mirror of
https://github.com/OpenBB-finance/OpenBB.git
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73e59804fc
* include plotly.js in reports html, implement new plotly multi-legends feature * use `ensure_backend` to `check_backend`, improve charts in reports * requirements files * fix files with `CRLF` line endings * Update widget_helpers.py * Update widget_helpers.py * handle unsupported plotly multiple legends version error
675 lines
17 KiB
Plaintext
Vendored
675 lines
17 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"attachments": {},
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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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"import pandas as pd\n",
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"\n",
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"from IPython.display import HTML\n",
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"\n",
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"import openbb_terminal.config_terminal as cfg\n",
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"from openbb_terminal.helper_classes import TerminalStyle\n",
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"from openbb_terminal import OpenBBFigure, theme\n",
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"\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": "03a286e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Suppressing sdk logs\n",
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"from openbb_terminal.core.session.current_system import set_system_variable\n",
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"\n",
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"set_system_variable(\"LOGGING_SUPPRESS\", True)\n",
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"\n",
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"# Import the OpenBB SDK\n",
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"from openbb_terminal.sdk import openbb, widgets"
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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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"from openbb_terminal.core.session.current_user import get_current_user\n",
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"\n",
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"user = get_current_user()\n",
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"\n",
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"cfg.theme = TerminalStyle(\n",
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" user.preferences.MPL_STYLE,\n",
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" user.preferences.PMF_STYLE,\n",
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" user.preferences.RICH_STYLE,\n",
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")\n",
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"\n",
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"theme.apply_style(\"light\")"
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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": "4b002bfd",
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"metadata": {},
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"outputs": [],
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"source": [
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"stylesheet = widgets.html_report_stylesheet()"
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]
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},
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{
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"attachments": {},
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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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"# Do not leave parameters blank as notebook will not run otherwise\n",
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"symbol = \"EURUSD\"\n",
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"report_name = \"Forex Report for EURUSD\""
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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": "613cea1d",
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"metadata": {},
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"outputs": [],
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"source": [
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"ticker = symbol"
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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": "d4897334",
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"metadata": {},
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"outputs": [],
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"source": [
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"author = \"\"\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": "07d09ce8",
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"metadata": {},
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"outputs": [],
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"source": [
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"from_symbol = ticker[:3]\n",
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"to_symbol = ticker[3:]\n",
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"last_year = datetime.datetime.now() - datetime.timedelta(days=365)\n",
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"\n",
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"ticker_data = openbb.forex.load(\n",
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" from_symbol=from_symbol,\n",
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" to_symbol=to_symbol,\n",
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" start_date=last_year.strftime(\"%Y-%m-%d\"),\n",
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" interval=\"1day\",\n",
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" resolution=\"d\",\n",
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")\n",
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"\n",
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"# 1 year historical data\n",
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"ticker_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": "88af0fbe",
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"metadata": {},
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"outputs": [],
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"source": [
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"last_year = datetime.datetime.now() - datetime.timedelta(days=365)\n",
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"\n",
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"ticker_data_interval_1month = openbb.forex.load(\n",
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" from_symbol=from_symbol,\n",
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" to_symbol=to_symbol,\n",
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" start_date=last_year.strftime(\"%Y-%m-%d\"),\n",
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" interval=\"1month\",\n",
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" resolution=\"d\",\n",
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")\n",
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"ticker_data_interval_1month = ticker_data_interval_1month.drop(columns=\"Volume\")\n",
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"ticker_data_interval_1month"
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]
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},
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{
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"attachments": {},
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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": "709a2a7e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# candle\n",
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"\n",
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"candle = openbb.forex.candle(\n",
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" data=ticker_data,\n",
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" to_symbol=to_symbol,\n",
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" from_symbol=from_symbol,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "05f54519",
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"metadata": {},
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"outputs": [],
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"source": [
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"# candle graph with moving averages 7/14 and 30/60\n",
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"candle_ma_7_14 = openbb.forex.candle(\n",
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" data=ticker_data,\n",
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" to_symbol=to_symbol,\n",
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" from_symbol=from_symbol,\n",
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" ma=[7, 14],\n",
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" external_axes=True,\n",
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").to_html()\n",
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"\n",
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"candle_ma_30_60 = openbb.forex.candle(\n",
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" data=ticker_data,\n",
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" to_symbol=to_symbol,\n",
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" from_symbol=from_symbol,\n",
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" ma=[30, 60],\n",
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" external_axes=True,\n",
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").to_html()"
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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": "fc17a601",
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"metadata": {},
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"outputs": [],
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"source": [
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"fwd = openbb.forex.fwd(\n",
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" to_symbol=to_symbol,\n",
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" from_symbol=from_symbol,\n",
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")\n",
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"\n",
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"# forward rates\n",
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"fwd"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "6c852082",
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"metadata": {},
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"source": [
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"# Technical analysis"
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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": "4ff6345d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# simple moving average\n",
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"ticker_data.index.names = [\"date\"]\n",
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"\n",
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"sma = openbb.ta.ma_chart(\n",
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" data=ticker_data[\"Close\"],\n",
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" ma_type=\"SMA\",\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "5d5089fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"# exponential moving average\n",
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"ema = openbb.ta.ma_chart(\n",
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" data=ticker_data[\"Close\"],\n",
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" ma_type=\"EMA\",\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "5facfbf4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# zero lag exponential moving average\n",
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"\n",
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"zlma = openbb.ta.ma_chart(\n",
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" data=ticker_data[\"Close\"],\n",
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" ma_type=\"ZLMA\",\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "df54f7ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"# commodity channel index\n",
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"\n",
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"cci = openbb.ta.cci_chart(\n",
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" data=ticker_data,\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "f53469eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# moving average convergence/divergence\n",
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"\n",
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"macd = openbb.ta.macd_chart(\n",
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" data=ticker_data[\"Adj Close\"],\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "dd79e6b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# fisher transform\n",
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"\n",
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"fisher = openbb.ta.fisher_chart(\n",
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" data=ticker_data,\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "b699e61f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# aroon indicator\n",
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"\n",
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"aroon = openbb.ta.aroon_chart(\n",
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" data=ticker_data,\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "79401027",
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"metadata": {},
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"outputs": [],
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"source": [
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"# bollinger bands\n",
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"\n",
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"bbands = openbb.ta.bbands_chart(\n",
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" data=ticker_data,\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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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": "0b5e2949",
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"metadata": {},
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"outputs": [],
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"source": [
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"# fibonacci retracement\n",
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"\n",
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"fib = openbb.ta.fib_chart(\n",
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" data=ticker_data,\n",
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" symbol=ticker,\n",
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" external_axes=True,\n",
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").to_html()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "e6f6ce8a",
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"metadata": {},
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"source": [
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"# Quantitative Analysis"
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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": "e77b373b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# summary\n",
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"\n",
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"summary = openbb.qa.summary(data=ticker_data)\n",
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"summary"
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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": "3ecfc93e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# normality statistics and tests\n",
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"\n",
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"normality = openbb.qa.normality(\n",
