IQ_API_ExploratoryAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ilton.aws@gmail.com\n"
     ]
    }
   ],
   "source": [
    "from iqoptionapi.stable_api import IQ_Option\n",
    "import time, json\n",
    "from datetime import datetime\n",
    "from dateutil import tz\n",
    "\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "import mplfinance as mpf\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "dev_env = True\n",
    "\n",
    "IQ_EMAIL = os.environ['IQ_EMAIL']\n",
    "IQ_PWD = os.environ['IQ_PWD']\n",
    "\n",
    "print(IQ_EMAIL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estabelecendo Conexão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conexão estabelecida!\n"
     ]
    }
   ],
   "source": [
    "API = IQ_Option(IQ_EMAIL, IQ_PWD)\n",
    "\n",
    "API.connect()\n",
    "API.change_balance('PRACTICE')  # PRACTICE / REAL\n",
    "\n",
    "con_tests = 0\n",
    "max_con_test = 10\n",
    "\n",
    "while True:\n",
    "    con_tests = con_tests + 1\n",
    "    if API.check_connect() is False:\n",
    "        print('Erro ao tentar se conectar!')\n",
    "        API.connect()\n",
    "    elif con_tests == max_con_test:\n",
    "        print('(error)Número máximo de tentativas excedidas!')\n",
    "        break\n",
    "    else:\n",
    "        print('Conexão estabelecida!')\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Requisitando opções em aberto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = API.get_all_open_time()\n",
    "payouts = API.get_all_profit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definindo Configuração da estratégia de operação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_payout = float(0.84)\n",
    "spend_bid = int(2)\n",
    "timeframe = 1\n",
    "index = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBPUSD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "CADCHF\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "EURNZD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GBPNZD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "AUDCHF\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "EURUSD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "USDCAD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "AUDJPY\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GBPJPY\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "EURAUD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "EURGBP\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "USDJPY\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "AUDCAD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "USDCHF\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GBPCHF\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GBPAUD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "AUDNZD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "USDNOK\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "AUDUSD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GBPCAD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "EURJPY\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "NZDUSD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "EURCAD\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "COKE\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "JPM\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "MCDON\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "YANDEX\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "MSFT\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "MORSTAN\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GS\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "BAIDU\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "TESLA\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "GOOGLE\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "ALIBABA\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "FACEBOOK\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "INTEL\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "NIKE\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "AMAZON\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "CITI\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "TWITTER\n",
      "defaultdict(<class 'dict'>, {'open': True})\n",
      "APPLE\n",
      "defaultdict(<class 'dict'>, {'open': True})\n"
     ]
    }
   ],
   "source": [
    "for pair in pairs['binary']:\n",
    "    if pairs['binary'][pair]['open'] is True:# and payout[par] >= payout_min:\n",
    "        print(pair)\n",
    "        print(pairs['binary'][pair])\n",
    "        \n",
    "#         bought, id = API.buy(spend_bid,pair,\"call\",timeframe)\n",
    "#         if bought:\n",
    "#             print(\"Ordem executada com sucesso!\")\n",
    "#         else:\n",
    "#             print(\"Não foi possível executar a ordem!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'dict'>, {'turbo': 0.9, 'binary': 0.6})\n"
     ]
    }
   ],
   "source": [
    "print(payouts['EURUSD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'dict'>, {'binary': 0.81})\n"
     ]
    }
   ],
   "source": [
    "print(payouts['NZDUSD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_ops = API.get_all_ACTIVES_OPCODE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial Information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ActiveId = active_ops['EURUSD'] #1\n",
    "fi = API.get_financial_information(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '',\n",
       " 'name': 'financial-information',\n",
       " 'msg': {'data': {'active': {'charts': {'dtd': {'change': 0.13476629136812646},\n",
       "     'm1': {'change': -1.8545400619502928},\n",
       "     'y1': {'change': 7.388511435281604},\n",
       "     'ytd': {'change': 4.892717271236193}},\n",
       "    'fininfo': {'base': {'name': 'Euro'},\n",
       "     'currency': {'name': 'US Dollar'},\n",
