FX Spiders

FX-Spider-01-Brokers.ipynb

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    "%load_ext nb_black"
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"import re\\nimport pandas as pd\";\n",
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   "source": [
    "import re\n",
    "import pandas as pd"
   ]
  },
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   "execution_count": 3,
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     "start_time": "2020-08-17T03:28:00.686831Z"
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"from selenium import webdriver\";\n",
       "                var nbb_formatted_code = \"from selenium import webdriver\";\n",
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   "source": [
    "from selenium import webdriver"
   ]
  },
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   "execution_count": 4,
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"str_date = pd.to_datetime(\\\"today\\\").strftime(\\\"%Y%m%d\\\")\";\n",
       "                var nbb_formatted_code = \"str_date = pd.to_datetime(\\\"today\\\").strftime(\\\"%Y%m%d\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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    "str_date = pd.to_datetime(\"today\").strftime(\"%Y%m%d\")"
   ]
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       "\n",
       "            setTimeout(function() {\n",
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       "                var nbb_unformatted_code = \"str_time = pd.to_datetime(\\\"today\\\").strftime(\\\"%H%M\\\")\";\n",
       "                var nbb_formatted_code = \"str_time = pd.to_datetime(\\\"today\\\").strftime(\\\"%H%M\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "            "
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   "source": [
    "str_time = pd.to_datetime(\"today\").strftime(\"%H%M\")"
   ]
  },
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   "execution_count": 6,
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    "ExecuteTime": {
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     "start_time": "2020-08-17T03:28:00.799830Z"
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"db_fx = dict()\\ndb_fx[str_date] = dict()\\ndb_fx[str_date][str_time] = dict()\";\n",
       "                var nbb_formatted_code = \"db_fx = dict()\\ndb_fx[str_date] = dict()\\ndb_fx[str_date][str_time] = dict()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "db_fx = dict()\n",
    "db_fx[str_date] = dict()\n",
    "db_fx[str_date][str_time] = dict()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:00.827830Z",
     "start_time": "2020-08-17T03:28:00.813833Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"var_regex = r\\\"[1-9]\\\\d*\\\\.[1-9]\\\\d*\\\"\";\n",
       "                var nbb_formatted_code = \"var_regex = r\\\"[1-9]\\\\d*\\\\.[1-9]\\\\d*\\\"\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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    "var_regex = r\"[1-9]\\d*\\.[1-9]\\d*\""
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   "source": [
    "## Environment variables"
   ]
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     "end_time": "2020-08-17T03:28:00.842830Z",
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    "var_headers = {\n",
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       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"CHROMEDRIVER_PATH = \\\"driver/chromedriver.exe\\\"\";\n",
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       "            "
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   "source": [
    "CHROMEDRIVER_PATH = \"driver/chromedriver.exe\""
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:00.873830Z",
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"chrome_options = webdriver.ChromeOptions()\\nchrome_options.headless = True\";\n",
       "                var nbb_formatted_code = \"chrome_options = webdriver.ChromeOptions()\\nchrome_options.headless = True\";\n",
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       "            "
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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   "source": [
    "chrome_options = webdriver.ChromeOptions()\n",
    "chrome_options.headless = True"
   ]
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   "execution_count": 11,
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     "end_time": "2020-08-17T03:28:03.945283Z",
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"driver = webdriver.Chrome(executable_path=CHROMEDRIVER_PATH, options=chrome_options)\";\n",
       "                var nbb_formatted_code = \"driver = webdriver.Chrome(executable_path=CHROMEDRIVER_PATH, options=chrome_options)\";\n",
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   "execution_count": 12,
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     "end_time": "2020-08-17T03:28:03.960310Z",
     "start_time": "2020-08-17T03:28:03.948284Z"
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"var_urls = dict(\\n    CambioSeguro=\\\"https://cambioseguro.com/\\\",\\n    CambistaInka=\\\"https://www.cambistainka.com/admin/obtenertipocambio.php\\\",\\n    DecisionCapital=\\\"https://www.decision-capital.com/fx/\\\",\\n    JetPeru=\\\"http://www.jetperu.com.pe/\\\",\\n    Kambista=\\\"https://kambista.com/\\\",\\n    Rextie=\\\"https://app.rextie.com/api/v1/fxrates/rate/?origin=home\\\",\\n    WesternUnion=\\\"https://www.westernunionperu.pe/cambiodemoneda\\\",\\n)\";\n",
       "                var nbb_formatted_code = \"var_urls = dict(\\n    CambioSeguro=\\\"https://cambioseguro.com/\\\",\\n    CambistaInka=\\\"https://www.cambistainka.com/admin/obtenertipocambio.php\\\",\\n    DecisionCapital=\\\"https://www.decision-capital.com/fx/\\\",\\n    JetPeru=\\\"http://www.jetperu.com.pe/\\\",\\n    Kambista=\\\"https://kambista.com/\\\",\\n    Rextie=\\\"https://app.rextie.com/api/v1/fxrates/rate/?origin=home\\\",\\n    WesternUnion=\\\"https://www.westernunionperu.pe/cambiodemoneda\\\",\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
