ml-finance-python

Price Momentum Factor Notebook.ipynb

(442468B)

 {
  "cells": [
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    "cell_type": "markdown",
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    "source": [
     "#Price Momentum Factor Algorithm\n",
     "\n",
     "By Gil Wassermann\n",
     "\n",
     "Strategy taken from \"130/30: The New Long-Only\" by Andrew Lo and Pankaj Patel\n",
     "\n",
     "Part of the Quantopian Lecture Series:\n",
     "* www.quantopian.com/lectures\n",
     "* github.com/quantopian/research_public\n",
     "\n",
     "Notebook released under the Creative Commons Attribution 4.0 License. Please do not remove this attribution."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Let us imagine that we are traders at a large bank, watching out screens as stock prices fluctuate up and down. Suddenly, everyone around us is buying one particular security. Demand has increased so the stock price increases. We panic. Is there some information that we missed out on -are we out of the loop? In our panic, we blindly decide to buy some shares so we do not miss the boat on the next big thing. Demand further increases as a result of the hype surrounding the stock, driving up the price even more. \n",
     "\n",
     "Now let us take a step back. From the observational perspective of a quant, the price of the security is increasing because of the animal spirits of investors. In essence, the price is going up because the price is going up. As quants, if we can identify these irrational market forces, we can profit from them.\n",
     "\n",
     "In this notebook we will go step-by-step through the contruction of an algorithm to find and trade equities experiencing momentum in price."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "First, let us import all the necessary libraries for our algorithm."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "from scipy.signal import argrelmin, argrelmax\n",
     "import statsmodels.api as sm\n",
     "import talib\n",
     "import matplotlib.pyplot as plt\n",
     "from quantopian.pipeline.classifiers.morningstar import Sector\n",
     "from quantopian.pipeline import Pipeline\n",
     "from quantopian.pipeline.factors import Latest\n",
     "from quantopian.pipeline.data.builtin import USEquityPricing\n",
     "from quantopian.research import run_pipeline\n",
     "from quantopian.pipeline.data import morningstar\n",
     "from quantopian.pipeline.factors import CustomFactor"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#Price Momentum\n",
     "\n",
     "In this notebook, we will use indicators outlined in \"130/30: The New Long Only\" by Andrew Lo and Pankaj Patel and combine them to create a single factor. It should be clarified that we are looking for long-term momentum as opposed to intra-day momentum. These indicators are:\n",
     "\n",
     "* Slope of the 52-Week Trendline (20-day Lag)\n",
     "* Percent Above 260-Day Low (20-day Lag)\n",
     "* 4/52-Week Oscillator (20-day Lag)\n",
     "* 39-Week Return (20-day Lag)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Lag\n",
     "\n",
     "One thing that all of the indicators have in common is that they are calculated using a 20-day lag. This lag is a way of smoothing out the stock signal so that we can filter out noise and focus on concrete, underlying trends. To calculate lag, we will take our desired data series and calculate its 20-day simple moving average, which is the arithmetic mean of window of the series' last 20 entries. \n",
     "\n",
     "Let's see an example of this for the closing price of Apple (AAPL) stock from August 2014 to August 2015. We will abstract out the lag calculation into a helper function because we will be needing it so often in the algorithm.\n",
     "\n",
     "NB: we remove the first 20 entries of the results as these will always be undefined (here, NaN) as because there is not a 20-day window with which to calculate the lag. We also have a check to determine if the entire row of data in NaN, as this can cause issues with the TA-Lib library."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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7yHWQa+BwMdfhUhMszT7P0ocffkhSUhIACQkJdOnShZMnT/KPf/wDq9WKxWJh3759RERE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\nw6hRo3D06FGEhITg888/R/fu3Wu9cTx48GDExcVp9Rbt3r0b/fv3h6Wlp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       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd4de36f90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# check if entire column is NaN. If yes, return True\n",
     "def nan_check(col):\n",
     "    if np.isnan(np.sum(col)):\n",
     "        return True\n",
     "    else: \n",
     "        return False\n",
     "\n",
     "# helper to calculate lag\n",
     "def lag_helper(col):\n",
     "    \n",
     "    # TA-Lib raises an error if whole colum is NaN,\n",
     "    # so we check if this is true and, if so, skip\n",
     "    # the lag calculation\n",
     "    if nan_check(col):\n",
     "        return np.nan\n",
     "    # 20-day simple moving average\n",
     "    else:\n",
     "        return talib.SMA(col, 20)[20:]\n",
     "\n",
     "AAPL_frame = get_pricing('AAPL', start_date='2014-08-08', end_date='2015-08-08', fields='close_price')\n",
     "\n",
     "# convert to np.array for helper function and save index of timeseries\n",
     "AAPL_index = AAPL_frame.index\n",
     "AAPL_frame = AAPL_frame.as_matrix()\n",
     "\n",
     "# calculate lag\n",
     "AAPL_frame_lagged = lag_helper(AAPL_frame)\n",
     "\n",
     "plt.plot(AAPL_index, AAPL_frame, label='Close')\n",
     "plt.plot(AAPL_index[20:], AAPL_frame_lagged, label='Lagged Close')\n",
     "plt.legend(loc=2)\n",
     "plt.xlabel('Date')\n",
     "plt.title('Close Prices vs Close Prices (20-Day Lag)')\n",
     "plt.ylabel('AAPL Price');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "As you can see from the graph, the lagged closing prices generally follow the same general pattern as the unlagged prices, but do not experience as extreme peaks and troughs. For the rest of the notebook we will use lagged prices as we are interested in long-term trends."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Slope of 52-Week Trendline\n",
     "\n",
     "One of the oldest indicators of price momentum is the trendline. The basic idea is to create a bounding line around stock prices that predict when a price should pivot. A trendline that predicts a ceiling is called a resistance trendline, and one that predicts a floor is a support trendline. \n",
     "\n",
     "To calculate a support trendline here, we take a lagged series, and find its pronounced local minima (here, a local minimum is defined as a data point lower than the five previous and five proceeding points). We then connect the first local minimum and the last local minimum by a straight line. For a resistance trendline, the process is the same, except it uses local maxima. This is just one of many methodologies for calculating trendlines.\n",
     "\n",
     "Let us code up a function to return the gradient of the trendline. We will include a boolean variable `support` that, when set to `True` gives a support trendline and when set to `False` gives a resistance trendline. Let us have a look at the same dataset of AAPL stock and plot its trendlines. \n",
     "\n",
     "NB: The y-intercepts used here are purely aesthetic and have no meaning as the indicator itself is only based on the slope of the trendline  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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EqWMGkD7bxpDYEHeXJ/KVFIRERERE5ILUNLSyLruI97Ydp7nNgdXLwjWpCSya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\nHAUhERERERHxOQpCIiIiIiLic/4fadBeYp8CHMYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd4c50b110>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Custom Factor 1 : Slope of 52-Week trendline\n",
     "def trendline_function(col, support):\n",
     "    \n",
     "    # NaN check for speed\n",
     "    if nan_check(col):\n",
     "        return np.nan  \n",
     "    \n",
     "    # lag transformation\n",
     "    col = lag_helper(col)\n",
     "    \n",
     "    # support trendline\n",
     "    if support:\n",
     "        \n",
     "        # get local minima\n",
     "        minima_index = argrelmin(col, order=5)[0]\n",
     "        \n",
     "        # make sure line can be drawn\n",
     "        if len(minima_index) < 2:\n",
     "            return np.nan\n",
     "        else:\n",
     "            # return gradient\n",
