{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Lime"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from lime import lime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named matplotlib",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-86-8abd7ac3caf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/magics/pylab.pyc\u001b[0m in \u001b[0;36mmatplotlib\u001b[0;34m(self, line)\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/magics/pylab.pyc\u001b[0m in \u001b[0;36mmatplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     78\u001b[0m         \"\"\"\n\u001b[1;32m     79\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_arguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_argstring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m         \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_matplotlib_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36menable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m   2929\u001b[0m         \"\"\"\n\u001b[1;32m   2930\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpylabtools\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2931\u001b[0;31m         \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_gui_and_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpylab_gui_select\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2933\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgui\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/administrator/dev/lime/env/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mfind_gui_and_backend\u001b[0;34m(gui, gui_select)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m     \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgui\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgui\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: No module named matplotlib"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import timedelta\n",
      "\n",
      "\n",
      "class LimeInvalidTicker(Exception):\n",
      "    '''\n",
      "    An Exception for handling invalid tickers\n",
      "    '''\n",
      "\n",
      "    def __init__(self):\n",
      "        self.msg = ('Lime was not able to find that ticker symbol on Netfonds.'\n",
      "                    'Please check your ticker and try again.')\n",
      "\n",
      "    def __str__(self):\n",
      "        return repr(self.msg)\n",
      "\n",
      "\n",
      "class LimeInvalidQuery(Exception):\n",
      "    '''\n",
      "    An Exception for handling invalid requests\n",
      "\n",
      "    ###Parameters\n",
      "    * url -- string, should be self._url used to access NetFonds\n",
      "    '''\n",
      "\n",
      "    def __init__(self, url):\n",
      "        self.url = url\n",
      "        self.msg = ('Lime was not able to contact Netfonds, based'\n",
      "                    ' on the information you provided.'\n",
      "                    'Please check your request and try again.')\n",
      "\n",
      "    def __str__(self):\n",
      "        return repr(\"Invalid Query:{}:\\n{}\".format(self.msg, self.url))\n",
      "\n",
      "\n",
      "class LimeInvalidDate(Exception):\n",
      "    '''\n",
      "    An Exception for handling invalid dates for requests Invalid\n",
      "\n",
      "    ###Parameters\n",
      "    * start -- datetime object, should be self.start_date\n",
      "    * end -- datetime object, should be self.end_date\n",
      "\n",
      "    '''\n",
      "\n",
      "    def __init__(self, start, end):\n",
      "        self.msg = self.check_date(start, end)\n",
      "        self.start = start\n",
      "        self.end = end\n",
      "\n",
      "    def check_date(self):\n",
      "        '''\n",
      "        returns the appropriate error message based\n",
      "        on the provided dates.\n",
      "        '''\n",
      "        if (self.end - self.start) > timedelta(21):\n",
      "            self.msg = ('Date range is greater than 21 days.'\n",
      "                        'Data does not exist on NetFonds beyond 21 days.\\n'\n",
      "                        'Please reconstruct your query and try again.')\n",
      "        if self.start > self.end:\n",
      "            self.msg = ('The Start Date is greater than the End Date.\\n'\n",
      "                        'You most likely reversed the begining_date and end_date.')\n",
      "\n",
      "    def __str__(self):\n",
      "        return repr(\"Invalid Date:{}:\\n{}\".format(self.msg, self.date))\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "from datetime import timedelta\n",
