Migrating HFT from Python to Go 1.24: How Swiss Tables Killed Our Latency Spikes (-41%)

Source: Dev.to

If you are running a trading bot on Python in 2026, you are likely paying a latency tax you can’t afford.
We learned this the hard way.

We (my friend and I) spent months fighting what J.P. Morgan and the community call Infrastructure Hell. We started where everyone starts: Python (with libraries like CCXT and frameworks like Freqtrade). It worked fine for prototyping, but when we scaled to processing tick data from seven major exchanges (Binance, OKX, Bybit, Kraken, Gate.io, Bitget, KuCoin) simultaneously, the cracks appeared.

Infrastructure Hell

Memory leaks

Chronic memory accumulation in watchOrderBook caches caused RSS growth that crashed our containers after roughly five days.

The GIL & jitter

Handling > 40 k WebSocket messages / sec blocked the Global Interpreter Lock, creating “phantom latency”: price updates arrived, but the interpreter couldn’t dispatch them fast enough.

We needed a compiled language with a scheduler capable of true parallelism, so we chose Go 1.24 (thanks, Google!).

Swiss Tables in Go 1.24

The most critical improvement for us was the new map implementation based on Swiss Tables. Our system maintains a massive in‑memory state of tickers (stored in Redis keys like tk:SYMBOL), making map performance a bottleneck.

Benchmark results (new engine vs. Python monolith)

By leveraging metadata fingerprinting and SIMD instructions, we effectively removed the GC pauses that used to plague our jitter buffers.

Architecture – The MIE Pipeline

Collector (Ingestor)

• Maintains persistent WebSocket connections to the seven exchanges.
• Normalizes “dirty” ticks into a unified struct.
• Uses a hot‑store strategy: atomic HSET operations to Redis keys tk:SYMBOL, guaranteeing sub‑millisecond snapshots.
• Sequences events with internal timestamps to correct exchange clock drift before publishing to Pub/Sub NEW_CANDLE:*.

Brain

• Subscribes to the Redis stream and performs heavy server‑side calculations (RSI, MACD, Pearson correlation).
• Implements a worker‑pool pattern with 8 concurrent goroutines.
• Processes pairs in batches of 100 with a 50 ms interval, maximizing CPU cache locality and minimizing Redis round‑trips.

API

• Read‑only layer that pulls from Redis (hot data) and TimescaleDB (cold history).
• Strictly separates ingestion from consumption, so spikes in user traffic cannot crash the collector.

“Candle Forge”

Speed is useless if the data is inaccurate. We introduced the concept of Conscious Latency: a deliberate 100–200 ms jitter buffer to cross‑validate prices.

• If Binance shows a 5 % spike but OKX and Kraken don’t reflect it within the buffer window, the Candle Forge algorithm flags it as a “Scam Wick” (liquidity void) and filters it out.
• We trade 100 ms of latency for arbitrage truth.

Conclusion

The transition to Go 1.24 wasn’t just about raw speed—it was about predictability.
By moving to a compiled language with Swiss Tables, we eliminated the memory bloat that killed our Python bots. We now deliver institutional‑grade data—normalized, validated, and computed—without the institutional price tag, democratizing this speed.

Tech docs:
Engine in action:
Main dev GitHub: