Cloned+from+_Introduction+to+Research_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Introduction to the Research Environment\n",
    "\n",
    "The research environment is powered by IPython notebooks, which allow one to perform a great deal of data analysis and statistical validation. We'll demonstrate a few simple techniques here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Code Cells vs. Text Cells\n",
    "\n",
    "As you can see, each cell can be either code or text. To select between them, choose from the 'Cell Type' dropdown menu on the top left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Executing a Command\n",
    "\n",
    "A code cell will be evaluated when you press play, or when you press the shortcut, -enter. Evaluating a cell evaluates each line of code in sequence, and prints the results of the last line below the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4+4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-2-62bc096bc7b2>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-2-62bc096bc7b2>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    - Add formatted text to your notebooks using Markdown cells\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# This is a 'Markdown' cell\n",
    "- Add formatted text to your notebooks using Markdown cells \n",
    "- You can change any cell into a Markdown cell using the Cell Type dropdown in the toolbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is number 0\n",
      "This is number 1\n",
      "This is number 2\n",
      "This is number 3\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 4):\n",
    "    print (\"This is number %s\" % i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes there is no result to be printed, as is the case with assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that only the result from the last line is printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 2\n",
    "3 + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, you can print whichever lines you want using the `print` statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 2 + 2\n",
    "3 + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Knowing When a Cell is Running\n",
    "\n",
    "While a cell is running, a `[*]` will display on the left. When a cell has yet to be executed, `[ ]` will display. When it has been run, a number will display indicating the order in which it was run during the execution of the notebook `[5]`. Try on this cell and note it happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49999995000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Take some time to run something\n",
    "c = 0\n",
    "for i in range(10000000):\n",
    "    c = c + i\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Importing Libraries\n",
    "\n",
    "The vast majority of the time, you'll want to use functions from pre-built libraries. You can't import every library on Quantopian due to security issues, but you can import most of the common scientific ones. Here I import numpy and pandas, the two most common and useful libraries in quant finance. I recommend copying this import statement to every new notebook.\n",
    "\n",
    "Notice that you can rename libraries to whatever you want after importing. The `as` statement allows this. Here we use `np` and `pd` as aliases for `numpy` and `pandas`. This is a very common aliasing and will be found in most code snippets around the web. The point behind this is to allow you to type fewer characters when you are frequently accessing these libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# This is a plotting library for pretty pictures.\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Tab Autocomplete\n",
    "\n",
    "Pressing tab will give you a list of IPython's best guesses for what you might want to type next. This is incredibly valuable and will save you a lot of time. If there is only one possible option for what you could type next, IPython will fill that in for you. Try pressing tab very frequently, it will seldom fill in anything you don't want, as if there is ambiguity a list will be shown. This is a great way to see what functions are available in a library.\n",
    "\n",
    "Try placing your cursor after the `.` and pressing tab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-8-1a778a4e80a5>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-8-1a778a4e80a5>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    np.random.\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "np.random."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Getting Documentation Help\n",
    "\n",
    "Placing a question mark after a function and executing that line of code will give you the documentation IPython has for that function. It's often best to do this in a new cell, as you avoid re-executing other code and running into bugs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.normal?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Sampling\n",
