{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streaming Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is about **real-time or streaming data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fxcmpy\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "con = fxcmpy.fxcmpy(config_file='fxcm.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Streaming the current market data is straightforward, one just need to **subscribe** to the instrument of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.subscribe_market_data('EUR/USD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list with the subscribed instruments is returned by the `con.get_subscribed_symbols()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EUR/USD']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_subscribed_symbols()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also check the subscription for a specified instruments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.is_subscribed('EUR/USD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the subscription, `fxcmpy` collects the data in a `pandas` `DataFrame`. The `con.get_prices()` method returns that `DataFrame` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:42.588</th>\n",
       "      <td>1.17718</td>\n",
       "      <td>1.17743</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:45.767</th>\n",
       "      <td>1.17717</td>\n",
       "      <td>1.17743</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:47.469</th>\n",
       "      <td>1.17716</td>\n",
       "      <td>1.17742</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:47.737</th>\n",
       "      <td>1.17716</td>\n",
       "      <td>1.17741</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:53.435</th>\n",
       "      <td>1.17716</td>\n",
       "      <td>1.17742</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:54.864</th>\n",
       "      <td>1.17718</td>\n",
       "      <td>1.17742</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:56.277</th>\n",
       "      <td>1.17719</td>\n",
       "      <td>1.17742</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:02:57.461</th>\n",
       "      <td>1.17719</td>\n",
       "      <td>1.17744</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:20.470</th>\n",
       "      <td>1.17720</td>\n",
       "      <td>1.17744</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:21.441</th>\n",
       "      <td>1.17720</td>\n",
       "      <td>1.17745</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:23.930</th>\n",
       "      <td>1.17721</td>\n",
       "      <td>1.17746</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:27.619</th>\n",
       "      <td>1.17722</td>\n",
       "      <td>1.17747</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:32.414</th>\n",
       "      <td>1.17721</td>\n",
       "      <td>1.17747</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:34.200</th>\n",
       "      <td>1.17721</td>\n",
       "      <td>1.17746</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 10:03:35.389</th>\n",
       "      <td>1.17721</td>\n",
       "      <td>1.17745</td>\n",
       "      <td>1.17751</td>\n",
       "      <td>1.17095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Bid      Ask     High      Low\n",
       "2018-06-06 10:02:42.588  1.17718  1.17743  1.17751  1.17095\n",
       "2018-06-06 10:02:45.767  1.17717  1.17743  1.17751  1.17095\n",
       "2018-06-06 10:02:47.469  1.17716  1.17742  1.17751  1.17095\n",
       "2018-06-06 10:02:47.737  1.17716  1.17741  1.17751  1.17095\n",
       "2018-06-06 10:02:53.435  1.17716  1.17742  1.17751  1.17095\n",
       "2018-06-06 10:02:54.864  1.17718  1.17742  1.17751  1.17095\n",
       "2018-06-06 10:02:56.277  1.17719  1.17742  1.17751  1.17095\n",
       "2018-06-06 10:02:57.461  1.17719  1.17744  1.17751  1.17095\n",
       "2018-06-06 10:03:20.470  1.17720  1.17744  1.17751  1.17095\n",
       "2018-06-06 10:03:21.441  1.17720  1.17745  1.17751  1.17095\n",
       "2018-06-06 10:03:23.930  1.17721  1.17746  1.17751  1.17095\n",
       "2018-06-06 10:03:27.619  1.17722  1.17747  1.17751  1.17095\n",
       "2018-06-06 10:03:32.414  1.17721  1.17747  1.17751  1.17095\n",
       "2018-06-06 10:03:34.200  1.17721  1.17746  1.17751  1.17095\n",
       "2018-06-06 10:03:35.389  1.17721  1.17745  1.17751  1.17095"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_prices('EUR/USD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also fetch only the last available quotes via `con.get_last_price()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bid     1.17729\n",
       "Ask     1.17754\n",
       "High    1.17756\n",
       "Low     1.17095\n",
       "Name: 2018-06-06 10:05:03.066000, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_last_price('EUR/USD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prevent the resulting `DataFrame` from growing too large, the number of entries can be limited by the class attribute `max_prices`. It is set to 10,000 by default.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_max_prices()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the value of `max_prices` with `set_prices()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "con.set_max_prices(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " `None` as value for max_prices means that there is no limit for the number of prices in the `DataFrame`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To stop the stream and delete (!) the `DataFrame`, use `con.unsubscribe_market_data()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.unsubscribe_market_data('EUR/USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Bid, Ask, High, Low]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_prices('EUR/USD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Callback Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can add an iterable (e.g. `tuple` or `list` object) with **callback functions** to the subscription, as in the example below.\n",
    "\n",
    "The function `print_data()` simply prints selected quotes as soon as they are retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_data(data, dataframe):\n",
    "    print('%3d | %s | %s, %s, %s, %s, %s' \n",
    "          % (len(dataframe), data['Symbol'], \n",
    "             pd.to_datetime(int(data['Updated']), unit='ms'), \n",
    "             data['Rates'][0], data['Rates'][1], data['Rates'][2],\n",
    "             data['Rates'][3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1 | EUR/USD | 2018-06-06 10:06:29.800000, 1.17725, 1.17749, 1.17756, 1.17095\n",
      "  2 | EUR/USD | 2018-06-06 10:06:34.421000, 1.17725, 1.17748, 1.17756, 1.17095\n"
     ]
    }
   ],
   "source": [
    "con.subscribe_market_data('EUR/USD', (print_data,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.unsubscribe_market_data('EUR/USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.close()"
   ]
  }
 ],
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