       "     'description': \"EUR/USD is an exchange rate that specifies how many USD (United States Dollar) can be converted into one EUR (Euro), therefore the base currency is EUR and the price currency is USD. If this rate declines, it means that USD appreciates relative to EUR and if this rate increases it means that the USD depreciates against the EUR. There are many macroeconomic factors/events (fundamentals) that affect EUR/USD exchange rate, which are usually common in both countries. Some of the most notable factors/events are GDP, Inflation or Consumer Price Index (CPI),  Interest Rates and other monetary policies applied by central banks. From the period since 2015 the exchange rate reached the lowest value of 1.04 in Dec of 2016 and the highest of 1.17 in Aug of 2017. Regarding the trade relationship between Eurozone and United States, the latter imports 10% of Eurozone's total exports, whereas Eurozone imports 19.2% of United States's total exports. The largest component of Eurozone's exports are machinery, nuclear reactor, boiler products and vehicles whereas United States's largest export components are machinery, nuclear reactor, boiler products and electrical/electronic equipment. Any significant shifts in the trade relationship between the two regions  and changes in the values of the aforementioned components are some of the factors that could have a material impact on the pair.\",\n",
       "     'type': 'major'},\n",
       "    'id': 1,\n",
       "    'index_fininfo': {},\n",
       "    'media': {'siteBackground': 'https://static.cdnpub.info/files/storage/public/5d/65/068c601b09g7e4c0h4.jpg'},\n",
       "    'name': 'EUR/USD',\n",
       "    'ticker': 'EURUSD'}}},\n",
       " 'status': 2000}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['request_id', 'name', 'msg', 'status'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': {'active': {'charts': {'dtd': {'change': 0.13476629136812646},\n",
       "    'm1': {'change': -1.8545400619502928},\n",
       "    'y1': {'change': 7.388511435281604},\n",
       "    'ytd': {'change': 4.892717271236193}},\n",
       "   'fininfo': {'base': {'name': 'Euro'},\n",
       "    'currency': {'name': 'US Dollar'},\n",
       "    'description': \"EUR/USD is an exchange rate that specifies how many USD (United States Dollar) can be converted into one EUR (Euro), therefore the base currency is EUR and the price currency is USD. If this rate declines, it means that USD appreciates relative to EUR and if this rate increases it means that the USD depreciates against the EUR. There are many macroeconomic factors/events (fundamentals) that affect EUR/USD exchange rate, which are usually common in both countries. Some of the most notable factors/events are GDP, Inflation or Consumer Price Index (CPI),  Interest Rates and other monetary policies applied by central banks. From the period since 2015 the exchange rate reached the lowest value of 1.04 in Dec of 2016 and the highest of 1.17 in Aug of 2017. Regarding the trade relationship between Eurozone and United States, the latter imports 10% of Eurozone's total exports, whereas Eurozone imports 19.2% of United States's total exports. The largest component of Eurozone's exports are machinery, nuclear reactor, boiler products and vehicles whereas United States's largest export components are machinery, nuclear reactor, boiler products and electrical/electronic equipment. Any significant shifts in the trade relationship between the two regions  and changes in the values of the aforementioned components are some of the factors that could have a material impact on the pair.\",\n",
       "    'type': 'major'},\n",
       "   'id': 1,\n",
       "   'index_fininfo': {},\n",
       "   'media': {'siteBackground': 'https://static.cdnpub.info/files/storage/public/5d/65/068c601b09g7e4c0h4.jpg'},\n",
       "   'name': 'EUR/USD',\n",
       "   'ticker': 'EURUSD'}}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi['msg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'active': {'charts': {'dtd': {'change': 0.13476629136812646},\n",
       "   'm1': {'change': -1.8545400619502928},\n",
       "   'y1': {'change': 7.388511435281604},\n",
       "   'ytd': {'change': 4.892717271236193}},\n",
       "  'fininfo': {'base': {'name': 'Euro'},\n",
       "   'currency': {'name': 'US Dollar'},\n",
       "   'description': \"EUR/USD is an exchange rate that specifies how many USD (United States Dollar) can be converted into one EUR (Euro), therefore the base currency is EUR and the price currency is USD. If this rate declines, it means that USD appreciates relative to EUR and if this rate increases it means that the USD depreciates against the EUR. There are many macroeconomic factors/events (fundamentals) that affect EUR/USD exchange rate, which are usually common in both countries. Some of the most notable factors/events are GDP, Inflation or Consumer Price Index (CPI),  Interest Rates and other monetary policies applied by central banks. From the period since 2015 the exchange rate reached the lowest value of 1.04 in Dec of 2016 and the highest of 1.17 in Aug of 2017. Regarding the trade relationship between Eurozone and United States, the latter imports 10% of Eurozone's total exports, whereas Eurozone imports 19.2% of United States's total exports. The largest component of Eurozone's exports are machinery, nuclear reactor, boiler products and vehicles whereas United States's largest export components are machinery, nuclear reactor, boiler products and electrical/electronic equipment. Any significant shifts in the trade relationship between the two regions  and changes in the values of the aforementioned components are some of the factors that could have a material impact on the pair.\",\n",
       "   'type': 'major'},\n",
       "  'id': 1,\n",
       "  'index_fininfo': {},\n",
       "  'media': {'siteBackground': 'https://static.cdnpub.info/files/storage/public/5d/65/068c601b09g7e4c0h4.jpg'},\n",
       "  'name': 'EUR/USD',\n",
       "  'ticker': 'EURUSD'}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi['msg']['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'charts': {'dtd': {'change': 0.13476629136812646},\n",
       "  'm1': {'change': -1.8545400619502928},\n",
       "  'y1': {'change': 7.388511435281604},\n",
       "  'ytd': {'change': 4.892717271236193}},\n",
       " 'fininfo': {'base': {'name': 'Euro'},\n",
       "  'currency': {'name': 'US Dollar'},\n",
