OpenBB/openbb_terminal/reports/templates/equity.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "70ba2c8a",
   "metadata": {},
   "source": [
    "## Notebook setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "driven-billion",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "from openbb_terminal import config_terminal as cfg\n",
    "from openbb_terminal.helper_classes import TerminalStyle\n",
    "from openbb_terminal import OpenBBFigure\n",
    "from openbb_terminal.core.plots.plotly_ta.ta_class import PlotlyTA\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "150e5103",
   "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": "cf31bcc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb_terminal.core.session.current_user import get_current_user\n",
    "\n",
    "user = get_current_user()\n",
    "\n",
    "from openbb_terminal import theme\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": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370",
   "metadata": {},
   "outputs": [],
   "source": [
    "stylesheet = widgets.html_report_stylesheet()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "807fda4e",
   "metadata": {},
   "source": [
    "## Select symbol\n"
   ]
  },
  {
   "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 = \"MSFT\"\n",
    "report_name = f\"Equity report for {symbol}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a441ddbc-d75b-4a18-84d4-e01b677674d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "if \".\" in symbol:\n",
    "    import sys\n",
    "\n",
    "    sys.exit(0)\n",
    "symbol = symbol.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker_data = openbb.stocks.load(\n",
    "    symbol=symbol, start_date=datetime.datetime.now() - datetime.timedelta(days=18 * 30)\n",
    ")\n",
    "ticker_data = openbb.stocks.process_candle(data=ticker_data)\n",
    "\n",
    "author = \"\"\n",
    "report_title = f\"Investment Research Report on {symbol.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": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "info = openbb.stocks.fa.overview(symbol=symbol, source=\"Finviz\").transpose()\n",
    "\n",
    "info\n",
    "try:\n",
    "    company_name = info[\"Company\"][0]\n",
    "except:\n",
    "    company_name = \"N/A\"\n",
    "try:\n",
    "    company_sector = info[\"Sector\"][0]\n",
    "except:\n",
    "    company_sector = \"N/A\"\n",
    "\n",
    "try:\n",
    "    company_industry = info[\"Industry\"][0]\n",
    "except:\n",
    "    company_industry = \"N/A\"\n",
    "\n",
    "\n",
    "overview = [company_name, company_sector, company_industry]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f8a0d747",
   "metadata": {},
   "source": [
    "## Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aca4b6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    (\n",
    "        df_year_estimates,\n",
    "        df_quarter_earnings,\n",
    "        df_quarter_revenues,\n",
    "    ) = openbb.stocks.fa.est(symbol=symbol)\n",
    "    df_quarter_revenues.transpose()\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    df_year_estimates = pd.DataFrame()\n",
    "    df_quarter_earnings = pd.DataFrame()\n",
    "    df_quarter_revenues = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0f0a1ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_year = sorted(df_year_estimates.columns.tolist())[:3]\n",
    "df_year_estimates = df_year_estimates[display_year].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6976c1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tables = openbb.stocks.news(symbol)\n",
    "\n",
    "for table in tables:\n",
    "    if not isinstance(table, pd.DataFrame):\n",
    "        continue\n",
    "\n",
    "    table[0].loc[\"URL\"] = table[0].loc[\"URL\"].apply(lambda x: f'<a href=\"{x}\">{x}</a>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e38d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "quote_data = info[[\"Price\", \"Prev Close\", \"Change\", \"Volume\", \"52W Range\", \"Perf YTD\"]]\n",
    "quote_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde46353",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df_institutional_shareholders = openbb.stocks.fa.shrs(\n",
    "        symbol, holder=\"institutional\"\n",
    "    )\n",
    "    df_institutional_shareholders.index += 1\n",
    "    df_institutional_shareholders\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    df_institutional_shareholders = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3022130e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this command works 1/2 the time - really need to fix this - but this will catch the errors and stop it from breaking the report\n",
    "try:\n",
    "    df_sec_filings = openbb.stocks.fa.sec(symbol)[\n",
    "        [\"Company Name\", \"Reporting Owner\", \"Form Type\", \"Period\", \"View\"]\n",
    "    ].head(5)\n",
    "    df_sec_filings\n",
    "except:\n",
    "    df_sec_filings = \"Error with Nasdaq API\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4662b1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_analyst = openbb.stocks.fa.pt(symbol=symbol).sort_index(ascending=False)\n",
    "# print(df_analyst)\n",
    "# if not df_analyst.empty:\n",
    "#    if \"target\" in df_analyst.columns:\n",
    "#        if \"target_to\" in df_analyst.columns:\n",
    "#            df_analyst[\"target\"] = df_analyst[\"target_to\"].combine_first(\n",
    "#                df_analyst[\"target\"]\n",
    "#            )\n",
    "#        else:\n",
    "#            df_analyst[\"target\"] = df_analyst[\"target\"]\n",
    "#        df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target\"]].rename(\n",
    "#            columns={\n",
    "#                \"category\": \"Category\",\n",
    "#                \"analyst\": \"Analyst\",\n",
    "#                \"rating\": \"Rating\",\n",
    "#                \"target\": \"Price Target\",\n",
    "#            }\n",
    "#        )\n",
    "df_analyst.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df8bdb7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df_rating = openbb.stocks.fa.rating(symbol)\n",
    "    df_rating\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    df_rating = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0df953b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcr_chart = openbb.stocks.options.pcr_chart(symbol, window=30, external_axes=True)\n",
    "\n",
    "if pcr_chart:\n",
    "    pcr_chart = pcr_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd79195c",
   "metadata": {},
   "outputs": [],
   "source": [
    "current_price = 0\n",
    "try:\n",
    "    current_price = float(ticker_data[\"Adj Close\"][-1])\n",
    "    expiry_dates = openbb.stocks.options.expirations(symbol)\n",
    "\n",
    "    if expiry_dates:\n",
    "        exp = expiry_dates[0]\n",
    "        option_chain = openbb.stocks.options.chains(symbol, expiration=exp)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    expiry_dates = []\n",
    "    option_chain = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad6d0c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "if expiry_dates:\n",
    "    vol_chart = openbb.stocks.options.vol(\n",
    "        option_chain, current_price, symbol, exp, external_axes=True\n",
    "    )\n",
    "\n",
    "    if vol_chart:\n",
    "        vol_chart = vol_chart.to_html()\n",
    "\n",
    "    voi_chart = openbb.stocks.options.voi(\n",
    "        option_chain, current_price, symbol, exp, external_axes=True\n",
    "    )\n",
    "\n",
    "    if voi_chart:\n",
    "        voi_chart = voi_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "324e2d4b-b2f6-4da3-bac4-ef785ad4e436",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_short_position = openbb.stocks.dps.spos_chart(\n",
    "    symbol=symbol,\n",
    "    limit=84,\n",
    "    raw=False,\n",
    "    export=\"\",\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if net_short_position:\n",
    "    net_short_position = net_short_position.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac0bfbda-e4b8-4b3b-b8e7-c02639fdb971",
   "metadata": {},
   "outputs": [],
   "source": [
    "dark_pools = openbb.stocks.dps.dpotc_chart(symbol=symbol, external_axes=True)\n",
    "\n",
    "if dark_pools:\n",
    "    dark_pools = dark_pools.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a7266f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtrades_chart = openbb.stocks.gov.gtrades_chart(\n",
    "    symbol,\n",
    "    past_transactions_months=12,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if gtrades_chart:\n",
