Docs should mention support for YAML/JSON5 package descriptor format

[adanski]

I was unable to find the details of the following issues outcome on the page.

pnpm/pnpm#1100
pnpm/pnpm#1799

[Mohstarclassnet]

To implement real-time data harvesting for your banking/investment platform, you need both:
1. A Web Scraper (Program) – To collect financial data from various sources.
2. An Application (Dashboard/API) – To process, store, and display the data for users.

1. Web Scraper (Python Program)

This Python script collects financial data (such as stock prices, exchange rates, and news) from web sources every minute.

Requirements

Install the necessary libraries:

pip install requests beautifulsoup4 schedule pandas

Python Code for Real-Time Data Harvesting

import requests
from bs4 import BeautifulSoup
import schedule
import time
import pandas as pd

Function to scrape financial data

def fetch_financial_data():
url = "https://www.example.com/finance" # Replace with actual financial data source
headers = {"User-Agent": "Mozilla/5.0"}

response = requests.get(url, headers=headers)
if response.status_code == 200:
    soup = BeautifulSoup(response.text, "html.parser")
    
    # Example: Extract stock prices (Modify as needed)
    stocks = soup.find_all("div", class_="stock-price")  
    data = [{"Stock": stock.text} for stock in stocks]
    
    # Save data to CSV (or database)
    df = pd.DataFrame(data)
    df.to_csv("financial_data.csv", mode='a', index=False, header=False)
    
    print("Data collected and saved.")
else:
    print("Failed to fetch data.")

Schedule the scraper to run every minute

schedule.every(1).minutes.do(fetch_financial_data)

print("Starting data harvesting...")
while True:
schedule.run_pending()
time.sleep(1)

What This Does:
• Scrapes financial data (e.g., stock prices).
• Stores it in a CSV file (can be extended to a database).
• Runs every minute automatically.

2. Application (Dashboard/API)

The backend application should:
1. Process the collected data.
2. Display insights to users.
3. Send alerts on financial trends.

Tech Stack Options:

•	Backend: Flask/Django (Python) or Node.js
•	Frontend: React.js/Vue.js
•	Database: PostgreSQL/MySQL/MongoDB
•	Hosting: AWS, Azure, or your preferred cloud service

Would you like me to generate a Flask API or a Full-Stack Web App for real-time financial data visualization?