      "from pandas import DataFrame, concat, date_range, read_csv\n",
      "\n",
      "from .exceptions import LimeInvalidQuery, LimeInvalidDate, LimeInvalidTicker\n",
      "\n",
      "\n",
      "class Lime:\n",
      "    '''\n",
      "    A simple API for extracting stock tick data.\n",
      "\n",
      "    ###Parameters\n",
      "    * start_date -- datetime, date beginning the retrieval window\n",
      "    * end_date -- datetime, date ending the retrieval window\n",
      "    * exchange -- string ( optional ), ticker's exchange: ['Nasdaq', 'Nyse', 'Amex']\n",
      "    * ticker -- string ( optional ), stock ticker symbol. With or with out\n",
      "        Netfonds exchange extension.\n",
      "    '''\n",
      "    def __init__(self, start_date, end_date=None, exchange=None, ticker=None):\n",
      "        self.start_date = self.initialize_date(start_date)\n",
      "        self.end_date = self.initialize_date(end_date)\n",
      "        self.ticker = None\n",
      "        self._exchange = exchange\n",
      "        self._file_format = 'csv'\n",
      "        self._df = None\n",
      "        self._exchanges = {\n",
      "            'Nasdaq': '.O',\n",
      "            'Nyse': '.N',\n",
      "            'Amex': '.A'\n",
      "        }\n",
      "        self.exchange_extensions = ['O', 'N', 'A']\n",
      "        self._url = 'http://www.netfonds.no/quotes/tradedump.php'\n",
      "        self.uri = None\n",
      "        \n",
      "    def get_exchange(self):\n",
      "        ''' Returns the exchange chosen '''\n",
      "        return self._exchange\n",
      "\n",
      "    def get_df(self):\n",
      "        ''' Gets the stored tick data '''\n",
      "        return self._df\n",
      "\n",
      "    def set_df(self, dataframe):\n",
      "        '''\n",
      "        Sets stored tick data\n",
      "        \n",
      "        Parameters\n",
      "        * dataframe -- pandas.DataFrame()\n",
      "        '''\n",
      "        self._df = concat([self.get_df(), dataframe]) if self._df is None else dataframe\n",
      "        self.process_data()\n",
      "\n",
      "    def initialize_date(self, date):\n",
      "        '''\n",
      "        Returns parsed todays date, a parsed supplied date\n",
      "\n",
      "        ###Parameters\n",
      "        * date -- datetime, date to be parsed\n",
      "        '''\n",
      "        if not date:\n",
      "            date = datetime.date.today()\n",
      "        return self.date_parse(date)\n",
      "\n",
      "    def date_parse(self, date):\n",
      "        '''\n",
      "        Parses date to YYYY/MM/DD.\n",
      "\n",
      "        ###Parameters\n",
      "        * date -- datetime, date to be parsed\n",
      "        '''\n",
      "        return date.strftime('%Y%m%d')\n",
      "\n",
      "    def check_date(self, start, end):\n",
      "        '''\n",
      "        Checks whether supplied dates are acceptable.\n",
      "\n",
      "        ###Parameters\n",
      "        * start -- datetime, date beginning the retrieval window\n",
      "        * end -- datetime, date ending the retrieval window\n",
      "        '''\n",
      "        if timedelta(0) > (end - start) > timedelta(21):\n",
      "            raise LimeInvalidDate(start, end)\n",
      "        return True\n",
      "\n",
      "    def format_ticker_with_exchange_extenstion(self):\n",
      "        self.ticker = \"{}{}\".format(self.ticker,\n",
      "                                    self._exchanges[self._exchange.title()])\n",
      "        return self.ticker\n",
      "\n",
      "    def validate_ticker_exchange_extenstion(self):\n",
      "        '''Checks if ticker has a valid exchange extension. '''\n",
      "        extension = self.ticker.split('.')[1]\n",
      "        if extension in self.exchange_extensions:\n",
      "            return True\n",
      "        return False\n",
      "\n",
      "    def check_ticker_exchange_extenstion(self):\n",
      "        '''\n",
      "        Check's whether the appropriate netfonds extension, ( '.N', '.O', '.A' ), has been added.\n",
      "        If it hasn't, but the ticker's exchange has, it adds the appropriate extension.\n",
      "        If neither have; it raises a LimeInvalidTicker exception.\n",
      "        '''\n",
      "        try:\n",
      "            self.validate_ticker_exchange_extenstion()\n",
      "        except IndexError:\n",
      "            if not self._exchange:\n",
      "                self.get_exchange_extension_from_ticker()\n",
      "            self.format_ticker_with_exchange_extenstion()\n",