    "    gtrades_chart = gtrades_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c670ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_contracts_chart = openbb.stocks.gov.contracts_chart(\n",
    "    symbol,\n",
    "    past_transaction_days=365,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if gov_contracts_chart:\n",
    "    gov_contracts_chart = gov_contracts_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caa3dae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "google_mentions_chart = openbb.stocks.ba.mentions_chart(\n",
    "    symbol,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if google_mentions_chart:\n",
    "    google_mentions_chart = google_mentions_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b77e60d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "google_regions_chart = openbb.stocks.ba.regions_chart(\n",
    "    symbol,\n",
    "    limit=10,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if google_regions_chart:\n",
    "    google_regions_chart = google_regions_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78593369",
   "metadata": {},
   "outputs": [],
   "source": [
    "similar_companies = openbb.stocks.ca.similar(symbol, source=\"Finviz\")[0:5]\n",
    "if similar_companies:\n",
    "    similar_companies.append(symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4639665b",
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_similar = openbb.stocks.ca.hist_chart(\n",
    "    similar_companies,\n",
    "    external_axes=True,\n",
    "    normalize=False,\n",
    ")\n",
    "\n",
    "if historical_similar:\n",
    "    historical_similar = historical_similar.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74565e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "hcorr_similar = openbb.stocks.ca.hcorr_chart(\n",
    "    similar_companies,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if hcorr_similar:\n",
    "    hcorr_similar = hcorr_similar.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936babfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "vol_similar = openbb.stocks.ca.volume_chart(\n",
    "    similar_companies,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if vol_similar:\n",
    "    vol_similar = vol_similar.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99388d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scorr_similar = openbb.stocks.ca.scorr_chart(\n",
    "    similar_companies,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if scorr_similar:\n",
    "    scorr_similar = scorr_similar.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaa67343",
   "metadata": {},
   "outputs": [],
   "source": [
    "income_comparison = openbb.stocks.ca.income(similar_companies)\n",
    "income_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13ad71cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df_related_queries = openbb.stocks.ba.queries(symbol)\n",
    "    df_related_queries.index += 1\n",
    "    df_related_queries.index\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58c06129",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df_rising_queries = openbb.stocks.ba.rise(symbol)\n",
    "    df_rising_queries.index += 1\n",
    "    df_rising_queries\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a467f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_histcont_chart = openbb.stocks.gov.histcont_chart(\n",
    "    symbol,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if gov_histcont_chart:\n",
    "    gov_histcont_chart = gov_histcont_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "730cae26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lobbying = openbb.stocks.gov.lobbying(symbol, limit=5)\n",
    "df_lobbying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8bb53c1-147e-4342-af61-0d0a9cc56e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "price_vs_short_interest = openbb.stocks.dps.psi_sg_chart(\n",
    "    symbol=symbol, external_axes=True\n",
    ")\n",
    "\n",
    "if price_vs_short_interest:\n",
    "    price_vs_short_interest = price_vs_short_interest.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394668e1-1158-4a2b-8a38-04c72a6abdd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "price_chart = openbb.stocks.candle(\n",
    "    symbol=symbol,\n",
