bonds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import Imputer\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import csv\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('ML_Bond_metadata.csv', header=0)\n",
    "data = pd.read_csv('dataset.csv', header=0)\n",
    "avg = pd.read_csv('avgvol.csv', header=0)\n",
    "det = pd.read_csv('details.csv', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([10000], dtype='int64')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=df[df['isin']=='isin10033']\n",
    "p.index+4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17261 entries, 0 to 17260\n",
      "Data columns (total 24 columns):\n",
      "isin                          17261 non-null object\n",
      "issuer                        17261 non-null object\n",
      "issue date                    17260 non-null object\n",
      "Market                        17261 non-null object\n",
      "amtIssued                     17261 non-null int64\n",
      "amtOutstanding                17261 non-null int64\n",
      "collateralType                17261 non-null object\n",
      "coupon                        17261 non-null float64\n",
      "couponFrequency               16553 non-null float64\n",
      "couponType                    17261 non-null object\n",
      "industryGroup                 17261 non-null object\n",
      "industrySector                17261 non-null object\n",
      "industrySubgroup              17261 non-null object\n",
      "maturity                      17023 non-null object\n",
      "maturityType                  17261 non-null object\n",
      "securityType                  17261 non-null object\n",
      "paymentRank                   17261 non-null object\n",
      "144aFlag                      17261 non-null object\n",
      "ratingAgency1Rating           17261 non-null object\n",
      "ratingAgency1Watch            17261 non-null int64\n",
      "ratingAgency1EffectiveDate    15480 non-null object\n",
      "ratingAgency2Rating           17261 non-null object\n",
      "ratingAgency2Watch            17261 non-null int64\n",
      "ratingAgency2EffectiveDate    15995 non-null object\n",
      "dtypes: float64(2), int64(4), object(18)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.head(10)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isin</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "      <th>side</th>\n",
       "      <th>volume</th>\n",
       "      <th>timeofday</th>\n",
       "      <th>date</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 16May16 02:51:27.000 pm</td>\n",
       "      <td>117.363618</td>\n",
       "      <td>S</td>\n",
       "      <td>730000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>16May2016</td>\n",
       "      <td>136.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 16May16 03:45:52.000 pm</td>\n",
       "      <td>115.715329</td>\n",
       "      <td>B</td>\n",
       "      <td>730000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>16May2016</td>\n",
       "      <td>136.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Wed  6Apr16 08:24:07.000 am</td>\n",
       "      <td>115.296450</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Apr2016</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Wed  6Apr16 08:24:07.000 am</td>\n",
       "      <td>115.296450</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Apr2016</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Wed  6Apr16 08:25:00.000 am</td>\n",
       "      <td>115.558250</td>\n",
       "      <td>S</td>\n",
       "      <td>5000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Apr2016</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Fri 22Apr16 11:13:28.000 am</td>\n",
       "      <td>122.588087</td>\n",
       "      <td>S</td>\n",
       "      <td>40000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>22Apr2016</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Fri 22Apr16 11:13:28.000 am</td>\n",
       "      <td>122.588087</td>\n",
       "      <td>S</td>\n",
       "      <td>40000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>22Apr2016</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Thu 19May16 09:00:03.000 am</td>\n",
       "      <td>123.529518</td>\n",
       "      <td>S</td>\n",
       "      <td>20000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>19May2016</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 18Apr16 12:44:32.000 pm</td>\n",
       "      <td>115.155079</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>18Apr2016</td>\n",
       "      <td>108.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 18Apr16 12:44:32.000 pm</td>\n",
       "      <td>115.155079</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>18Apr2016</td>\n",
       "      <td>108.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>isin100</td>\n",
       "      <td>Fri 25Mar16 11:46:33.000 am</td>\n",
       "      <td>104.839973</td>\n",
       "      <td>B</td>\n",
       "      <td>210000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>25Mar2016</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>isin100</td>\n",
       "      <td>Mon  6Jun16 11:40:18.000 am</td>\n",
       "      <td>104.881128</td>\n",
       "      <td>B</td>\n",
       "      <td>310000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Jun2016</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>isin100</td>\n",
       "      <td>Thu  9Jun16 10:14:39.000 am</td>\n",
       "      <td>104.845419</td>\n",
       "      <td>S</td>\n",
       "      <td>210000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>9Jun2016</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Wed 23Mar16 11:43:48.000 am</td>\n",
       "      <td>109.693943</td>\n",
       "      <td>S</td>\n",
       "      <td>3900000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>23Mar2016</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Wed 23Mar16 11:45:15.000 am</td>\n",
       "      <td>109.693943</td>\n",
       "      <td>S</td>\n",
       "      <td>3900000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>23Mar2016</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Wed 23Mar16 12:02:36.000 pm</td>\n",
       "      <td>109.589224</td>\n",
       "      <td>B</td>\n",
       "      <td>3900000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>23Mar2016</td>\n",
       "      <td>83.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Wed 23Mar16 02:55:23.000 pm</td>\n",
       "      <td>109.693943</td>\n",
       "      <td>S</td>\n",
       "      <td>3900000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>23Mar2016</td>\n",
       "      <td>83.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Wed 23Mar16 03:56:50.000 pm</td>\n",
       "      <td>109.191289</td>\n",
       "      <td>B</td>\n",
       "      <td>20000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>23Mar2016</td>\n",
       "      <td>83.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Mon 28Mar16 10:40:46.000 am</td>\n",
       "      <td>108.908545</td>\n",
       "      <td>B</td>\n",
       "      <td>420000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>28Mar2016</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>isin10272</td>\n",
       "      <td>Mon 28Mar16 04:33:25.000 pm</td>\n",
       "      <td>109.170345</td>\n",
       "      <td>B</td>\n",
       "      <td>1050000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>28Mar2016</td>\n",
       "      <td>88.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         isin                         time       price side   volume  \\\n",
       "0   isin10033  Mon 16May16 02:51:27.000 pm  117.363618    S   730000   \n",
       "1   isin10033  Mon 16May16 03:45:52.000 pm  115.715329    B   730000   \n",
       "2   isin10033  Wed  6Apr16 08:24:07.000 am  115.296450    B     5000   \n",
       "3   isin10033  Wed  6Apr16 08:24:07.000 am  115.296450    B     5000   \n",
       "4   isin10033  Wed  6Apr16 08:25:00.000 am  115.558250    S     5000   \n",
       "5   isin10033  Fri 22Apr16 11:13:28.000 am  122.588087    S    40000   \n",
       "6   isin10033  Fri 22Apr16 11:13:28.000 am  122.588087    S    40000   \n",
       "7   isin10033  Thu 19May16 09:00:03.000 am  123.529518    S    20000   \n",
       "8   isin10033  Mon 18Apr16 12:44:32.000 pm  115.155079    B     5000   \n",
       "9   isin10033  Mon 18Apr16 12:44:32.000 pm  115.155079    B     5000   \n",
       "10    isin100  Fri 25Mar16 11:46:33.000 am  104.839973    B   210000   \n",
       "11    isin100  Mon  6Jun16 11:40:18.000 am  104.881128    B   310000   \n",
       "12    isin100  Thu  9Jun16 10:14:39.000 am  104.845419    S   210000   \n",
       "13  isin10272  Wed 23Mar16 11:43:48.000 am  109.693943    S  3900000   \n",
       "14  isin10272  Wed 23Mar16 11:45:15.000 am  109.693943    S  3900000   \n",
       "15  isin10272  Wed 23Mar16 12:02:36.000 pm  109.589224    B  3900000   \n",
       "16  isin10272  Wed 23Mar16 02:55:23.000 pm  109.693943    S  3900000   \n",
       "17  isin10272  Wed 23Mar16 03:56:50.000 pm  109.191289    B    20000   \n",
       "18  isin10272  Mon 28Mar16 10:40:46.000 am  108.908545    B   420000   \n",
       "19  isin10272  Mon 28Mar16 04:33:25.000 pm  109.170345    B  1050000   \n",
       "\n",
       "    timeofday       date   days  \n",
       "0   Afternoon  16May2016  136.5  \n",
       "1   Afternoon  16May2016  136.5  \n",
       "2     Morning   6Apr2016   96.0  \n",
       "3     Morning   6Apr2016   96.0  \n",
       "4     Morning   6Apr2016   96.0  \n",
       "5     Morning  22Apr2016  112.0  \n",
       "6     Morning  22Apr2016  112.0  \n",
       "7     Morning  19May2016  139.0  \n",
       "8   Afternoon  18Apr2016  108.5  \n",
       "9   Afternoon  18Apr2016  108.5  \n",
       "10    Morning  25Mar2016   85.0  \n",
       "11    Morning   6Jun2016  156.0  \n",
       "12    Morning   9Jun2016  159.0  \n",
       "13    Morning  23Mar2016   83.0  \n",
