How to Reduce Latency in Real-Time Market Data Streaming

 

In financial applications, speed isn't just a feature—it’s a competitive edge. Whether you’re powering algorithmic trading, live dashboards, or high-frequency alerts, even a few milliseconds of delay in real-time market data can translate into missed opportunities or financial loss.

As user expectations and trading volumes increase, so does the need to reduce latency—the time it takes for market data to travel from the exchange (or aggregator) to your system and ultimately to your user interface.

In this guide, we’ll break down where latency comes from, how it affects your platform, and the most effective ways to optimize your infrastructure, code, and data pipelines to deliver market data as fast and reliably as possible.

 

Table of Contents

- Why Latency Matters in Market Data Delivery

- The Key Sources of Latency in Financial APIs

- Best Practices for Reducing WebSocket Latency

- Optimizing REST API Usage for Near-Real-Time Speed

- How Finage Supports Low-Latency Streaming

- Final Thoughts: Speed, Stability, and Scalable Data Flow

 

1. Why Latency Matters in Market Data Delivery

Latency refers to the time delay between a market event and your system’s ability to reflect or act on it. In real-time financial systems—especially those handling trading, pricing, or alerts—latency isn’t just a performance issue. It directly impacts your platform’s accuracy, competitiveness, and trustworthiness.

 

Key areas where latency causes real risk:

Trading Systems

Milliseconds can affect order timing, slippage, and execution price. Delayed price data leads to suboptimal trades, missed arbitrage opportunities, and potential user complaints.

Portfolio Management

Real-time portfolio values become inaccurate if price data lags behind actual market conditions—especially during high-volatility sessions.

Pricing Engines & Conversions

For apps converting between currencies, indices, or crypto assets, outdated prices can result in incorrect outputs, creating regulatory and financial liability.

Market Alerts & Signals

Latency can cause alerts to trigger too late or not at all, undermining trust in your notification system.

User Trust & Experience

In a multi-platform environment (mobile, web, desktop), users may compare prices and spot inconsistencies. If your data is slow, users may abandon your app—even if everything else works.

 

In high-frequency environments, latency isn't measured in seconds—it's measured in milliseconds or even microseconds. And as infrastructure grows more distributed (multi-region, cloud-native, global clients), reducing latency becomes a design priority, not a back-office task.

 

2. The Key Sources of Latency in Financial APIs

Before you can reduce latency, you need to understand where delays are introduced. In real-time market data delivery, latency can originate from multiple layers—some technical, others architectural.

Here are the most common sources:

 

Exchange Latency

This is the time it takes for an exchange to generate and transmit the initial market event. Even the fastest exchanges introduce small delays.

Key causes:

- Internal matching engine speed

- Network load

- Exchange throttling during high volatility

 

Aggregation and Normalization Delays

Data providers who source from multiple exchanges often need to aggregate, filter, and normalize that data before sending it to you. This can add 10–100 milliseconds or more if not optimized.

Examples:

- Delayed consolidation of bid/ask data

- Format conversions

- Regional peering latency across sources

 

API Gateway or Proxy Overhead

Many fintech APIs sit behind load balancers, CDNs, or API gateways that introduce latency through:

- TLS negotiation

- Request parsing

- Token validation

- Rate-limiting middleware

While necessary for security, these layers must be optimized to avoid adding friction.

 

WebSocket Setup and Subscription Latency

Even with a persistent connection, the initial handshake, subscription message, and symbol binding take time. If the platform doesn't stream all data by default, your system waits for confirmation + data routing per symbol.

 

Client-Side Rendering and Network Distance

Once data reaches your frontend, it can still be delayed due to:

- Slow WebSocket parsing in browsers or apps

- Network distance from the user to your edge servers

- Latency spikes from mobile or high-latency environments (e.g., VPNs, 4G)

Polling and REST-Based Delays

If you're using REST APIs to simulate real-time data (e.g., polling every few seconds), your system is inherently delayed by the request interval.

Even with fast infrastructure, polling introduces built-in latency that WebSockets or push-based architectures avoid entirely.

 

3. Best Practices for Reducing WebSocket Latency

WebSockets are the preferred method for real-time market data delivery because they allow persistent, low-latency connections between client and server. But they’re only as fast as the architecture and implementation behind them.

Here’s how to minimize WebSocket latency across your system:

Use WebSocket Compression Wisely

Enabling compression (e.g., permessage-deflate) can reduce bandwidth usage, but in high-frequency data feeds, it may increase CPU load and delay processing.

- Avoid compression for small, frequent messages (like stock tick updates).

- Disable it entirely unless you're sending large payloads (e.g., snapshots).

 

Establish Persistent Connections

Opening and closing connections repeatedly adds handshake overhead and introduces delays. Instead:

- Keep WebSocket sessions open for as long as the user session is active.

- Use connection pooling for back-end services where appropriate.

Subscribe Efficiently

When using Finage or similar APIs, avoid subscribing to too many symbols per connection.

- Group symbols logically (e.g., per asset type or user watchlist).

- If you're only showing 5–10 assets, subscribe only to those.

- Avoid wildcard subscriptions unless necessary—they increase traffic.

Avoid the Frontend Bottleneck

In browser-based apps or mobile apps:

- Minimize DOM updates triggered by each message.

- Debounce or batch updates where millisecond precision is not required.

- Offload parsing and transformation to Web Workers if using heavy libraries.

Deploy WebSocket Gateways Close to the User

Latency increases with geographic distance. Use edge servers or CDN-level WebSocket proxies to:

- Terminate connections closer to the client

- Reduce round-trip times (RTT)

- Improve responsiveness in global applications

If you're routing WebSocket traffic through your own server, host that service in the same region as your data provider, or use a regional proxy.