    "    data=ticker_data,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if price_chart:\n",
    "    price_chart = price_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "900686a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    price_target_chart = openbb.stocks.fa.pt_chart(\n",
    "        symbol=symbol,\n",
    "        start_date=\"2022-01-01\",\n",
    "        data=ticker_data,\n",
    "        limit=10,\n",
    "        raw=False,\n",
    "        external_axes=True,\n",
    "    )\n",
    "\n",
    "    if price_target_chart:\n",
    "        price_target_chart = price_target_chart.to_html()\n",
    "except Exception:\n",
    "    price_target_chart = OpenBBFigure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61d62e1a-199e-481d-b57d-38ab486951be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_analyst.copy()\n",
    "avg_ratings_last_30_days = 0\n",
    "days = 0\n",
    "if not df.empty:\n",
    "    df_ratings = df[datetime.datetime.now() - datetime.timedelta(days=days) :]\n",
    "    while df_ratings.empty:\n",
    "        days += 30\n",
    "        df_ratings = df[datetime.datetime.now() - datetime.timedelta(days=days) :]\n",
    "\n",
    "        if days > 100:\n",
    "            break\n",
    "\n",
    "    if not df_ratings.empty:\n",
    "        avg_ratings_last_30_days = round(np.mean(df_ratings[\"Price Target\"].values), 2)\n",
    "    else:\n",
    "        avg_ratings = 0\n",
    "\n",
    "last_price = round(ticker_data[\"Close\"][-1], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3733ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_over_time_chart = openbb.stocks.fa.rot_chart(\n",
    "    symbol=symbol,\n",
    "    limit=10,\n",
    "    raw=False,\n",
    "    export=\"\",\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if ratings_over_time_chart:\n",
    "    ratings_over_time_chart = ratings_over_time_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "924f8042-e58a-449c-91a3-7923158ff714",
   "metadata": {},
   "outputs": [],
   "source": [
    "ta_rsi = openbb.ta.rsi_chart(ticker_data[\"Close\"], external_axes=True)\n",
    "\n",
    "if ta_rsi:\n",
    "    ta_rsi = ta_rsi.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "953a340e-7ea1-4aa3-ae1d-872683dced41",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = openbb.ta.rsi(ticker_data[\"Close\"])\n",
    "rsi_value = round(df.values[-1][0], 2)\n",
    "rsi_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dce71711-20b3-4c69-af52-e0f9a69991f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression().fit(\n",
    "    np.array(range(len(ticker_data[\"Close\"][-30:].index))).reshape(-1, 1),\n",
    "    ticker_data[\"Close\"][-30:].values,\n",
    ")\n",
    "regression_slope = round(model.coef_[0], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6272e34b-9511-4b48-9eea-ea882ce9846a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_insider = pd.DataFrame.from_dict(openbb.stocks.ins.lins(symbol=symbol)).head(10)\n",
    "if not df_insider.empty:\n",
    "    df_insider[\"Val ($)\"] = df_insider[\"Value ($)\"].replace({\",\": \"\"}, regex=True)\n",
    "    df_insider[\"Trade\"] = df_insider.apply(\n",
    "        lambda row: (-1 * float(row[\"Val ($)\"]))\n",
    "        if row[\"Transaction\"] == \"Sale\"\n",
    "        else (float(row[\"Val ($)\"]) if row[\"Transaction\"] == \"Buy\" else 0),\n",
    "        axis=1,\n",
    "    )\n",
    "    last_10_insider_trading = round(sum(df_insider[\"Trade\"]) / 1_000_000, 2)\n",
    "    df_insider = df_insider.drop(columns=[\"Val ($)\", \"Trade\"])\n",
    "    df_insider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29254666-58c6-4686-87e6-2635ad8387e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "finbrain_sentiment = openbb.stocks.ba.headlines_chart(symbol=symbol, external_axes=True)\n",
    "\n",
    "if finbrain_sentiment:\n",
    "    finbrain_sentiment = finbrain_sentiment.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8251b47-8ecc-47be-83f3-d9f108596898",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df_sentiment_finbrain = openbb.stocks.ca.sentiment(symbols=[symbol])\n",
    "    finbrain_sentiment_val = float(df_sentiment_finbrain.values[-1][0])\n",
    "except Exception as e:\n",