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       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "var_urls = dict(\n",
    "    CambioSeguro=\"https://cambioseguro.com/\",\n",
    "    CambistaInka=\"https://www.cambistainka.com/admin/obtenertipocambio.php\",\n",
    "    DecisionCapital=\"https://www.decision-capital.com/fx/\",\n",
    "    JetPeru=\"http://www.jetperu.com.pe/\",\n",
    "    Kambista=\"https://kambista.com/\",\n",
    "    Rextie=\"https://app.rextie.com/api/v1/fxrates/rate/?origin=home\",\n",
    "    WesternUnion=\"https://www.westernunionperu.pe/cambiodemoneda\",\n",
    ")"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions for brokers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cambio Seguro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:03.992395Z",
     "start_time": "2020-08-17T03:28:03.962281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"def get_fx_cambioseguro(str_broker, dict_head, str_regex):\\n    # Libraries\\n    import re\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Extract bid and ask\\n    bid = soup.select(\\\".info-tasas\\\")[0].find_all(\\\"span\\\")[0].text.strip()\\n    ask = soup.select(\\\".info-tasas\\\")[0].find_all(\\\"span\\\")[1].text.strip()\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": re.findall(str_regex, bid)[0],\\n            \\\"ask\\\": re.findall(str_regex, ask)[0],\\n        }\\n    }\";\n",
       "                var nbb_formatted_code = \"def get_fx_cambioseguro(str_broker, dict_head, str_regex):\\n    # Libraries\\n    import re\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Extract bid and ask\\n    bid = soup.select(\\\".info-tasas\\\")[0].find_all(\\\"span\\\")[0].text.strip()\\n    ask = soup.select(\\\".info-tasas\\\")[0].find_all(\\\"span\\\")[1].text.strip()\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": re.findall(str_regex, bid)[0],\\n            \\\"ask\\\": re.findall(str_regex, ask)[0],\\n        }\\n    }\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_cambioseguro(str_broker, dict_head, str_regex):\n",
    "    # Libraries\n",
    "    import re\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Get request\n",
    "    s = requests.Session()\n",
    "    s.headers = dict_head\n",
    "    res = s.get(var_urls[str_broker])\n",
    "    if res.status_code != 200:\n",
    "        return dict(broker=str_broker, bid=None, ask=None)\n",
    "    # Parse html\n",
    "    soup = BeautifulSoup(res.text, \"html.parser\")\n",
    "    # Extract bid and ask\n",
    "    bid = soup.select(\".info-tasas\")[0].find_all(\"span\")[0].text.strip()\n",
    "    ask = soup.select(\".info-tasas\")[0].find_all(\"span\")[1].text.strip()\n",
    "    # Extract FX rate\n",
    "    return {\n",
    "        str_broker: {\n",
    "            \"bid\": re.findall(str_regex, bid)[0],\n",
    "            \"ask\": re.findall(str_regex, ask)[0],\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:04.853097Z",
     "start_time": "2020-08-17T03:28:03.993394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(\\n    get_fx_cambioseguro(\\\"CambioSeguro\\\", var_headers, var_regex)\\n)\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(\\n    get_fx_cambioseguro(\\\"CambioSeguro\\\", var_headers, var_regex)\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(\n",
    "    get_fx_cambioseguro(\"CambioSeguro\", var_headers, var_regex)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:04.868097Z",
     "start_time": "2020-08-17T03:28:04.855067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'}}}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cambista Inka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:40.094342Z",
     "start_time": "2020-08-17T03:28:40.063337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"def get_fx_cambioseguro(str_broker, dict_head):\\n    # Libraries\\n    import json\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    f_dict_payload = {\\n        \\\"cTipoOperacion\\\": \\\"01\\\",\\n        \\\"nMontoDolares\\\": \\\"0\\\",\\n        \\\"bEsInicial\\\": \\\"1\\\",\\n        \\\"bEsTCPref\\\": \\\"0\\\",\\n    }\\n    res = s.post(var_urls[str_broker], data=f_dict_payload, headers=s.headers)\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Parse to JSON\\n    f_dict_data = json.loads(str(soup))\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": f_dict_data[\\\"data\\\"][\\\"tipocambio\\\"][\\\"0\\\"][\\\"nTCCompra\\\"],\\n            \\\"ask\\\": f_dict_data[\\\"data\\\"][\\\"tipocambio\\\"][\\\"0\\\"][\\\"nTCVenta\\\"],\\n        }\\n    }\";\n",
       "                var nbb_formatted_code = \"def get_fx_cambioseguro(str_broker, dict_head):\\n    # Libraries\\n    import json\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    f_dict_payload = {\\n        \\\"cTipoOperacion\\\": \\\"01\\\",\\n        \\\"nMontoDolares\\\": \\\"0\\\",\\n        \\\"bEsInicial\\\": \\\"1\\\",\\n        \\\"bEsTCPref\\\": \\\"0\\\",\\n    }\\n    res = s.post(var_urls[str_broker], data=f_dict_payload, headers=s.headers)\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Parse to JSON\\n    f_dict_data = json.loads(str(soup))\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": f_dict_data[\\\"data\\\"][\\\"tipocambio\\\"][\\\"0\\\"][\\\"nTCCompra\\\"],\\n            \\\"ask\\\": f_dict_data[\\\"data\\\"][\\\"tipocambio\\\"][\\\"0\\\"][\\\"nTCVenta\\\"],\\n        }\\n    }\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_cambistainka(str_broker, dict_head):\n",
    "    # Libraries\n",