    "\n",
    "We'll sample some random data using a function from `numpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Sample 100 points with a mean of 0 and an std of 1. This is a standard normal distribution.\n",
    "X = np.random.normal(0, 1, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.29130359, -0.80604162,  0.66466472,  0.86983275,  0.17084047,\n",
       "       -0.12894033,  0.82727025,  0.50975483, -1.47579774,  1.03174115,\n",
       "        0.09993426, -1.22736157, -0.8095946 ,  0.2797116 , -0.01459062,\n",
       "        1.3725473 ,  0.75050037, -0.94093828, -1.62444048, -1.20526675,\n",
       "       -1.79578469,  1.33935964,  1.38418777, -0.53740364,  1.30492107,\n",
       "       -0.29394876,  1.31817188, -0.57374534, -0.35243766,  0.20627513,\n",
       "        0.37538725,  0.85282634, -0.27246947,  1.78458733, -0.74307283,\n",
       "       -0.00551155, -0.66513898,  1.15895799,  0.97643815, -0.96545985,\n",
       "        3.07159535,  0.27300258, -0.41901807, -0.63382816,  2.51944006,\n",
       "       -2.21990602,  0.49530831, -1.1904641 ,  0.50340042,  1.07370599,\n",
       "        0.04763298, -0.63279502, -0.65327408,  0.4608094 , -0.9226185 ,\n",
       "       -0.06273864, -0.01694652,  0.23171246, -0.34998428,  0.29062336,\n",
       "       -0.71374387,  1.92314419, -0.07026942,  2.16542588, -0.28626345,\n",
       "        0.01936706, -1.28803294,  0.6035862 ,  0.27148943,  0.17380116,\n",
       "       -0.98185916,  0.61867882, -0.37195412, -1.83382621, -0.46380868,\n",
       "        0.20731832, -0.71883348, -1.38838627,  0.18627113, -2.02559118,\n",
       "       -0.76517306,  0.13714887,  0.01063467,  0.06234413,  0.34373278,\n",
       "       -1.19476704,  0.98417906,  1.32895968,  0.20545005, -3.0563694 ,\n",
       "       -0.55923958,  0.67286906,  0.82483495, -1.39017832,  0.01978429,\n",
       "        0.6270854 ,  0.38160843,  0.85091572,  0.10525697,  0.92898269])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Plotting\n",
    "\n",
    "We can use the plotting library we imported as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f64a11aad10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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mqWiSJCERkzt2jo0VHmA9Bu7YUKFmL8ZzeEBlx6b+6xER9QMWNkQUOLtjs0kulMRhmPWj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\nbd1oj7zEJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64a0f96d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Squelching Line Output\n",
    "\n",
    "You might have noticed the annoying line of the form `[<matplotlib.lines.Line2D at 0x7f72fdbc1710>]` before the plots. This is because the `.plot` function actually produces output. Sometimes we wish not to display output, we can accomplish this with the semi-colon as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-3df3c123cb08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Adding Axis Labels\n",
    "\n",
    "No self-respecting quant leaves a graph without labeled axes. Here are some commands to help with that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-0979bf8673af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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 'np' is not defined"
     ]
    }
   ],
   "source": [
    "X = np.random.normal(0, 1, 100)\n",
    "X2 = np.random.normal(0, 1, 100)\n",
    "X3 = np.random.normal(0, 1, 100)\n",
    "\n",
    "plt.plot(X);\n",
    "plt.plot(X2);\n",
    "plt.plot(X3);\n",
    "plt.xlabel('Time') # The data we generated is unitless, but don't forget units in general.\n",
    "plt.ylabel('Returns')\n",
    "plt.legend(['X', 'X2', 'X3']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Generating Statistics\n",
    "\n",
    "Let's use `numpy` to take some simple statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0004110780517801804"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0305132860442268"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Getting Real Pricing Data\n",
    "\n",
    "Randomly sampled data can be great for testing ideas, but let's get some real data. We can use `get_pricing` to do that. You can use the `?` syntax as discussed above to get more information on `get_pricing`'s arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = get_pricing('MSFT', start_date='2012-1-1', end_date='2018-1-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is now a dataframe. You can see the datetime index and the colums with different pricing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open_price</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close_price</th>\n",
       "      <th>volume</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>22.608</td>\n",
