{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, being suspicious of market insiders.\n",
    "\n",
    "---\n",
    "\n",
    "Use the button below to show the code I've used to generate this article. Because there is a significant amount more code involved than most other posts I've written, it's hidden by default to allow people to concentrate on the important bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Market Just Knew\n",
    "\n",
    "I recently saw two examples of stock charts that have kept me thinking for a while. And now that the semester is complete, I finally have enough time to really look at them and give them the treatment they deserve. The first is good old Apple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UTujFyp588snxpS99qX/dY489FpL6E/rs2bPjsssui+7u7gFf3+LFi2PzzTeP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\nrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae3a9b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from secrets import QUANDL_KEY\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import date2num\n",
    "from matplotlib.finance import candlestick_ohlc\n",
    "from matplotlib.dates import DateFormatter, WeekdayLocator,\\\n",
    "    DayLocator, MONDAY\n",
    "import quandl\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "def fetch_ticker(ticker, start, end):\n",
    "    # Quandl is currently giving me issues with returning\n",
    "    # the entire dataset and not slicing server-side.\n",
    "    # So instead, we'll do it client-side!\n",
    "    q_format = '%Y-%m-%d'\n",
    "    ticker_data = quandl.get('YAHOO/' + ticker,\n",
    "                             start_date=start.strftime(q_format),\n",
    "                             end_date=end.strftime(q_format),\n",
    "                             authtoken=QUANDL_KEY)\n",
    "    return ticker_data\n",
    "\n",
    "def ohlc_dataframe(data, ax=None):\n",
    "    # Much of this code re-used from:\n",
    "    # http://matplotlib.org/examples/pylab_examples/finance_demo.html\n",
    "    if ax is None:\n",
    "        f, ax = plt.subplots()\n",
    "    \n",
    "    vals = [(date2num(date), *(data.loc[date]))\n",
    "            for date in data.index]\n",
    "    candlestick_ohlc(ax, vals)\n",
    "    \n",
    "    mondays = WeekdayLocator(MONDAY)\n",
    "    alldays = DayLocator()\n",
    "    weekFormatter = DateFormatter('%b %d')\n",
    "    ax.xaxis.set_major_locator(mondays)\n",
    "    ax.xaxis.set_minor_locator(alldays)\n",
    "    ax.xaxis.set_major_formatter(weekFormatter)\n",
    "    return ax\n",
    "\n",
    "AAPL = fetch_ticker('AAPL', datetime(2016, 3, 1), datetime(2016, 5, 1))\n",
    "ax = ohlc_dataframe(AAPL)\n",
    "plt.vlines(date2num(datetime(2016, 4, 26, 12)),\n",
    "           ax.get_ylim()[0], ax.get_ylim()[1],\n",
    "           color='b',\n",
    "          label='Earnings Release')\n",
    "plt.legend(loc=3)\n",
    "plt.title(\"Apple Price 3/1/2016 - 5/1/2016\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second chart is from Facebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gKCPV9HmNJ4v/yRkk7d/FiIegZ97vgHeAdcA/D2Q84Xh3AtMi37d1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\nczHjid0552LGE7tzzsXM/wfgLII59KYqMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae1b60f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FB = fetch_ticker('FB', datetime(2016, 3, 1), datetime(2016, 5, 5))\n",
    "ax = ohlc_dataframe(FB)\n",
    "plt.vlines(date2num(datetime(2016, 4, 27, 12)),\n",
    "           ax.get_ylim()[0], ax.get_ylim()[1],\n",
    "           color='b', label='Earnings Release')\n",
    "plt.title('Facebook Price 3/5/2016 - 5/5/2016')\n",
    "plt.legend(loc=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two charts demonstrate two very specific phonomena: how the market prepares for earnings releases. Let's look at those charts again, but with some extra information. As we're about the see, the market \"knew\" in advance that Apple was going to perform poorly. The market expected that Facebook was going to perform poorly, and instead shot the lights out. Let's see that trend in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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44ogPszxGE3BFJKYkJSVF9PzbtsHEiT5ZUKXKv/UOSpaM6GVFRERERGKCZiKI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\n33lBkmPpUjjvPNhnH/83nzsXHnvMt8YUERERkTI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mvzFrCLwKXOWcq7KR2aAMek+WcNIq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\ncAw4PwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae50d7630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hilo(ax, start, end, data):\n",
