/SimpleMLQuantopian Secret

## SimpleMLQuantopian
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simple Machine Learning Example - Data Explanation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook shows the data used to train the model in the algorithm by Grant Klein (May 31 2014).\n",
      "\n",
      "We use the daily trading scenario here - it feels easier to understand. We show what data is used to train a machine learning model. Please point out any issues.\n",
      "\n",
      "The issue "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import seaborn as sns\n",
      "\n",
      "from scipy import stats\n",
      "from zipline.data import load_from_yahoo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's grab more than 400 days' worth of data from yahoo for the following stocks.\n",
      "\n",
      "```\n",
      "context.stocks = [ sid(19662),  # XLY Consumer Discrectionary SPDR Fund\n",
      "                   sid(19656),  # XLF Financial SPDR Fund\n",
      "                   sid(19658),  # XLK Technology SPDR Fund\n",
      "                   sid(19655),  # XLE Energy SPDR Fund\n",
      "                   sid(19661),  # XLV Health Care SPRD Fund\n",
      "                   sid(19657),  # XLI Industrial SPDR Fund\n",
      "                   sid(19659),  # XLP Consumer Staples SPDR Fund\n",
      "                   sid(19654),  # XLB Materials SPDR Fund\n",
      "                   sid(19660),  # XLU Utilities SPRD Fund\n",
      "                   sid(8554) ]  # SPY SPDR S&P 500 ETF Trust\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stocks = ['XLY', 'XLF', 'XLK', 'XLE', 'XLV', 'XLI', 'XLP', 'XLB', 'XLU', 'SPY']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "end_date = pd.datetools.datetime(2014, 5, 1)\n",
      "\n",
      "# Ensure there is enough data (number of days: 800)\n",
      "start_date = end_date - pd.DateOffset(n=800)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = load_from_yahoo(stocks=stocks, start=start_date, end=end_date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "XLY\n",
        "XLF"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLK"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLE"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLV"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLI"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLP"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XLU"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "SPY"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10bc681d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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YUkhRuft10rhB4QxJCCIpLpAggx5wv+duGsA/bwz0/fr488ANoynv4qFtIcTZ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\n8N4PxWX7WE5QK9+rl467xmOhl3rhBPXyk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       "text": [
        "<matplotlib.figure.Figure at 0x10515c2d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check to ensure there is enough price data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.index[0]\n",
      "print data.index[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2012-02-21 00:00:00+00:00\n",
        "2014-05-01 00:00:00+00:00\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take 400 days of the data (as in the quantopian example)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prices = data.as_matrix()[:400]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prices.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(400, 10)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate the z score of each value in the sample, relative to the sample mean and standard deviation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes_all = stats.zscore(prices, axis=0, ddof=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes_all.shape # Quick look at the shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(400, 10)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A quick run through the next line.\n",
      "\n",
      "`changes_all[:,0:-1].shape` \u2013 All the stocks except the `SPY`.  \n",
      "`changes_all[:,-1]` \u2013 The SPY z-scores.  \n",
      "`(len(stocks)-1,1) = (9, 1)` in this case.  \n",
      "\n",
      "Therefore, this line simply subtracts all the `SPY` z-scores from the z-scores of everything else."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = changes_all[:,0:-1] - np.tile(changes_all[:,-1], (len(stocks)-1,1)).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a boolean matrix to indicate whether the z-score is positive or not. Indicate this as 1s or 0s."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = changes > 0\n",
