{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Historical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section illustrates how to retrieve **historical data** for different instruments.\n",
    "\n",
    "First, the API connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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": [
    "## Available Instruments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of **instruments** for which historical data is available is returned by `con. get_instruments_for_candles()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF']\n",
      "['EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD']\n",
      "['EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'AUD/JPY']\n",
      "['GBP/AUD', 'USD/CNH', 'FRA40', 'GER30']\n",
      "['UK100', 'US30', 'USDOLLAR', 'XAU/USD']\n",
      "['XAG/USD']\n"
     ]
    }
   ],
   "source": [
    "instruments = con.get_instruments_for_candles()\n",
    "for i in range(int(len(instruments)/4)):\n",
    "    print(instruments[i*4:(i+1)*4])\n",
    "print(instruments[(i+1)*4:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a simple case, the `con.get_candles()` method returns the most recent data points available for a specified `instrument` and `period` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>bidclose</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-24 21:00:00</th>\n",
       "      <td>110.069</td>\n",
       "      <td>109.228</td>\n",
       "      <td>110.085</td>\n",
       "      <td>108.945</td>\n",
       "      <td>110.096</td>\n",
       "      <td>109.275</td>\n",
       "      <td>110.132</td>\n",
       "      <td>108.965</td>\n",
       "      <td>454210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-25 21:00:00</th>\n",
       "      <td>109.228</td>\n",
       "      <td>109.352</td>\n",
       "      <td>109.734</td>\n",
       "      <td>109.112</td>\n",
       "      <td>109.275</td>\n",
       "      <td>109.419</td>\n",
       "      <td>109.755</td>\n",
       "      <td>109.131</td>\n",
       "      <td>394943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-27 21:00:00</th>\n",
       "      <td>109.504</td>\n",
       "      <td>109.736</td>\n",
       "      <td>109.806</td>\n",
       "      <td>109.500</td>\n",
       "      <td>109.563</td>\n",
       "      <td>109.796</td>\n",
       "      <td>109.848</td>\n",
       "      <td>109.563</td>\n",
       "      <td>733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-28 21:00:00</th>\n",
       "      <td>109.736</td>\n",
       "      <td>109.394</td>\n",
       "      <td>109.820</td>\n",
       "      <td>109.222</td>\n",
       "      <td>109.796</td>\n",
       "      <td>109.442</td>\n",
       "      <td>109.847</td>\n",
       "      <td>109.245</td>\n",
       "      <td>301699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-29 21:00:00</th>\n",
       "      <td>109.394</td>\n",
       "      <td>108.744</td>\n",
       "      <td>109.459</td>\n",
       "      <td>108.102</td>\n",
       "      <td>109.442</td>\n",
       "      <td>108.790</td>\n",
       "      <td>109.481</td>\n",
       "      <td>108.123</td>\n",
       "      <td>574325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 21:00:00</th>\n",
       "      <td>108.744</td>\n",
       "      <td>108.895</td>\n",
       "      <td>109.065</td>\n",
       "      <td>108.341</td>\n",
       "      <td>108.790</td>\n",
       "      <td>108.921</td>\n",
       "      <td>109.086</td>\n",
       "      <td>108.363</td>\n",
       "      <td>498453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>108.895</td>\n",
       "      <td>108.793</td>\n",
       "      <td>108.995</td>\n",
       "      <td>108.376</td>\n",
       "      <td>108.921</td>\n",
       "      <td>108.846</td>\n",
       "      <td>109.015</td>\n",
       "      <td>108.397</td>\n",
       "      <td>452231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01 21:00:00</th>\n",
       "      <td>108.793</td>\n",
       "      <td>109.478</td>\n",
       "      <td>109.723</td>\n",
       "      <td>108.712</td>\n",
       "      <td>108.846</td>\n",
       "      <td>109.545</td>\n",
       "      <td>109.752</td>\n",
       "      <td>108.733</td>\n",
       "      <td>368094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-04 21:00:00</th>\n",
