📰 How to Get Historic Stock News for Free with Python: A Step-by-Step Guide

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Breaking Through Information Barriers

As a data enthusiast and budget-conscious investor, I was tired of:

• Paying hefty subscription fees for financial news platforms

• Missing out on timely market insights

• Feeling like good information was locked behind expensive gates

That’s when I decided to create my own solution: a stock news scraper that pulls articles from Google News across multiple sectors.

Why This Matters? 🤔

Historic stock news is invaluable for traders, analysts, and researchers. It helps in:

• Understanding market sentiment 📈

• Evaluating company performance 🏢

• Making informed investment decisions 💡

But the challenge is finding free and reliable sources. Let’s resolve this issue with code! 💻✨

Step 1: Setting Up Your Environment 🛠️

First, install the necessary Python libraries:

!pip install feedparser pandas -q
import feedparser
import pandas as pd
from datetime import datetime, timedelta
from urllib.parse import quote_plus
import ipywidgets as widgets
from IPython.display import display, clear_output, HTML
from IPython.display import Javascript

These libraries will help us parse RSS feeds and handle data efficiently.

Step 2: Define Your Stock Universe 🌍

Let’s create a dictionary of popular stocks categorized by sectors:

def get_popular_stocks():
    return {
        "Technology": {
            "Apple": "AAPL",
            "Microsoft": "MSFT",
            "Amazon": "AMZN",
            "Alphabet (Google)": "GOOGL",
            "Nvidia": "NVDA"
        },
        # Add other sectors like Finance, Retail, etc.
    }

This ensures you can easily fetch news for companies like Apple 🍎 or Microsoft 🖥️.

Step 3: Fetch Historic News Articles 📜

The real magic happens here! Using Google News RSS feed, you can retrieve articles based on company names up to 90 days of news:

def get_stock_news(company_name, ticker, days=90): 
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)
    
    query = quote_plus(f'{company_name} OR {ticker} after:{start_date.strftime("%Y-%m-%d")} before:{end_date.strftime("%Y-%m-%d")}')
    rss_url = f'https://news.google.com/rss/search?q={query}&hl=en-US&gl=US&ceid=US:en'
    
    feed = feedparser.parse(rss_url)
    
    news_items = []
    for entry in feed.entries[:100]:  # Hardcoded max 100 articles
        try:
            news_items.append({
                'date': entry.get('published', 'N/A'),
                'title': entry.title,
                'url': entry.link,
                'source': entry.get('source', {}).get('title', 'N/A'),
                'company': company_name,
                'ticker': ticker
            })
        except:
            continue
    
    df = pd.DataFrame(news_items)
    if not df.empty:
        df['date'] = pd.to_datetime(df['date'])
    return df

This function fetches up to 100 articles based on the company name or ticker within a specified date range from Google RSS feed📅.

Key Features:

• Extended Date Range: The days parameter now defaults to 90, allowing retrieval of news articles from the past three months.

• Dynamic Query: Leverages Google News RSS feeds, fetching articles based on stock tickers or company names.

• Efficient Parsing: Limits results to 100 articles per query for performance optimization.

• Data Output: Returns a clean DataFrame containing article details such as date, title, URL, source, company name, and ticker.

Step 4: Build an Interactive UI 🎨

Want to make it user-friendly? Create a simple interface using ipywidgets:

def create_stock_news_ui():
    stocks = get_popular_stocks()
    result_df = pd.DataFrame()
    
    # Initialize with Technology stocks
    initial_category = "Technology"
    initial_stocks = [(f"{k} ({v})", (k, v)) for k, v in stocks[initial_category].items()]
    
    # Category dropdown
    category_dropdown = widgets.Dropdown(
        options=list(stocks.keys()),
        value=initial_category,
        description='Category:',
        disabled=False
    )
    
    # Stock selection - changed to Dropdown for single selection
    stock_dropdown = widgets.Dropdown(
        options=initial_stocks,
        description='Stock:',
        disabled=False,
        layout={'width': '500px'}
    )

This interface lets users select stocks and fetch news interactively 🔧

Step 5: Save Results as CSV 📂

Export your fetched data for further analysis:

def download_csv(df):
    filename = f"stock_news_{datetime.now().strftime('%Y%m%d_%H%M')}.csv"
    df.to_csv(filename, index=False)

Now you can save your data locally and analyze it anytime! ✅

Step 6: Test It Out

Run Google Colab Notebook and start fetching historic stock news for free. Whether you’re analyzing Tesla 🚗 or Amazon 🛒, this solution has covered you!

Final Thoughts 💭

This workflow is a game-changer for anyone looking to access historic stock news without breaking the bank 💵. With Python’s power and Google News RSS feed 🌐, you can unlock valuable insights effortlessly.