samples/tws.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%gui qt5\n",
    "import datetime\n",
    "from collections import defaultdict\n",
    "import ibapi\n",
    "from tws_async import TWSClientQt, iswrapper, util, Stock\n",
    "\n",
    "util.logToConsole()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-06-23 22:24:57,210 TWSClientQt INFO Connecting to 127.0.0.1:7497 with clientId 1...\n",
      "2017-06-23 22:24:57,228 TWSClientQt INFO Logged on to server version 112\n",
      "2017-06-23 22:24:57,231 root ERROR ERROR -1 2104 Market data farm connection is OK:eufarm\n",
      "2017-06-23 22:24:57,231 root ERROR ERROR -1 2104 Market data farm connection is OK:cashfarm\n",
      "2017-06-23 22:24:57,232 root ERROR ERROR -1 2104 Market data farm connection is OK:usfarm\n",
      "2017-06-23 22:24:57,232 root ERROR ERROR -1 2106 HMDS data farm connection is OK:ushmds\n"
     ]
    }
   ],
   "source": [
    "# sample application\n",
    "class TWS(TWSClientQt):\n",
    "    def __init__(self):\n",
    "        TWSClientQt.__init__(self)\n",
    "        self._reqIdSeq = 0\n",
    "        self._histData = defaultdict(list)\n",
    "\n",
    "    def popHistData(self, reqId):\n",
    "        \"\"\"\n",
    "        Remove and return the historical data that was downloaded for the given reqId.\n",
    "        \"\"\"\n",
    "        return self._histData.pop(reqId)\n",
    "    \n",
    "    @iswrapper\n",
    "    def tickPrice(self, reqId: int,\n",
    "            tickType: ibapi.ticktype.TickType,\n",
    "            price: float,\n",
    "            attrib: ibapi.common.TickAttrib):\n",
    "        print('{} price {}'.format(reqId, price))\n",
    "\n",
    "    @iswrapper\n",
    "    def tickSize(self, reqId: int,\n",
    "            tickType: ibapi.ticktype.TickType,\n",
    "            size: int):\n",
    "        print('{} size {}'.format(reqId, size))\n",
    "\n",
    "    @iswrapper\n",
    "    def historicalData(self, reqId: int, date: str, open: float, high: float,\n",
    "            low: float, close: float, volume: int, barCount: int,\n",
    "            WAP: float, hasGaps: int):\n",
    "        self._histData[reqId].append((date, open, high, low, close, volume))\n",
    "        \n",
    "    @iswrapper\n",
    "    def historicalDataEnd(self, reqId: int, start: str, end: str):\n",
    "        print('Historical request {} is finished'.format(reqId))\n",
    "\n",
    "\n",
    "if 'tws' in locals():\n",
    "    tws.disconnect()\n",
    "tws = TWS()\n",
    "tws.connect('127.0.0.1', 7497, clientId=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical request 488 is finished\n"
     ]
    }
   ],
   "source": [
    "# request historical bar data\n",
    "contract = Stock('TSLA')\n",
    "reqId = tws.getReqId()\n",
    "tws.reqHistoricalData(reqId, contract,\n",
    "        endDateTime=datetime.datetime.utcnow().strftime('%Y%m%d %H:%M:%S UTC'),\n",
    "        durationStr='60 D',\n",
    "        barSizeSetting='1 hour',\n",
    "        whatToShow='TRADES',\n",
    "        useRTH=False,\n",
    "        formatDate=1,\n",
    "        chartOptions=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fetch data when it's finished\n",
    "data = tws.popHistData(reqId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-30 14:00:00</td>\n",
       "      <td>277.26</td>\n",
       "      <td>277.76</td>\n",
       "      <td>277.26</td>\n",
       "      <td>277.65</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-30 15:00:00</td>\n",
       "      <td>277.59</td>\n",
       "      <td>282.00</td>\n",
       "      <td>277.21</td>\n",
       "      <td>280.72</td>\n",
       "      <td>11216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-30 16:00:00</td>\n",
       "      <td>280.86</td>\n",
       "      <td>281.37</td>\n",
       "      <td>279.21</td>\n",
       "      <td>280.13</td>\n",
       "      <td>7182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-30 17:00:00</td>\n",
       "      <td>280.17</td>\n",
       "      <td>281.60</td>\n",
       "      <td>278.81</td>\n",
       "      <td>278.90</td>\n",
       "      <td>6006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-30 18:00:00</td>\n",
       "      <td>278.80</td>\n",
       "      <td>279.59</td>\n",
       "      <td>278.42</td>\n",
       "      <td>278.68</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime    open    high     low   close  volume\n",