       "      <td>109.552</td>\n",
       "      <td>109.803</td>\n",
       "      <td>109.845</td>\n",
       "      <td>109.548</td>\n",
       "      <td>109.572</td>\n",
       "      <td>109.827</td>\n",
       "      <td>109.865</td>\n",
       "      <td>109.569</td>\n",
       "      <td>57968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-05 21:00:00</th>\n",
       "      <td>109.803</td>\n",
       "      <td>109.761</td>\n",
       "      <td>109.995</td>\n",
       "      <td>109.462</td>\n",
       "      <td>109.827</td>\n",
       "      <td>109.814</td>\n",
       "      <td>110.016</td>\n",
       "      <td>109.484</td>\n",
       "      <td>374545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2018-05-24 21:00:00  110.069   109.228  110.085  108.945  110.096   109.275   \n",
       "2018-05-25 21:00:00  109.228   109.352  109.734  109.112  109.275   109.419   \n",
       "2018-05-27 21:00:00  109.504   109.736  109.806  109.500  109.563   109.796   \n",
       "2018-05-28 21:00:00  109.736   109.394  109.820  109.222  109.796   109.442   \n",
       "2018-05-29 21:00:00  109.394   108.744  109.459  108.102  109.442   108.790   \n",
       "2018-05-30 21:00:00  108.744   108.895  109.065  108.341  108.790   108.921   \n",
       "2018-05-31 21:00:00  108.895   108.793  108.995  108.376  108.921   108.846   \n",
       "2018-06-01 21:00:00  108.793   109.478  109.723  108.712  108.846   109.545   \n",
       "2018-06-04 21:00:00  109.552   109.803  109.845  109.548  109.572   109.827   \n",
       "2018-06-05 21:00:00  109.803   109.761  109.995  109.462  109.827   109.814   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2018-05-24 21:00:00  110.132  108.965   454210  \n",
       "2018-05-25 21:00:00  109.755  109.131   394943  \n",
       "2018-05-27 21:00:00  109.848  109.563      733  \n",
       "2018-05-28 21:00:00  109.847  109.245   301699  \n",
       "2018-05-29 21:00:00  109.481  108.123   574325  \n",
       "2018-05-30 21:00:00  109.086  108.363   498453  \n",
       "2018-05-31 21:00:00  109.015  108.397   452231  \n",
       "2018-06-01 21:00:00  109.752  108.733   368094  \n",
       "2018-06-04 21:00:00  109.865  109.569    57968  \n",
       "2018-06-05 21:00:00  110.016  109.484   374545  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_candles('USD/JPY', period='D1')  # daily data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the method returns a `pandas` `DataFrame` object which simplifies the majority of typical analytics and visualizations tasks significantly (see http://pandas.pydata.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter `period` defines the frequency of the data to be retrieved. Below is a list of all currently available frequencies.\n",
    "\n",
    "* minutes: `m1`, `m5`, `m15` and `m30`,\n",
    "* hours: `H1`, `H2`, `H3`, `H4`, `H6` and `H8`,\n",
    "* one day: `D1`,\n",
    "* one week: `W1`,\n",
    "* one month: `M1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `con.get_candles()` returns data for the last 10 available periods, depending on the parameter value for `period`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>2017-07-31 21:00:00</th>\n",
       "      <td>1.18403</td>\n",
       "      <td>1.19081</td>\n",
       "      <td>1.20693</td>\n",
       "      <td>1.16610</td>\n",
       "      <td>1.18428</td>\n",
       "      <td>1.19110</td>\n",
       "      <td>1.20716</td>\n",
       "      <td>1.16634</td>\n",
       "      <td>6893224</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-31 21:00:00</th>\n",
       "      <td>1.19081</td>\n",
       "      <td>1.18108</td>\n",
       "      <td>1.20913</td>\n",
       "      <td>1.17160</td>\n",
       "      <td>1.19110</td>\n",
       "      <td>1.18197</td>\n",
       "      <td>1.20937</td>\n",
       "      <td>1.17181</td>\n",
       "      <td>6183348</td>\n",
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       "    <tr>\n",
       "      <th>2017-09-30 21:00:00</th>\n",
       "      <td>1.18108</td>\n",
       "      <td>1.16450</td>\n",
       "      <td>1.18792</td>\n",
       "      <td>1.15731</td>\n",
       "      <td>1.18197</td>\n",
       "      <td>1.16476</td>\n",
       "      <td>1.18810</td>\n",
       "      <td>1.15750</td>\n",