       "      <td>22.957</td>\n",
       "      <td>22.472</td>\n",
       "      <td>22.846</td>\n",
       "      <td>60891291.0</td>\n",
       "      <td>22.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>22.837</td>\n",
       "      <td>23.391</td>\n",
       "      <td>22.804</td>\n",
       "      <td>23.323</td>\n",
       "      <td>76534029.0</td>\n",
       "      <td>23.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>23.315</td>\n",
       "      <td>23.611</td>\n",
       "      <td>23.238</td>\n",
       "      <td>23.562</td>\n",
       "      <td>53479335.0</td>\n",
       "      <td>23.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>23.443</td>\n",
       "      <td>24.005</td>\n",
       "      <td>23.438</td>\n",
       "      <td>23.945</td>\n",
       "      <td>91671771.0</td>\n",
       "      <td>23.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>23.885</td>\n",
       "      <td>23.928</td>\n",
       "      <td>23.604</td>\n",
       "      <td>23.621</td>\n",
       "      <td>56352965.0</td>\n",
       "      <td>23.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10 00:00:00+00:00</th>\n",
       "      <td>23.783</td>\n",
       "      <td>23.970</td>\n",
       "      <td>23.630</td>\n",
       "      <td>23.707</td>\n",
       "      <td>54223945.0</td>\n",
       "      <td>23.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-11 00:00:00+00:00</th>\n",
       "      <td>23.357</td>\n",
       "      <td>23.826</td>\n",
       "      <td>23.306</td>\n",
       "      <td>23.604</td>\n",
       "      <td>62855941.0</td>\n",
       "      <td>23.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-12 00:00:00+00:00</th>\n",
       "      <td>23.732</td>\n",
       "      <td>23.860</td>\n",
       "      <td>23.540</td>\n",
       "      <td>23.843</td>\n",
       "      <td>46186121.0</td>\n",
       "      <td>23.843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-13 00:00:00+00:00</th>\n",
       "      <td>23.783</td>\n",
       "      <td>24.056</td>\n",
       "      <td>23.664</td>\n",
       "      <td>24.056</td>\n",
       "      <td>55326851.0</td>\n",
       "      <td>24.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-17 00:00:00+00:00</th>\n",
       "      <td>24.183</td>\n",
       "      <td>24.396</td>\n",
       "      <td>23.988</td>\n",
       "      <td>24.064</td>\n",
       "      <td>66537766.0</td>\n",
       "      <td>24.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-18 00:00:00+00:00</th>\n",
       "      <td>24.107</td>\n",
       "      <td>24.183</td>\n",
       "      <td>23.817</td>\n",
       "      <td>24.039</td>\n",
       "      <td>60912302.0</td>\n",
       "      <td>24.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-19 00:00:00+00:00</th>\n",
       "      <td>23.979</td>\n",
       "      <td>24.213</td>\n",
       "      <td>23.868</td>\n",
       "      <td>23.953</td>\n",
       "      <td>66169544.0</td>\n",
       "      <td>23.953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-20 00:00:00+00:00</th>\n",
       "      <td>24.541</td>\n",
       "      <td>25.324</td>\n",
       "      <td>24.481</td>\n",
       "      <td>25.299</td>\n",
       "      <td>157989713.0</td>\n",
       "      <td>25.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-23 00:00:00+00:00</th>\n",
       "      <td>25.163</td>\n",
       "      <td>25.503</td>\n",
       "      <td>24.992</td>\n",
       "      <td>25.316</td>\n",
       "      <td>70185739.0</td>\n",
       "      <td>25.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-24 00:00:00+00:00</th>\n",
       "      <td>25.094</td>\n",
       "      <td>25.180</td>\n",
       "      <td>24.848</td>\n",
       "      <td>24.984</td>\n",
       "      <td>48606276.0</td>\n",
       "      <td>24.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-25 00:00:00+00:00</th>\n",
       "      <td>24.754</td>\n",
       "      <td>25.248</td>\n",
       "      <td>24.754</td>\n",
       "      <td>25.180</td>\n",
       "      <td>55871304.0</td>\n",
       "      <td>25.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-26 00:00:00+00:00</th>\n",
       "      <td>25.214</td>\n",
       "      <td>25.290</td>\n",
       "      <td>25.035</td>\n",
       "      <td>25.129</td>\n",
       "      <td>46229333.0</td>\n",
       "      <td>25.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-27 00:00:00+00:00</th>\n",
       "      <td>25.077</td>\n",
       "      <td>25.146</td>\n",
       "      <td>24.839</td>\n",
       "      <td>24.882</td>\n",
       "      <td>41452599.0</td>\n",
       "      <td>24.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-30 00:00:00+00:00</th>\n",
       "      <td>24.669</td>\n",
       "      <td>25.222</td>\n",
       "      <td>24.550</td>\n",
       "      <td>25.214</td>\n",