       "14    Morning  23Mar2016   83.0  \n",
       "15  Afternoon  23Mar2016   83.5  \n",
       "16  Afternoon  23Mar2016   83.5  \n",
       "17  Afternoon  23Mar2016   83.5  \n",
       "18    Morning  28Mar2016   88.0  \n",
       "19  Afternoon  28Mar2016   88.5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep']\n",
    "tod = ['Morning', 'Afternoon']\n",
    "\n",
    "data['days'] = data['date'].apply(lambda x: months.index(x[-7:-4])*30 + int(x[:-7]))\n",
    "data['days'] += data['timeofday'].apply(lambda x: 0.5*tod.index(x))\n",
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isin</th>\n",
       "      <th>issuer</th>\n",
       "      <th>issue date</th>\n",
       "      <th>Market</th>\n",
       "      <th>amtIssued</th>\n",
       "      <th>amtOutstanding</th>\n",
       "      <th>collateralType</th>\n",
       "      <th>coupon</th>\n",
       "      <th>couponFrequency</th>\n",
       "      <th>couponType</th>\n",
       "      <th>...</th>\n",
       "      <th>securityType</th>\n",
       "      <th>paymentRank</th>\n",
       "      <th>144aFlag</th>\n",
       "      <th>ratingAgency1Rating</th>\n",
       "      <th>ratingAgency1Watch</th>\n",
       "      <th>ratingAgency1EffectiveDate</th>\n",
       "      <th>ratingAgency2Rating</th>\n",
       "      <th>ratingAgency2Watch</th>\n",
       "      <th>ratingAgency2EffectiveDate</th>\n",
       "      <th>avgvol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>isin0</td>\n",
       "      <td>issuer490</td>\n",
       "      <td>8Dec2011</td>\n",
       "      <td>Market2</td>\n",
       "      <td>183260000</td>\n",
       "      <td>183260000</td>\n",
       "      <td>19</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>flag0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.888000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>isin1</td>\n",
       "      <td>issuer1193</td>\n",
       "      <td>10-Sep-10</td>\n",
       "      <td>Market2</td>\n",
       "      <td>261800000</td>\n",
       "      <td>198970000</td>\n",
       "      <td>26</td>\n",
       "      <td>5.500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flag0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.843235e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>isin2</td>\n",
       "      <td>issuer2943</td>\n",
       "      <td>14-Nov-14</td>\n",
       "      <td>Market2</td>\n",
       "      <td>78540000</td>\n",
       "      <td>78540000</td>\n",
       "      <td>28</td>\n",
       "      <td>5.750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>flag1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.870000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>isin3</td>\n",
       "      <td>issuer3707</td>\n",
       "      <td>4Mar2013</td>\n",
       "      <td>Market1</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>28</td>\n",
       "      <td>4.750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>couponType6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>flag0</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>16-Mar-16</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2May2013</td>\n",
       "      <td>1.250755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>isin4</td>\n",
       "      <td>issuer3711</td>\n",
       "      <td>25-Nov-14</td>\n",
       "      <td>Market0</td>\n",
       "      <td>1309000000</td>\n",
       "      <td>1309000000</td>\n",
       "      <td>12</td>\n",
       "      <td>7.125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>couponType6</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>flag0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>16-Mar-16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.400000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>isin5</td>\n",
       "      <td>issuer3711</td>\n",
       "      <td>25-Nov-14</td>\n",
       "      <td>Market0</td>\n",
       "      <td>1309000000</td>\n",
       "      <td>1309000000</td>\n",
       "      <td>12</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>couponType6</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>flag0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>16-Mar-16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.483168e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>isin6</td>\n",
       "      <td>issuer3278</td>\n",
       "      <td>17-Dec-12</td>\n",
       "      <td>Market1</td>\n",
       "      <td>523600000</td>\n",
       "      <td>523600000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>flag0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>24-Dec-12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7Nov2014</td>\n",
       "      <td>8.056250e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>isin7</td>\n",
       "      <td>issuer2238</td>\n",
       "      <td>3Sep1985</td>\n",
       "      <td>Market1</td>\n",
       "      <td>523600000</td>\n",
       "      <td>135720000</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>couponType3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>flag0</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>19-Mar-15</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>11-Feb-14</td>\n",
       "      <td>3.900000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>isin8</td>\n",
       "      <td>issuer404</td>\n",
       "      <td>25-Nov-13</td>\n",
       "      <td>Market0</td>\n",
       "      <td>837760000</td>\n",
       "      <td>772920000</td>\n",
       "      <td>22</td>\n",
       "      <td>7.375</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>17-Apr-14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.878571e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isin9</td>\n",
       "      <td>issuer1167</td>\n",
       "      <td>27-Feb-14</td>\n",
       "      <td>Market1</td>\n",
       "      <td>418880000</td>\n",
       "      <td>418880000</td>\n",
       "      <td>22</td>\n",
       "      <td>2.803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Mar-14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>7Mar2014</td>\n",
       "      <td>4.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>isin10</td>\n",
       "      <td>issuer1167</td>\n",
       "      <td>27-Feb-14</td>\n",
       "      <td>Market1</td>\n",
       "      <td>418880000</td>\n",
       "      <td>418880000</td>\n",
       "      <td>22</td>\n",
       "      <td>3.839</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Mar-14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>7Mar2014</td>\n",
       "      <td>3.680000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>isin11</td>\n",
       "      <td>issuer1167</td>\n",
       "      <td>27-Feb-14</td>\n",
       "      <td>Market1</td>\n",
       "      <td>418880000</td>\n",
       "      <td>418880000</td>\n",
       "      <td>22</td>\n",
       "      <td>4.435</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Mar-14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>7Mar2014</td>\n",
       "      <td>6.885714e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>isin12</td>\n",
       "      <td>issuer1167</td>\n",
       "      <td>27-Feb-14</td>\n",
       "      <td>Market1</td>\n",
       "      <td>418880000</td>\n",
       "      <td>418880000</td>\n",
       "      <td>22</td>\n",
       "      <td>5.082</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Mar-14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>7Mar2014</td>\n",
       "      <td>1.143333e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>isin13</td>\n",
       "      <td>issuer1167</td>\n",
       "      <td>27-Feb-14</td>\n",
       "      <td>Market1</td>\n",
       "      <td>418880000</td>\n",
       "      <td>418880000</td>\n",
       "      <td>22</td>\n",
       "      <td>5.412</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Mar-14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>7Mar2014</td>\n",
       "      <td>1.765556e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>isin14</td>\n",
       "      <td>issuer11</td>\n",
       "      <td>10-Apr-14</td>\n",
       "      <td>Market2</td>\n",
       "      <td>424120000</td>\n",
       "      <td>480830000</td>\n",
       "      <td>22</td>\n",
       "      <td>12.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>flag1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.512143e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>isin15</td>\n",
       "      <td>issuer12</td>\n",
       "      <td>16-Feb-12</td>\n",
       "      <td>Market1</td>\n",
       "      <td>523600000</td>\n",
       "      <td>523600000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.625</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>flag0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15-Feb-12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>15-Feb-12</td>\n",
       "      <td>9.611494e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>isin16</td>\n",
       "      <td>issuer12</td>\n",
       "      <td>16-Feb-12</td>\n",
       "      <td>Market1</td>\n",
       "      <td>1309000000</td>\n",
       "      <td>1309000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.875</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>flag0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15-Feb-12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>15-Feb-12</td>\n",
       "      <td>3.598621e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>isin17</td>\n",
       "      <td>issuer12</td>\n",
       "      <td>16-Feb-12</td>\n",
       "      <td>Market1</td>\n",
       "      <td>785400000</td>\n",
       "      <td>785400000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.375</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>flag0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15-Feb-12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>15-Feb-12</td>\n",
