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.

Market making is essentially a constant balancing act between execution probability and risk control. If your quotes are too conservative, you won’t get filled. If they are too aggressive, you get picked off when the market moves sharply. T
Market making is essentially a constant balancing act between execution probability and risk control. If your quotes are too conservative, you won’t get filled. If they are too aggressive, you get picked off when the market moves sharply.
This tension becomes even more pronounced in the cryptocurrency market, where prices move 24/7, sentiment shifts rapidly, and the order book structure exposes supply and demand in real time.
This naturally leads to a key question:
Can we dynamically adjust market making quotes using Order Book Imbalance?
This article breaks down a practical framework for doing exactly that, with a Python-oriented implementation mindset.
Many people mistakenly treat market making as a forecasting problem. In reality, it is not about predicting direction.
A market maker mainly cares about three things:
In crypto markets (Cryptocurrency Market), the order book is one of the most direct sources of microstructure signals. If you can interpret its dynamics, you are already ahead of purely candle-based strategies.
A common definition is:
Order Imbalance = (Bid Volume – Ask Volume) / (Bid Volume + Ask Volume)
You can compute it across different depth levels (e.g., top 5 levels, top 10 levels):
The key insight is not directional prediction, but:
It reflects which side of liquidity is more likely to be consumed.
A traditional market making model uses:
Mid Price ± Spread / 2
However, this static approach fails when order book conditions shift.
We extend it to a dynamic model:
Bid Price = Mid – Spread/2 + α × Imbalance
Ask Price = Mid + Spread/2 + α × Imbalance
Where:
This allows quotes to adapt to real-time liquidity pressure.
To implement this, real-time order book data is essential.
Using a multi-asset streaming feed (e.g., WebSocket market data API), you can subscribe to Level 2 / depth updates.
import json
import websocket
import uuid
import time
API_KEY = "YOUR_API_KEY"
SYMBOLS = ["BTCUSDT", "ETHUSDT", "XAUUSD"]
WS_URL = f"wss://quote.alltick.co/quote-b-ws-api?token={API_KEY}"
def build_subscribe_msg(symbols):
return {
"cmd_id": 22004,
"seq_id": int(time.time()),
"trace": str(uuid.uuid4()),
"data": {
"symbol_list": [{"code": s} for s in symbols]
}
}
def on_open(ws):
print("WebSocket connected")
ws.send(json.dumps(build_subscribe_msg(SYMBOLS)))
def on_message(ws, message):
data = json.loads(message)
if data.get("cmd_id") == 22998:
tick = data["data"]
code = tick["code"]
price = float(tick["price"])
volume = float(tick.get("volume", 0))
direction = tick.get("trade_direction", 0)
print(f"[{code}] price={price} volume={volume} dir={direction}")
def on_close(ws, close_status_code, close_msg):
print("Reconnecting in 3s...")
time.sleep(3)
start()
def start():
ws = websocket.WebSocketApp(
WS_URL,
on_open=on_open,
on_message=on_message,
on_close=on_close
)
ws.run_forever(ping_interval=10, ping_timeout=5)
if __name__ == "__main__":
start()
This pipeline does three things:
The key risk in market making is adverse selection—being filled right before the market moves against you.
Order Book Imbalance helps by:
If imbalance < 0 (sell pressure), aggressive bid quoting increases risk of being consistently filled before drops.
Dynamic quotes improve execution probability in the direction of flow.
Imbalance alone can be noisy in high-volatility conditions.
A common adjustment:
if volatility > threshold:
alpha *= 0.5
Interpretation:
Once multiple assets are connected (BTC, ETH, FX, Gold), the model can expand into:
Examples:
Market making evolves from single-asset quoting to structured pricing across markets.
The order book is not just data—it is a real-time expression of market behavior.
The evolution of market making is not about increasingly complex prediction models, but:
Continuously adjusting quotes based on real-time liquidity pressure with low latency data.
In this sense, market making becomes less about “setting prices” and more about tracking the breathing rhythm of the market.
Generate a free API key in seconds and connect to every market from one endpoint.