linuxster/ES_prediction_RF.ipynb

## ES_prediction_RF.ipynb
{
 "metadata": {
  "name": "ES_prediction_RF"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from datetime import datetime\n",
      "from sklearn.ensemble import RandomForestClassifier\n",
      "from sklearn import cross_validation\n",
      "from sklearn.preprocessing import scale, MinMaxScaler\n",
      "from scipy.signal import detrend"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# utility function #1: append zero to time\n",
      "def append_zero(x):\n",
      "    '''input x is a string\n",
      "    '''\n",
      "    if len(x)==5:\n",
      "        y = '0' + x\n",
      "    else:\n",
      "        y = x\n",
      "        \n",
      "    return y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# utility function #2: detect if some events are observed\n",
      "def is_event(obj):\n",
      "    '''input obj is a dataframe with 4 rows and contains a 'Close' column\n",
      "    '''\n",
      "    if (obj.ix[1,'Close'] > obj.ix[0,'Close']) and \\\n",
      "       (obj.ix[2,'Close'] > obj.ix[1,'Close']) and \\\n",
      "       (obj.ix[3,'Close'] > obj.ix[2,'Close']) and \\\n",
      "       (obj.ix[3,'Close'] - obj.ix[0,'Close']) > 3. :\n",
      "        y = 1.\n",
      "    else:\n",
      "        y = 0.\n",
      "        \n",
      "    return y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('/Users/erriza/Dropbox/machine/ES #F_M5.CSV')\n",
      "times = ['Date', 'Time']\n",
      "df[times] = df[times].applymap(lambda x: str(x))\n",
      "df['Time'] = df['Time'].apply(append_zero)\n",
      "\n",
      "df['Volume'] = df['Volume'].astype(float)\n",
      "\n",
      "# create a datetime index, drop original date and time columns\n",
      "df.index = (df['Date'] + df['Time']).apply(lambda x: datetime.strptime(x, '%Y%m%d%H%M%S'))\n",
      "df = df.drop(times, axis=1)\n",
      "df[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Valid</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Tick</th>\n",
        "      <th>OI</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:05:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 984.75</td>\n",
        "      <td> 985.00</td>\n",
        "      <td> 732</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:10:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 984.75</td>\n",
        "      <td> 985.00</td>\n",
        "      <td>  67</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:15:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.00</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 984.75</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 109</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:20:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.25</td>\n",
        "      <td>  90</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:25:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 985.00</td>\n",
        "      <td> 985.25</td>\n",
        "      <td>  64</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:30:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 168</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:35:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 198</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:40:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.75</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 183</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:45:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 301</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:50:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 986.75</td>\n",
        "      <td>  61</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                     Valid    Open    High     Low   Close  Volume  Tick  OI\n",
        "2009-08-03 01:05:00      1  985.50  985.50  984.75  985.00     732     5   0\n",
        "2009-08-03 01:10:00      1  985.25  985.25  984.75  985.00      67     5   0\n",
        "2009-08-03 01:15:00      1  985.00  985.50  984.75  985.25     109     5   0\n",
        "2009-08-03 01:20:00      1  985.25  985.50  985.25  985.25      90     4   0\n",
        "2009-08-03 01:25:00      1  985.25  985.50  985.00  985.25      64     5   0\n",
        "2009-08-03 01:30:00      1  985.50  986.00  985.25  986.00     168     5   0\n",
        "2009-08-03 01:35:00      1  986.00  986.50  985.50  986.25     198     5   0\n",
        "2009-08-03 01:40:00      1  986.00  986.25  985.75  986.25     183     5   0\n",
        "2009-08-03 01:45:00      1  986.50  987.00  986.50  986.75     301     5   0\n",
        "2009-08-03 01:50:00      1  986.75  987.00  986.50  986.75      61     5   0"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# change the values of invalid data to NaN for missing value handling as below\n",
      "features = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
