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Artur Stankevicz
Artur Stankevicz

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I built a HFT crypto aggregator in Go 1.24 (and why "vibe coding" wouldn't survive it)

#go #architecture #performance #web3

Authored by a 19-year-old engineer tired of "Infrastructure Hell»

This article describes how we're creating a new crypto market standard, the challenges we encountered, and how we improved it. It will be useful for developers, Algo Traders, and Quants.

I am 19 years old. According to my SM feed, I should be building "AI wrappers" right now.
I should be "vibe coding" with Claude 4.6(overpriced), letting an LLM generate my entire backend while I focus on the CSS.

Instead, I spent the last year in what I call Infrastructure Hell.

My friend and I built Limpio Terminal, a high-frequency market data aggregator.
We connect to 7 major exchanges (Binance, Bybit, OKX, Kraken, etc.)

[no mexc their websockets are hell]

Normalize thousands of WebSocket streams, calculating technical indicators (RSI, MACD, Bollinger Bands) in real-time, and serve them via a unified API with maximum 200ms latency.

We didn't do this because we love pain. No no we are pain intolerant We did it because institutional data (Bloomberg/Refinitiv) costs $2,000/month, and public exchange APIs are a disaster of rate limits, dirty data, and random disconnects.

Okay so -

We wrote it in Go 1.24. We use Redis for hot windows, TimescaleDB for cold storage, and raw SQL locking for billing.

If I had tried to "vibe code" this, the project would have died in week two.

(Seriously, vibe coding literally interferes with engineering.)

Here is the technical post-mortem of why real engineering still matters, and how we solved the problems that LLMs don't even know exist.

Part 1: The "Vibe Coding» is enemy of engineering.

The modern narrative is that coding is dead. "Just prompt it."

I tried. I asked a leading coding agent to write a WebSocket manager for Binance. The code it gave me was syntactically correct Go. It compiled. It looked great.

But in production, it was a suicide note:

  1. No Rate Limit Awareness: It tried to open connection #51 immediately after #50, triggering Binance's aggressive WAF. And boom IP ban.

  2. Memory Leaks: It handled subscriptions but never cleaned up the maps when a client disconnected. In a long-running process, this is fatal.

  3. Naive Concurrency: It launched a goroutine for every single message. When volatility spiked (e.g., a Bitcoin flash crash), the runtime scheduler choked.

Real engineering isn't about syntax it's about constraints. It's about knowing that hardware is finite, networks are unreliable, and exchanges are hostile.

AND Here is how we actually built it.

Part 2: WebSocket Orchestration

Connecting to one exchange is easy. Connecting to seven, with thousands of trading pairs, is a distributed systems problem inside a single binary.

Problem: Rate Limits & Bans

Most exchanges enforce strict connection rate limits. Binance, for instance, allows only 5 incoming connection attempts per second from a single IP. If you restart your service and try to reconnect all 50+ WebSocket shards instantly, you look like a DDoS attack. You get BANNED.

Solution is staggered Start & chunking

We implemented a strict orchestration layer that negotiates connections rather than just opening them.

Code Pattern is Staggered Loop

This is from our internal/exchange/ws_manager.go. Note the explicit delay calculation based on the shard index.

Go 1.24

// ws_manager.go: avoiding the ban hammer
for i, provider := range orderedProviders {
    // Calculate a deterministic delay. 
    // Provider 0 starts at 0ms. Provider 1 at 400ms, etc.
    // This creates a "ramp" of traffic instead of a "wall".
    delay := time.Duration(i) * StartStaggerMs 

    go func(p Provider, d time.Duration) {
        if d > 0 {
             time.Sleep(d)
        }
        if err := p.Connect(ctx); err!= nil {
             logger.Error("Failed to connect %s: %v", p.Name(), err)
             // Backoff logic kicks in here
        }
    }(provider, delay)
}
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This isn't complex code. But it's the difference between a stable deployment and a frantic 3 AM debugging session trying to rotate IP addresses.

Anti-Leak Guard

We also enforce a lifecycle for temporary subscriptions (e.g., when a user views a specific chart). We track them in a map with expiration times. If the map grows beyond maxTempSubs, we actively delete the oldest entries. This is manual garbage collection for application state.

Go 1.24

if len(m.tempSubs) >= m.maxTempSubs {
    // Find and evict the oldest subscription to prevent memory creep
    delete(m.tempSubs, oldestSymbol)
}
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Part 3: Go 1.24, Swiss Tables, and RSS Regression

We upgraded to Go 1.24 immediately upon release. The headline feature was the new map implementation based on Swiss Tables (inspired by Abseil). The promise of Go 1.24 : faster lookups and lower memory overhead.

For a high-frequency aggregator that does millions of map lookups per minute (matching ticks to pairs), this sounded like free performance.(sweet performance)

The Reality:

We observed a non-trivial regression in RSS (Resident Set Size) memory usage in our production containers, despite Go heap metrics reporting lower usage.

It turns out that while the heap footprint of Swiss Tables is smaller, the interaction with the OS memory allocator under our specific workload (heavy churn of small objects + map writes) led to fragmentation that the OS didn't reclaim immediately.

We had to tune GOGC and our batch sizes to mitigate this. If I was just "prompting" code, I wouldn't even know what RSS is I'd just see my Kubernetes pods OOM-killing (Out Of Memory) and blame the cloud provider

Part 4: The "Candle Forge" TM

Handling 50,000+ ticks per second requires a robust pipeline. Writing every tick to Postgres is impossible (or prohibitively expensive).

