FTC_NLV_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mass Account Summary Extraction\n",
    "At the Financial Training Centre (FTC) of the School of Business Management in NYP, we conduct trading simulation using Interactive Brokers (IB) TWS. To encourage healthy competition, we extract their NLV from IB and rank them according to their profit and loss (PnL)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**import necessary libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**import IbPy library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import time\n",
    "from datetime import datetime\n",
    "from IBWrapper import IBWrapper, contract\n",
    "from ib.ext.EClientSocket import EClientSocket\n",
    "from ib.ext.ScannerSubscription import ScannerSubscription"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extract current batch of students' IB account number**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def obtain_ib_acct():\n",
    "    return pd.read_excel(\"G:/live/CURRENT_BATCH_IB.xlsx\", sheetname=\"2016S1R2\")[['ADM_NO','NAME','IB ACCT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = obtain_ib_acct()         # Obtain current batch IB account number        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "callback = IBWrapper()        # Instantiate IBWrapper. callback \n",
    "tws = EClientSocket(callback) # Instantiate EClientSocket and return data to callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "host = \"\"\n",
    "port = 4001\n",
    "clientId = 202"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Connect to TWS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Server Version: 76\n",
      "TWS Time at connection:20160513 11:43:38 SGT\n"
     ]
    }
   ],
   "source": [
    "tws.eConnect(host, port, clientId) # Connect to TWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tws.setServerLogLevel(5)           # Set error output to verbose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "create = contract()                # Instantiate contract class\n",
    "callback.initiate_variables()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Submit request for Account Summary. Asking for NLV for \"All\" accounts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reqID = 1001\n",
    "tws.reqAccountSummary(reqID,\"All\",\"NetLiquidation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TWS often send back duplicate data. We drop the duplicates and keep the last figure**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res = pd.DataFrame(callback.account_Summary,\n",
    "                  columns = [\"reqID\", \"IB ACCT\", \"Key\", \n",
    "                             \"NLV\", \"Currency\"])[[\"IB ACCT\", \"NLV\"]]\n",
    "res[\"IB ACCT\"].drop_duplicates(keep = \"last\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "current_batch_nlv = pd.merge(df, res, on=\"IB ACCT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate each student's PnL and visualise the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9444978>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D5MmT8fb2BmDq1KmsWLGCjRs3EhgYSExMjJrDEUII4SRVyWTo0KEMHTq03XUTJkxgwoQJ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\nGIsJEREp9j9/gAuGbM4ggQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x944cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Init_bal = 1000000\n",
    "current_batch_nlv[\"PnL\"] = current_batch_nlv[\"NLV\"].astype(float) - Init_bal\n",
    "current_batch_nlv.sort_values(by = \"PnL\", ascending=False, inplace=True)\n",
    "current_batch_nlv.plot(kind=\"bar\", x=current_batch_nlv[\"NAME\"].str.split(\" \").str.get(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Not a good trading period. Obviously**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tws.eDisconnect()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}