       "      <td>46520829.0</td>\n",
       "      <td>25.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31 00:00:00+00:00</th>\n",
       "      <td>25.256</td>\n",
       "      <td>25.290</td>\n",
       "      <td>24.890</td>\n",
       "      <td>25.154</td>\n",
       "      <td>40812893.0</td>\n",
       "      <td>25.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-01 00:00:00+00:00</th>\n",
       "      <td>25.367</td>\n",
       "      <td>25.588</td>\n",
       "      <td>25.341</td>\n",
       "      <td>25.452</td>\n",
       "      <td>64067228.0</td>\n",
       "      <td>25.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-02 00:00:00+00:00</th>\n",
       "      <td>25.461</td>\n",
       "      <td>25.691</td>\n",
       "      <td>25.299</td>\n",
       "      <td>25.499</td>\n",
       "      <td>49959760.0</td>\n",
       "      <td>25.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-03 00:00:00+00:00</th>\n",
       "      <td>25.665</td>\n",
       "      <td>25.886</td>\n",
       "      <td>25.622</td>\n",
       "      <td>25.733</td>\n",
       "      <td>38728694.0</td>\n",
       "      <td>25.733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-06 00:00:00+00:00</th>\n",
       "      <td>25.580</td>\n",
       "      <td>25.733</td>\n",
       "      <td>25.520</td>\n",
       "      <td>25.716</td>\n",
       "      <td>26067704.0</td>\n",
       "      <td>25.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-07 00:00:00+00:00</th>\n",
       "      <td>25.674</td>\n",
       "      <td>25.959</td>\n",
       "      <td>25.588</td>\n",
       "      <td>25.852</td>\n",
       "      <td>36751791.0</td>\n",
       "      <td>25.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-08 00:00:00+00:00</th>\n",
       "      <td>25.767</td>\n",
       "      <td>26.116</td>\n",
       "      <td>25.733</td>\n",
       "      <td>26.108</td>\n",
       "      <td>47382414.0</td>\n",
       "      <td>26.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-09 00:00:00+00:00</th>\n",
       "      <td>26.125</td>\n",
       "      <td>26.227</td>\n",
       "      <td>25.955</td>\n",
       "      <td>26.201</td>\n",
       "      <td>45580004.0</td>\n",
       "      <td>26.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-10 00:00:00+00:00</th>\n",
       "      <td>26.091</td>\n",
       "      <td>26.227</td>\n",
       "      <td>25.852</td>\n",
       "      <td>25.950</td>\n",
       "      <td>38526519.0</td>\n",
       "      <td>25.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-13 00:00:00+00:00</th>\n",
       "      <td>26.082</td>\n",
       "      <td>26.201</td>\n",
       "      <td>25.912</td>\n",
       "      <td>26.023</td>\n",
       "      <td>31447419.0</td>\n",
       "      <td>26.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-14 00:00:00+00:00</th>\n",
       "      <td>25.997</td>\n",
       "      <td>26.108</td>\n",
       "      <td>25.586</td>\n",
       "      <td>25.928</td>\n",
       "      <td>50770567.0</td>\n",
       "      <td>25.928</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-16 00:00:00+00:00</th>\n",
       "      <td>83.100</td>\n",
       "      <td>83.420</td>\n",
       "      <td>82.940</td>\n",
       "      <td>83.190</td>\n",
       "      <td>16198016.0</td>\n",
       "      <td>83.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 00:00:00+00:00</th>\n",
       "      <td>83.120</td>\n",
       "      <td>83.120</td>\n",
       "      <td>82.240</td>\n",
       "      <td>82.370</td>\n",
       "      <td>17427406.0</td>\n",
       "      <td>82.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-20 00:00:00+00:00</th>\n",
       "      <td>82.400</td>\n",
       "      <td>82.590</td>\n",
       "      <td>82.250</td>\n",
       "      <td>82.540</td>\n",
       "      <td>11458377.0</td>\n",
       "      <td>82.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21 00:00:00+00:00</th>\n",
       "      <td>82.740</td>\n",
       "      <td>83.840</td>\n",
       "      <td>82.740</td>\n",
       "      <td>83.730</td>\n",
       "      <td>14281714.0</td>\n",
       "      <td>83.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 00:00:00+00:00</th>\n",
       "      <td>83.830</td>\n",
       "      <td>83.900</td>\n",
       "      <td>83.040</td>\n",
       "      <td>83.100</td>\n",
       "      <td>12488234.0</td>\n",
       "      <td>83.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 00:00:00+00:00</th>\n",
       "      <td>83.010</td>\n",
       "      <td>83.430</td>\n",
       "      <td>82.780</td>\n",
       "      <td>83.270</td>\n",
       "      <td>5113940.0</td>\n",
       "      <td>83.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27 00:00:00+00:00</th>\n",
       "      <td>83.310</td>\n",
       "      <td>83.980</td>\n",