      "df.ix[df['Valid']==0, features] = NaN\n",
      "\n",
      "# fill up missing values:\n",
      "# first fill forward (fill missing data with current), \n",
      "# then backward (data in first rows don't have current value, so fill from the 'future')\n",
      "df = df.fillna(method='ffill').fillna(method='bfill')\n",
      "df[df['Valid']==0].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Valid</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Tick</th>\n",
        "      <th>OI</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2009-11-27 13:25:00</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-11-27 13:30:00</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-11-27 13:35:00</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-11-27 13:40:00</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-11-27 13:45:00</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 1089.25</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                     Valid     Open     High      Low    Close  Volume  Tick  OI\n",
        "2009-11-27 13:25:00      0  1089.25  1089.25  1089.25  1089.25       5     0   0\n",
        "2009-11-27 13:30:00      0  1089.25  1089.25  1089.25  1089.25       5     0   0\n",
        "2009-11-27 13:35:00      0  1089.25  1089.25  1089.25  1089.25       5     0   0\n",
        "2009-11-27 13:40:00      0  1089.25  1089.25  1089.25  1089.25       5     0   0\n",
        "2009-11-27 13:45:00      0  1089.25  1089.25  1089.25  1089.25       5     0   0"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add some derived features\n",
      "## difference of close values\n",
      "df['dclose'] = df['Close'].diff().fillna(0.)\n",
      "## rolling/moving average\n",
      "df['nmeanclose'] = pd.rolling_mean(df['Close'], 10).fillna(method='bfill')\n",
      "\n",
      "# add the event definition\n",
      "ev = []\n",
      "for ts in xrange(len(df.index)):\n",
      "    try:\n",
      "        if df['Close'][ts + 1] > df['Close'][ts] and \\\n",
      "           df['Close'][ts + 2] > df['Close'][ts + 1] and \\\n",
      "           df['Close'][ts + 3] > df['Close'][ts + 2] and \\\n",
      "           df['Close'][ts + 3] - df['Close'][ts] > 3.:\n",
      "            \n",
      "            ev.append(1.)\n",
      "        else:\n",
      "            ev.append(0.)\n",
      "    except:\n",
      "        ev.append(np.NaN)\n",
      "        \n",
      "df['event'] = ev\n",
      "df[:12]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Valid</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Tick</th>\n",
        "      <th>OI</th>\n",
        "      <th>dclose</th>\n",
        "      <th>nmeanclose</th>\n",
        "      <th>event</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:05:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 984.75</td>\n",
        "      <td> 985.00</td>\n",
        "      <td> 732</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:10:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 984.75</td>\n",
        "      <td> 985.00</td>\n",
        "      <td>  67</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:15:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.00</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 984.75</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 109</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:20:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.25</td>\n",
        "      <td>  90</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:25:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 985.00</td>\n",
        "      <td> 985.25</td>\n",
        "      <td>  64</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:30:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 168</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:35:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 985.50</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 198</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:40:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.75</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 183</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:45:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 301</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:50:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 986.50</td>\n",
        "      <td> 986.75</td>\n",
        "      <td>  61</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:55:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 987.00</td>\n",
        "      <td>  62</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 985.975</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 02:00:00</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 278</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 986.175</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                     Valid    Open    High     Low   Close  Volume  Tick  OI  dclose  \\\n",
