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 developing a stock market data system, many developers encounter a basic but important question after connecting to a stock API: what exactly do the open, high, low, and close fields returned by the API mean? For developers who are new
When developing a stock market data system, many developers encounter a basic but important question after connecting to a stock API: what exactly do the open, high, low, and close fields returned by the API mean?
For developers who are new to financial data APIs, these fields may look like simple price parameters. However, in real-world market data systems, quantitative trading platforms, and financial analysis applications, OHLC data is one of the most fundamental and important data structures.
Whether it is generating candlestick charts, analyzing historical market trends, or conducting strategy backtesting, these four fields are essential.
The stock market generates a large amount of tick-by-tick transaction data during trading hours. If every single tick is displayed directly, the amount of data becomes extremely large, making it difficult for users to quickly understand market movements.
Therefore, market data systems usually aggregate raw trading data based on fixed time intervals, such as 1-minute, 5-minute, hourly, or daily periods. The resulting aggregated data is known as OHLC data.
Simply put, OHLC uses four price points to describe market movement within a specific time period:
These four fields form a complete candlestick and represent one of the most common data formats returned by stock APIs.
In real-world applications, stock APIs typically return market data in a structure similar to the following:
{
"symbol": "XXXX",
"timestamp": 1784505600,
"open": 125.30,
"high": 128.60,
"low": 124.80,
"close": 127.90,
"volume": 356800
}
The meaning of each field is:
symbol: Stock symboltimestamp: Market data timestampopen: Opening pricehigh: Highest pricelow: Lowest priceclose: Closing pricevolume: Trading volumeDevelopers can use these fields to generate candlestick charts, analyze price movements, calculate volatility, and build additional market indicators.
Open represents the first valid traded price within a specific time period.
For example:
The importance of the opening price is that it shows where the market started trading during that period.
For example, after significant market news, if the stock API returns the following data:
Previous Close: 100
Open: 105
It indicates that the market experienced a significant price adjustment at the beginning of the trading session.
When developing quantitative strategies or market analysis systems, the Open field is commonly used for:
However, different markets and exchanges may define opening prices differently.
Some exchanges determine opening prices through auction mechanisms, while some data sources may use the first executed trade as the opening price. Therefore, when using a stock API, developers should confirm how the data provider defines the Open field.
High represents the highest executed price within a specific time period.
It shows the highest level that buyers pushed the price to during that period.
For example:
Open: 100
High: 108
Low: 98
Close: 105
This means the price reached a maximum level of 108 during this period.
The highest price does not represent the final market consensus price. Instead, it records the upper limit that the market explored during trading.
In market data systems, the High field is commonly used for:
For example, if the price repeatedly approaches a certain High level and then declines, that area may be considered an important price zone.
For quantitative developers, analyzing High alone is usually not enough. It is often combined with trading volume, tick data, or order book data.
If prices continue reaching new highs while trading volume increases, it may indicate stronger market participation. On the other hand, if prices rise while volume decreases, developers may need to further analyze liquidity changes.
Low represents the lowest executed price within a specific time period.
It shows how far the price dropped when selling pressure reached its strongest point.
Similar to High, Low is not the final trading result. Instead, it represents an extreme point reached during price movement.
For example:
{
"open": 80.20,
"high": 82.10,
"low": 79.50,
"close": 81.60
}
This means:
Low data helps developers analyze:
The difference between High and Low is also an important indicator of market activity.
Range = High - Low
When the price range suddenly expands, it often indicates increased market volatility. In such situations, trading systems or strategies may need to adjust their risk parameters.
Close represents the last executed price at the end of a specific time period.
Among the four OHLC fields, Close is often one of the most important data points used in analysis models.
The reason is simple: it represents the final price established by the market after a complete trading process.
Many market analysis methods are based on closing prices, including:
For example:
MA20 = average(last_20_close_prices)
The core data used in this calculation is the closing price from the previous 20 periods.
Therefore, if Close data returned by a stock API contains missing values, timestamp issues, or abnormal prices, it can directly affect the accuracy of subsequent analysis.
For developers, the most common application scenarios of OHLC data include the following.
Frontend market applications usually require the following data:
By combining these data points, developers can generate candlestick charts.
Using historical OHLC data, developers can perform various types of analysis, including:
For example:
price_change = (close - open) / open
This calculation can quickly determine the percentage price change within a specific period.
In real-world trading systems, developers rarely rely only on historical OHLC data.
A more common approach is to combine multiple types of market data.
Historical OHLC data is used to understand long-term market conditions.
Real-time tick data is used to capture the latest market movements.
Level 2 data is used to obtain order book depth and liquidity information.
By combining these three types of data, developers can build more complete market analysis systems.
Understanding OHLC fields is only the first step. In real-world development, data quality is equally important.
The first issue is time consistency.
Different markets have different trading schedules. If timestamps are handled incorrectly, candlestick periods may become misaligned. For example, minute-level data may contain duplicated or missing records.
Another important issue is stock price adjustment.
When events such as stock splits or dividends occur, historical prices may need to be adjusted.
Therefore, when conducting historical analysis, developers need to clearly understand whether they are using:
Different data processing methods can affect final backtesting results.
In addition, consistency between real-time data and historical data is extremely important.
If two different data sources are used, developers may encounter differences in:
In such cases, additional data cleaning and transformation processes are required.
For developers building market applications, quantitative systems, or financial data platforms, a stable data API is an essential foundation.
AllTick API provides stock real-time market data, historical candlestick data, and tick data APIs, allowing developers to access market information through a unified data structure.
By using a consistent data source, developers can reduce the complexity of:
For example, a quantitative backtesting system can use historical OHLC data to validate trading strategies while combining real-time market APIs to obtain the latest market changes. This creates a complete workflow from data acquisition to strategy execution.
OHLC data in a stock API is not simply four price values.
Instead, it represents the complete process of how market prices changed during a specific period.
Open shows where the market started.
High and Low represent the range of price movement.
Close records the final price level established by the market.
For developers, understanding these fields is not only important for correctly parsing stock API responses, but also for building more reliable market systems, trading tools, and data analysis platforms.
When OHLC data is combined with real-time market data, tick data, and order book information, the stock market is no longer just a list of prices. It becomes structured market data that can be understood and analyzed by software systems.
Generate a free API key in seconds and connect to every market from one endpoint.