    "    ax.plot([date2num(start), date2num(end)],\n",
    "            [data.loc[start]['High'], data.loc[end]['High']],\n",
    "            color='b')\n",
    "    ax.plot([date2num(start), date2num(end)],\n",
    "            [data.loc[start]['Low'], data.loc[end]['Low']],\n",
    "            color='b')\n",
    "\n",
    "f, axarr = plt.subplots(1, 2)\n",
    "\n",
    "ax_aapl = axarr[0]\n",
    "ax_fb = axarr[1]\n",
    "\n",
    "# Plot the AAPL trend up and down\n",
    "ohlc_dataframe(AAPL, ax=ax_aapl)\n",
    "plot_hilo(ax_aapl, datetime(2016, 3, 1), datetime(2016, 4, 15), AAPL)\n",
    "plot_hilo(ax_aapl, datetime(2016, 4, 18), datetime(2016, 4, 26), AAPL)\n",
    "ax_aapl.vlines(date2num(datetime(2016, 4, 26, 12)),\n",
    "               ax_aapl.get_ylim()[0], ax_aapl.get_ylim()[1],\n",
    "               color='g', label='Earnings Release')\n",
    "ax_aapl.legend(loc=2)\n",
    "ax_aapl.set_title('AAPL Price History')\n",
    "\n",
    "# Plot the FB trend down and up\n",
    "ohlc_dataframe(FB, ax=ax_fb)\n",
    "plot_hilo(ax_fb, datetime(2016, 3, 30), datetime(2016, 4, 27), FB)\n",
    "plot_hilo(ax_fb, datetime(2016, 4, 28), datetime(2016, 5, 5), FB)\n",
    "ax_fb.vlines(date2num(datetime(2016, 4, 27, 12)),\n",
    "             ax_fb.get_ylim()[0], ax_fb.get_ylim()[1],\n",
    "             color='g', label='Earnings Release')\n",
    "ax_fb.legend(loc=2)\n",
    "ax_fb.set_title('FB Price History')\n",
    "\n",
    "f.set_size_inches(18, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the market broke a prevailing trend on Apple in order to go down, and ultimately predict the earnings release. For Facebook, the opposite happened. While the trend was down, the earnings were fantastic and the market corrected itself much higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Formulating the Question\n",
    "\n",
    "While these are two specific examples, there are plenty of other examples you could cite one way or another. Even if the preponderance of evidence shows that the market correctly predicts earnings releases, we need not accuse people of collusion; for a company like Apple with many suppliers we can generally forecast how Apple has done based on those same suppliers.\n",
    "\n",
    "The question then, is this: **how well does the market predict the earnings releases?** It's an incredibly broad question that I want to disect in a couple of different ways:\n",
    "\n",
    "1. Given a stock that has been trending down over the past N days before an earnings release, how likely does it continue downward after the release?\n",
    "2. Given a stock trending up, how likely does it continue up?\n",
    "3. Is there a difference in accuracy between large- and small-cap stocks?\n",
    "4. How often, and for how long, do markets trend before an earnings release?\n",
    "\n",
    "**I want to especially thank Alejandro Saltiel for helping me retrieve the data.** He's great. And now for all of the interesting bits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Event Studies\n",
    "\n",
    "Before we go too much further, I want to introduce the actual event study. Each chart intends to capture a lot of information and present an easy-to-understand pattern:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bl1tuuYUvvvgCp9MZHH0FGD9+PH379gVgzJgxZGZmNhqia0a8p02bRlFREYWF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\nP9zuOAVrkaNEIVpERKSTiYiIYNiwYQw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lqKiItLQ0nnjiiSrcARGR4FLTKe4eBhJLX6cD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae4ffddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "from pandas.tseries.offsets import CustomBusinessDay\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# If you remove rules, it removes them from *all* calendars\n",
    "# To ensure we don't pop rules we don't want to, first make\n",
    "# sure to fully copy the object\n",
    "trade_calendar = USFederalHolidayCalendar()\n",
    "trade_calendar.rules.pop(6) # Remove Columbus day\n",
    "trade_calendar.rules.pop(7) # Remove Veteran's day\n",
    "TradeDay = lambda days: CustomBusinessDay(days, calendar=trade_calendar)\n",
    "\n",
    "def plot_study(array):\n",
