      "\tmyStrategy.run()", 
      "\tprint \"Result: %.2f\" % myStrategy.getResult()", 
      "", 
      "\tif plot:", 
      "\t\tplt.plot()", 
      "", 
      "if __name__ == \"__main__\":", 
      "\tmain(True)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Downloading aapl 2011", 
        "Downloading aapl 2012"
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "", 
        "Result: 1436098.00"
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        ""
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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FixeXWaYi7D7s7e25cMFw69xmZ2eTn59vsP3fKeoOo3qyaxfYPZHKICdH2rnB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\ngrlz77kqPWZmOtvwPdtNOTz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3brRt2xbQabtZ37/VVlo5jUaDVqtl1apVjBw5EgcHBwD9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\n"
      }
     ], 
     "prompt_number": 10
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [], 
     "language": "python", 
     "outputs": []
    }
   ]
  }
 ]
}