    "    import json\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Get request\n",
    "    s = requests.Session()\n",
    "    s.headers = dict_head\n",
    "    f_dict_payload = {\n",
    "        \"cTipoOperacion\": \"01\",\n",
    "        \"nMontoDolares\": \"0\",\n",
    "        \"bEsInicial\": \"1\",\n",
    "        \"bEsTCPref\": \"0\",\n",
    "    }\n",
    "    res = s.post(var_urls[str_broker], data=f_dict_payload, headers=s.headers)\n",
    "    if res.status_code != 200:\n",
    "        return dict(broker=str_broker, bid=None, ask=None)\n",
    "    # Parse html\n",
    "    soup = BeautifulSoup(res.text, \"html.parser\")\n",
    "    # Parse to JSON\n",
    "    f_dict_data = json.loads(str(soup))\n",
    "    # Extract FX rate\n",
    "    return {\n",
    "        str_broker: {\n",
    "            \"bid\": f_dict_data[\"data\"][\"tipocambio\"][\"0\"][\"nTCCompra\"],\n",
    "            \"ask\": f_dict_data[\"data\"][\"tipocambio\"][\"0\"][\"nTCVenta\"],\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:46.295620Z",
     "start_time": "2020-08-17T03:28:45.907826Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(get_fx_cambioseguro(\\\"CambistaInka\\\", var_headers))\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(get_fx_cambioseguro(\\\"CambistaInka\\\", var_headers))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(get_fx_cambistainka(\"CambistaInka\", var_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:47.299319Z",
     "start_time": "2020-08-17T03:28:47.282314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'},\n",
       "   'CambistaInka': {'bid': '3.5650', 'ask': '3.5850'}}}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T08:30:00.295345Z",
     "start_time": "2020-08-17T08:30:00.245345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 134;\n",
       "                var nbb_unformatted_code = \"def get_fx_DCFX(str_broker, dict_head):\\n    # Libraries\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    # If no connection then none\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse HTML\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Extract bid and ask\\n    f_var_data = soup.findAll(\\n        lambda tag: any([i.startswith(\\\"rate-\\\") for i in tag.attrs])\\n    )\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": f_var_data[0].attrs[\\\"rate-buying\\\"],\\n            \\\"ask\\\": f_var_data[0].attrs[\\\"rate-selling\\\"],\\n        }\\n    }\";\n",
       "                var nbb_formatted_code = \"def get_fx_DCFX(str_broker, dict_head):\\n    # Libraries\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    # If no connection then none\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse HTML\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Extract bid and ask\\n    f_var_data = soup.findAll(\\n        lambda tag: any([i.startswith(\\\"rate-\\\") for i in tag.attrs])\\n    )\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": f_var_data[0].attrs[\\\"rate-buying\\\"],\\n            \\\"ask\\\": f_var_data[0].attrs[\\\"rate-selling\\\"],\\n        }\\n    }\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_DCFX(str_broker, dict_head):\n",
    "    # Libraries\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Get request\n",
    "    s = requests.Session()\n",
    "    s.headers = dict_head\n",
    "    res = s.get(var_urls[str_broker])\n",
    "    # If no connection then none\n",
    "    if res.status_code != 200:\n",
    "        return dict(broker=str_broker, bid=None, ask=None)\n",
    "    # Parse HTML\n",
    "    soup = BeautifulSoup(res.text, \"html.parser\")\n",
    "    # Extract bid and ask\n",
    "    f_var_data = soup.findAll(\n",
    "        lambda tag: any([i.startswith(\"rate-\") for i in tag.attrs])\n",
    "    )\n",
    "    # Extract FX rate\n",
    "    return {\n",
    "        str_broker: {\n",
    "            \"bid\": f_var_data[0].attrs[\"rate-buying\"],\n",
    "            \"ask\": f_var_data[0].attrs[\"rate-selling\"],\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:54.172796Z",
     "start_time": "2020-08-17T03:28:52.522758Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(get_fx_DCFX(\\\"DecisionCapital\\\", var_headers))\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(get_fx_DCFX(\\\"DecisionCapital\\\", var_headers))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(get_fx_DCFX(\"DecisionCapital\", var_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:54.316087Z",
     "start_time": "2020-08-17T03:28:54.301986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'},\n",
       "   'CambistaInka': {'bid': '3.5650', 'ask': '3.5850'},\n",
       "   'DecisionCapital': {'bid': '3.55710', 'ask': '3.58710'}}}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JetPeru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:28:54.679121Z",
     "start_time": "2020-08-17T03:28:54.657820Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"def get_fx_jetperu(str_broker, sln_driver, str_regex):\\n    # Libraries\\n    import re\\n    from selenium import webdriver\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    sln_driver.get(var_urls[str_broker])\\n    # Parse html\\n    soup = BeautifulSoup(sln_driver.page_source, \\\"html.parser\\\")\\n    # Set a list with all website text\\n    f_lst_text = soup.find_all(text=True)\\n    # Get list with FX\\n    f_str_fx = [t.parent.parent for t in f_lst_text if \\\"D\\u00f3lares (US$)\\\" in t][\\n        0\\n    ].find_all(text=True)\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": [\\n                re.findall(var_regex, x)[0] for x in f_str_fx if \\\"compra\\\" in x.lower()\\n            ][0],\\n            \\\"ask\\\": [\\n                re.findall(var_regex, x)[0] for x in f_str_fx if \\\"venta\\\" in x.lower()\\n            ][0],\\n        }\\n    }\";\n",
