oandamomentumv1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import configparser\n",
    "# v1 OANDA API\n",
    "# pip install git+https://github.com/oanda/oandapy.git\n",
    "# Note: You can request a v1 instead of v20 account by contacting Oanda chat support\n",
    "import oandapy as opy\n",
    "\n",
    "config = configparser.ConfigParser()\n",
    "config.read('oanda.cfg')\n",
    "\n",
    "oanda = opy.API(environment='practice',\n",
    "                access_token=config['oanda']['access_token'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2658 entries, 2016-12-08 00:00:00 to 2016-12-09 21:59:00\n",
      "Data columns (total 10 columns):\n",
      "closeAsk    2658 non-null float64\n",
      "closeBid    2658 non-null float64\n",
      "complete    2658 non-null bool\n",
      "highAsk     2658 non-null float64\n",
      "highBid     2658 non-null float64\n",
      "lowAsk      2658 non-null float64\n",
      "lowBid      2658 non-null float64\n",
      "openAsk     2658 non-null float64\n",
      "openBid     2658 non-null float64\n",
      "volume      2658 non-null int64\n",
      "dtypes: bool(1), float64(8), int64(1)\n",
      "memory usage: 210.3 KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = oanda.get_history(instrument='EUR_USD',  # our instrument\n",
    "                         start='2016-12-08',  # start data\n",
    "                         end='2016-12-10',  # end date\n",
    "                         granularity='M1')  # minute bars\n",
    "\n",
    "df = pd.DataFrame(data['candles']).set_index('time')\n",
    "\n",
    "df.index = pd.DatetimeIndex(df.index)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "df['returns'] = np.log(df['closeAsk'] / df['closeAsk'].shift(1))\n",
    "\n",
    "cols = []\n",
    "\n",
    "for momentum in [15, 30, 60, 120]:\n",
    "    col = 'position_%s' % momentum\n",
    "    df[col] = np.sign(df['returns'].rolling(momentum).mean())\n",
    "    cols.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111242668>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Iyjv/iKw2GSztSWmM1lz1tclWiLmDg2Bj2SeHvSrQHYqi4KzfSLAXUR+9wdWw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LAAAg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\nCDPz/69XIn5e9N9dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11125da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "strats = ['returns']\n",
    "\n",
    "for col in cols:\n",
    "    strat = 'strategy_%s' % col.split('_')[1] \n",
    "    df[strat] = df[col].shift(1) * df['returns']\n",
    "    strats.append(strat)\n",
    "\n",
    "df[strats].dropna().cumsum().apply(np.exp).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class MomentumTrader(opy.Streamer): \n",
    "    def __init__(self, momentum, *args, **kwargs): \n",
    "        opy.Streamer.__init__(self, *args, **kwargs)\n",
    "        self.ticks = 0 \n",
    "        self.position = 0\n",
    "        self.df = pd.DataFrame()\n",
    "        self.momentum = momentum\n",
    "        self.units = 100000\n",
    "    def create_order(self, side, units):\n",
    "        order = oanda.create_order(config['oanda']['account_id'], \n",
    "            instrument='EUR_USD', units=units, side=side,\n",
    "            type='market') \n",
    "        print('\\n', order)\n",
    "    def on_success(self, data):\n",
    "        self.ticks += 1\n",
    "        # print(self.ticks, end=', ')\n",
    "        # appends the new tick data to the DataFrame object\n",
    "        self.df = self.df.append(pd.DataFrame(data['tick'],\n",
    "                                 index=[data['tick']['time']]))\n",
    "        # transforms the time information to a DatetimeIndex object\n",
    "        self.df.index = pd.DatetimeIndex(self.df['time'])\n",
    "        # resamples the data set to a new, homogeneous interval\n",
    "        dfr = self.df.resample('5s').last()\n",
    "        # calculates the log returns\n",
    "        dfr['returns'] = np.log(dfr['ask'] / dfr['ask'].shift(1))\n",
    "        # derives the positioning according to the momentum strategy\n",
    "        dfr['position'] = np.sign(dfr['returns'].rolling( \n",
    "                                      self.momentum).mean())\n",
    "        if dfr['position'].ix[-1] == 1:\n",
    "            # go long\n",
    "            if self.position == 0:\n",
    "                self.create_order('buy', self.units)\n",
    "            elif self.position == -1:\n",
    "                self.create_order('buy', self.units * 2)\n",
    "            self.position = 1\n",
    "        elif dfr['position'].ix[-1] == -1:\n",
    "            # go short\n",
    "            if self.position == 0:\n",
    "                self.create_order('sell', self.units)\n",
    "            elif self.position == 1:\n",
    "                self.create_order('sell', self.units * 2)\n",
    "            self.position = -1\n",
    "        if self.ticks == 250:\n",
    "            # close out the position\n",
    "            if self.position == 1:\n",
    "                self.create_order('sell', self.units)\n",
    "            elif self.position == -1:\n",
    "                self.create_order('buy', self.units)\n",
    "            self.disconnect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mt = MomentumTrader(momentum=12, environment='practice',\n",
    "                    access_token=config['oanda']['access_token'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-ec08ad91d1d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccount_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'oanda'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'account_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstruments\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'DE30_EUR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_heartbeat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/site-packages/oandapy/stream/stream.py\u001b[0m in \u001b[0;36mrates\u001b[0;34m(self, account_id, instruments, **params)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'instruments'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstruments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mendpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'v1/prices'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/site-packages/oandapy/stream/stream.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, endpoint, params)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mignore_heartbeat\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"heartbeat\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_success\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 354\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \"\"\"\n\u001b[0;32m--> 339\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m         \"\"\"\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)"
     ]
    }
   ],
   "source": [
    "mt.rates(account_id=config['oanda']['account_id'], instruments=['DE30_EUR'], ignore_heartbeat=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mystreamer = opy.Streamer(environment='practice',access_token=config['oanda']['access_token'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-8ff9e3858170>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmystreamer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccount_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"8ba5b3b5e28cd20292383895127b7991-e910b2fd79a67404fb7efcd8132f85e1\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstruments\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"DE30_EUR\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/site-packages/oandapy/stream/stream.py\u001b[0m in \u001b[0;36mrates\u001b[0;34m(self, account_id, instruments, **params)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'instruments'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstruments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mendpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'v1/prices'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/site-packages/oandapy/stream/stream.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, endpoint, params)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mignore_heartbeat\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"heartbeat\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_success\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 354\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \"\"\"\n\u001b[0;32m--> 339\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin.chodroff/anaconda3/anaconda/lib/python3.6/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m         \"\"\"\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)"
     ]
    }
   ],
   "source": [
    "mystreamer.rates(account_id=\"8ba5b3b5e28cd20292383895127b7991-e910b2fd79a67404fb7efcd8132f85e1\", instruments=\"DE30_EUR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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