Unified Crypto Market Data: Designing a Low-Latency WebSocket API for Informed Trading

Crypto markets don’t fail because traders lack tools. They fail because information is fragmented.

The same asset can surge on one exchange while remaining flat on another. Sometimes this divergence lasts seconds, sometimes minutes—but by the time it becomes obvious, the opportunity (or the risk) has already passed.

This project started as an attempt to close that visibility gap: a unified, low-latency view of market activity across major exchanges, delivered fast enough to matter and transparent enough to trust.

Why This Matters

Most trading decisions aren’t wrong—they’re incomplete.

A sudden move on Binance can look like genuine momentum until you realize OKX and Bybit show no corresponding volume. Without that context, traders are left guessing whether they’re seeing real demand or localized manipulation.

Crypto markets are structurally fragmented. Each exchange is its own micro-economy with its own latency profile, liquidity depth, and failure modes. Treating them as a single coherent market without reconciling those differences is a mistake.

The goal of this system is not to predict the market, but to remove blind spots.

What This Platform Does (and Does Not) Do

What It Does

• Aggregates real-time market data from multiple exchanges
• Normalizes and annotates that data into a consistent model
• Exposes it via a low-latency WebSocket API
• Surfaces cross-exchange discrepancies in price and volume

What It Does Not Do

• It is not a trading strategy or alpha engine
• It does not replace native exchange order books
• It is not a colocated HFT system competing on microseconds

This is an infrastructure layer—designed to be built upon, not to make decisions for the user.

High-Level Architecture

The system is built around a simple constraint:

Every millisecond of latency and every inconsistency should be explainable.

That constraint shapes every layer of the stack.

Exchange WebSocket ↓ Ingestion & Watchdogs ↓ Stream Buffer (Kafka / Redis Streams) ↓ Normalization & Validation ↓ Regional Fan-out (Redis) ↓ Client WebSocket API

Each stage is isolated, observable, and designed to fail independently.

Data Ingestion: The Reality of Exchange WebSockets

On paper, exchange WebSocket APIs look clean and well-documented. In practice, they are one of the least reliable components of the system.

Some failure modes I had to design around:

• Connections that stay open but silently stop delivering data
• Missing sequence numbers with no explicit error frames
• Aggressive reconnect throttling during volatility spikes
• Exchanges sending stale snapshots after reconnect

Each exchange stream is wrapped in a watchdog loop:

• Heartbeats are tracked per connection
• Message timestamps are sanity-checked
• Sequence gaps are flagged immediately
• REST polling is used as a last-resort verification path

A simplified Binance kline ingestion example:

import asyncio
import json
import websockets

async def listen_to_binance():
    url = "wss://stream.binance.com:9443/ws/btcusdt@kline_1m"

    async with websockets.connect(
        url,
        ping_interval=20,
        ping_timeout=10
    ) as ws:
        while True:
            msg = await ws.recv()
            data = json.loads(msg)

            yield {
                "exchange": "binance",
                "pair": "BTCUSDT",
                "price": data["k"]["c"],
                "volume": data["k"]["v"],
                "event_time": data["E"],
            }

The complexity isn’t in connecting—it’s in knowing when not to trust what you receive.

Data Unification & Normalization

Normalization turned out to be less about JSON shape and more about semantics.

Different exchanges disagree on:

• Whether volume is reported in base or quote asset
• Whether timestamps represent event time or publish time
• What “last price” actually means under low liquidity

Rather than forcing a single interpretation, the system exposes a consistent top-level model while preserving exchange-specific metadata.

exchange: "binance" pair: "BTCUSDT" price: 50000.25 volume: 10234.7 event_timestamp: 1672345678901 ingest_timestamp: 1672345678920

Discrepancies are surfaced—not hidden. Consumers decide how strict they want to be.

Cross-Exchange Consistency Checks

Perfect alignment across exchanges is not achievable. Instead of pretending otherwise, divergence is treated as a first-class signal.

Examples include:

• Price spikes isolated to a single exchange
• Volume surges without corresponding movement elsewhere
• Liquidity appearing briefly on one venue only

These conditions are flagged in real time and delivered alongside raw data. The platform does not infer intent—it provides context.

Stream Processing & Fan-out

Raw events flow into a lightweight stream buffer—Kafka or Redis Streams depending on deployment constraints.

Reasons for buffering:

• Backpressure during volatility
• Reconnect storms after exchange outages
• Replay during consumer restarts

Normalized events are fanned out via regional Redis clusters, minimizing hop count and avoiding cross-region chatter.

Latency & Deployment Strategy

Low latency is not just about speed—it’s about predictability.

Bare-metal deployment was a deliberate choice. Cloud VMs scale easily, but noisy neighbors and variable network paths become visible under load.

Regional Layout

• US-East — primary ingest and North American clients
• EU-West — European clients with local edge caching
• Asia-Pacific — optimized for Bybit and regional liquidity

Typical steady-state latency:

• Exchange → ingest node: 20–40ms
• Normalization & routing: 2–5ms
• Redis fan-out: <1ms
• Edge routing to client: 10–30ms

The goal isn’t zero latency. It’s latency that behaves consistently during stress.

WebSocket API Design

Clients connect to a globally routed WebSocket endpoint and are automatically steered to the nearest healthy region.

Key characteristics:

• Exchange-agnostic event schema
• Optional gzip/deflate compression
• Backpressure-aware subscriptions
• Per-client rate limiting

This allows dashboards, bots, and analytics systems to consume the same stream safely.

Security & Key Management

Because users can connect their own exchange accounts, key handling is treated as a first-class concern.

API keys are:

• Encrypted at rest using a managed KMS
• Scoped strictly by exchange and permission
• Never exposed to ingestion or aggregation services

Market data ingestion and trade execution are isolated services. A failure in one does not cascade into the other.

Failure Modes I Designed For

No system is defined by its happy path.

Explicitly handled scenarios include:

• WebSocket stalls without disconnects
• Partial regional outages
• Consumer lag during extreme volatility
• Inconsistent snapshots after reconnect

Each component assumes failure by default and recovers independently.

Cost vs Latency Trade-offs

Bare-metal, multi-region infrastructure is expensive.

The system scales selectively:

• High-volume pairs use ultra-low latency paths
• Lower-traffic assets flow through cheaper pipelines

This keeps costs predictable without compromising critical signals.

Who This Is For

• Traders who want cross-exchange visibility
• Engineers building trading dashboards or bots
• Teams operating in constrained or regulated environments

This is an infrastructure layer, not a finished product.

What’s Next

• Expand exchange coverage
• Add deeper order-book aggregation
• Improve anomaly detection heuristics
• Harden deployments for restricted and offline environments

Final Thought

This project isn’t about beating the market. It’s about removing blind spots.

In fragmented markets, clarity is often more valuable than speed. The goal here is to provide that clarity—consistently, transparently, and fast enough to matter.