Precision Over Pump: Building a Swap Sniper in the Fixed/Float Jungle

A deep dive into the infrastructure, architecture, and logic behind SwapHunt — no hype, just edge.

No shill. No screenshots. Just signal.

🧭 Introduction

Crypto is full of noise. Shitcoins pump on vibes, bots scream on-chain, and influencers sell screenshots of perfectly-timed trades with no code, no logic, no audit trail.

This isn’t one of those stories.

I’m SwapHunt — and I’ve spent months building a sniper bot that operates off-chain in the weird but alpha-rich world of fixed/float crypto swaps.

This is about infrastructure-level arbitrage. It’s about reading latency, exploiting rate decay, and trading like a machine. No hype. Just edge.

🧨 The Problem with Most Crypto Bots

Most bots today chase the same dopamine:

• Reflexive buying of pumped tokens
• Front-running memecoins on newly created pools
• Copying Telegram alerts like they’re alpha

And when it comes to fixed/float swap services — like FixedFloat, Exolix, ChangeNOW, etc — most ignore them entirely. But here’s the thing:

These services are inefficient. And inefficiency = opportunity.

They aren’t DEXes. They’re not CEXes. They’re quote engines — and they’re slow, asynchronous, and easily out-of-sync with each other.

Most people don’t track them.
I do.

🧠 The Philosophy Behind SwapHunt

SwapHunt is built on a few key beliefs:

• Latency is alpha. In traditional markets, milliseconds matter. In crypto swaps, latency is minutes — sometimes more. If you can detect quote delays across providers before others react, you can quietly capture mispriced routes before they vanish.
• Precision beats reaction. Most bots are wired to chase — reacting to candles, social sentiment, or liquidity spikes. SwapHunt is designed to anticipate. It uses historical behavior and quote decay models to act before the chaos, not after.
• No hype, no hope. This isn’t a YOLO memecoin play. There’s no Discord, no FOMO alerts. SwapHunt is built to act on cold data, under strict rules, and adapt only when the system sees concrete risk or opportunity.

I didn’t build a shill machine. I built a sniper framework — a quiet, focused observer in a noisy battlefield.

🧱 The Architecture: Modular and Paranoid

SwapHunt consists of several coordinated components:

• Buzzlings — These lightweight data collectors run in parallel and constantly fetch live quotes from multiple swap providers. Each one is scoped to a provider or pair, and optimized for minimum latency and retry logic.
• Hivemind — The aggregation engine. It cleans, normalizes, and ranks quotes. It knows which sources are trustworthy, which tend to lag, and adjusts accordingly. It’s also the layer that models spread trends across time.
• Jonglings — The tactical operators. They use rulesets to detect entry conditions and log decisions. They work independently per strategy/pair and are disposable by design — like scalpel blades, not hammers.
• ExitWorkers — Monitoring agents that track profitability windows post-entry. Some exits happen seconds later, some hours later. These workers watch swap quotes, CEX markets, and gas fees to time exits.
• WalletService — The wallet orchestrator. It creates, manages, and secures dedicated wallets per strategy. It supports XMR, LTC, and other coins, with built-in RPC health checks, fee estimation, and dust defragmentation.

Each runs in a Docker container, with logging, redundancy, and failover. It’s paranoid by design. One component failing won’t halt the system.

And outside of the sniper logic, the full ecosystem includes:

• Accounting & tax reporting tools — Every trade is logged with timestamp, fiat value at time of execution, and provider metadata. This enables tax-grade reporting and audit trails.
• Historical tracking modules — Strategy decisions, missed entries, and aborted routes are all archived for later analysis. These post-mortems help refine logic iteratively.
• Data warehouse connectors — Metrics and events stream into Grafana-style dashboards and long-term databases, providing insights on volume, latency, and strategy effectiveness over time.
• Strategy sandboxing — A simulation environment to A/B test new logic before deployment. It mimics quote feeds in real time without touching capital.

This isn’t just a bot — it’s an evolving infra layer for smarter, modular crypto automation.

📊 Strategy: Box Window Analysis

The strategy I currently run is simple by design:

• Define Box windows for each pair (e.g. 24h for XMR → LTC)
• Track quote spreads over time, normalized by volatility
• Trigger entry when deviation from the box exceeds a configurable threshold
• Wait for exit either via:
  - Return spread on another provider
  - Delayed profit on original path (asymmetrical return)

This is done across dozens of pairs, using both fixed and float options, simultaneously.