        "2009-08-03 01:05:00      1  985.50  985.50  984.75  985.00     732     5   0    0.00   \n",
        "2009-08-03 01:10:00      1  985.25  985.25  984.75  985.00      67     5   0    0.00   \n",
        "2009-08-03 01:15:00      1  985.00  985.50  984.75  985.25     109     5   0    0.25   \n",
        "2009-08-03 01:20:00      1  985.25  985.50  985.25  985.25      90     4   0    0.00   \n",
        "2009-08-03 01:25:00      1  985.25  985.50  985.00  985.25      64     5   0    0.00   \n",
        "2009-08-03 01:30:00      1  985.50  986.00  985.25  986.00     168     5   0    0.75   \n",
        "2009-08-03 01:35:00      1  986.00  986.50  985.50  986.25     198     5   0    0.25   \n",
        "2009-08-03 01:40:00      1  986.00  986.25  985.75  986.25     183     5   0    0.00   \n",
        "2009-08-03 01:45:00      1  986.50  987.00  986.50  986.75     301     5   0    0.50   \n",
        "2009-08-03 01:50:00      1  986.75  987.00  986.50  986.75      61     5   0    0.00   \n",
        "2009-08-03 01:55:00      1  986.75  987.00  986.75  987.00      62     5   0    0.25   \n",
        "2009-08-03 02:00:00      1  987.00  987.00  986.25  987.00     278     5   0    0.00   \n",
        "\n",
        "                     nmeanclose  event  \n",
        "2009-08-03 01:05:00     985.775      0  \n",
        "2009-08-03 01:10:00     985.775      0  \n",
        "2009-08-03 01:15:00     985.775      0  \n",
        "2009-08-03 01:20:00     985.775      0  \n",
        "2009-08-03 01:25:00     985.775      0  \n",
        "2009-08-03 01:30:00     985.775      0  \n",
        "2009-08-03 01:35:00     985.775      0  \n",
        "2009-08-03 01:40:00     985.775      0  \n",
        "2009-08-03 01:45:00     985.775      0  \n",
        "2009-08-03 01:50:00     985.775      0  \n",
        "2009-08-03 01:55:00     985.975      0  \n",
        "2009-08-03 02:00:00     986.175      0  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = 4100\n",
      "stop = 4220\n",
      "\n",
      "df['Close'][start:stop].plot()\n",
      "pd.rolling_mean(df['Close'], 5)[start:stop].plot(color='r')\n",
      "pd.rolling_mean(df['Close'], 10)[start:stop].plot(color='g')\n",
      "\n",
      "for ts in df.index[start:stop]:\n",
      "    if df['event'][ts] == 1.:\n",
      "        plot(ts, df['Close'][ts], 'rD', markersize=6)\n",
      "\n",
      "f = gcf()\n",
      "f.set_size_inches(10, 7);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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LyWQiMBCqqoyMGROGKTkZXn4ZY2c3dtvrl5SYuHABLl0yUlcHu3df3vh6Gm9Tk5GwMPv9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NSFVLjV4cMpXpMJ6utzNbcdOABTp4pksF4Ip2sLMl69E27xaqEbIeUPsbFY+0N58kjFxsI2ZSVs\n2SIa1CwRPqWLtTHUXPN1ckr+YhVEagsEsGWifBFSWh6phNpS6hPThD+q7GOWpGn7o1RiYvwXQc4Z\nqVBuxinRN548Urt3Q06O9jaJRCIJBXQjpPyp0w4aZBNS3jJS+9pmC1NVTo54AiGC2r5xG+n7/kJN\nlTBC+ZqRUhTPpb1hxjro6qIjfhhfVHxBYmwiY+PHeozXZApMRiqUm3GGWx0+nOI1maCz06y5LS8P\nsrP7dz19TThdW5Dx6p1wi1cL3Qgpf/C1tGcyQavFgHLjTXDjjdbnq6thyKJMlMFxjC4RQ521MlKq\nWLLHYhFZMBez+f/7fxAdzc7DwymPn4Up1uDVH6XSk4yUvdm8traWB+/4EpGRtf4dRCIJAOqdtM7t\nOBRFCCmZkZJIJKGMboSUP3Va+9KeJ7N5RIQQKC2P/RbuvBMQLQMaGyEh0UDtl9aw+OQmQDTlVFsg\nNDSIFgNVVQp3vncnH5780HrM+nqxzcUjlZcHn3/O8uwmfnDZJ8TEwLvH39X0RznH21OzucUiRNT6\nFSv41c63GXF6BbW1oSmmwq0OH07xinYcuS6jm06fFjdtdE9K0g3hdG1Bxqt3wi1eLXQjpPzB19Ke\nuq+avQLR1iAxUYisrlu/yZLat6GxkQkJEzjXeA5Lh4WGBkhNhe11v+MvhX/hrwf/an19QwOMHWtr\nj9DWBqaINtGhc+ZMIkzR1F8yci7mI07WnuTmGTd7jaenGamqKiGiHs7PZwKwpSWf9StCV0xJ9Et0\ntOPPGdiyUaHakkMikUhAR0LKX4+U+kvbk9kcHLNXIDJJ6uDTYdNS+HfEEnjjDaKMUYxPGE9JTQn1\n9ZA0Zy+7+BXvfO0dtpVss/aYqq8Xrx88WHzf1gbDakrErIuYGKKioKGxkw+N9/HY8seIidRWSL31\nSEVE1PLKOiGiErufSwQezg9NMRVudfhwi3fIELNLCwS9lvXC7drKePVNuMWrRdjN2gPX0p63jJS9\nkKqqsgmpuDh4iTVcu+lpIm67jWlJ01j/r/XsTxpPzYgtZJx9juWTlhMfE0/hhUIyR2XS0ADx8bbx\nMm1tkHTxCO9ePoyOoreIil7FuRGbiItI4MbpN2ovyomelPZaSm7jl1U2EaWSCPwoP5/1t93Gs1u2\n+HdQiaSHxMbCT37i2OZgyxZ47bXgrUkikUh8QTcZKX/7SPW0tFddbZvpZTDA3qSVGPbugbY2fr7k\n5yxNW4qhbiKrY54mtlSU5VZOXsm2EjFqpr4ehg7F2syzrQ0SLxbxiwll/PijH7MzfSpVc9dzc9xT\nGDzUNOzj/fWv4corfQ4fgJTMTayfkoVz3qkWeDwri4c3bfLvgH1MuNXhwy3eO+7IZe5cmDjR9m/9\nen024gy3ayvj1TfhFq8WYZuR8qWzubqvu9IewJDhsVjiJmL64guyMrPIGp3FcwfhhpvgkdfFPtdO\nvpZHPnmEnyz6iTUjZbGI7FZ7O8TUH+bo9Fqqv1PK6u8UkHuomCl3zPc5nssv9yP4bgYPTmTlT3ew\n/re28l4tsD4ri4d37CAx0TlXJZH0HVdcAbJCIJFIBiK6yUj1RR8p533BsbQH4vu6CZlQUGB9rr5e\n/EWttj9YkraEAxcOUN9ab81I2Zf2TjftZ1pcGrFRsYzuyqY9/7+8msd7W5eOjobo6EQe3rGD9VlZ\nlAK3JoWuiAq3OryMV7+EU6wg49U74RavFroRUv5gL468mc21SnvOQqpiVIaDkGpogInDG6muFr1w\nBkUN4orUK/io9CNrRkot7XVYOjkUeYqs8aLrYE+HEPuL2v4gMVGIqe/OWc2oVaEpoiQSiUQiCVV0\nI6T87SPlS0NOdV/n0p7qkQIhiE4Pswmp9naY3lpA0swUblZet75W9Uk1NNg8UlVVMKzxFPnjolgw\nXtyepK7FW5fx3tal7RtyJiYmsnzNFoYODV0RFW51eBmvfgmnWEHGq3fCLV4tdCOk/MG5tOfJI+Vc\n2nPOSCUnQ8nguVBYCIpCYyN8KfqfGK6+mic676XtsacBuGHqDbxx5A3e406aBx21