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.

Many failed backtests are not caused by the strategy itself, but by the fact that the data layer is already biased from the beginning. In cryptocurrency markets, this problem becomes even more pronounced: prices move 24/7, liquidity shifts
Many failed backtests are not caused by the strategy itself, but by the fact that the data layer is already biased from the beginning.
In cryptocurrency markets, this problem becomes even more pronounced: prices move 24/7, liquidity shifts rapidly, and execution structures are highly fragmented. If the historical data used for backtesting is incomplete or inconsistent, you are essentially validating a strategy against a distorted version of the market.
Backtesting is not simply about testing profitability. At a deeper level, it answers a more fundamental question: whether a strategy behaves consistently and meaningfully under historical market structures.
Many people treat backtesting as a “profit simulator over historical data,” but from an engineering perspective, it is closer to a market replay system.
You are not trying to predict returns. Instead, you reconstruct three core elements:
In the Cryptocurrency Market, this is critical because market structure changes rapidly. A strategy that works in one volatility regime may completely fail in another.
Using only OHLC candlestick data already removes a large amount of information.
A more realistic market representation should include:
At the microstructure level, the market is not a sequence of candles—it is a continuous stream of trading events.
For example, the same 1-minute bullish candle can represent:
Although visually identical, strategy outcomes can differ significantly.
Crypto data sources vary widely. The key differences are not “availability,” but:
In practical system design, unified data interfaces are often preferred to reduce integration complexity.
For example, using AllTick’s historical tick and K-line data capabilities allows developers to access structured multi-market datasets under a unified timeline, which is essential for cross-asset strategy evaluation.
The key is not data volume, but structural consistency.
A functional backtesting system typically consists of four layers:
Handles historical data ingestion:
Generates trading signals such as:
Simulates real market execution:
Tracks:
The system forms a closed loop:
Data → Strategy → Execution → Portfolio Feedback
import pandas as pd
class SimpleBacktest:
def __init__(self, data, fee=0.0005):
self.data = data
self.fee = fee
self.position = 0
self.cash = 10000
self.entry_price = 0
def signal(self, row):
if row["price"] > row["ma"]:
return 1
elif row["price"] < row["ma"]:
return -1
return 0
def run(self):
for _, row in self.data.iterrows():
sig = self.signal(row)
price = row["price"]
if sig == 1 and self.position == 0:
self.position = 1
self.entry_price = price
self.cash -= price * self.fee
elif sig == -1 and self.position == 1:
pnl = price - self.entry_price
self.cash += pnl
self.cash -= price * self.fee
self.position = 0
return self.cash
This simplified model already includes:
Real systems extend this with more accurate matching engines and data fidelity.
In real markets:
A realistic model should link slippage to volatility and volume conditions.
Common issues include:
Even if prices are correct, misaligned time series can invalidate strategy logic.
A strategy may look profitable under ideal fills, but once you introduce:
The performance curve can change significantly.
When extending from BTC or ETH to multi-asset environments, new structural relationships emerge:
Backtesting becomes less about a single strategy and more about validating a market system model.
Instead of focusing solely on PnL, more important metrics include:
A robust strategy is not necessarily the most profitable one in backtests, but one that remains structurally consistent across conditions.
The real value of backtesting is not answering “could this have made money in the past,” but rather:
When historical data is viewed as a market behavior record rather than a price archive, backtesting becomes a tool for understanding structure, not just performance.
Generate a free API key in seconds and connect to every market from one endpoint.