ml-finance-python

python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb

(139984B)
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      9     "# CBOE RVX Index\n",
     10     "\n",
     11     "In this notebook, we'll take a look at the CBOE RVX Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 02 Jan 2004 through the current day. This data has a daily frequency. Calculated by the CBOE, Quantopian sources this data from Quandl. The CBOE Russell 2000 Volatility Index (RVX) is a measure of market expectations of near-term volatility conveyed by Russell 2000 stock index option prices. Calculated by CBOE, provided by Quandl\n",
     12     "\n",
     13     "## Notebook Contents\n",
     14     "\n",
     15     "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
     16     "\n",
     17     "- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
     18     "- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
     19     "\n",
     20     "### Limits\n",
     21     "One key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
     22     "\n",
     23     "With preamble in place, let's get started:\n",
     24     "\n",
     25     "<a id='interactive'></a>\n",
     26     "#Interactive Overview\n",
     27     "### Accessing the data with Blaze and Interactive on Research\n",
     28     "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
     29     "\n",
     30     "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
     31     "\n",
     32     "It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
     33     "\n",
     34     "Helpful links:\n",
     35     "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
     36     "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
     37     "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
     38     "\n",
     39     "\n",
     40     "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
     41     "> `from odo import odo`  \n",
     42     "> `odo(expr, pandas.DataFrame)`\n",
     43     "\n",
     44     "\n",
     45     "###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
     46    ]
     47   },
     48   {
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     50    "execution_count": 1,
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     54    "outputs": [],
     55    "source": [
     56     "# For use in Quantopian Research, exploring interactively\n",
     57     "from quantopian.interactive.data.quandl import cboe_rvx as dataset\n",
     58     "\n",
     59     "# import data operations\n",
     60     "from odo import odo\n",
     61     "# import other libraries we will use\n",
     62     "import pandas as pd"
     63    ]
     64   },
     65   {
     66    "cell_type": "code",
     67    "execution_count": 2,
     68    "metadata": {
     69     "collapsed": false
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     72     {
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     75        "dshape(\"\"\"var * {\n",
     76        "  open_: float64,\n",
     77        "  high: float64,\n",
     78        "  low: float64,\n",
     79        "  close: float64,\n",
     80        "  asof_date: datetime,\n",
     81        "  timestamp: datetime\n",
     82        "  }\"\"\")"
     83       ]
     84      },
     85      "execution_count": 2,
     86      "metadata": {},
     87      "output_type": "execute_result"
     88     }
     89    ],
     90    "source": [
     91     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
     92     "dataset.dshape"
     93    ]
     94   },
     95   {
     96    "cell_type": "code",
     97    "execution_count": 3,
     98    "metadata": {
     99     "collapsed": false
    100    },
    101    "outputs": [
    102     {
    103      "data": {
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    105        "3118"
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    108        "3118"
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    115    ],
    116    "source": [
    117     "# And how many rows are there?\n",
    118     "# N.B. we're using a Blaze function to do this, not len()\n",
    119     "dataset.count()"
    120    ]
    121   },
    122   {
    123    "cell_type": "code",
    124    "execution_count": 4,
    125    "metadata": {
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    132        "<table border=\"1\" class=\"dataframe\">\n",
    133        "  <thead>\n",
    134        "    <tr style=\"text-align: right;\">\n",
    135        "      <th></th>\n",
    136        "      <th>open_</th>\n",
    137        "      <th>high</th>\n",
    138        "      <th>low</th>\n",
    139        "      <th>close</th>\n",
    140        "      <th>asof_date</th>\n",
    141        "      <th>timestamp</th>\n",
    142        "    </tr>\n",
    143        "  </thead>\n",
    144        "  <tbody>\n",
    145        "    <tr>\n",
    146        "      <th>0</th>\n",
    147        "      <td>23.60</td>\n",
    148        "      <td>24.56</td>\n",
    149        "      <td>23.60</td>\n",
    150        "      <td>24.17</td>\n",
    151        "      <td>2016-02-23</td>\n",
    152        "      <td>2016-02-24 12:03:08.086586</td>\n",
    153        "    </tr>\n",
    154        "    <tr>\n",
    155        "      <th>1</th>\n",
    156        "      <td>25.14</td>\n",
    157        "      <td>25.73</td>\n",
    158        "      <td>23.68</td>\n",
    159        "      <td>23.89</td>\n",
    160        "      <td>2016-02-24</td>\n",
    161        "      <td>2016-02-25 12:03:19.818291</td>\n",
    162        "    </tr>\n",
    163        "    <tr>\n",
    164        "      <th>2</th>\n",
    165        "      <td>23.82</td>\n",
    166        "      <td>24.51</td>\n",
    167        "      <td>23.00</td>\n",
    168        "      <td>23.02</td>\n",
    169        "      <td>2016-02-25</td>\n",
    170        "      <td>2016-02-26 12:03:12.136132</td>\n",
    171        "    </tr>\n",
    172        "  </tbody>\n",
    173        "</table>"
    174       ],
    175       "text/plain": [
    176        "   open_   high    low  close  asof_date                  timestamp\n",
    177        "0  23.60  24.56  23.60  24.17 2016-02-23 2016-02-24 12:03:08.086586\n",
    178        "1  25.14  25.73  23.68  23.89 2016-02-24 2016-02-25 12:03:19.818291\n",
    179        "2  23.82  24.51  23.00  23.02 2016-02-25 2016-02-26 12:03:12.136132"
    180       ]
    181      },
    182      "execution_count": 4,
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    186    ],
    187    "source": [
    188     "# Let's see what the data looks like. We'll grab the first three rows.\n",
    189     "dataset[:3]"
    190    ]
    191   },
    192   {
    193    "cell_type": "markdown",
    194    "metadata": {},
    195    "source": [
    196     "Let's go over the columns:\n",
    197     "- **open**: RVX at open\n",
    198     "- **high**: high for RVX on this date\n",
    199     "- **low**: low for RVX on this date\n",
    200     "- **close**: RVX at close\n",
    201     "- **asof_date**: the timeframe to which this data applies\n",
    202     "- **timestamp**: this is our timestamp on when we registered the data.\n",
    203     "\n",
    204     "We've done much of the data processing for you. Fields like `timestamp` are standardized across all our Store Datasets, so the datasets are easy to combine.\n",
    205     "\n",
    206     "We can select columns and rows with ease. Below, we'll do a simple plot of VVIX since 2007."
    207    ]
    208   },
    209   {
    210    "cell_type": "code",
    211    "execution_count": 5,
    212    "metadata": {
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    216     {
    217      "data": {
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    219        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    220        "<table border=\"1\" class=\"dataframe\">\n",
    221        "  <thead>\n",
    222        "    <tr style=\"text-align: right;\">\n",
    223        "      <th></th>\n",
    224        "      <th>open_</th>\n",
    225        "      <th>high</th>\n",
    226        "      <th>low</th>\n",
    227        "      <th>close</th>\n",
    228        "      <th>asof_date</th>\n",
    229        "      <th>timestamp</th>\n",
    230        "    </tr>\n",
    231        "  </thead>\n",
    232        "  <tbody>\n",
    233        "    <tr>\n",
    234        "      <th>0</th>\n",
    235        "      <td>23.60</td>\n",
    236        "      <td>24.56</td>\n",
    237        "      <td>23.60</td>\n",
    238        "      <td>24.17</td>\n",
    239        "      <td>2016-02-23</td>\n",
    240        "      <td>2016-02-24 12:03:08.086586</td>\n",
    241        "    </tr>\n",
    242        "    <tr>\n",
    243        "      <th>1</th>\n",
    244        "      <td>25.14</td>\n",
    245        "      <td>25.73</td>\n",
    246        "      <td>23.68</td>\n",
    247        "      <td>23.89</td>\n",
    248        "      <td>2016-02-24</td>\n",
    249        "      <td>2016-02-25 12:03:19.818291</td>\n",
    250        "    </tr>\n",
    251        "    <tr>\n",
    252        "      <th>2</th>\n",
    253        "      <td>23.82</td>\n",
    254        "      <td>24.51</td>\n",
    255        "      <td>23.00</td>\n",
    256        "      <td>23.02</td>\n",
    257        "      <td>2016-02-25</td>\n",
    258        "      <td>2016-02-26 12:03:12.136132</td>\n",
    259        "    </tr>\n",
    260        "    <tr>\n",
    261        "      <th>3</th>\n",
    262        "      <td>22.52</td>\n",
    263        "      <td>22.99</td>\n",
    264        "      <td>22.25</td>\n",
    265        "      <td>22.70</td>\n",
    266        "      <td>2016-02-26</td>\n",
    267        "      <td>2016-02-29 12:01:43.174743</td>\n",
    268        "    </tr>\n",
    269        "    <tr>\n",
    270        "      <th>4</th>\n",
    271        "      <td>23.35</td>\n",
    272        "      <td>23.48</td>\n",
    273        "      <td>22.40</td>\n",
    274        "      <td>23.19</td>\n",
    275        "      <td>2016-02-29</td>\n",
    276        "      <td>2016-03-01 12:02:50.996338</td>\n",
    277        "    </tr>\n",
    278        "  </tbody>\n",
    279        "</table>\n",
    280        "</div>"
    281       ],
    282       "text/plain": [
    283        "   open_   high    low  close  asof_date                  timestamp\n",
    284        "0  23.60  24.56  23.60  24.17 2016-02-23 2016-02-24 12:03:08.086586\n",
    285        "1  25.14  25.73  23.68  23.89 2016-02-24 2016-02-25 12:03:19.818291\n",
    286        "2  23.82  24.51  23.00  23.02 2016-02-25 2016-02-26 12:03:12.136132\n",
    287        "3  22.52  22.99  22.25  22.70 2016-02-26 2016-02-29 12:01:43.174743\n",
    288        "4  23.35  23.48  22.40  23.19 2016-02-29 2016-03-01 12:02:50.996338"
    289       ]
    290      },
    291      "execution_count": 5,
    292      "metadata": {},
    293      "output_type": "execute_result"
    294     }
    295    ],
    296    "source": [
    297     "# Plotting this DataFrame\n",
    298     "df = odo(dataset, pd.DataFrame)\n",
    299     "df.head(5)"
    300    ]
    301   },
    302   {
    303    "cell_type": "code",
    304    "execution_count": 6,
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    309     {
    310      "data": {
    311       "text/html": [
    312        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    313        "<table border=\"1\" class=\"dataframe\">\n",
    314        "  <thead>\n",
    315        "    <tr style=\"text-align: right;\">\n",
    316        "      <th></th>\n",
    317        "      <th>open_</th>\n",
    318        "      <th>high</th>\n",
    319        "      <th>low</th>\n",
    320        "      <th>close</th>\n",
    321        "      <th>timestamp</th>\n",
    322        "    </tr>\n",
    323        "    <tr>\n",
    324        "      <th>asof_date</th>\n",
    325        "      <th></th>\n",
    326        "      <th></th>\n",
    327        "      <th></th>\n",
    328        "      <th></th>\n",
    329        "      <th></th>\n",
    330        "    </tr>\n",
    331        "  </thead>\n",
    332        "  <tbody>\n",
    333        "    <tr>\n",
    334        "      <th>2016-02-23</th>\n",
    335        "      <td>23.60</td>\n",
    336        "      <td>24.56</td>\n",
    337        "      <td>23.60</td>\n",
    338        "      <td>24.17</td>\n",
    339        "      <td>2016-02-24 12:03:08.086586</td>\n",
    340        "    </tr>\n",
    341        "    <tr>\n",
    342        "      <th>2016-02-24</th>\n",
    343        "      <td>25.14</td>\n",
    344        "      <td>25.73</td>\n",
    345        "      <td>23.68</td>\n",
    346        "      <td>23.89</td>\n",
    347        "      <td>2016-02-25 12:03:19.818291</td>\n",
    348        "    </tr>\n",
    349        "    <tr>\n",
    350        "      <th>2016-02-25</th>\n",
    351        "      <td>23.82</td>\n",
    352        "      <td>24.51</td>\n",
    353        "      <td>23.00</td>\n",
    354        "      <td>23.02</td>\n",
    355        "      <td>2016-02-26 12:03:12.136132</td>\n",
    356        "    </tr>\n",
    357        "    <tr>\n",
    358        "      <th>2016-02-26</th>\n",
    359        "      <td>22.52</td>\n",
    360        "      <td>22.99</td>\n",
    361        "      <td>22.25</td>\n",
    362        "      <td>22.70</td>\n",
    363        "      <td>2016-02-29 12:01:43.174743</td>\n",
    364        "    </tr>\n",
    365        "    <tr>\n",
    366        "      <th>2016-02-29</th>\n",
    367        "      <td>23.35</td>\n",
    368        "      <td>23.48</td>\n",
    369        "      <td>22.40</td>\n",