       "0 2017-03-30 14:00:00  277.26  277.76  277.26  277.65      27\n",
       "1 2017-03-30 15:00:00  277.59  282.00  277.21  280.72   11216\n",
       "2 2017-03-30 16:00:00  280.86  281.37  279.21  280.13    7182\n",
       "3 2017-03-30 17:00:00  280.17  281.60  278.81  278.90    6006\n",
       "4 2017-03-30 18:00:00  278.80  279.59  278.42  278.68    2388"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>2017-06-23 18:00:00</td>\n",
       "      <td>385.58</td>\n",
       "      <td>385.58</td>\n",
       "      <td>383.01</td>\n",
       "      <td>383.82</td>\n",
       "      <td>4575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>2017-06-23 19:00:00</td>\n",
       "      <td>383.82</td>\n",
       "      <td>384.50</td>\n",
       "      <td>383.61</td>\n",
       "      <td>383.80</td>\n",
       "      <td>3449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>2017-06-23 20:00:00</td>\n",
       "      <td>383.81</td>\n",
       "      <td>385.10</td>\n",
       "      <td>382.23</td>\n",
       "      <td>382.53</td>\n",
       "      <td>5242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>2017-06-23 21:00:00</td>\n",
       "      <td>382.65</td>\n",
       "      <td>384.60</td>\n",
       "      <td>382.01</td>\n",
       "      <td>383.23</td>\n",
       "      <td>7657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>2017-06-23 22:00:00</td>\n",
       "      <td>383.18</td>\n",
       "      <td>383.50</td>\n",
       "      <td>383.00</td>\n",
       "      <td>383.02</td>\n",
       "      <td>2675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime    open    high     low   close  volume\n",
       "937 2017-06-23 18:00:00  385.58  385.58  383.01  383.82    4575\n",
       "938 2017-06-23 19:00:00  383.82  384.50  383.61  383.80    3449\n",
       "939 2017-06-23 20:00:00  383.81  385.10  382.23  382.53    5242\n",
       "940 2017-06-23 21:00:00  382.65  384.60  382.01  383.23    7657\n",
       "941 2017-06-23 22:00:00  383.18  383.50  383.00  383.02    2675"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# process data\n",
    "import pandas as pd  \n",
    "df = pd.DataFrame.from_records(data)\n",
    "df.columns = ['datetime', 'open', 'high', 'low', 'close', 'volume']\n",
    "df['datetime'] =  pd.to_datetime(df['datetime'])\n",
    "# df.set_index('datetime', inplace=True)\n",
    "display(df.head())\n",
    "display(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b2d3d550>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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GYSIiIiIAf3jnCN7YdgLH21248ZI5efs6wVAUgWAEVnNms4UHKtOL8S1dZViaJGHQSpVh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\n/0X9XYSELEl5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57b31e8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot data\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "df.plot(y='close', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489 price 383.02\n",
      "489 size 1\n",
      "489 size 1\n",
      "489 size 52869\n",
      "489 price 386.99\n",
      "489 price 379.35\n",
      "489 price 382.61\n",
      "489 price 382.45\n",
      "489 price 383.02\n",
      "489 size 1\n",
      "489 price 383.25\n",
      "489 size 10\n",
      "489 size 1\n",
      "489 size 10\n"
     ]
    }
   ],
   "source": [
    "# subscribe to realtime tick data\n",
    "reqId = tws.getReqId()\n",
    "tws.reqMktData(reqId, contract, genericTickList='', snapshot=False,\n",
    "        regulatorySnapshot=False, mktDataOptions=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cancel realtime ticks\n",
    "tws.cancelMktData(reqId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-06-23 22:25:10,980 quamash INFO Using Qt Implementation: PyQt5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting coroutine...\n",
      "coroutine finished\n"
     ]
    }
   ],
   "source": [
    "# asyncio integration\n",
    "import asyncio\n",
    "import quamash\n",
    "\n",
    "async def coro(seconds):\n",
    "    print('starting coroutine...')\n",
    "    await asyncio.sleep(seconds)\n",
    "    print('coroutine finished')\n",
    "\n",
    "loop = quamash.QEventLoop()\n",
    "asyncio.set_event_loop(loop)\n",
    "\n",
    "task = asyncio.ensure_future(coro(seconds=1))\n",
    "# note that the Qt event loop doesn't need to be started as it's already running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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