    "    finbrain_sentiment_val = \"N/A\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fe6e513-d568-41b4-8aa0-37cc578c9dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "(\n",
    "    watchlist_count,\n",
    "    n_cases,\n",
    "    n_bull,\n",
    "    n_bear,\n",
    ") = openbb.stocks.ba.bullbear(symbol=symbol)\n",
    "stocktwits_sentiment = f\"Watchlist count: {watchlist_count}</br>\"\n",
    "if n_cases > 0:\n",
    "    stocktwits_sentiment += f\"\\nLast {n_cases} sentiment messages:</br>\"\n",
    "    stocktwits_sentiment += f\"Bullish {round(100*n_bull/n_cases, 2)}%</br>\"\n",
    "    stocktwits_sentiment += f\"Bearish {round(100*n_bear/n_cases, 2)}%\"\n",
    "else:\n",
    "    stocktwits_sentiment += \"No messages found\"\n",
    "stocktwits_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfb75909-b877-4b3a-82a7-21ce174a7bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "snews = openbb.stocks.ba.snews_chart(symbol, external_axes=True)\n",
    "\n",
    "if snews:\n",
    "    snews = snews.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329c9e1b-e1de-4836-91fd-656cc87f7111",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker_data_all = openbb.stocks.load(\n",
    "    symbol=symbol,\n",
    "    start_date=datetime.datetime.now() - datetime.timedelta(days=5 * 12 * 21),\n",
    "    verbose=False,\n",
    ")\n",
    "ticker_data_all[\"Returns\"] = ticker_data_all[\"Adj Close\"].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37743ef3-2f16-4166-bfcc-a0fe3eb959bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "bw_month = openbb.qa.bw(\n",
    "    ticker_data_all,\n",
    "    \"Returns\",\n",
    "    symbol,\n",
    "    yearly=False,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if bw_month:\n",
    "    bw_month = bw_month.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16b33868-23dc-4c77-ac00-acb452da386c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bw_year = openbb.qa.bw(\n",
    "    ticker_data_all,\n",
    "    \"Returns\",\n",
    "    symbol,\n",
    "    yearly=True,\n",
    "    external_axes=True,\n",
    ")\n",
    "\n",
    "if bw_year:\n",
    "    bw_year = bw_year.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28ce0827",
   "metadata": {},
   "outputs": [],
   "source": [
    "income_df = openbb.stocks.fa.income(symbol, source=\"FinancialModelingPrep\")\n",
    "data_df = openbb.stocks.fa.overview(symbol, source=\"FinancialModelingPrep\")\n",
    "mgmt_df = openbb.stocks.fa.mgmt(symbol)\n",
    "try:\n",
    "    if not mgmt_df.empty:\n",
    "        mgmt_df[\"Info\"] = mgmt_df[\"Info\"].apply(lambda x: f'<a href=\"{x}\">{x}</a>')\n",
    "        mgmt_df[\"Insider Activity\"] = mgmt_df[\"Insider Activity\"].apply(\n",
    "            lambda x: f'<a href=\"{x}\">{x}</a>' if x != \"-\" else x\n",
    "        )\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    hist_dcf = openbb.stocks.fa.dcfc(symbol)\n",
    "except:\n",
    "    pass\n",
    "\n",
    "enterprise_df = openbb.stocks.fa.enterprise(symbol)\n",
    "score = openbb.stocks.fa.score(symbol, years=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42e289ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "indicators = dict(\n",
    "    ema=dict(length=[50]),\n",
    "    sma=dict(length=[50]),\n",
    "    wma=dict(length=[50]),\n",
    ")\n",
    "\n",
    "ma_chart = PlotlyTA.plot(ticker_data, indicators, symbol, candles=False, volume=False)\n",
    "\n",
    "if ma_chart:\n",
    "    ma_chart = ma_chart.show(external=True)\n",
    "    ma_chart = ma_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8846a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "macd_chart = openbb.ta.macd_chart(\n",
    "    ticker_data[\"Close\"], symbol=symbol, external_axes=True\n",
    ")\n",
    "\n",
    "if macd_chart:\n",
    "    macd_chart = macd_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48a7a427",
   "metadata": {},
   "outputs": [],
   "source": [
    "cci_chart = openbb.ta.cci_chart(ticker_data, symbol=symbol, external_axes=True)\n",
    "\n",
    "if cci_chart:\n",
    "    cci_chart = cci_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a1bd2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "stoch_chart = openbb.ta.stoch_chart(ticker_data, symbol=symbol, external_axes=True)\n",
    "\n",
    "if stoch_chart:\n",