       "                var nbb_formatted_code = \"def get_fx_jetperu(str_broker, sln_driver, str_regex):\\n    # Libraries\\n    import re\\n    from selenium import webdriver\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    sln_driver.get(var_urls[str_broker])\\n    # Parse html\\n    soup = BeautifulSoup(sln_driver.page_source, \\\"html.parser\\\")\\n    # Set a list with all website text\\n    f_lst_text = soup.find_all(text=True)\\n    # Get list with FX\\n    f_str_fx = [t.parent.parent for t in f_lst_text if \\\"D\\u00f3lares (US$)\\\" in t][\\n        0\\n    ].find_all(text=True)\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": [\\n                re.findall(var_regex, x)[0] for x in f_str_fx if \\\"compra\\\" in x.lower()\\n            ][0],\\n            \\\"ask\\\": [\\n                re.findall(var_regex, x)[0] for x in f_str_fx if \\\"venta\\\" in x.lower()\\n            ][0],\\n        }\\n    }\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_jetperu(str_broker, sln_driver, str_regex):\n",
    "    # Libraries\n",
    "    import re\n",
    "    from selenium import webdriver\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Get request\n",
    "    sln_driver.get(var_urls[str_broker])\n",
    "    # Parse html\n",
    "    soup = BeautifulSoup(sln_driver.page_source, \"html.parser\")\n",
    "    # Set a list with all website text\n",
    "    f_lst_text = soup.find_all(text=True)\n",
    "    # Get list with FX\n",
    "    f_str_fx = [t.parent.parent for t in f_lst_text if \"Dólares (US$)\" in t][\n",
    "        0\n",
    "    ].find_all(text=True)\n",
    "    # Extract FX rate\n",
    "    return {\n",
    "        str_broker: {\n",
    "            \"bid\": [\n",
    "                re.findall(var_regex, x)[0] for x in f_str_fx if \"compra\" in x.lower()\n",
    "            ][0],\n",
    "            \"ask\": [\n",
    "                re.findall(var_regex, x)[0] for x in f_str_fx if \"venta\" in x.lower()\n",
    "            ][0],\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:03.343953Z",
     "start_time": "2020-08-17T03:28:55.106772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(get_fx_jetperu(\\\"JetPeru\\\", driver, var_regex))\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(get_fx_jetperu(\\\"JetPeru\\\", driver, var_regex))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(get_fx_jetperu(\"JetPeru\", driver, var_regex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:03.486953Z",
     "start_time": "2020-08-17T03:29:03.471923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'},\n",
       "   'CambistaInka': {'bid': '3.5650', 'ask': '3.5850'},\n",
       "   'DecisionCapital': {'bid': '3.55710', 'ask': '3.58710'},\n",
       "   'JetPeru': {'bid': '3.562', 'ask': '3.586'}}}}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kambista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:03.645954Z",
     "start_time": "2020-08-17T03:29:03.615925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"def get_fx_kambista(str_broker, dict_head):\\n    # Libraries\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": soup.select(\\\"#valcompra\\\")[0].get_text(strip=True),\\n            \\\"ask\\\": soup.select(\\\"#valventa\\\")[0].get_text(strip=True),\\n        }\\n    }\";\n",
       "                var nbb_formatted_code = \"def get_fx_kambista(str_broker, dict_head):\\n    # Libraries\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    if res.status_code != 200:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"html.parser\\\")\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": soup.select(\\\"#valcompra\\\")[0].get_text(strip=True),\\n            \\\"ask\\\": soup.select(\\\"#valventa\\\")[0].get_text(strip=True),\\n        }\\n    }\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_kambista(str_broker, dict_head):\n",
    "    # Libraries\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Get request\n",
    "    s = requests.Session()\n",
    "    s.headers = dict_head\n",
    "    res = s.get(var_urls[str_broker])\n",
    "    if res.status_code != 200:\n",
    "        return dict(broker=str_broker, bid=None, ask=None)\n",
    "    # Parse html\n",
    "    soup = BeautifulSoup(res.text, \"html.parser\")\n",
    "    # Extract FX rate\n",
    "    return {\n",
    "        str_broker: {\n",
    "            \"bid\": soup.select(\"#valcompra\")[0].get_text(strip=True),\n",
    "            \"ask\": soup.select(\"#valventa\")[0].get_text(strip=True),\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:05.220687Z",
     "start_time": "2020-08-17T03:29:03.878925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(get_fx_kambista(\\\"Kambista\\\", var_headers))\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(get_fx_kambista(\\\"Kambista\\\", var_headers))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(get_fx_kambista(\"Kambista\", var_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:05.363602Z",
     "start_time": "2020-08-17T03:29:05.349601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'},\n",
       "   'CambistaInka': {'bid': '3.5650', 'ask': '3.5850'},\n",
       "   'DecisionCapital': {'bid': '3.55710', 'ask': '3.58710'},\n",
       "   'JetPeru': {'bid': '3.562', 'ask': '3.586'},\n",
       "   'Kambista': {'bid': '3.55', 'ask': '3.588'}}}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rextie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:05.522717Z",