       "      <td>2.580000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>isin18</td>\n",
       "      <td>issuer13</td>\n",
       "      <td>29-Mar-11</td>\n",
       "      <td>Market1</td>\n",
       "      <td>680680000</td>\n",
       "      <td>680680000</td>\n",
       "      <td>26</td>\n",
       "      <td>4.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flag1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>28-Mar-11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Apr-11</td>\n",
       "      <td>6.520000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>isin19</td>\n",
       "      <td>issuer18</td>\n",
       "      <td>6Nov1996</td>\n",
       "      <td>Market2</td>\n",
       "      <td>52360000</td>\n",
       "      <td>52360000</td>\n",
       "      <td>24</td>\n",
       "      <td>10.500</td>\n",
       "      <td>12.0</td>\n",
       "      <td>couponType0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>flag0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>isin20</td>\n",
       "      <td>issuer18</td>\n",
       "      <td>30-Sep-97</td>\n",
       "      <td>Market2</td>\n",
       "      <td>26180000</td>\n",
       "      <td>26180000</td>\n",
       "      <td>24</td>\n",
       "      <td>10.500</td>\n",
       "      <td>12.0</td>\n",
       "      <td>couponType0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>flag0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>isin21</td>\n",
       "      <td>issuer3144</td>\n",
       "      <td>9Mar1993</td>\n",
       "      <td>Market0</td>\n",
       "      <td>261800000</td>\n",
       "      <td>141960000</td>\n",
       "      <td>28</td>\n",
       "      <td>7.750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>flag0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9Jul2014</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>18-Dec-13</td>\n",
       "      <td>1.700000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>isin22</td>\n",
       "      <td>issuer3144</td>\n",
       "      <td>29-Jul-93</td>\n",
       "      <td>Market0</td>\n",
       "      <td>157080000</td>\n",
       "      <td>157080000</td>\n",
       "      <td>28</td>\n",
       "      <td>7.125</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>flag0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9Jul2014</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>18-Dec-13</td>\n",
       "      <td>3.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>isin23</td>\n",
       "      <td>issuer3142</td>\n",
       "      <td>19-Dec-03</td>\n",
       "      <td>Market0</td>\n",
       "      <td>522850000</td>\n",
       "      <td>522850000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.650</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>flag0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9Jul2014</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>18-Dec-13</td>\n",
       "      <td>1.656688e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>isin24</td>\n",
       "      <td>issuer24</td>\n",
       "      <td>7May2015</td>\n",
       "      <td>Market1</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>28</td>\n",
       "      <td>4.750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>flag1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>1May2015</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2Mar2016</td>\n",
       "      <td>4.385398e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>isin25</td>\n",
       "      <td>issuer29</td>\n",
       "      <td>27-Mar-13</td>\n",
       "      <td>Market0</td>\n",
       "      <td>523600000</td>\n",
       "      <td>523600000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>flag0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>27-Dec-13</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>26-Jan-12</td>\n",
       "      <td>7.664865e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>isin26</td>\n",
       "      <td>issuer3470</td>\n",
       "      <td>22-Aug-13</td>\n",
       "      <td>Market1</td>\n",
       "      <td>261800000</td>\n",
       "      <td>261800000</td>\n",
       "      <td>28</td>\n",
       "      <td>2.875</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType6</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>flag1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>9Jul2014</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>26-Mar-15</td>\n",
       "      <td>1.570000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>isin27</td>\n",
       "      <td>issuer24</td>\n",
       "      <td>8Nov2011</td>\n",
       "      <td>Market1</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>26</td>\n",
       "      <td>4.250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flag1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>24-Aug-12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2Mar2016</td>\n",
       "      <td>3.152927e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>isin28</td>\n",
       "      <td>issuer24</td>\n",
       "      <td>8Aug2013</td>\n",
       "      <td>Market1</td>\n",
       "      <td>1832600000</td>\n",
       "      <td>1832600000</td>\n",
       "      <td>26</td>\n",
       "      <td>2.500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>couponType1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flag1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5Aug2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2Mar2016</td>\n",
       "      <td>3.101071e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>isin29</td>\n",
       "      <td>issuer24</td>\n",
       "      <td>8Aug2013</td>\n",
       "      <td>Market1</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>1570800000</td>\n",
       "      <td>26</td>\n",
       "      <td>1.543</td>\n",
       "      <td>4.0</td>\n",
       "      <td>couponType3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flag1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5Aug2013</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2Mar2016</td>\n",
       "      <td>4.409091e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      isin      issuer issue date   Market   amtIssued  amtOutstanding  \\\n",
       "0    isin0   issuer490   8Dec2011  Market2   183260000       183260000   \n",
       "1    isin1  issuer1193  10-Sep-10  Market2   261800000       198970000   \n",
       "2    isin2  issuer2943  14-Nov-14  Market2    78540000        78540000   \n",
       "3    isin3  issuer3707   4Mar2013  Market1  1570800000      1570800000   \n",
       "4    isin4  issuer3711  25-Nov-14  Market0  1309000000      1309000000   \n",
       "5    isin5  issuer3711  25-Nov-14  Market0  1309000000      1309000000   \n",
       "6    isin6  issuer3278  17-Dec-12  Market1   523600000       523600000   \n",
       "7    isin7  issuer2238   3Sep1985  Market1   523600000       135720000   \n",
       "8    isin8   issuer404  25-Nov-13  Market0   837760000       772920000   \n",
       "9    isin9  issuer1167  27-Feb-14  Market1   418880000       418880000   \n",
       "10  isin10  issuer1167  27-Feb-14  Market1   418880000       418880000   \n",
       "11  isin11  issuer1167  27-Feb-14  Market1   418880000       418880000   \n",
       "12  isin12  issuer1167  27-Feb-14  Market1   418880000       418880000   \n",
       "13  isin13  issuer1167  27-Feb-14  Market1   418880000       418880000   \n",
       "14  isin14    issuer11  10-Apr-14  Market2   424120000       480830000   \n",
       "15  isin15    issuer12  16-Feb-12  Market1   523600000       523600000   \n",
       "16  isin16    issuer12  16-Feb-12  Market1  1309000000      1309000000   \n",
       "17  isin17    issuer12  16-Feb-12  Market1   785400000       785400000   \n",
       "18  isin18    issuer13  29-Mar-11  Market1   680680000       680680000   \n",
       "19  isin19    issuer18   6Nov1996  Market2    52360000        52360000   \n",
       "20  isin20    issuer18  30-Sep-97  Market2    26180000        26180000   \n",
       "21  isin21  issuer3144   9Mar1993  Market0   261800000       141960000   \n",
       "22  isin22  issuer3144  29-Jul-93  Market0   157080000       157080000   \n",
       "23  isin23  issuer3142  19-Dec-03  Market0   522850000       522850000   \n",
       "24  isin24    issuer24   7May2015  Market1  1570800000      1570800000   \n",
       "25  isin25    issuer29  27-Mar-13  Market0   523600000       523600000   \n",
       "26  isin26  issuer3470  22-Aug-13  Market1   261800000       261800000   \n",
       "27  isin27    issuer24   8Nov2011  Market1  1570800000      1570800000   \n",
       "28  isin28    issuer24   8Aug2013  Market1  1832600000      1832600000   \n",
       "29  isin29    issuer24   8Aug2013  Market1  1570800000      1570800000   \n",
       "\n",
       "    collateralType  coupon  couponFrequency   couponType      ...       \\\n",
       "0               19  10.000              2.0  couponType1      ...        \n",
       "1               26   5.500              2.0  couponType1      ...        \n",
       "2               28   5.750              2.0  couponType1      ...        \n",
       "3               28   4.750              1.0  couponType6      ...        \n",
       "4               12   7.125              1.0  couponType6      ...        \n",
       "5               12   7.000              1.0  couponType6      ...        \n",
       "6                5   1.500              1.0  couponType1      ...        \n",
       "7               12   1.000              4.0  couponType3      ...        \n",
       "8               22   7.375              2.0  couponType1      ...        \n",
       "9               22   2.803              2.0  couponType1      ...        \n",
       "10              22   3.839              2.0  couponType1      ...        \n",
       "11              22   4.435              2.0  couponType1      ...        \n",
       "12              22   5.082              2.0  couponType1      ...        \n",
       "13              22   5.412              2.0  couponType1      ...        \n",
       "14              22  12.000              2.0  couponType4      ...        \n",
       "15               5   1.625              2.0  couponType1      ...        \n",
       "16               5   2.875              2.0  couponType1      ...        \n",
       "17               5   4.375              2.0  couponType1      ...        \n",
       "18              26   4.000              2.0  couponType1      ...        \n",
       "19              24  10.500             12.0  couponType0      ...        \n",
       "20              24  10.500             12.0  couponType0      ...        \n",
       "21              28   7.750              2.0  couponType1      ...        \n",
       "22              28   7.125              2.0  couponType1      ...        \n",
       "23               5   4.650              2.0  couponType1      ...        \n",
       "24              28   4.750              2.0  couponType1      ...        \n",
       "25               5   6.750              2.0  couponType1      ...        \n",
       "26              28   2.875              2.0  couponType6      ...        \n",
       "27              26   4.250              2.0  couponType1      ...        \n",
       "28              26   2.500              2.0  couponType1      ...        \n",
       "29              26   1.543              4.0  couponType3      ...        \n",
       "\n",
       "   securityType paymentRank 144aFlag ratingAgency1Rating ratingAgency1Watch  \\\n",
       "0             3           3    flag0                   0                  4   \n",
       "1             5           8    flag0                   0                  4   \n",
       "2             5           9    flag1                   0                  4   \n",
       "3             2           9    flag0                  18                  4   \n",
       "4             3           5    flag0                  15                  4   \n",
       "5             3           5    flag0                  15                  4   \n",
       "6             2           8    flag0                   5                  4   \n",
       "7             3           5    flag0                  19                  4   \n",
       "8             5           2    flag1                  16                  4   \n",
       "9             5           2    flag1                  19                  4   \n",
       "10            5           2    flag1                  19                  4   \n",
       "11            5           2    flag1                  19                  4   \n",
       "12            5           2    flag1                  19                  4   \n",
       "13            5           2    flag1                  19                  4   \n",
       "14            5           2    flag1                   0                  4   \n",
       "15            4           8    flag0                   4                  4   \n",
       "16            4           8    flag0                   4                  4   \n",
       "17            4           8    flag0                   4                  4   \n",
       "18            5           8    flag1                   4                  4   \n",
       "19            6           7    flag0                   0                  4   \n",
       "20            6           7    flag0                   0                  4   \n",
       "21            7           9    flag0                  15                  4   \n",
       "22            6           9    flag0                  15                  4   \n",
       "23            4           9    flag0                  15                  4   \n",
       "24            5           9    flag1                  19                  4   \n",
       "25            4           8    flag0                  16                  4   \n",
       "26            5           9    flag1                  17                  4   \n",
       "27            5           8    flag1                   4                  4   \n",
       "28            5           8    flag1                   4                  4   \n",
       "29            5           8    flag1                   4                  4   \n",
       "\n",
       "    ratingAgency1EffectiveDate  ratingAgency2Rating ratingAgency2Watch  \\\n",
       "0                          NaN                    0                  4   \n",
       "1                          NaN                    0                  4   \n",
       "2                          NaN                    0                  4   \n",
       "3                    16-Mar-16                   22                  4   \n",
       "4                    16-Mar-16                    0                  4   \n",
       "5                    16-Mar-16                    0                  4   \n",
       "6                    24-Dec-12                    1                  4   \n",
       "7                    19-Mar-15                   16                  4   \n",
       "8                    17-Apr-14                    0                  4   \n",
       "9                    27-Mar-14                   16                  4   \n",
       "10                   27-Mar-14                   16                  4   \n",
       "11                   27-Mar-14                   16                  4   \n",
       "12                   27-Mar-14                   16                  4   \n",
       "13                   27-Mar-14                   16                  4   \n",
       "14                         NaN                    0                  4   \n",
       "15                   15-Feb-12                    2                  4   \n",
       "16                   15-Feb-12                    2                  4   \n",
       "17                   15-Feb-12                    2                  4   \n",
       "18                   28-Mar-11                    2                  4   \n",
       "19                         NaN                    0                  4   \n",
       "20                         NaN                    0                  4   \n",
       "21                    9Jul2014                   12                  4   \n",
       "22                    9Jul2014                   12                  4   \n",
       "23                    9Jul2014                   12                  4   \n",
       "24                    1May2015                   15                  4   \n",
       "25                   27-Dec-13                    8                  4   \n",
       "26                    9Jul2014                    1                  4   \n",
       "27                   24-Aug-12                    1                  4   \n",
       "28                    5Aug2013                    1                  4   \n",
       "29                    5Aug2013                    1                  4   \n",
       "\n",
       "    ratingAgency2EffectiveDate        avgvol  \n",
       "0                          NaN  3.888000e+06  \n",
       "1                          NaN  1.843235e+06  \n",
       "2                          NaN  3.870000e+06  \n",
       "3                     2May2013  1.250755e+06  \n",
       "4                          NaN  3.400000e+05  \n",
       "5                          NaN  1.483168e+06  \n",
       "6                     7Nov2014  8.056250e+05  \n",
       "7                    11-Feb-14  3.900000e+06  \n",
       "8                          NaN  7.878571e+05  \n",
       "9                     7Mar2014  4.000000e+05  \n",
       "10                    7Mar2014  3.680000e+06  \n",
       "11                    7Mar2014  6.885714e+05  \n",
       "12                    7Mar2014  1.143333e+06  \n",
       "13                    7Mar2014  1.765556e+06  \n",
       "14                         NaN  2.512143e+06  \n",
       "15                   15-Feb-12  9.611494e+05  \n",
       "16                   15-Feb-12  3.598621e+05  \n",
       "17                   15-Feb-12  2.580000e+05  \n",
       "18                   27-Apr-11  6.520000e+06  \n",
       "19                         NaN  1.000000e+04  \n",
       "20                         NaN  2.000000e+04  \n",
       "21                   18-Dec-13  1.700000e+05  \n",
       "22                   18-Dec-13  3.000000e+04  \n",
       "23                   18-Dec-13  1.656688e+05  \n",
       "24                    2Mar2016  4.385398e+06  \n",
       "25                   26-Jan-12  7.664865e+05  \n",
       "26                   26-Mar-15  1.570000e+06  \n",
       "27                    2Mar2016  3.152927e+06  \n",
       "28                    2Mar2016  3.101071e+06  \n",
       "29                    2Mar2016  4.409091e+06  \n",
       "\n",
       "[30 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"fw = open('avgvol.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'avgvol'))\n",
    "i=0\n",
    "while i<data.shape[0]:\n",
    "    j=i+1\n",
    "    while j<data.shape[0] and data['isin'][j]==data['isin'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    writer.writerow((data['isin'][i], data['volume'][i:j].mean()))\n",
    "    i=j\n",
    "    \n",
    "fw.close()\n",
    "\n",
    "for i in range(df.shape[0]):\n",
    "    df['collateralType'][i] = int(df['collateralType'][i][14:])\n",
    "    df['ratingAgency1Rating'][i] = int(df['ratingAgency1Rating'][i][6:])\n",
    "    df['ratingAgency2Rating'][i] = int(df['ratingAgency2Rating'][i][6:])\n",
    "    df['paymentRank'][i] = int(df['paymentRank'][i][11:])\n",
    "    df['securityType'][i] = int(df['securityType'][i][12:])\n",
    "    if i%1000==0:\n",
    "        print i\n",
    "\n",
    "fw = open('details.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'collateralType', 'ratingAgency1Rating', 'ratingAgency2Rating', 'paymentRank', 'securityType'))\n",
    "for i in range(df.shape[0]):\n",
