charts_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from binance import Client\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import mplfinance as mpl\n",
    "import mercury as mr\n",
    "from datetime import date\n",
    "import os\n",
    "api_key = os.environ.get('api_key')\n",
    "secret_key = os.environ.get('secret_key')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/mercury+json": {
       "allow_download": true,
       "code_uid": "App.0.40.25.2-rand5fb37859",
       "continuous_update": true,
       "description": "Report financial data for selected cryptocurrency.",
       "full_screen": true,
       "model_id": "mercury-app",
       "notify": "{}",
       "output": "app",
       "schedule": "",
       "show_code": false,
       "show_prompt": false,
       "show_sidebar": true,
       "static_notebook": false,
       "stop_on_error": false,
       "title": "Crypto charts",
       "widget": "App"
      },
      "text/html": [
       "<h3>Mercury Application</h3><small>This output won't appear in the web app.</small>"
      ],
      "text/plain": [
       "mercury.App"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client = Client(api_key, secret_key, tld='us')\n",
    "app = mr.App(title=\"Crypto charts\", description=\"Report financial data for selected cryptocurrency.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = client.get_all_tickers()\n",
    "ticker_df = pd.DataFrame(tickers)\n",
    "ticker_df['symbol'] = ticker_df['symbol'].astype('string')\n",
    "ticker_array = np.array(ticker_df['symbol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/mercury+json": {
       "code_uid": "Text.0.40.15.1-rand5b33c5ed",
       "disabled": false,
       "hidden": false,
       "label": "Enter Cryptocurrency:",
       "model_id": "4edf1bc05aa5419894704eda3171260b",
       "rows": 1,
       "url_key": "",
       "value": "BTCUSDT",
       "widget": "Text"
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4edf1bc05aa5419894704eda3171260b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Text"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/mercury+json": {
       "choices": [
        "1 Minute",
        "3 Minutes",
        "5 Minutes",
        "15 Minutes",
        "1 Hour",
        "4 Hours",
        "12 Hours",
        "Day",
        "Week"
       ],
       "code_uid": "Select.0.40.16.5-rand4f79accd",
       "disabled": false,
       "hidden": false,
       "label": "Choose your interval",
       "model_id": "1abcf4c26a39486e9a2625072239ebc0",
       "url_key": "",
       "value": "15 Minutes",
       "widget": "Select"
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1abcf4c26a39486e9a2625072239ebc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Select"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crypto_input = mr.Text(value=\"BTCUSDT\", label=\"Enter Cryptocurrency:\", rows=1)\n",
    "symbol = crypto_input.value.upper()\n",
    "\n",
    "interval_select = mr.Select(value=\"15 Minutes\", \n",
    "                    choices=['1 Minute', '3 Minutes', '5 Minutes', '15 Minutes', '1 Hour', '4 Hours', '12 Hours', 'Day', 'Week'], \n",
    "                    label=\"Choose your interval\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic info ✨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_res = client.get_server_time()\n",
    "current_time = time_res['serverTime']\n",
    "formated_server_time = pd.to_datetime(time_res['serverTime']/1000, unit='s')\n",
    "formated_server_time = formated_server_time.strftime(\"%H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Server time: **09:31**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mr.Md(f\"### Server time: **{formated_server_time}**\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/mercury+json": {
       "code_uid": "Slider.0.40.34.22-rand15ef8f76",
       "disabled": false,
       "hidden": false,
       "label": "Hours ago",
       "max": 168,
       "min": 12,
       "model_id": "ec46283474694e5c9e5ba0313a84bdfd",
       "step": 1,
       "url_key": "",
       "value": 24,
       "widget": "Slider"
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec46283474694e5c9e5ba0313a84bdfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Slider"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval = Client.KLINE_INTERVAL_1DAY\n",
    "interval_date = str(date.today())\n",
    "\n",
    "if interval_select.value == '1 Minute':\n",
    "    interval = Client.KLINE_INTERVAL_1MINUTE\n",
    "    time_ago = mr.Slider(value=180, min=60, max=360, label=\"Minutes ago\", step=1)\n",
    "    interval_date = str(time_ago.value) + ' minutes ago'\n",
    "\n",
    "elif interval_select.value == '3 Minutes':\n",
    "    interval = Client.KLINE_INTERVAL_3MINUTE\n",
    "    time_ago = mr.Slider(value=360, min=180, max=720, label=\"Minutes ago\", step=3)\n",
