mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-08 10:22:09 +00:00
698 lines
22 KiB
Plaintext
Vendored
698 lines
22 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "70ba2c8a",
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"metadata": {},
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"source": [
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"## Notebook setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "driven-billion",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import io\n",
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"import warnings\n",
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"import datetime\n",
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"import numpy as np\n",
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"import base64\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib_inline.backend_inline\n",
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"\n",
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"# import sys\n",
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"# sys.path.append('../../')\n",
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"\n",
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"from openbb_terminal import api as openbb\n",
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"from openbb_terminal.helper_classes import TerminalStyle\n",
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"\n",
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"%matplotlib inline\n",
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"matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370",
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" theme = TerminalStyle(\"light\", \"light\", \"light\")\n",
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"except:\n",
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" pass\n",
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"stylesheet = openbb.widgets.html_report_stylesheet()\n",
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"with open(\"./openbb_terminal/reports/OpenBB_reports_logo.png\", \"rb\") as image_file:\n",
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" openbb_image_encoded = base64.b64encode(image_file.read())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "807fda4e",
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"metadata": {},
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"source": [
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"## Select Ticker"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "intended-sherman",
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"# Parameters that will be replaced when calling this notebook\n",
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"ticker = \"TSLA\"\n",
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"report_name = \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a441ddbc-d75b-4a18-84d4-e01b677674d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"if \".\" in ticker:\n",
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" import sys\n",
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" sys.exit(0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b",
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"metadata": {},
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"outputs": [],
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"source": [
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"ticker_data = openbb.stocks.load(ticker, start=datetime.datetime.now() - datetime.timedelta(days=4*30))\n",
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"ticker_data = openbb.stocks.process_candle(ticker_data)\n",
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"\n",
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"author = \"Didier Rodrigues Lopes\"\n",
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"report_title = f\"INVESTMENT RESEARCH REPORT ON {ticker.upper()}\"\n",
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"report_date = datetime.datetime.now().strftime('%d %B, %Y')\n",
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"report_time = datetime.datetime.now().strftime('%H:%M') \n",
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"report_timezone = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo\n",
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"report_title, report_date, report_time, report_timezone"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"info = openbb.stocks.fa.models.yahoo_finance.get_info(ticker=ticker).transpose()\n",
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"\n",
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"if info[\"Long business summary\"][0] != \"NA\":\n",
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" overview = info[\"Long business summary\"][0]\n",
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"else:\n",
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" overview = info[\"Long name\"][0]\n",
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"overview"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8a0d747",
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"metadata": {},
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"source": [
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"## Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aca4b6ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"(\n",
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" df_year_estimates,\n",
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" df_quarter_earnings,\n",
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" df_quarter_revenues,\n",
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") = openbb.stocks.dd.models.business_insider.get_estimates(ticker)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c0f0a1ca",
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"metadata": {},
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"outputs": [],
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"source": [