    "    writer.writerow((df['isin'][i], df['collateralType'][i], df['ratingAgency1Rating'][i],\n",
    "                     df['ratingAgency2Rating'][i], df['paymentRank'][i], df['securityType'][i]))\n",
    "    if i%1000==0:\n",
    "        print i\n",
    "    \n",
    "fw.close()\"\"\"\n",
    "\n",
    "df['avgvol'] = avg['avgvol']\n",
    "df['collateralType'] = det['collateralType']\n",
    "df['ratingAgency1Rating'] = det['ratingAgency1Rating']\n",
    "df['ratingAgency2Rating'] = det['ratingAgency2Rating']\n",
    "df['paymentRank'] = det['paymentRank']\n",
    "df['securityType'] = det['securityType']\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94f6e74c90>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DwUWStJjPOVKzM7hIkpZg34uamcFFkrQU+17UrAwukqRlsu9FzcbgIklaLvte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\nJElSyzC4SJKklvH/Aeq0NRUzyY/tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94f67eaad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data[data['isin']=='isin0']\n",
    "a = temp[temp['side']=='B']\n",
    "plt.plot(a['volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[535275.47089112026, 8], [726730.55728049774, 18], [824072.23759645456, 17], [939232.74042480998, 19], [974208.06517225958, 15], [988381.20414095873, 0], [1001476.7833485882, 16], [1035412.80106927, 14], [1052834.7969855072, 6], [1098559.6949249217, 4], [1142627.5510204099, 3], [1173625.9485263387, 11], [1178461.0761930957, 25], [1209086.482984008, 12], [1254242.4681863913, 24], [1318889.2222976068, 26], [1358154.033908698, 13], [1390333.6318166712, 21], [1399704.4692102014, 23], [1415197.36842105, 2], [1425565.1026109548, 20], [1429341.2600836395, 5], [1477505.6438464925, 9], [1482876.3741255836, 7], [1513350.9677026868, 10], [1593894.9140502664, 22], [1744460.0938967101, 1]]\n",
      "[8, 18, 17, 19, 15, 0, 16, 14, 6, 4, 3, 11, 25, 12, 24, 26, 13, 21, 23, 2, 20, 5, 9, 7, 10, 22, 1]\n",
      "[535275.47089112026, 726730.55728049774, 824072.23759645456, 939232.74042480998, 974208.06517225958, 988381.20414095873, 1001476.7833485882, 1035412.80106927, 1052834.7969855072, 1098559.6949249217, 1142627.5510204099, 1173625.9485263387, 1178461.0761930957, 1209086.482984008, 1254242.4681863913, 1318889.2222976068, 1358154.033908698, 1390333.6318166712, 1399704.4692102014, 1415197.36842105, 1425565.1026109548, 1429341.2600836395, 1477505.6438464925, 1482876.3741255836, 1513350.9677026868, 1593894.9140502664, 1744460.0938967101]\n",
      "[[210830.98564075952, 22], [794951.65401766938, 14], [796820.12817960477, 15], [914720.324723572, 12], [927302.73562433117, 3], [938733.37379799434, 11], [938757.85742998624, 2], [1000355.6154423188, 23], [1007196.4937989194, 13], [1044899.9288844019, 16], [1059137.1561855921, 8], [1093091.3563735723, 9], [1166080.4117162148, 18], [1248931.4981518511, 0], [1259252.9317007354, 1], [1264160.0178640792, 10], [1308982.8299020159, 21], [1314306.4365542016, 6], [1346816.5142456852, 19], [1416979.8317957646, 20], [1421973.4734017139, 4], [1565710.9365773317, 17], [1697174.2612568527, 5], [2099103.4279058669, 7]]\n",
      "[22, 14, 15, 12, 3, 11, 2, 23, 13, 16, 8, 9, 18, 0, 1, 10, 21, 6, 19, 20, 4, 17, 5, 7]\n",
      "[210830.98564075952, 794951.65401766938, 796820.12817960477, 914720.324723572, 927302.73562433117, 938733.37379799434, 938757.85742998624, 1000355.6154423188, 1007196.4937989194, 1044899.9288844019, 1059137.1561855921, 1093091.3563735723, 1166080.4117162148, 1248931.4981518511, 1259252.9317007354, 1264160.0178640792, 1308982.8299020159, 1314306.4365542016, 1346816.5142456852, 1416979.8317957646, 1421973.4734017139, 1565710.9365773317, 1697174.2612568527, 2099103.4279058669]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94f678d790>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9nVQVVua6WVmVTKX45qM7ae8aAOD33rGcVYuqc9yqmUOBRUREJtXR3uN84/XvEEvG8Hv9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\nmVnx3LCYmZlZ8f4LZpmMcOicssYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9502f34050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings = []\n",
    "z = df.sort_values('ratingAgency1Rating').reset_index(drop=True)\n",
    "i=0\n",
    "while i<z.shape[0]:\n",
    "    j=i+1\n",
    "    while j<z.shape[0] and z['ratingAgency1Rating'][j]==z['ratingAgency1Rating'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    ratings.append([z[i:j]['avgvol'].mean(), z['ratingAgency1Rating'][i]])\n",
    "    i=j\n",
    "    \n",
    "ratings.sort()\n",
    "print ratings\n",
    "ratval1 = []\n",
    "for i in range(len(ratings)):\n",
    "    ratval1.append(ratings[i][0])\n",
    "    ratings[i] = ratings[i][1]\n",
    "print ratings\n",
    "print ratval1\n",
    "plt.plot(ratval1)\n",
    "\n",
    "ratings = []\n",
    "z = df.sort_values('ratingAgency2Rating').reset_index(drop=True)\n",
    "i=0\n",
    "while i<z.shape[0]:\n",
    "    j=i+1\n",
    "    while j<z.shape[0] and z['ratingAgency2Rating'][j]==z['ratingAgency2Rating'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    ratings.append([z[i:j]['avgvol'].mean(), z['ratingAgency2Rating'][i]])\n",
    "    i=j\n",
    "    \n",
    "ratings.sort()\n",
    "print ratings\n",
    "ratval2 = []\n",
    "for i in range(len(ratings)):\n",
    "    ratval2.append(ratings[i][0])\n",
    "    ratings[i] = ratings[i][1]\n",
    "print ratings\n",
    "print ratval2\n",
    "plt.plot(ratval2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rat1order = [8, 18, 17, 19, 15, 0, 16, 14, 6, 4, 3, 11, 25, 12, 24, 26, 13, 21, 23, 2, 20, 5, 9, 7, 10, 22, 1]\n",
    "rat2order = [22, 14, 15, 12, 3, 11, 2, 23, 13, 16, 8, 9, 18, 0, 1, 10, 21, 6, 19, 20, 4, 17, 5, 7]\n",
    "rat1val = [535275.47089112026, 726730.55728049774, 824072.23759645456, 939232.74042480998, \n",
    "           974208.06517225958, 988381.20414095873, 1001476.7833485882, 1035412.80106927, \n",
    "           1052834.7969855072, 1098559.6949249217, 1142627.5510204099, 1173625.9485263387, \n",
    "           1178461.0761930957, 1209086.482984008, 1254242.4681863913, 1318889.2222976068, \n",
    "           1358154.033908698, 1390333.6318166712, 1399704.4692102014, 1415197.36842105, \n",
    "           1425565.1026109548, 1429341.2600836395, 1477505.6438464925, 1482876.3741255836, \n",
    "           1513350.9677026868, 1593894.9140502664, 1744460.0938967101]\n",
    "rat2val = [210830.98564075952, 794951.65401766938, 796820.12817960477, 914720.324723572, \n",
    "           927302.73562433117, 938733.37379799434, 938757.85742998624, 1000355.6154423188, \n",
    "           1007196.4937989194, 1044899.9288844019, 1059137.1561855921, 1093091.3563735723, \n",
    "           1166080.4117162148, 1248931.4981518511, 1259252.9317007354, 1264160.0178640792, \n",
    "           1308982.8299020159, 1314306.4365542016, 1346816.5142456852, 1416979.8317957646, \n",
    "           1421973.4734017139, 1565710.9365773317, 1697174.2612568527, 2099103.4279058669]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n",
      "1700000\n",
      "1800000\n",
      "1900000\n",
      "2000000\n",
      "2100000\n",
      "2200000\n",
      "2300000\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "r1m = max(rat1val)\n",
    "r2m = max(rat2val)\n",
    "\n",
    "fw = open('rat_order.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "i=0\n",
    "while i<data.shape[0]:\n",
    "    j=i+1\n",
    "    while j<data.shape[0] and data['isin'][j]==data['isin'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    temp = data[i:j]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    temp = df[df['isin']==data['isin'][i]]\n",
    "    #print temp\n",
    "    p = rat1val[rat1order.index(temp['ratingAgency1Rating'].any())]\n",
    "    q = rat2val[rat2order.index(temp['ratingAgency2Rating'].any())]\n",
    "    \n",
    "    x*=((p/r1m)*(q/r2m))\n",
    "    y*=((p/r1m)*(q/r2m))\n",
    "    writer.writerow((data['isin'][i], x, y))\n",
    "    i=j\n",
    "    \n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n",
      "1700000\n",
      "1800000\n",
      "1900000\n",
      "2000000\n",
      "2100000\n",
      "2200000\n",
      "2300000\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "ami = log(df['amtIssued'].max())\n",
    "amo = log(df['amtOutstanding'].max())\n",
    "r1m = max(rat1val)\n",
    "r2m = max(rat2val)\n",
    "\n",
    "fw = open('amoi_log_rat.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "i=0\n",
    "while i<data.shape[0]:\n",
    "    j=i+1\n",
    "    while j<data.shape[0] and data['isin'][j]==data['isin'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    temp = data[i:j]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    temp = df[df['isin']==data['isin'][i]]\n",
    "    p = rat1val[rat1order.index(temp['ratingAgency1Rating'].item())]\n",
    "    q = rat2val[rat2order.index(temp['ratingAgency2Rating'].item())]\n",
    "    \n",
    "    x*=((log(temp['amtIssued'])/ami)*(log(temp['amtOutstanding'])/amo)*(p/r1m)*(q/r2m))\n",
    "    y*=((log(temp['amtIssued'])/ami)*(log(temp['amtOutstanding'])/amo)*(p/r1m)*(q/r2m))\n",
    "    writer.writerow((data['isin'][i], x, y))\n",
    "    i=j\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n",
      "1700000\n",
      "1800000\n",
      "1900000\n",
      "2000000\n",
      "2100000\n",
      "2200000\n",
      "2300000\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "ami = log(df['amtIssued'].max())\n",
    "amo = log(df['amtOutstanding'].max())\n",
    "\n",
    "fw = open('amoi_log_inv_w.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "i=0\n",
    "while i<data.shape[0]:\n",
    "    j=i+1\n",
    "    while j<data.shape[0] and data['isin'][j]==data['isin'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    temp = data[i:j]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    temp = df[df['isin']==data['isin'][i]]\n",
    "    \n",
    "    p=temp['ratingAgency1Watch'].item()\n",
    "    q=temp['ratingAgency2Watch'].item()\n",
    "    x*=((ami/log(temp['amtIssued']))*(amo/log(temp['amtOutstanding']))*p*q)\n",