    "    interval_date = str(time_ago.value) + ' minutes ago'\n",
    "\n",
    "elif interval_select.value == '5 Minutes':\n",
    "    interval = Client.KLINE_INTERVAL_5MINUTE\n",
    "    time_ago = mr.Slider(value=480, min=360, max=720, label=\"Minutes ago\", step=5)\n",
    "    interval_date = str(time_ago.value) + ' minutes ago'\n",
    "\n",
    "elif interval_select.value == '15 Minutes':\n",
    "    interval = Client.KLINE_INTERVAL_15MINUTE\n",
    "    time_ago = mr.Slider(value=24, min=12, max=168, label=\"Hours ago\", step=1)\n",
    "    interval_date = str(time_ago.value) + ' hours ago'\n",
    "\n",
    "elif interval_select.value == '1 Hour':\n",
    "    interval = Client.KLINE_INTERVAL_1HOUR\n",
    "    time_ago = mr.Slider(value=120, min=12, max=240, label=\"Hours ago\", step=1)\n",
    "    interval_date = str(time_ago.value) + ' hours ago'\n",
    "\n",
    "elif interval_select.value == '4 Hours':\n",
    "    interval = Client.KLINE_INTERVAL_4HOUR\n",
    "    time_ago = mr.Slider(value=120, min=48, max=480, label=\"Hours ago\", step=4)\n",
    "    interval_date = str(time_ago.value) + ' hours ago'\n",
    "        \n",
    "elif interval_select.value == '12 Hours':\n",
    "    interval = Client.KLINE_INTERVAL_12HOUR\n",
    "    time_ago = mr.Slider(value=50, min=10, max=100, label=\"Days ago\", step=1)\n",
    "    interval_date = str(time_ago.value) + ' days ago'\n",
    "    \n",
    "elif interval_select.value == 'Day':\n",
    "    interval = Client.KLINE_INTERVAL_1DAY\n",
    "    time_ago = mr.Slider(value=50, min=10, max=200, label=\"Days ago\", step=5)\n",
    "    interval_date = str(time_ago.value) + ' days ago'\n",
    "\n",
    "elif interval_select.value == 'Week':\n",
    "    interval = Client.KLINE_INTERVAL_1WEEK\n",
    "    time_ago = mr.Slider(value=50, min=5, max=200, label=\"Weeks ago\", step=5)\n",
    "    interval_date = str(time_ago.value) + ' weeks ago'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/mercury+json": {
       "code_uid": "Checkbox.0.40.11.1-rand509bc6b6",
       "disabled": false,
       "hidden": false,
       "label": "Volume",
       "model_id": "866b0578e1bb46c9844d58e0c3b0a8c4",
       "url_key": "",
       "value": true,
       "widget": "Checkbox"
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "866b0578e1bb46c9844d58e0c3b0a8c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Checkbox"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volume_bool = mr.Checkbox(value=True, label=\"Volume\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    error = False\n",
    "    if not symbol:\n",
    "        error = True\n",
    "        raise ValueError(\"Don't leave blank space\")\n",
    "    elif symbol not in ticker_array:\n",
    "        error = True\n",
    "        raise ValueError(\"Somethink gone wrong. Check your entry.\")\n",
    "    \n",
    "    hist = client.get_historical_klines(symbol, interval, interval_date)\n",
    "\n",
    "    histdf = pd.DataFrame(hist)\n",
    "\n",
    "    histdf = histdf.drop(columns=[6, 7, 8, 9, 10, 11])\n",
    "\n",
    "    histdf.columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume']\n",
    "\n",
    "    histdf['Date'] = pd.to_datetime(histdf['Date']/1000, unit='s')\n",
    "\n",
    "    histdf.set_index(histdf['Date'], inplace=True)\n",
    "\n",
    "    histdf = histdf.drop(columns=['Date'])\n",
    "\n",
    "    numeric_cols=['Open', 'High', 'Low', 'Close', 'Volume']\n",
    "    histdf[numeric_cols] = histdf[numeric_cols].apply(pd.to_numeric, axis=1)\n",
    "\n",
    "    mean_price = round((histdf.Open.mean()+histdf.Close.mean())/2,1)\n",
    "\n",
    "except ValueError as e:\n",
    "    mr.Md(f\"### **{e}**\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#<center>**BTCUSDT**</center>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not error:\n",
    "    mr.Md(f\"#<center>**{symbol}**</center>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"text-align: center; width: 100%; border: 1px solid lightgray; margin-right: 0px; margin-top: 15px; padding-top: 40px; padding-bottom: 30px; background: #FCEADE; border-radius:5px\">\n",
       "  <span style=\"font-size: 4em; color: black; font-family: monospace; \">26,594.99</span>\n",
       "  \n",
       "  <span style=\"font-size: 2em; color: gray; display: block; padding-top: 20px; font-family: monospace; line-height: 1.3em;\">Current close price</span>\n",
       "</div>\n",
       "  "
      ],
      "text/plain": [
       "<widgets.numberbox.NumberBox at 0x24653859c10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mr.NumberBox(data=histdf['Close'].iloc[-1], \n",
    "            title=f\"Current close price\",\n",
    "            background_color=\"#FCEADE\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "        .numberbox-container {\n",
       "        width: 100%;\n",