    370        "      <td>23.19</td>\n",
    371        "      <td>2016-03-01 12:02:50.996338</td>\n",
    372        "    </tr>\n",
    373        "  </tbody>\n",
    374        "</table>\n",
    375        "</div>"
    376       ],
    377       "text/plain": [
    378        "            open_   high    low  close                  timestamp\n",
    379        "asof_date                                                        \n",
    380        "2016-02-23  23.60  24.56  23.60  24.17 2016-02-24 12:03:08.086586\n",
    381        "2016-02-24  25.14  25.73  23.68  23.89 2016-02-25 12:03:19.818291\n",
    382        "2016-02-25  23.82  24.51  23.00  23.02 2016-02-26 12:03:12.136132\n",
    383        "2016-02-26  22.52  22.99  22.25  22.70 2016-02-29 12:01:43.174743\n",
    384        "2016-02-29  23.35  23.48  22.40  23.19 2016-03-01 12:02:50.996338"
    385       ]
    386      },
    387      "execution_count": 6,
    388      "metadata": {},
    389      "output_type": "execute_result"
    390     }
    391    ],
    392    "source": [
    393     "# So we can plot it, we'll set the index as the `asof_date`\n",
    394     "df['asof_date'] = pd.to_datetime(df['asof_date'])\n",
    395     "df = df.set_index(['asof_date'])\n",
    396     "df.head(5)"
    397    ]
    398   },
    399   {
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    401    "execution_count": 9,
    402    "metadata": {
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    405    "outputs": [
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    409        "<matplotlib.text.Text at 0x7f03135cefd0>"
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tyszMVHp6usrKyjR8+HCdeeaZeuihh/y+ftdrcXUb2759u9q1a6c2bdp4ZCgNwzAf836+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ATEbVUDeWgSKQpFsC9WPVNMaNfoY1DdYIfNakaXNMHR6ds9A8P75wDQzrL4w2PvrMOveyqwZOOx\nkH7f5qf3Qpe0RL9GSK/89IBkl0lxqj9562ChFp0LNlIVCDqLw/OSV1wkg8r2nj4kX211A1HG6IG+\n90mthufe34+RLTtvbC9ccEYf2XI6wBJPyExPknz2V1P66t3nYMq43hg3tJvsu2svGIjRA3PxzsOT\nxWUJVovqPMUGbXcfjv+G2f7gL+ujBpryeOYw77U1mRQdIeJYqrVaAARa020v/Yy/vbZCXPbPL7fK\nKFL5DC0v3Oh1IMbg8s3y7E8gDcCL9lfik4VCD0LWLiLF91aLKexzoAN4qV4mCDmv0spmtDvccLk5\ncT22seVBpt3A1v3BUebp4+/tp3b3+18O4UR1YA7PwrXSmi/O29vOH9odsXd41FgRpB7ezhyjlL7G\ne9eRUtoIQ8pfhocNeCk1Mg0VHcbhoWE1yw971pMXYcZ9k1TpboP6CPSA3Mwkxe8J2AnIYjbhr96o\nz5Xn9Ff9HU2b++Knffhw7i7ViSdYShvP87jjleW4+bklQf9Otm/KyQnWc2YdLrUHkeN4lFe3ILdL\nsmjsm0y+6xjOA0yiMaGmfSMVLQikEJ7c/0iX8Kg9V3aHG9uLqwKmK5yobkFJWYPmhBWoSlu8o1d+\nOhbMuApfPHepuOzu32kXLhuIHhJtFjGI0ENFlvWcMT1ly646r79i3Ul2F+1xPlYgASACf33hhvfP\nwf3Xn65IVctKT8KLd54tqTe0WX1S0+wYxc5tI/rLldDiHfdNG4ObLh0ifqZrnoJBemoCuuemon+P\nLiJ18LSeXSQOj8fDweXmxDlTy0l49D/KNRx0zQbH8TjB0NzDdnioAJ4S62G/Sq1FIHPED5TCbM88\nqSNAmn26PTw+orIYoYC+BqSg/aZnfsJ7c3bgrukr8Kfnl+CaRxfg88VCVtRfliRYWXX6vpZWajsz\ndBN6f5jJKOM5XdqZKwItx1pv+IRl1RweYQxxMMdInxe5n3Rw2cPxot3rr86QHev8BamDQYd0eJSo\nYvlZKZJMD4vX/n4uvnv1Cr/R9sz0REz/+7mSZeNHdMf8N6ZKONUs+vf0ZY7+93Mx5q85LFHxohFs\nhqc5RAUupQGapkEEkl6lUcc4PJJsFr28wY62drdM05401orEAxwqRz+c7BKNRJt/R4CkeSPt8Kil\n4GfN342nZm7AonWBqQnd+epy/OPNVZoDbGfJ8BDQ738s+40YkOP+60/HLZcPk1CEadCRwdumDscd\nV4/EwN5ZsFktGFIgHfvPGtFd12MNFazh6C/DEywsFhOcLg+G9s3GVy/8htmX1NB49OZxEd13NHDh\nmQW4atJp4udQ32GrxYyZj12Atx44D7+bPAB3XjMK9/5+jKSuyc6I+3g0aGBqczQdrWcbpgLh1/DQ\nNSFKBfdb9lcq/k7rXAiyqGwk7ai/N2cHjlX4qHuBqtepgS4VoLO7P3ol1om98O3yg2ixu8Qm2WoI\n9pFgr4VeNTR2b6YqkPX0QvHxerz62WbVfSj14aGhxnZyKzk8LKXNa/f6s93YLGtNgx07D1VHREK/\nQzo8oRhhVos54CaKQ/tl479PX4z/Pn2xuMyfo2QymTBprDQCWaMivxwslWzTHu0XXA3+0qeBpFdp\nsA4OUewgaGh2YOXWMvHBZDteEzpRONQ8An+RUTXYHe6I9OjQKjYm8GV4IkxpU4k0btwtaNYfpyaj\nQKDlUHc2hwcQhCf+dPkwUVDDQHwgKyMJv5syULWGx2IxY8RpOTh3TE9ceU5/XEFl3NMZ9U2t4FQs\nkZJkk2QZkwMYR4LB2u0nwPFCLYTNasF/Hp6McV6FzCkM9U+puWtHQFKiFVdNOg33TRvrf2UNmExC\nTVOCzYLLz+6HtJQEibHV1u6WFMRrZXjU5iPasNy42zePEzptJCltWj1eWASy36wMn8Oz42CNON8T\nRyRSoK+rP2P4rteW45OF2v2QtEStlMBmu1hq+Adzd2Hh2sNhN1tta3dLlCTVoAc1neDhf6/Buh3l\nWLH5uOL3Yg2PisND7g9bz047QCXeXkZ2Kljwxuwi1DYKdp+aCicBO/5/8MMuPPHeevzzS/U6tUCh\nuzUzffp0bN26FW63G3fccQdGjBiBRx55BBzHIS8vD9OnT0dCQnB868QIR8WUkNMleGOI3FCCQwy3\n1GQSUoaBZlYaWt14YdavOFha739lBfjbTyA8XhotdmFAffmus/H0zPXYf7QOtY128Vo988EGHC5v\nFJXtWB5oSpINOV2SUFoZnEFOQBdKhqq81dbuRkqSFUkJ1oAkKNWg5jy/eMdEPDlzvWRZtEQLCNhm\nr/5QWaeciQQ6p8MzpG92yB3XDcQWr9x1juLyzXuVI9nxCNrRCFS0IFAQyhTJTBR0y8Dd147G0ZNN\nKFSoWUtPSUBzmxN/uWpERI9Db9yu0/HSwSm7wy0xrtUMXo7j4XB5kJ5ikwWP6Cg3Pe+RGqJwFUtp\nh8etkLVRE+8IZO5PTZY+mzO/34WX7zo7yCP0Dzo74K+FA1tHnJ+VjComEBusY6IkyEC/lySDJdZ6\nedG3e4aoFscGd5XQ0OzQzPAM75+DPYdrdRUtIA6N2mPnT7SABItZxWL6vIh9wjJ5iJJgsJS2k7XC\nmLZm+wk88kf1rHR1vR0HS+uhdSd0zfBs3LgRhw4dwtdff42PPvoIL730Et5++23cdNNNmD17NgoK\nCjBnzpygtxuvMrN/vmKY5PPq7SdgNgkvBuDraRIopW3R5gZs2lshecmDSev5iyYEm+FxuDjkZ6dg\n5Gm5yM1MxrGKZtzy/FJxciXSjITKp0Q9LOiWgZrGdkWFIX+oqPUZ5oQPynE8ivZXBjxxtLW7kZJo\nw58uG+Z/ZQ2o3YeUZCseuUn6UkY6w+OPBhFsY1u1hn5A53R4DHRu/P7C+JVWZhFqptof6OZ+uZnJ\nGDe0q+I49M9/TMKd14zCleeq16eeSqDHu9Z2l0QB0MPxeH7WRslcM39NCdbvKgfPA2nJ8sCtxCGh\n5gxi1IVLaaNreJTG/WaVnnmB0KbcbumxpTENXSOVIaevi1qvJTUoSZAHmyFhs11NKtesmlGD/L8/\nnYEP/u9CJCda0dDswJdL9ovGvlJQssXukgXFaZBehdFQ8VXLpImUNhWThWRA2QzP8Uof24fcT1aC\nm1CSlZoo0wjVvr/vnyvxyqebNdfR1eE544wz8NZbbwEA0tPTYbfbsXnzZkyZMgUAMHnyZGzYsCGg\nbV10Zh9ccXY/TC7spdvxhovBBdn45uXLJUpQqck2ZHtTwyneztmB8DgBaUdmgmBeBn/7YQc0f3C6\nPOJDS0+orCdPHniLgrhEH68s+GdBFP8R0LUm5Ny+XVGMZz/ciK+W7A9oG3aHC8mJVpw7tidmP/8b\n/z9QgVodUl5mCvr30lYCDBdkLD1jWFfcf72c1hFIhIgekGlHkoXh8BjoCPjNWX3F///4m9g3zwwU\nkarhIaI8BIHSt7vlpOLys/tFPCjTUWGzmnHDxYMBAD+uOwq704PsjESxP8/mvZU47qV28TyPD+fu\nxmufbQEApKbIsym0Q0LbwKLDE2aGh57j3QrbUqMrf7OsWFHOnQYb1MvLlCvM5XZJCppRoLafwX2y\ngqabK9UiBZohaWxxYNHawzJVNDVqIOvwpCbb0D03FT3zhbq8r5YeEBXj1DJoR71B4dnP/wbvPTpF\nXP6fhyeLVK5oyFLPV6gnAwKv4Zm/+jDW7vD1HnzzK187FnLurLNNgtQ2i/bYRI9FrFP9+hdbxL5Y\nAFDbaMc1jy7Ako3HVJ17Gro6PBaLBSkpwsvw3Xff4bzzzkNbWxtsNmFgyM7ORlVVYBKC904bizuu\nGYUHbijU7XgjgeREK+68epT4OdFmEYv1ycAQsNOi8M4EE73wlw1yeYJ7sZwuj/hS0qlyp8snGQ74\nakyUPHXiKC2m1g8U73+/U7JPAFjqlafecVC5C7vHw4kZKI7jYXd4ROWxQJTW1EAiUaRzOkGXtARZ\n1DYSzeWUtpeWbMOUcX1kBp7aM/Lh3F14+b+bAEiVArWofZGm3BgwoAemXdRxsjo0lGSQQ8E9v5cG\nPsIZ2051dPX2+1m1rQxVdW3onpsmqQuxWc04VNYgqoYRpCnQx4jwwZUPzpP0KiGZCX8OzxPvrZPM\neyxog7+63i46YwRaRuBDb69W/Q7w2Q9EKpo9Vg/HwWwxhz2/Eaetb48MrN9ZHtRvlWj7jgBLBr5Z\nVoz3f9iFf3+7HYBPVImmztPnTGfFHrlpnEhNtVJ2zgdzd4HneZmK2agBgsPc6nWAU5Os6JWfjlfv\nPgfP//UsFHTLEN/ZaGR41HoJeTy8rF8XDVrOnTj6LEimUc1x9FfDQ4OlCq7edgJ/f+MX8fP6nSfh\ncnP4j/ce+kNURAuWLVuG77//Hk8//bRkeSw7yuoJWsq3prEdt181Ahed2UcsslR6oA+VNohSjwBw\nvKIJR6uEz/dNG4sp4wT5zWC8f3+DadAZHjcnRg7p1PMdry7HO9/t8G2XZHgUKG3hUDhoyUgySBL+\nbooKV/k/3+7AtCd+xM5D1WKGiAxs4cQ0/3DRYNxw8WD8g8mwmEwmWdQ2EiIJNMh9JYMSazQtXHcE\ni5geALtLajB/zWFs2HUSPM9LmoKt3nYCLAb1ycR908YGHCk2YCCWyM5IwrQLB+Hhm+I7IMYi3BoO\nAja4FGzzVgM+ZDJiDnmZyeCp+3TkRBPuf3MVvl1+ULKeUoan3emW9YkBIKrBaZlAPM9j56EaLFp3\nRHUdOpMwa/5u3P26YAx6PBxmzC7Cuh3qDoQWvUrYtjDHksbt7UwgbcRpubCYTKoiOjScLg9WbDmu\nWF9D7CFhrgluVp44Uq7G+POmY1iy8ajm7175dBPme2tzSG1VjlfKnnYSaWfO5T3Ox285A+dSAlX7\nj/lqrA8cq8f9b63C/W+tkuzv8rP7if+bTD5hheH9c8RmnmKGR0fRAjW43ByeeG8d9h2t07RXUpPl\nzzh7T90cBw/Hq0qiB2MPse8iC95fN2AGuodv16xZg5kzZ2LWrFlIS0tDSkoKnE4nEhISUFlZifz8\nwJr/FRUV6XykkcPxKmlh3dFDe3H2AKDsiBAR2lZcjc9/WINhfYSMT3WjC+8sqkReFyvuvlwojFu4\n2fcSZVmq0dwofN66fRe6Ziob91UNLmSmWUTaWXmd8OIO75OM6iY3nC4ODa2+l2n/gWLwrfKmZErw\ncDw4jke7vRVFRUVw2xtV162pEx70qsoKFBVJ08Bmh28wUbunm4pbUNPkxmXjfDQNu1P6UpWWlaOo\nyEfF4lwtittbtrkMAPDEe+tx/2+Fa9ve2iScAzVQh/J8DcoFqk8cRM+cBJyodeK6c7JRVFQkM2IO\nn2jErG9XY1S/FL9FmQDQ1ObBL2s2ISNF2Wipaxaclfq6WhQVFeHkSbkIxPs/7EK3ZN8z9OyXZeL/\nGzdtQWu7ehTs9NNSMXV8GoBqFBUpZ84M6IOONM7FG4bmA+DaUFTUcQQMDh8uwZBeyWHf99pmaUS5\noa7GeJZCxMk6aWS6uakeTmr++WGFct+Z9lbfnPjXS/PxwU9VOF5WgfYWeYPX+jph2Z69+9BYpSza\nRAcs2XtZ2eDC0q0N6JoltwW2bNmCo1UOrNwq3+/U8Vlobfdg+Y4mxe3SOFkhzB8nSgWH62RlNbZs\n8UX1zx9swv7DDjhcnN9nbdHmemw+2Iptu0tw/sgMyXfHvPZSfV013J7AJZmvPisLVfXyc6yut+M/\n3+5Arq1W9bfrd8qVb02ccN9LjhxHkXfupMsK/rtIELA4euQwEpzKyrmJNhNKyuS2EbmGgODsKl2v\nslK7d//HUJSo7CyEgzaGurd5yxbRHjlS2Y6dh4RraeL930+CLVu24LmvpAHT6upavP7JL2I2i8Xu\nXTsCrtNxO5QFrsjxlR4PTgBLV4enubkZ06dPx6effoqMDOEhnzhxIn766SdMnToVS5cuxaRJkwLa\nVmFhx4ncZZY1AMt8Hr7k2L0Px7oDDvzxaqHfz4Zd5QAqUd3ohjW9D0YPysPeqjC6K0kAACAASURB\nVH3YcrAYZrMJhYWF2Fm+B5sPHsKAgYMV+w0dO9mEZ78Uojsv3HEWnG4O6dkuAFUY2K8HXp06Ah/M\n3SXRzC/o2x+Fo3sEdE5t7S7g6xPIzclEYWEhUnPrsHqPcrO1xKQ0AO3o1asnCgsHS74rBPDR0nno\nmp2ieE8bmh149sufAACP3TYZ//v5ANbvOolLJxQA8EWrsnPzUFg4EpavT8DD8UhMzlB+RihDf8DA\noQAq0KtHVxQWjhYiWP8T7kc4z9eZZygs/No3CPA8MHdjPfr164cp3kZ5R082ISXRinwF/vOVD84D\nACyYcZXi/sqrW4AFFcjLy0Nh4RhUOY8CRTtk6xUWFqLV7sL/lhVLlvcbMMybKaxQ3H6PbvkoLByl\n+J0B/VBUVNShxjkDoePFjD5YsvEYfn/56di5Y1vY972yrg1Y4HufLztvpBg5NhAcahvtmPnTUvFz\nzx5d0SU1EXuOCwHLxORUAPLsSN/e3VF0SGjWOfHMMfjgp6U4XuPB1hJ5jWSP7t2A/YcwePBgVcVI\nt4cT5xH2+bj/zZUoqXCgpMIh+93Ysacj+Vg9sHytuOzFOyYiLysZPfLS8O3yYsDr8Kg9d1X1bdg7\nZwUA4PTRI/Dp8pXYc9yOEYMEut+YgXmYMH4cPlu1HK4Wp+bz6+F4PPvlfABAkytJtq7pQBWAahT0\n6YnSunJR+cwfBg8cAOuJBmCvYPTOuG8SHvyXj6ZXWFiI2kY7UpJs8lpUyi4g6J6fjePVFcjL74bC\nQqG5bbvTLdoILi8jpndBPxSO7qm4rfMLhfeaxeiRw4AVwrGlJZkVr5cptQpYswH5XXugsDA4iu6W\nfUKQh0jQK4HNOo0cOUakrCcdrhWfl6TEBM37mTznJOxe54l1dgAgo0smSsrVA+JnjCv0WzPYZX4V\nGluc6NenB7YfPiT7fsSoMUi0WVDaUgJsVd8XC10pbT/++CMaGhpw33334Y9//CNuvvlm3HnnnZg7\ndy5uvPFGNDU14eqrr9bzEGKC9BRfxObPVwyXfEea6g0uUB7kft0rTFrEA37mtgkAfJxstSaRpVU+\nT/epmRvwwqxfMcOrW062lctIbbNKG1ogVDqSdh1SkI0bLhmiuC7pXaDmxednp6hSOehCuFVby/C/\nZcUorWzGh/OEqNr44UKWxuXl6ZL06K97pMZ7W7sLCxlaFyngJINftAt13/xqqziY3/PGL7jtpZ9l\n69A0T7V7TdLs5PBtCtRBQJi0Plm4R9IxGwCe/XCjZs8Gm0GHMWBAV4wemIdH/jguYnRXeqz98PEL\nDWcnDGSkSmk0iTYLLplQIH42qdCuaLoPmSdrVGhjpChci9JIZ3i2F0trnbWIPB6Ol9VJjB6Uhx7e\npreBVBLc88YvolFLnxcJnhF7xGI2+6XO06I4SgwHQuFKtFnx9+tG+z84L2w2s6SWOJvqGwQI1LRb\nnl+Kp5k2EWogpQg0pUzp3NiShEljfM6PWj0s7XCdMyxdcR1yzzbsKhfphP/5djv+/Y3/+pTnPtqI\n5z7aqLkO2yaFLo+gG+5aVewJAn9tYZrbnJpiR4HYXe8/diE+eepi1XqfIq+D1xKAUAENXR2eadOm\nYc2aNfj888/x+eef47PPPkOPHj3w8ccfY/bs2Zg+fTosfhQbOiIyqK7EXXOkEfxpXslUmidKF9mR\nTtzkpSNa+EQS8vtf5N4uoD4IA77B9apJ/XHXtb4BxR2ELDVxMOhu54ML5JkmwMcNVlJpAwQDXU0r\nn1aOKTkh5z6P9Bb/EcltWpihvtk3udwzYyVm/rBL8ts3Zgtp0EjU8PjD2aOUM2df/3xA8vmHlYck\n50zzpMurldXTWK18uoaH8JABYWBWqvk6Ud2iODiOPE24tiP65yju14ABA/EJ2uHp5i26NxAaWCc0\nMcGCLmmJmDpJkO5Wa/eQRjkGWmIUF53ZR5yTtQr+aWP7qZlSNVstdT+3h5M4FueNlSrbkqChGnie\nl6iQKhmdZP9mk8lvHZq/Wm1S4J+UYEHX7MCf3USrBWlUcJkV6jh4XLAf6BobLZBaHnrOVDq1XvlS\nh4W28bYXyyngiQk+4SoAqiUJRN33UFkjvvAKYizZeAxLf5VnjGjsOaxO3dMCfZ60Kpu/5q/nqNg2\nBLtLanGkPLAsnRrSkm3IzUxWDQgR+enK+uB6KUZFtCBcPHjD6bE+hKCQlCB0gp5c2AtjB+VJvktJ\nssJqMaG+2YFbnl+Cj+btlkQUyOBAHkbiTV94Rh/kdknCtuJqRbnpbcXqanfkYbZYzPjNWX3x0I1C\nulJJ9loNe70FaPTYlZqkPOi2envsJKp451aLWVFBrq6pXSL9SPr50CD7dLk5cBwvqcOhiyerFCIt\nxLEgkRw9Ezyk0JOFzWqWTAAfL9iDx95ZC7vDjdtf+hmzf/LJa7e2K0uKkp+T+0oPUB8+fqFkPbVI\nC1uACgD3ThuDN+8/D2cMU0+LGzBgIP4QbHd5A4GDGLjEiTigYkCnpSTgrmtH49W7z1GNkt906RDc\nO20sLN5taWVHtJwhrQg6x/ESJyQrQ5qxKuiegQG9usBsNik6IyeZNgU2hWcrKVFwLsxm/yqktJKa\n0rzvs3Usfo1tyXHZzJIWIKxQx7+/2cb+RBPXXyTQ77UyPEkJFllJgdJcSuPZ2ydI7le+isPTu6vP\nkSIUNQItxd3H3lmr+p0WHE6PUJftdEtsJ3/KkbdOHa75fSSh9OzROF4RXA1Phxglz/fWPXQk3H7V\nCDxwQ6FMPcxkMiEjNQGHShtQ29iOeatL4HDJm5P50rzCS5ySZMOogXngOF6mn7/zULUib5TAwlj2\nZIBavF5d/YXG6m1lmOHNjqzZ7qOcZaUnqf0EgDo1ymo1o7nNJVEKK61s/v/27jsgqjPdH/j3TGfo\nINJBxIINUFSsKERNjGVj1JgYjW6Ma1nXuhpLYnRNMYm5Jm7ibrxp7q6Ju4km0bQ1q7n+zLVde2xB\nhRgrqKiggDDD/P4YznDOzJmhCAwM389fOgXOzMvMOc/7Pu/zYMLyf8sqhikFPOL7WWoqc0jJk34p\nGJ0EYwBg1Ft/hiAI6J8ShcmP1H7Xbi+9Bssm93C4XatWOcyI5eQV4lR2HnLyCvGlpD6+s/KUjilt\n0pKpalseb0mpWfblNX5wO6f19QHr31qrqAD25CBqZFx9run+iJNslV3Y6nVqDO7ZAh1aBitmN3z6\n8hCMKb+oFi/sXfXKcxUMuap2um3feVkQkqeQVhfga0BZmUVxY7l96pNGrXKofiiupqhUgsvjLDWV\n4X8OVexxUXqs9FqnOn3fdOXnup6dwvH806my7BPAeTqhkt8ObY/W5YGM9Lxrf7xK+6eLnGzOB4Cp\nIzqhY3wzeOk1iA3zRZ+kCIfjFAmCgGblGRrSlSvA+h4VFJY4HE9VezoquVdqxqK1P2L0oq9lqz1K\n111SSs1eAeCtuf3h76NcgKOmXAXAhcWluJhTUK2J60YR8Hga+xzh67cqPpjiH7B01kMkprUV2s38\nf/SV6yaeKrvZpjvle1myL+fj9Pk8XLvp2IBs664sW/rV6/+oqNgxSJLLHBLohQe6RWPybzritRl9\nHX6Gsw929iXrJrPRi7/Gxu9/xn/2n3eY0QCAywofPHEWJ/9uCQqL5O+D+N6d/iXPaWNQQF42fN7Y\nFAzvG+/0sfcjJcFxpeTwz7mKKQDSoFfkLOCxrfCUf9Ltv4Biy5u7lprLZAFP6+gAhy9M6d8XZ4mJ\nGqfK8u6p5sR08coadUrTyFQqwaFbvbSnmThx5+piWXqesC9uY5/SJm3Q+OFXJ/H9vl9t/7903bHn\nSlh5GtYVhfvsV3jUahXSOkfZLsYBwKv891e2h2fDd6dke0iVz31m22tSqwS8PT8d4XZpmWIfGymt\nVgWtRoXFE7uje4cwh4k66cX3Sx/uwz//87P9j7ARBME2uSxPaZMfr1KKlf0KmpR4fhUEAW/Pz8CC\n8V2dPhYA/jSlFwDr+EgncHPyCjH2+W+x/L29WP/1SVwuH7c7RdXbwyJ1r8SMk9nWzB3pNWVNSuX/\ndmh7tIz0x0tTezvc96ff9azxMTrbFgEAE//0b5SYyhAXXvVG77zCcYOIEPmHWfqFIKZo2a/wSP9d\nUFgCk7kMJ7Nv4Ner+YgNs1bAGzc4Acsn97R1ihbZbxR8qGdF0DJ/zS5MfXW7wzGu++InWXqVaPJv\nKlZDBEHA7Me7YHhaPBJaOM58OFvhkX6eNnx3Gm/98wg+2HpC8bH2xCDgRNYNvL9F/pxSUxmK75kw\n/8/y6nH2lUuqM4tU267fLlbcv6RUe99ZAzX7Pjz2q1liAFRaWia7EFLK7Zf+ZVS1VCQRNSwGnQar\nZvbFR0sHuftQPMKHz0vex/KvxRu3XQc89lxdN4qrRnecpC1bn1/xA+yzNOx73C2dlCpL6ZLu+2in\nUAVO3CustE9UmoIW3szbdg5pIWmyLRYyUKlc7+E5ZdeLxWQuw+nzeZj31k7bnltx5UwMDmLD/LDk\n6e6y5/kYtZj9eGes/H2fitsUesJI3b5TEQzsPX4V//jW8XpGJAgVv99VSptSf6tRGa0xfnA7WfEC\nkV4rPzdXlj0RWN5o805hqWzC89fy/UWHfs7FZzvO4E/v7QMAFN9zveroivTnS5uqVtfCCd3waHpr\nANYg1J6/pJfOnCeqtz3lbpHzz4dYVCNIEohXlq3DgMcNnhikXN0MAErLN0SKM/7SGXhxVuf5d/fg\nub/uxrNv/4jfv/4D/l95mtmg1Fh0SWiOJ+yqp9lfyGo1arSJqehx46qzr7QZqvhcJUofZGcrPPcj\nqXUImpc3Pd152LpULn4h//nTI7YKcVIGndr2BQ+gXptpKqUemBT2ThUpBDxr/nkYX+w853C7eCIU\n01haRwegf5coLJpgrY8tnoxu370nS3eTNosVlUiWxBnwEDVebWODEOzv+Bmn6msW4GVLlRG/T5W+\nt4GK6mD2VcKk7Ku1Gst/5i6Fps8i6cW2ucx5+jZgPRconccTWzXDxKGOey7Eim0XcytWeI6euYbC\nYvmF9l8WZEgaXFecH8QVKpXCXqRSk9m2N8h+P4i5zIJl/70Xmb/ewmfljVuVJndjQuWFAQRBwAPd\nYtBBUlDH1+g8faq5wrnOdgwKE44atQoatQpqleByhcdbocG50aDFYwPaYM9xx9489oUUKmM0aKHX\nqXHmwi3k5Vdk/ny6Xd5aQlzZsS9K5Cr4jIuwTow/Wl4pWPrc7f9XtX6MSqSrcTqF60PpqlhG1+pt\nT8l3UU1WJAaJACrN1mHA4wZiypGSnBvWzfZ3i01Qq+RfGNIPj7Qyh/gF5S9JlXvq4Xa2fyvldz/5\nYDuH2wBg/4mrsk1wy/57j+LjlCyb3AMvPFOxb0W6zF4bHurZAt5eWry7aAB8JV2tw5tZP3BnL9zC\n38urm0jptGrZh662SsFWxZLfdkf/lChsfPFhANZldvsvXJWgXF6x1FSG97ccx8Vc+cY88WQiDqta\nrcK8J1PQq7x6Skz535d9PwOlzYjSE5X9rCERUVO1dkEGpo9KQqso6+Tgs085piNFNffB6jn9sOS3\n3dEyUjm15olBbfFoeivZbeKF80/nrisW2AHkBYLsL2RNJvn/NWoVenR0rL6W3CZEcYJPnAAU09b3\nHr+C5/66G29uPGxLDZ81prMszVkac7WMtF48ixlHYmDw772/4NFnv8Jb/7QWDAgon91f/rueMBo0\nMJvLbFkF4mtSSt8XBAEfLR2Eh3u1AACMKl9BAKz7owd0i1FcbRmUGotu7UMR4KucZjZs3peywES8\nRulYXqFUr1PLVnjO2O1nMno5zw5R2k9T3clVlUrAwG4xyMsvxp6fKo7TfnO+GFzbt65wVdygpNSM\nAB+9LZ1R+jrFpqM1Ib2etb+2Wj65p+33udpX7Yw42RAZ4gMfLy2mjOjk8Bgfow4j01th1hjlQlFS\n7svtacIEQUBIoJfD3hlBAG7kF8NiseDKtTsI9JEPT2UfHmlgY5SkbSkFPHGR8m7H678+iXZxQVjx\nwT7Z7WclXYPFGQJnxD0rLzzTA/tPXkVMmPLjZ4xOwtufOjbKrIwYyWvUKvgadba+OuHBPriQY52p\nUjp5xEX42UpOAvUb8CS3aY7kNtaeGAE+evgYtQ4zhWUW4GZBxcqUTiOgRHJCW/H+Pry7SF59DXC+\nPC6O0/FzN2wzJNJUAGdc5csSETUlUc19ZSWIY+3OZwuf6oZeieEQBAE9OoY7PD/AV49bBfcUL0Kl\nF/e3795TbEB9/FzFRajZ7pxRapbP7KtVAuY9mYLRi76W3a7U9wYAQiSTkYdO59omUA+cykFo+bG0\nbylPhbt1p2LFoWV5ept4zigrs+DijQLbeX37/13A7Me72CqNtmsRBLXKWrDHoNfgTlEpfj5vTXcT\nL9rtV0OC/b0wbWQSpo2U9+b5TZrzWfw/PJYMwDoh7Kx62d+/sU6KPtq/FZ4Y1BZ5+cW2FS+dVm0L\nwMxlFqwsL38sUlrhEc15ogtWf3JIdltUqI+TRzsXWr5i4qwtBQDbSq59Kvxzf/lfvD4zzfb/81fy\nEd7Mu/x1lUGnU1ek7tmtDnnpNVVObRuUGgujQYNJw+UpZPZBaFiwEVqNGmsXZFSagqhkVEYrqFTA\nkN4t4W3QQBAEh3YjWo0K4wcrT+Db4xWOm7wzPwO9Eiu+JHsnRsDboMXpX/JwMjsPd4tNaOZnF/C4\nWB61LxrgJflgKpX2C/Q1YHjflrb/f7bjDFa8v8/hcaIOLYMxb2zVuoF3bReK6SOTnKZIPdijhcvn\ntwj3U/xwBEpSBqTlKKWbBqU198c+mIBZY5IxvG+821Z4pPQ6NYpLzLYT4APdotG3PO/3kiS1IMRf\n/tpNZRZcvlZxv9m2h0f594jV805k3bAFevarN1qNCq/OqAiCRqa3YqUnIqIqio/yd7knY+Xv+6Br\nu1AM7hnncF9U84oL4Zy8QmvFtKJSjH3+Wwyb9yWu3SyyNQ4HFFLaFFZ4DDoN/vXyENntzlbtVSoB\ng1Kte3lf+O892LLL2qS71FRmS3OzT08SJ+X6p0TZVn6kDVSnvbrD4fcUFpugUgkw6NRQl/ffaxVl\nDZbOXryN4nsmxZS2+9WhZTC2vvEbxXOkGGBptSoY9BpbsCMegxgI3FNo/O1qlaJ3UgS89I5BW3WJ\n13lnLzr2IRSJgZf43omri9Lrn8cWf40Zq37AyIVf4dvd2SgpNUOvVdn2FdkXv6qsHLXUHx5Ldgh2\nAMdtDOJriQ71lV2/VZVWo8aYAW3h46V1+llzldpojwGPm3jpNWgTbd3ob9CpMfuJzmgTGwhzmcU2\nMxHsJ7/wdVWKsl2cfDZG2oDSWSO6yY84Lg86k54Shdhw1ys8NaG0r8THqLWdEJJbV/QxCpIsU0tn\nxOxLf4vaxwVhQPdYqFSCrMKOs1mvumYuLyn+u1f+A8D6NyAGbhclAU3zAPmXam5eIV78cL/t/xUp\nbVV/HdIAEQB+PyoJ7eMq8qHDm1V/JoqIqClq1yKo0gavkSE+eOGZHornOINOg4e7WlPlXv3bAXy6\nPROrPzlka0j+2Q75ng1pSltJqdlhJl4MbLz0GrwyvaJSlqtV+47xFd//SpXW7GfrxYvsAMkmdPEc\nVKCQkv3j0Uu4U1QKo946M69Rq1BqtsiOPSev0DYBWBcTkWI2hHT/UV6+NXBT2m+i16lx/VYRPth6\nQnG1QymNzvZcrRr/fKki4Fzy2+5OH+uKGPiJKenSfof9u1ibyIrvmRi8DeoeAwCyyoDS41+76RhK\nSs2y9H77/TFxEX4ICfSSbYeoLrVaJXuP6mNPoXTyoDIMeNxocK8WGJXRGu8uGgCDToPn7D4gEUHy\nC3n7ih+i5Qpl/6RL1vbBUM3UTZCwdn6Gw21xEf62Ag1FJdKOzxUfJOkGUfuZCgBYML4rkiTBkvgl\nB8jLUtcn+7KmXnoN/MsDEWkqXliA44yFNCWvspQ2e+MeSkBEeUAjnuRi7PaRKXXTJiKiCp+tHIoX\nnumBV6qQIlwZg67iO3fj9z/j4OmK5uHSVQdAHvCMXPgVfjon33MhnZ2Pj6ooSOTtIo0orXOUy+Oz\nn61fPLE7eidFYFRGxX4aMZ46dNqx8fmrfzuAgsIS22Sbl16DomKTrGVE/t0S296Xusy8UNpfpXTO\nE1/z5/9zFj8evexwf2Vp34IgYM28/pg/LkUxzbEq7DN5UiU/x2jQQBBgK2ggrvB4GTSIDvW1jbe0\n4IGopNQMnUZtC47F9iQig06DD54bhNEPtKnRcYvEEuL9Kvn7qg3B/gakJDSv8uN5leNGRoMWE4a0\nt128azVq2cpMeJD8wldpI95fFz6ALm0dBzxYEvDUxlKxUkfm+zHnic6Y/JuOst4EorhwP9uHsqTU\njIlD2iPARy9r+qVUalOqT1KE7P9LftsdA7vHYPXsfpU2TK0vRoPWYeUFABKilY9PDHrE9Iaq9t54\nbEDFF9jSST3wxqw0tI6WlxFXmu0iIqIKeq212WVtVLT0kgQ8Qf5esr0+9pvRS0utlc+cbUqXHo+0\n7YKrOTG1SlAspSyybysR3swbC5/qJiszLAYANxUusAFrQCM+3li+R+S2ZGUh/26JbSKwOilVVTW4\nvOiBNAgUKVWcle5Zfu/L4wrPqXzc4yL8Kw0mXZGuoAHWTBWRl16DsCBv5OQV4p3PjmLNv44AsE6G\ne+mtKfPf7fkFE5b/2+Hnllmsf7+a8jGzX5WrraJFc8d2wegHWuOR/nXT3/DNOf1s/140oVu1GqUz\n4GlgbpR3B+6S0BxBdkULWiiklEWGKC/nSYMcV38Q4s98qGcLl8flY6z+hjNXMrrGYHj55kP7pd+o\n5r5oHmhNWYuL8MfIjNb4+/KHZLNVvRIjsGJKT3y2cqjiScD+NXdtF4qZYzqjVbTjF5+7GA0a+Nl9\nuc0flwJ/o/IK1PTXduD9LcdtRQ+quudG+l546TWywDG+PJ+6OsvCRER0f7x0Fd/L9pVb7xSW2i58\nW0UHoMxinc0vVmhfADj2lnt7fjoyukajZyWrDDMfr6hsZb+iU5WgTjwHSYvuSJWVWWyTekaDBiZz\nmSybIS+/2LZnqDoXrlU1fWQStqwaLiviJKpJP776KOxjnwIpDcxUKgFeeg3y8ovx3Z5fbLfrdWoY\ndBqUmsoUK9WKdFrnKzy11ZbC16jDUw+3t1U3rG3xUQFIiLVew1Q3ZY5V2hqoEf3iYS6Q10ZXqQSM\n6N9K1qjUlccGtHHZYwewbqwsKCzBkcxrTh+T0TUaqR1qtjxbFT06hmPTyqE4c+EWjp65hoQWgWgR\n4Ydgf4MtKFIiVj8TVyfio/xxTjJD09BJ9/CI0jpH4eDBHIQHezt0vAaAL3aeswW5VZkRe31mX5f3\nvzS1Ny7kFtTJ/iwiIlImXaG33y/yxc5zMOjUiA71RXiwN85euIXCeyYcyaxIHWsR7mfb52EfLMSG\n+VWpyaN0YjTQz4AcJyWynRH38Hy75xcAwNhBbfHxtp9ljxHPcREhPjhcfp1hKC/gc1XhHFfbBEGQ\nldcW2ff6caZlhD8SWzfDFzvPyfoA1RX74Eya6qcSBMWUfINObQvgXPWu0evU0JT/vIIiuxWeRlSl\n9U9TeuFmQXG1W580nlfYRLw0rRfGDGiDxFYhivc/PawDPn9tWJV+1vjB7RQraUh5e2kRFuyNQamx\nmDUmGWsXVOypaRnhj2WTe2DOE13qvLKZTqtGh5bBGPtgAgTBOosxZmDbKs3CPPFgW/RNjsSCcY59\nEhoyo14jq0bXs1NFUPn6zL4OzepEYvdhVzMy4uxg25hAp48BrOOfEFsbe7yIiKiqQvy1tv0HYosK\naWGi4hIz1CrBll2x48AFrP7ksO1+fx9rIFHV1ObKSPeOzH688p4mQMUeHlHfzo4pcmLAI51Ua1s+\nQ3/+ar7D4+uC0uRgtEI/RPsqawCQ2jEMk4Z3xOZXh9Z6b0El0jT/12b0lQc85RXv7IkrPJXRaVUV\nKW136yalrT546TW2fcnVwRWeBiaxVYjTYEekUauwaEI3xeovNaVSCRjQPRYWiwWjMlqjTUyg7AK8\nIQv0NWDBeGuws3ZBRp3kAteG6FBfWfEBL4NGVohhuqTfgL+PHiP6x+PDr044/JwdB60rf64Cnr8u\nfAAlJnOdpAkQEdH9UasELJvcE39Y9YNtpaZvciRCAo34+N+nAVjPyxHljbXXf33S7vnW81zAfe5J\n/f2oJPxreyaefDABr6z/P3SMD8YD3WKq+Brk51qlCUqxKEGLMGnAE4SjZ67j/JUCh8fXhVKTY6aL\n0t7m1XP6Y+rK7bb/D+0dhzHle2CV9vzUBen1S7u4IFlBCDGlzV5Vgh1AntJWYJfSdvuOclqiJ2HA\n00j1Soyo/EE1IAgCJgxpXyc/uz5EV3GZ2h1emtYLu49dwV83HwMAGPVaWcDja5fe5ixYEbsuq1wE\ndt5eWnijdvddERFR7UpuE2ILeAL9DLILz7zbxU6zHPokReDQz7kYcZ+bwx/q2cK2h/elab0QH1n1\nvRfZV+Qp5IG+BiS2aoZjZ6/jtRl98ZfNR5HeNRoAZKsjYnPTW+WvtbI9xPfLfhI0vJlySfHIEB+M\nfTDBFnC2iwtSTIera2/O6Wdrt+GQ0qbw9xAS4CWbTHVGr1Xb3gv7NMqDCpX2PA0DHqJ6EuhrwIM9\nYisCHoNGVhpTacXmD48lI8BHj/85dBG7jlyS3VdbmwyJiMg92scF44ud5wAAgb56dG4Tgq//NxuA\nNSDQ283eGw0avDytN+Ii/JHQIqhWJ/kqyy6x1yLcz7ZvtmWkP1QqAc9PSsW9EjP8ffRYMy/d9lhp\ng3D71ZXqlBauiUf6xePazSKMGdgGft46l6s1Twxqiy5tQ/Dtnl/Qs1PdTCxXRlpVTiu5RrBYLIp9\nBw16DUICvZB1uSIAnTS8A97fIs8Q0WnVTqv2KlWM9TQNM/eHyENp1CrEJ96qMgAAE4dJREFUhPlC\np1UjwFdfaTnoQamx6N4hDFNGdELnNvKTEQMeIqLGTdpiIdDX4LAx3v4C9b9m90N8VABUKsHtGQ3S\nPcITHrZmhhh0GlnpapFGrcKLU3uhd2IEuncIk92n1CenNhkNWsx6vDPCgr1hNGgr3ZPcNjYIsx+v\n+73LVSFtMJ57swjdO4TK7h/WtyUAYMboZNtt7eOC8Ei/Vviv2Wmy6r7eBi2CA7wU931NH5XkcJun\n4QoPUT1bM7e/tQO1QauYW6zE30ePP03phb9uPmab/XPHUjsREdUesfgAAAQHGBxSme2rdkl79bmb\nr1GHt+b2h1otIDas8kqfSa1DbA3BH+kXb1vZCqmHYgCNlf1e7cRWIZg/LgURIT6IlwSK0j6N58vT\n3ltHB2LNvP4Y/sctAKyrOGqVAK1GDZO5IqWtb3IketfRNomGhFdMRPVMrVbZZsC0GjUWT+yGt+b2\nr9Jz28RULHWrWZCAiKhRkwY4Svtn2rZo2E2iW0b6VynYsSdeYOu0ahbXcSHQ14C5Y7vA16jDyIxW\nAKztK1pFBUAQBNl7FxZs3RtllvQmlN4vpq1J9+9M/k1HzB1beQlzT8AVHiI3q06esHTDor+v5+fc\nEhE1FUopVPYVuKracLqhaxMTiEnDOyKxVTN3H0qDl54SjfSU6EofN+eJLnj27R/x1MPKhad8jY7X\nDNYUt6ax9tE0XiWRh5AGPDWpQ09ERA3LwO4xSJP0sHn7j+kI9NXbKqbOezLFXYdWZ1QqAY/0i6/z\n/TtNSfu4YPzzpYcxtE+c4v3BAY7pkJ4RPlcNV3iIGhFpwBPk13ByuYmIqGZmjpE3+owN98Pflj1k\n+3+gr2MRACIlSlXcXnimBy5du4PmgUaH+7q2C3W4zVMx4CFqRKRdmD0ltYGIiJzT6xrWvh1qXLq2\nC1UMbBZP7CbrBejpmNJG1IiIObhi4zYiIvJsznqnEN2PQN+mlSXCFR6iRiTIz4A18/ojRGFpmoiI\nPA9XeKg2rV2QgZ/OXUeCpAdUU8CAh6iRiYvgJk8ioqaiqVTRovoRHerr9qa17sBPEREREVEDJW5E\nVypbTURVwxUeIiIiogbKx0uL12f2VayyRURVw4CHiIiIqAFLiG1a+y2IahvXR4mIiIiIyGMx4CEi\nIiIiIo/FgIeIiIiIiDwWAx4iIiIiIvJYDHiIiIiIiMhjMeAhIiIiIiKPxYCHiIiIiIg8FgMeIiIi\nIiLyWAx4iIiIiIjIYzHgISIiIiIij8WAh4iIiIiIPFadBzynT5/GgAEDsGHDBgDAlStXMH78eDz5\n5JOYPXs2SkpK6voQiIiIiIioiarTgKeoqAivvvoq+vTpY7ttzZo1GDduHDZs2IDY2Fhs2rSpLg+B\niIiIiIiasDoNeHQ6Hd599100a9bMdtv+/fuRkZEBAEhPT8eePXvq8hCIiIiIiKgJ09