Lukman Hakim
// quant developer & on-chain analyst

Lukman Hakim

 

I build systems that generate value autonomously — trading bots, on-chain forensics tools, and automation that runs 24/7 without supervision.

scroll ↓
ABOUT

From hospitality floors to trading systems.

Hi, I'm Lukman. My path into tech wasn't the usual one. I graduated from a vocational high school in hospitality and took on service jobs around Jakarta before finding my way into tech. No CS degree, no bootcamp — just curiosity and a refusal to stay still.

In 2021 I discovered crypto, and everything clicked. I taught myself to earn online — farming airdrops, running testnets, learning how markets actually move. That curiosity turned into code: I started building trading bots, on-chain forensics tools, and automation that run on their own, around the clock.

Today I'm a self-taught quant developer & on-chain analyst. I don't chase hype — I build systems that actually work. Every project here was born from a real problem I hit, then engineered into something measurable and live.

0
live systems
0
uptime
0
self-built
LIVE PERFORMANCE

Numbers from systems that actually run.

Pulled directly from my bots' databases — not mock data. The scanner runs continuously across the market; the Spot Challenge is a live $2,000 virtual-capital track record with fixed sizing and honest, close-based stops. Transparent by design — wins and losses both show.

—
market scans
—
symbols tracked
—
spot equity
—
spot ROI
Spot Challenge — Equity ($2,000 start)
—
auto-synced from live bot databases · last build
JOURNEY

The road here.

2017
Graduated — SMK Negeri 32 Jakarta
Vocational high school, majoring in Hospitality. Far from code, but where the work ethic started.
after school
Working in the Real World — Jakarta
Took on hospitality and service jobs around Jakarta. Different from tech, but it taught me how to show up, adapt fast, and keep going — and left me wanting to build something of my own.
2021
Discovered Crypto — Self-Taught
Found crypto and started teaching myself to earn online: airdrop farming, running testnets, and learning how markets really work. The turning point.
2022 — 2025
From Trader to Builder
Went deep on trading and on-chain analysis, then started writing code to automate it — my first bots, scanners, and on-chain forensics tools.
2026 — now
Self-Taught Quant Developer & On-Chain Analyst
Running multiple live systems — trading bots, smart-money trackers, and automation — entirely self-built, on my own infrastructure.
SELECTED WORK

What I've built.

Python · SQLite · Telegram · Live

OB Confluence Scanner

A trading bot that detects order blocks through multi-timeframe confluence, paired with a paper-trade engine and automated Telegram signals. Built to validate candidates before entry rather than fire blindly.

▾
click to expand →
PROBLEM
Manual chart-scanning across dozens of pairs is slow and emotional. I needed a system to surface only high-confluence setups and track their outcomes objectively.
BUILD
Multi-timeframe OB detection (1H/30m/15m) with a hybrid SMC + legacy merge, fresh-impulse leg validation, volume gating, and a paper-trade engine that logs every entry to SQLite. Signals fire to Telegram with full gate diagnostics.
RESULT
17.5k+ market scans across 170+ pairs, 200+ qualified setups logged. The engine now feeds the live $2,000 Spot Challenge — every signal tracked transparently, no cherry-picking.
PythonSQLiteBinance APISMC / OBTelegram BotPM2
Arkham API · On-Chain · BSC / ETH

On-Chain Forensics Engine

An investigation tool that traces capital flow on-chain — surfacing scam-pump patterns, CEX pre-positioning, and relay-chain dumps. Designed to expose footprints that were deliberately obscured.

▾
click to expand →
PROBLEM
Insider accumulation and coordinated dumps leave on-chain traces, but they're buried across thousands of transactions and intentionally obfuscated through relay wallets.
BUILD
Queries the Arkham API to trace wallet lineage, detect fresh-wallet insider fingerprints, classify accumulation vs distribution, and filter out institutional custody (Grayscale, BlackRock) to avoid false positives.
RESULT
A defensive recon layer that flags scam-pump candidates before they list — built purely to avoid rugs, not to shill.
PythonArkham APIBSC / ETHWallet LineageHeuristics
Arkham · Cron · Real-time Alerts

Whale / Smart-Money Tracker

A real-time alert system that detects whale accumulation and distribution, with a confirmation layer that filters out false signals and fresh-wallet insider fingerprints before flagging.

▾
click to expand →
PROBLEM
Raw whale alerts are noisy and often reversed — a single large transfer doesn't mean a real move. I needed confirmation, not just notification.
BUILD
Five staggered cron jobs (scan, pump-watch, dump-watch, daily outcome, weekly recap) with a confirmation layer comparing average entry and wallet behaviour — distinguishing real distribution from fake-out. Alerts in plain Indonesian.
RESULT
A "smart-money" alert feed in the spirit of Nansen / Info Bandar, but self-hosted and tuned to filter the false signals that burned me before.
PythonArkham APICronTelegramConfirmation Logic
Python · Equity Engine · Spot

Spot Trading Challenge

A live virtual-capital experiment that runs the OB strategy on spot markets with close-based stop logic and running equity tracking — a transparent test of the system's real edge over time.

▾
click to expand →
PROBLEM
Paper futures with auto-follow don't reflect how I'd actually trade. I wanted a realistic, disciplined spot experiment with fixed sizing and honest stop logic.
BUILD
$2,000 virtual capital, $300 fixed size per trade, close-based 1H stop (wick-proof — only a candle close below OB invalidation exits), TP at ~3R, and a running equity ledger in its own database with daily recap.
RESULT
A transparent, ongoing track record — the same data feeds the live equity curve on this site. No cherry-picking.
PythonSQLiteEquity EngineClose-based SLSpot
Hyperliquid API · Snapshot-Diff

Hyperliquid Cluster Tracker

A smart-money monitor that snapshots top-performing Hyperliquid wallets and diffs their positions, flagging coordinated clusters when multiple proven traders enter the same asset.

▾
click to expand →
PROBLEM
One profitable wallet entering a position is noise. Several proven traders entering the same asset at once is signal — but spotting that needs continuous cross-wallet comparison.
BUILD
Filters the Hyperliquid leaderboard to ~450 wallets with proven weekly + monthly profit and >$500k size, snapshots their open positions on a 30-minute cron, and diffs against the prior snapshot to detect coordinated clusters (3+ wallets, altcoins only).
RESULT
Real-time alerts when smart money converges — surfacing conviction trades the moment a cluster forms.
PythonHyperliquid APISnapshot-DiffCronNo-key
STACK

Daily tools.

PythonTrading BotsOn-Chain Analysis Web3 / Multi-chainArkham / NansenData Analysis VPS / DevOpsDocker · PM2AutomationSQLite · Pandas