{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "70ba2c8a",
   "metadata": {},
   "source": [
    "## Notebook setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "driven-billion",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import io\n",
    "import warnings\n",
    "import datetime\n",
    "import numpy as np\n",
    "import base64\n",
    "import pandas as pd\n",
    "from typing import List\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "import openbb_terminal.config_terminal as cfg\n",
    "from openbb_terminal.helper_classes import TerminalStyle\n",
    "from openbb_terminal import OpenBBFigure, theme\n",
    "\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.5f\" % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24ed42f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppressing sdk logs\n",
    "from openbb_terminal.core.session.current_system import set_system_variable\n",
    "\n",
    "set_system_variable(\"LOGGING_SUPPRESS\", True)\n",
    "\n",
    "# Import the OpenBB SDK\n",
    "from openbb_terminal.sdk import openbb, widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb_terminal.core.session.current_user import get_current_user\n",
    "\n",
    "user = get_current_user()\n",
    "\n",
    "cfg.theme = TerminalStyle(\n",
    "    user.preferences.MPL_STYLE,\n",
    "    user.preferences.PMF_STYLE,\n",
    "    user.preferences.RICH_STYLE,\n",
    ")\n",
    "theme.apply_style(\"light\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c52f83e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "stylesheet = widgets.html_report_stylesheet()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "807fda4e",
   "metadata": {},
   "source": [
    "## Select Ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "intended-sherman",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters that will be replaced when calling this notebook\n",
    "# Do not leave parameters blank as notebook will not run otherwise\n",
    "symbol = \"BTC\"\n",
    "report_name = f\"Crypto Report for {symbol}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c46efb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4897334",
   "metadata": {},
   "outputs": [],
   "source": [
    "author = \"\"\n",
    "report_title = f\"INVESTMENT RESEARCH REPORT ON {ticker.upper()}\"\n",
    "report_date = datetime.datetime.now().strftime(\"%d %B, %Y\")\n",
    "report_time = datetime.datetime.now().strftime(\"%H:%M\")\n",
    "report_timezone = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo\n",
    "report_title, report_date, report_time, report_timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35d9a943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic info\n",
    "\n",
    "basic_info = openbb.crypto.dd.basic(ticker)\n",
    "if not basic_info.empty:\n",
    "    basic_info[\"Metric\"] = basic_info[\"Metric\"].str.title()\n",
    "    basic_info[\"Metric\"] = basic_info[\"Metric\"].str.replace(\"_\", \" \")\n",
    "    basic_info = basic_info.set_index(\"Metric\")\n",
    "    basic_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "091d305e",
   "metadata": {},
   "outputs": [],
   "source": [
    "news = openbb.crypto.dd.news(symbol=ticker).head(10)\n",
    "\n",
    "if (isinstance(news, pd.DataFrame) and not news.empty) or (\n",
    "    not isinstance(news, pd.DataFrame) and news\n",
    "):\n",
    "    news = news.set_index(\"published_at\")\n",
    "    news.sort_index()\n",
    "    news[\"link\"] = news[\"link\"].apply(lambda x: f'<a href=\"{x}\">{x}</a>')\n",
    "    news\n",
    "else:\n",
    "    news = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd281ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "links = openbb.crypto.dd.links(ticker)\n",
    "\n",
    "if (isinstance(links, pd.DataFrame) and not links.empty) or (\n",
    "    not isinstance(links, pd.DataFrame) and links\n",
    "):\n",
    "    links[\"Link\"] = links[\"Link\"].apply(lambda x: f'<a href=\"{x}\">{x}</a>')\n",
    "    links\n",
    "else:\n",
    "    links = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07d09ce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "five_year = datetime.datetime.now() - datetime.timedelta(days=1825)\n",
    "three_year = datetime.datetime.now() - datetime.timedelta(days=1095)\n",
    "one_year = datetime.datetime.now() - datetime.timedelta(days=365)\n",
    "three_months = datetime.datetime.now() - datetime.timedelta(days=90)\n",
    "\n",
    "dates = {\n",
    "    \"three_months\": three_months,\n",
    "    \"one_year\": one_year,\n",
    "    \"three_year\": three_year,\n",
    "    \"five_year\": five_year,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6d6d43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter.filedialog import Open\n",