       "  'description': \"EUR/USD is an exchange rate that specifies how many USD (United States Dollar) can be converted into one EUR (Euro), therefore the base currency is EUR and the price currency is USD. If this rate declines, it means that USD appreciates relative to EUR and if this rate increases it means that the USD depreciates against the EUR. There are many macroeconomic factors/events (fundamentals) that affect EUR/USD exchange rate, which are usually common in both countries. Some of the most notable factors/events are GDP, Inflation or Consumer Price Index (CPI),  Interest Rates and other monetary policies applied by central banks. From the period since 2015 the exchange rate reached the lowest value of 1.04 in Dec of 2016 and the highest of 1.17 in Aug of 2017. Regarding the trade relationship between Eurozone and United States, the latter imports 10% of Eurozone's total exports, whereas Eurozone imports 19.2% of United States's total exports. The largest component of Eurozone's exports are machinery, nuclear reactor, boiler products and vehicles whereas United States's largest export components are machinery, nuclear reactor, boiler products and electrical/electronic equipment. Any significant shifts in the trade relationship between the two regions  and changes in the values of the aforementioned components are some of the factors that could have a material impact on the pair.\",\n",
       "  'type': 'major'},\n",
       " 'id': 1,\n",
       " 'index_fininfo': {},\n",
       " 'media': {'siteBackground': 'https://static.cdnpub.info/files/storage/public/5d/65/068c601b09g7e4c0h4.jpg'},\n",
       " 'name': 'EUR/USD',\n",
       " 'ticker': 'EURUSD'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi['msg']['data']['active']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static.cdnpub.info/files/storage/public/5d/65/068c601b09g7e4c0h4.jpg\" width=\"100\" height=\"100\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= fi['msg']['data']['active']['media']['siteBackground'], width=100, height=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Financial Candles\n",
    "\n",
    "```python\n",
    "    goal=\"EURUSD\"\n",
    "    print(\"get candles\")\n",
    "    print(I_want_money.get_candles(goal,60,111,time.time()))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#interval:duration of candles (in seconds), 1 minute = 60\n",
    "#count:how many candles you want to get from now to past -> max=1000\n",
    "candles = API.get_candles(ACTIVES='EURUSD', interval=60, count=1000, endtime=time.time())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1089357,\n",
       " 'from': 1601500620,\n",
       " 'at': 1601500680024333233,\n",
       " 'to': 1601500680,\n",
       " 'open': 1.1723,\n",
       " 'close': 1.17233,\n",
       " 'min': 1.1723,\n",
       " 'max': 1.17233,\n",
       " 'volume': 3}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(candles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-09-30 21:18:00'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.utcfromtimestamp(candles[0]['to']).strftime('%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dct_struct = {'Date':[], 'Open':[], 'Close':[], 'High':[], 'Low':[], 'Volume':[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_timestamp(x, pattern='%Y-%m-%d %H:%M:%S'):\n",
    "    return datetime.utcfromtimestamp(x).strftime(pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for candle in candles:\n",
    "    dct_struct['Open'].append(candle['open'])\n",
    "    dct_struct['Close'].append(candle['close'])\n",
    "    dct_struct['High'].append(candle['max'])\n",
    "    dct_struct['Low'].append(candle['min'])\n",
    "    dct_struct['Volume'].append(candle['volume'])\n",
    "    dct_struct['Date'].append(convert_timestamp(candle['to']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-30 21:18:00</td>\n",
       "      <td>1.17230</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.17230</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-30 21:19:00</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.17235</td>\n",
       "      <td>1.17235</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date     Open    Close     High      Low  Volume\n",
       "0  2020-09-30 21:18:00  1.17230  1.17233  1.17233  1.17230       3\n",
       "1  2020-09-30 21:19:00  1.17233  1.17235  1.17235  1.17233       1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles_df = pd.DataFrame(dct_struct)\n",
    "candles_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-30 21:18:00</th>\n",
       "      <td>2020-09-30 21:18:00</td>\n",
       "      <td>1.17230</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.17230</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30 21:19:00</th>\n",
       "      <td>2020-09-30 21:19:00</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1.17235</td>\n",
       "      <td>1.17235</td>\n",
       "      <td>1.17233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Date     Open    Close     High      Low  \\\n",
       "Date                                                                           \n",
       "2020-09-30 21:18:00  2020-09-30 21:18:00  1.17230  1.17233  1.17233  1.17230   \n",
       "2020-09-30 21:19:00  2020-09-30 21:19:00  1.17233  1.17235  1.17235  1.17233   \n",
       "\n",
       "                     Volume  \n",
       "Date                         \n",
       "2020-09-30 21:18:00       3  \n",
       "2020-09-30 21:19:00       1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the index as date\n",
    "candles_df.index = pd.to_datetime(candles_df.Date)\n",
    "candles_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(candles_df,type='candle',mav=(7,12),show_nontrading=True,volume=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dt_pattern = '%Y-%#m-%#d'\n",
    "start_dt = convert_timestamp(candles[0]['to'], dataset_dt_pattern)\n",
    "end_dt = convert_timestamp(candles[-1]['to'], dataset_dt_pattern)\n",
    "\n",
    "candles_df.to_csv('datasets/candles_iqoption_' +start_dt+ '_' +end_dt+ '.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate Occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1089357,\n",
       " 'from': 1601500620,\n",
       " 'at': 1601500680024333233,\n",
       " 'to': 1601500680,\n",
       " 'open': 1.1723,\n",
       " 'close': 1.17233,\n",
       " 'min': 1.1723,\n",
       " 'max': 1.17233,\n",
       " 'volume': 3}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = []\n",
    "for candle in candles:\n",