    "    # Given a 2-d array, we assume the event happens at index `lookback`,\n",
    "    # and create all of our summary statistics from there.\n",
    "    lookback = int((array.shape[1] - 1) / 2)\n",
    "    norm_factor = np.repeat(array[:,lookback].reshape(-1, 1), array.shape[1], axis=1)\n",
    "    centered_data = array / norm_factor - 1\n",
    "    lookforward = centered_data.shape[1] - lookback\n",
    "    means = centered_data.mean(axis=0)\n",
    "    lookforward_data = centered_data[:,lookforward:]\n",
    "    std_dev = np.hstack([0, lookforward_data.std(axis=0)])\n",
    "    maxes = lookforward_data.max(axis=0)\n",
    "    mins = lookforward_data.min(axis=0)\n",
    "    \n",
    "    f, axarr = plt.subplots(1, 2)\n",
    "    range_begin = -lookback\n",
    "    range_end = lookforward\n",
    "    axarr[0].plot(range(range_begin, range_end), means)\n",
    "    axarr[1].plot(range(range_begin, range_end), means)\n",
    "    axarr[0].fill_between(range(0, range_end),\n",
    "                     means[-lookforward:] + std_dev,\n",
    "                     means[-lookforward:] - std_dev,\n",
    "                    alpha=.5, label=\"$\\pm$ 1 s.d.\")\n",
    "    axarr[1].fill_between(range(0, range_end),\n",
    "                     means[-lookforward:] + std_dev,\n",
    "                     means[-lookforward:] - std_dev,\n",
    "                    alpha=.5, label=\"$\\pm$ 1 s.d.\")\n",
    "    \n",
    "    max_err = maxes - means[-lookforward+1:]\n",
    "    min_err = means[-lookforward+1:] - mins\n",
    "    axarr[0].errorbar(range(1, range_end),\n",
    "                  means[-lookforward+1:],\n",
    "                  yerr=[min_err, max_err], label='Max & Min')\n",
    "    axarr[0].legend(loc=2)\n",
    "    axarr[1].legend(loc=2)\n",
    "    \n",
    "    axarr[0].set_xlim((-lookback-1, lookback+1))\n",
    "    axarr[1].set_xlim((-lookback-1, lookback+1))\n",
    "    \n",
    "def plot_study_small(array):\n",
    "    # Given a 2-d array, we assume the event happens at index `lookback`,\n",
    "    # and create all of our summary statistics from there.\n",
    "    lookback = int((array.shape[1] - 1) / 2)\n",
    "    norm_factor = np.repeat(array[:,lookback].reshape(-1, 1), array.shape[1], axis=1)\n",
    "    centered_data = array / norm_factor - 1\n",
    "    lookforward = centered_data.shape[1] - lookback\n",
    "    means = centered_data.mean(axis=0)\n",
    "    lookforward_data = centered_data[:,lookforward:]\n",
    "    std_dev = np.hstack([0, lookforward_data.std(axis=0)])\n",
    "    maxes = lookforward_data.max(axis=0)\n",
    "    mins = lookforward_data.min(axis=0)\n",
    "    \n",
    "    range_begin = -lookback\n",
    "    range_end = lookforward\n",
    "    plt.plot(range(range_begin, range_end), means)\n",
    "    plt.fill_between(range(0, range_end),\n",
    "                     means[-lookforward:] + std_dev,\n",
    "                     means[-lookforward:] - std_dev,\n",
    "                    alpha=.5, label=\"$\\pm$ 1 s.d.\")\n",
    "    \n",
    "    max_err = maxes - means[-lookforward+1:]\n",
    "    min_err = means[-lookforward+1:] - mins\n",
    "    plt.errorbar(range(1, range_end),\n",
    "                  means[-lookforward+1:],\n",
    "                  yerr=[min_err, max_err], label='Max & Min')\n",
    "    plt.legend(loc=2)\n",
    "    plt.xlim((-lookback-1, lookback+1))\n",
    "    \n",
    "def fetch_event_data(ticker, events, horizon=5):\n",
    "    # Use horizon+1 to account for including the day of the event,\n",
    "    # and half-open interval - that is, for a horizon of 5,\n",
    "    # we should be including 11 events. Additionally, using the\n",
    "    # CustomBusinessDay means we automatically handle issues if\n",
    "    # for example a company reports Friday afternoon - the date\n",
    "    # calculator will turn this into a \"Saturday\" release, but\n",
    "    # we effectively shift that to Monday with the logic below.\n",
    "    td_back = TradeDay(horizon+1)\n",
    "    td_forward = TradeDay(horizon+1)\n",
    "    \n",
    "    start_date = min(events) - td_back\n",
    "    end_date = max(events) + td_forward\n",
    "    total_data = fetch_ticker(ticker, start_date, end_date)\n",
    "    event_data = [total_data.ix[event-td_back:event+td_forward]\\\n",
    "                      [0:horizon*2+1]\\\n",
    "                      ['Adjusted Close']\n",
    "                  for event in events]\n",
