mirror of
https://github.com/OpenBB-finance/OpenBB.git
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780 lines
21 KiB
Plaintext
Vendored
780 lines
21 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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"import pandas as pd\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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"from IPython.display import HTML\n",
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"\n",
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"from openbb_terminal.sdk import widgets\n",
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"from openbb_terminal.sdk import openbb\n",
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"from openbb_terminal.helper_classes import TerminalStyle\n",
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"from openbb_terminal.core.config.paths import REPOSITORY_DIRECTORY\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 = widgets.html_report_stylesheet()"
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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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"# 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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"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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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"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=[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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"candle = 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": "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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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\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=[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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"candle_ma_7_14 = f.getvalue().decode(\"utf-8\")\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\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=[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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"candle_ma_30_60 = 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": "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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"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": "39afc6a7",
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"metadata": {},
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"outputs": [],
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"source": []
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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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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"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=[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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"sma = 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": "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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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"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=[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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"ema = 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": "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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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"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=[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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"zlma = 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": "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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"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
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"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=[ax1, ax2],\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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"cci = 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": "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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"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
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"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=[ax1, ax2],\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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"macd = 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": "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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"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 11), dpi=150)\n",
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"ax3 = ax2.twinx()\n",
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"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=[ax1, ax2, ax3],\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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"fisher = 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": "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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"fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, figsize=(11, 11), dpi=150)\n",
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"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=[ax1, ax2, ax3],\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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"aroon = 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": "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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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"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=[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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"bbands = 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": "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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"fig, ax1 = plt.subplots(figsize=(11, 5), dpi=150)\n",
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"ax2 = ax1.twinx()\n",
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"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=[ax1, ax2],\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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"fib = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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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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"# fig, _ = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"# openbb.qa.normality_chart(\n",
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"# data=ticker_data,\n",
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"# target=\"Close\",\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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"\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": "8abe96b0",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# box and whisker plot\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"openbb.qa.bw(\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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" yearly=False,\n",
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" external_axes=[ax],\n",
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")\n",
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"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"bw = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "66a7dccc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# rolling mean and std deviation of prices\n",
|
|
"\n",
|
|
"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
|
|
"openbb.qa.rolling_chart(\n",
|
|
" symbol=ticker,\n",
|
|
" data=ticker_data,\n",
|
|
" target=\"Close\",\n",
|
|
" external_axes=[ax1, ax2],\n",
|
|
")\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"rolling = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d6b43dd7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# rolling kurtosis of distribution of prices\n",
|
|
"\n",
|
|
"fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n",
|
|
"openbb.qa.kurtosis_chart(\n",
|
|
" symbol=ticker,\n",
|
|
" data=ticker_data,\n",
|
|
" target=\"Close\",\n",
|
|
" external_axes=[ax1, ax2],\n",
|
|
")\n",
|
|
"fig.tight_layout()\n",
|
|
"f = io.BytesIO()\n",
|
|
"fig.savefig(f, format=\"svg\")\n",
|
|
"kurtosis = f.getvalue().decode(\"utf-8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fc31624a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# latest news\n",
|
|
"\n",
|
|
"news = openbb.news(term=ticker).head(10)\n",
|
|
"\n",
|
|
"news[\"published\"] = pd.to_datetime(news[\"published\"])\n",
|
|
"news[\"published\"] = pd.to_datetime(\n",
|
|
" news[\"published\"], format=\"%d/%m/%y %H:%M:%S\"\n",
|
|
").dt.strftime(\"%Y-%m-%d\")\n",
|
|
"news = news[[\"published\", \"title\", \"link\"]]\n",
|
|
"\n",
|
|
"news[\"link\"] = news[\"link\"].apply(lambda x: f'<a href=\"{x}\">{x}</a>')\n",
|
|
"news = news.set_index(\"published\")\n",
|
|
"news.sort_index()\n",
|
|
"news"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "077fb111",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"news.index"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fd5136de",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# TODO: add prediction"
|
|
]
|
|
},
|
|
{
|
|
"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 = (\n",
|
|
" str(REPOSITORY_DIRECTORY)\n",
|
|
" + \"/openbb_terminal/reports/templates/OpenBB_reports_logo.png\"\n",
|
|
")\n",
|
|
"floppy_disk_img = (\n",
|
|
" str(REPOSITORY_DIRECTORY) + \"/openbb_terminal/reports/templates/floppy-disc.png\"\n",
|
|
")\n",
|
|
"body += widgets.header(\n",
|
|
" img,\n",
|
|
" floppy_disk_img,\n",
|
|
" author,\n",
|
|
" report_date,\n",
|
|
" report_time,\n",
|
|
" report_timezone,\n",
|
|
" f\"<b>INVESTMENT RESEARCH REPORT:</b> {ticker}\",\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, \"Box and whisker plot\")])\n",
|
|
"htmlcode += widgets.row([bw])\n",
|
|
"htmlcode += widgets.row([widgets.h(3, \"Rolling mean and std deviation of prices\")])\n",
|
|
"htmlcode += widgets.row([rolling])\n",
|
|
"htmlcode += widgets.row([widgets.h(3, \"Rolling kurtosis of distribution of prices\")])\n",
|
|
"htmlcode += widgets.row([kurtosis])\n",
|
|
"body += widgets.add_tab(\"Quantitative Analysis\", htmlcode, 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)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7f749ab6-2631-41d9-988b-c0a95765856d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "30e917a5-e702-44c9-8c0c-7acbb59dc2da",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Tags",
|
|
"kernelspec": {
|
|
"display_name": "Python 3.9.6 ('obb')",
|
|
"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": "3.9.6"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "cb66ba39c97f15743fbb79e204a841ea03600987d49579cfb0ffc8e8dd934c69"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|