    "    if candle['close'] > candle['open']:\n",
    "        variants.append('positive')\n",
    "    elif candle['close'] == candle['open']:\n",
    "        variants.append('neutro')\n",
    "    else:\n",
    "        variants.append('negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>variants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variants\n",
       "0  positive\n",
       "1  positive\n",
       "2    neutro\n",
       "3  positive\n",
       "4  positive"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf = pd.DataFrame()\n",
    "vdf['variants'] = variants\n",
    "vdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdf_gp = vdf.groupby([vdf['variants'].ne(vdf['variants'].shift()).cumsum(), 'variants']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='variants,variants'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vdf_gp.plot(figsize=(25,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf_gp.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     337\n",
       "2     136\n",
       "3      64\n",
       "4      20\n",
       "5      10\n",
       "6       5\n",
       "8       2\n",
       "16      1\n",
       "7       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf_gp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "576"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf_gp.value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-09-30 21:18:00'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_dt_chart = convert_timestamp(candles[0]['to'])\n",
    "start_dt_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-10-01 13:57:00'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_dt_chart = convert_timestamp(candles[-1]['to'])\n",
    "end_dt_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "            {'binary': defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "                         {'USDPLN': defaultdict(dict, {'open': False}),\n",
       "                          'USDHKD': defaultdict(dict, {'open': False}),\n",
       "                          'GBPUSD': defaultdict(dict, {'open': True}),\n",
       "                          'NZDUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'AUDCAD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'USDSGD': defaultdict(dict, {'open': False}),\n",
       "                          'USDCHF-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'CADCHF': defaultdict(dict, {'open': True}),\n",
       "                          'EURNZD': defaultdict(dict, {'open': True}),\n",
       "                          'GBPNZD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDCHF': defaultdict(dict, {'open': True}),\n",
       "                          'EURUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'EURUSD': defaultdict(dict, {'open': True}),\n",
       "                          'USDRUB': defaultdict(dict, {'open': False}),\n",
       "                          'GBPUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'USDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'GBPJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURAUD': defaultdict(dict, {'open': True}),\n",
       "                          'EURCHF': defaultdict(dict, {'open': False}),\n",
       "                          'USDTRY': defaultdict(dict, {'open': False}),\n",
       "                          'EURGBP': defaultdict(dict, {'open': True}),\n",
       "                          'USDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'AUDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'USDCHF': defaultdict(dict, {'open': True}),\n",
       "                          'EURGBP-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'GBPCHF': defaultdict(dict, {'open': True}),\n",
       "                          'USDZAR': defaultdict(dict, {'open': False}),\n",
       "                          'GBPAUD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDNZD': defaultdict(dict, {'open': True}),\n",
       "                          'USDNOK': defaultdict(dict, {'open': True}),\n",
       "                          'AUDUSD': defaultdict(dict, {'open': True}),\n",
       "                          'GBPCAD': defaultdict(dict, {'open': True}),\n",
       "                          'USDINR': defaultdict(dict, {'open': False}),\n",
       "                          'USDBRL': defaultdict(dict, {'open': False}),\n",
       "                          'EURJPY': defaultdict(dict, {'open': True}),\n",
       "                          'NZDUSD': defaultdict(dict, {'open': True}),\n",
       "                          'EURCAD': defaultdict(dict, {'open': True}),\n",
       "                          'COKE': defaultdict(dict, {'open': True}),\n",
       "                          'JPM': defaultdict(dict, {'open': True}),\n",
       "                          'MCDON': defaultdict(dict, {'open': True}),\n",
       "                          'YANDEX': defaultdict(dict, {'open': True}),\n",
       "                          'MSFT': defaultdict(dict, {'open': True}),\n",
       "                          'MORSTAN': defaultdict(dict, {'open': True}),\n",
       "                          'GS': defaultdict(dict, {'open': True}),\n",
       "                          'BAIDU': defaultdict(dict, {'open': True}),\n",
       "                          'TESLA': defaultdict(dict, {'open': True}),\n",
       "                          'GOOGLE': defaultdict(dict, {'open': True}),\n",
       "                          'ALIBABA': defaultdict(dict, {'open': True}),\n",
       "                          'FACEBOOK': defaultdict(dict, {'open': True}),\n",
       "                          'INTEL': defaultdict(dict, {'open': True}),\n",
       "                          'NIKE': defaultdict(dict, {'open': True}),\n",
       "                          'AMAZON': defaultdict(dict, {'open': True}),\n",
       "                          'CITI': defaultdict(dict, {'open': True}),\n",
       "                          'TWITTER': defaultdict(dict, {'open': True}),\n",
       "                          'APPLE': defaultdict(dict, {'open': True})}),\n",
       "             'turbo': defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "                         {'GBPUSD': defaultdict(dict, {'open': True}),\n",