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"display_year = sorted(df_year_estimates.columns.tolist())[:3]\n",
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"df_year_estimates = df_year_estimates[display_year].head(5)\n",
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"df_year_estimates"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fde46353",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_quarter_earnings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51dfd9d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_quarter_revenues"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3022130e",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_sec_filings = openbb.stocks.dd.models.marketwatch.get_sec_filings(ticker)[\n",
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" [\"Type\", \"Category\", \"Link\"]\n",
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"].head(5)\n",
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"df_sec_filings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4662b1ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_analyst = openbb.stocks.dd.models.finviz.get_analyst_data(ticker)\n",
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"df_analyst[\"target_to\"] = df_analyst[\"target_to\"].combine_first(df_analyst[\"target\"])\n",
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"df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target_to\"]].rename(\n",
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" columns={\n",
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" \"category\": \"Category\",\n",
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" \"analyst\": \"Analyst\",\n",
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" \"rating\": \"Rating\",\n",
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" \"target_to\": \"Price Target\",\n",
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" }\n",
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")\n",
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"df_analyst"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "324e2d4b-b2f6-4da3-bac4-ef785ad4e436",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax1 = plt.subplots(figsize=(11, 5), dpi=150)\n",
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"ax2 = ax1.twinx()\n",
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"openbb.stocks.dps.spos(\n",
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" ticker,\n",
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" num=84,\n",
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" raw=False,\n",
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" export=\"\",\n",
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" external_axes=[ax1, ax2],\n",
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")\n",
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"fig.tight_layout()\n",
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"\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"net_short_position = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ac0bfbda-e4b8-4b3b-b8e7-c02639fdb971",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
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"openbb.stocks.dps.dpotc(\n",
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" ticker,\n",
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" external_axes=[ax1, ax2],\n",
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")\n",
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"fig.tight_layout()\n",
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"\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"dark_pools = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e40a2b8f-8076-4220-89ad-388def38971c",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(11, 5), dpi=150)\n",
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"openbb.stocks.dps.volexch(\n",
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" ticker,\n",
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" external_axes=[ax],\n",
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")\n",
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"fig.tight_layout()\n",
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"\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"volexch = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8bb53c1-147e-4342-af61-0d0a9cc56e59",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
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"ax3 = ax1.twinx()\n",
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"openbb.stocks.dps.psi_sg(\n",
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" ticker,\n",
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" external_axes=[ax1, ax2, ax3],\n",
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")\n",
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"fig.tight_layout()\n",
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"\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"price_vs_short_interest = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "394668e1-1158-4a2b-8a38-04c72a6abdd6",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (candles, volume) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
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"openbb.stocks.candle(\n",
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" s_ticker=ticker,\n",
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" df_stock=ticker_data,\n",
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" use_matplotlib=True,\n",
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" external_axes=[candles, volume],\n",
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")\n",
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"candles.set_xticklabels(\"\")\n",
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"fig.tight_layout()\n",
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"\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"price_chart = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "900686a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.dd.pt(\n",