     "            return (col[minima_index[-1]] - col[minima_index[0]]) / (minima_index[-1] - minima_index[0])\n",
     "    \n",
     "    # resistance trandline\n",
     "    else:\n",
     "        \n",
     "        # get local maxima\n",
     "        maxima_index = argrelmax(col, order=5)[0]\n",
     "        if len(maxima_index) < 2:\n",
     "            return np.nan\n",
     "        else:\n",
     "            return (col[maxima_index[-1]] - col[maxima_index[0]]) / (maxima_index[-1] - maxima_index[0])\n",
     "\n",
     "# make the lagged frame the default \n",
     "AAPL_frame = AAPL_frame_lagged\n",
     "\n",
     "# use day count rather than dates to ensure straight lines\n",
     "days = list(range(0,len(AAPL_frame),1))\n",
     "\n",
     "# get points to plot\n",
     "points_low = [(101.5 + (trendline_function(AAPL_frame, True)*day)) for day in days]\n",
     "points_high = [94 + (trendline_function(AAPL_frame, False)*day) for day in days]\n",
     "\n",
     "# create graph\n",
     "plt.plot(days, points_low, label='Support')\n",
     "plt.plot(days, points_high, label='Resistance')\n",
     "plt.plot(days, AAPL_frame, label='Lagged Closes')\n",
     "plt.xlim([0, max(days)])\n",
     "plt.xlabel('Days Elapsed')\n",
     "plt.ylabel('AAPL Price')\n",
     "plt.legend(loc=2);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "As you can see, at the beginning of the time frame these lines seem to describe the pivot points of the curve well. Therefore it appears that betting against the stock when its price nears the resistance line and betting on the stock when its price nears the support line is a decent strategy. One issue with this is that these trendlines change over time. Even at the end of the above graph, it appears that the lines need to be redrawn in order to accomodate new prevailing price trends."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now let us create our factor. In order to maintain flexibility between the types of trendlines, we need a way to pass the variable `support` into our Pipeline calculation. To do this we create a function that returns a `CustomFactor class` that *can* take a variable that is in scope of our indicator.\n",
     "\n",
     "Also, we have abstracted out the trendline calculation so that we can use the builtin Numpy function `apply_along_axis` instead of creating and appending the results of the trendline calculation for each column to a list, which is a slower process."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/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></th>\n",
        "      <th>Trendline</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">2015-08-10 00:00:00+00:00</th>\n",
        "      <th>Equity(2 [AA])</th>\n",
        "      <td>-0.020134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(24 [AAPL])</th>\n",
        "      <td>0.134210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(25 [AA_PR])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(31 [ABAX])</th>\n",
        "      <td>0.026059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(39 [DDC])</th>\n",
        "      <td>0.027155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(41 [ARCB])</th>\n",
        "      <td>-0.015297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(52 [ABM])</th>\n",
        "      <td>0.032766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(53 [ABMD])</th>\n",
        "      <td>0.185881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(62 [ABT])</th>\n",
        "      <td>0.030551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(64 [ABX])</th>\n",
        "      <td>-0.044889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(66 [AB])</th>\n",
        "      <td>0.034333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(67 [ADSK])</th>\n",
        "      <td>-0.009252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(69 [ACAT])</th>\n",
        "      <td>-0.014448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(70 [VBF])</th>\n",
        "      <td>-0.000941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(76 [TAP])</th>\n",
        "      <td>0.006323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(84 [ACET])</th>\n",
        "      <td>0.026783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(86 [ACG])</th>\n",
        "      <td>0.001448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(88 [ACI])</th>\n",
        "      <td>-1.289812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(100 [IEP])</th>\n",
        "      <td>-0.078825</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                              Trendline\n",
        "2015-08-10 00:00:00+00:00 Equity(2 [AA])      -0.020134\n",
        "                          Equity(21 [AAME])         NaN\n",
        "                          Equity(24 [AAPL])    0.134210\n",
        "                          Equity(25 [AA_PR])        NaN\n",
        "                          Equity(31 [ABAX])    0.026059\n",
        "                          Equity(39 [DDC])     0.027155\n",
        "                          Equity(41 [ARCB])   -0.015297\n",
        "                          Equity(52 [ABM])     0.032766\n",
        "                          Equity(53 [ABMD])    0.185881\n",
        "                          Equity(62 [ABT])     0.030551\n",
        "                          Equity(64 [ABX])    -0.044889\n",
        "                          Equity(66 [AB])      0.034333\n",
        "                          Equity(67 [ADSK])   -0.009252\n",
        "                          Equity(69 [ACAT])   -0.014448\n",
        "                          Equity(70 [VBF])    -0.000941\n",
        "                          Equity(76 [TAP])     0.006323\n",
        "                          Equity(84 [ACET])    0.026783\n",
        "                          Equity(86 [ACG])     0.001448\n",
        "                          Equity(88 [ACI])    -1.289812\n",
        "                          Equity(100 [IEP])   -0.078825"
       ]
      },
      "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def create_trendline_factor(support):\n",
     "    \n",
     "    class Trendline(CustomFactor):\n",
     "\n",
     "        # 52 week + 20d lag\n",
     "        window_length = 272\n",
     "        inputs=[USEquityPricing.close]\n",
     "\n",
     "        def compute(self, today, assets, out, close): \n",
     "            out[:] = np.apply_along_axis(trendline_function, 0, close, support)\n",
     "    return Trendline\n",
     "    \n",
     "temp_pipe_1 = Pipeline()\n",
     "trendline = create_trendline_factor(support=True)\n",
     "temp_pipe_1.add(trendline(), 'Trendline')\n",
     "results_1 = run_pipeline(temp_pipe_1, '2015-08-08', '2015-08-08')\n",
     "results_1.head(20)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Percent Above 260-Day Low\n",
     "\n",
     "This indicator is relatively self explanitory. Whereas the trendline metric gives a more indepth picture of price momentum (as the line itself shows how this momentum has evolves over time), this metric is fairly blunt. It is calculated as the price of a stock today less the minimum price in a retrospective 260-day window, all divided by that minimum price.\n",
     "\n",
     "Let us have a look at a visualization of this metric for the same window of AAPL stock."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Percent above 260-day Low: 25.114962%\n"
      ]
     },
     {
      "data": {
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IiIiIiBxIPM8jlUp1dhkHrWAw+IU3Iv+8Dp01TkRERETkYJFKpRSEOslXOfb7fdY4ERER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\nhAAAgOIIIQAAoDhCCAAAKM7/A+2imTUWNG9dAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd4c4c52d0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Custom Factor 2 : % above 260 day low\n",
     "def percent_helper(col):\n",
     "    if nan_check(col):\n",
     "        return np.nan \n",
     "    else:\n",
     "        col = lag_helper(col)\n",
     "        return (col[-1] - min(col)) / min(col)\n",
     "\n",
     "print 'Percent above 260-day Low: %f%%' % (percent_helper(AAPL_frame) * 100)\n",
     "\n",
     "# create the graph\n",
     "plt.plot(days, AAPL_frame)\n",
     "plt.axhline(min(AAPL_frame), color='r', label='260-Day Low')\n",
     "plt.axhline(AAPL_frame[-1], color='y', label='Latest Price')\n",
     "plt.fill_between(days, AAPL_frame)\n",
     "plt.xlabel('Days Elapsed')\n",
     "plt.ylabel('AAPL Price')\n",
     "plt.xlim([0, max(days)])\n",
     "plt.title('Percent Above 260-Day Low')\n",