      "        else:\n",
      "            raise LimeInvalidTicker()\n",
      "        \n",
      "        return self.ticker\n",
      "\n",
      "    def get_exchange_extension_from_ticker(self):\n",
      "        '''\n",
      "        Loops through the three exchanges Netfonds supports, ( Nasdaq, NYSE, Amex),\n",
      "        and returns the correct exchange extension if it exists.\n",
      "        '''\n",
      "        for key in self._exchanges.keys():\n",
      "            self.ticker = \"{}{}\".format(self.ticker, self._exchanges[key])\n",
      "            self._get_tick_data()\n",
      "            if self._df is not None and (len(self._df.columns) > 1):\n",
      "                self._exchange = key\n",
      "                self.format_ticker_with_exchange_extenstion()\n",
      "                return self._exchange\n",
      "\n",
      "        raise LimeInvalidTicker()\n",
      "\n",
      "    def set_start_end_dates(self, start, end=None):\n",
      "        '''\n",
      "        Parses and Prepares Start and End dates.\n",
      "\n",
      "        ###Parameters\n",
      "        * start -- datetime\n",
      "        * end -- ( optional ) datetime, defaults to today's date\n",
      "        '''\n",
      "        self.start_date = self.date_parse(start)\n",
      "        self.end_date = self.date_parse(end) if end else self.get_date_today()\n",
      "        self.check_date(start, end)\n",
      "    \n",
      "    def process_data(self):\n",
      "        '''\n",
      "        Cleans data after its retrieved from Netfonds\n",
      "        '''\n",
      "        df = self.get_df()\n",
      "        try:\n",
      "            df.time = df.time.apply(lambda x: datetime.datetime.strptime(x, '%Y%m%dT%H%M%S'))\n",
      "            df = df.set_index(df.time)\n",
      "        except AttributeError:\n",
      "            raise LimeInvalidQuery(self.uri)\n",
      "   \n",
      "    def _get_tick_data(self):\n",
      "        '''\n",
      "        Retrieves tick data from Netfonds from a known ticker.\n",
      "        '''\n",
      "        self.uri = '{}?date={}&paper={}&csv_format={}'.format(self._url,\n",
      "                                                              self.start_date,\n",
      "                                                              self.ticker,\n",
      "                                                              self._file_format)\n",
      "\n",
      "        self.set_df(read_csv(self.uri))\n",
      "\n",
      "    def get_trades(self, ticker, exchange=None):\n",
      "        '''\n",
      "        Gets the trades made for a ticker on a specified day.\n",
      "\n",
      "        ###Parameters\n",
      "        * ticker -- string, stock ticker symbol\n",
      "        '''\n",
      "        if exchange:\n",
      "            self.exchange = exchange\n",
      "            \n",
      "        self.ticker = ticker\n",
      "        self.check_ticker_exchange_extenstion()\n",
      "        self._get_tick_data()\n",
      "        \n",
      "        return self.get_df()\n",
      "    \n",
      "    def get_trade_history(self, ticker, start_date, end_date=None):\n",
      "        '''\n",
      "        Retrieves the trades made for a ticker from a range of days.\n",
      "\n",
      "        ###Parameters\n",
      "        * ticker -- string, stock ticker symbol\n",
      "        * start_date -- datetime, starting date of retrieval window\n",
      "        * end_date  -- datetime (optional), ending date of retrieval window.\n",
      "            defaults to today, if committed.\n",
      "\n",
      "        Note: Tick data only persist for 21 days on Netfonds. Any queries greater\n",
      "        than that window will raise a LimeInvalidQuery exception.\n",
      "        '''\n",
      "        self.ticker = ticker\n",
      "        self.set_start_end_dates(start_date, end_date)\n",
      "\n",
      "        for day in date_range(start=start_date, end=self.end_date, freq='B'):\n",
      "            self.start_date = self.date_parse(day)\n",
      "            self.set_df(self.get_trades(self.ticker))\n",
      "\n",
      "        return self.get_df()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start_date = datetime.datetime.today() - timedelta(days=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lime = Lime(start_date, exchange=\"Nasdaq\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AAPL_single_day = lime.get_trades('AAPL')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AAPL_single_day"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>time</th>\n",