      "changes = changes.astype(np.int)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check once more to make sure the shape is the same. There are 400 changes for each stock in our portfolio."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(400, 9)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "The training data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we look at what exactly we are using to train the model.\n",
      "\n",
      "For each stock in the portfolio, now independent of the others, split the changes into 20 training samples and take the last sample as the labels. The original code:\n",
      "\n",
      "```\n",
      "for k in range(len(context.stocks)-1):\n",
      "    X = np.split(changes[:,k],20)\n",
      "    Y = X[-1]\n",
      "    \n",
      "    context.classifier.fit(X, Y)\n",
      "    \n",
      "    context.prediction[k] = context.classifier.predict(Y)\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are simple splitting the changes into 20 training samples."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.split(changes[:,0], 20)\n",
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "[array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1]),\n",
        " array([0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
        " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
        " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]),\n",
        " array([0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]),\n",
        " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
        " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]),\n",
        " array([0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0]),\n",
        " array([1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
        " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]),\n",
        " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0]),\n",
        " array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
        " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If unsure, we can inspect and see that this is a straightforward split from the changes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
        "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
        "       1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n",
        "       0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Y is the last 20 changes - the final split."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Y = X[-1]\n",
      "Y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A model is then trained on the split changes, with the last 20 changes acting as the labels/classes. However, this doesn't seem right and we can see where the data is coming from by using the dates as the data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Quickly reduce the dates to a list of easily readable strings."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "full_dates = data.index[:400]\n",
      "dates = []\n",
      "\n",
      "for date in full_dates:\n",
      "    new_date = str(date)[:10]\n",
      "    dates.append(new_date)\n",
      "\n",
      "dates = np.array(dates)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check what they look like."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array(['2012-02-21', '2012-02-22', '2012-02-23', '2012-02-24',\n",
        "       '2012-02-27', '2012-02-28', '2012-02-29', '2012-03-01',\n",
        "       '2012-03-02', '2012-03-05'], \n",
        "      dtype='|S10')"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Split the dates in the same way as the training data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates_split = np.array(np.split(dates, 20))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates_split.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(20, 20)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that each split is a set of 20 consecutive days."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates_split[:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "array([['2012-02-21', '2012-02-22', '2012-02-23', '2012-02-24',\n",
        "        '2012-02-27', '2012-02-28', '2012-02-29', '2012-03-01',\n",
        "        '2012-03-02', '2012-03-05', '2012-03-06', '2012-03-07',\n",
        "        '2012-03-08', '2012-03-09', '2012-03-12', '2012-03-13',\n",
        "        '2012-03-14', '2012-03-15', '2012-03-16', '2012-03-19'],\n",
        "       ['2012-03-20', '2012-03-21', '2012-03-22', '2012-03-23',\n",
        "        '2012-03-26', '2012-03-27', '2012-03-28', '2012-03-29',\n",
        "        '2012-03-30', '2012-04-02', '2012-04-03', '2012-04-04',\n",