    "    return np.array(event_data)\n",
    "\n",
    "# Generate a couple of random events\n",
    "\n",
    "event_dates = [datetime(2016, 5, 27) - timedelta(days=1) - TradeDay(x*20) for x in range(1, 40)]\n",
    "data = fetch_event_data('CELG', event_dates)\n",
    "plot_study_small(data)\n",
    "plt.legend(loc=3)\n",
    "plt.gcf().set_size_inches(12, 6);\n",
    "\n",
    "\n",
    "plt.annotate('Mean price for days leading up to each event',\n",
    "             (-5, -.01), (-4.5, .025),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "plt.annotate('', (-.1, .005), (-.5, .02),\n",
    "             arrowprops={'facecolor': 'black', 'shrink': .05})\n",
    "plt.annotate('$\\pm$ 1 std. dev. each day', (5, .055), (2.5, .085),\n",
    "            arrowprops={'facecolor': 'black', 'shrink': .05})\n",
    "plt.annotate('Min/Max each day', (.9, -.07), (-1, -.1),\n",
    "            arrowprops={'facecolor': 'black', 'shrink': .05});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And as a quick textual explanation as well:\n",
    "\n",
    "- The blue line represents the mean price for each day, represented as a percentage of the price on the '0-day'. For example, if we defined an 'event' as whenever the stock price dropped for three days, we would see a decreasing blue line to the left of the 0-day.\n",
    "\n",
    "- The blue shaded area represents one standard deviation above and below the mean price for each day following an event. This is intended to give us an idea of what the stock price does in general following an event.\n",
    "\n",
    "- The green bars are the minimum and maximum price for each day following an event. This instructs us as to how much it's possible for the stock to move."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Event Type 1: Trending down over the past N days\n",
    "\n",
    "The first type of event I want to study is how stocks perform when they've been trending down over the past couple of days prior to a release. However, we need to clarify what exactly is meant by \"trending down.\" To do so, we'll use the following metric: **the midpoint between each day's opening and closing price goes down over a period of N days**.\n",
    "\n",
    "It's probably helpful to have an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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CBPj880JHJ0mqi0kESZKkZmTx4qzOwKmnwkMPwcEHFzqilbPaavC978HTT8OV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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae6c517b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(1, 2)\n",
    "f.set_size_inches(18, 6)\n",
    "\n",
    "FB_plot = axarr[0]\n",
    "ohlc_dataframe(FB[datetime(2016, 4, 18):], FB_plot)\n",
    "\n",
    "FB_truncated = FB[datetime(2016, 4, 18):datetime(2016, 4, 27)]\n",
    "midpoint = FB_truncated['Open']/2 + FB_truncated['Close']/2\n",
    "FB_plot.plot(FB_truncated.index, midpoint, label='Midpoint')\n",
    "FB_plot.vlines(date2num(datetime(2016, 4, 27, 12)),\n",
    "               ax_fb.get_ylim()[0], ax_fb.get_ylim()[1],\n",
    "               color='g', label='Earnings Release')\n",
    "FB_plot.legend(loc=2)\n",
    "FB_plot.set_title('FB Midpoint Plot')\n",
    "\n",
    "AAPL_plot = axarr[1]\n",
    "ohlc_dataframe(AAPL[datetime(2016, 4, 10):], AAPL_plot)\n",
    "AAPL_truncated = AAPL[datetime(2016, 4, 10):datetime(2016, 4, 26)]\n",
    "midpoint = AAPL_truncated['Open']/2 + AAPL_truncated['Close']/2\n",
    "AAPL_plot.plot(AAPL_truncated.index, midpoint, label='Midpoint')\n",
    "AAPL_plot.vlines(date2num(datetime(2016, 4, 26, 12)),\n",
    "                 ax_aapl.get_ylim()[0], ax_aapl.get_ylim()[1],\n",
    "                 color='g', label='Earnings Release')\n",
    "AAPL_plot.legend(loc=3)\n",
    "AAPL_plot.set_title('AAPL Midpoint Plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given these charts, we can see that FB was trending down for the four days preceding the earnings release, and AAPL was trending down for a whopping 8 days (we don't count the peak day). This will define the methodology that we will use for the study.\n",
    "\n",
    "So what are the results? For a given horizon, how well does the market actually perform?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (47578 of 47578) |###########################################################| Elapsed Time: 0:21:38 Time: 0:21:38\n"
     ]
    },
    {
     "data": {
