Low Latency Market Feed vs Traditional Market Data: What Is the Difference?

A market monitoring application may display the latest price, but how quickly does that information reach the screen? The answer depends on how market data is collected, delivered, and processed.

For fintech developers building real-time monitoring dashboards, this difference matters. Traditional market data delivery often relies on periodic requests, while a Low Latency Market Feed is designed to deliver updates as soon as they become available.

Understanding how these approaches differ helps developers choose the right data architecture, reduce unnecessary delays, and handle incoming market information more efficiently.

Low Latency Market Feed vs Traditional Market Data

The main difference lies in how data reaches an application.

Traditional market data is commonly accessed through HTTP requests, periodic snapshots, or scheduled data delivery. The application requests information and processes the response when it arrives.

A Low Latency Market Feed focuses on reducing the time between a market event becoming available and the application receiving its update. Event-driven streaming, commonly implemented through WebSocket, allows the server to push data through an established connection without waiting for another client request.

These approaches are not mutually exclusive. A traditional data provider may also offer streaming services, and using WebSocket does not automatically guarantee low latency. Actual performance depends on the data source, network conditions, processing infrastructure, and client application.

How the Difference Affects a EURUSD Monitoring Application

Consider a developer building a dashboard to monitor EURUSD price movements.

With HTTP polling, the application might request the latest price every second. If the price changes immediately after a request completes, the application may not see the update until its next polling cycle.

This creates two limitations. First, intermediate price changes may be missed. Second, the application continues sending requests even when no new information is available.

A WebSocket-based real-time data feed works differently. The application establishes a persistent connection, subscribes to EURUSD, and receives updates as they become available.

This approach removes the waiting time associated with the next polling request and provides a continuous stream for updating the dashboard.

The choice also depends on data requirements. A monitoring interface may only need the latest price, while an application analyzing individual market events may need to retain incoming Tick updates. Developers should select the appropriate subscription rather than assume every streaming connection provides all market data types.

Connecting to a Real-Time Market Data Feed with Python

AllTick provides a WebSocket interface for Forex market data. Developers can subscribe to EURUSD through its general market data gateway.

The following example demonstrates the basic connection, subscription, heartbeat, and reconnection logic.

Install the required library:

pip install websockets

Set your API token as the ALLTICK_API_TOKEN environment variable.

import asyncio
import json
import os
import uuid
import random
import websockets

TOKEN = os.environ["ALLTICK_API_TOKEN"]

WS_URL = (
    "wss://quote.alltick.co/"
    f"quote-b-ws-api?token={TOKEN}"
)


def make_message(cmd_id, seq_id, data=None):
    return json.dumps({
        "cmd_id": cmd_id,
        "seq_id": seq_id,
        "trace": str(uuid.uuid4()),
        "data": data or {}
    })


async def connect_market_data():
    async with websockets.connect(
        WS_URL,
        open_timeout=10,
        close_timeout=5
    ) as ws:

        await ws.send(make_message(
            22004,
            1,
            {"symbol_list": [{"code": "EURUSD"}]}
        ))

        async def heartbeat():
            seq = 2
            while True:
                await asyncio.sleep(10)
                await ws.send(make_message(22000, seq))
                seq += 1

        task = asyncio.create_task(heartbeat())

        try:
            async for message in ws:
                data = json.loads(message)
                print(data)

        finally:
            task.cancel()
            try:
                await task
            except asyncio.CancelledError:
                pass


async def main():
    delay = 1

    while True:
        try:
            await connect_market_data()

        except asyncio.CancelledError:
            raise

        except Exception as error:
            print(f"Connection interrupted: {error}")

        await asyncio.sleep(delay + random.uniform(0, 0.5))
        delay = min(delay * 2, 30)


if __name__ == "__main__":
    asyncio.run(main())

The subscription uses cmd_id: 22004 for latest trade-price updates, while cmd_id: 22000 sends heartbeat requests every 10 seconds.

The example also implements exponential backoff when the connection is interrupted. Once reconnected, the application sends the subscription again.

For production applications, developers should additionally validate subscription responses, handle authentication errors, monitor connection health, and process incoming messages according to their actual response structure.

How to Measure Market Data Latency

A streaming connection alone does not tell developers how much latency an application actually experiences.

A useful approach is to record three timestamps:

• Source event time: The timestamp associated with the market event.
• Client receive time: When the application receives the message.
• Processing completion time: When the application finishes handling the message.

These measurements help separate data delivery delays from application processing overhead.

For example, if the source and client clocks are synchronized and the source timestamp is understood, the difference between event time and receive time can help estimate delivery latency.

The difference between receive time and processing completion time measures local processing overhead.

For more reliable monitoring, track p50, p95, and p99 latency instead of relying only on averages. Also monitor message backlogs and reconnection frequency, especially when the application subscribes to multiple instruments.

One important detail: source timestamps and local timestamps must use compatible units and synchronized clocks. Otherwise, the calculated latency may be misleading.

Using AllTick for Real-Time Market Data Streaming

For developers who need both historical queries and continuous market updates, AllTick provides REST and WebSocket API access to financial market data across Forex, stocks, cryptocurrencies, commodities, and indices.

In a EURUSD monitoring application, the WebSocket interface can deliver latest trade-price updates through a persistent connection. AllTick also provides a separate subscription for order book depth:

These subscriptions serve different purposes and should not be treated as interchangeable.

AllTick requires heartbeat requests every 10 seconds, and WebSocket does not support K-line push. Applications requiring historical K-line data should use the appropriate HTTP API instead.

A practical architecture is to use REST for historical data queries and initial data loading, then use WebSocket for continuous updates.

Developers can explore the AllTick WebSocket API documentation and WebSocket request examples to review connection and subscription requirements. The REST API documentation explains request-based data access.

If you are planning to integrate a real-time data feed, create an AllTick API account to review the available API access and subscription options.

Please review AllTick's Terms of Service before implementation. Market data is provided for informational purposes, is not intended for trading or investment purposes, and must not be redistributed without authorization.