There’s no indicators, no candles, no gut feeling.
Just quote math.

📈 The Testing: 5 Million+ Combos and Counting

I’m not posting screenshots of 100x flips.
But I’m not guessing either.

Over the last 3 months, I’ve tested over 5,000,000 strategy combinations — spanning:

• Entry thresholds
• Window durations
• Risk regimes
• Swap types (fixed/float)
• Slippage buffers
• Route prioritization

This isn’t theoretical. It’s simulated against real-time swap rates, fetched every 30–60 seconds from each provider.

And yes — the system finds edge. Small. Consistent. Exploitable.

🤖 Live Trades via CEX (and Beyond)

While SwapHunt itself focuses on off-chain swaps, I also run Binance bots — with related sniper logic.

They’re simpler, faster, and optimized for short-term candle-based volatility patterns.

Some of the core strategies include:

• CDLENGULFING: Looks for a large bullish or bearish engulfing pattern on lower timeframes (e.g., 3m). If the second candle fully engulfs the previous one and volume aligns, the system enters quickly — but only when confirmed by quote direction.
• CDLHAMMER: Identifies potential reversals using hammer or inverted hammer candles. These are monitored in real time across 5m and 15m intervals. The bot reacts only when additional momentum confirms the bounce, and risk is low-to-medium.
• CDLDOJI: Tracks periods of indecision in the market, especially when volatility is compressing. If a doji forms after a sharp move, and swap rates remain stable, the bot prepares for a potential breakout — but may reduce size due to risk asymmetry.

Each pattern is tied into dynamic risk models — so during high-impact news windows or unstable quote spreads, these patterns are paused automatically.

The core principle is the same throughout: reduce position size during chaos, scale into clarity.

📡 The Narrative Layer (Twitter-as-Audit) The Narrative Layer (Twitter-as-Audit)

What’s the point of running bots if no one knows what they’re doing?

That’s why I built an automated Twitter layer — a system that posts 24x per day with:

• Volatility alerts
• Strategy pauses
• Pattern-specific risk adjustments
• Commentary on ETF flows, sentiment spikes, or quote instability

These are auto-posts based on a news aggregator that digests real market events from 20+ sources and converts it into strategy-aware insight.

Example tweet:

“System: High vol alert. CDLHAMMER & CDLENGULFING paused on 3m. XRP risk HIGH. 5m/15m MED. Logs > lambos. #CryptoAutomation”

Behind the scenes? The system reads and adjusts continuously. Twitter is just the surface.

🧠 Lessons from 5 Million Simulations

1. Latency is the real alpha. Seconds matter more than patterns. I’ve seen profitable spreads vanish in under 3 seconds — and some stick around for 90. The only way to know is to measure everything.
2. Most swap services are dumb. No dynamic hedging. Many use stale pricing. Their APIs might look professional, but under the hood, many just mirror CEX quotes with 20–60s lag. That’s exploitable — if you’re faster.
3. You don’t need to win big. You just need to not lose stupidly. Most “edge” comes from managing risk. SwapHunt exits early when quotes turn, scales down during volatility, and avoids getting trapped in float routes when slippage spikes.
4. Backtests lie unless modeled in real-time conditions. I rebuilt my pipeline twice before results made sense. Simulating swap rate delay, gas variance, and stale quotes was critical to make tests reflect reality.
5. Twitter bots are undervalued. They build trust if they show real-time decisions. Posting raw signals, pauses, or volatility calls in public helps build transparency — and proves the bot is doing real work, not just claiming it.

🔮 What’s Next?

SwapHunt is still maturing, but here’s what’s cooking:

• Live signal service for followers
• Shared dashboard with real-time route profitability
• Signal mirrors to CEX (Binance) and DEX for hybrid execution
• Controlled copy trading tests

And yeah — I might run other bots too.
Not here to be pure. Here to learn and earn.

🧵 Follow the Signals

This isn’t a paid course. There’s no NFT alpha group.
But I post live system updates — 24/7 — from the bots I actually run.

What you see on Twitter is filtered.
The real work happens in code, in logs, in live trades — and in continuous strategy design.

📡 Follow @SwapHunt for:

• Bot behavior breakdowns
• Strategy pause/resume signals
• Risk regime shifts
• News flow impact assessments
• And yes — experiments with other bots too.

I’m not here to shill.
I’m here to outlearn the noise.

— SwapHunt

Precision Over Pump: Building a Swap Sniper in the Fixed/Float Jungle was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.