     "plt.legend();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now we will create the `CustomFactor` for this metric. We will use the same abstraction process as above for run-time efficiency."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/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></th>\n",
        "      <th>Percent Above Low</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">2015-08-10 00:00:00+00:00</th>\n",
        "      <th>Equity(2 [AA])</th>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(24 [AAPL])</th>\n",
        "      <td>0.314374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(25 [AA_PR])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(31 [ABAX])</th>\n",
        "      <td>0.189924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(39 [DDC])</th>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(41 [ARCB])</th>\n",
        "      <td>0.020620</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(52 [ABM])</th>\n",
        "      <td>0.327307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(53 [ABMD])</th>\n",
        "      <td>2.256132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(62 [ABT])</th>\n",
        "      <td>0.238078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(64 [ABX])</th>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(66 [AB])</th>\n",
        "      <td>0.175879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(67 [ADSK])</th>\n",
        "      <td>0.006031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(69 [ACAT])</th>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(70 [VBF])</th>\n",
        "      <td>0.004431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(76 [TAP])</th>\n",
        "      <td>0.029064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(84 [ACET])</th>\n",
        "      <td>0.415219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(86 [ACG])</th>\n",
        "      <td>0.054047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(88 [ACI])</th>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(100 [IEP])</th>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                              Percent Above Low\n",
        "2015-08-10 00:00:00+00:00 Equity(2 [AA])               0.000000\n",
        "                          Equity(21 [AAME])                 NaN\n",
        "                          Equity(24 [AAPL])            0.314374\n",
        "                          Equity(25 [AA_PR])                NaN\n",
        "                          Equity(31 [ABAX])            0.189924\n",
        "                          Equity(39 [DDC])             0.000000\n",
        "                          Equity(41 [ARCB])            0.020620\n",
        "                          Equity(52 [ABM])             0.327307\n",
        "                          Equity(53 [ABMD])            2.256132\n",
        "                          Equity(62 [ABT])             0.238078\n",
        "                          Equity(64 [ABX])             0.000000\n",
        "                          Equity(66 [AB])              0.175879\n",
        "                          Equity(67 [ADSK])            0.006031\n",
        "                          Equity(69 [ACAT])            0.000000\n",
        "                          Equity(70 [VBF])             0.004431\n",
        "                          Equity(76 [TAP])             0.029064\n",
        "                          Equity(84 [ACET])            0.415219\n",
        "                          Equity(86 [ACG])             0.054047\n",
        "                          Equity(88 [ACI])             0.000000\n",
        "                          Equity(100 [IEP])            0.000000"
       ]
      },
      "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "class Percent_Above_Low(CustomFactor):\n",
     "    \n",
     "    # 260 days + 20 lag\n",
     "    window_length = 280\n",
     "    inputs=[USEquityPricing.close]\n",
     "    \n",
     "    def compute(self, today, asseys, out, close):\n",
     "        out[:] = np.apply_along_axis(percent_helper, 0, close)\n",
     "\n",
     "temp_pipe_2 = Pipeline()\n",
     "temp_pipe_2.add(Percent_Above_Low(), 'Percent Above Low')\n",
     "results_2 = run_pipeline(temp_pipe_2, '2015-08-08', '2015-08-08')\n",
     "results_2.head(20)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "NB: There are a lot of 0's here for this output. Although this might seem odd at first, it makes sense when we consider that there are many securities on a downwards trend. These stocks would be prime candidates to give a value of 0 as their current price is as low as it has ever been in this lookback window."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##4/52-Week Price Oscillator\n",
     "\n",
     "This is calculated as the average close price over 4 weeks over the average close price over 52 weeks, all subtracted by 1. To understand this value measures, let us consider what happens to the oscillator in different scenarios. This particular oscillator gives a sense of relative performance between the previous four weeks and the previous year. A value given by this oscillator could be \"0.05\", which would indicate that the stocks recent closes are outperforming its previous year's performance by 5%. A positive value is an indicator of momentum as more recent performance is stronger than normal and the larger the number, the more momentum.\n",
     "\n",
     "As close prices can not be negative, this oscillator is bounded by -1 and positive infinity. Let us create a graph to show how, given a particular 52-week average, the value of the oscillator is affected by its four-week average."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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dlM033zx1dXWpq6vLFVdckaamplKMCQBQVfSWYO0UPTQ9+eSTefnllzN69OhMnTo1w4YN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\ngg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd4dd5da90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# set 48-week average\n",
     "av_52w = 100.\n",
     "\n",
     "# create list of possible last four-week averages\n",
     "av_4w = xrange(0,200)\n",
     "\n",
     "# create list of oscillator values\n",
     "osc = [(x / av_52w) - 1 for x in av_4w]\n",
     "\n",
     "# draw graph\n",
     "plt.plot(av_4w, osc)\n",
     "plt.axvline(100, color='r', label='52-Week Average')\n",
     "plt.xlabel('Four-Week Average')\n",
     "plt.ylabel('4/52 Oscillator')\n",
     "plt.legend();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now let us create a Pipeline factor and observe some values."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/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></th>\n",
        "      <th>Price Oscillator</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">2015-08-10 00:00:00+00:00</th>\n",
        "      <th>Equity(2 [AA])</th>\n",
        "      <td>-0.271287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(24 [AAPL])</th>\n",
        "      <td>0.089667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(25 [AA_PR])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(31 [ABAX])</th>\n",
        "      <td>-0.046013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(39 [DDC])</th>\n",
        "      <td>-0.159902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(41 [ARCB])</th>\n",
        "      <td>-0.163490</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(52 [ABM])</th>\n",
        "      <td>0.120786</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(53 [ABMD])</th>\n",
        "      <td>0.473859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(62 [ABT])</th>\n",
        "      <td>0.107602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(64 [ABX])</th>\n",
        "      <td>-0.269565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(66 [AB])</th>\n",
        "      <td>0.059691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(67 [ADSK])</th>\n",
        "      <td>-0.083954</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(69 [ACAT])</th>\n",
        "      <td>-0.063122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(70 [VBF])</th>\n",
        "      <td>-0.022971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(76 [TAP])</th>\n",
        "      <td>-0.030247</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(84 [ACET])</th>\n",
        "      <td>0.154912</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(86 [ACG])</th>\n",
        "      <td>0.011582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(88 [ACI])</th>\n",
