REST vs WebSocket: Quantitative Trading API Architecture Comparison
ClawDUX TeamMarch 29, 20266 min read0 views
REST vs WebSocket: Quantitative Trading API Architecture Comparison
Choosing the wrong API transport for your trading frequency can cost you real money. Here's the definitive comparison.
The Fundamental Difference
REST: Request → Response. You ask, server answers. One round trip per query.
WebSocket: Persistent connection. Server pushes data the moment it's available.
Latency Comparison
python
import time
import requests
import asyncio
import websockets
import json
# REST latency test
def measure_rest_latency(url: str, n: int = 100) -> dict:
latencies = []
for _ in range(n):
start = time.perf_counter()
requests.get(url)
elapsed = (time.perf_counter() - start) * 1000
latencies.append(elapsed)
return {
'mean_ms': sum(latencies) / len(latencies),
'p50_ms': sorted(latencies)[len(latencies) // 2],
'p99_ms': sorted(latencies)[int(len(latencies) * 0.99)],
'min_ms': min(latencies),
}
# WebSocket latency test
async def measure_ws_latency(url: str, n: int = 100):
latencies = []
async with websockets.connect(url) as ws:
for _ in range(n):
start = time.perf_counter()
await ws.recv()
elapsed = (time.perf_counter() - start) * 1000
latencies.append(elapsed)
return {
'mean_ms': sum(latencies) / len(latencies),
'p50_ms': sorted(latencies)[len(latencies) // 2],
'p99_ms': sorted(latencies)[int(len(latencies) * 0.99)],
}Benchmark Results (Typical)
| Metric | REST (polling 1s) | WebSocket |
|---|---|---|
| Data latency | 500-1000ms avg | 1-50ms |
| Requests/sec | 1-10 | N/A (push) |
| CPU usage | Higher (HTTP overhead) | Lower |
| Memory | Lower (no persistent conn) | Higher (conn buffers) |
| Reliability | Simpler (stateless) | Complex (reconnection) |
| Bandwidth | Higher (headers each req) | Lower (frames only) |
When to Use Each
Use REST for:
- Account management: Check balance, list positions (infrequent)
- Order placement: Submit/cancel orders (critical, need confirmation)
- Historical data: Backfill OHLCV bars (bulk download)
- Low-frequency strategies: Rebalancing daily/weekly
Use WebSocket for:
- Live market data: Tick-by-tick prices, order book updates
- Order status: Execution reports, fill notifications
- High-frequency strategies: Sub-second signal generation
- Multi-exchange monitoring: Concurrent price feeds
The Hybrid Architecture
python
class TradingEngine:
"""Hybrid REST + WebSocket architecture."""
def __init__(self):
self.rest_client = None # For orders, account
self.ws_client = None # For market data
self.latest_prices = {}
async def start(self):
# WebSocket for real-time data
ws_task = asyncio.create_task(self._ws_loop())
# REST for periodic account sync
sync_task = asyncio.create_task(self._sync_loop())
await asyncio.gather(ws_task, sync_task)
async def _ws_loop(self):
"""Receive real-time market data."""
# ... WebSocket connection with reconnection
pass
async def _sync_loop(self):
"""Periodically sync account state via REST."""
while True:
account = self.rest_client.get_account()
positions = self.rest_client.get_positions()
await asyncio.sleep(30) # Every 30 seconds
def on_signal(self, symbol: str, side: str):
"""Signal from WS data → Order via REST."""
self.rest_client.place_order(
symbol=symbol,
side=side,
type='market',
)Decision Matrix
| Strategy Type | Data Source | Order Execution |
|---|---|---|
| HFT (< 1s) | WebSocket only | WebSocket/FIX |
| Intraday (mins) | WebSocket | REST |
| Swing (days) | REST polling | REST |
| Portfolio rebal | REST batch | REST |
The ClawDUX platform uses this same hybrid approach — WebSocket for real-time strategy monitoring during the 72-hour verification window, and REST for order management and escrow operations.
The core logic discussed in this article has been integrated into the ClawDUX API. Access ClawDUX-core for full permissions, or browse the marketplace to discover verified trading strategies.
#rest#websocket#architecture#latency#api-design