       "                          'NZDUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'AUDCAD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'USDCHF-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'CADCHF': defaultdict(dict, {'open': False}),\n",
       "                          'EURNZD': defaultdict(dict, {'open': False}),\n",
       "                          'GBPNZD': defaultdict(dict, {'open': True}),\n",
       "                          'EURJPY-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'CADJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'EURUSD': defaultdict(dict, {'open': True}),\n",
       "                          'GBPUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'USDCAD': defaultdict(dict, {'open': False}),\n",
       "                          'AUDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'GBPJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURAUD': defaultdict(dict, {'open': False}),\n",
       "                          'EURCHF': defaultdict(dict, {'open': False}),\n",
       "                          'EURGBP': defaultdict(dict, {'open': True}),\n",
       "                          'USDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'AUDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'USDCHF': defaultdict(dict, {'open': True}),\n",
       "                          'EURGBP-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'GBPCHF': defaultdict(dict, {'open': True}),\n",
       "                          'GBPAUD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDUSD': defaultdict(dict, {'open': True}),\n",
       "                          'GBPCAD': defaultdict(dict, {'open': True}),\n",
       "                          'CHFJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURJPY': defaultdict(dict, {'open': False}),\n",
       "                          'NZDUSD': defaultdict(dict, {'open': False}),\n",
       "                          'EURCAD': defaultdict(dict, {'open': False})}),\n",
       "             'digital': defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "                         {'EURUSD': defaultdict(dict, {'open': True}),\n",
       "                          'EURGBP': defaultdict(dict, {'open': True}),\n",
       "                          'GBPJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURJPY': defaultdict(dict, {'open': True}),\n",
       "                          'GBPUSD': defaultdict(dict, {'open': True}),\n",
       "                          'USDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'AUDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'EURUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'EURGBP-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'EURJPY-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'NZDUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'GBPUSD-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'GBPJPY-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'USDJPY-OTC': defaultdict(dict, {'open': False}),\n",
       "                          'AUDUSD': defaultdict(dict, {'open': True}),\n",
       "                          'USDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'GBPCAD': defaultdict(dict, {'open': True}),\n",
       "                          'GBPCHF': defaultdict(dict, {'open': True})}),\n",
       "             'cfd': defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "                         {'FXI': defaultdict(dict, {'open': True}),\n",
       "                          'IBM:US': defaultdict(dict, {'open': True}),\n",
       "                          'IDXX': defaultdict(dict, {'open': True}),\n",
       "                          'AIRP-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'ABBV:US': defaultdict(dict, {'open': True}),\n",
       "                          'CXO:US': defaultdict(dict, {'open': True}),\n",
       "                          'VZ:US': defaultdict(dict, {'open': True}),\n",
       "                          'ULVRL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'LMT:US': defaultdict(dict, {'open': True}),\n",
       "                          'VIAC': defaultdict(dict, {'open': True}),\n",
       "                          'ETR:US': defaultdict(dict, {'open': True}),\n",
       "                          'FCAM-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'NVDA:US': defaultdict(dict, {'open': True}),\n",
       "                          'EOAND-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'CTXS:US': defaultdict(dict, {'open': True}),\n",
       "                          'BBVAE-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'PM:US': defaultdict(dict, {'open': True}),\n",
       "                          'ED:US': defaultdict(dict, {'open': True}),\n",
       "                          'NFLX:US': defaultdict(dict, {'open': True}),\n",
       "                          'ALVD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'SLB:US': defaultdict(dict, {'open': True}),\n",
       "                          'XLY': defaultdict(dict, {'open': True}),\n",
       "                          'BHPl-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'MJ': defaultdict(dict, {'open': True}),\n",
       "                          'ATVI:US': defaultdict(dict, {'open': True}),\n",
       "                          'HAS:US': defaultdict(dict, {'open': True}),\n",
       "                          'BMY:US': defaultdict(dict, {'open': True}),\n",
       "                          'EQR:US': defaultdict(dict, {'open': True}),\n",
       "                          'SU': defaultdict(dict, {'open': True}),\n",
       "                          'CTAS:US': defaultdict(dict, {'open': True}),\n",
       "                          'MORSTAN': defaultdict(dict, {'open': True}),\n",
       "                          'DVN:US': defaultdict(dict, {'open': True}),\n",
       "                          'XME': defaultdict(dict, {'open': True}),\n",
       "                          'FDJP': defaultdict(dict, {'open': True}),\n",
       "                          'BA:US': defaultdict(dict, {'open': True}),\n",
       "                          'GWPH': defaultdict(dict, {'open': True}),\n",
       "                          'XLK': defaultdict(dict, {'open': True}),\n",
       "                          'DBX': defaultdict(dict, {'open': True}),\n",