TlD1er1VCr\n1bLbioqKoNVqAQBBQUHIzc2ty0MgIiIiIqImzK1FCywWizt/PRERERERebg6XeFRYjQaUVJSAp1O\nh5ycHDRv3rzS5xw8eLAejqxhasqvvSnjuDdNHPemiePeNHHcmyaOu3vUS8AjXcnp1asXvvvuOwwf\nPhzbtm1DWlqay+empKTU9eEREREREZGHEix1mFd25MgRPP/887hx4wbUajUCAgLw3nvvYdGiRbh3\n7x4iIyPxyiuvOOzzISIiIiIiqg11GvAQERERERG5k1uLFhAREREREdUlBjxEREREROSxGPAQERER\nEZHHYsDjZgUFBe4+BHKDnJwcAOxF1RRxzImIPBuv7RoeBjxuUlBQgFWrVmH9+vUoKSlx9+FQPSko\nKMDq1asxatQoXLlyBYIguPuQqB7k5+fj/fffR1ZWFu7evQuAgU9TUFBQgOzsbHcfBrlBfn4+/vzn\nP2Pnzp3Iy8sDwM98U8Bru4aLAY8bfPzxx5gwYQL8/PwwefJk6HQ6dx8S1YONGzdi6tSpAIAxY8ZA\npVLxBNgE7N69G9OmTcO1a9fwzTffYOXKlQDAYNfDmUwmTJw4EevWrcPFixfdfThUj7Zv347p06ej\nsLAQu3fvxqpVqwDwM+/peG3XsDHgqWd5eXk4fPgwunfvjt/97nfQ6/XIz8+33c8LYM909uxZ5Obm\nYtWqVZgzZw6OHj2KkpISCILAMfdQJpMJgDV9sVu3bli4cCFmzJiBgwcPYtu2bQCAsrIydx4i1aHL\nly/Dy8sLWq0Wp06d4mxvE3L16lWMGDECzz77LAYOHIiWLVva7uP3vWe6fPkyjh07htTUVF7bNVDq\nZcuWLXP3QXi6zMxMvPvuu8jOzkbnzp1hNBqRm5uL69ev46OPPsLOnTuxb98+pKWlcQbIg0jHvWfP\nnujduzd8fX0BABcvXoRWq0WLFi045h4mMzMT69atQ3Z2NhISEnDs2DEIgoCIiAj4+voiMzMTn376\nKcaPH8+x9yC//vorfvjhByQkJACwBrxpaWkAgEOHDiEmJgZBQUHuPESqI/ZjL37nm0wmzJo1C1qt\nFjk5OUhMTORn3oNIx93X1xeCICAnJwd5eXn48MMPeW3XwDDgqSMWiwWCICA7OxvLli1DWloajh49\niiNHjiAuLg63bt3Cpk2bMHjwYDz11FP429/+hsuXL6N79+4oKyvjh6ORUhr3Y8eOYe/evYiMjERw\ncDBMJhN27NiBtm3bIjIyEmazGSoVF1sbM2fjfurUKYSEhOD8+fPYvXs3Dh06hMjISFy4cAGFhYVI\nTk62PZcaH+nYPffcc9i9ezeioqIQHR0NtVqN4OBgxMbG4ocffkBZWRkiIyNhMBj4mfcASmMfERGB\nmJgYtG3bFr6+vrh27RqCg4MxbNgwrFu3DpcuXUJqairP8Y2Y0rhHRkYiJiYGRqMRmZmZ+OKLL3ht\n1wDxG7eOlJaWArCmMgUFBWHEiBFYvHgxtFotzp49i3bt2mHmzJkYMmQIAgICsHz5cnz99de4d+8e\nT4SNmLNx9/Pzw65du5CbmwuNRoPIyEisX78eAKBWq915yFQLnI27xWLB3bt3MWTIEPTs2RM+Pj4Y\nP348nnnmGVy+fJknwEZOHPesrCzodDqMGDECX375JcrKyqDX62EymeDl5YX09HQcOXIEt2/fBsBU\nRk+gNPZbt26Vfaajo6MxevRoxMXFYfny5di2bRuKi4t5jm/EXH3mw8LCkJ6ejilTpmDo0KG8tmtg\nuMJTy/bu3YtXX30Vhw8fho+PD1q3bm1b8gwLCwMAnDhxAhEREejfvz+Kioqg0+lw4sQJqFQq9O/f\n370vgGqksnFXqVQ4fvw49Ho94uLiEB8fj++//x6RkZEICwvjLH8jVZXP+7FjxxAREYEHHngACQkJ\nMBgM+Oabb9C8eXMkJye7+RVQTUjH3dvbGx06dEDbtm0RHx+PQ4cO4ebNm2jfvj3KysqgUqnQsmVL\nnDp1Ctu3b8cbb7wBg8GAjh07uvtlUA1UdexLS0uRlZWFmzdvIigoCMeOHYPFYkF6ejq/6xshV+N+\n+PBh5OXloUOHDggKCkLr1q15bdcAadx9AJ4kNzcXb775JqZNm4b8/Hx8+eWXCA8PR1paGnbs2IH4\n+Hj07NkTBw4cwMWLF3Hq1Cls2rQJZ86cgVqtxqRJk9z9EqgGqjLuqamp2L9/Py5cuADAOkvUpk0b\nHD58GMnJyTwBNkLV+bxnZWWhVatW+OSTT7B3715otVrMmTPH3S+BakA67gUFBfj888/xyy+/YNSo\nUSgsLES/fv2wdetW9O3bF6GhoQCAkpIS7Nq1C4WFhZg3bx4GDhzo5ldBNVHdsT9z5gx+/PFH5OTk\noKysDE8//TS/6xuhqo57WloaQkNDcfToUXz77bc4efIkNBoNr+0aCK7w3Cez2Yx33nkHmZmZyMrK\nQkxMDB599FHExsYiICAAn3zyCTp06ICrV69CrVYjKioKpaWl2LhxI6ZMmYLExESEhIRgzpw5iImJ\ncffLoSqqybibTCb84x//wOjRo+Hl5YWYmBjbpmZqHGr6ed+wYQMmTJiA1NRUhIeHY9asWQgPD3f3\ny6EqcjXu/v7++OCDD5CRkQE/Pz/o9Xr8+uuvyMnJQVJSEs6dO4cTJ04gPDwcK1euRHx8vLtfDlVD\nTcb+ypUrttXbIUOGIDQ0FDNnzkRsbKybXw1VVU3G/erVq0hKSsKdO3fQp08fNG/enNd2DQgTCu9D\nTk4OZs+ejYKCAhgMBrz44ovYsmULCgsLYTAYkJycjG7duuHgwYPo1KkT3nnnHZSUlOD27dvo1KkT\niouLERAQgAEDBrj7pVA11HTcb926hZSUFNy7dw8AEBER4eZXQtVxP5/3zp07o6ioCIIgoHfv3u5+\nKVQNrsZdr9cjJSUFiYmJeP/99wEAUVFRGDJkCDZs2IDevXvjp59+Qp8+fTBu3Dg3vxKqrpqO/Sef\nfII+ffrY0p/69u3r5ldC1VHTcf/4449t4x4UFMRruwaGKzz34eLFi/j++++xevVqdOjQAefPn8eB\nAwdw48YNpKenw2KxICAgAEePHsWTTz6Jy5cvY+vWrdi7dy+mTp1qS3egxuV+x7158+bufglUA/y8\nN01VGffg4GDs2bMHiYmJKCgowMyZMxEeHo4VK1agX79+3KzcSN3v2GdkZLj7JVANcNw9E/fw3Ieg\noCBMnToVZrMZFosF0dHRWLduHRYuXIjjx4+jY8eO8Pb2hkajgbe3N2bNmoXCwkL4+fm5+9DpPnDc\nmyaOe9NUlXH38fGBXq9Hs2bNcOvWLUyZMgXDhg1z96HTfeLYN00cd8/EFZ774O3tjZiYGKhUKlu+\n58SJE+Ht7Y2NGzciJCQEBw8eRFZWFjIyMqDX66HX69192HSfOO5NE8e9aarKuB84cADnzp1D//79\nERAQgLZt27r7sKkWcOybJo67Z+IKTy05c+YMLBYL/P39MX78eHh5eWHfvn3Izc3FCy+8AKPR6O5D\npDrAcW+aOO5NU2Xj7uPj4+5DpDrCsW+aOO6egwFPLcnJycHQoUORm5uLpUuXIjExEbNnz2YJSg/H\ncW+aOO5NE8e96eLYN00cd8/BgKeW3Lp1Cy+//DL+85//4JFHHsHw4cPdfUhUDzjuTRPHvWniuDdd\nHPumiePuOQSLxWJx90F4gv379+PkyZMYO3YsdDqduw+H6gnHvWniuDdNHPemi2PfNHHcPQcDHiIi\nIiIi8lhsDkBERERERB6LAQ8REREREXksBjxEREREROSxGPAQEREREZHHYsBDREREREQeiwEPERER\nERF5LAY8RETUIOXn52Po0KGYMWNGlZ/zxz/+EZ9//rnLx+zcuRO3b9++38MjIqJGggEPERE1SJmZ\nmTAajXj77ber/BxBECAIgsvHrF+/ngEPEVETonH3ARARkeexWCxYunQpzp07B7PZjE6dOmHu3LmY\nO3cuCgoKYDKZkJ6ejqlTp+L69etYsmQJCgsLUVpaimeeeQa9evXCihUrcOnSJcycORNr1qxR/D1l\nZWVYsmQJMjMzERkZicLCQtt9b731Fvbs2QOVSoXQ0FC8/vrr+Ne//oUDBw5g/vz5ePnll1FaWorX\nXnsNpaWlMJlMWLp0Kdq1a1dfbxMREdUDBjxERFTr8vPz0aZNG6xYsQIAMHjwYAQFBcFsNmPDhg2w\nWCxYv349LBYL1qxZg9TUVDz99NPIy8vD8OHDsW3bNixZsgRvvvmm02AHAHbv3o3s7Gxs2rQJxcXF\nGDBgAIYOHQqz2QwvLy9s2LABarUakyZNwo8//oixY8fivffew6pVqxAdHY1hw4Zh7dq1iI6OxunT\np7F48WJs3ry5vt4mIiKqBwx4iIio1vn6+uLq1at4/PHHodVqce3aNcTFxeHbb7/F7Nmz0a9fP4wZ\nMwaCIODYsWMYO3YsACAoKAihoaHIysqCxWKp9PdkZmaic+fOAACDwYCkpCQAgFqthkqlwrhx46DR\naJCVlYVbt27Jnnvjxg1kZ2dj8eLFttvu3r1bW28BERE1EAx4iIio1n311Vc4fvw4Pv74Y6hUKowc\nORLNmjXDli1bcPjwYWzfvh0jR47E559/7rDnxmKxQKWq+hZT6fPNZjMA4ODBg9i8eTM2b94Mg8GA\nmTNnOjxPp9NBp9Ph73//ew1fJRERNQYsWkBERLUuLy8PcXFxUKlUOH78OM6fP4979+7hhx9+QJcu\nXTB//nwYjUbcuHEDSUlJ2LVrFwAgJyfHthpUFfHx8Th69CgA4M6dOzh27BgsFgvy8vIQGRkJg8GA\nS5cu4ciRI7h37x4AQKVSoaSkBL6+voiMjMTOnTsBANnZ2XjnnXfq4N0gIiJ3EixVyRkgIiKqhqtX\nr2Lq1Knw9vZGcnIyvL29sWnTJvj4+MDHxwcqlQopKSmYPXs28vLysHjxYty9exclJSWYPn06+vXr\nh/379+Ott97Chg0bnP6esrIyLFiwAOfPn0dERARMJhMGDhyIQYMGYdKkSbBYLIiPj0dycjLWrl2L\nDz/8EB999BF2796N1157DXq9Hi+++CIEQYDJZMKiRYtsaXFEROQZGPAQEREREZHH4h4eIiJq0I4c\nOYI33nhD8b7Vq1ejWbNm9XxERETUmHCFh4iIiIiIPBaLFhARERERkcdiwENERERERB6LAQ8RERER\nEXksBjxEREREROSxGPAQEREREZHHYsBDREREREQe6/8Dstsyxz8HYL0AAAAASUVORK5CYII=\n",
    419       "text/plain": [
    420        "<matplotlib.figure.Figure at 0x7f031364a650>"
    421       ]
    422      },
    423      "metadata": {},
    424      "output_type": "display_data"
    425     }
    426    ],
    427    "source": [
    428     "import matplotlib.pyplot as plt\n",
    429     "df['open_'].plot(label=str(dataset))\n",
    430     "plt.ylabel(str(dataset))\n",
    431     "plt.legend()\n",
    432     "plt.title(\"Graphing %s since %s\" % (str(dataset), min(df.index)))"
    433    ]
    434   },
    435   {
    436    "cell_type": "markdown",
    437    "metadata": {},
    438    "source": [
    439     "<a id='pipeline'></a>\n",
    440     "\n",
    441     "#Pipeline Overview\n",
    442     "\n",
    443     "### Accessing the data in your algorithms & research\n",
    444     "The only method for accessing partner data within algorithms running on Quantopian is via the pipeline API. Different data sets work differently but in the case of this data, you can add this data to your pipeline as follows:\n",
    445     "\n",
    446     "Import the data set here\n",
    447     "> `from quantopian.pipeline.data.quandl import cboe_rvx`\n",
    448     "\n",
    449     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    450     "> `pipe.add(cboe_rvx.open_.latest, 'open_rvx')`\n",
    451     "\n",
    452     "Pipeline usage is very similar between the backtester and Research so let's go over how to import this data through pipeline and view its outputs."