        "      <td>-0.816684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(100 [IEP])</th>\n",
        "      <td>-0.108132</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                              Price Oscillator\n",
        "2015-08-10 00:00:00+00:00 Equity(2 [AA])             -0.271287\n",
        "                          Equity(21 [AAME])                NaN\n",
        "                          Equity(24 [AAPL])           0.089667\n",
        "                          Equity(25 [AA_PR])               NaN\n",
        "                          Equity(31 [ABAX])          -0.046013\n",
        "                          Equity(39 [DDC])           -0.159902\n",
        "                          Equity(41 [ARCB])          -0.163490\n",
        "                          Equity(52 [ABM])            0.120786\n",
        "                          Equity(53 [ABMD])           0.473859\n",
        "                          Equity(62 [ABT])            0.107602\n",
        "                          Equity(64 [ABX])           -0.269565\n",
        "                          Equity(66 [AB])             0.059691\n",
        "                          Equity(67 [ADSK])          -0.083954\n",
        "                          Equity(69 [ACAT])          -0.063122\n",
        "                          Equity(70 [VBF])           -0.022971\n",
        "                          Equity(76 [TAP])           -0.030247\n",
        "                          Equity(84 [ACET])           0.154912\n",
        "                          Equity(86 [ACG])            0.011582\n",
        "                          Equity(88 [ACI])           -0.816684\n",
        "                          Equity(100 [IEP])          -0.108132"
       ]
      },
      "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Custom Factor 3: 4/52 Price Oscillator\n",
     "def oscillator_helper(col):\n",
     "    if nan_check(col):\n",
     "        return np.nan   \n",
     "    else:\n",
     "        col = lag_helper(col)\n",
     "        return np.nanmean(col[-20:]) / np.nanmean(col) - 1\n",
     "\n",
     "class Price_Oscillator(CustomFactor):\n",
     "    inputs = [USEquityPricing.close]\n",
     "    window_length = 272\n",
     "    \n",
     "    def compute(self, today, assets, out, close):\n",
     "        out[:] = np.apply_along_axis(oscillator_helper, 0, close)\n",
     "        \n",
     "temp_pipe_3 = Pipeline()\n",
     "temp_pipe_3.add(Price_Oscillator(), 'Price Oscillator')\n",
     "results_3 = run_pipeline(temp_pipe_3, '2015-08-08', '2015-08-08')\n",
     "results_3.head(20)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
    "source": [
     "Once again, let us use AAPL stock as an example."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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2vPzyy3z55Zdlbb/xxhssWLAAHx8f5syZw9q1a7njjjuu/gLdAAUlEREREZEqlJCQwKRJ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\ngU5mFyo3v0RnCkqUV1Aiq8Uih90qb4fX+T/tNjnsNjVs4FB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       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd3dca0290>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# get two averages\n",
     "av_4w = np.nanmean(AAPL_frame[-20:])\n",
     "av_52w = np.nanmean(AAPL_frame)\n",
     "\n",
     "# create the graph\n",
     "plt.plot(days, AAPL_frame)\n",
     "plt.fill_between(days[-20:], AAPL_frame[-20:])\n",
     "plt.axhline(av_4w, color='y', label='Four-week Average' )\n",
     "plt.axhline(av_52w, color='r', label='Year-long Average')\n",
     "plt.ylim([80,140])\n",
     "plt.xlabel('Days Elapsed')\n",
     "plt.ylabel('AAPL Price')\n",
     "plt.title('4/52 Week Oscillator')\n",
     "plt.legend();\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The section shaded blue under the graph represents the last four weeks of close prices. The fact that this average (shown by the yellow line) is greater than the year-long average (shown by the red line), means that the 4/52 week oscillator for this date will be positive. This fact is backed by our pipeline output, which gives the value of the metric to be 9.4%. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##39-Week Return\n",
     "\n",
     "This is calculated as the difference price between today and 39-weks prior, all over the price 39-weeks prior.\n",
     "\n",
     "Although returns as a metric might seem too ubitquitous to be useful or special, the important thing to highlight hear is the window length chosen. By choosing a larger window length (here, 39-weeks) as opposed to daily returns, we see larger fluctuations in value. This is because a larger time window exposes the metric to larger trends and higher volatility. \n",
     "\n",
     "In the graph below, we illustrate this point by plotting returns calculated over different time windows. To do this we will look at a AAPL close prices between 2002 and 2016. We will also mark important dates in the history of Apple in order to highlight this metric's descriptive power for larger trends.\n",
     "\n",
     "NB: 39-week return is not a metric that is event driven. The inclusion of these dates is illustrative as opposed to predictive."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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ATp068eWXX9KzZ08qKiq48847GTVqFBdddNHx/qgapVAkIiIiItKA3DMnNFjVaUnTp0/H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\nSIMiBXH53Iz0/3K9ZG5eXcA/f+ENfOGPXkYywUqZxUzMFmHhSFqOzp2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t7RZcvzSPSChR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vdGYHMYoV4nnhhrITQygtCNm3Ds3QO1PvfNRmMuPShKhcKYevY7OPXJT4NLyWeYNDotKNMk/QEk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\nNPBAsRgt3L7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       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd5b989a10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# create a new longer frame of AAPL close prices\n",
     "AAPL_frame = get_pricing('AAPL', start_date='2002-08-08', end_date='2016-01-01', fields='close_price')\n",
     "\n",
     "# use dates as index\n",
     "AAPL_index = AAPL_frame.index[20:]\n",
     "AAPL_frame = lag_helper(AAPL_frame.as_matrix())\n",
     "\n",
     "# 1d returns\n",
     "AAPL_1d_returns = ((AAPL_frame - np.roll(AAPL_frame, 1))/ np.roll(AAPL_frame,1))[1:]\n",
     "\n",
     "# 1w returns\n",
     "AAPL_1w_returns = ((AAPL_frame - np.roll(AAPL_frame, 5))/ np.roll(AAPL_frame, 5))[5:]\n",
     "\n",
     "# 1m returns\n",
     "AAPL_1m_returns = ((AAPL_frame - np.roll(AAPL_frame, 30))/ np.roll(AAPL_frame, 30))[30:]\n",
     "\n",
     "# 39w returns\n",
     "AAPL_39w_returns = ((AAPL_frame - np.roll(AAPL_frame, 215))/ np.roll(AAPL_frame, 215))[215:]\n",
     "\n",
     "# plot close prices\n",
     "plt.plot(AAPL_index[1:], AAPL_1d_returns, label='1-day Returns')\n",
     "plt.plot(AAPL_index[5:], AAPL_1w_returns, label='1-week Returns')\n",
     "plt.plot(AAPL_index[30:], AAPL_1m_returns, label='1-month Returns')\n",
     "plt.plot(AAPL_index[215:], AAPL_39w_returns, label='39-week Returns')\n",
     "\n",
     "# show events\n",
     "# iPhone release\n",
     "plt.axvline('2007-07-29')\n",
     "# iPod mini 2nd gen. release\n",
     "plt.axvline('2005-02-23')\n",
     "# iPad release\n",
     "plt.axvline('2010-04-03')\n",
     "# iPhone 5 release\n",
     "plt.axvline('2012-09-21')\n",
     "# Apple Watch\n",
     "plt.axvline('2015-04-24')\n",
     "\n",
     "# labels\n",
     "plt.xlabel('Days')\n",
     "plt.ylabel('Returns')\n",
     "plt.title('Returns')\n",
     "plt.legend();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "There are a few important characteristics to note on the graph above.\n",
     "\n",
     "Firstly, as we expected, the amplitude of the signal of returns with a longer window length is larger.\n",
     "\n",
     "Secondly, these new releases, many of which were announced several months before, all lie in or adjacent to a peak in the 39-week return price. Therefore, it would seem that this window length is a useful tool for capturing information on larger trends.\n",
     "\n",
     "Now let us create the custom factor and run the Pipeline."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/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></th>\n",
        "      <th>39 Week Return</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">2015-08-10 00:00:00+00:00</th>\n",
        "      <th>Equity(2 [AA])</th>\n",
        "      <td>-0.381646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(24 [AAPL])</th>\n",