        "      <th>price</th>\n",
        "      <th>quantity</th>\n",
        "      <th>board</th>\n",
        "      <th>source</th>\n",
        "      <th>buyer</th>\n",
        "      <th>seller</th>\n",
        "      <th>initiator</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9700</td>\n",
        "      <td>    428</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9550</td>\n",
        "      <td>    205</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9400</td>\n",
        "      <td> 248807</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9    </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10   </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11   </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12   </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13   </th>\n",
        "      <td>2014-10-06 15:30:00</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    500</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9550</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9687</td>\n",
        "      <td>   1400</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>   2000</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    200</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9400</td>\n",
        "      <td>    300</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9500</td>\n",
        "      <td>    200</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29   </th>\n",
        "      <td>2014-10-06 15:30:01</td>\n",
        "      <td> 99.9600</td>\n",
        "      <td>    200</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44863</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    187</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44864</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6400</td>\n",
        "      <td>    413</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44865</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44866</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6361</td>\n",
        "      <td>    179</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44867</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    300</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44868</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6499</td>\n",
        "      <td>    778</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44869</th>\n",
        "      <td>2014-10-06 21:59:57</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    120</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44870</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>   3561</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44871</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6200</td>\n",
        "      <td>    400</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44872</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    700</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44873</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6499</td>\n",
        "      <td>    600</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44874</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6451</td>\n",
        "      <td>    700</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44875</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    200</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44876</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6400</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44877</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    703</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44878</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6400</td>\n",
        "      <td>    400</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44879</th>\n",
        "      <td>2014-10-06 21:59:58</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    540</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44880</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>   2937</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44881</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6499</td>\n",
        "      <td>   5700</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44882</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6400</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44883</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6500</td>\n",
        "      <td>    180</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44884</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6400</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44885</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6300</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44886</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6400</td>\n",
