Low-Latency Execution in Algorithmic Trading Systems

In financial markets, latency can be the difference between a profitable trade and a slippage loss. Optimizing order execution speed is paramount.

Techniques for Speed Optimization

1. Persistent WebSocket Connections: Maintaining persistent WebSocket feeds instead of polling REST endpoints for market ticks.
2. In-Memory Order Processing: Processing signals in memory arrays using NumPy and TA-Lib C-extensions before firing order payloads.
3. Asynchronous I/O: Leveraging asyncio to prevent network calls from blocking technical indicator calculations.

import asyncio
import websockets

async def stream_market_ticks(ticker):
    async with websockets.connect(WS_URL) as ws:
        while True:
            tick = await ws.recv()
            process_tick_signal(tick)

Minimizing execution latency unlocks competitive edges in high-volatility derivative markets.