Forex API
Tick-level quotes for 50+ currency pairs including majors, minors and exotics.
Stream tick-level Forex, Crypto, Stock, Commodity and Index data over a single WebSocket and REST API. Get a free key in seconds — no sales call required.
| Symbol | Asset class | Price | Latest move |
|---|---|---|---|
| EUR/USD ForexEuro / US Dollar | Forex | - | - |
| BTC/USDT CryptoBitcoin | Crypto | - | - |
| ETH/USDT CryptoEthereum | Crypto | - | - |
| AAPL StockApple Inc. | Stock | - | - |
| XAU/USD CommodityGold Spot | Commodity | - | - |
| USD/JPY ForexUS Dollar / Yen | Forex | - | - |
| NVDA StockNVIDIA Corp. | Stock | - | - |
| SPX IndexS&P 500 Index | Index | - | - |
Every market AllTick covers is available through the same unified REST and WebSocket interface.
Tick-level quotes for 50+ currency pairs including majors, minors and exotics.
Real-time spot and derivatives data, normalized into one feed.
Equities across US, Hong Kong and mainland China with trades and quotes.
Live pricing for precious metals and energy.
Benchmark index values and constituents for major global indices.
Compare coverage, latency and data types across every AllTick market.
Browse productsHow AllTick compares to a typical legacy market-data vendor.
| Capability | AllTick | Typical Legacy Vendor |
|---|---|---|
| Median WebSocket latency | ~150ms | 400–800ms |
| Asset classes in one API | 5 (FX, Crypto, Stock, Commodities, Indices) | 1–2 |
| Uptime SLA | 99.95% | 99.5% or none |
| Free tier | Yes — instant API key | Sales call required |
| WebSocket streaming | Native | Polling / limited |
Connect over WebSocket and subscribe to any symbol across any market.
# AllTick realtime financial data API
# forex crypto stock commodities indices
import asyncio, json, uuid
import websockets
subscribe = {
"cmd_id": 22004,
"seq_id": 1,
"trace": str(uuid.uuid4()),
"data": {"symbol_list": [{"code": "EURUSD"}]},
}
heartbeat = {"cmd_id": 22000, "seq_id": 1, "trace": "heartbeat", "data": {}}
async def stream():
uri = "wss://quote.alltick.co/quote-b-ws-api?token=YOUR_API_KEY"
async with websockets.connect(uri) as socket:
await socket.send(json.dumps(subscribe))
async def keep_alive():
while True:
await asyncio.sleep(10)
await socket.send(json.dumps(heartbeat))
asyncio.create_task(keep_alive())
async for message in socket:
print(json.loads(message))
asyncio.run(stream())Cut market-data costs by 60% while adding crypto coverage.
“Migrating to AllTick let us consolidate three vendors into one WebSocket feed and ship our trading app a quarter early.”Read case study
Served 40k concurrent users with sub-200ms quote updates.
“The 99.95% SLA and consistent latency were exactly what our retail brokerage needed to scale globally.”Read case study
Backtested 12 years of tick data across 5 asset classes.
“Having historical and live data from a single normalized API removed weeks of data-engineering work.”Read case study
Generate a free API key in seconds and connect to every market from one endpoint.
Practical writing on market data engineering, streaming APIs and building low-latency financial applications.
Generate a free API key in seconds and connect to every market from one endpoint.

When building a forex trading system, quantitative strategy platform, or financial data application, developers usually focus on factors such as market data latency, coverage, and API stability. However, one fundamental issue is often overl
When building a forex trading system, quantitative strategy platform, or financial data application, developers usually focus on factors such as market data latency, coverage, and API stability. However, one fundamental issue is often overlooked: timestamp management.
The forex market connects multiple financial centers around the world, including London, New York, Tokyo, and Sydney. Since these markets operate in different local time zones, inaccurate time handling can lead to incorrect trading session detection, shifted candlestick periods, and inconsistent backtesting results.
Therefore, understanding UTC timestamps returned by forex data APIs and establishing a reliable timezone processing workflow are essential for building stable market data systems.
Unlike markets operated by a single exchange, the forex market is a truly global market. Since different regions follow different local times, returning market data based on local time would require developers to manage numerous timezone conversions.
To simplify this process, most financial data providers use UTC (Coordinated Universal Time) as the standard reference time. UTC is not affected by geographical location and does not shift due to daylight saving time changes, making it suitable as a universal time standard for global market data.
