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EmilyL
EmilyL

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How to Pick a Low-Latency US Stock API: Key Metrics & WebSocket Test

#api #data #performance #testing

As a FinTech tech lead, I’ve onboarded several real-time market data feeds for US equities. The performance gap between a well-optimized API and a generic one can be over 200ms — enough to turn a profitable strategy into a loser. Here’s how I evaluate them, complete with code.

Breaking Down Latency

Latency comes from three main places:

  • Transport: HTTP adds overhead; WebSocket delivers push in real time.
  • Location: Servers near exchange data centers send data faster.
  • Serialization: JSON parsing slows you down at scale.

Mandatory Metrics

Metric Target
End-to-end latency < 80ms median
Throughput ≥ 1500 tick/s under load
Packet loss < 0.001%

Messages must be sequenced and in order.

Test Script

I benchmark providers by running the same WebSocket client code during market open:

import websocket
import json

def on_message(ws, message):
    data = json.loads(message)
    # Display tick data
    print(f"Time: {data['ts']} Symbol: {data['symbol']} Price: {data['price']} Volume: {data['volume']}")

def on_open(ws):
    # Subscription payload
    sub_msg = {
        "action": "subscribe",
        "symbols": ["AAPL", "MSFT", "NVDA"]
    }
    ws.send(json.dumps(sub_msg))

ws = websocket.WebSocketApp(
    "wss://api.alltick.co/stock/ws",
    on_open=on_open,
    on_message=on_message
)
ws.run_forever()
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In my tests, clean APIs like AllTick showed steady 60ms latency, while others varied wildly.

The Bigger Picture

In quantitative research, latency stability outweighs peak speed. A stream with low jitter delivers cleaner factor signals and reproducible backtests. Choose stability over hype.

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