    453    ]
    454   },
    455   {
    456    "cell_type": "code",
    457    "execution_count": 10,
    458    "metadata": {
    459     "collapsed": true
    460    },
    461    "outputs": [],
    462    "source": [
    463     "# Import necessary Pipeline modules\n",
    464     "from quantopian.pipeline import Pipeline\n",
    465     "from quantopian.research import run_pipeline\n",
    466     "from quantopian.pipeline.factors import AverageDollarVolume"
    467    ]
    468   },
    469   {
    470    "cell_type": "code",
    471    "execution_count": 11,
    472    "metadata": {
    473     "collapsed": false
    474    },
    475    "outputs": [],
    476    "source": [
    477     "# Import the datasets available\n",
    478     "from quantopian.pipeline.data.quandl import cboe_rvx"
    479    ]
    480   },
    481   {
    482    "cell_type": "markdown",
    483    "metadata": {},
    484    "source": [
    485     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    486     "\n",
    487     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    488    ]
    489   },
    490   {
    491    "cell_type": "code",
    492    "execution_count": 12,
    493    "metadata": {
    494     "collapsed": false
    495    },
    496    "outputs": [
    497     {
    498      "name": "stdout",
    499      "output_type": "stream",
    500      "text": [
    501       "Here are the list of available fields per dataset:\n",
    502       "---------------------------------------------------\n",
    503       "\n",
    504       "Dataset: cboe_rvx\n",
    505       "\n",
    506       "Fields:\n",
    507       "open_ - float64\n",
    508       "high - float64\n",
    509       "low - float64\n",
    510       "close - float64\n",
    511       "\n",
    512       "\n",
    513       "---------------------------------------------------\n",
    514       "\n"
    515      ]
    516     }
    517    ],
    518    "source": [
    519     "print \"Here are the list of available fields per dataset:\"\n",
    520     "print \"---------------------------------------------------\\n\"\n",
    521     "\n",
    522     "def _print_fields(dataset):\n",
    523     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    524     "    print \"Fields:\"\n",
    525     "    for field in list(dataset.columns):\n",
    526     "        print \"%s - %s\" % (field.name, field.dtype)\n",
    527     "    print \"\\n\"\n",
    528     "\n",
    529     "_print_fields(cboe_rvx)\n",
    530     "\n",
    531     "\n",
    532     "print \"---------------------------------------------------\\n\""
    533    ]
    534   },
    535   {
    536    "cell_type": "markdown",
    537    "metadata": {},
    538    "source": [
    539     "Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.\n",
    540     "\n",
    541     "\n",
    542     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    543     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    544    ]
    545   },
    546   {
    547    "cell_type": "code",
    548    "execution_count": 13,
    549    "metadata": {
    550     "collapsed": false
    551    },
    552    "outputs": [],
    553    "source": [
    554     "pipe = Pipeline()\n",
    555     "       \n",
    556     "pipe.add(cboe_rvx.open_.latest, 'open_rvx')"
    557    ]
    558   },
    559   {
    560    "cell_type": "code",
    561    "execution_count": 14,
    562    "metadata": {
    563     "collapsed": false
    564    },
    565    "outputs": [],
    566    "source": [
    567     "# Setting some basic liquidity strings (just for good habit)\n",
    568     "dollar_volume = AverageDollarVolume(window_length=20)\n",
    569     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    570     "\n",
    571     "pipe.set_screen(top_1000_most_liquid & cboe_rvx.open_.latest.notnan())"
    572    ]
    573   },
    574   {
    575    "cell_type": "code",
    576    "execution_count": 15,
    577    "metadata": {
    578     "collapsed": false
    579    },
    580    "outputs": [
    581     {
    582      "data": {
    583       "image/png": 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+dq4IziggIECRkZGSpCeeeEJVqlSxc0UAAMDRESYBJ+fr66sOHTpIkjp27Chf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\nP0wNTU52tUurAAAAAElFTkSuQmCC\n",
    584       "text/plain": [
    585        "<IPython.core.display.Image object>"
    586       ]
    587      },
    588      "execution_count": 15,
    589      "metadata": {},
    590      "output_type": "execute_result"
    591     }
    592    ],
    593    "source": [
    594     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    595     "pipe.show_graph(format='png')"
    596    ]
    597   },
    598   {
    599    "cell_type": "code",
    600    "execution_count": 16,
    601    "metadata": {
    602     "collapsed": false
    603    },
    604    "outputs": [
    605     {
    606      "data": {
    607       "text/html": [
    608        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    609        "<table border=\"1\" class=\"dataframe\">\n",
    610        "  <thead>\n",
    611        "    <tr style=\"text-align: right;\">\n",
    612        "      <th></th>\n",
    613        "      <th></th>\n",
    614        "      <th>open_rvx</th>\n",
    615        "    </tr>\n",
    616        "  </thead>\n",
    617        "  <tbody>\n",
    618        "    <tr>\n",
    619        "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
    620        "      <th>Equity(21 [AAME])</th>\n",
    621        "      <td>16.76</td>\n",
    622        "    </tr>\n",
    623        "    <tr>\n",
    624        "      <th>Equity(25 [AA_PR])</th>\n",
    625        "      <td>16.76</td>\n",
    626        "    </tr>\n",
    627        "    <tr>\n",
    628        "      <th>Equity(117 [AEY])</th>\n",
    629        "      <td>16.76</td>\n",
    630        "    </tr>\n",
    631        "    <tr>\n",
    632        "      <th>Equity(225 [AHPI])</th>\n",
    633        "      <td>16.76</td>\n",
    634        "    </tr>\n",
    635        "    <tr>\n",
    636        "      <th>Equity(312 [ALOT])</th>\n",
    637        "      <td>16.76</td>\n",
    638        "    </tr>\n",
    639        "    <tr>\n",
    640        "      <th>Equity(392 [AMS])</th>\n",
    641        "      <td>16.76</td>\n",
    642        "    </tr>\n",
    643        "    <tr>\n",
    644        "      <th>Equity(468 [API])</th>\n",
    645        "      <td>16.76</td>\n",
    646        "    </tr>\n",
    647        "    <tr>\n",
    648        "      <th>Equity(548 [ASBI])</th>\n",
    649        "      <td>16.76</td>\n",
    650        "    </tr>\n",
    651        "    <tr>\n",
    652        "      <th>Equity(717 [BAMM])</th>\n",
    653        "      <td>16.76</td>\n",
    654        "    </tr>\n",
    655        "    <tr>\n",
    656        "      <th>Equity(790 [BDL])</th>\n",
    657        "      <td>16.76</td>\n",
    658        "    </tr>\n",
    659        "    <tr>\n",
    660        "      <th>Equity(880 [BIO_B])</th>\n",
    661        "      <td>16.76</td>\n",
    662        "    </tr>\n",
    663        "    <tr>\n",
    664        "      <th>Equity(925 [BKSC])</th>\n",
    665        "      <td>16.76</td>\n",
    666        "    </tr>\n",
    667        "    <tr>\n",
    668        "      <th>Equity(1088 [BRID])</th>\n",
    669        "      <td>16.76</td>\n",
    670        "    </tr>\n",
    671        "    <tr>\n",
    672        "      <th>Equity(1095 [BRN])</th>\n",
    673        "      <td>16.76</td>\n",
    674        "    </tr>\n",
    675        "    <tr>\n",
    676        "      <th>Equity(1157 [BTUI])</th>\n",
    677        "      <td>16.76</td>\n",
    678        "    </tr>\n",
    679        "    <tr>\n",
    680        "      <th>Equity(1190 [BWIN_A])</th>\n",
    681        "      <td>16.76</td>\n",
    682        "    </tr>\n",
    683        "    <tr>\n",
    684        "      <th>Equity(1193 [BWL_A])</th>\n",
    685        "      <td>16.76</td>\n",
    686        "    </tr>\n",
    687        "    <tr>\n",
    688        "      <th>Equity(1323 [CAW])</th>\n",
    689        "      <td>16.76</td>\n",
    690        "    </tr>\n",
    691        "    <tr>\n",
    692        "      <th>Equity(1653 [MOC])</th>\n",
    693        "      <td>16.76</td>\n",
    694        "    </tr>\n",
    695        "    <tr>\n",
    696        "      <th>Equity(1668 [CMS_PRB])</th>\n",
    697        "      <td>16.76</td>\n",
    698        "    </tr>\n",
    699        "    <tr>\n",
    700        "      <th>Equity(1988 [CUO])</th>\n",
    701        "      <td>16.76</td>\n",
    702        "    </tr>\n",
    703        "    <tr>\n",
    704        "      <th>Equity(2078 [DAIO])</th>\n",
    705        "      <td>16.76</td>\n",
    706        "    </tr>\n",
    707        "    <tr>\n",
    708        "      <th>Equity(2103 [ESCR])</th>\n",
    709        "      <td>16.76</td>\n",
    710        "    </tr>\n",
    711        "    <tr>\n",
    712        "      <th>Equity(2124 [DD_PRA])</th>\n",
    713        "      <td>16.76</td>\n",
    714        "    </tr>\n",
    715        "    <tr>\n",
    716        "      <th>Equity(2209 [DGSE])</th>\n",
    717        "      <td>16.76</td>\n",
    718        "    </tr>\n",
    719        "    <tr>\n",
    720        "      <th>Equity(2292 [DRCO])</th>\n",
    721        "      <td>16.76</td>\n",
    722        "    </tr>\n",
    723        "    <tr>\n",
    724        "      <th>Equity(2344 [DRAM])</th>\n",
    725        "      <td>16.76</td>\n",
    726        "    </tr>\n",
    727        "    <tr>\n",
    728        "      <th>Equity(2382 [DXR])</th>\n",
    729        "      <td>16.76</td>\n",
    730        "    </tr>\n",
    731        "    <tr>\n",
    732        "      <th>Equity(2389 [COBR])</th>\n",
    733        "      <td>16.76</td>\n",
    734        "    </tr>\n",
    735        "    <tr>\n",
    736        "      <th>Equity(2391 [DYNT])</th>\n",
    737        "      <td>16.76</td>\n",
    738        "    </tr>\n",
    739        "    <tr>\n",
    740        "      <th>...</th>\n",
    741        "      <th>...</th>\n",
    742        "      <td>...</td>\n",
    743        "    </tr>\n",
    744        "    <tr>\n",
    745        "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
    746        "      <th>Equity(45179 [ERW])</th>\n",
    747        "      <td>16.62</td>\n",
    748        "    </tr>\n",
    749        "    <tr>\n",
    750        "      <th>Equity(45195 [LGL_WS])</th>\n",
    751        "      <td>16.62</td>\n",
    752        "    </tr>\n",
    753        "    <tr>\n",
    754        "      <th>Equity(45203 [NASH])</th>\n",
    755        "      <td>16.62</td>\n",
    756        "    </tr>\n",
    757        "    <tr>\n",
    758        "      <th>Equity(45222 [QPAC_U])</th>\n",
    759        "      <td>16.62</td>\n",
    760        "    </tr>\n",
    761        "    <tr>\n",
    762        "      <th>Equity(45240 [INTL_L])</th>\n",
    763        "      <td>16.62</td>\n",
    764        "    </tr>\n",
    765        "    <tr>\n",
    766        "      <th>Equity(45270 [TIPT])</th>\n",
    767        "      <td>16.62</td>\n",
    768        "    </tr>\n",
    769        "    <tr>\n",
    770        "      <th>Equity(45288 [EMHD])</th>\n",
    771        "      <td>16.62</td>\n",
    772        "    </tr>\n",
    773        "    <tr>\n",
    774        "      <th>Equity(45301 [TRC_WS])</th>\n",
    775        "      <td>16.62</td>\n",
    776        "    </tr>\n",
    777        "    <tr>\n",
    778        "      <th>Equity(45390 [CPXX])</th>\n",
    779        "      <td>16.62</td>\n",
    780        "    </tr>\n",
    781        "    <tr>\n",
    782        "      <th>Equity(45412 [EAGL])</th>\n",