ZqRSG4+wd4yB\nBaMXADYh1R8ZKedZe6E8IkYikUgkklBEN0Kqp32k/L1rT8sjdbplOAwZAqWl1NfDNco2+N73WDst\nj9i/PAdvvsmkYZMo+m4RxpaR/KZuMcXR/6C6GlJaCzg92EJ6Sjrgu5AKhEfKftZeqAupcKvDy3j1\nSzjFCjJevRNu8WqhGyHlD/blup405HQu7VVXA5nCcH7pbB2z2gtg8WLaR43j1LV3wYdiRMyIISNI\nPfEgP5j4Mm/XbqCySqEjPo/pEalEGUVaLJgZqdjYvj2nRCKRSCR6QzdCqi/7SHkr7VVXAxnCJ2X8\n14ccGnolxMaSlASnxuSIFs3dNDTA0tRrMBoVThk/oG7YF8xJyLBuV8uM/eGRss9ItbSEdkYq3Orw\nMl79Ek6xgoxX74RbvFroRkj5g79mc3Xfri4xI2/YMNt21TSuCqkhn2yjYNS1QLd/akgGlJQIBYXa\nkNPAXRk/4Hzak5wacY65E6+2Hq+/MlL2ZnMI/dKeRCKRSCShiG6EVG/6SPlqNq+vFzPy7Pe3lvYy\nMuDAAZLzt3EsbaV1W2V9tCj77dkDYG1/sHbB1+lMKOCDKZ3Mm7zUerz+8kip7Q9UQl1IhVsdXsar\nX8IpVpDx6p1wi1cL3Qgpf+jpiJjz52HkSMftI0eK55W0CdDQQEdENC1jpwB2IivHVt5TG3ImnDnJ\nfXvaaImIYmbKNOvxgpmRkh4piUQikUj8QzdCqqd9pPwxm5eXQ2qq4/ahQyEiAuoaImDuXIqnXEv8\nUDEjz0FI7d6NokBjI8QdzgOzmYbj/0v7269gijFaj+drQ87e1qWdzebSIxVayHj1SzjFCjJevRNu\n8WqhGyHlDz01m2sJKRDPlZcD//3f5M24naFDxfPJyd3+qexs+OwzLjV0MczUTOStX4UXX+SzUd+B\nL77icH5fG3L2loHW/kAikUgkklBEN0KqL2ftqdkrT0LqzBngK1/haGwm8fHieWtGKiUFkpJo3lfE\njyKeFMLq+uutd//Ze6760yM1kMzm4VaHl/Hql3CKFWS8eifc4tUiMtgLCAbOd+15Mpvbl/bOnIHL\nL3fdx5qRAusIGLATUgA5OUS8+QZ3ND8Dj+Zbt0dFgcFgO5YqpDyJu0CglZGSHimJRCKRSPxDNxmp\nnnikFCXApT2EmVzNSFlLewA5OSQ/90veGXE7TJhg3e587uho8c9eXGkRyFl7ID1SoYaMV7+EU6wg\n49U74RavFroRUv4QESGERGurd7O5r6U9rYxUXJzI+rS1AWYzLcPH8ea0B6yvUzNSKrW1tbz8yJeI\niantXYA+MNDaH0gkEolEEop4FVIbN25k9uzZpKens3HjRgAKCwvJzs5mzpw5rFq1isbGRgD27NlD\nZmYmmZmZzJkzh9dee61vV2+Hv3VaVSD50pCzuVlkr3wVUmpGymAQzTurq4Hp0/nnxhNEJg21vi4p\nyXbu2tpa1q9YwROfv016ywpqaz2LqUDM2pMeqdBFxqtfwilWkPHqnXCLVwuPQurw4cO88MIL7N27\nl8LCQt577z1OnDjB7bffzmOPPcbBgwe58cYbefzxxwGYPXs2+/bt48CBA+zYsYPvfve7dHZ29ksg\n/qKW7Hwt7dXVgdFoE0n2OJf2htq0koNPquFShMu26GibiHo4P58JwD878lm/wruY6g1a7Q+kR0oi\nkUgkEv/wKKSOHj3KwoULMZlMGI1GlixZwubNmykuLmbRokUALFu2jM2bNwMQGxtLRIQ4ZEtLC0OH\nDsVoNLo9fiDxt06rZpp86Wze0uI+GwW2u/YUxTEjBY4+KXv/lLrNaLSJqMTu5xOBh/M9i6lAz9oL\n9YxUuNXhZbz6JZxiBRmv3gm3eLXweNdeeno669evp6amBpPJxPvvv09WVhbp6em8/fbbrF69mtdf\nf51yNR2DKO/ddtttlJaW8uqrr2oed82aNaSlpQGQkJBARkaGNT2oXhR/H6v4uv+gQWZaWqCmJpdD\nh+CKK7T337Mnl6YmKC83k5rq+XhVVVBbm0tBAaxYIbZ3deXy8cdi/4YGcb7cXPE4KQk6K25g0Smb\niFKjMQM/ys9nzQ03cO+GDb2O1/nxsGFm2trEY0WB1lYzJlPPj9fXj3sb70B7LOMNrfUF8nFBQUFI\nrUfGK+OV8To+Vr8vKyvDFwyKoiiedvjzn//Ms88+y+DBg5k1axYxMTHceeedrFu3jurqalatWsUz\nzzxDlfX2NMHRo0dZuXIlhYWFDLWrZxkMBrycsl/IzoannoI774SXX4a5c7X3UxSRsXr6aSgshD/9\nSXu/uXPhuedg6VLHTM/99wvT+c9+BvfeK7JX990ntlkssGVLLR8/4ZiRAqgF1mdl8fCOHSQm2m8J\nDEePwurVcOyYMNwPHuxY6pNIJBKJROJdt3gs7QGsXbuW/Px8Pv74YxISEpg2bRrTpk1j+/bt5Ofn\n87WvfY1Jkya5vG769OlMmjSJkpKS3kXQR6gtELx5pAwGse+xY+5LeyC2ffGFq4fKbsyeS9kvJga+\n+tVEHt6xg/VZWahFvL4WUeq5VeEU6q0PJBKJRCIJVbwKqYqKCgBOnz7NW2+9xa233kplZSUAXV1d\nbNiwgbvuuguAsrIyOjo6ADh16hTFxcVMmTKlr9bugH1KzhfszeaePFLqvr4KKXszOVinw9DV5WpE\nV0lMtImpUnwTUf7G60x0tC1zFur+KOh9vAMNGa9+CadYQcard8ItXi28CqlbbrmFWbNmsWrVKp59\n9lni4+N59dVXmTZtGjNmzGDs2LGsWbMGgE8++YSMjAwyMzP58pe/zPPPP0+81m1uIYC92dxTRgqE\nkDp+vGcZqREjxN15R4+6ZqTsUcXU46tX92kmSiU62paRGghCSiKRSCSSUMSrRyrgJwwRj9S3vgVX\nXQX/8z9QUAAjR7rfd+ZMIYSKimDaNO19XnlF+KGmT4edOx23/ed/wpIl8MILwmulNWamv2logLFj\nxdfjx+GGG0TWTSKRSCQSiY1ee6T0iq99pNR9FcV7RurCBe2Mk+qTcm5/EEzsS3vSIyWRSCQSSc/Q\njZDyt07rb2lv2DDx1R2qyNLyQKlCyn58TG8JhEeqrY3u1gehL6TCrQ4v49Uv4RQryHj1TrjFq4XH\nPlJ6xn5EjDezeWys52wUiDIZaGec0tPh3DlxvlDJSEVEQGSkaH0wEISURCKRSCShiG4yUmpDLV8Z\nNAiamoSQ8OWuPW9CKiYGUlK0M05GIyxcCJ2dMGSIX8t0i7/xaqG2QGhtDf3xMIGIdyAh49Uv4RQr\nyHj1TrjFq4VuhJS/xMaKUltkpMjOeMIXIQViH3cZp5wcsc1g8H+tfYXqk5IeKYlEIpFIeoZuhFRP\n+kjV13v3R4FvpT3wLKSyswNb1gtEXVr1SQ2E0l641eFlvPolnGIFGa/eCbd4tQhrj1RdnW9C6o47\nRC8ob/zwhzavlDNmM/zmN34tsc+xL+2FupCSSCQSiSQUCds+Um+8IXo6HT8O3c3bw44pU+Cf/4SP\nPoKDB8WsQIlEIpFIJDZkHyk3+FPa0ysyIyWRSCQSSe/QjZDqSR+pgSykAuWRslgGhpAKtzq8jFe/\nhFOsIOPVO+EWrxa6EVL+IjNSMiMlkUgkEklvCVuP1KFDMGeOaJZ56FCwVxMcliyBhx6C99+H5GQx\nK1AikUgkEokN6ZFyg9qAMpwzUgOp/YFEIpFIJKGIboRUT/pIwcAVUoGoSw+k0l641eFlvPolnGIF\nGa/eCbd4tdCNkPKXgS6kAsFAMptLJBKJRBKKhK1HymIR4mHZMvjgg2CvJjh8/euwahW8+SZ85Svw\n5S8He0USiUQikYQW0iPlhuhoMWPP28BiPSMzUhKJRCKR9A7dCCl/67QGgzCcD9TSnvRI6RsZr34J\np1hBxqt3wi1eLXQjpHrCoEEDV0gFApmRkkgkEomkd4StRwogLQ2uvBL++tdgr68FedAAAByFSURB\nVCQ43HcfjBkDr74Kf/gDZGUFe0USiUQikYQW0iPlgYFc2gsEA6m0J5FIJBJJKKIbIdWTOu2gQQPX\nbC5n7ekbGa9+CadYQcard8ItXi10I6R6Qrh7pNSMVEtL6AspiUQikUhCkbD2SK1YAbNnw5NPBnsl\nweGJJ+D8eXjpJTh+HJKSgr0iiUQikUhCC+mR8oDMSEmPlEQikUgkvUE3QqonddqBbDYPtEcqJqb3\na+pLwq0OL+PVL+EUK8h49U64xauFboRUTxjIZvNAEB0Nly6JDu+RkcFejUQikUgkA4+w9kh973uQ\nmgr33x/slQSHv/1N/Nu1Cxoagr0aiUQikUhCD2+6JazzELfeCnFxwV5F8IiOhvp66Y+SSCQSiaSn\n6Ka015M6bXY2pKcHfi39QaBm7TU0DAwhFW51eBmvfgmnWEHGq3fCLV4tdCOkJP4jM1ISiUQikfSO\nsPZIhTu5uXDjjTBuHBQWBns1EolEIpGEHrKPlMQt0dEDp7QnkUgkEkkoohshFW512kD1kerqGhhC\nSl5ffRNO8YZTrCDj1TvhFq8WuhFSEv9Rm3AOBCElkUgkEkko4tUjtXHjRl544QUUReHb3/423//+\n9yksLOTOO++kqamJtLQ0/u///n979x0W1Zn2cfyHSLOCFVuuWC6xoIJBEUGNWdcCEQJoFogFWRFd\nNeqCYkQvywUYUdeO4oouEtZecEWxRLAhBmJBFFksCFhiAwKCMDD3+wcvZ0GJBRmGec79+ScOMyc+\n3xxyeJjznDMRaNy4MU6dOoUffvgBxcXF0NXVxcqVKzF06NDKfyGvkaozUlOBbt0ABwfg8GF