We built a component called Candle Forge. It acts as a high-speed reduction gear.

Architecture

  1. Hot Store (Redis): We use Redis Lists as a circular buffer.

  2. Compaction: Ticks are aggregated into 1-minute bars in memory.

  3. Persistence: Only finished hourly bars are written to TimescaleDB (Cold Store).

Code Pattern: The Redis Ring

We use LPUSH + LTRIM to keep a fixed-size window of history in Redis. This ensures $O(1)$ time complexity for inserts and strictly bounds memory usage.

Go 1.24

// internal/collector/candle_forge.go

// Push the new minute bar
c.hotStore.LPush(ctx, CandlesKeyPrefix+pairID, string(body))

// TRIM the list to keep exactly CandleForgeWindowSize elements.
// This guarantees that Redis memory usage never grows unbounded,
// regardless of how long the system runs.
c.hotStore.LTrim(ctx, CandlesKeyPrefix+pairID, 0, CandleForgeWindowSize-1)

// Notify downstream calculators via Pub/Sub
c.pub.Publish(ctx, NewCandleChannelPrefix+pairID, pairID)
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This pattern allows our API to serve "sparkline" data (last 24 hours) instantly from Redis RAM, while TimescaleDB handles the heavy analytical queries for historical data (years of data).

Part 5: Billing Race Conditions (When Mutex Isn't Enough)

We offer a free tier (100k units/day) till this Friday to be honest. This means we have to count every request.

The Race Condition:

Imagine two requests come in for the same API key at the exact same microsecond (common in crypto trading bots).

  1. Request A reads DB: UnitsUsed = 99,999.

  2. Request B reads DB: UnitsUsed = 99,999.

  3. Both see Limit = 100,000. Both allow the request.

  4. Request A writes UnitsUsed = 100,000.

  5. Request B writes UnitsUsed = 100,000.

Result: The user got 100,001 requests but was only charged for 100,000. Scale this up, and you have a massive revenue leak.

Solution is Row-Level Locking

Standard Go mutexes only work within a single process. Since we run multiple API instances, we need database-level locking.

We use PostgreSQL's SELECT... FOR UPDATE via GORM's locking clauses.

Go 1.24

// internal/service/usage_service.go

err = database.DB.Transaction(func(tx *gorm.DB) error {
    var entry models.UsageEntry

    // CLAUSE.LOCKING logic is critical here.
    // "Strength: UPDATE" tells Postgres to lock this specific row.
    // Any other transaction trying to read this row will WAIT 
    // until this transaction commits or rolls back.
    if err := tx.Clauses(clause.Locking{Strength: "UPDATE"}).
        Where("api_key_id =? AND date =?", apiKey.ID, today).
        First(&entry).Error; err!= nil {
        return err
    }

    if entry.UnitsUsed + cost > limit {
        return ErrLimitReached
    }

    entry.UnitsUsed += cost
    return tx.Save(&entry).Error
})
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Is this slower? Yes. It serializes requests for a single user.

Is it correct? Yes.

In fintech, correctness > latency (usually). For everything else, we have Redis.

Part 6: Why We Panic in Production

Look at our main.go snippet provided in the architecture docs:

Go 1.24

redisCache, err := cache.NewRedisCache(cfg.Redis)
if err!= nil {
    if cfg.Env == "production" {
        logger.Error("Production requires Redis. Fix REDIS_HOST. DIE.")
        os.Exit(1) // Fail Fast
    }
    // In Dev, degrade gracefully to memory
    cacheManager = cache.NewMemoryCache()
}
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This violates the "always stay up" dogma of some web devs. But in our domain, running without Redis means running with a split-brain state. One API node might serve price A, and another serves price B because they aren't syncing.

I would rather the API return 502 Bad Gateway (and wake me up) than return 200 OK with stale data that causes a user to liquidate their portfolio.

Explicit degradation strategy is an engineered feature, not an accident.

Conclusion: We Are 19, and We Are Tired

We built Limpio Crypto Engine because we wanted to trade, but we spent a year building infrastructure instead. We solved the "Infrastructure Hell" so you don't have to.

We don't have a QA team. We don't have VC funding. We have Go 1.24, rigorous locking, and a hatred for dirty data.

We opened a Free Tier (100k units/day). Go ahead, try to break it. Flood our WebSockets. Hammer our billing logic.

If it breaks, I'll fix it. I won't ask an AI to do it for me.

I think we've taken on a serious task in trying to democratize institutional data, which is hard for young guys with no money and a part-time schedule, so if you have any suggestions, Limpio needs YOU!

Check the docs: docs.limpioterminal.pro

See the architecture: limpioterminal.pro

For feedback, you can use LinkedIn or email.
linkedin: https://www.linkedin.com/in/arturstankevicz/

email : okartur01@gmail.com

Top comments (1)

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clarabennettdev profile image
Clara Bennett •

Love the take on vibe coding vs. production-grade systems. HFT is where Go really shines — the goroutine scheduler handles thousands of concurrent WebSocket connections without breaking a sweat, and the GC pauses in 1.24 are impressively low. Curious about your approach to order book synchronization across exchanges — did you use lock-free data structures or stick with mutexes? In my experience the bottleneck usually isn't the network but the contention on shared state when aggregating across multiple feeds.