       "                          'FIS:US': defaultdict(dict, {'open': True}),\n",
       "                          'MMM:US': defaultdict(dict, {'open': True}),\n",
       "                          'DAID-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'BAIDU': defaultdict(dict, {'open': True}),\n",
       "                          'WMT:US': defaultdict(dict, {'open': True}),\n",
       "                          'XOM:US': defaultdict(dict, {'open': True}),\n",
       "                          'RTN:US': defaultdict(dict, {'open': True}),\n",
       "                          'EBAY:US': defaultdict(dict, {'open': True}),\n",
       "                          'DISCA:US': defaultdict(dict, {'open': True}),\n",
       "                          'MSFT': defaultdict(dict, {'open': True}),\n",
       "                          'CVX:US': defaultdict(dict, {'open': True}),\n",
       "                          'WORK': defaultdict(dict, {'open': True}),\n",
       "                          'LLOYL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'TLT': defaultdict(dict, {'open': True}),\n",
       "                          'ADSD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'CMCSA:US': defaultdict(dict, {'open': True}),\n",
       "                          'DHR:US': defaultdict(dict, {'open': True}),\n",
       "                          'ALGN': defaultdict(dict, {'open': True}),\n",
       "                          'VOW3D-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'FISV:US': defaultdict(dict, {'open': True}),\n",
       "                          'ORP-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'DHI:US': defaultdict(dict, {'open': True}),\n",
       "                          'DTED-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'GDX': defaultdict(dict, {'open': True}),\n",
       "                          'XAUUSD': defaultdict(dict, {'open': True}),\n",
       "                          'EXR:US': defaultdict(dict, {'open': True}),\n",
       "                          'FERRARI': defaultdict(dict, {'open': True}),\n",
       "                          'IYR': defaultdict(dict, {'open': True}),\n",
       "                          'DOV:US': defaultdict(dict, {'open': True}),\n",
       "                          'HON:US': defaultdict(dict, {'open': True}),\n",
       "                          'MKSL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'MANU': defaultdict(dict, {'open': True}),\n",
       "                          'SANE-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'ABT:US': defaultdict(dict, {'open': True}),\n",
       "                          'UBER': defaultdict(dict, {'open': True}),\n",
       "                          'FMC:US': defaultdict(dict, {'open': True}),\n",
       "                          'SNAP': defaultdict(dict, {'open': True}),\n",
       "                          'TKAD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'FBHS:US': defaultdict(dict, {'open': True}),\n",
       "                          'UKOUSD': defaultdict(dict, {'open': True}),\n",
       "                          'COKE': defaultdict(dict, {'open': True}),\n",
       "                          'ESS:US': defaultdict(dict, {'open': True}),\n",
       "                          'MRWL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'GILD:US': defaultdict(dict, {'open': True}),\n",
       "                          'MA:US': defaultdict(dict, {'open': True}),\n",
       "                          'LYFT': defaultdict(dict, {'open': True}),\n",
       "                          'DBKD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'TMUS': defaultdict(dict, {'open': True}),\n",
       "                          'MCDON': defaultdict(dict, {'open': True}),\n",
       "                          'INTEL': defaultdict(dict, {'open': True}),\n",
       "                          'FITB:US': defaultdict(dict, {'open': True}),\n",
       "                          'BBY:US': defaultdict(dict, {'open': True}),\n",
       "                          'EQT:US': defaultdict(dict, {'open': True}),\n",
       "                          'BPL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'ETFC:US': defaultdict(dict, {'open': True}),\n",
       "                          'SYK:US': defaultdict(dict, {'open': True}),\n",
       "                          'PSTM-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'DIA': defaultdict(dict, {'open': True}),\n",
       "                          'BMWD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'PYPL:US': defaultdict(dict, {'open': True}),\n",
       "                          'AA:US': defaultdict(dict, {'open': True}),\n",
       "                          'CVS:US': defaultdict(dict, {'open': True}),\n",
       "                          'IWM': defaultdict(dict, {'open': True}),\n",
       "                          'CRON': defaultdict(dict, {'open': True}),\n",
       "                          'WBA:US': defaultdict(dict, {'open': True}),\n",
       "                          'ANSS': defaultdict(dict, {'open': True}),\n",
       "                          'XLE': defaultdict(dict, {'open': True}),\n",
       "                          'ECL:US': defaultdict(dict, {'open': True}),\n",
       "                          'SBUX:US': defaultdict(dict, {'open': True}),\n",
       "                          'HEIOA-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'PINS': defaultdict(dict, {'open': True}),\n",
       "                          'DGX:US': defaultdict(dict, {'open': True}),\n",
       "                          'EZJL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'AMD': defaultdict(dict, {'open': True}),\n",
       "                          'CL:US': defaultdict(dict, {'open': True}),\n",
       "                          'CRM:US': defaultdict(dict, {'open': True}),\n",
       "                          'QQQ': defaultdict(dict, {'open': True}),\n",
       "                          'DTE:US': defaultdict(dict, {'open': True}),\n",
       "                          'TSCOL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'MELI': defaultdict(dict, {'open': True}),\n",
       "                          'FSLR:US': defaultdict(dict, {'open': True}),\n",
       "                          'PFE:US': defaultdict(dict, {'open': True}),\n",