    "\n",
    "\n",
    "def get_historical_data(ticker, dict_of_dates, is_plot=True) -> List[pd.DataFrame]:\n",
    "    list_of_historical_data = []\n",
    "\n",
    "    for date in dict_of_dates:\n",
    "        data = openbb.crypto.load(\n",
    "            symbol=ticker,\n",
    "            to_symbol=\"usd\",\n",
    "            start_date=dict_of_dates[date],\n",
    "            source=\"YahooFinance\",\n",
    "        )\n",
    "\n",
    "        if data.empty:\n",
    "            continue\n",
    "\n",
    "        # we'll be using the adjusted close price as same as close price\n",
    "        data[\"Adj Close\"] = data[\"Close\"]\n",
    "\n",
    "        if is_plot:\n",
    "            fig = OpenBBFigure(yaxis_title=\"USDT\").set_title(\n",
    "                f\"{date} historical data for {ticker.upper()}\"\n",
    "            )\n",
    "            fig.add_scatter(x=data.index, y=data[\"Adj Close\"], name=\"Close\")\n",
    "\n",
    "            plot_data = fig.to_html()\n",
    "\n",
    "            list_of_historical_data.append(plot_data)\n",
    "        else:\n",
    "            list_of_historical_data.append(data)\n",
    "\n",
    "    return list_of_historical_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed19a708",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    historical_data = get_historical_data(ticker, dates)\n",
    "except Exception as e:\n",
    "    raise KeyboardInterrupt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f815dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data_one_year = get_historical_data(ticker, {\"one_year\": one_year}, False)[0]\n",
    "historical_data_one_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "081a4bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb_terminal.cryptocurrency.cryptocurrency_helpers import show_quick_performance\n",
    "\n",
    "show_quick_performance(\n",
    "    crypto_df=historical_data_one_year,\n",
    "    symbol=ticker,\n",
    "    current_currency=\"USDT\",\n",
    "    source=\"YahooFinance\",\n",
    "    exchange=\"binance\",\n",
    "    interval=\"1440\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88af0fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "headlines = openbb.crypto.dd.headlines_chart(ticker, external_axes=True)\n",
    "\n",
    "if headlines:\n",
    "    headlines = headlines.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4509c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "candle_one_year = openbb.crypto.chart(\n",
    "    historical_data_one_year,\n",
    "    to_symbol=ticker,\n",
    "    from_symbol=\"USDT\",\n",
    "    source=\"YahooFinance\",\n",
    "    exchange=\"binance\",\n",
    "    interval=\"1440\",\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if candle_one_year:\n",
    "    candle_one_year = candle_one_year.to_html()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7f83daae",
   "metadata": {},
   "source": [
    "## Due Diligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93d66cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# market cap dominance\n",
    "mcapdom_df = openbb.crypto.dd.mcapdom(symbol=ticker)\n",
    "\n",
    "if (isinstance(mcapdom_df, pd.DataFrame) and not mcapdom_df.empty) or (\n",
    "    not isinstance(mcapdom_df, pd.DataFrame) and mcapdom_df\n",
    "):\n",
    "    mcapdom = openbb.crypto.dd.mcapdom_chart(symbol=ticker, external_axes=True)\n",
    "\n",
    "    if mcapdom:\n",
    "        mcapdom = mcapdom.to_html()\n",
    "else:\n",
    "    mcapdom_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "284b0586",
   "metadata": {},
   "outputs": [],
   "source": [
    "# roadmap\n",
    "roadmap_model = openbb.crypto.dd.rm(symbol=ticker)\n",
    "\n",
    "if (isinstance(roadmap_model, pd.DataFrame) and not roadmap_model.empty) or (\n",
    "    not isinstance(roadmap_model, pd.DataFrame) and roadmap_model\n",
    "):\n",
    "    roadmap_model = roadmap_model.set_index(\"Date\")\n",
    "    roadmap_model.sort_index()\n",
    "\n",
    "    roadmap_view = openbb.crypto.dd.rm_chart(symbol=ticker, external_axes=True)\n",
    "    if roadmap_view:\n",
    "        roadmap_view = roadmap_view.to_html()\n",
    "\n",
    "else:\n",
    "    roadmap_model = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ddbc654",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenomics_model = openbb.crypto.dd.rm(symbol=ticker)\n",
    "\n",
    "if (isinstance(tokenomics_model, pd.DataFrame) and not tokenomics_model.empty) or (\n",
    "    not isinstance(tokenomics_model, pd.DataFrame) and tokenomics_model\n",
    "):\n",
    "    tokenomics_model = tokenomics_model.set_index(\"Date\")\n",
    "    tokenomics_model.sort_index()\n",
    "\n",