       "      <td>83.300</td>\n",
       "      <td>83.860</td>\n",
       "      <td>13338797.0</td>\n",
       "      <td>83.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28 00:00:00+00:00</th>\n",
       "      <td>84.070</td>\n",
       "      <td>85.060</td>\n",
       "      <td>84.020</td>\n",
       "      <td>84.880</td>\n",
       "      <td>15938330.0</td>\n",
       "      <td>84.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29 00:00:00+00:00</th>\n",
       "      <td>84.710</td>\n",
       "      <td>84.917</td>\n",
       "      <td>83.175</td>\n",
       "      <td>83.330</td>\n",
       "      <td>21804872.0</td>\n",
       "      <td>83.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 00:00:00+00:00</th>\n",
       "      <td>83.510</td>\n",
       "      <td>84.520</td>\n",
       "      <td>83.340</td>\n",
       "      <td>84.100</td>\n",
       "      <td>19968980.0</td>\n",
       "      <td>84.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 00:00:00+00:00</th>\n",
       "      <td>83.600</td>\n",
       "      <td>84.810</td>\n",
       "      <td>83.220</td>\n",
       "      <td>84.260</td>\n",
       "      <td>22993261.0</td>\n",
       "      <td>84.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04 00:00:00+00:00</th>\n",
       "      <td>84.420</td>\n",
       "      <td>84.420</td>\n",
       "      <td>80.700</td>\n",
       "      <td>81.080</td>\n",
       "      <td>31896125.0</td>\n",
       "      <td>81.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05 00:00:00+00:00</th>\n",
       "      <td>81.340</td>\n",
       "      <td>82.680</td>\n",
       "      <td>80.980</td>\n",
       "      <td>81.590</td>\n",
       "      <td>19991077.0</td>\n",
       "      <td>81.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06 00:00:00+00:00</th>\n",
       "      <td>81.550</td>\n",
       "      <td>83.140</td>\n",
       "      <td>81.430</td>\n",
       "      <td>82.790</td>\n",
       "      <td>16109181.0</td>\n",
       "      <td>82.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07 00:00:00+00:00</th>\n",
       "      <td>82.540</td>\n",
       "      <td>82.800</td>\n",
       "      <td>82.000</td>\n",
       "      <td>82.490</td>\n",
       "      <td>15963260.0</td>\n",
       "      <td>82.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08 00:00:00+00:00</th>\n",
       "      <td>83.630</td>\n",
       "      <td>84.580</td>\n",
       "      <td>83.330</td>\n",
       "      <td>84.160</td>\n",
       "      <td>19414814.0</td>\n",
       "      <td>84.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11 00:00:00+00:00</th>\n",
       "      <td>84.290</td>\n",
       "      <td>85.370</td>\n",
       "      <td>84.120</td>\n",
       "      <td>85.220</td>\n",
       "      <td>14466119.0</td>\n",
       "      <td>85.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12 00:00:00+00:00</th>\n",
       "      <td>85.310</td>\n",
       "      <td>86.050</td>\n",
       "      <td>85.080</td>\n",
       "      <td>85.620</td>\n",
       "      <td>17803908.0</td>\n",
       "      <td>85.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13 00:00:00+00:00</th>\n",
       "      <td>85.740</td>\n",
       "      <td>86.000</td>\n",
       "      <td>85.170</td>\n",
       "      <td>85.350</td>\n",
       "      <td>16782602.0</td>\n",
       "      <td>85.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14 00:00:00+00:00</th>\n",
       "      <td>85.430</td>\n",
       "      <td>85.870</td>\n",
       "      <td>84.530</td>\n",
       "      <td>84.720</td>\n",
       "      <td>13605834.0</td>\n",
       "      <td>84.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15 00:00:00+00:00</th>\n",
       "      <td>85.260</td>\n",
       "      <td>87.090</td>\n",
       "      <td>84.880</td>\n",
       "      <td>86.820</td>\n",
       "      <td>33797786.0</td>\n",
       "      <td>86.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18 00:00:00+00:00</th>\n",
       "      <td>87.120</td>\n",
       "      <td>87.500</td>\n",
       "      <td>86.230</td>\n",
       "      <td>86.430</td>\n",
       "      <td>16400158.0</td>\n",
       "      <td>86.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19 00:00:00+00:00</th>\n",
       "      <td>86.350</td>\n",
       "      <td>86.350</td>\n",
       "      <td>85.270</td>\n",
       "      <td>85.830</td>\n",
       "      <td>16977241.0</td>\n",
       "      <td>85.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20 00:00:00+00:00</th>\n",
       "      <td>86.200</td>\n",
       "      <td>86.300</td>\n",
       "      <td>84.710</td>\n",
       "      <td>85.590</td>\n",
       "      <td>15114756.0</td>\n",
       "      <td>85.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21 00:00:00+00:00</th>\n",