1j4Yx\nxhirez5pjVRycjK2bduGhIQEXL9+HUePHsXdu3cxefJkBAUFISkpCY6Ojli5ciUAoGXLljh69CiS\nkpIQFhaG8ePH12wNq1H8jhRjjDH2ad45kbp9+zYsLS2hr68PbW1tDBkyBAcOHEBaWhoGDRoEABg2\nbBgOHDgAADAzM4OxsTEAoEePHigsLIRCoVBxQhm5naetqTVSgGZMpHj/ik1OvXJqBbhXdHLrrco7\n72xuamoKPz8/vHz5Evr6+jh27BgsLCxgamqKyMhIODg4YN++fcjMzHxr2wMHDuCLL76AThUfZufu\n7o7PP/8cAGBoaAgzMzN8+eWXAP63Uz72cbnqbq9pj2uit+wdqVi8fAkAdauP9y/3ivr42rVrdWo8\n3Mu93Fv5cfmf09PT8SHeu0Zq+/btCA4ORsOGDdGzZ0/o6elh6tSp+P777/HixQvY29tj/fr1eP78\nubTNzZs34eDggFOnTqFjx46V/0JeI1Vn5OUBTZoAs2cDa9aoezSMMcZY3fPJ95Hy8PBAYmIizp49\nC0NDQ5iYmMDExAQnTpxAYmIiXFxc0LlzZ+n1WVlZcHJyQnh4+FuTKFa36OqW/VMTTu0xxhhjddF7\nJ1JPnz4FAGRkZODQoUNwc3PDs2fPAABKpRL+/v6YNm0aACAnJwd2dnZYsWIFrKysVDjst1V8S04O\naqJXkyZSvH/FJqdeObUC3Cs6ufVW5b0TqTFjxqBnz56wt7dHcHAwmjRpgl27dsHExATdu3dH+/bt\n4e7uDgDYuHEj7t69i6VLl8Lc3Bzm5uaVTvmxukVLC9DR0YyJFGOMMVYX8WftyVzjxkBAAPD99+oe\nCWOMMVb38GftsXfS1eV3pBhjjLHqEmYiJbfztDXRm52djaavvkFpafanD0jFeP+KTU69cmoFuFd0\ncuutijATKfZxsrOz4Td8OH4uisTZVcORnV33J1OMMcZYXcNrpGSofBIVkJgIIwDZAPwsLBBw8iSM\njIzUPTzGGGOszuA1UqySNydRAGAEICAxEX7D+Z0pxhhj7GMIM5GS23na6vb6TZqEuRUmUeWMAMxN\nTITfpEmfOjSV4P0rNjn1yqkV4F7Rya23KsJMpNiHCdixAystLPDm+07ZAFZaWCBgxw51DIsxxhjT\nSLxGSoZ4jRRjjDH2Yd43b+GJlEyVT6bmJiaWvRPFkyjGGGPsLbJZbC6387Sf2mtkZISAkyex0sFB\nIyZRvH/FJqdeObUC3Cs6ufVWpb66B8DUx8jICMGHD6t7GIwxxpjG4lN7jDHGGGN/QDan9hhjjDHG\napswEym5naflXrFxr7jk1Apwr+jk1lsVYSZSjDHGGGO1jddIMcYYY4z9AV4jxRhjjDGmIsJMpOR2\nnpZ7xca94pJTK8C9opNbb1WEmUgxxhhjjNU2XiPFGGOMMfYHeI0UY4wxxpiKCDORktt5Wu4VG/eK\nS06tAPeKTm69VRFmIsUYY4wxVtt4jRRjjDHG2B/gNVKMMcYYYyoizERKbudpuVds3CsuObUC3Cs6\nufVWRZiJFGOMMcZYbeM1Uowxxhhjf4DXSDHGGGOMqYgwEym5naflXrFxr7jk1Apwr+jk1lsVYSZS\njDHGGGO1jddIMcYYY4z9AV4jxRhjjDGmIsJMpOR2npZ7xca94pJTK8C9opNbb1WEmUhdu3ZN3UOo\nVdwrNu4Vl5xaAe4Vndx6qyLMRConJ0fdQ6hV3Cs27hWXnFoB7hWd3Hqr8t6J1Lp169CrVy+Ymppi\n3bp1AIDr16/DysoKvXv3hr29PfLy8gAAL1++xNChQ9G4cWPMnDlTtSNnjDHGGFOzd06kkpOTsW3b\nNiQkJOD69es4evQo7t69i8mTJyMoKAhJSUlwdHTEypUrAQD6+vrw9/fHqlWramXwFaWnp9f636lO\n3Cs27hWXnFoB7hWd3Hqr8s7bH+zfvx/R0dHYtm0bAMDf3x+6uroIDAyU3s7LzMzEyJEjcfPmTWm7\nf/3rX/j111+xYcOGt/9CLa2abmCMMcYYU5l33f6g/rs2NDU1hZ+fH16+fAl9fX0cO3YMFhYWMDU1\nRWRkJBwcHLBv3z5kZmZW2u5dkyW+hxRjjDHGRPHOU3vdunWDr68vhg8fjlGjRsHMzAza2toIDQ1F\ncHAwLCwskJ+fD11d3doaL2OMMcZYnfHexeYeHh5ITEzE2bNnYWhoCBMTE5iYmODEiRNITEyEi4sL\nOnfuXBtjZYwxxhirU947kXr69CkAICMjA4cOHYKbmxuePXsGAFAqlfD398e0adMqbcOn7xhjjDEm\nB+9cIwUAY8aMwYsXL6Cjo4Pg4GA0adIE69evx6ZNmwAAzs7OcHd3l17/+eefIy8vD8XFxYiMjMTJ\nkyfRrVu3Gh+4UqlEvXrC3AaLMcaEILdjM/eyWv/Q4k+Rn5+PHTt2wNbWFm3atEGDBg1AREJfCVhc\nXCybNWhyagW4V3Ry6pXbsZl7xe79WNpLlixZou5BfIgzZ87A3t4eBQUFuHr1KmJiYmBrayv0jly7\ndi2mT5+Ox48f49WrV+jatauw37xyagW4l3vFIbdjM/eK3VstpCHCw8Np8eLFRET022+/kbm5OW3b\nto2IiEpLS9U4MtU4ffo09e/fn65cuUIRERHUt29fio+PJyLxeuXUSsS93CsWuR2buVfs3uqosyc6\nMzIycOXKFenx7du30bBhQwBAq1atsGLFCixatAgAhDlfq1AopD8/f/4ctra2MDc3h5ubGyZMmICp\nU6cCEKNXTq0A93KvOL1yOzZzr9i9NULdM7mq+Pn5Ufv27WnYsGHk4+ND2dnZdOHCBerYsWOl140e\nPZqWLVumplHWnOLiYpozZw7NmjWLTp8+TURE+/fvpy+//LLS63r27Enbt28nIiKlUlnr46wJcmol\n4l4i7iUSp1dux2buFbu3ptS56eTz58/x3//+F3fu3MHevXtRv359LF26FNbW1ujevTsWLFggvdbD\nwwO//fZbpd8GNY1SqcT06dPx/Plz9O3bF8uXL0dISAicnZ3x9OlTRERESK/19/fH/v37AWjmR+3I\nqRXgXu4Vq1dux2buFbu3Jr339ge1TUdHB/Hx8Xj27Bnat2+Pb7/9Fjt37kR4eDi2bt2KgQMHYuTI\nkRg8eDBSU1PRrl076OjoqHvY1Zabm4ukpCTExMTAwMAALVu2xJEjR3D27Fls2rQJEydOxJgxY6Cn\np4e2bduie/fuKC0thZaWlsa9rSqnVoB7uVesXrkdm7lX7N6apPar9uj/r2QpP8Do6+vj4cOHuHv3\nLmxsbNCyZUvk5+fjwoULGDt2LIyMjBAdHY2goCAkJCTAw8MDnTp1UmfCB6M3rtpRKpVo0KABTp06\nhezsbPTr1w+tW7dGTk4OTp48iRkzZuDWrVuIjo7G69evERISAh0dHdjb29f532rl1ApwL/eK1QvI\n69gMcK/ovaqktonUli1bUL9+fTRs2BB6enqoV6+edIApLCxEXFwcOnXqhDZt2qCwsBBRUVGwtbWF\nlZUV/vSnP6Fdu3ZYs2aNRu3IijcyK/+zUqlESUkJLl68CCsrKzRr1gylpaW4ceMGunfvjtGjR0Nf\nXx/h4eEwNTXF6tWr1VzxYeTUCnAv94rTK7djM/eK3VsrantRVnJyMvXp04fs7OzIy8uLJk6cKD03\nbtw4+uWXXygrK4sCAgLIw8NDes7GxoZSUlJqe7g1ovwS6FmzZtHu3bulrx85coRSUlLowYMH5O3t\nTT/++KP03IABA+jSpUvS4+Li4lodc3XJqZWIe8txr+b3yu3YzL0TpedE7K1NtT6ROnPmDE2dOpWI\niPLy8sjOzo58fHyIiOjRo0fS6548eUKDBg0iT09P6t+/P40dO5ZycnJqe7if7NatW9S3b1+KjY2l\nI0eO0ODBgykiIoKIiHbu3Em3bt0ihUJBMTExNHDgQDp48CClpaXRV199RQkJCWoe/ceRUysR93Kv\nWL1yOzZzr9i9tUnlE6ns7Gy6fPmy9FvZ5s2baebMmdLz9+7do6ZNm1JWVhYRVb7B19OnT+nkyZMU\nFham6mHWqIoNMTExlXqPHTtGbdu2rXK7yMhIcnd3p65du1JwcLDKx1kT5NRKxL3cK06v3I7N3Ct2\nrzqpdCIVEhJCLVu2JFtbW5owYQJlZmZSZmYmtW7dmp4/fy69bvbs2TRhwgTp8T//+U/KzMxU5dBU\nZsmSJTRt2jTau3cvERElJiaSmZlZpdeMGDGCfH19K32t/Jv49evXGnO3WDm1EnEv94rTK7djM/eK\n3atuKrsGt7CwEJcuXcL58+cRFRWFzz77DMuXL0fjxo3h5uaGKVOmSK8dP348SktLkZOTAwDQ09PT\nyMsq/f39ERcXh5EjR2LDhg1YtWoVvvjiC7Rt2xYLFy6UXrdy5UqcO3cOubm5AID58+dj9+7dACAt\n/qvr5NQKcC/3lhGhV27HZu4Vu7dOUOUszcTEhM6dO0dERKmpqbRo0SJavnw5KRQK6ty5s/Sb3969\ne2nGjBmqHIrKKRQKGj58OF2/fp2IiGJjY2nOnDn0008/0YMHD6hZs2bSTD8rK4u8vLyk8865ublq\nG3d1yKmViHu5V6xeInkdm4m4V/Redavx2x+UlJQAKLt7b0FBAa5cuYLhw4ejefPmKCoqQnx8PCwt\nLWFubo7jx49j7dq1iIyMhJubG3r37l2TQ1EZeuOeMiUlJahfvz6uXr2KlJQUDBs2DO3bt8erV69w\n8uRJODo6AgAiIiJQXFyMiIgIZGVlYdy4cdDW1oaenp66Uj6anFoB7uVecXpLS0sBiH1sroh7xe6t\nSz55IrV582bcuHEDANCmTZtK96TQ0tLCpUuX0KBBA3Tq1An16tXDoUOHYG1tDSsrK4wYMQJt27ZF\nQEAALCwsPjmmthQWFkpvf5aWlqJ+/bIbxGtpaeHixYvo2rUrjI2Noa2tjZSUFDRv3hyurq5o0qQJ\nIiMjoaenh5CQEOjr66sz44O8fv1a6hO9FQAyMzPRtGlTKJXKSnegFrU3MjIS2traaN68OQAI3/v4\n8WM0aNAA9erVkyZRgJi9O3fuRE5ODgwNDaGvry/8sXnv3r3Izc1Fo0aNYGBgIHyv3PZvnVbdt7LS\n0tLI2tqabG1tyd/fn8zNzenFixdEROTr60t79+6lnJwc2rhxI/3lL38hhUJBRESjRo2in3/+uUbe\nTqttp0+fpoEDB5K7uzuFh4dLX4+Li6OYmBj6/fffafHixTRv3jzpOXt7e+mSaSLNuadMdHQ0jRw5\nkiZPnlzpyo1Lly4J10pUtlD4u+++oy5dulT6UNn4+Hghe3/99Vfq3bs3OTs7040bN6SvX758Wcje\nU6dOkbW1NTk6OtKUKVOkr4v2/axUKunhw4c0ZMgQGjp0KHl6epKrqys9ffqUiIh8fHyEOzafP3+e\n+vfvT8OHD6dx48aRh4cHZWdnExHRvHnzhOqV4/7VBB+9MrL8QwqTkpIwaNAgREVFwc/PD1ZWVtJr\n5s2bh7Fjx6Jp06Zwc3ODlpYWXFxcMGrUKOTn56Nr1641NxOsJS9evMDChQsxe/ZsjB8/Hvv378ey\nZcsAlH3mFhGhcePG+Prrr5GcnIx//OMfyM7OhkKhQNOmTaV/T11eyEdEKCkpwYoVK7BgwQLMmDED\nQ4cORXR0NA4fPgwAyMvLE6L1Tbq6ulAoFMjLy0N4eLj0dVF79+zZg5kzZ2L//v0wNTWVvi7K93JF\nt27dwvz58zF79mxs3rwZGRkZ+PnnnwGItX9LSkqgpaWFvLw8tGvXDmfOnEFwcDCaNWsmLTD29fUV\n5tisVCqhUCiwa9cuzJo1CydOnMCiRYugr6+PuLg4AGUXA4jSq1AoZLV/NcqHzrhKSkrI29ubZsyY\nQfHx8bR8+XJycXGhS5cu0bRp06hDhw60fv16SkpKIqLKv70VFRXR2bNnKSQkpCYngSpXWloqXc6c\nlJREnp6eVFJSQkRlC/gMDQ3p4cOHb2139epVmjhxIvXq1YsWLlxYq2OurtLSUqktIiKCUlNTiYjo\n999/Jx8fH9qzZw8RUaV3a4g0s5Wo8r4tv2x9zZo1tG/fPurSpcsf3oBOhN6SkhKaNGkS/fLLL0RE\nFBwcTJcvX6b8/Py3thOhNywsTLoRYW5uLjk7O1NmZiYVFRW9tZ0m9paUlND8+fNp3rx5FBMTQ0eO\nHKl0SXtJSQm1atWKYmNjiYikdyiINPPYXN7r4+NDcXFxlJycLDUVFxeTra0txcfHE9HbxytN7fX2\n9qaZM2dSbGwsHT58mCZNmlTpeZH2ryb6oIlUaWkpTZ06ldzc3CgsLIxGjx5Nq1atotDQUBoxYgR9\n/fXXdPXqVVq8eDGZmppK2x08eFD6htY0oaGhZGxsTAsWLCCispuXmZqa0pMnT6TXTJ8+nZycnCpt\nV34VT1FRERUUFNTegD9BeesPP/xARESvXr2i0tJSaTLs4uJCoaGhb22nia1E/+v18/OTvqZQKOir\nr76iV69e0aRJk2jBggXSVS/lROnNzs6mcePG0YEDB+ibb76hCRMmkJOTE9nb21faTtN7y7+fU1JS\npNPUHTp0oEGDBpGbmxt99913lbbTxN7Y2Fjq06cPTZ06lbZu3Uo2NjZ0/Phx6tChA12+fFl6XXBw\nMA0ZMkR6rKnH5jd7+/fvL00giouLSalUkoODA124cKHSdpraW/Fn786dO8nOzo6CgoKoRYsW0lWm\nROLsX031Qaf28vLycO3aNWzZsgUTJkyAl5cXMjIyAAA9evRAaGgozMzMsGTJEmhpaSE2NhZA2YK3\nJk2aqOzdNFXJz89HZGQkfH19cezYMaSmpqJjx47o27cvZs2aJb0uMDAQmZmZSEtLAwBs2rQJmzZt\nAlB2qsjAwEAt4/8YFVuPHz+OO3fuSItxdXR0UFxcjKKiIvTr16/Sdhs2bNC4VqByb1RUFO7cuSN9\n3cbGBg0aNMCf//xnrF69Gl5eXigoKAARITg4WIje1NRUGBoaomPHjggMDISVlRXCwsKwZ88epKSk\n4PTp0wCAjRs3anzv8ePHkZqaim7duuHgwYPo2LEjFi5ciHPnziE0NBTR0dHSKSBN7dXS0oK3tzc2\nb94MT09PmJqa4v79+1i6dCmmTZsGoOwiEUdHR7Rs2RLp6enSdpp4bH6z19LSEtHR0QDKTr2mp6cj\nPT0d1tbWAMp+dgFlyxY0sbfiz97x48fDy8sLWlpaMDY2hre3NwCx9q/G+tAZl4uLC61bt46Iyk73\n7Nixg/7+979T06ZNadeuXUREdP/+fXJycqLHjx+rYtJXqx48eEBEZQvnv/32WyIq+3yiFi1a0MWL\nF4mo7F0MT09P6bWa8lvsmyq2urq6EtH/3hJ/8uQJjRgxgojK7qGzb98+ItLcVqLKvS4uLkRU1tOj\nRw8aOnQo9e7dmxwcHMjZ2VnaRpTe8u/lgoICsrS0pKVLl0qn9Ly9vWn79u3S85qqqv2rUCjIysqq\n0mLb6dOn03/+8x8i0tzegoICKiwslE7L//TTTzR//nwiIurTp490zE5ISJD+W2iyN3v//e9/09y5\nc6XnY2JiyMfHh4qKisjDw4MWLVqkrqHWmIo/e3NzcyksLIzmzp1LrVq1opCQEFIqlcLsX031wYvN\nnZyccO3aNTx+/BiNGzdGt27d0Lp1a/j4+CAoKAgeHh5wcHCAmZkZjI2NVTn3qxWfffYZAGD27Nm4\nd+8ejh49ikaNGmHx4sXw9/fHjh074O/vj6SkJDRq1AgANOa32DdVbE1LS8OJEyeky2jv37+PnJwc\nrF27FnZ2dnjy5AkAzW0FKvfeuXMHx44dg4GBAZycnDBgwABcv34dhw8fRnJyMm7evAlAnN579+4h\nKioKBgYGmDNnDjIzM7F161YEBATg+PHj0kUjovSW79/69evDzs4Oc+bMwe3btxEYGIjz58+jR48e\nADS318DAAPr6+tDW1gYAnDp1Ci1atAAAbN++HSkpKbCzs4Orqyv69u2rzqHWiDd7T5w4gfbt20vP\n379/H+vWrYOlpSU6dOggXRCkySr+7G3SpAm6du0KIyMjLFu2DNeuXcPo0aOF2b8a60NnXI8ePaK5\nc+dSYGCg9LUBAwZQUlISPXz4kHbv3k0ZGRkqme2p25YtW8jGxkZ6HBUVRXPnziVXV1fhmrds2UKD\nBg2SHq9Zs4a0tbXJy8tL+k1fJJs3b6bBgwdX+VxVi6813Zvfy1evXqVVq1bR3/72N0pPT1fjyFTj\nzd4FCxbQxIkTycXFRaj/dxUKBZWUlNDIkSMpLS2NiMpuUfPy5Us6f/68cJ+fVlXv/fv3adKkSeTq\n6kqPHj1S8whrTlU/e62srCghIYGIiM6cOSPc/tU0WkREHzrpiouLg6+vL2bOnIl+/frBw8MDP/74\nIywtLVU511Mr+v+7mDs7O8PY2Bj16tXD5MmT0bt370p3NxfBm63NmzdHu3bt0L17dwwePFjdw6tx\nFXvbtWsHpVKJcePGwdLSUrh9C1TubdOmDQBgypQpwt7VuGJv69atoa+vD1dXV/Tq1Usjbqj5sV6/\nfg1PT084OjoiNDQULVq0wIYNG4RdK1Oxd+vWrejSpQsWLVqE1q1bq3toNe7Nn71//etfERgYiAED\nBqh7aAz4+BtyRkVFkbu7O5mYmNCGDRtqdFZXV7169YpsbGyoefPm0rlqUVVsXbt2rbqHo3Jy2rdE\n3CuyuLg40tLSImtra9q2bZu6h6NycuuV489eTfFR70iVKy4uhra2tnSeWnSrV69GRkYGgoKCNOqz\ntapDTq0A94pOTr1ZWVnYuXMnfHx8oKurq+7hqJzcegH5/ezVFNWaSMmNUqmUPoNMdHJqBbhXdHLr\nZYzVPp5IMcYYY4xVE/+qxhhjjDFWTTyRYowxxhirJp5IMcYYY4xVE0+kGGOMMcaqiSdSjDHGGGPV\n9H/EbZvpmHFhXgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import EventProfiler as ep\n",
      "\n",
      "eventmat = df['event'].copy()\n",
      "eventmat[eventmat==0.] = np.NaN\n",
      "\n",
      "ep_profiler = ep.EventProfiler(df, pd.DataFrame(eventmat), df.index[0], df.index[-1], verbose=False)\n",
      "f = ep_profiler.study(plotErrorBars=True, plotEvents=False)\n",
      "f.set_size_inches(8, 5)\n",
      "#f.savefig('event_study.png', dpi=150)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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3WLY8RNQzky3xpKQkFBQUoLGxEYGBgUhPT4dWqwUArF27FvHx8cjJyUFwcDDc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\nUFCAxsZGBAYGIj09HVqtFgC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      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create complete training/test dataset (FeatureFrame)\n",