      "image/png": 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zS5IkSVLJvPbaazz77LM8+eSzPPzwPFasaAJ2o1+/fRk69H306TMw7xKl3JQi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\n6dOn65ZbbrnS3tfXpzNnzigajepnP4uqufk1HTsW1YkTF2U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae7308128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the events for each stock;\n",
    "# The file was created using the first code block in the Appendix\n",
    "import yaml\n",
    "from dateutil.parser import parse\n",
    "from progressbar import ProgressBar\n",
    "\n",
    "data_str = open('earnings_dates.yaml', 'r').read()\n",
    "# Need to remove invalid lines\n",
    "filtered = filter(lambda x: '{' not in x, data_str.split('\\n'))\n",
    "earnings_data = yaml.load('\\n'.join(filtered))\n",
    "\n",
    "# Convert our earnings data into a list of (ticker, date) pairs\n",
    "# to make it easy to work with.\n",
    "# This is horribly inefficient, but should get us what we need\n",
    "ticker_dates = []\n",
    "for ticker, date_list in earnings_data.items():\n",
    "    for iso_str in date_list:\n",
    "        ticker_dates.append((ticker, parse(iso_str)))\n",
    "\n",
    "def does_trend_down(ticker, event, horizon):\n",
    "    # Figure out if the `event` has a downtrend for\n",
    "    # the `horizon` days preceding it\n",
    "    # As an interpretation note: it is assumed that\n",
    "    # the closing price of day `event` is the reference\n",
    "    # point, and we want `horizon` days before that.\n",
    "    # The price_data.hdf was created in the second appendix code block\n",
    "    try:\n",
    "        ticker_data = pd.read_hdf('price_data.hdf', ticker)\n",
    "        data = ticker_data[event-TradeDay(horizon):event]\n",
    "        midpoints = data['Open']/2 + data['Close']/2\n",
    "\n",
    "        # Shift dates one forward into the future and subtract\n",
    "        # Effectively: do we trend down over all days?\n",
    "        elems = midpoints - midpoints.shift(1)\n",
    "        return len(elems)-1 == len(elems.dropna()[elems <= 0])\n",
    "    except KeyError:\n",
    "        # If the stock doesn't exist, it doesn't qualify as trending down\n",
    "        # Mostly this is here to make sure the entire analysis doesn't \n",
    "        # blow up if there were issues in data retrieval\n",
    "        return False\n",
    "\n",
    "def study_trend(horizon, trend_function):\n",
    "    five_day_events = np.zeros((1, horizon*2 + 1))\n",
    "    invalid_events = []\n",
    "    for ticker, event in ProgressBar()(ticker_dates):\n",
    "        if trend_function(ticker, event, horizon):\n",
    "            ticker_data = pd.read_hdf('price_data.hdf', ticker)\n",
    "            event_data = ticker_data[event-TradeDay(horizon):event+TradeDay(horizon)]['Close']\n",
    "\n",
    "            try:\n",
    "                five_day_events = np.vstack([five_day_events, event_data])\n",
    "            except ValueError:\n",
    "                # Sometimes we don't get exactly the right number of values due to calendar\n",
    "                # issues. I've fixed most everything I can, and the few issues that are left\n",
    "                # I assume don't systemically bias the results (i.e. data could be missing\n",
    "                # because it doesn't exist, etc.). After running through, ~1% of events get\n",
    "                # discarded this way\n",
    "                invalid_events.append((ticker, event))\n",
    "            \n",
    "\n",
    "    # Remove our initial zero row\n",
    "    five_day_events = five_day_events[1:,:]\n",
    "    plot_study(five_day_events)\n",
    "    plt.gcf().suptitle('Action over {} days: {} events'\n",
    "                       .format(horizon,five_day_events.shape[0]))\n",
    "    plt.gcf().set_size_inches(18, 6)\n",
    "    \n",
    "# Start with a 5 day study\n",
    "study_trend(5, does_trend_down)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a stock has been trending down for 5 days, once the earnings are announced it really doesn't move on average. However, the variability is *incredible*. This implies two important things:\n",
    "\n",
    "1. The market is just as often wrong about an earnings announcement before it happens as it is correct\n",
    "2. The incredible width of the min/max bars and standard deviation area tell us that the market reacts *violently* after the earnings are released.\n",
