Forem: Artur Stankevicz

Limpio, the new crypto trading meta.

Artur Stankevicz — Wed, 25 Feb 2026 09:49:17 +0000

I was recently recommended a cool product, I'll tell you about it

so What is Limpio ?

Limpio Terminal isn't just another API; it's a fundamental infrastructure layer that physically removes the barriers that prevent professionals from earning. They outperform all competitors in every key metric, and here's why their dominance is inevitable:

1. Technological "Concrete" vs. Fragile Workarounds

While competitors use Node.js or Python, which choke on dense WebSocket threads due to the GIL (Global Interpreter Lock), Limpio's Go-based computing core is designed from the ground up for extreme loads.

Extreme Efficiency: Limpio process data on over 1,000 trading pairs from 7 leading exchanges, consuming only 500 MB of RAM—competitors require entire clusters for this.

Latency < 20 ms: Limpio delivers data faster than most top-tier professional feed services.

Death of Error 1006: Limpio encapsulated the fight against exchange imperfections within the Go orchestrator; where other bots are blinded by WebSocket interruptions, Limpio's multiplexer seamlessly transmits ticks without a single millisecond of downtime.

2. Total Data Purity (Market Intelligence)

Limpio transforms the chaos of market quotes into "clean energy" for your algorithms.

Neighbor Protection: Limpio's anomaly filtering algorithm automatically isolates exchanges that are "lying" or catching flash crashes, preventing false liquidations and losing trades.

Data Quality Score: Limpio implemented a quality rating system so you can see the real state of your data stream—from "Excellent" to "Limited"—not just raw numbers.

Candle Forge: Limpio collects raw ticks and reconstruct candlesticks ourselves, ensuring 100% accuracy for backtesting, which is impossible to achieve with standard OHLCV data.

3. Cost-Effectiveness: Eliminates TCO

Limpio offer institutional-grade data for 5% of the cost of corporate monopolies.

95% Savings: Developing such infrastructure yourself costs between $165,000 and $205,000 per year. With Limpio Terminal, you get the same (or better) results for $948 per year.

Zero-Infrastructure Math: Limpio completely eliminates the need for heavy Python backends; all calculations (RSI, MACD, Bollinger, etc.) are performed on our servers and delivered to you in <20 ms.

4. Continuous Development and Expansion

Limpio has no intention of stopping there. The plan is to become the standard Market Intelligence Engine (MIE) by 2026.

MIE Transformation: They building a system that doesn't just transmit data, but consumes it and produces "ready-to-use intelligence" free of artifacts.

Market Capture: Limpio is aggressively entering the RWA, DEX aggregator, and proprietary trading segments, where Limpio Terminal's technological superiority gives clients an unfair advantage over the rest of the market.

Deterministic Stability: Limpio creates an environment where data is a transparent and instantly executable asset, freeing engineers from the "infrastructure hell" of alpha-seeking.

*Limpio Terminal delivers professional, clutter-free data, designed for those who value the accuracy and speed of their infrastructure. Limpio is here to rewrites the rules of the game.
*

There's an open beta for early adopters, and there's a special offer for lifetime access for $100.
https://limpioterminal.pro

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

Artur Stankevicz — Wed, 18 Feb 2026 10:26:20 +0000

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:

No Rate Limit Awareness: It tried to open connection #51 immediately after #50, triggering Binance's aggressive WAF. And boom IP ban.
Memory Leaks: It handled subscriptions but never cleaned up the maps when a client disconnected. In a long-running process, this is fatal.
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)
}

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)
}

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

Hot Store (Redis): We use Redis Lists as a circular buffer.
Compaction: Ticks are aggregated into 1-minute bars in memory.
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)

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).

Request A reads DB: UnitsUsed = 99,999.
Request B reads DB: UnitsUsed = 99,999.
Both see Limit = 100,000. Both allow the request.
Request A writes UnitsUsed = 100,000.
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
})

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()
}

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

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

Artur Stankevicz — Tue, 17 Feb 2026 11:03:41 +0000

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 (me and my friend) spent months fighting what Jp Morgan and community call "Infrastructure Hell". We started where everyone starts: Python (specifically libraries like CCXT and frameworks like Freqtrade).