      "# this cell is the bottleneck of all computations in this notebook\n",
      "# look back in time = (dt - 1) * 5 minutes\n",
      "# number of features = len(features) * dt\n",
      "# event indicator is stored in column 'event', look forward = (lf - 1) * 5 minutes\n",
      "# number of training/test examples = numdata\n",
      "dt = 7\n",
      "jj = dt\n",
      "lf = 4\n",
      "numdata = 2000\n",
      "\n",
      "features = ['Close', 'nmeanclose']\n",
      "\n",
      "FeatureFrame = pd.DataFrame( [tuple(np.ravel(df.ix[jj-dt:jj,features]))], index=[df.ix[jj-1].name] )\n",
      "FeatureFrame['event'] = df.ix[jj-1,'event']\n",
      "\n",
      "for i in xrange(jj+1, numdata+dt):\n",
      "    temp_df = pd.DataFrame( [tuple(np.ravel(df.ix[i-dt:i,features]))], index=[df.ix[i-1].name] )\n",
      "    FeatureFrame = pd.concat( [FeatureFrame, temp_df] )\n",
      "    FeatureFrame.ix[-1,'event'] = df.ix[i-1,'event']\n",
      "    \n",
      "FeatureFrame.shape\n",
      "FeatureFrame.ix[:5,-10::]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>11</th>\n",
        "      <th>12</th>\n",
        "      <th>13</th>\n",
        "      <th>event</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:35:00</strong></td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:40:00</strong></td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 985.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:45:00</strong></td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.00</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:50:00</strong></td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2009-08-03 01:55:00</strong></td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.25</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 986.75</td>\n",
        "      <td> 985.775</td>\n",
        "      <td> 987.00</td>\n",
        "      <td> 985.975</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "                           5       6        7       8        9      10       11  \\\n",
        "2009-08-03 01:35:00  985.775  985.25  985.775  985.25  985.775  986.00  985.775   \n",
        "2009-08-03 01:40:00  985.775  985.25  985.775  986.00  985.775  986.25  985.775   \n",
        "2009-08-03 01:45:00  985.775  986.00  985.775  986.25  985.775  986.25  985.775   \n",
        "2009-08-03 01:50:00  985.775  986.25  985.775  986.25  985.775  986.75  985.775   \n",
        "2009-08-03 01:55:00  985.775  986.25  985.775  986.75  985.775  986.75  985.775   \n",
        "\n",
        "                         12       13  event  \n",
        "2009-08-03 01:35:00  986.25  985.775      0  \n",
        "2009-08-03 01:40:00  986.25  985.775      0  \n",
        "2009-08-03 01:45:00  986.75  985.775      0  \n",
        "2009-08-03 01:50:00  986.75  985.775      0  \n",
        "2009-08-03 01:55:00  987.00  985.975      0  "
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check percentage of events in the data\n",
      "print '%.3f%%' % (float(len(FeatureFrame[FeatureFrame['event']==1.])) / float(len(FeatureFrame)) * 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.250%\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# split into training and test set; TBD: use sklearn cross validation\n",
      "numtrain = .7 * numdata\n",
      "\n",
      "vols = [x for x in range(dt * len(features)) if (x%len(features))==1]\n",
      "ohlc = [x for x in range(dt * len(features)) if x not in vols]\n",
      "\n",
      "train = FeatureFrame.ix[:numtrain,:-1].copy()\n",
      "#train.ix[:,ohlc] = scale(detrend(train.ix[:,ohlc]))\n",
      "train.ix[:,ohlc] = MinMaxScaler(feature_range=(0,1)).fit_transform(train.ix[:,ohlc])\n",
      "train.ix[:,vols] = MinMaxScaler(feature_range=(0,1)).fit_transform(train.ix[:,vols])\n",
      "target = FeatureFrame.ix[:numtrain,'event']\n",
      "\n",
      "test = FeatureFrame.ix[numtrain:,:-1].copy()\n",
      "#test.ix[:,ohlc] = scale(detrend(test.ix[:,ohlc]))\n",
      "test.ix[:,ohlc] = MinMaxScaler(feature_range=(0,1)).fit_transform(test.ix[:,ohlc])\n",
      "test.ix[:,vols] = MinMaxScaler(feature_range=(0,1)).fit_transform(test.ix[:,vols])\n",
      "test_target = FeatureFrame.ix[numtrain:,'event']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax1 = subplots(ncols=1)\n",
      "\n",
      "#ax1.plot(train.ix[target==1., ohlc].T, 'o')\n",
      "bp1 = ax1.boxplot(np.matrix( train.ix[target==1.,:] ), patch_artist=True)\n",
      "setp(bp1['boxes'], facecolor='r', alpha=0.5)\n",
      "setp(bp1['medians'], color='r')\n",
      "setp(bp1['caps'], color='r')\n",
      "setp(bp1['whiskers'], color='r')\n",
      "setp(bp1['fliers'], color='r')\n",
      "#ax1.set_xlim(-1,6)\n",
      "#ax1.set_ylim(0,1)\n",