       "        display: flex;\n",
       "        flex-direction: row;\n",
       "        }\n",
       "        @media (max-width: 800px) {\n",
       "        .numberbox-container {\n",
       "            flex-direction: column;\n",
       "        }\n",
       "        }\n",
       "        </style><div class=\"numberbox-container\" style=\"display: flex; background: #fff;\">\n",
       "<div style=\"text-align: center; width: 100%; border: 1px solid lightgray; margin-right: 15px; margin-top: 15px; padding-top: 40px; padding-bottom: 30px; background: #FEF5EF; border-radius:5px\">\n",
       "  <span style=\"font-size: 4em; color: black; font-family: monospace; \">26,814.9</span>\n",
       "  \n",
       "  <span style=\"font-size: 2em; color: gray; display: block; padding-top: 20px; font-family: monospace; line-height: 1.3em;\">Highest price</span>\n",
       "</div>\n",
       "  \n",
       "<div style=\"text-align: center; width: 100%; border: 1px solid lightgray; margin-right: 15px; margin-top: 15px; padding-top: 40px; padding-bottom: 30px; background: #FEF5EF; border-radius:5px\">\n",
       "  <span style=\"font-size: 4em; color: black; font-family: monospace; \">26,567.4</span>\n",
       "  \n",
       "  <span style=\"font-size: 2em; color: gray; display: block; padding-top: 20px; font-family: monospace; line-height: 1.3em;\">Mean price</span>\n",
       "</div>\n",
       "  \n",
       "<div style=\"text-align: center; width: 100%; border: 1px solid lightgray; margin-right: 0px; margin-top: 15px; padding-top: 40px; padding-bottom: 30px; background: #FEF5EF; border-radius:5px\">\n",
       "  <span style=\"font-size: 4em; color: black; font-family: monospace; \">26,269.2</span>\n",
       "  \n",
       "  <span style=\"font-size: 2em; color: gray; display: block; padding-top: 20px; font-family: monospace; line-height: 1.3em;\">Lowest price</span>\n",
       "</div>\n",
       "  </div>"
      ],
      "text/plain": [
       "<widgets.numberbox.NumberBox at 0x2465dd24090>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mr.NumberBox([\n",
    "        mr.NumberBox(data=histdf.High.max().round(1), title=f\"Highest price\", background_color=\"#FEF5EF\"),\n",
    "        mr.NumberBox(data=mean_price, title=f\"Mean price\", background_color=\"#FEF5EF\"),\n",
    "        mr.NumberBox(data=histdf.Low.min().round(1), title=f\"Lowest price\", background_color=\"#FEF5EF\")\n",
    "        ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# 📈Chart📈"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not error:\n",
    "    mr.Md(\"# 📈Chart📈\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/mercury+json": {
       "choices": [
        "candle",
        "line"
       ],
       "code_uid": "Select.0.40.16.1-randbf8ee086",
       "disabled": false,
       "hidden": false,
       "label": "Chart type:",
       "model_id": "6489168c8950415898a979383579be36",
       "url_key": "",
       "value": "candle",
       "widget": "Select"
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6489168c8950415898a979383579be36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Select"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/mercury+json": {
       "choices": [
        "classic",
        "default",
        "charles",
        "binance",
        "blueskies",
        "brasil",
        "checkers",
        "kenan",
        "ibd",
        "mike",
        "nightclouds",
        "sas",
        "starsandstripes",
        "yahoo"
       ],
       "code_uid": "Select.0.40.16.10-rande9d593c8",
       "disabled": false,
       "hidden": false,
       "label": "Candle style:",
       "model_id": "b7a293d8aefa4cd094d4dc93634231e1",
       "url_key": "",
       "value": "charles",
       "widget": "Select"
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7a293d8aefa4cd094d4dc93634231e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Select"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_type = mr.Select(value=\"candle\", choices=['candle', 'line'], label=\"Chart type:\")\n",
    "candle_style = 'charles'\n",
    "color = 'blue'\n",
    "\n",
    "if plot_type.value == 'line':\n",
    "    color_select = mr.Select(value=\"blue\", choices=['blue', 'red', 'green', 'black', 'purple'], label=\"Line color:\")\n",
    "    color = color_select.value\n",
    "\n",
    "else:\n",
    "    style_select = mr.Select(value='charles', choices=['classic', 'default', 'charles', 'binance', 'blueskies', 'brasil', 'checkers', 'kenan', 'ibd', 'mike', 'nightclouds', 'sas', 'starsandstripes', 'yahoo'], label='Candle style:')\n",
    "    candle_style = style_select.value\n",
    "\n",
    "if not error:\n",
    "    mpl.plot(histdf, type=plot_type.value, volume=volume_bool.value, linecolor=color, ylabel='Price in $', style=candle_style, figsize=(20,10), warn_too_much_data=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}