Architecture

Building Real-Time Trading Platforms

Rajat Kumar R
8 min read
Building Real-Time Trading Platforms

Building Real-Time Trading Platforms

After spending months building Tredye, a real-time trading platform, I've accumulated valuable insights into the unique challenges of financial technology systems. Here's what I learned.

The Challenge of Real-Time

Trading platforms have unique requirements that push traditional web architectures to their limits:

  • Sub-millisecond latency: Price updates must reach users instantly
  • High throughput: Thousands of events per second per user
  • Data consistency: Order execution must be atomic and reliable
  • Always-on reliability: Downtime means lost money

Architecture Decisions

Message-Driven Architecture

We chose Apache Kafka as our event backbone. Every price tick, order, and trade is an event flowing through Kafka topics. This provides:

  1. Durability: Events are persisted and can be replayed
  2. Ordering: Critical for maintaining price history
  3. Scalability: Easy to add more consumers as load increases

WebSocket Strategy

REST APIs don't cut it for real-time data. We implemented a WebSocket layer with:

  • Connection pooling: Efficient resource management
  • Heartbeat mechanism: Detect and recover from stale connections
  • Reconnection logic: Automatic recovery with exponential backoff
  • Message batching: Aggregate updates within animation frames

State Management

The frontend uses a carefully designed state architecture:

// Tick batching for smooth updates
const tickBuffer = new Map<string, PriceTick>();
const BATCH_INTERVAL = 16; // 60fps

setInterval(() => {
  if (tickBuffer.size > 0) {
    updatePrices(Array.from(tickBuffer.values()));
    tickBuffer.clear();
  }
}, BATCH_INTERVAL);

Lessons Learned

1. Measure Everything

Without metrics, you're flying blind. We instrumented:

  • WebSocket message latency
  • Order execution time
  • UI render performance
  • Database query duration

2. Plan for Failure

In trading systems, failure modes must be well-defined:

  • What happens when Kafka is down?
  • How do we handle database failover?
  • What's the degraded experience for users?

3. Test Under Load

We discovered critical bugs only under production-like load. Invest in load testing infrastructure early.

Conclusion

Building real-time trading platforms is challenging but rewarding. The key is embracing event-driven architecture, investing in observability, and planning for failure from day one.

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