Building a Low-Latency OMS

In the world of quantitative trading, execution is just as important as the strategy itself. You can have a signal with a 90% theoretical win rate, but if you're entering 2 seconds late, that edge can vanish entirely.

When we set out to build our custom Order Management System (OMS), we had one goal: minimize "wire-to-wire" latency. Here's how we achieved sub-50ms execution times.

The Bottleneck: HTTP vs WebSockets

Most retail trading APIs (Dhan, Zerodha) offer REST endpoints for placing orders. Opening a new HTTPS connection for every trade is expensive. It involves DNS resolution, TCP handshake, and SSL handshake, which can add 200ms+ overhead.

To solve this, we implemented persistent connections and connection pooling. But we went a step further for market data: WebSockets. By maintaining a live pipe to the exchange, we receive tick data instantly, rather than polling for it.

Redis for State Management

Tracking the state of hundreds of orders in memory (RAM) is risky. If the server crashes, you lose track of your positions. Writing to a traditional SQL database (PostgreSQL) is too slow for high frequency.

Enter Redis. We use Redis as an in-memory data structure store. It allows us to read/write order states in microseconds.

# Example: Atomic order update in Redis
async def update_order_status(order_id, status):
    await redis.hset(f"order:{order_id}", "status", status)
    await redis.publish("order_updates", f"{order_id}:{status}")

Asynchronous Python

We built the core engine using asyncio in Python. This allows the system to handle thousands of concurrent connections without waiting for one to finish before starting the next. This is crucial when you need to broadcast a "Sell" signal to 50 different client accounts simultaneously.

Conclusion

Building your own OMS is complex, but for serious algorithmic traders, it's a necessary investment. The control you gain over execution logic—slippage protection, retry mechanisms, and smart routing—is unparalleled.