        "        '2012-04-05', '2012-04-09', '2012-04-10', '2012-04-11',\n",
        "        '2012-04-12', '2012-04-13', '2012-04-16', '2012-04-17']], \n",
        "      dtype='|S10')"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The labels are the last split."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates_labels = dates_split[-1]\n",
      "dates_labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array(['2013-08-26', '2013-08-27', '2013-08-28', '2013-08-29',\n",
        "       '2013-08-30', '2013-09-03', '2013-09-04', '2013-09-05',\n",
        "       '2013-09-06', '2013-09-09', '2013-09-10', '2013-09-11',\n",
        "       '2013-09-12', '2013-09-13', '2013-09-16', '2013-09-17',\n",
        "       '2013-09-18', '2013-09-19', '2013-09-20', '2013-09-23'], \n",
        "      dtype='|S10')"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "View these labels alongside the dates. We can see that changes in Feb-March 2012 are being used to predict the change on 26 August 2013. Then March-April 2012 are being used to predict 27th August 2013. April-May 2012 used to predict 28th August 2013.  \n",
      "\n",
      "This gap slowly closes with changes for July-August 2013 to predict 20th September 2013. Finally, August-September 2013 to predict 23rd September 2013. A crucial note: the last training set actually includes the target value.  \n",
      "\n",
      "The labels are then used to predict the next day.  \n",
      "\n",
      "As far as we can tell, this is the structure of the data used for training a model and predicting the next value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x_dates, label in zip(dates_split, dates_labels):\n",
      "    print 'Target:', label\n",
      "    print 'Training:', x_dates\n",
      "    print ''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Target: 2013-08-26\n",
        "Training: ['2012-02-21' '2012-02-22' '2012-02-23' '2012-02-24' '2012-02-27'\n",
        " '2012-02-28' '2012-02-29' '2012-03-01' '2012-03-02' '2012-03-05'\n",
        " '2012-03-06' '2012-03-07' '2012-03-08' '2012-03-09' '2012-03-12'\n",
        " '2012-03-13' '2012-03-14' '2012-03-15' '2012-03-16' '2012-03-19']\n",
        "\n",
        "Target: 2013-08-27\n",
        "Training: ['2012-03-20' '2012-03-21' '2012-03-22' '2012-03-23' '2012-03-26'\n",
        " '2012-03-27' '2012-03-28' '2012-03-29' '2012-03-30' '2012-04-02'\n",
        " '2012-04-03' '2012-04-04' '2012-04-05' '2012-04-09' '2012-04-10'\n",
        " '2012-04-11' '2012-04-12' '2012-04-13' '2012-04-16' '2012-04-17']\n",
        "\n",
        "Target: 2013-08-28\n",
        "Training: ['2012-04-18' '2012-04-19' '2012-04-20' '2012-04-23' '2012-04-24'\n",
        " '2012-04-25' '2012-04-26' '2012-04-27' '2012-04-30' '2012-05-01'\n",
        " '2012-05-02' '2012-05-03' '2012-05-04' '2012-05-07' '2012-05-08'\n",
        " '2012-05-09' '2012-05-10' '2012-05-11' '2012-05-14' '2012-05-15']\n",
        "\n",
        "Target: 2013-08-29\n",
        "Training: ['2012-05-16' '2012-05-17' '2012-05-18' '2012-05-21' '2012-05-22'\n",
        " '2012-05-23' '2012-05-24' '2012-05-25' '2012-05-29' '2012-05-30'\n",
        " '2012-05-31' '2012-06-01' '2012-06-04' '2012-06-05' '2012-06-06'\n",
        " '2012-06-07' '2012-06-08' '2012-06-11' '2012-06-12' '2012-06-13']\n",
        "\n",
        "Target: 2013-08-30\n",
        "Training: ['2012-06-14' '2012-06-15' '2012-06-18' '2012-06-19' '2012-06-20'\n",
        " '2012-06-21' '2012-06-22' '2012-06-25' '2012-06-26' '2012-06-27'\n",
        " '2012-06-28' '2012-06-29' '2012-07-02' '2012-07-03' '2012-07-05'\n",
        " '2012-07-06' '2012-07-09' '2012-07-10' '2012-07-11' '2012-07-12']\n",
        "\n",
        "Target: 2013-09-03\n",
        "Training: ['2012-07-13' '2012-07-16' '2012-07-17' '2012-07-18' '2012-07-19'\n",
        " '2012-07-20' '2012-07-23' '2012-07-24' '2012-07-25' '2012-07-26'\n",
        " '2012-07-27' '2012-07-30' '2012-07-31' '2012-08-01' '2012-08-02'\n",
        " '2012-08-03' '2012-08-06' '2012-08-07' '2012-08-08' '2012-08-09']\n",
        "\n",
        "Target: 2013-09-04\n",
        "Training: ['2012-08-10' '2012-08-13' '2012-08-14' '2012-08-15' '2012-08-16'\n",
        " '2012-08-17' '2012-08-20' '2012-08-21' '2012-08-22' '2012-08-23'\n",
        " '2012-08-24' '2012-08-27' '2012-08-28' '2012-08-29' '2012-08-30'\n",
        " '2012-08-31' '2012-09-04' '2012-09-05' '2012-09-06' '2012-09-07']\n",
        "\n",
        "Target: 2013-09-05\n",
        "Training: ['2012-09-10' '2012-09-11' '2012-09-12' '2012-09-13' '2012-09-14'\n",
        " '2012-09-17' '2012-09-18' '2012-09-19' '2012-09-20' '2012-09-21'\n",