    "    stoch_chart = stoch_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2611d3c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "adx_chart = openbb.ta.adx_chart(ticker_data, symbol=symbol, external_axes=True)\n",
    "\n",
    "if adx_chart:\n",
    "    adx_chart = adx_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ecb6e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "bbands_chart = openbb.ta.bbands_chart(ticker_data, symbol=symbol, external_axes=True)\n",
    "\n",
    "if bbands_chart:\n",
    "    bbands_chart = bbands_chart.to_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e42fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_chart = openbb.ta.ad_chart(ticker_data, symbol=symbol, external_axes=True)\n",
    "\n",
    "if ad_chart:\n",
    "    ad_chart = ad_chart.to_html()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac501a32-124d-4d21-aae1-a8d147bb1071",
   "metadata": {},
   "source": [
    "## Render the report template to a file\n"
   ]
  },
  {
   "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",
    "    report_title,\n",
    "    plotly_js=True,\n",
    ")\n",
    "\n",
    "body += widgets.tablinks(\n",
    "    [\n",
    "        \"SUMMARY\",\n",
    "        \"Overview\",\n",
    "        \"Fundamental Analysis\",\n",
    "        \"Technical Analysis\",\n",
    "        \"Behavioural Analysis\",\n",
    "        \"Government Menu\",\n",
    "        \"Comparison Menu\",\n",
    "        \"Options\",\n",
    "        \"Dark Pool and Shorts\",\n",
    "        \"Quantitative Analysis\",\n",
    "        \"Analyst Opinions\",\n",
    "        \"Insider Trading\",\n",
    "        #    \"Prediction Techniques\",\n",
    "    ]\n",
    ")\n",
    "\n",
    "htmlcode = widgets.h(3, \"KPIs\")\n",
    "htmlcode += widgets.kpi(\n",
    "    [last_price],\n",
    "    [\n",
    "        \"Last closing price is above the average price ratings of last 30 days\",\n",
    "        \"Average price ratings of last 30 day is above last closing price\",\n",
    "    ],\n",
    "    avg_ratings_last_30_days,\n",
    ")\n",
    "htmlcode += widgets.kpi(\n",
    "    [30, 70],\n",
    "    [\"RSI level is oversold\", \"RSI level is normal\", \"RSI level is overbought\"],\n",
    "    rsi_value,\n",
    ")\n",
    "try:\n",
    "    htmlcode += widgets.kpi(\n",
    "        [0],\n",
    "        [\n",
    "            \"The sum of last 10 insider trading (in millions) was negative\",\n",
    "            \"The sum of last 10 insider trading (in millions) was positive\",\n",
    "        ],\n",
    "        last_10_insider_trading,\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    htmlcode += widgets.kpi(\n",
    "        [-0.1, 0.1],\n",
    "        [\n",
    "            \"Last FinBrain sentiment is bearish\",\n",
    "            \" Last FinBrain sentiment is neutral\",\n",
    "            \"Last FinBrain sentiment is bullish\",\n",
    "        ],\n",
    "        finbrain_sentiment_val,\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "if score and score[\"Total Score\"]:\n",
    "    htmlcode += widgets.kpi(\n",
    "        [25, 75],\n",
    "        [\n",
    "            \"Buffet Score is not favourable\",\n",
    "            \"Buffet Score is neutral\",\n",
    "            \"Buffet Score is favourable\",\n",
    "        ],\n",
    "        float(score[\"Total Score\"]),\n",
    "    )\n",
    "body += widgets.add_tab(\"SUMMARY\", htmlcode)\n",
    "\n",
    "htmlcode = \"\"\n",
    "if overview:\n",
    "    titles = [\"Company\", \"Sector\", \"Industry\"]\n",
    "    m = 0\n",
    "    for x in overview:\n",
    "        print(x)\n",
    "        htmlcode += widgets.row([widgets.h(3, titles[m]) + widgets.p(x)])\n",
    "        m += 1\n",
    "htmlcode += widgets.row([widgets.h(3, \"Price Chart\") + (price_chart or \"\")])\n",
    "\n",
    "try:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Quote\") + quote_data.to_html()])\n",
    "except:\n",
    "    pass\n",
    "\n",
    "htmlcode += widgets.row([widgets.h(3, \"Latest News for \" + symbol)])\n",
    "try:\n",
    "    for table in tables:\n",
    "        htmlcode += widgets.row(\n",
    "            [widgets.h(4, table[1][\"title\"]) + table[0].to_html(escape=False)]\n",
    "        )\n",
    "except:\n",
    "    pass\n",
    "\n",
    "if not mgmt_df.empty:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, f\"Management team of {symbol}\") + mgmt_df.to_html(escape=False)]\n",
    "    )\n",
    "body += widgets.add_tab(\"Overview\", htmlcode)\n",
    "\n",
    "htmlcode = widgets.row(\n",
    "    [widgets.h(3, \"Price Target Chart\") + (price_target_chart or \"\")]\n",
    ")\n",
    "htmlcode += widgets.row(\n",
    "    [widgets.h(3, \"Analyst Ratings over time\") + (ratings_over_time_chart or \"\")]\n",
    ")\n",
    "try:\n",
    "    htmlcode += widgets.row([widgets.h(3, \"Analyst Ratings\") + df_analyst.to_html()])\n",
    "except:\n",
    "    pass\n",
    "# try:\n",
    "#    htmlcode += widgets.row(\n",
    "#        [widgets.h(3, \"Analyst Recommendations\") + df_rating.to_html()]\n",
    "#    )\n",
    "# except:\n",
    "#    pass\n",
    "\n",
    "body += widgets.add_tab(\"Analyst Opinions\", htmlcode)\n",
    "\n",
    "try:\n",
    "    htmlcode = widgets.row(\n",
    "        [widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html()]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "\n",
    "if not df_quarter_earnings.empty:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, \"Earnings\") + df_quarter_earnings.head().to_html()]\n",
    "    )\n",
    "if not df_quarter_revenues.empty:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, \"Revenues\") + df_quarter_revenues.head().to_html()]\n",
    "    )\n",
    "try:\n",
    "    htmlcode += widgets.row(\n",
    "        [\n",
    "            widgets.h(3, \"Major Institutional Shareholders\")\n",
    "            + df_institutional_shareholders.head().to_html()\n",
    "        ]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, f\"Historical DCF for {symbol}\") + hist_dcf.to_html()]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, f\"Enterprise data for {symbol}\") + enterprise_df.to_html()]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, f\"Income Statement for {symbol}\") + income_df.to_html()]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    htmlcode += widgets.row([widgets.h(3, f\"Data for {symbol}\") + data_df.to_html()])\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, \"SEC filings\") + df_sec_filings.to_html(escape=False)]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "\n",
    "body += widgets.add_tab(\"Fundamental Analysis\", htmlcode)\n",
    "\n",
    "htmlcode = widgets.row([widgets.h(3, \"Put to call ratio\") + (pcr_chart or \"\")])\n",
    "\n",
    "if expiry_dates:\n",
    "    htmlcode += widgets.row(\n",
    "        [widgets.h(3, \"Option Volume for closest expiry date\") + (vol_chart or \"\")]\n",
    "    )\n",
    "    htmlcode += widgets.row(\n",
    "        [\n",
    "            widgets.h(3, \"Volume and Open Interest for closest expiry date\")\n",
    "            + (voi_chart or \"\")\n",
    "        ]\n",
    "    )\n",
    "\n",
    "body += widgets.add_tab(\"Options\", htmlcode)\n",
    "\n",
    "htmlcode = widgets.row([net_short_position or \"\"])\n",
    "htmlcode += widgets.row([price_vs_short_interest or \"\"])\n",
    "# htmlcode += widgets.row([dark_pools])\n",
    "body += widgets.add_tab(\"Dark Pool and Shorts\", htmlcode)\n",
    "\n",
    "htmlcode = widgets.row(\n",
    "    [\n",
    "        widgets.h(3, \"Congress trading in the past 12 months for \" + symbol)\n",
    "        + (gtrades_chart or \"\")\n",
    "    ]\n",
    ")\n",
    "htmlcode += widgets.row([gov_histcont_chart or \"\"])\n",
    "htmlcode += widgets.row([gov_contracts_chart or \"\"])\n",
    "htmlcode += widgets.row([widgets.h(3, \"Recent Corporate Lobbying by \" + symbol)])\n",
    "try:\n",
    "    for _, row in df_lobbying.iterrows():\n",
    "        amount = (\n",
    "            \"$\" + str(int(float(row[\"Amount\"]))) if row[\"Amount\"] is not None else \"N/A\"\n",
    "        )\n",
    "        htmlcode += widgets.row([widgets.p(f\"{row['Date']}: {row['Client']} {amount}\")])\n",
    "        if (row[\"Amount\"] is not None) and (row[\"Specific_Issue\"] is not None):\n",
    "            htmlcode += widgets.row(\n",
    "                [\n",
    "                    widgets.p(\n",
    "                        \"\\t\"\n",
    "                        + row[\"Specific_Issue\"].replace(\"\\n\", \" \").replace(\"\\r\", \"\")\n",