    783        "      <td>16.62</td>\n",
    784        "    </tr>\n",
    785        "    <tr>\n",
    786        "      <th>Equity(45414 [EAGL_W])</th>\n",
    787        "      <td>16.62</td>\n",
    788        "    </tr>\n",
    789        "    <tr>\n",
    790        "      <th>Equity(45420 [ROIQ_U])</th>\n",
    791        "      <td>16.62</td>\n",
    792        "    </tr>\n",
    793        "    <tr>\n",
    794        "      <th>Equity(45432 [SPCB])</th>\n",
    795        "      <td>16.62</td>\n",
    796        "    </tr>\n",
    797        "    <tr>\n",
    798        "      <th>Equity(45510 [MLPC])</th>\n",
    799        "      <td>16.62</td>\n",
    800        "    </tr>\n",
    801        "    <tr>\n",
    802        "      <th>Equity(45524 [NVEE])</th>\n",
    803        "      <td>16.62</td>\n",
    804        "    </tr>\n",
    805        "    <tr>\n",
    806        "      <th>Equity(45525 [NVEE_W])</th>\n",
    807        "      <td>16.62</td>\n",
    808        "    </tr>\n",
    809        "    <tr>\n",
    810        "      <th>Equity(45527 [JASN])</th>\n",
    811        "      <td>16.62</td>\n",
    812        "    </tr>\n",
    813        "    <tr>\n",
    814        "      <th>Equity(45536 [JASN_W])</th>\n",
    815        "      <td>16.62</td>\n",
    816        "    </tr>\n",
    817        "    <tr>\n",
    818        "      <th>Equity(45562 [ESBA])</th>\n",
    819        "      <td>16.62</td>\n",
    820        "    </tr>\n",
    821        "    <tr>\n",
    822        "      <th>Equity(45563 [OGCP])</th>\n",
    823        "      <td>16.62</td>\n",
    824        "    </tr>\n",
    825        "    <tr>\n",
    826        "      <th>Equity(45564 [FISK])</th>\n",
    827        "      <td>16.62</td>\n",
    828        "    </tr>\n",
    829        "    <tr>\n",
    830        "      <th>Equity(45646 [CHNA])</th>\n",
    831        "      <td>16.62</td>\n",
    832        "    </tr>\n",
    833        "    <tr>\n",
    834        "      <th>Equity(45678 [SLQD])</th>\n",
    835        "      <td>16.62</td>\n",
    836        "    </tr>\n",
    837        "    <tr>\n",
    838        "      <th>Equity(45680 [ADXS_W])</th>\n",
    839        "      <td>16.62</td>\n",
    840        "    </tr>\n",
    841        "    <tr>\n",
    842        "      <th>Equity(45717 [FTGC])</th>\n",
    843        "      <td>16.62</td>\n",
    844        "    </tr>\n",
    845        "    <tr>\n",
    846        "      <th>Equity(45768 [KODK_WS])</th>\n",
    847        "      <td>16.62</td>\n",
    848        "    </tr>\n",
    849        "    <tr>\n",
    850        "      <th>Equity(45792 [FTSD])</th>\n",
    851        "      <td>16.62</td>\n",
    852        "    </tr>\n",
    853        "    <tr>\n",
    854        "      <th>Equity(45824 [ROIQ_W])</th>\n",
    855        "      <td>16.62</td>\n",
    856        "    </tr>\n",
    857        "    <tr>\n",
    858        "      <th>Equity(45854 [PGAL])</th>\n",
    859        "      <td>16.62</td>\n",
    860        "    </tr>\n",
    861        "    <tr>\n",
    862        "      <th>Equity(45895 [EMSH])</th>\n",
    863        "      <td>16.62</td>\n",
    864        "    </tr>\n",
    865        "  </tbody>\n",
    866        "</table>\n",
    867        "<p>16983 rows × 1 columns</p>\n",
    868        "</div>"
    869       ],
    870       "text/plain": [
    871        "                                                   open_rvx\n",
    872        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])           16.76\n",
    873        "                          Equity(25 [AA_PR])          16.76\n",
    874        "                          Equity(117 [AEY])           16.76\n",
    875        "                          Equity(225 [AHPI])          16.76\n",
    876        "                          Equity(312 [ALOT])          16.76\n",
    877        "                          Equity(392 [AMS])           16.76\n",
    878        "                          Equity(468 [API])           16.76\n",
    879        "                          Equity(548 [ASBI])          16.76\n",
    880        "                          Equity(717 [BAMM])          16.76\n",
    881        "                          Equity(790 [BDL])           16.76\n",
    882        "                          Equity(880 [BIO_B])         16.76\n",
    883        "                          Equity(925 [BKSC])          16.76\n",
    884        "                          Equity(1088 [BRID])         16.76\n",
    885        "                          Equity(1095 [BRN])          16.76\n",
    886        "                          Equity(1157 [BTUI])         16.76\n",
    887        "                          Equity(1190 [BWIN_A])       16.76\n",
    888        "                          Equity(1193 [BWL_A])        16.76\n",
    889        "                          Equity(1323 [CAW])          16.76\n",
    890        "                          Equity(1653 [MOC])          16.76\n",
    891        "                          Equity(1668 [CMS_PRB])      16.76\n",
    892        "                          Equity(1988 [CUO])          16.76\n",
    893        "                          Equity(2078 [DAIO])         16.76\n",
    894        "                          Equity(2103 [ESCR])         16.76\n",
    895        "                          Equity(2124 [DD_PRA])       16.76\n",
    896        "                          Equity(2209 [DGSE])         16.76\n",
    897        "                          Equity(2292 [DRCO])         16.76\n",
    898        "                          Equity(2344 [DRAM])         16.76\n",
    899        "                          Equity(2382 [DXR])          16.76\n",
    900        "                          Equity(2389 [COBR])         16.76\n",
    901        "                          Equity(2391 [DYNT])         16.76\n",
    902        "...                                                     ...\n",
    903        "2013-11-25 00:00:00+00:00 Equity(45179 [ERW])         16.62\n",
    904        "                          Equity(45195 [LGL_WS])      16.62\n",
    905        "                          Equity(45203 [NASH])        16.62\n",
    906        "                          Equity(45222 [QPAC_U])      16.62\n",
    907        "                          Equity(45240 [INTL_L])      16.62\n",
    908        "                          Equity(45270 [TIPT])        16.62\n",
    909        "                          Equity(45288 [EMHD])        16.62\n",
    910        "                          Equity(45301 [TRC_WS])      16.62\n",
    911        "                          Equity(45390 [CPXX])        16.62\n",
    912        "                          Equity(45412 [EAGL])        16.62\n",
    913        "                          Equity(45414 [EAGL_W])      16.62\n",
    914        "                          Equity(45420 [ROIQ_U])      16.62\n",
    915        "                          Equity(45432 [SPCB])        16.62\n",
    916        "                          Equity(45510 [MLPC])        16.62\n",
    917        "                          Equity(45524 [NVEE])        16.62\n",
    918        "                          Equity(45525 [NVEE_W])      16.62\n",
    919        "                          Equity(45527 [JASN])        16.62\n",
    920        "                          Equity(45536 [JASN_W])      16.62\n",
    921        "                          Equity(45562 [ESBA])        16.62\n",
    922        "                          Equity(45563 [OGCP])        16.62\n",
    923        "                          Equity(45564 [FISK])        16.62\n",
    924        "                          Equity(45646 [CHNA])        16.62\n",
    925        "                          Equity(45678 [SLQD])        16.62\n",
    926        "                          Equity(45680 [ADXS_W])      16.62\n",
    927        "                          Equity(45717 [FTGC])        16.62\n",
    928        "                          Equity(45768 [KODK_WS])     16.62\n",
    929        "                          Equity(45792 [FTSD])        16.62\n",
    930        "                          Equity(45824 [ROIQ_W])      16.62\n",
    931        "                          Equity(45854 [PGAL])        16.62\n",
    932        "                          Equity(45895 [EMSH])        16.62\n",
    933        "\n",
    934        "[16983 rows x 1 columns]"
    935       ]
    936      },
    937      "execution_count": 16,
    938      "metadata": {},
    939      "output_type": "execute_result"
    940     }
    941    ],
    942    "source": [
    943     "# run_pipeline will show the output of your pipeline\n",
    944     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
    945     "pipe_output"
    946    ]
    947   },
    948   {
    949    "cell_type": "markdown",
    950    "metadata": {},
    951    "source": [
    952     "Here, you'll notice that each security is mapped to the corresponding value, so you could grab any security to get what you need.\n",
    953     "\n",
    954     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    955    ]
    956   },
    957   {
    958    "cell_type": "code",
    959    "execution_count": 75,
    960    "metadata": {
    961     "collapsed": false
    962    },
    963    "outputs": [],
    964    "source": [
    965     "# This section is only importable in the backtester\n",
    966     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    967     "\n",
    968     "# General pipeline imports\n",
    969     "from quantopian.pipeline import Pipeline\n",
    970     "from quantopian.pipeline.factors import AverageDollarVolume\n",
    971     "\n",
    972     "# For use in your algorithms via the pipeline API\n",
    973     "from quantopian.pipeline.data.quandl import cboe_rvx\n",
    974     "\n",
    975     "def make_pipeline():\n",
    976     "    # Create our pipeline\n",
    977     "    pipe = Pipeline()\n",
    978     "    \n",
    979     "    # Screen out penny stocks and low liquidity securities.\n",
    980     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    981     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    982     "    \n",
    983     "    # Create the mask that we will use for our percentile methods.\n",
    984     "    base_universe = (is_liquid)\n",
    985     "\n",
    986     "    # Add the datasets available\n",
    987     "    pipe.add(cboe_rvx.open_.latest, 'rvx_open')\n",
    988     "\n",
    989     "    # Set our pipeline screens\n",
    990     "    pipe.set_screen(is_liquid)\n",
    991     "    return pipe\n",
    992     "\n",
    993     "def initialize(context):\n",
    994     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    995     "    \n",
    996     "def before_trading_start(context, data):\n",
    997     "    results = pipeline_output('pipeline')"
    998    ]
    999   },
   1000   {
   1001    "cell_type": "markdown",
   1002    "metadata": {},
   1003    "source": [
   1004     "Now you can take that and begin to use it as a building block for your algorithms, for more examples on how to do that you can visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
   1005    ]
   1006   }
   1007  ],
   1008  "metadata": {
   1009   "kernelspec": {
   1010    "display_name": "Python 2",
   1011    "language": "python",
   1012    "name": "python2"
   1013   },
   1014   "language_info": {
   1015    "codemirror_mode": {
   1016     "name": "ipython",
   1017     "version": 2
   1018    },
   1019    "file_extension": ".py",
   1020    "mimetype": "text/x-python",
   1021    "name": "python",
   1022    "nbconvert_exporter": "python",
   1023    "pygments_lexer": "ipython2",
   1024    "version": "2.7.11"
   1025   }
   1026  },
   1027  "nbformat": 4,
   1028  "nbformat_minor": 0
   1029 }