     "start_time": "2020-08-17T03:29:05.492719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"def get_fx_rextie(str_broker, dict_head):\\n    # Libraries\\n    import json\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Set payload\\n    f_var_payload = {\\n        \\\"source_currency\\\": \\\"USD\\\",\\n        \\\"target_currency\\\": \\\"PEN\\\",\\n        \\\"source_amount\\\": \\\"1000\\\",\\n    }\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.post(var_urls[str_broker], data=f_var_payload)\\n    if res.status_code != 200 and res.status_code != 201:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"lxml\\\")\\n    # Convert to JSON\\n    f_var_data = json.loads(soup.select(\\\"p\\\")[0].text)\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": f_var_data[\\\"fx_rate_buy\\\"],\\n            \\\"ask\\\": f_var_data[\\\"fx_rate_sell\\\"],\\n        }\\n    }\";\n",
       "                var nbb_formatted_code = \"def get_fx_rextie(str_broker, dict_head):\\n    # Libraries\\n    import json\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Set payload\\n    f_var_payload = {\\n        \\\"source_currency\\\": \\\"USD\\\",\\n        \\\"target_currency\\\": \\\"PEN\\\",\\n        \\\"source_amount\\\": \\\"1000\\\",\\n    }\\n    # Get request\\n    s = requests.Session()\\n    s.headers = dict_head\\n    res = s.post(var_urls[str_broker], data=f_var_payload)\\n    if res.status_code != 200 and res.status_code != 201:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(res.text, \\\"lxml\\\")\\n    # Convert to JSON\\n    f_var_data = json.loads(soup.select(\\\"p\\\")[0].text)\\n    # Extract FX rate\\n    return {\\n        str_broker: {\\n            \\\"bid\\\": f_var_data[\\\"fx_rate_buy\\\"],\\n            \\\"ask\\\": f_var_data[\\\"fx_rate_sell\\\"],\\n        }\\n    }\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_rextie(str_broker, dict_head):\n",
    "    # Libraries\n",
    "    import json\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Set payload\n",
    "    f_var_payload = {\n",
    "        \"source_currency\": \"USD\",\n",
    "        \"target_currency\": \"PEN\",\n",
    "        \"source_amount\": \"1000\",\n",
    "    }\n",
    "    # Get request\n",
    "    s = requests.Session()\n",
    "    s.headers = dict_head\n",
    "    res = s.post(var_urls[str_broker], data=f_var_payload)\n",
    "    if res.status_code != 200 and res.status_code != 201:\n",
    "        return dict(broker=str_broker, bid=None, ask=None)\n",
    "    # Parse html\n",
    "    soup = BeautifulSoup(res.text, \"lxml\")\n",
    "    # Convert to JSON\n",
    "    f_var_data = json.loads(soup.select(\"p\")[0].text)\n",
    "    # Extract FX rate\n",
    "    return {\n",
    "        str_broker: {\n",
    "            \"bid\": f_var_data[\"fx_rate_buy\"],\n",
    "            \"ask\": f_var_data[\"fx_rate_sell\"],\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:06.417797Z",
     "start_time": "2020-08-17T03:29:05.760934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(get_fx_rextie(\\\"Rextie\\\", var_headers))\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(get_fx_rextie(\\\"Rextie\\\", var_headers))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(get_fx_rextie(\"Rextie\", var_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:06.556672Z",
     "start_time": "2020-08-17T03:29:06.542666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'},\n",
       "   'CambistaInka': {'bid': '3.5650', 'ask': '3.5850'},\n",
       "   'DecisionCapital': {'bid': '3.55710', 'ask': '3.58710'},\n",
       "   'JetPeru': {'bid': '3.562', 'ask': '3.586'},\n",
       "   'Kambista': {'bid': '3.55', 'ask': '3.588'},\n",
       "   'Rextie': {'bid': '3.5566', 'ask': '3.5860'}}}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WesternUnion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probar con dict_head different than `{\"User-Agent\": \"Mozilla/5.0\"}`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T08:31:14.672697Z",
     "start_time": "2020-08-17T08:31:14.633668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 135;\n",
       "                var nbb_unformatted_code = \"def get_fx_WUFX(str_broker, dict_head):\\n    # Libraries\\n    import json\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    #     s.headers = {\\\"User-Agent\\\": \\\"Mozilla/5.0\\\"}\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    # Parse HTML\\n    soup = BeautifulSoup(res.text, \\\"lxml\\\")\\n    # Get payload\\n    f_var_payload = {\\n        item[\\\"name\\\"]: item.get(\\\"value\\\", \\\"\\\") for item in soup.select(\\\"input[name]\\\")\\n    }\\n\\n    # Update payload\\n    f_var_payload[\\\"monto\\\"] = 1000\\n    f_var_payload[\\\"moneda\\\"] = 2\\n    f_var_payload[\\\"tipo\\\"] = 1\\n    del f_var_payload[\\\"SolesToDolares\\\"]\\n    del f_var_payload[\\\"InpAmount1\\\"]\\n    del f_var_payload[\\\"InpAmount2\\\"]\\n    del f_var_payload[\\\"captcha\\\"]\\n\\n    # Post request\\n    req = s.post(\\n        \\\"https://www.westernunionperu.pe/cambiodemoneda/Operation/PostTipoCambio\\\",\\n        data=f_var_payload,\\n    )\\n    if req.status_code != 200 and req.status_code != 201:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(req.text, \\\"lxml\\\")\\n    # Convert to JSON\\n    f_var_data = json.loads(soup.select(\\\"p\\\")[0].text)\\n    # Extract FX rate\\n    return {str_broker: {\\\"bid\\\": f_var_data[\\\"DT_Compra\\\"], \\\"ask\\\": f_var_data[\\\"DT_Venta\\\"]}}\";\n",