    "                    )\n",
    "                ]\n",
    "            )\n",
    "except:\n",
    "    pass\n",
    "\n",
    "body += widgets.add_tab(\"Government Menu\", htmlcode)\n",
    "\n",
    "htmlcode = widgets.row(\n",
    "    [\n",
    "        widgets.h(3, f\"Price over the past year for companies similar to {symbol}\")\n",
    "        + (historical_similar or \"\")\n",
    "    ]\n",
    ")\n",
    "htmlcode += widgets.row(\n",
    "    [\n",
    "        widgets.h(3, f\"Price correlation with similar companies for {symbol}\")\n",
    "        + (hcorr_similar or \"\")\n",
    "    ]\n",
    ")\n",
    "htmlcode += widgets.row(\n",
    "    [\n",
    "        widgets.h(3, f\"Volume over the past year for companies similar to {symbol}\")\n",
    "        + (vol_similar or \"\")\n",
    "    ]\n",
    ")\n",
    "htmlcode += widgets.row(\n",
    "    [\n",
    "        widgets.h(3, f\"Sentiment correlation with similar companies for {symbol}\")\n",
    "        + (scorr_similar or \"\")\n",
    "    ]\n",
    ")\n",
    "if isinstance(income_comparison, pd.DataFrame):\n",
    "    htmlcode += widgets.row(\n",
    "        [\n",
    "            widgets.h(3, f\"Income data for similar companies to {symbol}\")\n",
    "            + income_comparison.to_html()\n",
    "        ]\n",
    "    )\n",
    "htmlcode += widgets.row(\n",
    "    [\n",
    "        widgets.p(\n",
    "            \"Note that similar companies have been found using openbb.stocks.ca.similar\"\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "body += widgets.add_tab(\"Comparison Menu\", htmlcode)\n",
    "\n",
    "htmlcode = widgets.row([widgets.h(3, f\"Moving Averages for {symbol}\") + ma_chart])\n",
    "htmlcode += widgets.row([macd_chart or \"\"])\n",
    "htmlcode += widgets.row([ta_rsi or \"\"])\n",
    "htmlcode += widgets.row([stoch_chart or \"\"])\n",
    "htmlcode += widgets.row([cci_chart or \"\"])\n",
    "htmlcode += widgets.row([ad_chart or \"\"])\n",
    "htmlcode += widgets.row([bbands_chart or \"\"])\n",
    "htmlcode += widgets.row([adx_chart or \"\"])\n",
    "body += widgets.add_tab(\"Technical Analysis\", htmlcode)\n",
    "\n",
    "if not df_insider.empty:\n",
    "    htmlcode = widgets.row(\n",
    "        [widgets.h(3, \"Last Activity\") + df_insider.head(10).to_html(col_space=\"75px\")]\n",
    "    )\n",
    "else:\n",
    "    htmlcode = widgets.row(\n",
    "        [widgets.h(3, \"Last Activity\") + widgets.p(\"No insider trading data found.\")]\n",
    "    )\n",
    "\n",
    "body += widgets.add_tab(\"Insider Trading\", htmlcode)\n",
    "\n",
    "\n",
    "htmlcode = widgets.row([bw_year or \"\"])\n",
    "htmlcode += widgets.row([bw_month or \"\"])\n",
    "body += widgets.add_tab(\"Quantitative Analysis\", htmlcode)\n",
    "\n",
    "\n",
    "htmlcode = widgets.row([finbrain_sentiment or \"\"])\n",
    "htmlcode += widgets.row([snews or \"\"])\n",
    "htmlcode += widgets.row(\n",
    "    [\n",
    "        widgets.h(3, \"Interest in \" + symbol + \" based on google analytics\")\n",
    "        + (google_mentions_chart or \"\")\n",
    "    ]\n",
    ")\n",
    "htmlcode += widgets.row(\n",
    "    [\n",
    "        widgets.h(3, f\"Regions with highest interest in {symbol}\")\n",
    "        + (google_regions_chart or \"\")\n",
    "    ]\n",
    ")\n",
    "try:\n",
    "    htmlcode += widgets.row(\n",
    "        [\n",
    "            widgets.h(3, f\"Top queries related to {symbol}\")\n",
    "            + df_related_queries.to_html()\n",
    "        ]\n",
    "    )\n",
    "    htmlcode += widgets.row(\n",
    "        [\n",
    "            widgets.h(3, f\"Top rising queries related to {symbol}\")\n",
    "            + df_rising_queries.to_html()\n",
    "        ]\n",
    "    )\n",
    "except:\n",
    "    pass\n",
    "htmlcode += widgets.row(\n",
    "    [widgets.h(3, f\"Stocktwits sentiment for {symbol}\") + stocktwits_sentiment]\n",
    ")\n",
    "body += widgets.add_tab(\"Behavioural Analysis\", htmlcode)\n",
    "\n",
    "body += widgets.tab_clickable_and_save_evt()\n",
    "\n",
    "report = widgets.html_report(\n",
    "    title=report_name, stylesheet=widgets.html_report_stylesheet(), body=body\n",
    ")\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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