        "      <td>0.243775</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(25 [AA_PR])</th>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(31 [ABAX])</th>\n",
        "      <td>0.020425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(39 [DDC])</th>\n",
        "      <td>-0.074306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(41 [ARCB])</th>\n",
        "      <td>-0.126575</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(52 [ABM])</th>\n",
        "      <td>0.227928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(53 [ABMD])</th>\n",
        "      <td>2.081958</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(62 [ABT])</th>\n",
        "      <td>0.206293</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(64 [ABX])</th>\n",
        "      <td>-0.515709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(66 [AB])</th>\n",
        "      <td>0.092714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(67 [ADSK])</th>\n",
        "      <td>-0.043848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(69 [ACAT])</th>\n",
        "      <td>-0.153872</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(70 [VBF])</th>\n",
        "      <td>-0.013950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(76 [TAP])</th>\n",
        "      <td>-0.023681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(84 [ACET])</th>\n",
        "      <td>0.191040</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(86 [ACG])</th>\n",
        "      <td>0.045309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(88 [ACI])</th>\n",
        "      <td>-0.943585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(100 [IEP])</th>\n",
        "      <td>-0.207704</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                              39 Week Return\n",
        "2015-08-10 00:00:00+00:00 Equity(2 [AA])           -0.381646\n",
        "                          Equity(21 [AAME])              NaN\n",
        "                          Equity(24 [AAPL])         0.243775\n",
        "                          Equity(25 [AA_PR])             NaN\n",
        "                          Equity(31 [ABAX])         0.020425\n",
        "                          Equity(39 [DDC])         -0.074306\n",
        "                          Equity(41 [ARCB])        -0.126575\n",
        "                          Equity(52 [ABM])          0.227928\n",
        "                          Equity(53 [ABMD])         2.081958\n",
        "                          Equity(62 [ABT])          0.206293\n",
        "                          Equity(64 [ABX])         -0.515709\n",
        "                          Equity(66 [AB])           0.092714\n",
        "                          Equity(67 [ADSK])        -0.043848\n",
        "                          Equity(69 [ACAT])        -0.153872\n",
        "                          Equity(70 [VBF])         -0.013950\n",
        "                          Equity(76 [TAP])         -0.023681\n",
        "                          Equity(84 [ACET])         0.191040\n",
        "                          Equity(86 [ACG])          0.045309\n",
        "                          Equity(88 [ACI])         -0.943585\n",
        "                          Equity(100 [IEP])        -0.207704"
       ]
      },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Custom Fator 4: 39-week Returns\n",
     "def return_helper(col):\n",
     "    if nan_check(col):\n",
     "        return np.nan \n",
     "    else:\n",
     "        col = lag_helper(col)\n",
     "        return (col[-1] - col[-215]) / col[-215]\n",
     "\n",
     "class Return_39_Week(CustomFactor):\n",
     "    inputs = [USEquityPricing.close]\n",
     "    window_length = 235\n",
     "    \n",
     "    def compute(self, today, assets, out, close):\n",
     "        out[:] = np.apply_along_axis(return_helper, 0, close)\n",
     "        \n",
     "temp_pipe_4 = Pipeline()\n",
     "temp_pipe_4.add(Return_39_Week(), '39 Week Return')\n",
     "results_4 = run_pipeline(temp_pipe_4, '2015-08-08','2015-08-08')\n",
     "results_4.head(20)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
    "source": [
     "##Aggregation\n",
     "\n",
     "Let us create the full Pipeline. Once again we will need a proxy for the S&P500 for the ordering logic. Also, given the large window lengths needed for the algorithm, we will employ the trick of multiple outputs per factor. This is explained in detail here (https://www.quantopian.com/posts/new-feature-multiple-output-pipeline-custom-factors). Instead of having to process several data frames, we only need to deal with one large one and then apply our helper functions. This will speed up out computation exponentially in the backtester."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/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></th>\n",
        "      <th>Oscillator</th>\n",
        "      <th>Percent</th>\n",
        "      <th>Returns</th>\n",
        "      <th>SPY Proxy</th>\n",
        "      <th>Trendline</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"20\" valign=\"top\">2015-08-10 00:00:00+00:00</th>\n",
        "      <th>Equity(2 [AA])</th>\n",
        "      <td>-0.271287</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-0.381646</td>\n",
        "      <td>1.189590e+10</td>\n",
        "      <td>-0.020134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>7.612130e+07</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(24 [AAPL])</th>\n",
        "      <td>0.089667</td>\n",
        "      <td>0.314374</td>\n",
        "      <td>0.243775</td>\n",
        "      <td>6.537600e+11</td>\n",
        "      <td>0.134210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(25 [AA_PR])</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(31 [ABAX])</th>\n",
        "      <td>-0.046013</td>\n",
        "      <td>0.189924</td>\n",
        "      <td>0.020425</td>\n",
        "      <td>1.118860e+09</td>\n",
        "      <td>0.026059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(39 [DDC])</th>\n",
        "      <td>-0.159902</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-0.074306</td>\n",
        "      <td>1.039750e+09</td>\n",
        "      <td>0.027155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(41 [ARCB])</th>\n",
        "      <td>-0.163490</td>\n",
        "      <td>0.020620</td>\n",
        "      <td>-0.126575</td>\n",
        "      <td>8.785140e+08</td>\n",
        "      <td>-0.015297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(52 [ABM])</th>\n",
        "      <td>0.120786</td>\n",
        "      <td>0.327307</td>\n",
        "      <td>0.227928</td>\n",
        "      <td>1.857790e+09</td>\n",
        "      <td>0.032766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(53 [ABMD])</th>\n",
        "      <td>0.473859</td>\n",
        "      <td>2.256132</td>\n",
        "      <td>2.081958</td>\n",
        "      <td>3.706250e+09</td>\n",
        "      <td>0.185881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(62 [ABT])</th>\n",
        "      <td>0.107602</td>\n",
        "      <td>0.238078</td>\n",
        "      <td>0.206293</td>\n",
        "      <td>7.519710e+10</td>\n",
        "      <td>0.030551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(64 [ABX])</th>\n",
        "      <td>-0.269565</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-0.515709</td>\n",
        "      <td>7.919750e+09</td>\n",
        "      <td>-0.044889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(66 [AB])</th>\n",
        "      <td>0.059691</td>\n",
        "      <td>0.175879</td>\n",
        "      <td>0.092714</td>\n",
        "      <td>2.922680e+09</td>\n",
        "      <td>0.034333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(67 [ADSK])</th>\n",
        "      <td>-0.083954</td>\n",
        "      <td>0.006031</td>\n",
        "      <td>-0.043848</td>\n",
        "      <td>1.232740e+10</td>\n",
        "      <td>-0.009252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(69 [ACAT])</th>\n",
        "      <td>-0.063122</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-0.153872</td>\n",
        "      <td>3.598240e+08</td>\n",
        "      <td>-0.014448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(70 [VBF])</th>\n",
        "      <td>-0.022971</td>\n",
        "      <td>0.004431</td>\n",
        "      <td>-0.013950</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-0.000941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(76 [TAP])</th>\n",
        "      <td>-0.030247</td>\n",