    "\n",
    "Let's repeat the same study, but over a time horizon of 8 days and 3 days. Presumably if a stock has been going down for 8 days at a time before the earnings, the market should be more accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (47578 of 47578) |###########################################################| Elapsed Time: 0:20:29 Time: 0:20:29\n"
     ]
    },
    {
     "data": {
      "image/png": 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ln7OISOu1q/aIhkWIiIiIiIiISJMoXBARERERERGRJlG4ICIiIiIiIiJNonBBRERERERE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\nAAAAKMr/B9pAH9lY/+K6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae6c59668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8 day study next\n",
    "study_trend(8, does_trend_down)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, looking only at stocks that trended down for 8 days prior to a release, the same pattern emerges: on average, the stock doesn't move, but the market reaction is often incredibly violent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (47578 of 47578) |###########################################################| Elapsed Time: 0:26:26 Time: 0:26:26\n"
     ]
    },
    {
     "data": {
      "image/png": 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6nZh0KVKTMECQJEmSpD3w5JMzqKg4nDZtOiVditQkDBAkSZIkaTetWbOGRx99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\nSTfmKmb+uQ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae7326828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3 day study after that\n",
    "study_trend(3, does_trend_down)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, when we look at a 3-day horizon, we start getting some incredible outliers. Stocks have a potential to move over ~300% up, and the standard deviation width is again, incredible. The results for a 3-day horizon follow the same pattern we've seen in the 5- and 8-day horizons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Event Type 2: Trending up for N days\n",
    "\n",
    "We're now going to repeat the analysis, but do it for uptrends instead. That is, instead of looking at stocks that have been trending down over the past number of days, we focus only on stocks that have been trending up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (47578 of 47578) |###########################################################| Elapsed Time: 0:22:51 Time: 0:22:51\n"
     ]
    },
    {
     "data": {
      "image/png": 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GLwAniwABAAAAQMKrr6/X+++vVY8eQ/0uBYhbBAgAAAAAEt6aNWtUV9dX6ek5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\nJGnmzHfDNXhT5OvI+dbaHG/9yf6UpKl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae6e8d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def does_trend_up(ticker, event, horizon):\n",
    "    # Figure out if the `event` has an uptrend for\n",
    "    # the `horizon` days preceding it\n",
    "    # As an interpretation note: it is assumed that\n",
    "    # the closing price of day `event` is the reference\n",
    "    # point, and we want `horizon` days before that.\n",
    "    # The price_data.hdf was created in the second appendix code block\n",
    "    try:\n",
    "        ticker_data = pd.read_hdf('price_data.hdf', ticker)\n",
    "        data = ticker_data[event-TradeDay(horizon):event]\n",
    "        midpoints = data['Open']/2 + data['Close']/2\n",
    "\n",
    "        # Shift dates one forward into the future and subtract\n",
    "        # Effectively: do we trend down over all days?\n",
    "        elems = midpoints - midpoints.shift(1)\n",
    "        return len(elems)-1 == len(elems.dropna()[elems >= 0])\n",
    "    except KeyError:\n",
    "        # If the stock doesn't exist, it doesn't qualify as trending down\n",
    "        # Mostly this is here to make sure the entire analysis doesn't \n",
    "        # blow up if there were issues in data retrieval\n",
    "        return False\n",
    "\n",
    "study_trend(5, does_trend_up)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The patterns here are very similar. With the exception of noting that stocks can go to nearly 400% after an earnings announcement (most likely this included a takeover announcement, etc.), we still see large min/max bars and wide standard deviation of returns.\n",
    "\n",