    "    y*=((ami/log(temp['amtIssued']))*(amo/log(temp['amtOutstanding']))*p*q)\n",
    "    writer.writerow((data['isin'][i], x, y))\n",
    "    i=j\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7ff0258ba490>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ukCul6oHHgVs0TTsFfBoIn09jHCAajRXdHJCT/QF+MpdEqqPdm7UO8sOHT/Cpezowm4xZ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\nD64GviQij2AC2M0DbgaeAC7EzG+vAD7njJVzMAvmi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff014890590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8Lo9MrqYdG21HM76b0nJZDZeWYyXOOAB8/biEXYI+ItqftbQSF7Zx2GXZGCB5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\niRzYXVOukv4MAI+cOIs90/zM0S5BWH2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5YgycGnVwf+PeYoLOYuafqnBg3xBmmadEJmeZ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\n2UYIzWEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0141ad990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['amtIssued', 'amtOutstanding', 'coupon', 'couponFrequency', 'collateralType',\n",
    "        'ratingAgency1Rating', 'ratingAgency2Rating', 'paymentRank']\n",
    "sns.pairplot(df, x_vars=cols[:4], y_vars=['avgvol'])\n",
    "sns.pairplot(df, x_vars=cols[4:], y_vars=['avgvol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff004064710>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DTJ48mUMOeUrT9zNnzpNGZD9jXbMC4yc+8RG2bNn25tzjx3fyrnf9XUP3045h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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff014154750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['ratingAgency1Watch', 'couponType', 'securityType', 'ratingAgency2Watch']\n",
    "sns.boxplot(x=cols[3], y='avgvol', data=df.sort_values(cols[3]).reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3973.61712601 5968.09676455\n",
      "500 22251.2330956 41526.0888845\n",
      "1000 219.173586653 11283\n",
      "1500 8499.17216903 3249.68347639\n",
      "2000 3857.45512509 37142.6171514\n",
      "2500 10872 1037.42164349\n",
      "3000 1176.2315817 1132.39686437\n",
      "3500 382348.321916 351116.085818\n",
      "4000 39378.1877353 70410.2452908\n",
      "4500 167469.076404 237591.473718\n",
      "5000 16867.5992288 21601.7487005\n",
      "5500 2659.30618472 3907.1344714\n",
      "6000 29.9595837655 20.7882826128\n",
      "6500 43.3184566541 41.1021635229\n",
      "7000 10872 58.446289774\n",
      "7500 854.776987945 1065.18363113\n",
      "8000 6325.3497108 8411.88225573\n",
      "8500 26569.6833313 38644.6867986\n",
      "9000 61901.9266569 44302.2876487\n",
      "9500 2040.50609174 688.205062089\n",
      "10000 56920.8416109 63501.8938395\n",
      "10500 19725.6227987 473.41494717\n",
      "11000 3053.81864069 7174.28206976\n",
      "11500 13.1504151992 11283\n",
      "12000 16238.8288864 609.248991408\n",
      "12500 3430.79720974 3553.53441826\n",
      "13000 260487.807737 258149.956146\n",
      "13500 184339.597947 80480.5410189\n",
      "14000 1718.32091936 859.160459679\n",
      "14500 35462.2863204 27162.9131725\n",
      "15000 172445.777978 242639.771997\n",
      "15500 842.787182559 129.659566548\n",
      "16000 3942.20224526 3775.6303194\n",
      "16500 26.3008303983 11283\n",
      "17000 10872 4523.74282851\n"
     ]
    }
   ],
   "source": [
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "amo = df['amtOutstanding'].max()\n",
    "\n",
    "fw = open('amo.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)*(float(df['amtOutstanding'][i])/amo)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)*(float(df['amtOutstanding'][i])/amo)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3973.61712601 5968.09676455\n",
      "500 22251.2330956 41526.0888845\n",
      "1000 219.173586653 11283\n",
      "1500 12916.6300401 4938.71148591\n",
      "2000 3857.45512509 37142.6171514\n",
      "2500 10872 1037.42164349\n",
      "3000 1176.2315817 1132.39686437\n",
      "3500 382348.321916 351116.085818\n",
      "4000 39378.1877353 70410.2452908\n",
      "4500 167469.076404 237591.473718\n",
      "5000 16867.5992288 21601.7487005\n",
      "5500 2659.30618472 3907.1344714\n",
      "6000 29.9595837655 20.7882826128\n",
      "6500 43.3184566541 41.1021635229\n",
      "7000 10872 58.446289774\n",
      "7500 854.776987945 1065.18363113\n",
      "8000 6325.3497108 8411.88225573\n",
      "8500 26569.6833313 38644.6867986\n",
      "9000 61901.9266569 44302.2876487\n",
      "9500 2040.50609174 688.205062089\n",
      "10000 56920.8416109 63501.8938395\n",
      "10500 21917.3586653 526.016607966\n",
      "11000 3053.81864069 7174.28206976\n",
      "11500 13.1504151992 11283\n",
      "12000 26327.1312287 987.742297181\n",
      "12500 3430.79720974 3553.53441826\n",
      "13000 260487.807737 258149.956146\n",
      "13500 184339.597947 80480.5410189\n",
      "14000 1718.32091936 859.160459679\n",
      "14500 35462.2863204 27162.9131725\n",
      "15000 172445.777978 242639.771997\n",
      "15500 842.787182559 129.659566548\n",
      "16000 3942.20224526 3775.6303194\n",
      "16500 26.3008303983 11283\n",
      "17000 10872 4523.74282851\n"
     ]
    }
   ],
   "source": [
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "ami = df['amtIssued'].max()\n",
    "\n",
    "fw = open('ami.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)*(float(df['amtIssued'][i])/amo)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)*(float(df['amtIssued'][i])/amo)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 212374.317669 318971.465533\n",
      "500 616283.254474 1150131.01042\n",
      "1000 13555.5776046 9176.85492676\n",
      "1500 129219.651338 49407.5137467\n",
      "2000 39384.1578352 379221.70158\n",
      "2500 9081.92103201 39539.8818539\n",
      "3000 44830.4294259 43159.7301926\n",
      "3500 2653293.65036 2436558.57144\n",
      "4000 564488.886577 1009335.4533\n",
      "4500 1995835.26473 2831528.37543\n",
      "5000 411546.139105 527052.851743\n",
      "5500 73653.7100267 108214.297039\n",
      "6000 11897.8862984 8255.67620707\n",
      "6500 25458.622493 24156.088319\n",
      "7000 8327.82109427 10213.1727916\n",
      "7500 52866.7526579 65880.1071583\n",
      "8000 391213.969669 520263.068464\n",
      "8500 648263.599574 942877.017242\n",
      "9000 1221837.74737 874451.091711\n",
      "9500 243134.441176 82002.3786566\n",
      "10000 1388789.96838 1549358.55903\n",
      "10500 430340.676591 10328.1762382\n",
      "11000 60277.1365988 141608.009904\n",
      "11500 813.334656276 9176.85492676\n",
      "12000 395335.692765 14832.2193534\n",
      "12500 83706.716443 86701.3349188\n",
      "13000 2150498.69804 2131198.19086\n",
      "13500 972448.572117 424559.823654\n",
      "14000 28520.5981727 14260.2990863\n",
      "14500 699964.643662 536149.267642\n",
      "15000 3403783.57004 4789292.48976\n",
      "15500 18634.905509 2866.90853985\n",
      "16000 21838.5671429 20915.8107847\n",
      "16500 573.787568788 9711.06770796\n",
      "17000 9302.14059116 110373.080966\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "amo = log(df['amtOutstanding'].max())\n",
    "\n",
    "fw = open('amo_log.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "fw1 = open('amo_log1.csv', 'w')\n",
    "wr = csv.writer(fw1)\n",
    "wr.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    x*=(log(df['amtOutstanding'][i])/amo)\n",
    "    y*=(log(df['amtOutstanding'][i])/amo)\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "    wr.writerow((df['isin'][i], x/10.0, y/10.0))\n",
    "fw.close()\n",
    "fw1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 209896.958301 315250.643935\n",
      "500 609094.272725 1136714.66204\n",
      "1000 13397.4509651 9069.80635438\n",
      "1500 130338.89955 49835.4615926\n",
      "2000 38924.7392321 374798.057303\n",
      "2500 8975.9798693 39078.6467206\n",
      "3000 44307.4797325 42656.2693077\n",
      "3500 2622342.81811 2408135.96711\n",
      "4000 557904.089289 997561.493679\n",
      "4500 1972553.72465 2798498.42425\n",
      "5000 406745.428293 520904.748085\n",
      "5500 72794.5350074 106951.970665\n",
      "6000 11759.0967291 8159.37324063\n",
      "6500 25161.646109 23874.3060755\n",
      "7000 8230.67655332 10094.0355081\n",
      "7500 52250.058764 65111.6116905\n",
      "8000 386650.434854 514194.168042\n",
      "8500 640701.56515 931878.299333\n",
      "9000 1207584.93553 864250.566396\n",
      "9500 240298.263092 81045.8159085\n",
      "10000 1372589.6487 1531285.19695\n",
      "10500 427564.594194 10261.5502606\n",
      "11000 59574.0001243 139956.143833\n",
      "11500 803.847057908 9069.80635438\n",
      "12000 400232.612684 15015.9421786\n",
      "12500 82730.2724907 85689.9585594\n",
      "13000 2125412.99958 2106337.63399\n",
      "13500 961104.900217 419607.307419\n",
      "14000 28187.9036556 14093.9518278\n",
      "14500 691799.513405 529895.053737\n",
      "15000 3364078.22711 4733425.10079\n",
      "15500 18417.5281998 2833.46587689\n",
      "16000 21583.8189253 20671.8265764\n",
      "16500 567.094301805 9597.7875109\n",
      "17000 9193.63055386 109085.572245\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "ami = log(df['amtIssued'].max())\n",
    "\n",
    "fw = open('ami_log.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "fw1 = open('ami_log1.csv', 'w')\n",
    "wr = csv.writer(fw1)\n",
    "wr.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    x*=(log(df['amtIssued'][i])/ami)\n",
    "    y*=(log(df['amtIssued'][i])/ami)\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "    wr.writerow((df['isin'][i], x/10.0, y/10.0))\n",
    "fw.close()\n",
    "fw1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n",
      "1700000\n",
      "1800000\n",
      "1900000\n",
      "2000000\n",
      "2100000\n",
      "2200000\n",
      "2300000\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "ami = df['amtIssued'].max()\n",
    "amo = df['amtOutstanding'].max()\n",
