Live Flow only matters if it is fast. In this post, we walk through how we built a real-time Options Live Flow using WebSockets and Redis Stack, and how we keep live data streaming smoothly even during peak traffic.
🎯 The Problem
Options traders need to see live flow data - large options orders, unusual activity, and sweep orders - the moment they hit the tape. A delay of even a few seconds can mean the difference between a profitable trade and a missed opportunity. Our challenge was to ingest, process, and deliver this data to thousands of concurrent users with sub-100ms latency.
🏗️ System Architecture
Our pipeline consists of a market data feed that ingests raw options flow from exchange APIs, a processing layer built in Python that filters, enriches, and scores each flow event, Redis Streams for buffering and fan-out, a Node.js WebSocket server that pushes events to connected clients, and a React frontend that renders the live feed in real time.
🔴 Why We Chose Redis Stack
Redis Streams gave us persistent, ordered storage of flow events with consumer group support for reliable processing. Pub/Sub enabled instant fan-out to all connected WebSocket servers. The in-memory architecture kept our end-to-end latency consistently under 50ms even at peak load.
📊 Handling Peak Traffic
During market open and major economic events, our system processes thousands of flow events per second. We handle this with Redis Cluster for horizontal scaling, multiple WebSocket server instances behind a load balancer, and client-side filtering so each user only receives the flow data relevant to their watchlist.
⚡ Key Engineering Decisions
We serialize flow events as compact JSON to minimize payload size. We use Redis TTLs to automatically expire old flow data and keep memory usage bounded. We implemented a circuit breaker pattern to gracefully degrade when upstream data feeds experience issues.
✅ What We Learned
Building real-time financial data systems requires obsessing over every millisecond. Redis Stack proved to be the right foundation - fast, reliable, and flexible enough to handle our evolving requirements. The combination of WebSockets and Redis Streams is now the backbone of our entire live data platform.