It worked fine for prototyping. But when we scaled to processing tick data from 7 major exchanges (Binance, OKX, Bybit, Kraken, Gate.io, Bitget, KuCoin) simultaneously, the cracks appeared.

Here is the post-mortem of why we killed our Python monolith and rewrote our entire Market Intelligence Engine (MIE) in Go 1.24, achieving a 41% reduction in map insertion time and flattening our memory profile.

Sooo

The crypto market of 2026 is fragmented. Price discovery doesn't happen on one exchange; it happens across a web of venues.

Our Python infrastructure faced two fatal bottlenecks
The first one are Memory Leaks. We noticed chronic memory accumulation in watchOrderBook caches. In high-throughput scenarios, our containers would crash after roughly 5 days due to RSS growth.

The second ones are The GIL & Jitter. Handling 40k+ WebSocket messages/sec blocked the Global Interpreter Lock. This created "phantom latency"—price updates were arriving, but the interpreter couldn't dispatch them fast enough.

We needed a compiled language with a scheduler capable of true parallelism. We chose Go 1.24.(thank you google!)

Swiss Tables in Go 1.24

We didn't just swap syntax we architected around the specific performance breakthroughs in the latest Go release. The most critical for us was the new Map Implementation based on Swiss Tables.

For a system that maintains a massive in-memory state of tickers (stored in Redis keys like tk:SYMBOL), map performance is the bottleneck.

We tested our ingestion engine before and after the migration. The impact on our Redis Hot-Store updates was violent

Map Insertion Time: Reduced by 41% (from 103.01 ms to 60.78 ms)
Map Lookup Time: Reduced by 25% (from 318.45 ms to 240.22 ms)
Memory Footprint: Reduced by ~70% (from 726 MiB to 217 MiB)

(The data was collected from tests of our brand new engine.)

By utilizing metadata fingerprinting and SIMD instructions, we effectively removed the Garbage Collection (GC) pauses that used to plague our jitter buffers

Architecture! "The MIE Pipeline»

To solve the "Data Silos" problem, we split the system into three specialized Go microservices.

Collector (Ingestor)
It maintains persistent WebSocket connections to 7 exchanges.
Instead of pushing raw data, it normalizes "dirty" ticks into a unified struct. Critically, it uses a Hot-Store strategy instead of writing to disk, it performs atomic HSET operations to Redis key tk:SYMBOL. This ensures sub-millisecond snapshots. It sequences events using internal timestamps to fix exchange clock drift before pushing to Pub/Sub NEW_CANDLE:*.

Brain

This is where the magic happens. The Calculator service subscribes to the Redis stream and performs heavy math server-side (RSI, MACD, Pearson Correlation).

To handle the load, we implemented a Worker Pool pattern

8 concurrent goroutines.

Then we process pairs in batches of 100 with a 50ms interval. This maximizes CPU cache locality and minimizes Redis round-trips.

API

A read-only layer that pulls from Redis (Hot) and TimescaleDB (Cold History). It strictly separates ingestion from consumption, so a spike in user traffic cannot crash the data collector.

"Candle Forge" !

Speed is useless if the data is inaccurate. We introduced a concept "Conscious Latency".

In an industry obsessed with "zero latency," we deliberately introduced a 100-200ms Jitter Buffer. Why? To cross-validate prices.

If Binance shows a 5% spike, but OKX and Kraken don't reflect it within the buffer window, our Candle Forge algorithm flags it as a "Scam Wick" (liquidity void) and filters it out of the stream. We trade 100ms 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.

We are democratizing this speed.

Check out our Tech Docs: https://docs.limpioterminal.pro
See the Engine in Action: https://limpioterminal.pro
Main Dev git: https://github.com/psychosomat