       "                var nbb_formatted_code = \"def get_fx_WUFX(str_broker, dict_head):\\n    # Libraries\\n    import json\\n    import requests\\n    from bs4 import BeautifulSoup\\n\\n    # Get request\\n    s = requests.Session()\\n    #     s.headers = {\\\"User-Agent\\\": \\\"Mozilla/5.0\\\"}\\n    s.headers = dict_head\\n    res = s.get(var_urls[str_broker])\\n    # Parse HTML\\n    soup = BeautifulSoup(res.text, \\\"lxml\\\")\\n    # Get payload\\n    f_var_payload = {\\n        item[\\\"name\\\"]: item.get(\\\"value\\\", \\\"\\\") for item in soup.select(\\\"input[name]\\\")\\n    }\\n\\n    # Update payload\\n    f_var_payload[\\\"monto\\\"] = 1000\\n    f_var_payload[\\\"moneda\\\"] = 2\\n    f_var_payload[\\\"tipo\\\"] = 1\\n    del f_var_payload[\\\"SolesToDolares\\\"]\\n    del f_var_payload[\\\"InpAmount1\\\"]\\n    del f_var_payload[\\\"InpAmount2\\\"]\\n    del f_var_payload[\\\"captcha\\\"]\\n\\n    # Post request\\n    req = s.post(\\n        \\\"https://www.westernunionperu.pe/cambiodemoneda/Operation/PostTipoCambio\\\",\\n        data=f_var_payload,\\n    )\\n    if req.status_code != 200 and req.status_code != 201:\\n        return dict(broker=str_broker, bid=None, ask=None)\\n    # Parse html\\n    soup = BeautifulSoup(req.text, \\\"lxml\\\")\\n    # Convert to JSON\\n    f_var_data = json.loads(soup.select(\\\"p\\\")[0].text)\\n    # Extract FX rate\\n    return {str_broker: {\\\"bid\\\": f_var_data[\\\"DT_Compra\\\"], \\\"ask\\\": f_var_data[\\\"DT_Venta\\\"]}}\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fx_WUFX(str_broker, dict_head):\n",
    "    # Libraries\n",
    "    import json\n",
    "    import requests\n",
    "    from bs4 import BeautifulSoup\n",
    "\n",
    "    # Get request\n",
    "    s = requests.Session()\n",
    "    #     s.headers = {\"User-Agent\": \"Mozilla/5.0\"}\n",
    "    s.headers = dict_head\n",
    "    res = s.get(var_urls[str_broker])\n",
    "    # Parse HTML\n",
    "    soup = BeautifulSoup(res.text, \"lxml\")\n",
    "    # Get payload\n",
    "    f_var_payload = {\n",
    "        item[\"name\"]: item.get(\"value\", \"\") for item in soup.select(\"input[name]\")\n",
    "    }\n",
    "\n",
    "    # Update payload\n",
    "    f_var_payload[\"monto\"] = 1000\n",
    "    f_var_payload[\"moneda\"] = 2\n",
    "    f_var_payload[\"tipo\"] = 1\n",
    "    del f_var_payload[\"SolesToDolares\"]\n",
    "    del f_var_payload[\"InpAmount1\"]\n",
    "    del f_var_payload[\"InpAmount2\"]\n",
    "    del f_var_payload[\"captcha\"]\n",
    "\n",
    "    # Post request\n",
    "    req = s.post(\n",
    "        \"https://www.westernunionperu.pe/cambiodemoneda/Operation/PostTipoCambio\",\n",
    "        data=f_var_payload,\n",
    "    )\n",
    "    if req.status_code != 200 and req.status_code != 201:\n",
    "        return dict(broker=str_broker, bid=None, ask=None)\n",
    "    # Parse html\n",
    "    soup = BeautifulSoup(req.text, \"lxml\")\n",
    "    # Convert to JSON\n",
    "    f_var_data = json.loads(soup.select(\"p\")[0].text)\n",
    "    # Extract FX rate\n",
    "    return {str_broker: {\"bid\": f_var_data[\"DT_Compra\"], \"ask\": f_var_data[\"DT_Venta\"]}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:07.947019Z",
     "start_time": "2020-08-17T03:29:06.968846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 34;\n",
       "                var nbb_unformatted_code = \"db_fx[str_date][str_time].update(get_fx_WUFX(\\\"WesternUnion\\\", var_headers))\";\n",
       "                var nbb_formatted_code = \"db_fx[str_date][str_time].update(get_fx_WUFX(\\\"WesternUnion\\\", var_headers))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx[str_date][str_time].update(get_fx_WUFX(\"WesternUnion\", var_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:08.086827Z",
     "start_time": "2020-08-17T03:29:08.073231Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200816': {'2228': {'CambioSeguro': {'bid': '3.5600', 'ask': '3.5880'},\n",
       "   'CambistaInka': {'bid': '3.5650', 'ask': '3.5850'},\n",
       "   'DecisionCapital': {'bid': '3.55710', 'ask': '3.58710'},\n",
       "   'JetPeru': {'bid': '3.562', 'ask': '3.586'},\n",
       "   'Kambista': {'bid': '3.55', 'ask': '3.588'},\n",
       "   'Rextie': {'bid': '3.5566', 'ask': '3.5860'},\n",
       "   'WesternUnion': {'bid': 3.563, 'ask': 3.588}}}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"db_fx\";\n",
       "                var nbb_formatted_code = \"db_fx\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_fx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:26.324621Z",
     "start_time": "2020-08-17T03:29:26.314981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 36;\n",
       "                var nbb_unformatted_code = \"import json\";\n",
       "                var nbb_formatted_code = \"import json\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:26.775880Z",
     "start_time": "2020-08-17T03:29:26.762908Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"20200816\": {\n",
      "        \"2228\": {\n",
      "            \"CambioSeguro\": {\n",
      "                \"bid\": \"3.5600\",\n",
      "                \"ask\": \"3.5880\"\n",
      "            },\n",
      "            \"CambistaInka\": {\n",
      "                \"bid\": \"3.5650\",\n",
      "                \"ask\": \"3.5850\"\n",
      "            },\n",
      "            \"DecisionCapital\": {\n",
      "                \"bid\": \"3.55710\",\n",
      "                \"ask\": \"3.58710\"\n",
      "            },\n",
      "            \"JetPeru\": {\n",
      "                \"bid\": \"3.562\",\n",
      "                \"ask\": \"3.586\"\n",
      "            },\n",
      "            \"Kambista\": {\n",
      "                \"bid\": \"3.55\",\n",
      "                \"ask\": \"3.588\"\n",
      "            },\n",
      "            \"Rextie\": {\n",
      "                \"bid\": \"3.5566\",\n",
      "                \"ask\": \"3.5860\"\n",
      "            },\n",
      "            \"WesternUnion\": {\n",
      "                \"bid\": 3.563,\n",
      "                \"ask\": 3.588\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 37;\n",
       "                var nbb_unformatted_code = \"print(json.dumps(db_fx, indent=4))\";\n",
       "                var nbb_formatted_code = \"print(json.dumps(db_fx, indent=4))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(json.dumps(db_fx, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:31.914752Z",