    "\n",
    "fw = open('amoi_log_z.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "i=0\n",
    "while i<data.shape[0]:\n",
    "    j=i+1\n",
    "    while j<data.shape[0] and data['isin'][j]==data['isin'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    temp = data[i:j]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    temp = df[df['isin']==data['isin'][i]]\n",
    "    \n",
    "    p=(log(temp['amtIssued']))/(log(float(ami)))\n",
    "    q=(log(temp['amtOutstanding']))/(log(float(amo)))\n",
    "    p=p*p\n",
    "    q=q*q\n",
    "    #p=pow(2, log(float(ami))/log(temp['amtIssued']))\n",
    "    #q=pow(2, log(float(amo))/log(temp['amtOutstanding']))\n",
    "    x*=(p*q)\n",
    "    y*=(p*q)\n",
    "    writer.writerow((data['isin'][i], x, y))\n",
    "    i=j\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n",
      "1700000\n",
      "1800000\n",
      "1900000\n",
      "2000000\n",
      "2100000\n",
      "2200000\n",
      "2300000\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "ami = log(df['amtIssued'].max())\n",
    "amo = log(df['amtOutstanding'].max())\n",
    "\n",
    "fw = open('amoi_log_inv.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "i=0\n",
    "while i<data.shape[0]:\n",
    "    j=i+1\n",
    "    while j<data.shape[0] and data['isin'][j]==data['isin'][i]:\n",
    "        if j%100000==0:\n",
    "            print j\n",
    "        j=j+1\n",
    "    temp = data[i:j]\n",
    "    x = (temp['volume'][temp['side'] == 'B'].sum()/30.0)\n",
    "    y = (temp['volume'][temp['side'] == 'S'].sum()/30.0)\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    temp = df[df['isin']==data['isin'][i]]\n",
    "    x*=((ami/log(temp['amtIssued']))*(amo/log(temp['amtOutstanding'])))\n",
    "    y*=((ami/log(temp['amtIssued']))*(amo/log(temp['amtOutstanding'])))\n",
    "    writer.writerow((data['isin'][i], x, y))\n",
    "    i=j\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isin</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "      <th>side</th>\n",
       "      <th>volume</th>\n",
       "      <th>timeofday</th>\n",
       "      <th>date</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Wed  6Apr16 08:24:07.000 am</td>\n",
       "      <td>115.296450</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Apr2016</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Wed  6Apr16 08:24:07.000 am</td>\n",
       "      <td>115.296450</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Apr2016</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Wed  6Apr16 08:25:00.000 am</td>\n",
       "      <td>115.558250</td>\n",
       "      <td>S</td>\n",
       "      <td>5000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>6Apr2016</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 18Apr16 12:44:32.000 pm</td>\n",
       "      <td>115.155079</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>18Apr2016</td>\n",
       "      <td>108.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 18Apr16 12:44:32.000 pm</td>\n",
       "      <td>115.155079</td>\n",
       "      <td>B</td>\n",
       "      <td>5000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>18Apr2016</td>\n",
       "      <td>108.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Fri 22Apr16 11:13:28.000 am</td>\n",
       "      <td>122.588087</td>\n",
       "      <td>S</td>\n",
       "      <td>40000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>22Apr2016</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Fri 22Apr16 11:13:28.000 am</td>\n",
       "      <td>122.588087</td>\n",
       "      <td>S</td>\n",
       "      <td>40000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>22Apr2016</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 16May16 02:51:27.000 pm</td>\n",
       "      <td>117.363618</td>\n",
       "      <td>S</td>\n",
       "      <td>730000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>16May2016</td>\n",
       "      <td>136.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Mon 16May16 03:45:52.000 pm</td>\n",
       "      <td>115.715329</td>\n",
       "      <td>B</td>\n",
       "      <td>730000</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>16May2016</td>\n",
       "      <td>136.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isin10033</td>\n",
       "      <td>Thu 19May16 09:00:03.000 am</td>\n",
       "      <td>123.529518</td>\n",
       "      <td>S</td>\n",
       "      <td>20000</td>\n",
       "      <td>Morning</td>\n",
       "      <td>19May2016</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        isin                         time       price side  volume  timeofday  \\\n",
       "0  isin10033  Wed  6Apr16 08:24:07.000 am  115.296450    B    5000    Morning   \n",
       "1  isin10033  Wed  6Apr16 08:24:07.000 am  115.296450    B    5000    Morning   \n",
       "2  isin10033  Wed  6Apr16 08:25:00.000 am  115.558250    S    5000    Morning   \n",
       "3  isin10033  Mon 18Apr16 12:44:32.000 pm  115.155079    B    5000  Afternoon   \n",
       "4  isin10033  Mon 18Apr16 12:44:32.000 pm  115.155079    B    5000  Afternoon   \n",
       "5  isin10033  Fri 22Apr16 11:13:28.000 am  122.588087    S   40000    Morning   \n",
       "6  isin10033  Fri 22Apr16 11:13:28.000 am  122.588087    S   40000    Morning   \n",
       "7  isin10033  Mon 16May16 02:51:27.000 pm  117.363618    S  730000  Afternoon   \n",
       "8  isin10033  Mon 16May16 03:45:52.000 pm  115.715329    B  730000  Afternoon   \n",
       "9  isin10033  Thu 19May16 09:00:03.000 am  123.529518    S   20000    Morning   \n",
       "\n",
       "        date   days  \n",
       "0   6Apr2016   96.0  \n",
       "1   6Apr2016   96.0  \n",
       "2   6Apr2016   96.0  \n",
       "3  18Apr2016  108.5  \n",
       "4  18Apr2016  108.5  \n",
       "5  22Apr2016  112.0  \n",
       "6  22Apr2016  112.0  \n",
       "7  16May2016  136.5  \n",
       "8  16May2016  136.5  \n",
       "9  19May2016  139.0  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = data[data['isin'] == 'isin10033'].sort_values('days')\n",
    "t1.reset_index(drop=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fbf65506a50>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfbef47190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(t1[t1['side']=='B'], x_vars=['days'], y_vars=['volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfbcd4f490>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FHyCyCQUfERHpS6VymflcHsIxYtrHIx2i4CMiIn2l0WgwO5ej5oZIJBR4pLMUfEREpC+4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfbedae9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1 = data[data['isin'] == 'isin10003'].sort_values('days')\n",
    "sns.regplot(x='days', y='volume', data=t1[t1['side']=='B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf65588210>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MZ+nvjvG9Fy5w+PSlitcWsnmyOZPrdlXfcnJua34hx2wyQzhslL787W1vGaq++lyqLG7r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfbee8c990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='days', y='volume', data=data[data['side']=='S'][:5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 7770000 11670000\n",
      "500 21755000 40600000\n",
      "1000 500000 0\n",
      "1500 4420000 1690000\n",
      "2000 1320000 12710000\n",
      "2500 0 1420000\n",
      "3000 1610000 1550000\n",
      "3500 87225000 80100000\n",
      "4000 19250000 34420000\n",
      "4500 67420000 95650000\n",
      "5000 14430000 18480000\n",
      "5500 2600000 3820000\n",
      "6000 490000 340000\n",
      "6500 1075000 1020000\n",
      "7000 0 400000\n",
      "7500 1950000 2430000\n",
      "8000 14430000 19190000\n",
      "8500 22730000 33060000\n",
      "9000 42365000 30320000\n",
      "9500 9310000 3140000\n",
      "10000 48695000 54325000\n",
      "10500 15000000 360000\n",
      "11000 2090000 4910000\n",
      "11500 30000 0\n",
      "12000 13860000 520000\n",
      "12500 2935000 3040000\n",
      "13000 71310000 70670000\n",
      "13500 31540000 13770000\n",
      "14000 980000 490000\n",
      "14500 24270000 18590000\n",
      "15000 118020000 166060000\n",
      "15500 650000 100000\n",
      "16000 710000 680000\n",
      "16500 20000 0\n",
      "17000 0 3870000\n"
     ]
    }
   ],
   "source": [
    "fw = open('x1.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = temp['volume'][temp['side'] == 'B'].sum()\n",
    "    y = temp['volume'][temp['side'] == 'S'].sum()\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x/30, y/30))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 259000 389000\n",
      "500 725166 1353333\n",
      "1000 16666 11283\n",
      "1500 147333 56333\n",
      "2000 44000 423666\n",
      "2500 10872 47333\n",
      "3000 53666 51666\n",
      "3500 2907500 2670000\n",
      "4000 641666 1147333\n",
      "4500 2247333 3188333\n",
      "5000 481000 616000\n",
      "5500 86666 127333\n",
      "6000 16333 11333\n",
      "6500 35833 34000\n",
      "7000 10872 13333\n",
      "7500 65000 81000\n",
      "8000 481000 639666\n",
      "8500 757666 1102000\n",
      "9000 1412166 1010666\n",
      "9500 310333 104666\n",
      "10000 1623166 1810833\n",
      "10500 500000 12000\n",
      "11000 69666 163666\n",
      "11500 1000 11283\n",
      "12000 462000 17333\n",
      "12500 97833 101333\n",
      "13000 2377000 2355666\n",
      "13500 1051333 459000\n",
      "14000 32666 16333\n",
      "14500 809000 619666\n",
      "15000 3934000 5535333\n",
      "15500 21666 3333\n",
      "16000 23666 22666\n",
      "16500 666 11283\n",
      "17000 10872 129000\n"
     ]
    }
   ],
   "source": [
    "bm = data['volume'][data['side'] == 'B'].sum()/(30*data.shape[0])\n",
    "sm = data['volume'][data['side'] == 'S'].sum()/(30*data.shape[0])\n",
    "\n",
    "fw = open('x2.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = temp['volume'][temp['side'] == 'B'].sum()/30\n",
    "    y = temp['volume'][temp['side'] == 'S'].sum()/30\n",