       "                          'EMN:US': defaultdict(dict, {'open': True}),\n",
       "                          'JPM': defaultdict(dict, {'open': True}),\n",
       "                          'QID': defaultdict(dict, {'open': True}),\n",
       "                          'GOOGLE': defaultdict(dict, {'open': True}),\n",
       "                          'HSBAL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'DVA:US': defaultdict(dict, {'open': True}),\n",
       "                          'XLU': defaultdict(dict, {'open': True}),\n",
       "                          'PIRCM-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'BUD': defaultdict(dict, {'open': True}),\n",
       "                          'FRT:US': defaultdict(dict, {'open': True}),\n",
       "                          'CBKD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'AMGN:US': defaultdict(dict, {'open': True}),\n",
       "                          'XAGUSD': defaultdict(dict, {'open': True}),\n",
       "                          'RMD': defaultdict(dict, {'open': True}),\n",
       "                          'EFX:US': defaultdict(dict, {'open': True}),\n",
       "                          'DLR:US': defaultdict(dict, {'open': True}),\n",
       "                          'ADBE:US': defaultdict(dict, {'open': True}),\n",
       "                          'ADSK:US': defaultdict(dict, {'open': True}),\n",
       "                          'BARCL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'CGC': defaultdict(dict, {'open': True}),\n",
       "                          'SPOT': defaultdict(dict, {'open': True}),\n",
       "                          'SQQQ': defaultdict(dict, {'open': True}),\n",
       "                          'STANL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'ALIBABA': defaultdict(dict, {'open': True}),\n",
       "                          'TITM-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'FCX:US': defaultdict(dict, {'open': True}),\n",
       "                          'ZM': defaultdict(dict, {'open': True}),\n",
       "                          'DAL:US': defaultdict(dict, {'open': True}),\n",
       "                          'TFC': defaultdict(dict, {'open': True}),\n",
       "                          'XLB': defaultdict(dict, {'open': True}),\n",
       "                          'AMAZON': defaultdict(dict, {'open': True}),\n",
       "                          'BYND': defaultdict(dict, {'open': True}),\n",
       "                          'SMH': defaultdict(dict, {'open': True}),\n",
       "                          'SPY': defaultdict(dict, {'open': True}),\n",
       "                          'CAT:US': defaultdict(dict, {'open': True}),\n",
       "                          'COP:US': defaultdict(dict, {'open': True}),\n",
       "                          'FLS:US': defaultdict(dict, {'open': True}),\n",
       "                          'GS': defaultdict(dict, {'open': True}),\n",
       "                          'EIX:US': defaultdict(dict, {'open': True}),\n",
       "                          'USOUSD': defaultdict(dict, {'open': True}),\n",
       "                          'EWW': defaultdict(dict, {'open': True}),\n",
       "                          'GE': defaultdict(dict, {'open': True}),\n",
       "                          'MU:US': defaultdict(dict, {'open': True}),\n",
       "                          'TEFE-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'PG:US': defaultdict(dict, {'open': True}),\n",
       "                          'ACN:US': defaultdict(dict, {'open': True}),\n",
       "                          'COST:US': defaultdict(dict, {'open': True}),\n",
       "                          'ENIM-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'MDT:US': defaultdict(dict, {'open': True}),\n",
       "                          'EEM': defaultdict(dict, {'open': True}),\n",
       "                          'EXPD:US': defaultdict(dict, {'open': True}),\n",
       "                          'FACEBOOK': defaultdict(dict, {'open': True}),\n",
       "                          'TESLA': defaultdict(dict, {'open': True}),\n",
       "                          'MRKD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'FE:US': defaultdict(dict, {'open': True}),\n",
       "                          'MO:US': defaultdict(dict, {'open': True}),\n",
       "                          'IEMG': defaultdict(dict, {'open': True}),\n",
       "                          'BAYND-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'UCGM-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'TLRY': defaultdict(dict, {'open': True}),\n",
       "                          'COND-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'QCOM:US': defaultdict(dict, {'open': True}),\n",
       "                          'VODL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'V:US': defaultdict(dict, {'open': True}),\n",
       "                          'T:US': defaultdict(dict, {'open': True}),\n",
       "                          'TFX': defaultdict(dict, {'open': True}),\n",
       "                          'TWITTER': defaultdict(dict, {'open': True}),\n",
       "                          'RBSL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'XOP': defaultdict(dict, {'open': True}),\n",
       "                          'SDS': defaultdict(dict, {'open': True}),\n",
       "                          'DPWD-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'GM': defaultdict(dict, {'open': True}),\n",
       "                          'BAC:US': defaultdict(dict, {'open': True}),\n",
       "                          'NIKE': defaultdict(dict, {'open': True}),\n",
       "                          'DRI:US': defaultdict(dict, {'open': True}),\n",
       "                          'DFS:US': defaultdict(dict, {'open': True}),\n",
       "                          'EMR:US': defaultdict(dict, {'open': True}),\n",
       "                          'AAP:US': defaultdict(dict, {'open': True}),\n",
       "                          'JUVEM': defaultdict(dict, {'open': True}),\n",
       "                          'DRE': defaultdict(dict, {'open': True}),\n",
       "                          'RRL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'DIS:US': defaultdict(dict, {'open': True}),\n",
