Crypto Liquidity Trading Bot

Self-hosted market-making, liquidity-signal, and arbitrage bot for crypto spot markets. It watches the order book, posts inventory-aware quotes, and scans for cross-exchange and triangular edges.

The runtime is Node.js (not Python). Control it with ADAMANT messenger commands, optional Telegram/CLI, and a local health API.

• Why liquidity matters
• Who this is for
• Commercial use
• Strategy concept
• Architecture
• New in 7.1
• Quick start
• What you get
• How liquidity hunting works
• Installation
• Supported exchanges
• Project layout
• Use cases
• Related projects
• FAQ
• Contributing

Why liquidity matters

Most crypto trading bots rely only on price and technical indicators. Professional traders, however, monitor order book liquidity, because price often moves toward zones where liquidity is concentrated—and away when that liquidity is swept.

This project focuses on liquidity-aware trading signals instead of lagging indicators: it detects gaps, walls, and sweeps so you can act on structure, not just price.

Who this is for

This project may be useful for:

• Crypto trading firms building in-house liquidity and execution tools
• Quant researchers studying order book and market microstructure
• Exchanges building surveillance or liquidity analytics
• Developers building AI trading agents or signal systems

Commercial use

If you are interested in custom crypto trading bots (liquidity, arbitrage, execution), AI trading signal systems, or liquidity detection algorithms and exchange API integrations:

For collaboration or development work:

• Telegram — @k02_xx

New in 7.1

Classic wash-volume market-making is still here. On top of it:

Live arbitrage is opt-in. Scan mode only alerts. Cross-exchange live legs need API keys on both venues (arbitrage.accounts plus the home account). REST latency and fees often consume small edges — treat live mode as experimental.

Backtest performance

This release does not ship a verified historical backtest. Paper-trade /enable arb paper and /start mm smart on your pair before any live size. Live results depend on fees, delay, inventory, and venue quality.

Example signal:

{
  "symbol": "ADM/USDT",
  "direction": "LONG",
  "strength": 0.61,
  "reason": "liquidity_sweep_detected",
  "ts": "2026-08-25T01:00:00Z"
}

Strategy concept

Price indicators lag. Liquidity moves first.

Large orders and stop-loss clusters sit in the order book before price reaches them. When price sweeps those levels, liquidity is consumed and moves tend to accelerate. This bot identifies those levels and signals sweep events so you can trade with the flow instead of chasing price.

Architecture

Exchange public/private APIs
        ↓
Order-book analytics (microprice, imbalance, walls, gaps)
        ↓
   ┌────┴────┬────────────┬──────────────┐
   ↓         ↓            ↓              ↓
Smart     Volume MM    Arb scanner    Signal engine
quoter    (optional)   (cross + tri)  (alerts)
   ↓         ↓            ↓
Risk manager (inventory / daily loss / order caps)
        ↓
ADAMANT / Slack / Discord /health /status

Core paths:

• app.js — boot, MongoDB, engines
• trade/engines/ — pure math (quotes, arb, risk, book analytics)
• trade/mm_*.js — live loops
• modules/commandTxs.js — chat commands
• trade/trader_*.js — exchange adapters

Quick start

Quick start (Node)

git clone https://github.com/asonglin/crypto-liquidity-ai-trading-bot.git && cd crypto-liquidity-ai-trading-bot
npm install
cp config.default.jsonc config.jsonc
# set exchange keys, pair, and (optionally) ADAMANT passPhrase
node app.js

Then in chat:

/start mm smart
/enable arb scan
/signals
/arb
/risk

What you get

Use it for liquidity grabs, order book imbalance strategies, market microstructure research, and algorithmic trading—whether you trade manually or automate.

How liquidity hunting works

Liquidity hunting targets zones where lots of orders sit (e.g. stop-loss clusters). When price sweeps those levels, liquidity is “taken” and price can move fast. This bot helps you find and watch those zones.

1. Find where large stop-loss clusters or thin book zones sit.
2. Detect liquidity sweeps and wall removals in real time.
3. Alert so you can enter when liquidity is taken or book vacuum appears.
4. Extend with your own execution (manual or automated).

Signals you can get: stop-loss clusters, sudden order book vacuum, liquidity wall removal, aggressive market order flow.

Installation

git clone https://github.com/asonglin/crypto-liquidity-ai-trading-bot.git
cd crypto-liquidity-ai-trading-bot

Quick start (Node) — main engine:

npm install
cp config.default.jsonc config.jsonc
# Edit config.jsonc, then:
node app.js

Then: /start mm smart, /enable arb scan, /signals.

Production hardening checklist

• Keep api.host set to 127.0.0.1 unless you explicitly front the API with a trusted reverse proxy.
• Keep api.debug disabled in production; if enabled, configure api.debugToken and api.debugAllowlist.
• Prefer env vars for secrets (BOT_PASSPHRASE, EXCHANGE_API_KEY, EXCHANGE_API_SECRET) over plain config values.
• Never commit config.jsonc or .env with real credentials.

Container deployment

Build and run with Docker Compose:

cp .env.example .env
cp config.default.jsonc config.jsonc
docker compose up --build -d

The compose stack starts:

• bot (this project)
• mongo (MongoDB 7)

Supported exchanges

Adapters in this repo (spot):

The arb scanner uses public tickers on every name listed in config.exchanges. Live execution uses the home exchange keys, plus optional arbitrage.accounts for a second venue.

Project layout

crypto-liquidity-ai-trading-bot/
├── app.js                 # entry point
├── config.default.jsonc   # config template
├── package.json
├── helpers/               # shared utils
├── modules/               # api, DB, config, commands
├── routes/                # /ping, /status, debug
├── trade/
│   ├── engines/           # analytics, quotes, arb math, risk
│   ├── mm_smart_quoter.js
│   ├── mm_arbitrage.js
│   ├── mm_signal_engine.js
│   ├── mm_trader.js       # volume MM (optional)
│   └── trader_*.js        # exchange adapters
├── tests/unit/
└── assets/

Use cases

• Crypto algorithmic trading — Feed signals into your execution engine.
• Quant research — Order book and liquidity analysis.
• AI/ML strategy dev — Use liquidity events as features or triggers.
• Market microstructure — Study gaps, walls, and sweep behavior.

FAQ

What is liquidity hunting?
A strategy that focuses on levels where lots of stop-loss or passive orders sit; when those levels are hit, liquidity is consumed and price often moves sharply.

Is the bot fully automated?
Volume MM, smart quoting, and live arb can place orders. Arb defaults to scan (alerts only). Smart quotes need /enable smart or /start mm smart.

Who is it for?
Token issuers and traders who want self-hosted market-making, microstructure signals, and a conservative arb scanner on the supported CEX adapters.

Contributing

We welcome pull requests and issues. Fork → branch → PR. See CONTRIBUTING.md if present.

License: MIT © 2026