    "    if x==0:\n",
    "        x = bm\n",
    "    if y==0:\n",
    "        y = sm\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3885000.0 3890000.0\n",
      "500 1450333.33333 1765217.3913\n",
      "1000 125000.0 nan\n",
      "1500 736666.666667 422500.0\n",
      "2000 660000.0 6355000.0\n",
      "2500 nan 473333.333333\n",
      "3000 230000.0 516666.666667\n",
      "3500 555573.248408 305725.19084\n",
      "4000 740384.615385 1496521.73913\n",
      "4500 2043030.30303 3298275.86207\n",
      "5000 1110000.0 1421538.46154\n",
      "5500 288888.888889 424444.444444\n",
      "6000 70000.0 15454.5454545\n",
      "6500 51190.4761905 30000.0\n",
      "7000 nan 36363.6363636\n",
      "7500 27857.1428571 39836.0655738\n",
      "8000 1603333.33333 2132222.22222\n",
      "8500 988260.869565 1001818.18182\n",
      "9000 1176805.55556 618775.510204\n",
      "9500 2327500.0 785000.0\n",
      "10000 87267.0250896 42809.2986604\n",
      "10500 3750000.0 360000.0\n",
      "11000 2090000.0 1227500.0\n",
      "11500 30000.0 nan\n",
      "12000 1980000.0 520000.0\n",
      "12500 45153.8461538 98064.516129\n",
      "13000 3565500.0 2944583.33333\n",
      "13500 2867272.72727 1147500.0\n",
      "14000 490000.0 490000.0\n",
      "14500 591951.219512 244605.263158\n",
      "15000 5131304.34783 4488108.10811\n",
      "15500 216666.666667 100000.0\n",
      "16000 236666.666667 226666.666667\n",
      "16500 20000.0 nan\n",
      "17000 nan 3870000.0\n"
     ]
    }
   ],
   "source": [
    "fw = open('a2.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "bm = data['volume'][data['side'] == 'B'].mean()\n",
    "sm = data['volume'][data['side'] == 'S'].mean()\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    x = temp['volume'][temp['side'] == 'B'].mean()\n",
    "    y = temp['volume'][temp['side'] == 'S'].mean()\n",
    "    if np.isnan(x):\n",
    "        x = bm\n",
    "    if np.isnan(y):\n",
    "        y = sm\n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "799975.162284 571544.31174\n",
      "0 3885000.0 3890000.0\n",
      "500 1450333.33333 40000.0\n",
      "1000 10000.0 571544.31174\n",
      "1500 10000.0 43730000.0\n",
      "2000 660000.0 6355000.0\n",
      "2500 799975.162284 473333.333333\n",
      "3000 230000.0 516666.666667\n",
      "3500 555573.248408 305725.19084\n",
      "4000 150000.0 5080000.0\n",
      "4500 2043030.30303 3298275.86207\n",
      "5000 1110000.0 1421538.46154\n",
      "5500 180000.0 424444.444444\n",
      "6000 70000.0 15454.5454545\n",
      "6500 51190.4761905 20000.0\n",
      "7000 799975.162284 30000.0\n",
      "7500 50000.0 50000.0\n",
      "8000 1603333.33333 2132222.22222\n",
      "8500 988260.869565 50000.0\n",
      "9000 1176805.55556 140000.0\n",
      "9500 2327500.0 785000.0\n",
      "10000 30000.0 70000.0\n",
      "10500 3750000.0 360000\n",
      "11000 2090000 1227500.0\n",
      "11500 30000 571544.31174\n",
      "12000 30000.0 520000\n",
      "12500 10000.0 1000000.0\n",
      "13000 3565500.0 2944583.33333\n",
      "13500 2867272.72727 1147500.0\n",
      "14000 490000.0 490000\n",
      "14500 591951.219512 244605.263158\n",
      "15000 5131304.34783 11720000.0\n",
      "15500 216666.666667 100000\n",
      "16000 236666.666667 226666.666667\n",
      "16500 20000 571544.31174\n",
      "17000 799975.162284 3870000\n"
     ]
    }
   ],
   "source": [
    "fw = open('last_two.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "bm = data['volume'][data['side'] == 'B'].mean()\n",
    "sm = data['volume'][data['side'] == 'S'].mean()\n",
    "print bm, sm\n",
    "\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    db = temp[temp['side'] == 'B'].sort_values('days').reset_index(drop=True)\n",
    "    ds = temp[temp['side'] == 'S'].sort_values('days').reset_index(drop=True)\n",
    "    x = db['volume'].mean()\n",
    "    y = ds['volume'].mean()\n",
    "    #print ds.head(10), db.head(10)\n",
    "    \n",
    "    if db.shape[0]==0 or np.isnan(x):\n",
    "        x = bm\n",
    "    elif db.shape[0]==1:\n",
    "        x = db['volume'][0]\n",
    "    else:\n",
    "        j = db.shape[0]-1\n",
    "        x2, y2 = db['days'][j], db['volume'][j]\n",
    "        while j>=0 and np.isnan(y2):\n",
    "            j=j-1\n",
    "            x2, y2 = db['days'][j], db['volume'][j]\n",
    "        j=j-1\n",
    "        x1, y1 = db['days'][j], db['volume'][j]\n",
    "        while j>=0 and np.isnan(y1):\n",
    "            j=j-1\n",
    "            x1, y1 = db['days'][j], db['volume'][j]\n",
    "        if j<0:\n",
    "            x = y2\n",
    "        elif x1==x2:\n",
    "            x2=x1+0.5\n",
    "            x = y1 + ((y2-y1)*(162-x1))/(x2-x1)\n",
    "            if x<0:\n",
    "                x = db['volume'].mean()\n",
    "        \n",
    "    if ds.shape[0]==0 or np.isnan(y):\n",
    "        y = sm\n",
    "    elif ds.shape[0]==1:\n",
    "        y = ds['volume'][0]\n",
    "    else:\n",
    "        j = ds.shape[0]-1\n",
    "        x2, y2 = ds['days'][j], ds['volume'][j]\n",
    "        while j>=0 and np.isnan(y2):\n",
    "            j=j-1\n",
    "            x2, y2 = ds['days'][j], ds['volume'][j]\n",
    "        j=j-1\n",
    "        x1, y1 = ds['days'][j], ds['volume'][j]\n",
    "        while j>=0 and np.isnan(y1):\n",
    "            j=j-1\n",
    "            x1, y1 = ds['days'][j], ds['volume'][j]\n",
    "        if j<0:\n",
    "            y = y2\n",
    "        elif x1==x2:\n",
    "            x2=x1+0.5\n",
    "            y = y1 + ((y2-y1)*(162-x1))/(x2-x1)\n",
    "            if y<0:\n",
    "                y = ds['volume'].mean()\n",
    "        \n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "799975.162284 571544.31174\n",
      "0 3885000.0 3890000.0\n",
      "500 1450333.33333 40000.0\n",
      "1000 10000.0 571544.31174\n",
      "1500 10000.0 12570000\n",
      "2000 660000.0 6355000.0\n",
      "2500 799975.162284 473333.333333\n",
      "3000 230000.0 516666.666667\n",
      "3500 555573.248408 305725.19084\n",
      "4000 150000.0 5080000.0\n",
      "4500 2043030.30303 3298275.86207\n",
      "5000 1110000.0 1421538.46154\n",
      "5500 180000.0 424444.444444\n",
      "6000 70000.0 15454.5454545\n",
      "6500 51190.4761905 20000.0\n",
      "7000 799975.162284 30000.0\n",
      "7500 50000.0 50000.0\n",
      "8000 1603333.33333 2132222.22222\n",
      "8500 988260.869565 50000.0\n",
      "9000 1176805.55556 140000.0\n",
      "9500 2327500.0 785000.0\n",
      "10000 30000.0 70000.0\n",
      "10500 3750000.0 360000\n",
      "11000 2090000 1227500.0\n",
      "11500 30000 571544.31174\n",
      "12000 30000.0 520000\n",
      "12500 10000.0 1000000.0\n",
      "13000 3565500.0 2944583.33333\n",
      "13500 2867272.72727 1147500.0\n",
      "14000 490000.0 490000\n",
      "14500 591951.219512 244605.263158\n",
      "15000 5131304.34783 11720000.0\n",
      "15500 216666.666667 100000\n",
      "16000 236666.666667 226666.666667\n",
      "16500 20000 571544.31174\n",
      "17000 799975.162284 3870000\n"
     ]
    }
   ],
   "source": [
    "fw = open('last_two_max_lim.csv', 'w')\n",
    "writer = csv.writer(fw)\n",
    "writer.writerow(('isin', 'buyvolume', 'sellvolume'))\n",
    "bm = data['volume'][data['side'] == 'B'].mean()\n",
    "sm = data['volume'][data['side'] == 'S'].mean()\n",
    "bmax = data['volume'][data['side'] == 'B'].max()\n",
    "smax = data['volume'][data['side'] == 'S'].max()\n",
    "print bm, sm\n",
    "\n",
    "for i in range(df.shape[0]):\n",
    "    temp = data[data['isin'] == df['isin'][i]]\n",
    "    db = temp[temp['side'] == 'B'].sort_values('days').reset_index(drop=True)\n",
    "    ds = temp[temp['side'] == 'S'].sort_values('days').reset_index(drop=True)\n",
    "    x = db['volume'].mean()\n",
    "    y = ds['volume'].mean()\n",
    "    #print ds.head(10), db.head(10)\n",
    "    \n",
    "    if db.shape[0]==0 or np.isnan(x):\n",
    "        x = bm\n",
    "    elif db.shape[0]==1:\n",
    "        x = db['volume'][0]\n",
    "    else:\n",
    "        j = db.shape[0]-1\n",
    "        x2, y2 = db['days'][j], db['volume'][j]\n",
    "        while j>=0 and np.isnan(y2):\n",
    "            j=j-1\n",
    "            x2, y2 = db['days'][j], db['volume'][j]\n",
    "        j=j-1\n",
    "        x1, y1 = db['days'][j], db['volume'][j]\n",
    "        while j>=0 and np.isnan(y1):\n",
    "            j=j-1\n",
    "            x1, y1 = db['days'][j], db['volume'][j]\n",
    "        if j<0:\n",
    "            x = y2\n",
    "        elif x1==x2:\n",
    "            x2=x1+0.5\n",
    "            x = y1 + ((y2-y1)*(162-x1))/(x2-x1)\n",
    "            if x<0:\n",
    "                x = db['volume'].mean()\n",
    "    if x>bmax:\n",
    "        x=bmax\n",
    "        \n",
    "    if ds.shape[0]==0 or np.isnan(y):\n",
    "        y = sm\n",
    "    elif ds.shape[0]==1:\n",
    "        y = ds['volume'][0]\n",
    "    else:\n",
    "        j = ds.shape[0]-1\n",
    "        x2, y2 = ds['days'][j], ds['volume'][j]\n",
    "        while j>=0 and np.isnan(y2):\n",
    "            j=j-1\n",
    "            x2, y2 = ds['days'][j], ds['volume'][j]\n",
    "        j=j-1\n",
    "        x1, y1 = ds['days'][j], ds['volume'][j]\n",
    "        while j>=0 and np.isnan(y1):\n",
    "            j=j-1\n",
    "            x1, y1 = ds['days'][j], ds['volume'][j]\n",
    "        if j<0:\n",
    "            y = y2\n",
    "        elif x1==x2:\n",
    "            x2=x1+0.5\n",
    "            y = y1 + ((y2-y1)*(162-x1))/(x2-x1)\n",
    "            if y<0:\n",
    "                y = ds['volume'].mean()\n",
    "    if y>smax:\n",
    "        y=smax\n",
    "        \n",
    "    if i%500 == 0:\n",
    "        print i, x, y\n",
    "    writer.writerow((df['isin'][i], x, y))\n",
    "fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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