    "    tokenomics_view = openbb.crypto.dd.tk_chart(symbol=ticker, external_axes=True)\n",
    "    if tokenomics_view:\n",
    "        tokenomics_view = tokenomics_view.to_html()\n",
    "\n",
    "else:\n",
    "    tokenomics_model = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d02fcc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fundraise\n",
    "fundraise = openbb.crypto.dd.fr(symbol=ticker)  # this returns a tuple\n",
    "\n",
    "if fundraise:\n",
    "    fundraise_info = fundraise[0]\n",
    "    fundraise_df = fundraise[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e625a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_pair_info = openbb.crypto.dd.trading_pair_info(f\"{ticker}-USDT\")\n",
    "if (isinstance(trading_pair_info, pd.DataFrame) and not trading_pair_info.empty) or (\n",
    "    not isinstance(trading_pair_info, pd.DataFrame) and trading_pair_info\n",
    "):\n",
    "    trading_pair_info[\"Metric\"] = trading_pair_info[\"Metric\"].str.title()\n",
    "    trading_pair_info[\"Metric\"] = trading_pair_info[\"Metric\"].str.replace(\"_\", \" \")\n",
    "    trading_pair_info = trading_pair_info.set_index(\"Metric\")\n",
    "    trading_pair_info\n",
    "else:\n",
    "    trading_pair_info = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f33b4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openbb_terminal.cryptocurrency.due_diligence.pycoingecko_model as gecko\n",
    "from openbb_terminal.cryptocurrency.cryptocurrency_helpers import get_coingecko_id\n",
    "\n",
    "ath = pd.DataFrame()\n",
    "try:\n",
    "    cg_id = get_coingecko_id(symbol=ticker)\n",
    "    coin = gecko.Coin(ticker)\n",
    "    ath = coin.get_all_time_high(currency=cg_id)\n",
    "\n",
    "except Exception as e:\n",
    "    print(\"Reached coingecko API limit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a82e990",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openbb_terminal.cryptocurrency.due_diligence.pycoingecko_model as gecko\n",
    "from openbb_terminal.cryptocurrency.cryptocurrency_helpers import get_coingecko_id\n",
    "\n",
    "atl = pd.DataFrame()\n",
    "try:\n",
    "    cg_id = get_coingecko_id(symbol=ticker)\n",
    "    coin = gecko.Coin(ticker)\n",
    "    atl = coin.get_all_time_low(currency=cg_id)\n",
    "\n",
    "except Exception as e:\n",
    "    print(\"Reached coingecko API limit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e512e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# market stats about loaded coin\n",
    "\n",
    "market = openbb.crypto.dd.mkt(ticker)\n",
    "\n",
    "if (isinstance(market, pd.DataFrame) and not market.empty) or (\n",
    "    not isinstance(market, pd.DataFrame) and market\n",
    "):\n",
    "    market = market.set_index(\"exchange\")\n",
    "\n",
    "    market[\"market_url\"] = market[\"market_url\"].apply(\n",
    "        lambda x: f'<a href=\"{x}\">{x}</a>'\n",
    "    )\n",
    "    market\n",
    "else:\n",
    "    market = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d6e76cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all exchanges where loaded coin is listed\n",
    "ex = openbb.crypto.dd.ex(ticker)\n",
    "\n",
    "if (isinstance(ex, pd.DataFrame) and not ex.empty) or (\n",
    "    not isinstance(ex, pd.DataFrame) and ex\n",
    "):\n",
    "    ex = ex.set_index(\"id\")\n",
    "    ex\n",
    "else:\n",
    "    ex = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fcdbc67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# last trades\n",
    "trades = openbb.crypto.dd.trades(symbol=ticker, exchange_id=\"gemini\", to_symbol=\"USD\")\n",
    "if (isinstance(trades, pd.DataFrame) and not trades.empty) or (\n",
    "    not isinstance(trades, pd.DataFrame) and trades\n",
    "):\n",
    "    trades = trades.set_index(\"Date\")\n",
    "    trades\n",
    "else:\n",
    "    trades = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "262d190a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# order book\n",
    "order_book_raw = openbb.crypto.dd.ob(\n",
    "    symbol=ticker, exchange=\"gemini\", to_symbol=\"USD\"\n",
    ")  # this returns a dict\n",
    "\n",
    "if order_book_raw:\n",
    "    order_book = openbb.crypto.dd.ob_chart(\n",
    "        symbol=ticker, exchange=\"gemini\", to_symbol=\"USD\", external_axes=True\n",
    "    )\n",
    "    if order_book:\n",
    "        order_book = order_book.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "679d0d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# price and supply related metrics for loaded coin\n",
    "\n",
    "ps = openbb.crypto.dd.ps(ticker)\n",
    "if (isinstance(ps, pd.DataFrame) and not ps.empty) or (\n",
    "    not isinstance(ps, pd.DataFrame) and ps\n",
    "):\n",
    "    ps = ps.set_index(\"Metric\")\n",
