Real-Time Data Streaming for Modern Trading

The Need for Speed

In trading, stale data is dangerous data. Real-time data streaming ensures you're always working with the latest market information.

WebSocket vs REST

REST APIs

•Request-response model
•Higher latency
•Polling required
•Simpler to implement
•Good for historical data

WebSocket

•Persistent connection
•Low latency
•Server push model
•Ideal for real-time feeds
•More complex to manage

Architecture Patterns

Event-Driven Design

Modern trading systems use event-driven architectures:

•Market data events trigger analysis
•Signals trigger order generation
•Fills trigger position updates
•Each component reacts independently

Message Queues

For handling high-throughput data:

•Buffer against spikes
•Decouple producers from consumers
•Enable replay capabilities
•Ensure no data loss

Data Types

Level 1 Data

•Last traded price
•Best bid/ask
•Volume
•Open/High/Low/Close

Level 2 Data

•Full order book
•Market depth
•Individual orders
•Order modifications

Tick Data

•Every trade execution
•Timestamps to microseconds
•Trade direction
•Venue information

Implementation Considerations

•Connection management and reconnection
•Data normalization across venues
•Timestamp synchronization
•Compression for bandwidth
•Failover and redundancy

Building with APIs

Modern trading platforms provide:

•WebSocket endpoints for streaming
•REST endpoints for snapshots
•Client libraries for easy integration
•Comprehensive documentation

Conclusion

Real-time data streaming is the backbone of modern trading infrastructure. Understanding these concepts is essential for building responsive, reliable trading systems.