     "start_time": "2020-08-17T03:29:31.905750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 38;\n",
       "                var nbb_unformatted_code = \"import pandas as pd\";\n",
       "                var nbb_formatted_code = \"import pandas as pd\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:29:32.483480Z",
     "start_time": "2020-08-17T03:29:32.355226Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 39;\n",
       "                var nbb_unformatted_code = \"import plotly.express as px\";\n",
       "                var nbb_formatted_code = \"import plotly.express as px\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T08:37:35.519982Z",
     "start_time": "2020-08-17T08:37:35.487967Z"
    }
   },
   "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>CambioSeguro</th>\n",
       "      <th>CambistaInka</th>\n",
       "      <th>DecisionCapital</th>\n",
       "      <th>JetPeru</th>\n",
       "      <th>Kambista</th>\n",
       "      <th>Rextie</th>\n",
       "      <th>WesternUnion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bid</th>\n",
       "      <td>3.5600</td>\n",
       "      <td>3.5650</td>\n",
       "      <td>3.55710</td>\n",
       "      <td>3.562</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.5566</td>\n",
       "      <td>3.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ask</th>\n",
       "      <td>3.5880</td>\n",
       "      <td>3.5850</td>\n",
       "      <td>3.58710</td>\n",
       "      <td>3.586</td>\n",
       "      <td>3.588</td>\n",
       "      <td>3.5860</td>\n",
       "      <td>3.588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CambioSeguro CambistaInka DecisionCapital JetPeru Kambista  Rextie  \\\n",
       "bid       3.5600       3.5650         3.55710   3.562     3.55  3.5566   \n",
       "ask       3.5880       3.5850         3.58710   3.586    3.588  3.5860   \n",
       "\n",
       "     WesternUnion  \n",
       "bid         3.563  \n",
       "ask         3.588  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 136;\n",
       "                var nbb_unformatted_code = \"tmp_df = pd.DataFrame(db_fx[str_date][str_time])\\ntmp_df\";\n",
       "                var nbb_formatted_code = \"tmp_df = pd.DataFrame(db_fx[str_date][str_time])\\ntmp_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp_df = pd.DataFrame(db_fx[str_date][str_time])\n",
    "tmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T03:44:03.034219Z",
     "start_time": "2020-08-17T03:44:03.018194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 62;\n",
       "                var nbb_unformatted_code = \"tmp_df.loc[\\\"spread\\\"] = tmp_df.loc[\\\"ask\\\"].astype(float) - tmp_df.loc[\\\"bid\\\"].astype(float)\";\n",
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     "end_time": "2020-08-17T04:46:08.559776Z",
     "start_time": "2020-08-17T04:46:08.535774Z"
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       "      <th></th>\n",
       "      <th>CambioSeguro</th>\n",
       "      <th>CambistaInka</th>\n",
       "      <th>DecisionCapital</th>\n",
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       "      <th>Kambista</th>\n",
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       "       CambioSeguro CambistaInka DecisionCapital JetPeru Kambista  Rextie  \\\n",
       "bid          3.5600       3.5650         3.55710   3.562     3.55  3.5566   \n",
       "ask          3.5880       3.5850         3.58710   3.586    3.588  3.5860   \n",
       "spread        0.028         0.02            0.03   0.024    0.038  0.0294   \n",
       "\n",
       "        WesternUnion  \n",
       "bid            3.563  \n",
       "ask            3.588  \n",
       "spread         0.025  "
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     "start_time": "2020-08-17T05:03:26.752722Z"
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 120;\n",
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    "tmp_df_raw = tmp_df.stack().reset_index()"
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   "execution_count": 121,
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     "start_time": "2020-08-17T05:03:27.583199Z"
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 121;\n",
       "                var nbb_unformatted_code = \"tmp_df_raw.columns = [\\\"quote\\\", \\\"broker\\\", \\\"rate\\\"]\";\n",
       "                var nbb_formatted_code = \"tmp_df_raw.columns = [\\\"quote\\\", \\\"broker\\\", \\\"rate\\\"]\";\n",
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    "tmp_df_raw.columns = [\"quote\", \"broker\", \"rate\"]"
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   "execution_count": 122,
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     "start_time": "2020-08-17T05:03:28.311075Z"
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       "quote     object\n",
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     "start_time": "2020-08-17T05:03:32.055702Z"
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 123;\n",
       "                var nbb_unformatted_code = \"tmp_df_raw[\\\"rate\\\"] = pd.to_numeric(tmp_df_raw[\\\"rate\\\"])\";\n",
       "                var nbb_formatted_code = \"tmp_df_raw[\\\"rate\\\"] = pd.to_numeric(tmp_df_raw[\\\"rate\\\"])\";\n",
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       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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     "start_time": "2020-08-17T05:03:32.918760Z"
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     "start_time": "2020-08-17T05:03:36.335146Z"
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    {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>5</th>\n",
       "      <td>bid</td>\n",
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       "      <td>3.5566</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bid</td>\n",