       "                          'CITI': defaultdict(dict, {'open': True}),\n",
       "                          'CISCO': defaultdict(dict, {'open': True}),\n",
       "                          'APPLE': defaultdict(dict, {'open': True}),\n",
       "                          'EWJ': defaultdict(dict, {'open': True}),\n",
       "                          'FFIV:US': defaultdict(dict, {'open': True}),\n",
       "                          'KHC:US': defaultdict(dict, {'open': True}),\n",
       "                          'BATSL-CHIX': defaultdict(dict, {'open': True}),\n",
       "                          'ENELM-CHIX': defaultdict(dict, {'open': True})}),\n",
       "             'forex': defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "                         {'EURSGD': defaultdict(dict, {'open': True}),\n",
       "                          'EURJPY': defaultdict(dict, {'open': True}),\n",
       "                          'CADCHF': defaultdict(dict, {'open': True}),\n",
       "                          'EURSEK': defaultdict(dict, {'open': True}),\n",
       "                          'USDDKK': defaultdict(dict, {'open': True}),\n",
       "                          'NZDUSD': defaultdict(dict, {'open': True}),\n",
       "                          'EURCHF': defaultdict(dict, {'open': True}),\n",
       "                          'USDTHB': defaultdict(dict, {'open': True}),\n",
       "                          'AUDUSD': defaultdict(dict, {'open': True}),\n",
       "                          'USDCHF': defaultdict(dict, {'open': True}),\n",
       "                          'SEKJPY': defaultdict(dict, {'open': True}),\n",
       "                          'AUDNZD': defaultdict(dict, {'open': True}),\n",
       "                          'GBPJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURAUD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDCHF': defaultdict(dict, {'open': True}),\n",
       "                          'AUDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'NZDCHF': defaultdict(dict, {'open': True}),\n",
       "                          'USDSEK': defaultdict(dict, {'open': True}),\n",
       "                          'EURDKK': defaultdict(dict, {'open': True}),\n",
       "                          'EURGBP': defaultdict(dict, {'open': True}),\n",
       "                          'EURCAD': defaultdict(dict, {'open': True}),\n",
       "                          'USDCZK': defaultdict(dict, {'open': True}),\n",
       "                          'EURNZD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDSGD': defaultdict(dict, {'open': True}),\n",
       "                          'NZDCAD': defaultdict(dict, {'open': True}),\n",
       "                          'CHFSGD': defaultdict(dict, {'open': True}),\n",
       "                          'EURHUF': defaultdict(dict, {'open': True}),\n",
       "                          'GBPCAD': defaultdict(dict, {'open': True}),\n",
       "                          'USDTRY': defaultdict(dict, {'open': True}),\n",
       "                          'USDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'EURUSD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDSEK': defaultdict(dict, {'open': True}),\n",
       "                          'USDPLN': defaultdict(dict, {'open': True}),\n",
       "                          'USDCNH': defaultdict(dict, {'open': True}),\n",
       "                          'USDHUF': defaultdict(dict, {'open': True}),\n",
       "                          'CHFJPY': defaultdict(dict, {'open': True}),\n",
       "                          'GBPILS': defaultdict(dict, {'open': True}),\n",
       "                          'NZDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'CADJPY': defaultdict(dict, {'open': True}),\n",
       "                          'GBPUSD': defaultdict(dict, {'open': True}),\n",
       "                          'CADPLN': defaultdict(dict, {'open': True}),\n",
       "                          'NZDSGD': defaultdict(dict, {'open': True}),\n",
       "                          'AUDJPY': defaultdict(dict, {'open': True}),\n",
       "                          'USDCAD': defaultdict(dict, {'open': True})}),\n",
       "             'crypto': defaultdict(<function iqoptionapi.stable_api.nested_dict.<locals>.<lambda>()>,\n",
       "                         {'ETHUSD': defaultdict(dict, {'open': True}),\n",
       "                          'IOTUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'EOSUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'QTMUSD': defaultdict(dict, {'open': True}),\n",
       "                          'EOSUSD': defaultdict(dict, {'open': True}),\n",
       "                          'BCHUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'ATOMUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'BTCUSD': defaultdict(dict, {'open': True}),\n",
       "                          'TRXUSD': defaultdict(dict, {'open': True}),\n",
       "                          'ONTUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'XRPUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'XLMUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'ETHUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'ZECUSD': defaultdict(dict, {'open': True}),\n",
       "                          'LTCUSD': defaultdict(dict, {'open': True}),\n",
       "                          'BCHUSD': defaultdict(dict, {'open': True}),\n",
       "                          'NEOUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'BTCUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'OMGUSD': defaultdict(dict, {'open': True}),\n",
       "                          'TRXUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'ADAUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'DSHUSD': defaultdict(dict, {'open': True}),\n",
       "                          'LTCUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'XRPUSD': defaultdict(dict, {'open': True}),\n",
       "                          'BNBUSD-L': defaultdict(dict, {'open': True}),\n",
       "                          'ETCUSD': defaultdict(dict, {'open': True})})})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs"
   ]
  }
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