       "      <td>86.050</td>\n",
       "      <td>86.100</td>\n",
       "      <td>85.400</td>\n",
       "      <td>85.570</td>\n",
       "      <td>11191800.0</td>\n",
       "      <td>85.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22 00:00:00+00:00</th>\n",
       "      <td>85.400</td>\n",
       "      <td>85.630</td>\n",
       "      <td>84.920</td>\n",
       "      <td>85.580</td>\n",
       "      <td>10309676.0</td>\n",
       "      <td>85.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26 00:00:00+00:00</th>\n",
       "      <td>85.310</td>\n",
       "      <td>85.535</td>\n",
       "      <td>85.030</td>\n",
       "      <td>85.400</td>\n",
       "      <td>7347118.0</td>\n",
       "      <td>85.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27 00:00:00+00:00</th>\n",
       "      <td>85.650</td>\n",
       "      <td>85.980</td>\n",
       "      <td>85.215</td>\n",
       "      <td>85.700</td>\n",
       "      <td>9685222.0</td>\n",
       "      <td>85.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28 00:00:00+00:00</th>\n",
       "      <td>85.900</td>\n",
       "      <td>85.930</td>\n",
       "      <td>85.550</td>\n",
       "      <td>85.700</td>\n",
       "      <td>7708547.0</td>\n",
       "      <td>85.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29 00:00:00+00:00</th>\n",
       "      <td>85.630</td>\n",
       "      <td>86.050</td>\n",
       "      <td>85.500</td>\n",
       "      <td>85.590</td>\n",
       "      <td>11170183.0</td>\n",
       "      <td>85.590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1509 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           open_price    high     low  close_price  \\\n",
       "2012-01-03 00:00:00+00:00      22.608  22.957  22.472       22.846   \n",
       "2012-01-04 00:00:00+00:00      22.837  23.391  22.804       23.323   \n",
       "2012-01-05 00:00:00+00:00      23.315  23.611  23.238       23.562   \n",
       "2012-01-06 00:00:00+00:00      23.443  24.005  23.438       23.945   \n",
       "2012-01-09 00:00:00+00:00      23.885  23.928  23.604       23.621   \n",
       "2012-01-10 00:00:00+00:00      23.783  23.970  23.630       23.707   \n",
       "2012-01-11 00:00:00+00:00      23.357  23.826  23.306       23.604   \n",
       "2012-01-12 00:00:00+00:00      23.732  23.860  23.540       23.843   \n",
       "2012-01-13 00:00:00+00:00      23.783  24.056  23.664       24.056   \n",
       "2012-01-17 00:00:00+00:00      24.183  24.396  23.988       24.064   \n",
       "2012-01-18 00:00:00+00:00      24.107  24.183  23.817       24.039   \n",
       "2012-01-19 00:00:00+00:00      23.979  24.213  23.868       23.953   \n",
       "2012-01-20 00:00:00+00:00      24.541  25.324  24.481       25.299   \n",
       "2012-01-23 00:00:00+00:00      25.163  25.503  24.992       25.316   \n",
       "2012-01-24 00:00:00+00:00      25.094  25.180  24.848       24.984   \n",
       "2012-01-25 00:00:00+00:00      24.754  25.248  24.754       25.180   \n",
       "2012-01-26 00:00:00+00:00      25.214  25.290  25.035       25.129   \n",
       "2012-01-27 00:00:00+00:00      25.077  25.146  24.839       24.882   \n",
       "2012-01-30 00:00:00+00:00      24.669  25.222  24.550       25.214   \n",
       "2012-01-31 00:00:00+00:00      25.256  25.290  24.890       25.154   \n",
       "2012-02-01 00:00:00+00:00      25.367  25.588  25.341       25.452   \n",
       "2012-02-02 00:00:00+00:00      25.461  25.691  25.299       25.499   \n",
       "2012-02-03 00:00:00+00:00      25.665  25.886  25.622       25.733   \n",
       "2012-02-06 00:00:00+00:00      25.580  25.733  25.520       25.716   \n",
       "2012-02-07 00:00:00+00:00      25.674  25.959  25.588       25.852   \n",
       "2012-02-08 00:00:00+00:00      25.767  26.116  25.733       26.108   \n",
       "2012-02-09 00:00:00+00:00      26.125  26.227  25.955       26.201   \n",
       "2012-02-10 00:00:00+00:00      26.091  26.227  25.852       25.950   \n",
       "2012-02-13 00:00:00+00:00      26.082  26.201  25.912       26.023   \n",
       "2012-02-14 00:00:00+00:00      25.997  26.108  25.586       25.928   \n",
       "...                               ...     ...     ...          ...   \n",
       "2017-11-16 00:00:00+00:00      83.100  83.420  82.940       83.190   \n",
       "2017-11-17 00:00:00+00:00      83.120  83.120  82.240       82.370   \n",
       "2017-11-20 00:00:00+00:00      82.400  82.590  82.250       82.540   \n",
       "2017-11-21 00:00:00+00:00      82.740  83.840  82.740       83.730   \n",
       "2017-11-22 00:00:00+00:00      83.830  83.900  83.040       83.100   \n",
       "2017-11-24 00:00:00+00:00      83.010  83.430  82.780       83.270   \n",
       "2017-11-27 00:00:00+00:00      83.310  83.980  83.300       83.860   \n",