When developers obtain real-time market data through a forex API, the response usually contains information such as trading symbol, price, and timestamp. For example:
{
"symbol": "EURUSD",
"price": 1.0856,
"timestamp": 1783900800
}
The timestamp represents the exact event time in UTC rather than the user’s local time. After receiving the data, the system should convert it according to application requirements instead of directly treating it as local time.
For simple data display scenarios, a few hours of difference may only create a visual issue. However, for trading systems, quantitative models, and market analysis platforms, timestamp errors can directly affect strategy logic.
For example, a strategy designed around European trading session volatility needs to accurately identify the London market opening time. If the system incorrectly processes UTC timestamps, the strategy may trigger signals too early or too late, causing differences between backtesting and live trading environments.
Similar problems can occur when generating candlestick data. If one system builds hourly candles based on UTC boundaries while another uses local time boundaries, the resulting open prices, closing prices, and technical indicators may differ.
Therefore, timestamps in forex market data are not simply additional fields. They define the structure and sequence of market activity.
A reliable market data system usually follows a principle of “store in UTC, convert when needed.”
During the data ingestion stage, developers should preserve the original UTC timestamp returned by the API. This ensures consistency regardless of where users are located or where the system servers are deployed.
For example, in Python, developers can explicitly define UTC during timestamp conversion:
from datetime import datetime, timezone
timestamp = 1783900800
utc_time = datetime.fromtimestamp(
timestamp,
timezone.utc
)
print(utc_time)
This approach prevents differences caused by the server’s default timezone configuration.
When data needs to be displayed to users, it can then be converted into the appropriate local timezone. For example, applications targeting US users can display New York time, while applications serving Asian users can display Beijing time, while the strategy engine continues using UTC internally.
This architecture helps avoid timezone conflicts between different system components and is widely used in financial data applications.
In real-time forex market data systems, tick data represents the smallest unit of market movement. Each price update corresponds to a specific point in time, and these continuous events together form the actual market activity sequence.
If tick data contains timestamp issues, such as out-of-order events, delayed updates, or inconsistent timezone standards between sources, short-term trading strategies may generate incorrect signals.
For example, high-frequency trading and short-term quantitative strategies often analyze price movement speed, transaction density, and market activity levels. All of these calculations rely heavily on accurate timestamps.
Therefore, when selecting a forex API, developers should not only consider whether it provides real-time prices but also evaluate timestamp accuracy, data format consistency, and time standard reliability.
When using historical forex data APIs, many developers focus on OHLC price fields while overlooking the time rules behind candlestick generation.
However, a candlestick is not simply a price aggregation result. It also depends on clearly defined time boundaries.
For example, a daily candlestick generated from UTC 00:00 will be different from one generated according to the New York market closing time. These differences can affect technical analysis, indicator calculations, and quantitative strategy backtesting.
Therefore, when analyzing historical forex data, developers should confirm:
Only when time-related rules remain consistent can analysis results maintain reliability.
For developers, the biggest challenge in building a forex market data system is often not obtaining prices, but ensuring that data remains stable, standardized, and suitable for different application scenarios.
A complete forex data workflow usually needs to handle real-time market data streaming, historical data queries, tick data processing, multi-timeframe candlestick generation, and timezone conversion for global users.
By using AllTick API for forex market data access, developers can integrate real-time market data, historical data, and tick-level data through a unified interface. This reduces the workload of data cleaning and format conversion, allowing teams to focus more on trading logic, risk management, and application development.
For quantitative trading platforms, financial applications, and market terminals, consistent data structures and clear timestamp standards are fundamental components of a reliable system.
In global financial markets, price movements are generated by participants across different regions, while timestamps record the exact sequence of these market events.
UTC is not an additional technical limitation. Instead, it provides a unified framework that allows market data from different regions to be processed consistently.
When a forex data API returns UTC timestamps, the correct approach is not simply adding or subtracting several hours. Developers need to establish a complete time management workflow: store data consistently in UTC, convert time according to application requirements, handle daylight saving time changes, and ensure that strategy calculations use the same time standard.
For any system relying on real-time market data, accurate timestamp processing is just as important as data speed and coverage. A reliable time foundation allows trading strategies, analytical models, and financial applications to operate with greater accuracy and stability.
Generate a free API key in seconds and connect to every market from one endpoint.