        "      <td>    400</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44887</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6300</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44888</th>\n",
        "      <td>2014-10-06 21:59:59</td>\n",
        "      <td> 99.6200</td>\n",
        "      <td>    100</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44889</th>\n",
        "      <td>2014-10-06 22:00:00</td>\n",
        "      <td> 99.6200</td>\n",
        "      <td>    794</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44890</th>\n",
        "      <td>2014-10-06 22:00:00</td>\n",
        "      <td> 99.6250</td>\n",
        "      <td>    400</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44891</th>\n",
        "      <td>2014-10-06 22:00:00</td>\n",
        "      <td> 99.6200</td>\n",
        "      <td> 866345</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44892</th>\n",
        "      <td>2014-10-06 22:00:04</td>\n",
        "      <td> 99.6200</td>\n",
        "      <td>    800</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Auto trade</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>44893 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "                     time    price  quantity  board      source  buyer  \\\n",
        "0     2014-10-06 15:30:00  99.9500       100    NaN  Auto trade    NaN   \n",
        "1     2014-10-06 15:30:00  99.9600       100    NaN  Auto trade    NaN   \n",
        "2     2014-10-06 15:30:00  99.9700       428    NaN  Auto trade    NaN   \n",
        "3     2014-10-06 15:30:00  99.9500       100    NaN  Auto trade    NaN   \n",
        "4     2014-10-06 15:30:00  99.9550       205    NaN  Auto trade    NaN   \n",
        "5     2014-10-06 15:30:00  99.9600       100    NaN  Auto trade    NaN   \n",
        "6     2014-10-06 15:30:00  99.9400    248807    NaN  Auto trade    NaN   \n",
        "7     2014-10-06 15:30:00  99.9500       100    NaN  Auto trade    NaN   \n",
        "8     2014-10-06 15:30:00  99.9600       100    NaN  Auto trade    NaN   \n",
        "9     2014-10-06 15:30:00  99.9500       100    NaN  Auto trade    NaN   \n",
        "10    2014-10-06 15:30:00  99.9600       100    NaN  Auto trade    NaN   \n",
        "11    2014-10-06 15:30:00  99.9500       100    NaN  Auto trade    NaN   \n",
        "12    2014-10-06 15:30:00  99.9600       100    NaN  Auto trade    NaN   \n",
        "13    2014-10-06 15:30:00  99.9500       100    NaN  Auto trade    NaN   \n",
        "14    2014-10-06 15:30:01  99.9500       500    NaN  Auto trade    NaN   \n",
        "15    2014-10-06 15:30:01  99.9550       100    NaN  Auto trade    NaN   \n",
        "16    2014-10-06 15:30:01  99.9500       100    NaN  Auto trade    NaN   \n",
        "17    2014-10-06 15:30:01  99.9600       100    NaN  Auto trade    NaN   \n",
        "18    2014-10-06 15:30:01  99.9687      1400    NaN  Auto trade    NaN   \n",
        "19    2014-10-06 15:30:01  99.9600       100    NaN  Auto trade    NaN   \n",
        "20    2014-10-06 15:30:01  99.9500       100    NaN  Auto trade    NaN   \n",
        "21    2014-10-06 15:30:01  99.9600       100    NaN  Auto trade    NaN   \n",
        "22    2014-10-06 15:30:01  99.9500       100    NaN  Auto trade    NaN   \n",
        "23    2014-10-06 15:30:01  99.9600       100    NaN  Auto trade    NaN   \n",
        "24    2014-10-06 15:30:01  99.9500       100    NaN  Auto trade    NaN   \n",
        "25    2014-10-06 15:30:01  99.9600      2000    NaN  Auto trade    NaN   \n",
        "26    2014-10-06 15:30:01  99.9500       200    NaN  Auto trade    NaN   \n",
        "27    2014-10-06 15:30:01  99.9400       300    NaN  Auto trade    NaN   \n",
        "28    2014-10-06 15:30:01  99.9500       200    NaN  Auto trade    NaN   \n",
        "29    2014-10-06 15:30:01  99.9600       200    NaN  Auto trade    NaN   \n",
        "...                   ...      ...       ...    ...         ...    ...   \n",
        "44863 2014-10-06 21:59:57  99.6500       187    NaN  Auto trade    NaN   \n",
        "44864 2014-10-06 21:59:57  99.6400       413    NaN  Auto trade    NaN   \n",
        "44865 2014-10-06 21:59:57  99.6500       100    NaN  Auto trade    NaN   \n",
        "44866 2014-10-06 21:59:57  99.6361       179    NaN  Auto trade    NaN   \n",
        "44867 2014-10-06 21:59:57  99.6500       300    NaN  Auto trade    NaN   \n",