    "We'll repeat the pattern for stocks going up for both 8 and 3 days straight, but at this point, the results should be very predictable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (47578 of 47578) |###########################################################| Elapsed Time: 0:20:51 Time: 0:20:51\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gjs5HRGQkqaur45VX3mTduh1s2bKTSCQXKMK5InJzTyQ/v4iSkjFp3RvhcAT8LmAk6us1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\nSIQHpzdOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae76e6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "study_trend(8, does_trend_up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (47578 of 47578) |###########################################################| Elapsed Time: 0:26:56 Time: 0:26:56\n"
     ]
    },
    {
     "data": {
      "image/png": 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Un39Zy24MQLMQIAAAAABJoKZGWr78yP0LZs2SsrK8sODqq6X//m+p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fae7851390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "study_trend(3, does_trend_up)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion and Summary\n",
    "\n",
    "I guess the most important thing to summarize with is this: **looking at the entire market, stock performance prior to an earnings release has no bearing on the stock's performance.** Honestly: given the huge variability of returns after an earnings release, even when the stock has been trending for a long time, you're best off divesting before an earnings release and letting the market sort itself out.\n",
    "\n",
    "*However*, there is a big caveat. These results are taken when we look at the entire market. So while we can say that the market as a whole knows nothing and just reacts violently, I want to take a closer look into this data. Does the market typically perform poorly on large-cap/high liquidity stocks? Do smaller companies have investors that know them better and can thus predict performance better? Are specific market sectors better at prediction? Presumably technology stocks are more volatile than the industrials.\n",
    "\n",
    "So there are some more interesting questions I still want to ask with this data. Knowing that the hard work of data processing is largely already done, it should be fairly simple to continue this analysis and get much more refined with it. Until next time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix\n",
    "\n",
    "Export event data for Russell 3000 companies:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "from html.parser import HTMLParser\n",
    "from datetime import datetime, timedelta\n",
    "import requests\n",
    "import re\n",
    "from dateutil import parser\n",
    "import progressbar\n",
    "from concurrent import futures\n",
    "import yaml\n",
    "\n",
    "class EarningsParser(HTMLParser):\n",
    "    store_dates = False\n",
    "    earnings_offset = None\n",
    "    dates = []\n",
    "    \n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.dates = []\n",
    "    \n",
    "    def handle_starttag(self, tag, attrs):\n",
    "        if tag == 'table':\n",
    "            self.store_dates = True\n",
    "            \n",
    "    def handle_data(self, data):\n",
    "        if self.store_dates:\n",
    "            match = re.match(r'\\d+/\\d+/\\d+', data)\n",
    "            if match:\n",
    "                self.dates.append(match.group(0))\n",
    "        \n",
    "        # If a company reports before the bell, record the earnings date\n",
    "        # being at midnight the day before. Ex: WMT reports 5/19/2016,\n",
    "        # but we want the reference point to be the closing price on 5/18/2016\n",
    "        if 'After Close' in data:\n",
    "            self.earnings_offset = timedelta(days=0)\n",
    "        elif 'Before Open' in data:\n",
    "            self.earnings_offset = timedelta(days=-1)\n",
    "                \n",
    "    def handle_endtag(self, tag):\n",
    "        if tag == 'table':\n",
    "            self.store_dates = False\n",
    "            \n",
    "def earnings_releases(ticker):\n",
    "    #print(\"Looking up ticker {}\".format(ticker))\n",
    "    user_agent = 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:46.0) '\\\n",
    "        'Gecko/20100101 Firefox/46.0'\n",
    "    headers = {'user-agent': user_agent}\n",
    "    base_url = 'http://www.streetinsider.com/ec_earnings.php?q={}'\\\n",
    "        .format(ticker)\n",
    "    e = EarningsParser()\n",
