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.

In the field of quantitative trading, the choice and execution of strategies are crucial. XTrader, with its powerful data analysis and strategy execution capabilities, has gradually become the platform of choice for many traders. By applyin
In the field of quantitative trading, the choice and execution of strategies are crucial. XTrader, with its powerful data analysis and strategy execution capabilities, has gradually become the platform of choice for many traders. By applying the trader-x contract quantitative strategy, traders can achieve efficient market analysis and automated trading, improving the execution of their strategies. This article will delve into what XTrader is, share practical experiences of the trader-x contract quantitative strategy, and provide actionable examples.
XTrader is a comprehensive trading platform designed for quantitative traders, supporting various financial assets such as forex, stocks, and cryptocurrencies. It provides real-time market data, strategy backtesting, and automated trading capabilities, allowing users to design and optimize quantitative trading strategies. Whether it’s trend-following, mean-reversion, or the trader-x contract quantitative strategy, XTrader offers flexible solutions for traders, helping them execute strategies in complex market environments.
Core FeaturesDescriptionPractical ApplicationReal-Time DataProvides real-time market data for various financial assetsFetch real-time prices for EUR/USD and perform trend analysisStrategy Design and BacktestingSupports strategy design, simulation, backtesting, and optimizationDesign a trend-following strategy based on moving averages and test it on historical dataAutomated Trading ExecutionSupports automated execution of strategies to reduce manual interventionSet rules for automatic buy when short-term MA crosses above long-term MAWebSocket APIProvides low-latency real-time data streams, suitable for high-frequency tradingReceive real-time market data and execute high-frequency trading strategies with millisecond response times
With these features, XTrader provides a complete trading framework for quantitative traders, enabling them to flexibly design and automate various strategies. Next, we will analyze several common quantitative strategies in detail and demonstrate how to implement them using XTrader.
In quantitative trading, strategy design requires not only technical support but also flexibility to adapt to market changes. The trader-x contract quantitative strategy is based on contract trading, typically used to capture market volatility and achieve quick capital growth through contract trades. This strategy relies on real-time market data and precise execution, and XTrader offers strong API and real-time data support to help traders implement it.
1. Trend-Following Strategy
The trend-following strategy is based on the assumption that market trends will persist over time. XTrader provides rich technical indicator analysis features to help traders accurately identify trend directions and make trading decisions.
Trend-Following StrategyDescriptionCore TechnologyMoving Average CrossoversIdentifying trend changes by crossing short-term and long-term moving averagesUse short-term MA (e.g., 50-day MA) and long-term MA (e.g., 200-day MA) crossovers to generate buy or sell signals
Code Example:
import requests
def get_data():
url = "https://apis.alltick.co/market_data"
params = {'symbol': 'EURUSD'}
response = requests.get(url, params=params)
return response.json()
def moving_average_strategy(data):
short_window = 50
long_window = 200
short_ma = sum(data[-short_window:]) / short_window
long_ma = sum(data[-long_window:]) / long_window
if short_ma > long_ma:
return "BUY"
else:
return "SELL"
data = get_data()
action = moving_average_strategy(data['prices'])
print(action)
2. Mean-Reversion Strategy
The mean-reversion strategy is based on the assumption that market prices will revert to their long-term average. When prices deviate from a certain range, the market tends to reverse or correct. XTrader’s real-time data interface helps traders react quickly when prices deviate from the mean.
Mean-Reversion StrategyDescriptionCore TechnologyZ-score MethodUsing the standard deviation of prices from the mean to judge if the market is overbought or oversoldWhen the Z-score exceeds a threshold, execute a sell; when it falls below a threshold, execute a buy
Code Example:
import numpy as np
def mean_reversion_strategy(data, threshold=2):
prices = np.array(data['prices'])
mean_price = np.mean(prices)
std_dev = np.std(prices)
z_score = (prices[-1] - mean_price) / std_dev
if z_score > threshold:
return "SELL"
elif z_score < -threshold:
return "BUY"
return "HOLD"
data = get_data()
action = mean_reversion_strategy(data)
print(action)
3. High-Frequency Trading Strategy
High-frequency trading (HFT) strategies rely on ultra-short-term market fluctuations for trading. XTrader’s WebSocket API supports real-time, low-latency data streams, making it ideal for implementing high-frequency trading strategies.
High-Frequency Trading StrategyDescriptionCore TechnologyMillisecond Market ResponseExecute trades based on market fluctuations in a very short time frameUse WebSocket API to receive real-time market data and react in milliseconds
1. Moving Average Crossovers
To help better understand the trend-following strategy, we illustrate the concept of moving average crossovers as shown below:
Price
^
|
| ------
| / \
| / \
| / \
| / \
-------------------------> Time
Short-term MA
Long-term MA
In the diagram, when the short-term moving average crosses above the long-term moving average, a buy signal is generated (upward crossover); conversely, when the short-term MA crosses below the long-term MA, a sell signal is generated (downward crossover).
2. Z-score Method
For the mean-reversion strategy, the Z-score is used to measure the deviation between the current price and the mean. When the price deviates beyond a certain standard deviation, a reversion is expected. The Z-score is calculated as follows:
[
Z = \frac{X – \mu}{\sigma}
]
Where ( X ) is the current price, ( \mu ) is the mean price, and ( \sigma ) is the standard deviation of the price. When the Z-score exceeds a certain threshold, a buy or sell operation is triggered.Z=σX−μ
Generate a free API key in seconds and connect to every market from one endpoint.