       "2017-11-28 00:00:00+00:00      84.070  85.060  84.020       84.880   \n",
       "2017-11-29 00:00:00+00:00      84.710  84.917  83.175       83.330   \n",
       "2017-11-30 00:00:00+00:00      83.510  84.520  83.340       84.100   \n",
       "2017-12-01 00:00:00+00:00      83.600  84.810  83.220       84.260   \n",
       "2017-12-04 00:00:00+00:00      84.420  84.420  80.700       81.080   \n",
       "2017-12-05 00:00:00+00:00      81.340  82.680  80.980       81.590   \n",
       "2017-12-06 00:00:00+00:00      81.550  83.140  81.430       82.790   \n",
       "2017-12-07 00:00:00+00:00      82.540  82.800  82.000       82.490   \n",
       "2017-12-08 00:00:00+00:00      83.630  84.580  83.330       84.160   \n",
       "2017-12-11 00:00:00+00:00      84.290  85.370  84.120       85.220   \n",
       "2017-12-12 00:00:00+00:00      85.310  86.050  85.080       85.620   \n",
       "2017-12-13 00:00:00+00:00      85.740  86.000  85.170       85.350   \n",
       "2017-12-14 00:00:00+00:00      85.430  85.870  84.530       84.720   \n",
       "2017-12-15 00:00:00+00:00      85.260  87.090  84.880       86.820   \n",
       "2017-12-18 00:00:00+00:00      87.120  87.500  86.230       86.430   \n",
       "2017-12-19 00:00:00+00:00      86.350  86.350  85.270       85.830   \n",
       "2017-12-20 00:00:00+00:00      86.200  86.300  84.710       85.590   \n",
       "2017-12-21 00:00:00+00:00      86.050  86.100  85.400       85.570   \n",
       "2017-12-22 00:00:00+00:00      85.400  85.630  84.920       85.580   \n",
       "2017-12-26 00:00:00+00:00      85.310  85.535  85.030       85.400   \n",
       "2017-12-27 00:00:00+00:00      85.650  85.980  85.215       85.700   \n",
       "2017-12-28 00:00:00+00:00      85.900  85.930  85.550       85.700   \n",
       "2017-12-29 00:00:00+00:00      85.630  86.050  85.500       85.590   \n",
       "\n",
       "                                volume   price  \n",
       "2012-01-03 00:00:00+00:00   60891291.0  22.846  \n",
       "2012-01-04 00:00:00+00:00   76534029.0  23.323  \n",
       "2012-01-05 00:00:00+00:00   53479335.0  23.562  \n",
       "2012-01-06 00:00:00+00:00   91671771.0  23.945  \n",
       "2012-01-09 00:00:00+00:00   56352965.0  23.621  \n",
       "2012-01-10 00:00:00+00:00   54223945.0  23.707  \n",
       "2012-01-11 00:00:00+00:00   62855941.0  23.604  \n",
       "2012-01-12 00:00:00+00:00   46186121.0  23.843  \n",
       "2012-01-13 00:00:00+00:00   55326851.0  24.056  \n",
       "2012-01-17 00:00:00+00:00   66537766.0  24.064  \n",
       "2012-01-18 00:00:00+00:00   60912302.0  24.039  \n",
       "2012-01-19 00:00:00+00:00   66169544.0  23.953  \n",
       "2012-01-20 00:00:00+00:00  157989713.0  25.299  \n",
       "2012-01-23 00:00:00+00:00   70185739.0  25.316  \n",
       "2012-01-24 00:00:00+00:00   48606276.0  24.984  \n",
       "2012-01-25 00:00:00+00:00   55871304.0  25.180  \n",
       "2012-01-26 00:00:00+00:00   46229333.0  25.129  \n",
       "2012-01-27 00:00:00+00:00   41452599.0  24.882  \n",
       "2012-01-30 00:00:00+00:00   46520829.0  25.214  \n",
       "2012-01-31 00:00:00+00:00   40812893.0  25.154  \n",
       "2012-02-01 00:00:00+00:00   64067228.0  25.452  \n",
       "2012-02-02 00:00:00+00:00   49959760.0  25.499  \n",
       "2012-02-03 00:00:00+00:00   38728694.0  25.733  \n",
       "2012-02-06 00:00:00+00:00   26067704.0  25.716  \n",
       "2012-02-07 00:00:00+00:00   36751791.0  25.852  \n",
       "2012-02-08 00:00:00+00:00   47382414.0  26.108  \n",
       "2012-02-09 00:00:00+00:00   45580004.0  26.201  \n",
       "2012-02-10 00:00:00+00:00   38526519.0  25.950  \n",
       "2012-02-13 00:00:00+00:00   31447419.0  26.023  \n",
       "2012-02-14 00:00:00+00:00   50770567.0  25.928  \n",
       "...                                ...     ...  \n",
       "2017-11-16 00:00:00+00:00   16198016.0  83.190  \n",
       "2017-11-17 00:00:00+00:00   17427406.0  82.370  \n",
       "2017-11-20 00:00:00+00:00   11458377.0  82.540  \n",
       "2017-11-21 00:00:00+00:00   14281714.0  83.730  \n",
       "2017-11-22 00:00:00+00:00   12488234.0  83.100  \n",
       "2017-11-24 00:00:00+00:00    5113940.0  83.270  \n",
       "2017-11-27 00:00:00+00:00   13338797.0  83.860  \n",
       "2017-11-28 00:00:00+00:00   15938330.0  84.880  \n",
       "2017-11-29 00:00:00+00:00   21804872.0  83.330  \n",
       "2017-11-30 00:00:00+00:00   19968980.0  84.100  \n",
       "2017-12-01 00:00:00+00:00   22993261.0  84.260  \n",
       "2017-12-04 00:00:00+00:00   31896125.0  81.080  \n",
       "2017-12-05 00:00:00+00:00   19991077.0  81.590  \n",
       "2017-12-06 00:00:00+00:00   16109181.0  82.790  \n",
       "2017-12-07 00:00:00+00:00   15963260.0  82.490  \n",
       "2017-12-08 00:00:00+00:00   19414814.0  84.160  \n",
       "2017-12-11 00:00:00+00:00   14466119.0  85.220  \n",
       "2017-12-12 00:00:00+00:00   17803908.0  85.620  \n",