        "44868 2014-10-06 21:59:57  99.6499       778    NaN  Auto trade    NaN   \n",
        "44869 2014-10-06 21:59:57  99.6500       120    NaN  Auto trade    NaN   \n",
        "44870 2014-10-06 21:59:58  99.6500      3561    NaN  Auto trade    NaN   \n",
        "44871 2014-10-06 21:59:58  99.6200       400    NaN  Auto trade    NaN   \n",
        "44872 2014-10-06 21:59:58  99.6500       700    NaN  Auto trade    NaN   \n",
        "44873 2014-10-06 21:59:58  99.6499       600    NaN  Auto trade    NaN   \n",
        "44874 2014-10-06 21:59:58  99.6451       700    NaN  Auto trade    NaN   \n",
        "44875 2014-10-06 21:59:58  99.6500       200    NaN  Auto trade    NaN   \n",
        "44876 2014-10-06 21:59:58  99.6400       100    NaN  Auto trade    NaN   \n",
        "44877 2014-10-06 21:59:58  99.6500       703    NaN  Auto trade    NaN   \n",
        "44878 2014-10-06 21:59:58  99.6400       400    NaN  Auto trade    NaN   \n",
        "44879 2014-10-06 21:59:58  99.6500       540    NaN  Auto trade    NaN   \n",
        "44880 2014-10-06 21:59:59  99.6500      2937    NaN  Auto trade    NaN   \n",
        "44881 2014-10-06 21:59:59  99.6499      5700    NaN  Auto trade    NaN   \n",
        "44882 2014-10-06 21:59:59  99.6400       100    NaN  Auto trade    NaN   \n",
        "44883 2014-10-06 21:59:59  99.6500       180    NaN  Auto trade    NaN   \n",
        "44884 2014-10-06 21:59:59  99.6400       100    NaN  Auto trade    NaN   \n",
        "44885 2014-10-06 21:59:59  99.6300       100    NaN  Auto trade    NaN   \n",
        "44886 2014-10-06 21:59:59  99.6400       400    NaN  Auto trade    NaN   \n",
        "44887 2014-10-06 21:59:59  99.6300       100    NaN  Auto trade    NaN   \n",
        "44888 2014-10-06 21:59:59  99.6200       100    NaN  Auto trade    NaN   \n",
        "44889 2014-10-06 22:00:00  99.6200       794    NaN  Auto trade    NaN   \n",
        "44890 2014-10-06 22:00:00  99.6250       400    NaN  Auto trade    NaN   \n",
        "44891 2014-10-06 22:00:00  99.6200    866345    NaN  Auto trade    NaN   \n",
        "44892 2014-10-06 22:00:04  99.6200       800    NaN  Auto trade    NaN   \n",
        "\n",
        "       seller  initiator  \n",
        "0         NaN        NaN  \n",
        "1         NaN        NaN  \n",
        "2         NaN        NaN  \n",
        "3         NaN        NaN  \n",
        "4         NaN        NaN  \n",
        "5         NaN        NaN  \n",
        "6         NaN        NaN  \n",
        "7         NaN        NaN  \n",
        "8         NaN        NaN  \n",
        "9         NaN        NaN  \n",
        "10        NaN        NaN  \n",
        "11        NaN        NaN  \n",
        "12        NaN        NaN  \n",
        "13        NaN        NaN  \n",
        "14        NaN        NaN  \n",
        "15        NaN        NaN  \n",
        "16        NaN        NaN  \n",
        "17        NaN        NaN  \n",
        "18        NaN        NaN  \n",
        "19        NaN        NaN  \n",
        "20        NaN        NaN  \n",
        "21        NaN        NaN  \n",
        "22        NaN        NaN  \n",
        "23        NaN        NaN  \n",
        "24        NaN        NaN  \n",
        "25        NaN        NaN  \n",
        "26        NaN        NaN  \n",
        "27        NaN        NaN  \n",
        "28        NaN        NaN  \n",
        "29        NaN        NaN  \n",
        "...       ...        ...  \n",
        "44863     NaN        NaN  \n",
        "44864     NaN        NaN  \n",
        "44865     NaN        NaN  \n",
        "44866     NaN        NaN  \n",
        "44867     NaN        NaN  \n",
        "44868     NaN        NaN  \n",
        "44869     NaN        NaN  \n",
        "44870     NaN        NaN  \n",
        "44871     NaN        NaN  \n",
        "44872     NaN        NaN  \n",
        "44873     NaN        NaN  \n",
        "44874     NaN        NaN  \n",
        "44875     NaN        NaN  \n",
        "44876     NaN        NaN  \n",
        "44877     NaN        NaN  \n",
        "44878     NaN        NaN  \n",
        "44879     NaN        NaN  \n",
        "44880     NaN        NaN  \n",
        "44881     NaN        NaN  \n",
        "44882     NaN        NaN  \n",
        "44883     NaN        NaN  \n",
        "44884     NaN        NaN  \n",
        "44885     NaN        NaN  \n",
        "44886     NaN        NaN  \n",
        "44887     NaN        NaN  \n",
        "44888     NaN        NaN  \n",
        "44889     NaN        NaN  \n",
        "44890     NaN        NaN  \n",
        "44891     NaN        NaN  \n",
        "44892     NaN        NaN  \n",
        "\n",