    "    s = requests.Session()\n",
    "    a = requests.adapters.HTTPAdapter(max_retries=0)\n",
    "    s.mount('http://', a)\n",
    "    e.feed(str(s.get(base_url, headers=headers).content))\n",
    "    \n",
    "    if e.earnings_offset is not None:\n",
    "        dates = map(lambda x: parser.parse(x) + e.earnings_offset, e.dates)\n",
    "        past = filter(lambda x: x < datetime.now(), dates)\n",
    "        return list(map(lambda d: d.isoformat(), past))\n",
    "\n",
    "# Use a Russell-3000 ETF tracker (ticker IWV) to get a list of holdings\n",
    "r3000 = pd.read_csv('https://www.ishares.com/us/products/239714/'\n",
    "                    'ishares-russell-3000-etf/1449138789749.ajax?'\n",
    "                    'fileType=csv&fileName=IWV_holdings&dataType=fund',\n",
    "                    header=10)\n",
    "r3000_equities = r3000[(r3000['Exchange'] == 'NASDAQ') |\n",
    "                       (r3000['Exchange'] == 'New York Stock Exchange Inc.')]\n",
    "\n",
    "dates_file = open('earnings_dates.yaml', 'w')\n",
    "\n",
    "with futures.ThreadPoolExecutor(max_workers=8) as pool:\n",
    "    fs = {pool.submit(earnings_releases, r3000_equities.ix[t]['Ticker']): t\n",
    "          for t in r3000_equities.index}\n",
    "    pbar = progressbar.ProgressBar(term_width=80,\n",
    "                                   max_value=r3000_equities.index.max())\n",
    "    \n",
    "    for future in futures.as_completed(fs):\n",
    "        i = fs[future]\n",
    "        pbar.update(i)\n",
    "        dates_file.write(yaml.dump({r3000_equities.ix[i]['Ticker']:\n",
    "            future.result()}))\n",
    "```\n",
    "\n",
    "Downloading stock price data needed for the event studies:\n",
    "\n",
    "```python\n",
    "from secrets import QUANDL_KEY\n",
    "import pandas as pd\n",
    "import yaml\n",
    "from dateutil.parser import parse\n",
    "from datetime import timedelta\n",
    "import quandl\n",
    "from progressbar import ProgressBar\n",
    "\n",
    "def fetch_ticker(ticker, start, end):\n",
    "    # Quandl is currently giving me issues with returning\n",
    "    # the entire dataset and not slicing server-side.\n",
    "    # So instead, we'll do it client-side!\n",
    "    q_format = '%Y-%m-%d'\n",
    "    ticker_data = quandl.get('YAHOO/' + ticker,\n",
    "                             start_date=start.strftime(q_format),\n",
    "                             end_date=end.strftime(q_format),\n",
    "                             authtoken=QUANDL_KEY)\n",
    "    return ticker_data\n",
    "    \n",
    "data_str = open('earnings_dates.yaml', 'r').read()\n",
    "# Need to remove invalid lines\n",
    "filtered = filter(lambda x: '{' not in x, data_str.split('\\n'))\n",
    "earnings_data = yaml.load('\\n'.join(filtered))\n",
    "\n",
    "# Get the first 1500 keys - split up into two statements\n",
    "# because of Quandl rate limits\n",
    "tickers = list(earnings_data.keys())\n",
    "\n",
    "price_dict = {}\n",
    "invalid_tickers = []\n",
    "for ticker in ProgressBar()(tickers[0:1500]):\n",
    "    try:\n",
    "        # Replace '.' with '-' in name for some tickers\n",
    "        fixed = ticker.replace('.', '-')\n",
    "        event_strs = earnings_data[ticker]\n",
    "        events = [parse(event) for event in event_strs]\n",
    "        td = timedelta(days=20)\n",
    "        price_dict[ticker] = fetch_ticker(fixed,\n",
    "            min(events)-td, max(events)+td)\n",
    "    except quandl.NotFoundError:\n",
    "        invalid_tickers.append(ticker)\n",
    "        \n",
    "# Execute this after 10 minutes have passed\n",
    "for ticker in ProgressBar()(tickers[1500:]):\n",
    "    try:\n",
    "        # Replace '.' with '-' in name for some tickers\n",
    "        fixed = ticker.replace('.', '-')\n",
    "        event_strs = earnings_data[ticker]\n",
    "        events = [parse(event) for event in event_strs]\n",
    "        td = timedelta(days=20)\n",
    "        price_dict[ticker] = fetch_ticker(fixed,\n",
    "            min(events)-td, max(events)+td)\n",
    "    except quandl.NotFoundError:\n",
    "        invalid_tickers.append(ticker)\n",
    " \n",
    "prices_store = pd.HDFStore('price_data.hdf')\n",
    "for ticker, prices in price_dict.items():\n",
    "    prices_store[ticker] = prices\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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