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" ticker=ticker,\n",
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" start=\"2022-01-01\",\n",
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" interval=\"1440min\",\n",
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" stock=ticker_data,\n",
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" num=10,\n",
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" raw=False,\n",
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" external_axes=[ax],\n",
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")\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"price_target_chart = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "61d62e1a-199e-481d-b57d-38ab486951be",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = openbb.stocks.dd.models.business_insider.get_price_target_from_analysts(ticker)\n",
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"avg_ratings_last_30_days = round(np.mean(df[datetime.datetime.now() - datetime.timedelta(days=30):][\"Price Target\"].values), 2)\n",
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"last_price = round(ticker_data[\"Close\"][-1], 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f3733ff2",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.dd.rot(\n",
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" ticker=ticker,\n",
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" num=10,\n",
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" raw=False,\n",
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" export=\"\",\n",
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" external_axes=[ax],\n",
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")\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"ratings_over_time_chart = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "924f8042-e58a-449c-91a3-7923158ff714",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.ta.rsi(ticker_data[\"Close\"], external_axes=[ax1, ax2])\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"ta_rsi = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "953a340e-7ea1-4aa3-ae1d-872683dced41",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = openbb.stocks.ta.models.momentum.rsi(ticker_data[\"Close\"])\n",
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"rsi_value = round(df.values[-1][0], 2)\n",
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"rsi_value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dce71711-20b3-4c69-af52-e0f9a69991f8",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LinearRegression\n",
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"model = LinearRegression().fit(np.array(range(len(ticker_data[\"Close\"][-30:].index))).reshape(-1,1), ticker_data[\"Close\"][-30:].values)\n",
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"regression_slope = round(model.coef_[0], 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6272e34b-9511-4b48-9eea-ea882ce9846a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"df_insider = pd.DataFrame.from_dict(openbb.stocks.ins.models.finviz.get_last_insider_activity(ticker)).head(10)\n",
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"df_insider[\"Val ($)\"] = df_insider[\"Value ($)\"].replace({',': ''}, regex=True)\n",
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"df_insider[\"Trade\"] = df_insider.apply(\n",
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" lambda row: (-1*float(row[\"Val ($)\"])) if row[\"Transaction\"] == \"Sale\" else (float(row[\"Val ($)\"]) if row[\"Transaction\"] == \"Buy\" else 0),\n",
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" axis=1,\n",
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" )\n",
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"last_10_insider_trading = round(sum(df_insider[\"Trade\"])/1_000_000, 2)\n",
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"df_insider = df_insider.drop(columns=[\"Val ($)\", \"Trade\"])\n",
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"df_insider"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "29254666-58c6-4686-87e6-2635ad8387e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.ba.headlines(ticker, external_axes=[ax])\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"finbrain_sentiment = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b8251b47-8ecc-47be-83f3-d9f108596898",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_sentiment_finbrain = openbb.stocks.ba.models.finbrain.get_sentiment(ticker)\n",
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"finbrain_sentiment_val = float(df_sentiment_finbrain.values[-1][0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fe6e513-d568-41b4-8aa0-37cc578c9dc3",
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"metadata": {},
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"outputs": [],
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"source": [
|
|
"watchlist_count, n_cases, n_bull, n_bear = openbb.stocks.ba.models.stocktwits.get_bullbear(ticker)\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\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bfb75909-b877-4b3a-82a7-21ce174a7bb4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
|
|
"openbb.stocks.ba.snews(ticker, external_axes=[ax1, ax2])\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"snews = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "329c9e1b-e1de-4836-91fd-656cc87f7111",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ticker_data_all = openbb.stocks.load(ticker, start=datetime.datetime.now() - datetime.timedelta(days=5*12*21))\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": [
|
|
"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
|
|
"openbb.stocks.qa.bw(ticker, ticker_data_all, \"Returns\", False, external_axes=[ax])\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"bw_month = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "16b33868-23dc-4c77-ac00-acb452da386c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
|
|