        "[44893 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize(18,4), dpi=1600)\n",
      "AAPL_single_day.price.plot()\n",
      "plt.title(\"Apple stock movements on: \" + str(lime.start_date))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'plt' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-85-1fe5304ed581>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mAAPL_single_day\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Apple stock movements on: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AAPL_many_days = lime.get_many(begining_date=datetime.date(2013,5,29), end_date=datetime.date(2013,6,18), ticker='AAPL')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AAPL_many_days"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "DatetimeIndex: 42094 entries, 2013-05-29 15:30:00 to 2013-05-29 22:00:01\n",
        "Data columns (total 8 columns):\n",
        "time         42094  non-null values\n",
        "price        42094  non-null values\n",
        "quantity     42094  non-null values\n",
        "board        0  non-null values\n",
        "source       42094  non-null values\n",
        "buyer        0  non-null values\n",
        "seller       0  non-null values\n",
        "initiator    0  non-null values\n",
        "dtypes: datetime64[ns](1), float64(5), int64(1), object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 42094 entries, 2013-05-29 15:30:00 to 2013-05-29 22:00:01\n",
        "Data columns (total 8 columns):\n",
        "time         42094  non-null values\n",
        "price        42094  non-null values\n",
        "quantity     42094  non-null values\n",
        "board        0  non-null values\n",
        "source       42094  non-null values\n",
        "buyer        0  non-null values\n",
        "seller       0  non-null values\n",
        "initiator    0  non-null values\n",
        "dtypes: datetime64[ns](1), float64(5), int64(1), object(1)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize(18,4), dpi=1600)\n",
      "AAPL_many_days.price.plot()\n",
      "plt.title('Apple price movements over the last 21 days')\n",
      "savefig('Apple.png', bbox_inches=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D5uIbAMBvv6nTymHvlKZPBxQ9OHD7No1YUKmS5XPn58tDIwYEANHRgKIhs93e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\niIiIiMgiNiAQERERERERkUX/D0KOusbTPCF1AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x15345d30>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "goog = lime.get_many(begining_date=datetime.date(2013,5,29), end_date=datetime.date(2013,6,18), ticker='GOOG')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize(18,4), dpi=1600)\n",
      "goog.price.plot()\n",
      "plt.title(\"Google's price movements over the last 21 days\")\n",
      "savefig('GOOG.png', bbox_inches=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eeeYZAEBCQoI5eaIqKCgIL730Epo3b47o6Gg0bdoUnTt3znchy5SxXufrq80f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rZt23Dp0iWUK1cOTz/9NObOnYt58+Zh+vTpaNy4MVq2bImHHnoIv//+O8qWLYvg4GBvHzb5IcYa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\nERERERFl6v9hXlZtp0V+lwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1392b2e8>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "#df = pandas.merge(AAPL_many_days, goog, on='time', suffixes=('_AAPL', '_GOOG'))\n",
      "plt.figure(figsize(18,8), dpi=1600)\n",
      "plt.subplot(212)\n",
      "df.price_AAPL.plot()\n",
      "legend(loc='best')\n",
      "\n",
      "plt.subplot(211)\n",
      "df.price_GOOG.plot(color='g')\n",
      "plt.title(\"Apple's and Google's price movements\"); legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.legend.Legend at 0xe7b26d8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BExMTLF68GH5+fhJTBkq+j/379yM/P5/b2cDX17fMzo9Xr17B19cXBgYGcHV1Rbdu3crd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OnQUq1fC4cWALya9grbErb0d+6fo3mTpWT+vPanWt3jKY9zNKagYCP2NEFeIORMkFcIWRPkBULO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ty8QoZE1qaioSEhLw7rvvYtKkSRg2bBjef/99FCtWLG2bu3fvMs4o15w+fRrvv/8+hg8fjsWL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\n7KnAXOlKAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe7a24e0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Version Control:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!git add lime_demo.ipynb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!git commit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[master 7040744] updated with date index parsing\n",
        " 1 file changed, 333 insertions(+), 322 deletions(-)\n",
        " rewrite lime_demo.ipynb (91%)\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}