    "    ps\n",
    "else:\n",
    "    ps = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e76d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# events\n",
    "\n",
    "events = openbb.crypto.dd.events(ticker)\n",
    "\n",
    "if (isinstance(events, pd.DataFrame) and not events.empty) or (\n",
    "    not isinstance(events, pd.DataFrame) and events\n",
    "):\n",
    "    events = events.set_index(\"date\")\n",
    "    events[\"link\"] = events[\"link\"].apply(lambda x: f'<a href=\"{x}\">{x}</a>')\n",
    "else:\n",
    "    events = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a14130db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# social media\n",
    "\n",
    "from openbb_terminal.cryptocurrency.due_diligence.pycoingecko_view import display_social\n",
    "\n",
    "display_social(ticker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f89627f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tweets for loaded coin\n",
    "\n",
    "tweets = openbb.crypto.dd.twitter(ticker)\n",
    "tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bb75443",
   "metadata": {},
   "outputs": [],
   "source": [
    "# github activity over time\n",
    "\n",
    "gh_data = openbb.crypto.dd.gh(symbol=ticker)\n",
    "\n",
    "if (isinstance(gh_data, pd.DataFrame) and not gh_data.empty) or (\n",
    "    not isinstance(gh_data, pd.DataFrame) and gh_data\n",
    "):\n",
    "    gh = openbb.crypto.dd.gh_chart(symbol=ticker, external_axes=True)\n",
    "    if gh:\n",
    "        gh = gh.to_html()\n",
    "else:\n",
    "    gh_data = pd.DataFrame()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fcaa973d",
   "metadata": {},
   "source": [
    "## TA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "477317b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker_data = historical_data_one_year.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af3695c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple moving average\n",
    "ticker_data.index.names = [\"date\"]\n",
    "\n",
    "sma = openbb.ta.ma_chart(\n",
    "    data=ticker_data,\n",
    "    ma_type=\"SMA\",\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if sma:\n",
    "    sma = sma.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d5089fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# exponential moving average\n",
    "ema = openbb.ta.ma_chart(\n",
    "    data=ticker_data,\n",
    "    ma_type=\"EMA\",\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if ema:\n",
    "    ema = ema.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5facfbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# zero lag exponential moving average\n",
    "\n",
    "zlma = openbb.ta.ma_chart(\n",
    "    data=ticker_data,\n",
    "    ma_type=\"ZLMA\",\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if zlma:\n",
    "    zlma = zlma.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df54f7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# commodity channel index\n",
    "\n",
    "cci = openbb.ta.cci_chart(\n",
    "    data=ticker_data,\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if cci:\n",
    "    cci = cci.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f53469eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# moving average convergence/divergence\n",
    "\n",
    "macd = openbb.ta.macd_chart(\n",
    "    data=ticker_data,\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if macd:\n",
    "    macd = macd.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd79e6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fisher transform\n",
    "\n",
    "fisher = openbb.ta.fisher_chart(\n",
    "    data=ticker_data,\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if fisher:\n",
    "    fisher = fisher.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b699e61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# aroon indicator\n",
    "\n",
    "aroon = openbb.ta.aroon_chart(\n",
    "    data=ticker_data,\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if aroon:\n",
    "    aroon = aroon.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79401027",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bollinger bands\n",
    "\n",
    "bbands = openbb.ta.bbands_chart(\n",
    "    data=ticker_data,\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if bbands:\n",
    "    bbands = bbands.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b5e2949",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fibonacci retracement\n",
    "\n",
    "fib = openbb.ta.fib_chart(\n",
    "    data=ticker_data,\n",
    "    symbol=ticker,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if fib:\n",
    "    fib = fib.to_html()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "48847a7f",