       "      <td>4598301</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-31 21:00:00</th>\n",
       "      <td>1.16450</td>\n",
       "      <td>1.19026</td>\n",
       "      <td>1.19601</td>\n",
       "      <td>1.15528</td>\n",
       "      <td>1.16476</td>\n",
       "      <td>1.19051</td>\n",
       "      <td>1.19624</td>\n",
       "      <td>1.15549</td>\n",
       "      <td>4392023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 22:00:00</th>\n",
       "      <td>1.19026</td>\n",
       "      <td>1.20038</td>\n",
       "      <td>1.20244</td>\n",
       "      <td>1.17165</td>\n",
       "      <td>1.19051</td>\n",
       "      <td>1.20103</td>\n",
       "      <td>1.20267</td>\n",
       "      <td>1.17187</td>\n",
       "      <td>3678417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31 22:00:00</th>\n",
       "      <td>1.20038</td>\n",
       "      <td>1.24122</td>\n",
       "      <td>1.25369</td>\n",
       "      <td>1.19144</td>\n",
       "      <td>1.20103</td>\n",
       "      <td>1.24148</td>\n",
       "      <td>1.25392</td>\n",
       "      <td>1.19166</td>\n",
       "      <td>6771090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31 22:00:00</th>\n",
       "      <td>1.24122</td>\n",
       "      <td>1.21922</td>\n",
       "      <td>1.25547</td>\n",
       "      <td>1.21867</td>\n",
       "      <td>1.24148</td>\n",
       "      <td>1.21962</td>\n",
       "      <td>1.25566</td>\n",
       "      <td>1.21890</td>\n",
       "      <td>6552190</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-01 20:00:00</th>\n",
       "      <td>1.23228</td>\n",
       "      <td>1.23191</td>\n",
       "      <td>1.23290</td>\n",
       "      <td>1.23191</td>\n",
       "      <td>1.23306</td>\n",
       "      <td>1.23252</td>\n",
       "      <td>1.23330</td>\n",
       "      <td>1.23252</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-01 20:00:00</th>\n",
       "      <td>1.21214</td>\n",
       "      <td>1.19907</td>\n",
       "      <td>1.21377</td>\n",
       "      <td>1.19801</td>\n",
       "      <td>1.21291</td>\n",
       "      <td>1.19948</td>\n",
       "      <td>1.21400</td>\n",
       "      <td>1.19822</td>\n",
       "      <td>477918</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-01 20:00:00</th>\n",
       "      <td>1.16534</td>\n",
       "      <td>1.16561</td>\n",
       "      <td>1.17273</td>\n",
       "      <td>1.15091</td>\n",
       "      <td>1.16596</td>\n",
       "      <td>1.16624</td>\n",
       "      <td>1.17297</td>\n",
       "      <td>1.15112</td>\n",
       "      <td>1806820</td>\n",
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      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2017-07-31 21:00:00  1.18403   1.19081  1.20693  1.16610  1.18428   1.19110   \n",
       "2017-08-31 21:00:00  1.19081   1.18108  1.20913  1.17160  1.19110   1.18197   \n",
       "2017-09-30 21:00:00  1.18108   1.16450  1.18792  1.15731  1.18197   1.16476   \n",
       "2017-10-31 21:00:00  1.16450   1.19026  1.19601  1.15528  1.16476   1.19051   \n",
       "2017-11-30 22:00:00  1.19026   1.20038  1.20244  1.17165  1.19051   1.20103   \n",
       "2017-12-31 22:00:00  1.20038   1.24122  1.25369  1.19144  1.20103   1.24148   \n",
       "2018-01-31 22:00:00  1.24122   1.21922  1.25547  1.21867  1.24148   1.21962   \n",
       "2018-04-01 20:00:00  1.23228   1.23191  1.23290  1.23191  1.23306   1.23252   \n",
       "2018-05-01 20:00:00  1.21214   1.19907  1.21377  1.19801  1.21291   1.19948   \n",
       "2018-06-01 20:00:00  1.16534   1.16561  1.17273  1.15091  1.16596   1.16624   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2017-07-31 21:00:00  1.20716  1.16634  6893224  \n",
       "2017-08-31 21:00:00  1.20937  1.17181  6183348  \n",
       "2017-09-30 21:00:00  1.18810  1.15750  4598301  \n",
       "2017-10-31 21:00:00  1.19624  1.15549  4392023  \n",
       "2017-11-30 22:00:00  1.20267  1.17187  3678417  \n",
       "2017-12-31 22:00:00  1.25392  1.19166  6771090  \n",
       "2018-01-31 22:00:00  1.25566  1.21890  6552190  \n",
       "2018-04-01 20:00:00  1.23330  1.23252       39  \n",
       "2018-05-01 20:00:00  1.21400  1.19822   477918  \n",
