notebooks/bar_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IB connected to 127.0.0.1:7497 clientId=14>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ib_insync import *\n",
    "util.startLoop()\n",
    "\n",
    "ib = IB()\n",
    "ib.connect('127.0.0.1', 7497, clientId=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Historical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the earliest date of available bar data the \"head timestamp\" can be requested:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 6, 29, 8, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contract = Stock('TSLA', 'SMART', 'USD')\n",
    "\n",
    "ib.reqHeadTimeStamp(contract, whatToShow='TRADES', useRTH=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To request hourly data of the last 60 trading days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bars = ib.reqHistoricalData(\n",
    "        contract,\n",
    "        endDateTime='',\n",
    "        durationStr='60 D',\n",
    "        barSizeSetting='1 hour',\n",
    "        whatToShow='TRADES',\n",
    "        useRTH=True,\n",
    "        formatDate=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BarData(date=datetime.datetime(2019, 10, 4, 15, 30), open=231.82, high=234.78, low=230.29, close=233.06, volume=12066, average=232.914, barCount=5981)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bars[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the list of bars to a data frame and print the first and last rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>average</th>\n",
       "      <th>barCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-04 15:30:00</td>\n",
       "      <td>231.82</td>\n",
       "      <td>234.78</td>\n",
       "      <td>230.29</td>\n",
       "      <td>233.06</td>\n",
       "      <td>12066</td>\n",
       "      <td>232.914</td>\n",
       "      <td>5981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-10-04 16:00:00</td>\n",
       "      <td>233.02</td>\n",
       "      <td>233.19</td>\n",
       "      <td>228.07</td>\n",
       "      <td>228.69</td>\n",
       "      <td>15847</td>\n",
       "      <td>229.846</td>\n",
       "      <td>7664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-10-04 17:00:00</td>\n",
       "      <td>228.62</td>\n",
       "      <td>230.87</td>\n",
       "      <td>228.30</td>\n",
       "      <td>230.56</td>\n",
       "      <td>8396</td>\n",
       "      <td>229.421</td>\n",
       "      <td>4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-10-04 18:00:00</td>\n",
       "      <td>230.56</td>\n",
       "      <td>231.49</td>\n",
       "      <td>230.28</td>\n",
       "      <td>230.85</td>\n",
       "      <td>7069</td>\n",
       "      <td>230.899</td>\n",
       "      <td>3605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-10-04 19:00:00</td>\n",
       "      <td>230.84</td>\n",
       "      <td>231.48</td>\n",
       "      <td>230.04</td>\n",
       "      <td>231.00</td>\n",
       "      <td>3518</td>\n",
       "      <td>230.765</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date    open    high     low   close  volume  average  \\\n",
       "0 2019-10-04 15:30:00  231.82  234.78  230.29  233.06   12066  232.914   \n",
       "1 2019-10-04 16:00:00  233.02  233.19  228.07  228.69   15847  229.846   \n",
       "2 2019-10-04 17:00:00  228.62  230.87  228.30  230.56    8396  229.421   \n",
       "3 2019-10-04 18:00:00  230.56  231.49  230.28  230.85    7069  230.899   \n",
       "4 2019-10-04 19:00:00  230.84  231.48  230.04  231.00    3518  230.765   \n",
       "\n",
       "   barCount  \n",
       "0      5981  \n",
       "1      7664  \n",
       "2      4324  \n",
       "3      3605  \n",
       "4      2125  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>average</th>\n",
       "      <th>barCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2019-12-30 17:00:00</td>\n",
       "      <td>413.03</td>\n",
       "      <td>421.06</td>\n",
       "      <td>412.61</td>\n",
       "      <td>419.18</td>\n",
       "      <td>14920</td>\n",
       "      <td>416.886</td>\n",
       "      <td>8149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2019-12-30 18:00:00</td>\n",
       "      <td>419.18</td>\n",
       "      <td>420.45</td>\n",
       "      <td>417.32</td>\n",
       "      <td>418.65</td>\n",
       "      <td>7629</td>\n",
       "      <td>419.017</td>\n",
       "      <td>4769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2019-12-30 19:00:00</td>\n",
       "      <td>418.70</td>\n",
       "      <td>420.78</td>\n",
       "      <td>416.13</td>\n",
       "      <td>416.42</td>\n",
       "      <td>7476</td>\n",
       "      <td>418.900</td>\n",
       "      <td>3883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2019-12-30 20:00:00</td>\n",
       "      <td>416.41</td>\n",
       "      <td>416.78</td>\n",
       "      <td>413.51</td>\n",
       "      <td>414.90</td>\n",
       "      <td>6936</td>\n",
       "      <td>415.059</td>\n",
       "      <td>3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2019-12-30 21:00:00</td>\n",
       "      <td>415.00</td>\n",
       "      <td>415.00</td>\n",
       "      <td>413.00</td>\n",
       "      <td>414.62</td>\n",
       "      <td>9204</td>\n",
       "      <td>414.130</td>\n",