   "metadata": {},
   "source": [
    "## QA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77b373b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# summary\n",
    "\n",
    "summary = openbb.qa.summary(data=ticker_data)\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ecfc93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normality statistics and tests\n",
    "\n",
    "# openbb.qa.normality_chart(\n",
    "#     data=ticker_data,\n",
    "#     target=\"Close\",\n",
    "# )\n",
    "\n",
    "normality = openbb.qa.normality(\n",
    "    data=ticker_data[\"Close\"],\n",
    ")\n",
    "normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8abe96b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# box and whisker plot\n",
    "\n",
    "bw = openbb.qa.bw(\n",
    "    symbol=ticker,\n",
    "    data=ticker_data,\n",
    "    target=\"Close\",\n",
    "    yearly=False,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if bw:\n",
    "    bw = bw.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66a7dccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rolling mean and std deviation of prices\n",
    "\n",
    "rolling = openbb.qa.rolling_chart(\n",
    "    symbol=ticker,\n",
    "    data=ticker_data,\n",
    "    target=\"Close\",\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if rolling:\n",
    "    rolling.update_layout(\n",
    "        legend=dict(\n",
    "            x=0.01,\n",
    "            y=0.99,\n",
    "            xanchor=\"left\",\n",
    "            yanchor=\"top\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            bgcolor=\"rgba(0, 0, 0, 0)\",\n",
    "        ),\n",
    "        legend2=dict(bgcolor=\"rgba(0, 0, 0, 0)\"),\n",
    "    )\n",
    "    rolling = rolling.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b43dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rolling kurtosis of distribution of prices\n",
    "\n",
    "kurtosis = openbb.qa.kurtosis_chart(\n",
    "    symbol=ticker,\n",
    "    data=ticker_data,\n",
    "    target=\"Close\",\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if kurtosis:\n",
    "    kurtosis = kurtosis.to_html()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac501a32-124d-4d21-aae1-a8d147bb1071",
   "metadata": {},
   "source": [
    "## Render the report template to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12",
   "metadata": {},
   "outputs": [],
   "source": [
    "body = \"\"\n",
    "\n",
    "\n",
    "body += widgets.header(\n",
    "    author,\n",
    "    report_date,\n",
    "    report_time,\n",
    "    report_timezone,\n",
    "    f\"<b>INVESTMENT RESEARCH REPORT:</b> {ticker}\",\n",
    "    plotly_js=True,\n",
    ")\n",
    "\n",
    "body += widgets.tablinks(\n",
    "    [\n",
    "        \"SUMMARY\",\n",
    "        \"Due Diligence\",\n",
    "        \"Technical Analysis\",\n",
    "        \"Quantitative Analysis\",\n",
    "    ]\n",
    ")\n",
    "\n",
    "comment = \"\"\n",
    "\n",
    "# Summary tab\n",
    "htmlcode = \"\"\n",
    "htmlcode += widgets.row(\n",
    "    [widgets.h(3, \"Basic information about loaded coin\") + basic_info.to_html()]\n",
    ")\n",
    "if not links.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Links\") + links.to_html(escape=False)])\n",
    "\n",
    "if not news.empty:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, \"Published news\") + news.to_html(escape=False)]\n",
    "    )\n",
    "\n",
    "htmlcode += widgets.row([widgets.h(3, \"Historical data\")])\n",
    "for data in historical_data:\n",
    "    htmlcode += widgets.row([data])\n",
    "\n",
    "htmlcode += widgets.row([widgets.h(3, \"1-year historical data candlestick chart\")])\n",
    "htmlcode += widgets.row([candle_one_year])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Sentiment analysis\")])\n",
    "htmlcode += widgets.row([headlines])\n",
    "# TODO: print rich table\n",
    "# htmlcode += widgets.row([widgets.h(3, \"Quick performance\")])\n",
    "# htmlcode += widgets.row([quick_performance])\n",
    "body += widgets.add_tab(\"SUMMARY\", htmlcode)\n",
    "\n",
    "# # Due diligence tab\n",
    "htmlcode = \"\"\n",
    "if not trading_pair_info.empty:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, f\"Trading pair info {ticker}/USDT\") + trading_pair_info.to_html()]\n",
    "    )\n",
    "if not mcapdom_df.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Market cap dominance\")])\n",
    "    htmlcode += widgets.row([mcapdom])\n",
    "\n",
    "if not roadmap_model.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Roadmap\") + roadmap_model.to_html()])\n",
    "    htmlcode += widgets.row([roadmap_view])\n",
    "\n",
    "if not tokenomics_model.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Tokenomics\") + tokenomics_model.to_html()])\n",