       "2018-06-01 20:00:00  1.17297  1.15112  1806820  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_candles('EUR/USD', period='M1')  # monthly data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A number different from 10 can also be defined via the `number` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-06 08:52:00</th>\n",
       "      <td>1.17460</td>\n",
       "      <td>1.17442</td>\n",
       "      <td>1.17461</td>\n",
       "      <td>1.17429</td>\n",
       "      <td>1.17484</td>\n",
       "      <td>1.17464</td>\n",
       "      <td>1.17484</td>\n",
       "      <td>1.17453</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 08:53:00</th>\n",
       "      <td>1.17442</td>\n",
       "      <td>1.17430</td>\n",
       "      <td>1.17442</td>\n",
       "      <td>1.17420</td>\n",
       "      <td>1.17465</td>\n",
       "      <td>1.17453</td>\n",
       "      <td>1.17466</td>\n",
       "      <td>1.17445</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 08:54:00</th>\n",
       "      <td>1.17429</td>\n",
       "      <td>1.17436</td>\n",
       "      <td>1.17440</td>\n",
       "      <td>1.17425</td>\n",
       "      <td>1.17453</td>\n",
       "      <td>1.17460</td>\n",
       "      <td>1.17463</td>\n",
       "      <td>1.17449</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 08:55:00</th>\n",
       "      <td>1.17437</td>\n",
       "      <td>1.17448</td>\n",
       "      <td>1.17457</td>\n",
       "      <td>1.17437</td>\n",
       "      <td>1.17460</td>\n",
       "      <td>1.17470</td>\n",
       "      <td>1.17480</td>\n",
       "      <td>1.17460</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 08:56:00</th>\n",
       "      <td>1.17448</td>\n",
       "      <td>1.17479</td>\n",
       "      <td>1.17487</td>\n",
       "      <td>1.17448</td>\n",
       "      <td>1.17470</td>\n",
       "      <td>1.17502</td>\n",
       "      <td>1.17510</td>\n",
       "      <td>1.17469</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2018-06-06 08:52:00  1.17460   1.17442  1.17461  1.17429  1.17484   1.17464   \n",
       "2018-06-06 08:53:00  1.17442   1.17430  1.17442  1.17420  1.17465   1.17453   \n",
       "2018-06-06 08:54:00  1.17429   1.17436  1.17440  1.17425  1.17453   1.17460   \n",
       "2018-06-06 08:55:00  1.17437   1.17448  1.17457  1.17437  1.17460   1.17470   \n",
       "2018-06-06 08:56:00  1.17448   1.17479  1.17487  1.17448  1.17470   1.17502   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2018-06-06 08:52:00  1.17484  1.17453      213  \n",
       "2018-06-06 08:53:00  1.17466  1.17445      241  \n",
       "2018-06-06 08:54:00  1.17463  1.17449      223  \n",
       "2018-06-06 08:55:00  1.17480  1.17460      351  \n",
       "2018-06-06 08:56:00  1.17510  1.17469      292  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_candles('EUR/USD', period='m1', number=5)  # five one-minute bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Time Windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, one can specifiy `start` and `stop` values to specifiy the time window for data retrieval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-16 21:00:00</th>\n",
       "      <td>1.18366</td>\n",
       "      <td>1.18052</td>\n",
       "      <td>1.18530</td>\n",
       "      <td>1.17620</td>\n",
       "      <td>1.18389</td>\n",
       "      <td>1.18092</td>\n",
       "      <td>1.18553</td>\n",
       "      <td>1.17644</td>\n",
       "      <td>398818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-17 21:00:00</th>\n",
       "      <td>1.18052</td>\n",
       "      <td>1.17920</td>\n",
       "      <td>1.18363</td>\n",
       "      <td>1.17752</td>\n",
       "      <td>1.18092</td>\n",
       "      <td>1.17964</td>\n",
       "      <td>1.18388</td>\n",
       "      <td>1.17776</td>\n",
       "      <td>308010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-18 21:00:00</th>\n",
       "      <td>1.17920</td>\n",
       "      <td>1.17673</td>\n",
       "      <td>1.18209</td>\n",
       "      <td>1.17486</td>\n",
       "      <td>1.17964</td>\n",
       "      <td>1.17754</td>\n",
       "      <td>1.18233</td>\n",
       "      <td>1.17508</td>\n",
       "      <td>212112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-20 21:00:00</th>\n",
       "      <td>1.17630</td>\n",
       "      <td>1.17667</td>\n",
       "      <td>1.17785</td>\n",