       "      <td>5979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date    open    high     low   close  volume  average  \\\n",
       "409 2019-12-30 17:00:00  413.03  421.06  412.61  419.18   14920  416.886   \n",
       "410 2019-12-30 18:00:00  419.18  420.45  417.32  418.65    7629  419.017   \n",
       "411 2019-12-30 19:00:00  418.70  420.78  416.13  416.42    7476  418.900   \n",
       "412 2019-12-30 20:00:00  416.41  416.78  413.51  414.90    6936  415.059   \n",
       "413 2019-12-30 21:00:00  415.00  415.00  413.00  414.62    9204  414.130   \n",
       "\n",
       "     barCount  \n",
       "409      8149  \n",
       "410      4769  \n",
       "411      3883  \n",
       "412      3700  \n",
       "413      5979  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = util.df(bars)\n",
    "\n",
    "display(df.head())\n",
    "display(df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instruct the notebook to draw plot graphics inline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the close data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='close');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a utility function to plot bars as a candlestick plot. It can accept either a DataFrame or a list of bars. Here it will print the last 100 bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "util.barplot(bars[-100:], title=contract.symbol);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Historical data with realtime updates\n",
    "\n",
    "A new feature of the API is to get live updates for historical bars. This is done by setting `endDateTime` to an empty string and the `keepUpToDate` parameter to `True`.\n",
    "\n",
    "Let's get some bars with an keepUpToDate subscription:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "contract = Forex('EURUSD')\n",
    "\n",
    "bars = ib.reqHistoricalData(\n",
    "        contract,\n",
    "        endDateTime='',\n",
    "        durationStr='900 S',\n",
    "        barSizeSetting='10 secs',\n",
    "        whatToShow='MIDPOINT',\n",
    "        useRTH=True,\n",
    "        formatDate=1,\n",
    "        keepUpToDate=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replot for every change of the last bar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def onBarUpdate(bars, hasNewBar):\n",
    "    plt.close()\n",
    "    plot = util.barplot(bars)\n",
    "    clear_output(wait=True)\n",
    "    display(plot)\n",
    "\n",
    "bars.updateEvent += onBarUpdate\n",
    "\n",
    "ib.sleep(10)\n",
    "ib.cancelHistoricalData(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realtime bars\n",
    "------------------\n",
    "\n",
    "With ``reqRealTimeBars`` a subscription is started that sends a new bar every 5 seconds.\n",
    "\n",
    "First we'll set up a event handler for bar updates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def onBarUpdate(bars, hasNewBar):\n",
    "    print(bars[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then do the real request and connect the event handler,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bars = ib.reqRealTimeBars(contract, 5, 'MIDPOINT', False)\n",
    "bars.updateEvent += onBarUpdate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let it run for half a minute and then cancel the realtime bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error 162, reqId 5: Historical Market Data Service error message:API historical data query cancelled: 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RealTimeBar(time=datetime.datetime(2019, 12, 31, 12, 27, 5, tzinfo=datetime.timezone.utc), endTime=-1, open_=1.122925, high=1.122975, low=1.122925, close=1.122975, volume=-1, wap=-1.0, count=-1)\n",
      "RealTimeBar(time=datetime.datetime(2019, 12, 31, 12, 27, 10, tzinfo=datetime.timezone.utc), endTime=-1, open_=1.122975, high=1.122975, low=1.122975, close=1.122975, volume=-1, wap=-1.0, count=-1)\n",
      "RealTimeBar(time=datetime.datetime(2019, 12, 31, 12, 27, 15, tzinfo=datetime.timezone.utc), endTime=-1, open_=1.122975, high=1.122975, low=1.122975, close=1.122975, volume=-1, wap=-1.0, count=-1)\n",
      "RealTimeBar(time=datetime.datetime(2019, 12, 31, 12, 27, 20, tzinfo=datetime.timezone.utc), endTime=-1, open_=1.122975, high=1.123025, low=1.122975, close=1.123025, volume=-1, wap=-1.0, count=-1)\n",
      "RealTimeBar(time=datetime.datetime(2019, 12, 31, 12, 27, 25, tzinfo=datetime.timezone.utc), endTime=-1, open_=1.123025, high=1.123025, low=1.122975, close=1.122975, volume=-1, wap=-1.0, count=-1)\n",
      "RealTimeBar(time=datetime.datetime(2019, 12, 31, 12, 27, 30, tzinfo=datetime.timezone.utc), endTime=-1, open_=1.122975, high=1.122975, low=1.122975, close=1.122975, volume=-1, wap=-1.0, count=-1)\n"
     ]
    }
   ],
   "source": [
    "ib.sleep(30)\n",
    "ib.cancelRealTimeBars(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantage of reqRealTimeBars is that it behaves more robust when the connection to the IB server farms is interrupted. After the connection is restored, the bars from during the network outage will be backfilled and the live bars will resume.\n",
    "\n",
    "reqHistoricalData + keepUpToDate will, at the moment of writing, leave the whole API inoperable after a network interruption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ib.disconnect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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