"openbb.stocks.qa.bw(ticker, ticker_data_all, \"Returns\", True, external_axes=[ax])\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"bw_year = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c688fa89-3773-41fe-8ab8-bb6a610f2287",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"regression_val = round(openbb.stocks.pred.models.regression.get_regression_model(ticker_data_all[\"Close\"], 1, 80, 20, 1)[0][-1], 2)\n",
|
|
"regression_val"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "66e51d40-3dca-46c0-abd6-4c1f04f59732",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
|
|
"openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"regression = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "90e77854-0652-4f62-9c0b-89d3fda456c6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(11, 3), dpi=150)\n",
|
|
"openbb.stocks.pred.mc(ticker_data[\"Close\"], 30, 100, external_axes=[ax1, ax2])\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"mc = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bb97c78c-4c4e-4e24-a2a1-29cdf47839c5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
|
|
"openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"regression = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"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",
|
|
"img = f'<img src=\"data:image/png;base64,{openbb_image_encoded.decode()}\" alt=\"OpenBB\" style=\"width:144px;\">'\n",
|
|
"body += openbb.widgets.header(img, author, report_date, report_time, report_timezone, f'<b>INVESTMENT RESEARCH REPORT:</b> {ticker}')\n",
|
|
"\n",
|
|
"body += openbb.widgets.tablinks([\"SUMMARY\", \"Overview\", \"Analyst Opinions\", \"Fundamental Analysis\", \"Dark Pool and Shorts\", \"Technical Analysis\", \"Insider Trading\", \"Behavioural Analysis\", \"Quantitative Analysis\", \"Prediction Techniques\"])\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.h(3, \"KPIs\")\n",
|
|
"htmlcode += openbb.widgets.kpi([last_price], [\"Last closing price is above the average price ratings of last 30 days\", \"Average price ratings of last 30 day is above last closing price\"], avg_ratings_last_30_days)\n",
|
|
"htmlcode += openbb.widgets.kpi([0], [\"Regression (dollars per market day) on last 30 market days is negative\", \"Regression (dollars per market day) on last 30 market days is positive\"], regression_slope)\n",
|
|
"htmlcode += openbb.widgets.kpi([30, 70], [\"RSI level is oversold\", \"RSI level is normal\", \"RSI level is overbought\"], rsi_value) \n",
|
|
"htmlcode += openbb.widgets.kpi([0], [\"The sum of last 10 insider trading (in millions) was negative\", \"The sum of last 10 insider trading (in millions) was positive\"], last_10_insider_trading)\n",
|
|
"htmlcode += openbb.widgets.kpi([-.1, .1], [\"Last FinBrain sentiment is bearish\", \" Last FinBrain sentiment is neutral\", \"Last FinBrain sentiment is bullish\"], finbrain_sentiment_val) \n",
|
|
"htmlcode += openbb.widgets.kpi([0], [\"The regression for the next 20th market price is below closing price\", \"The regression for the next 20th market price is above closing price\"], round(regression_val - last_price, 2)) \n",
|
|
"body += openbb.widgets.add_tab(\"SUMMARY\", htmlcode)\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([price_chart])\n",
|
|
"htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Description\") + openbb.widgets.p(overview)])\n",
|
|
"body += openbb.widgets.add_tab(\"Overview\", htmlcode)\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([price_target_chart])\n",
|
|
"htmlcode += openbb.widgets.row([ratings_over_time_chart])\n",
|
|
"htmlcode += openbb.widgets.row([df_analyst.to_html()])\n",
|
|
"body += openbb.widgets.add_tab(\"Analyst Opinions\", htmlcode)\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([openbb.widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html()])\n",
|
|
"htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Earnings\") + df_quarter_earnings.head().to_html()])\n",
|
|
"htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Revenues\") + df_quarter_revenues.head().to_html()])\n",
|
|
"htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"SEC filings\") + df_sec_filings.head().to_html()])\n",
|
|
"body += openbb.widgets.add_tab(\"Fundamental Analysis\", htmlcode)\n",
|
|
" \n",
|
|
"htmlcode = openbb.widgets.row([net_short_position])\n",
|
|
"htmlcode += openbb.widgets.row([price_vs_short_interest])\n",
|
|
"htmlcode += openbb.widgets.row([dark_pools])\n",
|
|
"htmlcode += openbb.widgets.row([volexch])\n",
|
|
"body += openbb.widgets.add_tab(\"Dark Pool and Shorts\", htmlcode)\n",
|
|
"\n",
|
|
"body += openbb.widgets.add_tab(\"Technical Analysis\", openbb.widgets.row([ta_rsi]))\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([openbb.widgets.h(3, \"Last Activity\") + df_insider.head(10).to_html(col_space='75px')])\n",
|
|
"body += openbb.widgets.add_tab(\"Insider Trading\", htmlcode)\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([finbrain_sentiment])\n",
|
|
"htmlcode += openbb.widgets.row([snews])\n",
|
|
"htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Stocktwits\") + stocktwits_sentiment])\n",
|
|
"body += openbb.widgets.add_tab(\"Behavioural Analysis\", htmlcode)\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([bw_month])\n",
|
|
"htmlcode += openbb.widgets.row([bw_year])\n",
|
|
"body += openbb.widgets.add_tab(\"Quantitative Analysis\", htmlcode)\n",
|
|
"\n",
|
|
"htmlcode = openbb.widgets.row([regression])\n",
|
|
"htmlcode += openbb.widgets.row([mc])\n",
|
|
"body += openbb.widgets.add_tab(\"Prediction Techniques\", htmlcode)\n",
|
|
"\n",
|
|
"body += openbb.widgets.tab_clickable_evt()\n",
|
|
"\n",
|
|
"report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)\n",
|
|
"\n",
|
|
"# to save the results\n",
|
|
"with open(report_name + \".html\", \"w\") as fh:\n",
|
|
" fh.write(report)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e15cef06-0d10-4153-b6d3-9762be0ed1dd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Tags",
|
|
"interpreter": {
|
|
"hash": "aa1c4d436101635411b2afa2ce061827695b6e3d0efa7f05338cbf11ad26165e"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.8.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|