       "      <td>1.17623</td>\n",
       "      <td>1.17698</td>\n",
       "      <td>1.17745</td>\n",
       "      <td>1.17810</td>\n",
       "      <td>1.17698</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-21 21:00:00</th>\n",
       "      <td>1.17667</td>\n",
       "      <td>1.17900</td>\n",
       "      <td>1.17945</td>\n",
       "      <td>1.17155</td>\n",
       "      <td>1.17745</td>\n",
       "      <td>1.17930</td>\n",
       "      <td>1.17968</td>\n",
       "      <td>1.17179</td>\n",
       "      <td>194031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-22 21:00:00</th>\n",
       "      <td>1.17900</td>\n",
       "      <td>1.17765</td>\n",
       "      <td>1.18287</td>\n",
       "      <td>1.17553</td>\n",
       "      <td>1.17930</td>\n",
       "      <td>1.17810</td>\n",
       "      <td>1.18310</td>\n",
       "      <td>1.17578</td>\n",
       "      <td>276164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-23 21:00:00</th>\n",
       "      <td>1.17765</td>\n",
       "      <td>1.16944</td>\n",
       "      <td>1.17886</td>\n",
       "      <td>1.16747</td>\n",
       "      <td>1.17810</td>\n",
       "      <td>1.16980</td>\n",
       "      <td>1.17909</td>\n",
       "      <td>1.16770</td>\n",
       "      <td>381970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-24 21:00:00</th>\n",
       "      <td>1.16944</td>\n",
       "      <td>1.17182</td>\n",
       "      <td>1.17494</td>\n",
       "      <td>1.16897</td>\n",
       "      <td>1.16979</td>\n",
       "      <td>1.17212</td>\n",
       "      <td>1.17515</td>\n",
       "      <td>1.16921</td>\n",
       "      <td>320736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-25 21:00:00</th>\n",
       "      <td>1.17182</td>\n",
       "      <td>1.16469</td>\n",
       "      <td>1.17325</td>\n",
       "      <td>1.16447</td>\n",
       "      <td>1.17212</td>\n",
       "      <td>1.16529</td>\n",
       "      <td>1.17349</td>\n",
       "      <td>1.16473</td>\n",
       "      <td>317164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-27 21:00:00</th>\n",
       "      <td>1.16534</td>\n",
       "      <td>1.16849</td>\n",
       "      <td>1.16868</td>\n",
       "      <td>1.16534</td>\n",
       "      <td>1.16596</td>\n",
       "      <td>1.16885</td>\n",
       "      <td>1.16907</td>\n",
       "      <td>1.16596</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-28 21:00:00</th>\n",
       "      <td>1.16849</td>\n",
       "      <td>1.16231</td>\n",
       "      <td>1.17273</td>\n",
       "      <td>1.16063</td>\n",
       "      <td>1.16885</td>\n",
       "      <td>1.16260</td>\n",
       "      <td>1.17297</td>\n",
       "      <td>1.16084</td>\n",
       "      <td>244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-29 21:00:00</th>\n",
       "      <td>1.16231</td>\n",
       "      <td>1.15382</td>\n",
       "      <td>1.16385</td>\n",
       "      <td>1.15091</td>\n",
       "      <td>1.16260</td>\n",
       "      <td>1.15407</td>\n",
       "      <td>1.16409</td>\n",
       "      <td>1.15112</td>\n",
       "      <td>421212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 21:00:00</th>\n",
       "      <td>1.15383</td>\n",
       "      <td>1.16599</td>\n",
       "      <td>1.16753</td>\n",
       "      <td>1.15176</td>\n",
       "      <td>1.15408</td>\n",
       "      <td>1.16673</td>\n",
       "      <td>1.16774</td>\n",
       "      <td>1.15199</td>\n",
       "      <td>441679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>1.16599</td>\n",
       "      <td>1.16890</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.16400</td>\n",
       "      <td>1.16673</td>\n",
       "      <td>1.16963</td>\n",
       "      <td>1.17254</td>\n",
       "      <td>1.16423</td>\n",
       "      <td>384090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01 21:00:00</th>\n",
       "      <td>1.16890</td>\n",
       "      <td>1.16561</td>\n",
       "      <td>1.17168</td>\n",
       "      <td>1.16158</td>\n",
       "      <td>1.16962</td>\n",
       "      <td>1.16624</td>\n",
       "      <td>1.17193</td>\n",
       "      <td>1.16180</td>\n",
       "      <td>314941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2018-05-16 21:00:00  1.18366   1.18052  1.18530  1.17620  1.18389   1.18092   \n",