        " '2012-09-24' '2012-09-25' '2012-09-26' '2012-09-27' '2012-09-28'\n",
        " '2012-10-01' '2012-10-02' '2012-10-03' '2012-10-04' '2012-10-05']\n",
        "\n",
        "Target: 2013-09-06\n",
        "Training: ['2012-10-08' '2012-10-09' '2012-10-10' '2012-10-11' '2012-10-12'\n",
        " '2012-10-15' '2012-10-16' '2012-10-17' '2012-10-18' '2012-10-19'\n",
        " '2012-10-22' '2012-10-23' '2012-10-24' '2012-10-25' '2012-10-26'\n",
        " '2012-10-31' '2012-11-01' '2012-11-02' '2012-11-05' '2012-11-06']\n",
        "\n",
        "Target: 2013-09-09\n",
        "Training: ['2012-11-07' '2012-11-08' '2012-11-09' '2012-11-12' '2012-11-13'\n",
        " '2012-11-14' '2012-11-15' '2012-11-16' '2012-11-19' '2012-11-20'\n",
        " '2012-11-21' '2012-11-23' '2012-11-26' '2012-11-27' '2012-11-28'\n",
        " '2012-11-29' '2012-11-30' '2012-12-03' '2012-12-04' '2012-12-05']\n",
        "\n",
        "Target: 2013-09-10\n",
        "Training: ['2012-12-06' '2012-12-07' '2012-12-10' '2012-12-11' '2012-12-12'\n",
        " '2012-12-13' '2012-12-14' '2012-12-17' '2012-12-18' '2012-12-19'\n",
        " '2012-12-20' '2012-12-21' '2012-12-24' '2012-12-26' '2012-12-27'\n",
        " '2012-12-28' '2012-12-31' '2013-01-02' '2013-01-03' '2013-01-04']\n",
        "\n",
        "Target: 2013-09-11\n",
        "Training: ['2013-01-07' '2013-01-08' '2013-01-09' '2013-01-10' '2013-01-11'\n",
        " '2013-01-14' '2013-01-15' '2013-01-16' '2013-01-17' '2013-01-18'\n",
        " '2013-01-22' '2013-01-23' '2013-01-24' '2013-01-25' '2013-01-28'\n",
        " '2013-01-29' '2013-01-30' '2013-01-31' '2013-02-01' '2013-02-04']\n",
        "\n",
        "Target: 2013-09-12\n",
        "Training: ['2013-02-05' '2013-02-06' '2013-02-07' '2013-02-08' '2013-02-11'\n",
        " '2013-02-12' '2013-02-13' '2013-02-14' '2013-02-15' '2013-02-19'\n",
        " '2013-02-20' '2013-02-21' '2013-02-22' '2013-02-25' '2013-02-26'\n",
        " '2013-02-27' '2013-02-28' '2013-03-01' '2013-03-04' '2013-03-05']\n",
        "\n",
        "Target: 2013-09-13\n",
        "Training: ['2013-03-06' '2013-03-07' '2013-03-08' '2013-03-11' '2013-03-12'\n",
        " '2013-03-13' '2013-03-14' '2013-03-15' '2013-03-18' '2013-03-19'\n",
        " '2013-03-20' '2013-03-21' '2013-03-22' '2013-03-25' '2013-03-26'\n",
        " '2013-03-27' '2013-03-28' '2013-04-01' '2013-04-02' '2013-04-03']\n",
        "\n",
        "Target: 2013-09-16\n",
        "Training: ['2013-04-04' '2013-04-05' '2013-04-08' '2013-04-09' '2013-04-10'\n",
        " '2013-04-11' '2013-04-12' '2013-04-15' '2013-04-16' '2013-04-17'\n",
        " '2013-04-18' '2013-04-19' '2013-04-22' '2013-04-23' '2013-04-24'\n",
        " '2013-04-25' '2013-04-26' '2013-04-29' '2013-04-30' '2013-05-01']\n",
        "\n",
        "Target: 2013-09-17\n",
        "Training: ['2013-05-02' '2013-05-03' '2013-05-06' '2013-05-07' '2013-05-08'\n",
        " '2013-05-09' '2013-05-10' '2013-05-13' '2013-05-14' '2013-05-15'\n",
        " '2013-05-16' '2013-05-17' '2013-05-20' '2013-05-21' '2013-05-22'\n",
        " '2013-05-23' '2013-05-24' '2013-05-28' '2013-05-29' '2013-05-30']\n",
        "\n",
        "Target: 2013-09-18\n",
        "Training: ['2013-05-31' '2013-06-03' '2013-06-04' '2013-06-05' '2013-06-06'\n",
        " '2013-06-07' '2013-06-10' '2013-06-11' '2013-06-12' '2013-06-13'\n",
        " '2013-06-14' '2013-06-17' '2013-06-18' '2013-06-19' '2013-06-20'\n",
        " '2013-06-21' '2013-06-24' '2013-06-25' '2013-06-26' '2013-06-27']\n",
        "\n",
        "Target: 2013-09-19\n",
        "Training: ['2013-06-28' '2013-07-01' '2013-07-02' '2013-07-03' '2013-07-05'\n",
        " '2013-07-08' '2013-07-09' '2013-07-10' '2013-07-11' '2013-07-12'\n",
        " '2013-07-15' '2013-07-16' '2013-07-17' '2013-07-18' '2013-07-19'\n",
        " '2013-07-22' '2013-07-23' '2013-07-24' '2013-07-25' '2013-07-26']\n",
        "\n",
        "Target: 2013-09-20\n",
        "Training: ['2013-07-29' '2013-07-30' '2013-07-31' '2013-08-01' '2013-08-02'\n",
        " '2013-08-05' '2013-08-06' '2013-08-07' '2013-08-08' '2013-08-09'\n",
        " '2013-08-12' '2013-08-13' '2013-08-14' '2013-08-15' '2013-08-16'\n",
        " '2013-08-19' '2013-08-20' '2013-08-21' '2013-08-22' '2013-08-23']\n",
        "\n",
        "Target: 2013-09-23\n",
        "Training: ['2013-08-26' '2013-08-27' '2013-08-28' '2013-08-29' '2013-08-30'\n",
        " '2013-09-03' '2013-09-04' '2013-09-05' '2013-09-06' '2013-09-09'\n",
        " '2013-09-10' '2013-09-11' '2013-09-12' '2013-09-13' '2013-09-16'\n",
        " '2013-09-17' '2013-09-18' '2013-09-19' '2013-09-20' '2013-09-23']\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "####End"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}