SSE vs WebSockets: Choosing the Right Tool for Stock Market Applications

When building stock market applications, real-time communication is non-negotiable. Whether you’re streaming live price updates, processing user trades, or delivering market news, choosing the right technology for real-time communication can make or break the user experience.

Two popular tools for server-to-client updates are:

• Server-Sent Events (SSE)
• WebSockets

Each serves different use cases, and choosing the wrong one could lead to performance bottlenecks or unnecessary complexity. In this post, we’ll break down both, specifically from a stock market application perspective.

What Are SSE and WebSockets?

Use Cases in Stock Market Apps

Let’s break this down by functionality commonly found in trading or stock apps:

Use SSE When:

1. Streaming Live Price Tickers (Read-Only)

If you’re building a component that only listens to price changes from the server — think of a public stock ticker or an index dashboard — SSE is a great fit. It’s lightweight, efficient, and automatically handles reconnections.

🟢 Ideal for: Nifty/Bank Nifty live prices, top gainers/losers, index trackers.

2. News Feed or Alert Broadcast

For real-time market news, earnings announcements, or alerts pushed from the server to all clients, SSE provides a simple way to push these messages with minimal overhead.

🟢 Ideal for: Market announcements, trade signals, global news banners.

Use WebSockets When:

1. Placing Orders and Trade Confirmations

Trading apps need bi-directional communication — users place orders (client ➝ server), and get confirmations or rejections (server ➝ client). This real-time interaction demands WebSockets.

🔁 Example: A trader clicks “Buy 100 shares of TCS” → server confirms the order or returns an error → client updates UI.

2. Real-Time Portfolio and P&L Updates

A user’s P&L might change every second as prices move. You’ll want to update positions, margin, and net worth dynamically — while also letting users act (square-off, hedge, etc.).

🔁 Example: Auto-refreshing portfolio page, live MTM tracking.

3. Depth (Level 2) and Order Book Visualizations

High-frequency data like order book changes and market depth require ultra-low latency and structured data. WebSockets can efficiently handle this binary/JSON stream.

🧠 Tip: Pair WebSocket streams with throttling or batching if displaying thousands of price changes/sec.

Summary: When to Use What?

You can even combine both in your app. For example:

• Use SSE for market-wide public data
• Use WebSockets inside authenticated dashboards for account-level interactions

Python Frameworks & Libraries for Real-Time Stock Dashboards

If you’re planning to build a real-time stock market dashboard in Python, here are solid tools worth considering:

🔹 FastAPI

• Asynchronous & high-performance
• Native support for both WebSockets and SSE
• Great for real-time APIs, broker integrations
• Works well with libraries like uvicorn, httpx, and websockets

# Example WebSocket endpoint with FastAPI
@app.websocket("/ws/portfolio")
async def portfolio_stream(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = get_user_portfolio()
        await websocket.send_json(data)
        await asyncio.sleep(1)

🔹 Flask (with Flask-SSE or Flask-SocketIO)

• Simpler than FastAPI
• Great for smaller dashboards or admin panels
• Use Flask-SocketIO for WebSocket support
• Use Flask-SSE for one-way event streams (built on Redis)

🔹 Dash by Plotly

• Good for data visualization dashboards
• Limited in interactive real-time updates, but can be paired with Flask/FastAPI backend

🔹 Streamlit

• Extremely fast to prototype dashboards
• Better suited for research, less ideal for trade execution or production systems

Final Thoughts

Building a stock market application? Here’s the bottom line:

• Use SSE when your app just needs to listen to the market.
• Use WebSockets when your app needs to talk back — place trades, update portfolios, or handle interactive features.
• Choose FastAPI if you’re building a robust backend — it plays well with both technologies and is production-ready.