 

Monitor Packet Drops and Reconnection Events

Silent reconnections, message buffering, and packet loss can degrade performance without obvious errors. Track:

- Latency between message timestamp and receipt

- Reconnection frequency

- Packet drop percentages (e.g., missed ticks or out-of-order updates)

Finage WebSocket streams include timestamps with each tick, allowing you to measure real latency on the client side.

By combining efficient symbol management, persistent connections, client-side optimizations, and geographic alignment, you can reduce latency to tens of milliseconds or less—even in production systems.

 

4. Optimizing REST API Usage for Near-Real-Time Speed

While WebSockets are the standard for live streaming data, REST APIs still play a key role—especially for historical data, snapshot views, and back-end processing. In some cases, teams also use REST endpoints to simulate real-time functionality through polling or scheduled fetches.

To minimize latency and increase performance when using REST APIs for market data, consider the following best practices:

 

Use Batched Endpoints Instead of Repeated Requests

Avoid sending multiple individual requests per symbol. Instead, use APIs that support batch queries—where you can request data for multiple assets in a single call.

Example (Finage batch quote API):

GET https://api.finage.co.uk/last/stock/AAPL?apikey=YOUR_API_KEY

 

This reduces:

- TCP overhead

- API processing time

- Total network latency

Shorten Polling Intervals with Caution

If you’re polling for near-real-time updates (e.g., every 1–5 seconds), ensure that:

- Your polling frequency is tuned to match data update intervals

- Your backend or frontend handles polling asynchronously, without blocking the UI or queueing excess requests

- You monitor for rate limits or potential throttling

Excessively frequent polling can simulate real-time poorly and introduce jitter, retries, and rate-limit errors under load.

 

Use CDN Acceleration Only for Static or Delayed Data

Many REST APIs are cached behind CDNs for performance. However, if you need true real-time or near-real-time data, avoid relying on cached responses—request direct, uncached endpoints when milliseconds matter.

Finage REST APIs are designed to bypass CDN caching for endpoints like /quote to ensure real-time accuracy.

 

Leverage Interval Granularity Where Possible

When using historical APIs (e.g., OHLCV), avoid requesting excessive data or full-day candles if you only need the most recent interval.

For example:

GET https://api.finage.co.uk/agg/stock/AAPL/1/day/2020-02-05/2020-02-07?apikey=YOUR_API_KEY

This is faster and more efficient than pulling an entire day’s data and parsing the final record client-side.

 

Process Data Asynchronously on the Client or Server

Avoid blocking UI updates or trade actions while waiting for REST responses. Instead:

- Use asynchronous fetch patterns (e.g., Promise.all, async/await)

- Handle timeout or fallback scenarios gracefully

- Preload or cache critical values where acceptable

REST APIs won’t match the speed of WebSockets, but with smart design, you can still achieve sub-second latency for snapshot-style market data workflows—especially when paired with real-time feeds or used for pre-trade validation, analytics, and reporting.

 

5. How Finage Supports Low-Latency Streaming

Finage is designed for fintech teams, trading platforms, and developers who need real-time data at minimal latency. Whether you’re powering a high-frequency trading bot or a live dashboard, Finage offers infrastructure and features built to minimize delay across all asset classes.

Here’s how Finage enables low-latency performance:

 

Real-Time WebSocket API for All Asset Types

Finage provides a unified WebSocket API with millisecond-level delivery across:

- Stocks

- Crypto

- Forex

- Indices

- ETFs

Clients can stream quotes, trades, or full bid/ask data through a single, persistent connection, reducing round trips and avoiding polling delays.

 

Optimized Global Infrastructure

Finage routes real-time data through geographically distributed edge nodes, reducing physical network distance and RTT (round-trip time). This ensures consistent delivery performance across regions.

 

High-Frequency Tick Data

The WebSocket stream supports tick-by-tick updates, which means users receive:

- Every trade or quote change

- Timestamped data

- Immediate symbol-level push notifications

This is especially important for trading systems where speed and precision affect outcomes.

 

Low Latency REST Endpoints for Snapshots and History

For backtesting, reporting, or hybrid models, Finage also offers fast REST APIs with:

- Batched requests

- Granular time intervals (down to 1-minute OHLCV)

- UTC-timestamped historical data

- Lightweight, cache-optimized JSON responses

Stable, Scalable Rate Limits

Finage’s infrastructure is built to handle real-time workloads at scale without dropping messages or forcing reconnections. Users on higher-tier plans benefit from:

- Increased throughput

- Higher message delivery priority

- Support for high-symbol subscriptions per session

Finage’s stack is designed to meet the demands of low-latency trading, compliance workflows, and global data distribution, allowing developers to stream real-time financial data reliably and efficiently.

 

6. Final Thoughts: Speed, Stability, and Scalable Data Flow

In fintech, latency is more than a technical metric—it’s a competitive variable. Whether you're building a trading bot, a pricing engine, or a real-time portfolio dashboard, the ability to stream market data instantly, accurately, and at scale is foundational to both user trust and system performance.

Reducing latency means:

- Acting on data before your competitors

- Delivering a smoother, more responsive user experience

- Meeting regulatory and reporting expectations with timestamped accuracy

- Preventing costly errors caused by outdated information

With Finage, you get a platform built for speed:

- Real-time WebSocket streams across asset classes

- Fast REST endpoints with clean JSON

- Low-latency infrastructure and scalable delivery architecture

- Unified API keys and consistent symbol formats to reduce integration friction

Whether you’re optimizing for milliseconds or just making your app feel faster and more accurate, Finage gives you the tools to build low-latency systems without sacrificing stability or coverage.

You can get your Real-Time and Historical Market Data with a free API key.

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