        "      <td>0.029064</td>\n",
        "      <td>-0.023681</td>\n",
        "      <td>1.291870e+10</td>\n",
        "      <td>0.006323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(84 [ACET])</th>\n",
        "      <td>0.154912</td>\n",
        "      <td>0.415219</td>\n",
        "      <td>0.191040</td>\n",
        "      <td>6.839320e+08</td>\n",
        "      <td>0.026783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(86 [ACG])</th>\n",
        "      <td>0.011582</td>\n",
        "      <td>0.054047</td>\n",
        "      <td>0.045309</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.001448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(88 [ACI])</th>\n",
        "      <td>-0.816684</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-0.943585</td>\n",
        "      <td>3.215040e+07</td>\n",
        "      <td>-1.289812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(100 [IEP])</th>\n",
        "      <td>-0.108132</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-0.207704</td>\n",
        "      <td>9.785680e+09</td>\n",
        "      <td>-0.078825</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                              Oscillator   Percent   Returns  \\\n",
        "2015-08-10 00:00:00+00:00 Equity(2 [AA])       -0.271287  0.000000 -0.381646   \n",
        "                          Equity(21 [AAME])          NaN       NaN       NaN   \n",
        "                          Equity(24 [AAPL])     0.089667  0.314374  0.243775   \n",
        "                          Equity(25 [AA_PR])         NaN       NaN       NaN   \n",
        "                          Equity(31 [ABAX])    -0.046013  0.189924  0.020425   \n",
        "                          Equity(39 [DDC])     -0.159902  0.000000 -0.074306   \n",
        "                          Equity(41 [ARCB])    -0.163490  0.020620 -0.126575   \n",
        "                          Equity(52 [ABM])      0.120786  0.327307  0.227928   \n",
        "                          Equity(53 [ABMD])     0.473859  2.256132  2.081958   \n",
        "                          Equity(62 [ABT])      0.107602  0.238078  0.206293   \n",
        "                          Equity(64 [ABX])     -0.269565  0.000000 -0.515709   \n",
        "                          Equity(66 [AB])       0.059691  0.175879  0.092714   \n",
        "                          Equity(67 [ADSK])    -0.083954  0.006031 -0.043848   \n",
        "                          Equity(69 [ACAT])    -0.063122  0.000000 -0.153872   \n",
        "                          Equity(70 [VBF])     -0.022971  0.004431 -0.013950   \n",
        "                          Equity(76 [TAP])     -0.030247  0.029064 -0.023681   \n",
        "                          Equity(84 [ACET])     0.154912  0.415219  0.191040   \n",
        "                          Equity(86 [ACG])      0.011582  0.054047  0.045309   \n",
        "                          Equity(88 [ACI])     -0.816684  0.000000 -0.943585   \n",
        "                          Equity(100 [IEP])    -0.108132  0.000000 -0.207704   \n",
        "\n",
        "                                                 SPY Proxy  Trendline  \n",
        "2015-08-10 00:00:00+00:00 Equity(2 [AA])      1.189590e+10  -0.020134  \n",
        "                          Equity(21 [AAME])   7.612130e+07        NaN  \n",
        "                          Equity(24 [AAPL])   6.537600e+11   0.134210  \n",
        "                          Equity(25 [AA_PR])           NaN        NaN  \n",
        "                          Equity(31 [ABAX])   1.118860e+09   0.026059  \n",
        "                          Equity(39 [DDC])    1.039750e+09   0.027155  \n",
        "                          Equity(41 [ARCB])   8.785140e+08  -0.015297  \n",
        "                          Equity(52 [ABM])    1.857790e+09   0.032766  \n",
        "                          Equity(53 [ABMD])   3.706250e+09   0.185881  \n",
        "                          Equity(62 [ABT])    7.519710e+10   0.030551  \n",
        "                          Equity(64 [ABX])    7.919750e+09  -0.044889  \n",
        "                          Equity(66 [AB])     2.922680e+09   0.034333  \n",
        "                          Equity(67 [ADSK])   1.232740e+10  -0.009252  \n",
        "                          Equity(69 [ACAT])   3.598240e+08  -0.014448  \n",
        "                          Equity(70 [VBF])             NaN  -0.000941  \n",
        "                          Equity(76 [TAP])    1.291870e+10   0.006323  \n",
        "                          Equity(84 [ACET])   6.839320e+08   0.026783  \n",
        "                          Equity(86 [ACG])             NaN   0.001448  \n",
        "                          Equity(88 [ACI])    3.215040e+07  -1.289812  \n",
        "                          Equity(100 [IEP])   9.785680e+09  -0.078825  "
       ]
      },
      "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# This factor creates the synthetic S&P500\n",
     "class SPY_proxy(CustomFactor):\n",
     "    inputs = [morningstar.valuation.market_cap]\n",
     "    window_length = 1\n",
     "    \n",
     "    def compute(self, today, assets, out, mc):\n",
     "        out[:] = mc[-1]\n",
     "\n",
     "# using helpers to boost speed\n",
     "class Pricing_Pipe(CustomFactor):\n",
     "    \n",
     "    inputs = [USEquityPricing.close]\n",
     "    outputs = ['trendline', 'percent', 'oscillator', 'returns']\n",
     "    window_length=280\n",
     "    \n",
     "    def compute(self, today, assets, out, close):\n",
     "        out.trendline[:] = np.apply_along_axis(trendline_function, 0, close[-272:], True)\n",
     "        out.percent[:] = np.apply_along_axis(percent_helper, 0, close)\n",
     "        out.oscillator[:] =  np.apply_along_axis(oscillator_helper, 0, close[-272:])\n",
     "        out.returns[:] = np.apply_along_axis(return_helper, 0, close[-235:])\n",
     "        \n",
     "def Data_Pull():\n",
     "    \n",
     "    # create the piepline for the data pull\n",
     "    Data_Pipe = Pipeline()\n",
     "    \n",
     "    # create SPY proxy\n",
     "    Data_Pipe.add(SPY_proxy(), 'SPY Proxy')\n",
     "\n",
     "    # run all on same dataset for speed\n",
     "    trendline, percent, oscillator, returns = Pricing_Pipe()\n",
     "    \n",
     "    # add the calculated values\n",
     "    Data_Pipe.add(trendline, 'Trendline')\n",
     "    Data_Pipe.add(percent, 'Percent')\n",
     "    Data_Pipe.add(oscillator, 'Oscillator')\n",
     "    Data_Pipe.add(returns, 'Returns')\n",
     "        \n",
     "    return Data_Pipe\n",
     "\n",
     "results = run_pipeline(Data_Pull(), '2015-08-08', '2015-08-08')\n",
     "results.head(20)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We will now use the Lo/Patel ranking logic described in the Traditional Value notebook (https://www.quantopian.com/posts/quantopian-lecture-series-long-slash-short-traditional-value-case-study) in order to combine these desriptive metrics into a single factor.\n",
     "\n",
     "NB: `standard_frame_compute` and `composite_score` have been combined into a single function called `aggregate_data`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(2015-08-10 00:00:00+00:00, Equity(40442 [ANAC]))    5.909706\n",
        "(2015-08-10 00:00:00+00:00, Equity(39270 [ANTH]))    5.829848\n",
        "(2015-08-10 00:00:00+00:00, Equity(46354 [EGRX]))    5.827141\n",
        "(2015-08-10 00:00:00+00:00, Equity(22651 [HRTX]))    5.825644\n",
        "(2015-08-10 00:00:00+00:00, Equity(47191 [ADPT]))    5.564333\n",
        "(2015-08-10 00:00:00+00:00, Equity(46871 [RDUS]))    5.417749\n",
        "(2015-08-10 00:00:00+00:00, Equity(39804 [VLTC]))    5.415383\n",
        "(2015-08-10 00:00:00+00:00, Equity(33062 [SRNE]))    5.396920\n",
        "(2015-08-10 00:00:00+00:00, Equity(44935 [BLUE]))    5.154398\n",
        "(2015-08-10 00:00:00+00:00, Equity(46869 [ALDR]))    5.152219\n",
        "(2015-08-10 00:00:00+00:00, Equity(32497 [HBI]))     5.131514\n",
        "(2015-08-10 00:00:00+00:00, Equity(44689 [RCPT]))    5.039724\n",
        "(2015-08-10 00:00:00+00:00, Equity(40992 [ADXS]))    5.007482\n",
        "(2015-08-10 00:00:00+00:00, Equity(13867 [IMH]))     4.982655\n",
        "(2015-08-10 00:00:00+00:00, Equity(31341 [ZIOP]))    4.851253\n",
        "(2015-08-10 00:00:00+00:00, Equity(32331 [SGYP]))    4.805818\n",
        "(2015-08-10 00:00:00+00:00, Equity(41766 [HZNP]))    4.788361\n",
        "(2015-08-10 00:00:00+00:00, Equity(41663 [RTRX]))    4.779483\n",
        "(2015-08-10 00:00:00+00:00, Equity(11512 [LJPC]))    4.747644\n",