       "2018-05-17 21:00:00  1.18052   1.17920  1.18363  1.17752  1.18092   1.17964   \n",
       "2018-05-18 21:00:00  1.17920   1.17673  1.18209  1.17486  1.17964   1.17754   \n",
       "2018-05-20 21:00:00  1.17630   1.17667  1.17785  1.17623  1.17698   1.17745   \n",
       "2018-05-21 21:00:00  1.17667   1.17900  1.17945  1.17155  1.17745   1.17930   \n",
       "2018-05-22 21:00:00  1.17900   1.17765  1.18287  1.17553  1.17930   1.17810   \n",
       "2018-05-23 21:00:00  1.17765   1.16944  1.17886  1.16747  1.17810   1.16980   \n",
       "2018-05-24 21:00:00  1.16944   1.17182  1.17494  1.16897  1.16979   1.17212   \n",
       "2018-05-25 21:00:00  1.17182   1.16469  1.17325  1.16447  1.17212   1.16529   \n",
       "2018-05-27 21:00:00  1.16534   1.16849  1.16868  1.16534  1.16596   1.16885   \n",
       "2018-05-28 21:00:00  1.16849   1.16231  1.17273  1.16063  1.16885   1.16260   \n",
       "2018-05-29 21:00:00  1.16231   1.15382  1.16385  1.15091  1.16260   1.15407   \n",
       "2018-05-30 21:00:00  1.15383   1.16599  1.16753  1.15176  1.15408   1.16673   \n",
       "2018-05-31 21:00:00  1.16599   1.16890  1.17233  1.16400  1.16673   1.16963   \n",
       "2018-06-01 21:00:00  1.16890   1.16561  1.17168  1.16158  1.16962   1.16624   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2018-05-16 21:00:00  1.18553  1.17644   398818  \n",
       "2018-05-17 21:00:00  1.18388  1.17776   308010  \n",
       "2018-05-18 21:00:00  1.18233  1.17508   212112  \n",
       "2018-05-20 21:00:00  1.17810  1.17698      168  \n",
       "2018-05-21 21:00:00  1.17968  1.17179   194031  \n",
       "2018-05-22 21:00:00  1.18310  1.17578   276164  \n",
       "2018-05-23 21:00:00  1.17909  1.16770   381970  \n",
       "2018-05-24 21:00:00  1.17515  1.16921   320736  \n",
       "2018-05-25 21:00:00  1.17349  1.16473   317164  \n",
       "2018-05-27 21:00:00  1.16907  1.16596      947  \n",
       "2018-05-28 21:00:00  1.17297  1.16084   244898  \n",
       "2018-05-29 21:00:00  1.16409  1.15112   421212  \n",
       "2018-05-30 21:00:00  1.16774  1.15199   441679  \n",
       "2018-05-31 21:00:00  1.17254  1.16423   384090  \n",
       "2018-06-01 21:00:00  1.17193  1.16180   314941  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = dt.datetime(2018, 5, 15)\n",
    "end = dt.datetime(2018, 6, 1)\n",
    "con.get_candles('EUR/USD', period='D1',\n",
    "                start=start, end=end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python ecosystem provides a number of alternatives to **visualize financial time series data**. The standard plotting library is `matplotlib` (see http://matplotlib.org) which is tighly integrated with `pandas` `DataFrame` objects, allowing for efficient visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `columns` parameter, one can specifiy which data columns are returned. Here, just one column is specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = con.get_candles('EUR/USD', period='m1',\n",
    "                       columns=['askopen'], number=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code visualizes the only financial time series in the `DataFrame` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y2lA8CNwMtBqGcRa4GwgAmKZ5j2EYHcB+oAFwDMP4GLDXNM0RwzCiwK3AndNu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\nQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6), lw=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specifying the `columns` parameter to be `asks` (`bids`) returns all columns related to ask (bid) prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = con.get_candles('EUR/USD', period='m1',\n",
    "                       columns=['asks'], number=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4m7kjzqba6eDZ3S9yTeYVjDCndInThWjJH2shf6wFl9vLSx/tY9XmEs6fNnJA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\n6UNJexmpgUlD9n1gQTyfHKrge48+TvXP/i+Ww4dQ+vmh8PVD4efX+2s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5frjJZKoB/gr4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W2wt7bS8OzTxN95Nwn33HvG3LGzNbT1YgkyERYSSGtfG29Vvcc3lt7vM4FzuVw89UYZN12eSaSX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\nCCHENPE/s33kn+e84P8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6), lw=0.8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}