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 quantitative trading and asset management, relying on a single market’s data often provides an incomplete view of strategy opportunities. Multi-asset data analysis allows you to observe price fluctuations and trends across different mark
In quantitative trading and asset management, relying on a single market’s data often provides an incomplete view of strategy opportunities. Multi-asset data analysis allows you to observe price fluctuations and trends across different markets, helping to identify potential trading opportunities more accurately. This article demonstrates, using Python, how to combine historical market data with real-time tick data to analyze multiple assets and optimize trading strategies.
The first step in multi-asset analysis is obtaining reliable historical data. For example, you can fetch OHLC data for foreign exchange and cryptocurrency markets via the AllTick API, then unify the data format for easier analysis:
import requests
import pandas as pd
API_URL = "https://api.alltick.co/forex/history"
API_KEY = "your_api_key_here"
symbols = ["EURUSD", "BTCUSD", "ETHUSD"]
dfs = []
for symbol in symbols:
params = {
"symbol": symbol,
"interval": "1h",
"limit": 500,
"apikey": API_KEY
}
resp = requests.get(API_URL, params=params)
df = pd.DataFrame(resp.json())
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['symbol'] = symbol
dfs.append(df)
data = pd.concat(dfs)
print(data.head())
By processing multi-asset data in a consistent format, you can simplify the calculation of indicators and subsequent strategy analysis.
In multi-asset analysis, common indicators such as moving averages (MA), volatility, and the Relative Strength Index (RSI) are used to determine trend direction, market volatility, and overbought/oversold conditions:
def calculate_indicators(df):
df = df.copy()
df['MA20'] = df['close'].rolling(20).mean()
df['MA50'] = df['close'].rolling(50).mean()
df['returns'] = df['close'].pct_change()
df['volatility'] = df['returns'].rolling(20).std()
delta = df['close'].diff()
up, down = delta.clip(lower=0), -delta.clip(upper=0)
roll_up = up.rolling(14).mean()
roll_down = down.rolling(14).mean()
rs = roll_up / roll_down
df['RSI'] = 100 - (100 / (1 + rs))
return df
data = data.groupby('symbol').apply(calculate_indicators)
print(data[['symbol','close','MA20','MA50','volatility','RSI']].tail())
These indicators provide a foundation for trend analysis, volatility observation, and market strength assessment, supporting the generation of trading signals.
Once data for different assets is prepared, potential trading signals can be explored. For instance, a moving average crossover on one asset combined with an oversold RSI on another may indicate cross-market arbitrage or hedging opportunities:
def generate_signal(df):
df['signal'] = 0
df.loc[df['MA20'] > df['MA50'], 'signal'] = 1
df.loc[df['MA20'] < df['MA50'], 'signal'] = -1
return df
signals = data.groupby('symbol').apply(generate_signal)
print(signals[['symbol','close','MA20','MA50','RSI','signal']].tail())
Analyzing multiple asset indicators simultaneously allows for more refined trading opportunities beyond trends in a single market.
Historical data provides trend references, while real-time tick data captures every price movement, supporting dynamic strategy analysis. A stable tick data stream allows you to:
Combining historical indicators with real-time tick data forms a complete multi-asset strategy analysis loop, extending from backtesting to live monitoring.
The main benefits of multi-asset analysis include:
By integrating historical market data with real-time tick data, you can build a comprehensive multi-asset strategy optimization workflow, improving data efficiency and providing a reliable foundation for testing and refining complex strategies.
Generate a free API key in seconds and connect to every market from one endpoint.