    "    htmlcode += widgets.row([tokenomics_view])\n",
    "\n",
    "if fundraise:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Fundraising Details\")])\n",
    "    htmlcode += widgets.row([widgets.p(fundraise_info)])\n",
    "    htmlcode += widgets.row([fundraise_df.to_html()])\n",
    "\n",
    "if not ath.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"All time high\")])\n",
    "    htmlcode += widgets.row([ath.to_html()])\n",
    "\n",
    "if not atl.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"All time low\")])\n",
    "    htmlcode += widgets.row([atl.to_html()])\n",
    "\n",
    "if not market.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"All markets\")])\n",
    "    htmlcode += widgets.row([market.sample(n=20).sort_index().to_html(escape=False)])\n",
    "\n",
    "if not ex.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Exchanges where loaded coin is listed\")])\n",
    "    htmlcode += widgets.row([ex.sample(n=20).sort_index().to_html()])\n",
    "\n",
    "if not trades.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Last trades\")])\n",
    "    htmlcode += widgets.row([trades.tail(20).to_html()])\n",
    "\n",
    "if order_book_raw:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Order book\")])\n",
    "    htmlcode += widgets.row([order_book])\n",
    "\n",
    "if not ps.empty:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, \"Price and supply related metrics for loaded coin\")]\n",
    "    )\n",
    "    htmlcode += widgets.row([ps.to_html()])\n",
    "\n",
    "if not events.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Events\")])\n",
    "    htmlcode += widgets.row([events.to_html(escape=False)])\n",
    "\n",
    "# TODO: print rich table\n",
    "# htmlcode += widgets.row([widgets.h(3, \"Tweets for loaded coin\")])\n",
    "# htmlcode += widgets.row([tweets])\n",
    "# htmlcode += widgets.row([widgets.h(3, \"Social media\")])\n",
    "# htmlcode += widgets.row([social.to_html()])\n",
    "\n",
    "if not gh_data.empty:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Github activity over time\")])\n",
    "    htmlcode += widgets.row([gh])\n",
    "else:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Github activity over time\")])\n",
    "    htmlcode += widgets.row([widgets.p(\"No data available.\")])\n",
    "\n",
    "body += widgets.add_tab(\"Due Diligence\", htmlcode)\n",
    "\n",
    "# Technical Analysis tab\n",
    "htmlcode = \"\"\n",
    "htmlcode = widgets.row([widgets.h(3, \"Simple moving average\")])\n",
    "htmlcode += widgets.row([sma])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Exponential moving average\")])\n",
    "htmlcode += widgets.row([ema])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Zero lag exponential moving average\")])\n",
    "htmlcode += widgets.row([zlma])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Commodity channel index\")])\n",
    "htmlcode += widgets.row([cci])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Moving average convergence/divergence\")])\n",
    "htmlcode += widgets.row([macd])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Fisher transform\")])\n",
    "htmlcode += widgets.row([fisher])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Aroon indicator\")])\n",
    "htmlcode += widgets.row([aroon])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Bollinger bands\")])\n",
    "htmlcode += widgets.row([bbands])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Fibonacci retracement\")])\n",
    "htmlcode += widgets.row([fib])\n",
    "body += widgets.add_tab(\"Technical Analysis\", htmlcode)\n",
    "\n",
    "# Quantitative Analysis tab\n",
    "htmlcode = \"\"\n",
    "htmlcode = widgets.row([widgets.h(3, \"Summary\") + summary.to_html()])\n",
    "htmlcode += widgets.row(\n",
    "    [widgets.h(3, \"Normality statistics and tests\") + normality.to_html()]\n",
    ")\n",
    "htmlcode += widgets.row([widgets.h(3, \"Box and whisker plot\")])\n",
    "htmlcode += widgets.row([bw])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Rolling mean and std deviation of prices\")])\n",
    "htmlcode += widgets.row([rolling])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Rolling kurtosis of distribution of prices\")])\n",
    "htmlcode += widgets.row([kurtosis])\n",
    "body += widgets.add_tab(\"Quantitative Analysis\", htmlcode)\n",
    "\n",
    "body += widgets.tab_clickable_and_save_evt()\n",
    "report = widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)\n",
    "\n",
    "# # to save the results\n",
    "with open(report_name + \".html\", \"w\", encoding=\"utf-8\") as fh:\n",
    "    fh.write(report)"
   ]
  }
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