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       "    <tr>\n",
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       "      <td>ask</td>\n",
       "      <td>CambistaInka</td>\n",
       "      <td>3.5850</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ask</td>\n",
       "      <td>DecisionCapital</td>\n",
       "      <td>3.5871</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ask</td>\n",
       "      <td>JetPeru</td>\n",
       "      <td>3.5860</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ask</td>\n",
       "      <td>Kambista</td>\n",
       "      <td>3.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ask</td>\n",
       "      <td>Rextie</td>\n",
       "      <td>3.5860</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ask</td>\n",
       "      <td>WesternUnion</td>\n",
       "      <td>3.5880</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>spread</td>\n",
       "      <td>CambioSeguro</td>\n",
       "      <td>0.0280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>spread</td>\n",
       "      <td>CambistaInka</td>\n",
       "      <td>0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spread</td>\n",
       "      <td>DecisionCapital</td>\n",
       "      <td>0.0300</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spread</td>\n",
       "      <td>JetPeru</td>\n",
       "      <td>0.0240</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spread</td>\n",
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       "      <td>0.0380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spread</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>spread</td>\n",
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      ],
      "text/plain": [
       "     quote           broker    rate\n",
       "0      bid     CambioSeguro  3.5600\n",
       "1      bid     CambistaInka  3.5650\n",
       "2      bid  DecisionCapital  3.5571\n",
       "3      bid          JetPeru  3.5620\n",
       "4      bid         Kambista  3.5500\n",
       "5      bid           Rextie  3.5566\n",
       "6      bid     WesternUnion  3.5630\n",
       "7      ask     CambioSeguro  3.5880\n",
       "8      ask     CambistaInka  3.5850\n",
       "9      ask  DecisionCapital  3.5871\n",
       "10     ask          JetPeru  3.5860\n",
       "11     ask         Kambista  3.5880\n",
       "12     ask           Rextie  3.5860\n",
       "13     ask     WesternUnion  3.5880\n",
       "14  spread     CambioSeguro  0.0280\n",
       "15  spread     CambistaInka  0.0200\n",
       "16  spread  DecisionCapital  0.0300\n",
       "17  spread          JetPeru  0.0240\n",
       "18  spread         Kambista  0.0380\n",
       "19  spread           Rextie  0.0294\n",
       "20  spread     WesternUnion  0.0250"
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 126;\n",
       "                var nbb_unformatted_code = \"tmp_df_px = tmp_df_raw[\\n    (tmp_df_raw[\\\"quote\\\"] == \\\"bid\\\") | (tmp_df_raw[\\\"quote\\\"] == \\\"ask\\\")\\n]\\n# tmp_df_px.columns = [\\\"quote\\\", \\\"broker\\\", \\\"rate\\\"]\";\n",
       "                var nbb_formatted_code = \"tmp_df_px = tmp_df_raw[(tmp_df_raw[\\\"quote\\\"] == \\\"bid\\\") | (tmp_df_raw[\\\"quote\\\"] == \\\"ask\\\")]\\n# tmp_df_px.columns = [\\\"quote\\\", \\\"broker\\\", \\\"rate\\\"]\";\n",
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    "tmp_df_px = tmp_df_raw[(tmp_df_raw[\"quote\"] == \"bid\") | (tmp_df_raw[\"quote\"] == \"ask\")]\n",
    "# tmp_df_px.columns = [\"quote\", \"broker\", \"rate\"]"
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   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-17T05:04:05.214360Z",
     "start_time": "2020-08-17T05:04:05.191358Z"
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     "data": {
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       "quote      object\n",
       "broker     object\n",
       "rate      float64\n",
       "dtype: object"
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     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>bid</td>\n",
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       "      <td>ask</td>\n",
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       "      <td>3.5880</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ask</td>\n",
       "      <td>Rextie</td>\n",
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       "    <tr>\n",
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       "      <td>ask</td>\n",
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       "   quote           broker    rate\n",
       "0    bid     CambioSeguro  3.5600\n",
       "1    bid     CambistaInka  3.5650\n",
       "2    bid  DecisionCapital  3.5571\n",
       "3    bid          JetPeru  3.5620\n",
       "4    bid         Kambista  3.5500\n",
       "5    bid           Rextie  3.5566\n",
       "6    bid     WesternUnion  3.5630\n",
       "7    ask     CambioSeguro  3.5880\n",
       "8    ask     CambistaInka  3.5850\n",
       "9    ask  DecisionCapital  3.5871\n",
       "10   ask          JetPeru  3.5860\n",
       "11   ask         Kambista  3.5880\n",
       "12   ask           Rextie  3.5860\n",
       "13   ask     WesternUnion  3.5880"
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     "execution_count": 128,
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       "                var nbb_cell_id = 133;\n",
       "                var nbb_unformatted_code = \"fig = px.box(tmp_df_px, x=\\\"broker\\\", y=\\\"rate\\\", color=\\\"broker\\\")\\nfig.show()\";\n",
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    "print(\"Best Ask:\" + str(min(tmp_df_raw[tmp_df_raw[\"quote\"] == \"ask\"][\"rate\"])))"
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