        "(2015-08-10 00:00:00+00:00, Equity(17331 [ZNH]))     4.706597\n",
        "(2015-08-10 00:00:00+00:00, Equity(42455 [CEMP]))    4.704212\n",
        "(2015-08-10 00:00:00+00:00, Equity(43730 [PRTA]))    4.697790\n",
        "(2015-08-10 00:00:00+00:00, Equity(44989 [ESPR]))    4.695852\n",
        "(2015-08-10 00:00:00+00:00, Equity(45239 [XON]))     4.633739\n",
        "(2015-08-10 00:00:00+00:00, Equity(14972 [NBIX]))    4.610307\n",
        "(2015-08-10 00:00:00+00:00, Equity(43495 [AMBA]))    4.607808\n",
        "(2015-08-10 00:00:00+00:00, Equity(21383 [EXEL]))    4.511022\n",
        "(2015-08-10 00:00:00+00:00, Equity(40807 [NPTN]))    4.456241\n",
        "(2015-08-10 00:00:00+00:00, Equity(24099 [NCT]))     4.301719\n",
        "(2015-08-10 00:00:00+00:00, Equity(36742 [TREE]))    4.272909\n",
        "                                                       ...   \n",
        "(2015-08-10 00:00:00+00:00, Equity(46939 [CVEO]))   -2.371987\n",
        "(2015-08-10 00:00:00+00:00, Equity(45133 [LIQD]))   -2.376540\n",
        "(2015-08-10 00:00:00+00:00, Equity(44107 [NSLP]))   -2.381352\n",
        "(2015-08-10 00:00:00+00:00, Equity(42820 [MPO]))    -2.386834\n",
        "(2015-08-10 00:00:00+00:00, Equity(6825 [SFY]))     -2.390722\n",
        "(2015-08-10 00:00:00+00:00, Equity(39287 [NSPR]))   -2.400395\n",
        "(2015-08-10 00:00:00+00:00, Equity(43552 [NETE]))   -2.401344\n",
        "(2015-08-10 00:00:00+00:00, Equity(41717 [XGTI]))   -2.413051\n",
        "(2015-08-10 00:00:00+00:00, Equity(40182 [RNO]))    -2.419800\n",
        "(2015-08-10 00:00:00+00:00, Equity(33984 [VTG]))    -2.442377\n",
        "(2015-08-10 00:00:00+00:00, Equity(1663 [CRK]))     -2.449673\n",
        "(2015-08-10 00:00:00+00:00, Equity(30148 [VPCO]))   -2.451658\n",
        "(2015-08-10 00:00:00+00:00, Equity(39627 [NOR]))    -2.453439\n",
        "(2015-08-10 00:00:00+00:00, Equity(8163 [WG]))      -2.487326\n",
        "(2015-08-10 00:00:00+00:00, Equity(26126 [BBD]))    -2.494402\n",
        "(2015-08-10 00:00:00+00:00, Equity(22660 [BTU]))    -2.499573\n",
        "(2015-08-10 00:00:00+00:00, Equity(26906 [WRES]))   -2.516253\n",
        "(2015-08-10 00:00:00+00:00, Equity(46192 [HELI]))   -2.531585\n",
        "(2015-08-10 00:00:00+00:00, Equity(13363 [GDP]))    -2.554851\n",
        "(2015-08-10 00:00:00+00:00, Equity(10458 [SPDC]))   -2.585789\n",
        "(2015-08-10 00:00:00+00:00, Equity(36189 [RGSE]))   -2.599698\n",
        "(2015-08-10 00:00:00+00:00, Equity(27747 [HERO]))   -2.609222\n",
        "(2015-08-10 00:00:00+00:00, Equity(30049 [LPTN]))   -2.615332\n",
        "(2015-08-10 00:00:00+00:00, Equity(46270 [EIGR]))   -2.617463\n",
        "(2015-08-10 00:00:00+00:00, Equity(39191 [DXM]))    -2.696096\n",
        "(2015-08-10 00:00:00+00:00, Equity(26560 [EOX]))    -2.801408\n",
        "(2015-08-10 00:00:00+00:00, Equity(88 [ACI]))       -2.939352\n",
        "(2015-08-10 00:00:00+00:00, Equity(27917 [FREE]))   -3.078166\n",
        "(2015-08-10 00:00:00+00:00, Equity(26663 [CPL]))    -3.169475\n",
        "(2015-08-10 00:00:00+00:00, Equity(10583 [MLNK]))   -3.438196\n",
        "dtype: float64"
       ]
      },
      "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# limit effect of outliers\n",
     "def filter_fn(x):\n",
     "    if x <= -10:\n",
     "        x = -10.0\n",
     "    elif x >= 10:\n",
     "        x = 10.0\n",
     "    return x   \n",
     "\n",
     "# combine data\n",
     "def aggregate_data(df):\n",
     "\n",
     "    # basic clean of dataset to remove infinite values\n",
     "    df = df.replace([np.inf, -np.inf], np.nan)\n",
     "    df = df.dropna()\n",
     "\n",
     "    # need standardization params from synthetic S&P500\n",
     "    df_SPY = df.sort(columns='SPY Proxy', ascending=False)\n",
     "\n",
     "    # create separate dataframe for SPY\n",
     "    # to store standardization values\n",
     "    df_SPY = df_SPY.head(500)\n",
     "\n",
     "    # get dataframes into numpy array\n",
     "    df_SPY = df_SPY.as_matrix()\n",
     "\n",
     "    # store index values\n",
     "    index = df.index.values\n",
     "\n",
     "    # get data intp a numpy array for speed\n",
     "    df = df.as_matrix()\n",
     "\n",
     "    # get one empty row on which to build standardized array\n",
     "    df_standard = np.empty(df.shape[0])\n",
     "\n",
     "    for col_SPY, col_full in zip(df_SPY.T, df.T):\n",
     "\n",
     "        # summary stats for S&P500\n",
     "        mu = np.mean(col_SPY)\n",
     "        sigma = np.std(col_SPY)\n",
     "        col_standard = np.array(((col_full - mu) / sigma))\n",
     "\n",
     "        # create vectorized function (lambda equivalent)\n",
     "        fltr = np.vectorize(filter_fn)\n",
     "        col_standard = (fltr(col_standard))\n",
     "\n",
     "        # make range between -10 and 10\n",
     "        col_standard = (col_standard / df.shape[1])\n",
     "\n",
     "        # attach calculated values as new row in df_standard\n",
     "        df_standard = np.vstack((df_standard, col_standard))\n",
     "\n",
     "    # get rid of first entry (empty scores)\n",
     "    df_standard = np.delete(df_standard, 0, 0)\n",
     "\n",
     "    # sum up transformed data\n",
     "    df_composite = df_standard.sum(axis=0)\n",
     "\n",
     "    # put into a pandas dataframe and connect numbers\n",
     "    # to equities via reindexing\n",
     "    df_composite = pd.Series(data=df_composite, index=index)\n",
     "\n",
     "    # sort descending\n",
     "    df_composite.sort(ascending=False)\n",
     "\n",
     "    return df_composite\n",
     "\n",
     "ranked_scores = aggregate_data(results)\n",
     "ranked_scores"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Stock Choice\n",
     "\n",
     "Now that we have our ranking system, let us have a look at the histogram of the ranked scores. This will allow us to see general trends in the metric and diagnose any issues with our ranking system as a factor. The red lines give our cut-off points for our trading baskets"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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fn5133jmf/vSns+mmm1YO57r55ptz9tlnr3b7vNvuu++eCRMmVA6FfPrppzNy5MgkyXe/\n+928+OKLSZYfErnBBhusdNGJXr16ZcKECZVDFMeNG5devXqt8Fn4oOuVLP8Mzp07N8nyz+Att9yS\nZcuWZcGCBbnzzjuz1157vef6v2OrrbbKWWedlXvvvTfjx49faY599903f//73/PQQw8lWf7+jhkz\nJltttVW+8IUvvOf8q/LOBU0+97nPVfYU7bPPPrnpppuybNmyFEWRX/ziF6sMwVV9L9bU1KSuri57\n7bVXbrjhhiTLP4enn376ai97DtAQ9igB65yGXha5Q4cO2WOPPTJ06NA0a9YszZs3r/yC3KdPn1x0\n0UWZNm1aTj311LzwwguVc3N23nnnHHbYYUmSc845J2eeeWauvPLKfPWrX03nzp0rr//uOb785S/n\nt7/9bfr06ZNNNtkkp59+ep5++ukcf/zxK+1tKc//Xutz6aWX5uyzz86YMWPSqlWrjB49uvKv7h/k\n0tD7779/fvWrX2XcuHE59NBD8+Uvfzn9+vVLx44dc+qpp2bixIkZPnx4Ro8e3aDn/fa3v537778/\n119/fQ499ND87W9/y+DBg1NTU5Otttoq/+f//J/Ktjv99NNz2WWXZa+99qqcI1T2q1/9KhdccEEG\nDhxYef2vfvWrOfLII5Mk//7v/55TTjkll1xySTp16lR5D99v+7zbxhtvnJ/85Cf5wQ9+kPr6+rRu\n3bqyR2T48OE56aSTUl9fnySVy3e/W+fOnXPeeefle9/7XpYuXZotttgiP/nJT1a7nVa3Xv369cuJ\nJ56Y4447LsOHD8/LL7+c/fbbL7W1tRkwYEDlqn/l9X9nr8w7z7nhhhvmRz/6UU4//fTceeedKxzW\n2b59+1xxxRW56KKLcv7556coiuywww4ZM2ZM5T4N/Ty+czXHd97fd7bXK6+8UjmPb7vttqus37uf\np0OHDvnyl7+8yu/Fc845J2effXZuvvnm1NTU5Ctf+Uo6d+682u0LsDo1RUP/WfFDuvDCCzNx4sQs\nXbo03/72t7P99tvnlFNOSVEU2XjjjXPhhRemefPmufPOO3Pttdemrq4uBx54YIYOHZr6+vqcdtpp\nefXVV1NXV5dRo0at9th6gLXZrrvumquvvjrbbLNNtUcBAN5Hox5698QTT+TFF1/MuHHj8stf/jIj\nR47M6NGjc9hhh+W6667LVlttlVtvvTULFy7M2LFjc8011+Taa6/NNddck7feeit333132rVrlxtu\nuCHf/e53c8kllzTmuAAfu+OPPz6//OUvk6Ty94bWxj/aCgCsqFFD6Utf+lLl5NcNNtggCxYsyJNP\nPpnevXsnSfbee+88+uijeeqpp9K9e/e0bt06LVu2zI477pgJEybkscceq1w6d7fddsvEiRMbc1yA\nj93xxx+f++67L/369cvIkSNz0UUXrXDVMQBg7dSo5yjV1tZm/fXXT5Lccsst2WuvvfLwww9X/sbE\nRhttlNdeey2zZs1Khw4dKo/r0KFDXn/99cycObOyvKamJrW1tamvr0+zZk6tAj4Z/u3f/q1ywQMA\n4JNjjRTHfffdl1tvvTVXXnll+vbtW1n+XqdHvdfy1f1xviSZMGHChxsSAABYZ/Ts2fN9v97oofTQ\nQw/liiuuyJVXXpk2bdqkdevWWbx4cVq0aJEZM2akc+fO6dSpU15//fXKY2bMmJEePXqkU6dOmTlz\nZrbZZpvKFYUasjdpdSvNGvbpTy/////4RzWnaBQTJkzweWON8XljTfJ5Y01bqz9zTfh3mXVVQ3au\nNOo5SvPmzctFF12Uyy67rPJXu3fdddfce++9SZJ77703e+yxR7p3757Jkydn3rx5mT9/fiZNmpSe\nPXumV69eueeee5Jkhb8dAQAA0JgadY/S73//+8yePTsnnHBCiqJITU1NLrjggpxxxhm58cYbs9lm\nm2Xw4MGpq6vLSSedlKOPPjq1tbU59thj06ZNmwwcODCPPPJIhg0blpYtW+b8889vzHEBAACSNHIo\nHXTQQTnooINWWn7VVVettKxv374rnL+ULL8YxDt/GA8AAGBNadRD7wAAAD6JhBIAAECJUAIAACgR\nSgAAACVCCQAAoEQoAQAAlAglAACAEqEEAABQIpQAAABKhBIAAECJUAIAACgRSgAAACVCCQAAoEQo\nAQAAlAglAACAEqEEAABQIpQAAABKhBIAAECJUAIAACgRSgAAACXNqj0AQGNbunRppkyZUu0xPrKp\nU6embdu2jfb8Xbp0SV1dXaM9PwB8kggloMmbMmVKho+4Ia3adar2KB/d3dMb5WkXzHktvxk1LF27\ndm2U5weATxqhBKwTWrXrlDbtN6/2GADAJ4RzlAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgB\nAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQA\nAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAA\nQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAA\nJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACU\nCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAi\nlAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQ\nAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJ\nAACgRCgBAACUCCUAAIASoQQAAFDS6KH0/PPPZ999983111+fJBkxYkQGDRqUww8/PIcffngefPDB\nJMmdd96ZoUOH5utf/3puueWWJEl9fX1OPvnkDBs2LMOHD8+0adMae1wAAIA0a8wnX7hwYS644IL0\n6tVrheUnn3xy9txzzxXuN3bs2Nx6661p1qxZhg4dmr59++b+++9Pu3btcvHFF+eRRx7JJZdckksv\nvbQxRwYAAGjcPUotW7bM5Zdfno4dO77v/Z566ql07949rVu3TsuWLbPjjjtmwoQJeeyxx9KnT58k\nyW677ZaJEyc25rgAAABJGjmUamtr06JFi5WWX3fddTniiCNy0kkn5c0338zMmTPToUOHytc7dOiQ\n119/fYXlNTU1qa2tTX19fWOODAAA0LiH3q3KAQcckA033DDbbrttrrjiiowZMyY9evRY4T5FUazy\nscuWLWvQa0yYMOEjz8nHZ7vFi5Mkk5vo++LztvabOnVqtUf4RJg8eXLmzp1b7TFYi/j5xpq2tn7m\nmvrvMqzaGg+lXXbZpfLf++yzT370ox+lf//++fOf/1xZPmPGjPTo0SOdOnXKzJkzs80221T2JDVr\ntvqRe/bs+fEPzof3f/cqNsX3ZcKECU1yvZqatm3bJndPr/YYa73tttsuXbt2rfYYrCX8fGNNW6s/\nc034d5l1VUOifI1fHvy4447LCy+8kCQZP358unbtmu7du2fy5MmZN29e5s+fn0mTJqVnz57p1atX\n7rnnniTJ/fffn5133nlNjwsAAKyDGnWP0lNPPZUzzzwzb7zxRurq6jJu3Lgcd9xxGTFiRFq3bp3W\nrVtn5MiRadmyZU466aQcffTRqa2tzbHHHps2bdpk4MCBeeSRRzJs2LC0bNky559/fmOOCwAAkKSR\nQ2mHHXbIXXfdtdLyfffdd6Vlffv2Td++fVdYVltbm1GjRjXafAAAAKuyxg+9AwAAWNsJJQAAgBKh\nBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQS\nAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoA\nAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEA\nAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAA\nUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABA\niVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAl\nQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQI\nJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgJIGhVJRFI09\nBwAAwFqjQaG0995759JLL83LL7/c2PMAAABUXYNC6aabbkr79u1zyimn5Mgjj8xdd92VxYsXN/Zs\nAAAAVdGgUOrUqVOOPPLIjBs3LqeddlquuOKK7LHHHrn00kuzaNGixp4RAABgjWrwxRwef/zxnHLK\nKfn+97+fPffcMzfccEM22GCDHH/88Y05HwAAwBrXrCF36tOnT7bYYoscdNBBGTlyZJo3b54k6dKl\nS+67775GHRAAAGBNa1AoXXnllSmKIp/+9KeTJM8991w+//nPJ0luuOGGRhsOAACgGhp06N1tt92W\nyy+/vHL78ssvz8UXX5wkqampaZzJAAAAqqRBofTEE09k1KhRldujR4/O//zP/zTaUAAAANXUoFBa\nsmTJCpcDnz9/fpYuXdpoQwEAAFRTg85ROvjggzNw4MBst912WbZsWZ555pn84Ac/aOzZAAAAqqJB\noXTggQemV69eeeaZZ1JTU5MRI0Zk0003bezZAAAAqqJBobRo0aI899xzmTdvXoqiyCOPPJIkGTp0\naKMOBwAAUA0NCqVvfOMbqa2tzeabb77CcqEEAAA0RQ0Kpfr6+owbN66xZwEAAFgrNOiqd5/5zGfy\n5ptvNvYsAAAAa4UG7VGaPn16+vbtmy5duqSurq6y/Prrr2+0wYCGWbp0aaZMmVLtMdZqL730UrVH\nAAA+YRoUSt/+9rcbew7gQ5oyZUqGj7ghrdp1qvYoa61Z0/43G23xuWqPAQB8gjQolHbaaac88MAD\nmTZtWg477LD885//zJZbbtnYswEN1Kpdp7Rpv/nq77iOWjBnRrVHAAA+YRp0jtJFF12UW265Jbfd\ndluS5K677sp5553XqIMBAABUS4NC6cknn8yYMWPSunXrJMkxxxyTZ599tkEv8Pzzz2ffffetnM80\nffr0DB8+PIcddlhOPPHELFmyJEly5513ZujQofn617+eW265Jcnyq+2dfPLJGTZsWIYPH55p06Z9\n4BUEAAD4oBoUSi1btkyS1NTUJFl+8vjSpUtX+7iFCxfmggsuSK9evSrLRo8eneHDh+e6667LVltt\nlVtvvTULFy7M2LFjc8011+Taa6/NNddck7feeit333132rVrlxtuuCHf/e53c8kll3yYdQQAAPhA\nGhRKO+64Y0aMGJHXXnstv/71r3PYYYdlp512Wu3jWrZsmcsvvzwdO3asLBs/fnz23nvvJMnee++d\nRx99NE899VS6d++e1q1bp2XLltlxxx0zYcKEPPbYY+nTp0+SZLfddsvEiRM/zDoCAAB8IA26mMOJ\nJ56Ye+65J+utt16mT5+eo446Kn379l3t42pra9OiRYsVli1cuDDNmzdPkmy00UZ57bXXMmvWrHTo\n0KFynw4dOuT111/PzJkzK8trampSW1ub+vr6NGvWoLEBAAA+lAYVx8svv5xu3bqlW7duKyz7qFe+\nK4riAy1ftmzZR3o9AACAhmhQKB1xxBGV85MWL16cN954I5/97Gdzxx13fOAXbN26dRYvXpwWLVpk\nxowZ6dy5czp16pTXX3+9cp8ZM2akR48e6dSpU2bOnJltttkm9fX1ywduwN6kCRMmfOC5aDzbLV6c\nJJncRN82+7BvAAAYI0lEQVSXan/epk6dWtXXp+mYPHly5s6dW+0xWItU++cb65619TPX1H+XYdUa\nFEr333//Crf/9re/Va5M90HtuuuuuffeezNo0KDce++92WOPPdK9e/eceeaZmTdvXmpqajJp0qSc\nccYZmTt3bu6555706tUr999/f3beeecGvUbPnj0/1Gw0kv97+GVTfF8mTJhQ9fVq27Ztcvf0qs5A\n07Dddtula9eu1R6DtcTa8PONdcta/Zlrwr/LrKsaEuUf6mSfz372sw26PPhTTz2VM888M2+88Ubq\n6uoybty4XHnllTnttNNy4403ZrPNNsvgwYNTV1eXk046KUcffXRqa2tz7LHHpk2bNhk4cGAeeeSR\nDBs2LC1btsz555//YcYFAAD4QBoUSqNHj17h9vTp0/PWW2+t9nE77LBD7rrrrpWWX3XVVSst69u3\n70oXiKitrc2oUaMaMiIAAMDHpkGXB6+rq1vh/7bZZpv88pe/bOzZAAAAqqJBe5S+973vVS7m8G7v\nXIWutrZBvQUAAPCJ0KBQ2mGHHbJ06dKVlhdFkZqamvzv//7vxz4YAABAtTQolI455ph85jOfSa9e\nvVJTU5M///nPmTJlSo499tjGng8AAGCNa9Axc48//nj23XfftGrVKuuvv34GDhyYJ598srFnAwAA\nqIoGhdLs2bPz4IMPZv78+Zk/f34efPDBvPnmm409GwAAQFU06NC7n/zkJzn//PNz4oknJkm6du2a\nc845p1EHAwAAqJYGhVL37t1zww03VC7eAAAA0JQ16NC7559/PkOGDMmAAQOSJGPHjs1TTz3VqIMB\nAABUS4NC6dxzz83IkSOz8cYbJ0kGDBiQUaNGNepgAAAA1dKgUGrWrFm23Xbbyu2tt946zZo16Kg9\nAACAT5wGh9LLL79cOT/pwQcfTFEUjToYAABAtTRot9Cpp56a73//+3nppZfSs2fPbL755rnwwgsb\nezYAAICqaFAobbjhhrnrrrvyxhtvpEWLFmnTpk1jzwUAAFA1DTr07uSTT06SdOjQQSQBAABNXoP2\nKG299db5//6//y89evRI8+bNK8uHDh3aaIMBAABUy/uG0vPPP59tt902S5YsSV1dXR588MG0b9++\n8nWhBAAANEXvG0ojR47MtddeW/mbSYcffnguu+yyNTIYAABAtbzvOUouAQ4AAKyL3jeU3vm7Se8Q\nTgAAwLqgQVe9e0c5nAAAAJqi9z1HadKkSdlrr70qt2fNmpW99torRVGkpqYmDzzwQCOPBwAAsOa9\nbyjdc889a2oOAACAtcb7htLmm2++puYAAABYa3ygc5QAAADWBUIJAACgRCgBAACUCCUAAIASoQQA\nAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAA\nQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAA\nJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACU\nCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAi\nlAAAAEqEEgAAQIlQAgAAKGlW7QEAqL5i2bK89NJL1R5jrdelS5fU1dVVewwA1gChBEAWzn09Z18x\nM63aTan2KGutBXNey29GDUvXrl2rPQoAa4BQAiBJ0qpdp7Rpv3m1xwCAtYJzlAAAAEqEEgAAQIlQ\nAgAAKBFKAAAAJUIJAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJ\nAACgRCgBAACUCCUAAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJUIJAACgRCgBAACUCCUA\nAIASoQQAAFAilAAAAEqEEgAAQIlQAgAAKBFKAAAAJc3W9AuOHz8+xx9/fD772c+mKIpss802+eY3\nv5lTTjklRVFk4403zoUXXpjmzZvnzjvvzLXXXpu6uroceOCBGTp06JoeFwAAWAet8VBKkp122imj\nR4+u3B4xYkSGDx+evn375tJLL82tt96aAw44IGPHjs2tt96aZs2aZejQoenbt2822GCDaowMAACs\nQ6py6F1RFCvcHj9+fPbee+8kyd57751HH300Tz31VLp3757WrVunZcuW2XHHHTNx4sRqjAsAAKxj\nqrJHacqUKfn+97+fOXPm5Jhjjsnbb7+d5s2bJ0k22mijvPbaa5k1a1Y6dOhQeUyHDh3y+uuvV2Nc\nAABgHbPGQ+lTn/pUfvCDH2TAgAF5+eWXc/jhh6e+vr7y9fLeptUtX5UJEyZ85Dn5+Gy3eHGSZHIT\nfV+q/XmbOnVqVV8f1iWTJ0/O3Llzqz3GGlPtn2+se9bWz1xT/12GVVvjodS5c+cMGDAgSbLlllum\nY8eOmTx5chYvXpwWLVpkxowZ6dy5czp16rTCHqQZM2akR48eDXqNnj17NsrsfEgtWiRpmu/LhAkT\nqr5ebdu2Te6eXtUZYF2x3XbbpWvXrtUeY41YG36+sW5Zqz9zTfh3mXVVQ6J8jZ+jdNddd2XMmDFJ\nklmzZmXWrFkZMmRI7rnnniTJvffemz322CPdu3fP5MmTM2/evMyfPz+TJk3y4QQAANaINb5HqXfv\n3jnppJNyyCGHpCiK/PjHP862226bU089NTfddFM222yzDB48OHV1dTnppJNy9NFHp7a2Nscee2za\ntGmzpscFAADWQWs8lFq3bp3LLrtspeVXXXXVSsv69u2bvn37romxAAAAKqpyeXAAAIC1mVACAAAo\nEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBE\nKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKh\nBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQS\nAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoA\nAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEA\nAJQIJQAAgJJm1R4A3s/SpUszZcqUao/xnqZOnZq2bdtWdYaXXnqpqq8PANAUCSXWalOmTMnwETek\nVbtO1R7lvd09vaovP2va/2ajLT5X1RkAAJoaocRar1W7TmnTfvNqj7HWWjBnRrVHAABocpyjBAAA\nUCKUAAAASoQSAABAiVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASoQSAABA\niVACAAAoEUoAAAAlQgkAAKBEKAEAAJQIJQAAgBKhBAAAUCKUAAAASppVewAA+CQoli3LSy+9VO0x\n1pipU6embdu2H/hxXbp0SV1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       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fdd3db5b550>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# histogram\n",
     "ranked_scores.hist()\n",
     "\n",
     "# baskets\n",
     "plt.axvline(ranked_scores[26], color='r')\n",
     "plt.axvline(ranked_scores[-6], color='r')\n",
     "plt.xlabel('Ranked Scores')\n",
     "plt.ylabel('Frequency')\n",
     "plt.title('Histogram of Ranked Scores of Stock Universe');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
    "source": [
     "Although there does appear to be some positive skew, this looks to be a robust metric as the tails of this distribution are very thin. A thinner tail means that our ranking system has identified special characteristics about our stock universe possessed by only a few equities. More thorough statistical analysis would have to be conducted in order to see if this strategy could generate good alpha returns. This robust factor analysis will be covered in a later notebook.\n",
     "\n",
     "Please see the attached algorithm for a full implementation!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "*The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian.*\n",
     "\n",
     "*In addition, the content of the website neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have 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": "2.7.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }