mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-27 20:42:26 +00:00
Create comparison analysis report (#993)
* Improve comparison analysis - yf model/view * similar analysis report
This commit is contained in:
DidierRLopes
369
gamestonk_terminal/reports/similar_analysis.ipynb
vendored
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369
gamestonk_terminal/reports/similar_analysis.ipynb
vendored
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "phantom-facility",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import most generic modules\n",
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"import importlib\n",
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"import pathlib\n",
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"import os\n",
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"import sys\n",
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"from datetime import datetime, timedelta\n",
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"import pandas as pd\n",
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"from IPython.display import display, Markdown\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"module_path = os.path.abspath(os.path.join('../..'))\n",
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"\n",
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"if module_path not in sys.path:\n",
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" sys.path.append(module_path)"
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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": "compact-count",
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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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"\n",
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"report_name=f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_comparison_{ticker.upper()}\""
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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": "possible-constraint",
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"metadata": {},
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"outputs": [],
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"source": [
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"display(Markdown(f\"# {ticker.upper()} - Comparison - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"))"
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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": "b9eaca4a-a46d-45a3-9306-e80e74234f12",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_model, yahoo_finance_model\n",
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"\n",
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"finviz_similar = finviz_compare_model.get_similar_companies(ticker, [\"Sector\", \"Industry\", \"Country\"])[0]\n",
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"finviz_similar.remove(ticker)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ffea4266-e801-4a4e-8cac-3d60e735aa4e",
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"metadata": {},
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"source": [
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"### Similar companies and descriptions"
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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": "21122245-457b-4347-b89f-d4aabb5bdc9a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.fundamental_analysis import yahoo_finance_model\n",
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"\n",
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"df_info = yahoo_finance_model.get_info(ticker)\n",
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"if \"Long business summary\" in df_info.index:\n",
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" summary = df_info.loc[\"Long business summary\"].values[0]\n",
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"if \"Website\" in df_info.index:\n",
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" website = df_info.loc[\"Website\"].values[0]\n",
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" \n",
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"if finviz_similar:\n",
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" print(f\"{ticker}: {website}\")\n",
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" print(summary)\n",
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" \n",
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" for symbol in finviz_similar:\n",
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" df_info = yahoo_finance_model.get_info(symbol)\n",
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" if \"Long business summary\" in df_info.index:\n",
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" summary = df_info.loc[\"Long business summary\"].values[0]\n",
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" if \"Website\" in df_info.index:\n",
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" website = df_info.loc[\"Website\"].values[0]\n",
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" \n",
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" print(\"\")\n",
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" print(f\"{symbol}: {website}\")\n",
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" print(summary)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "temporal-clerk",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Historical prices"
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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": "1fdfac4c-62cd-4bd3-b7df-41ba67263798",
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"from gamestonk_terminal.stocks.comparison_analysis import yahoo_finance_view\n",
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"\n",
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"for i in range(math.ceil(len(finviz_similar)/4)):\n",
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" yahoo_finance_view.display_historical(\n",
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" ticker=ticker,\n",
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" similar_tickers=finviz_similar[4*(i):4*(i+1)],\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a07f866-7939-4fb7-8b14-c0701087a8ae",
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"metadata": {},
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"source": [
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"### Historical correlation"
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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": "eb1850dc-1d62-4ad4-971f-1bbce6209726",
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"\n",
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"plt.figure(figsize=(25,10))\n",
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"yahoo_finance_view.display_correlation(\n",
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" ticker=ticker,\n",
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" similar_tickers=finviz_similar,\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "21005668-7f21-4884-a231-846130695602",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Historical volumes"
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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": "a7bf528c-427c-4d84-8cb9-ec5656f920e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(math.ceil(len(finviz_similar)/4)):\n",
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" yahoo_finance_view.display_volume(\n",
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" ticker=ticker,\n",
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" similar_tickers=finviz_similar[4*(i):4*(i+1)],\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6e370470-8014-4e97-95f2-13b4f66645c5",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Overview"
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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": "f82b7162-f7e5-4d5c-8ca9-50466f789f48",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_view\n",