      "#ax1.set_xticks(train.ix[target==1., ohlc].columns)\n",
      "#ax1.set_xticklabels(train.ix[target==1., ohlc].columns + 1)\n",
      "ax1.grid()\n",
      "ax1.set_xlabel('Feature number')\n",
      "ax1.set_title('Normalized feature values, red:=\"event=1\"')\n",
      "\n",
      "bp2 = ax1.boxplot(np.matrix( train.ix[target==0.,:] ), patch_artist=True)\n",
      "setp(bp2['boxes'], alpha=0.7)\n",
      "setp(bp2['medians'], color='b')\n",
      "setp(bp2['caps'], color='b')\n",
      "setp(bp2['whiskers'], color='b')\n",
      "setp(bp2['fliers'], color='b')\n",
      "#ax2.set_ylim(0,1)\n",
      "#ax2.grid()\n",
      "#ax2.set_xlabel('Feature number')\n",
      "#ax2.set_title('Normalized feature values for event==0')\n",
      "\n",
      "f.set_size_inches(7,5)\n",
      "f.tight_layout();\n",
      "f.savefig('featuredist.png', dpi=300);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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DwP8MvNVHS/RCnzFD2oE8zIDoArdFZgAwA8D4DCxRwO12h6h+aqqsPsAMAMDhkPcgnYMO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\n2GpgEyQAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# note there's a general uptrend in the data\n",
      "plot(range(numdata), FeatureFrame.ix[:,1], range(numdata), FeatureFrame.ix[:,2], range(numdata), FeatureFrame.ix[:,3]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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RQgpbi7dS6mq7qMvpCAiMqobrbwK7AgfaSTchh2SyKiWCWR1vmLph9A3sSoff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\n"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check how the scaled and detrended data looks like\n",
      "plot(train.ix[:,ohlc]);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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kBO749SMoogJ2Zhpn+Hw+dA1kUSIaH8AhcR0QjcYYN2gcmxPdy87eF93eZ1iW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\nRK5CYII=\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# what about volume?\n",
      "f, (ax1, ax2) = subplots(ncols=2)\n",
      "\n",
      "ax1.plot(FeatureFrame.ix[:,4])\n",
      "#ax2.plot(train.ix[:,vols]); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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1b19OQIVCrk3U58xRrksk5g34zp8PDBhg/HGjRqkO2gp7/oZq9YpN7qbL7WMI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\n4k8QBOGEkPgTBEE4IST+BEEQTsj/A/ZQOYfs1rnCAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rf = RandomForestClassifier(n_estimators=500, n_jobs=2, min_samples_leaf=10) #, compute_importances=True)\n",
      "rf.fit(train, target)\n",
      "predicted_probs = rf.predict_proba(test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(predicted_probs[:,1], bins=20);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# TBD: use cross validation to determine the decision threshold\n",
      "# TBD: define performance measure that include risks/losses; precision alone is not enough!\n",
      "thr = 0.1\n",
      "idx = predicted_probs[:,1]>thr\n",
      "test_target[idx]\n",
      "\n",
      "# the model doesn't generalize well!!!!!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "2009-08-14 11:45:00    0\n",
        "2009-08-14 11:50:00    0\n",
        "2009-08-14 13:45:00    0\n",
        "2009-08-14 13:50:00    0\n",
        "2009-08-17 02:25:00    0\n",
        "Name: event"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_target[test_target==1.]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "2009-08-12 14:45:00    1\n",
        "2009-08-13 10:30:00    1\n",
        "2009-08-13 15:50:00    1\n",
        "2009-08-13 15:55:00    1\n",
        "2009-08-14 15:30:00    1\n",
        "2009-08-14 15:50:00    1\n",
        "2009-08-14 15:55:00    1\n",
        "2009-08-14 16:00:00    1\n",
        "2009-08-17 04:15:00    1\n",
        "Name: event"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# a side note: performance on training set\n",
      "# setting min_samples_leaf removes the overfitting somewhat\n",
      "rf = RandomForestClassifier(n_estimators=500, n_jobs=2, min_samples_leaf=10)\n",
      "rf.fit(train, target)\n",
      "predicted_probs = rf.predict_proba(train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thr = 0.1\n",
      "idx = predicted_probs[:,1]>thr\n",
      "target[idx]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "2009-08-03 15:50:00    1\n",
        "2009-08-03 15:55:00    0\n",
        "2009-08-04 15:50:00    1\n",
        "2009-08-04 15:55:00    1\n",
        "2009-08-05 14:35:00    1\n",
        "2009-08-07 08:30:00    1\n",
        "2009-08-07 10:45:00    1\n",
        "2009-08-07 10:50:00    1\n",
        "2009-08-12 09:30:00    1\n",
        "2009-08-12 09:35:00    1\n",
        "2009-08-12 09:40:00    1\n",
        "2009-08-12 09:45:00    1\n",
        "2009-08-12 09:50:00    1\n",
        "Name: event"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}