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" data=ticker_data[\"Close\"],\n",
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")\n",
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"normality"
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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": "66a7dccc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# rolling mean and std deviation of prices\n",
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"\n",
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"rolling = (\n",
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" openbb.qa.rolling_chart(\n",
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" symbol=ticker,\n",
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" data=ticker_data,\n",
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" target=\"Close\",\n",
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" external_axes=True,\n",
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" )\n",
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" .update_layout(\n",
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" legend=dict(\n",
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" x=0.01,\n",
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" y=0.99,\n",
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" xanchor=\"left\",\n",
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" yanchor=\"top\",\n",
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" xref=\"paper\",\n",
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" yref=\"paper\",\n",
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" bgcolor=\"rgba(0, 0, 0, 0)\",\n",
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" ),\n",
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" legend2=dict(bgcolor=\"rgba(0, 0, 0, 0)\"),\n",
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" )\n",
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" .to_html()\n",
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")"
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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": "d6b43dd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# rolling kurtosis of distribution of prices\n",
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"\n",
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"kurtosis = openbb.qa.kurtosis_chart(\n",
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" symbol=ticker,\n",
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" data=ticker_data,\n",
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" target=\"Close\",\n",
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" external_axes=True,\n",
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").to_html()"
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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": "fc31624a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# latest news\n",
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"try:\n",
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" news = openbb.news(term=ticker).head(10)\n",
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" news.columns = news.columns.str.title()\n",
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"\n",
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" news = news.rename(\n",
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" columns={\"Date\": \"Published\", \"Description\": \"Title\", \"Url\": \"Link\"}\n",
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" )\n",
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" news[\"Title\"] = news.apply(\n",
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" lambda x: f'<a href=\"{x[\"Link\"]}\">{x[\"Title\"]}</a>', axis=1\n",
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" )\n",
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" news = news[[\"Title\", \"Published\"]]\n",
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" news[\"Published\"] = pd.to_datetime(news[\"Published\"]).dt.strftime(\"%Y-%m-%d %H:%M\")\n",
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"\n",
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" news = news.set_index(\"Published\")\n",
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" news.sort_index()\n",
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"except:\n",
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" news = pd.DataFrame()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "ac501a32-124d-4d21-aae1-a8d147bb1071",
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"metadata": {},
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"source": [
|
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"## Render the report template to a file"
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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": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12",
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"metadata": {},
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"outputs": [],
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"source": [
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"body = \"\"\n",
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"\n",
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"\n",
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"body += widgets.header(\n",
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" author,\n",
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" report_date,\n",
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" 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",
|
|
" \"Overview\",\n",
|
|
" \"Technical Analysis\",\n",
|
|
" \"Quantitative Analysis\",\n",
|
|
" ]\n",
|
|
")\n",
|
|
"\n",
|
|
"comment = \"Every analysis displayed considered every day data YTD\"\n",
|
|
"\n",
|
|
"\n",
|
|
"# TODO: add KPIs\n",
|
|
"\n",
|
|
"htmlcode = \"\"\n",
|
|
"\n",
|
|
"# htmlcode += widgets.h(3, \"KPIs\")\n",
|
|
"# htmlcode += widgets.kpi(\n",
|
|
"# [30, 70],\n",
|
|
"# [\"RSI level is oversold\", \"RSI level is normal\", \"RSI level is overbought\"],\n",
|
|
"# 999,\n",
|
|
"# )\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",
|
|
"# 999,\n",
|
|
"# )\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",
|
|
"# 999,\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# Summary tab\n",
|
|
"htmlcode += widgets.row([widgets.h(3, \"Latest news\") + news.to_html(escape=False)])\n",
|
|
"htmlcode += widgets.row(\n",
|
|
" [\n",
|
|
" widgets.h(3, \"One year historical data for 1st day each month\")\n",
|
|
" + ticker_data_interval_1month.to_html()\n",
|
|
" ]\n",
|
|
")\n",
|
|
"body += widgets.add_tab(\"SUMMARY\", htmlcode)\n",
|
|
"\n",
|
|
"# Overview tab\n",
|
|
"htmlcode = \"\"\n",
|
|
"htmlcode = widgets.row([widgets.h(3, \"Candlestick chart\")])\n",
|
|
"htmlcode += widgets.row([candle])\n",
|
|
"htmlcode += widgets.row(\n",
|
|
" [widgets.h(3, \"Candlestick chart with moving averages 7 and 14\")]\n",
|
|
")\n",
|
|
"htmlcode += widgets.row([candle_ma_7_14])\n",
|
|
"htmlcode += widgets.row(\n",
|
|
" [widgets.h(3, \"Candlestick chart with moving averages 30 and 60\")]\n",
|
|
")\n",
|
|
"htmlcode += widgets.row([candle_ma_30_60])\n",
|
|
"htmlcode += widgets.row([widgets.h(3, \"Forward rates\")])\n",
|
|
"htmlcode += widgets.row([fwd.to_html()])\n",
|
|
"body += widgets.add_tab(\"Overview\", htmlcode, True, comment)\n",
|
|
"\n",
|
|
"# Technical Analysis tab\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, True, comment)\n",
|
|
"\n",
|
|
"# Quantitative Analysis tab\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, \"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, True, comment)\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)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Tags",
|
|
"kernelspec": {
|
|
"display_name": "venv",
|
|
"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": "3.9.13"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "381a209f752e9104bff95ec5e80679ea2e476866b80e4326f1ee6b7658af6049"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|