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"\n",
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"finviz_compare_view.screener(\n",
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" ticker=ticker,\n",
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" similar=finviz_similar,\n",
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" data_type=\"overview\",\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5277ae24-b7d7-48e6-b46c-fcffdf84631f",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Valuation"
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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": "caed21ad-a31d-4063-a4cb-f5b6fd1c63ed",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_view\n",
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"\n",
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"finviz_compare_view.screener(\n",
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" ticker=ticker,\n",
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" similar=finviz_similar,\n",
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" data_type=\"valuation\",\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98030d12-9a77-4f08-b4ad-2c558d9ad225",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Financial"
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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": "e98bf90d-bf2b-4eea-b533-08211e8eec47",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_view\n",
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"\n",
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"finviz_compare_view.screener(\n",
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" ticker=ticker,\n",
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" similar=finviz_similar,\n",
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" data_type=\"financial\",\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7cf8774-5162-47ea-8bfe-5005ed2fb9d9",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Ownership"
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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": "d3f90d1f-7a40-403f-bf3e-17703e151f4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_view\n",
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"\n",
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"finviz_compare_view.screener(\n",
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" ticker=ticker,\n",
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" similar=finviz_similar,\n",
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" data_type=\"ownership\",\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c1833238-5ea8-486b-a6e7-6043e7c0aa0e",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Performance"
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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": "62057b47-2364-49ed-80e7-7d6938f2819d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_view\n",
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"\n",
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"finviz_compare_view.screener(\n",
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" ticker=ticker,\n",
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" similar=finviz_similar,\n",
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" data_type=\"performance\",\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6d0494c0-e66a-414a-ba2d-89e4072e1a8f",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Technical"
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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": "1178a080-83ce-4170-a355-9faa8cf9abe6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gamestonk_terminal.stocks.comparison_analysis import finviz_compare_view\n",
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"\n",
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"finviz_compare_view.screener(\n",
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" ticker=ticker,\n",
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" similar=finviz_similar,\n",
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" data_type=\"technical\",\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": "sought-phase",
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"metadata": {},
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"outputs": [],
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"source": [
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"!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input"
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]
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}
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],
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"metadata": {
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"celltoolbar": "Tags",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -52,7 +52,7 @@ def get_historical(
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# where all tickers are columns.
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return yf.download(all_tickers, start=start, progress=False, threads=False)[
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d_candle_types[candle_type]
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]
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][all_tickers]
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def get_1y_sp500() -> pd.DataFrame:
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@@ -75,7 +75,7 @@ def get_1y_sp500() -> pd.DataFrame:
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def get_sp500_comps_tsne(
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ticker: str, lr: int = 200, no_plot: bool = False
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ticker: str, lr: int = 200, no_plot: bool = False, num_tickers: int = 10
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) -> List[str]:
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"""
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Runs TSNE on SP500 tickers (along with ticker if not in SP500).
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@@ -91,6 +91,8 @@ def get_sp500_comps_tsne(
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Learning rate for TSNE
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no_plot : bool
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Flag to hold off on plotting
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num_tickers : int
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Number of tickers to return
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Returns
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-------
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@@ -128,4 +130,4 @@ def get_sp500_comps_tsne(
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x0, y0 = data.loc[ticker]
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data["dist"] = (data.X - x0) ** 2 + (data.Y - y0) ** 2
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data = data.sort_values(by="dist")
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return data.iloc[1:11].index.to_list()
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return data.iloc[1 : num_tickers + 1].index.to_list()
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@@ -75,7 +75,6 @@ def display_historical(
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)
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df_similar.plot(ax=ax)
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ax.set_title(f"Similar companies to {ticker}")
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ax.plot(df_similar.index, df_similar[ticker].values)
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ax.set_xlabel("Time")
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ax.set_ylabel(f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}")
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ax.grid(b=True, which="major", color="#666666", linestyle="-")
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