       "2017-12-13 00:00:00+00:00   16782602.0  85.350  \n",
       "2017-12-14 00:00:00+00:00   13605834.0  84.720  \n",
       "2017-12-15 00:00:00+00:00   33797786.0  86.820  \n",
       "2017-12-18 00:00:00+00:00   16400158.0  86.430  \n",
       "2017-12-19 00:00:00+00:00   16977241.0  85.830  \n",
       "2017-12-20 00:00:00+00:00   15114756.0  85.590  \n",
       "2017-12-21 00:00:00+00:00   11191800.0  85.570  \n",
       "2017-12-22 00:00:00+00:00   10309676.0  85.580  \n",
       "2017-12-26 00:00:00+00:00    7347118.0  85.400  \n",
       "2017-12-27 00:00:00+00:00    9685222.0  85.700  \n",
       "2017-12-28 00:00:00+00:00    7708547.0  85.700  \n",
       "2017-12-29 00:00:00+00:00   11170183.0  85.590  \n",
       "\n",
       "[1509 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a pandas dataframe, so we can index in to just get price like this. For more info on pandas, please [click here](http://pandas.pydata.org/pandas-docs/stable/10min.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = data['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because there is now also date information in our data, we provide two series to `.plot`. `X.index` gives us the datetime index, and `X.values` gives us the pricing values. These are used as the X and Y coordinates to make a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lcV+ea8/L19T8PD3zs/NUUlGvu59eaVwzeVyOzjp1sGZPGaKIpM+3FOvj9Yd0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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6485ede710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X.index, X.values)\n",
    "plt.ylabel('Price')\n",
    "plt.legend(['MSFT']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get statistics again on real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.68511265738895"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.983702777071748"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Getting Returns from Prices\n",
    "\n",
    "We can use the `pct_change` function to get returns. Notice how we drop the first element after doing this, as it will be `NaN` (nothing -> something results in a NaN percent change)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "R = X.pct_change()[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the returns distribution as a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.hist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AFeAzTAAAAAUEEwAAQAHBBAAAUEAwAQAAFBBMAAAABQQTAABAAcEEAABQQDAB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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f648533cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(R, bins=20)\n",
    "plt.xlabel('Return')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend(['MSFT Returns']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get statistics again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0009732185733803189"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01393781942117986"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(R)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's go backwards and generate data out of a normal distribution using the statistics we estimated from Microsoft's returns. We'll see that we have good reason to suspect Microsoft's returns may not be normal, as the resulting normal distribution looks far different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpF/OECVJakvZbJa3tzeen58pl8ucnZ1Rr9cZHR1laWmJ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\nUgsMUZIkSZLUgo84MIXvPLhhhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64a114f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.normal(np.mean(R), np.std(R), 10000), bins=20)\n",
    "plt.xlabel('Return')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend(['Normally Distributed Returns']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Generating a Moving Average\n",
    "\n",
    "`pandas` has some nice tools to allow us to generate rolling statistics. Here's an example. Notice how there's no moving average for the first 60 days, as we don't have 60 days of data on which to generate the statistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=60,center=False).mean()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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0A5uP7+SKLsNrPK4RHhERERER8TmXQa0jPLU5fYTn9O3TbcjcVuc5FHhERERERMTn6hrh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\ncdfa4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6485cbb650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the average of the last 60 days at each timepoint.\n",
    "MAVG = pd.rolling_mean(X, window=60)\n",
    "plt.plot(X.index, X.values)\n",
    "plt.plot(MAVG.index, MAVG.values)\n",
    "plt.ylabel('Price')\n",
    "plt.legend(['MSFT', '60-day MAVG']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}