AI Market Maker Skill

Purpose

This skill provides tooling and documentation to run, inspect, and extend the multi-agent LangGraph trading workflow in OpenClaw environments.

It includes a hard Risk Guard veto before any execution, structured tracing for transparency, and dedicated OpenClaw integration tools.

Quick Start

Installation

# From OpenClaw
claw install https://github.com/olaxbt/ai-market-maker

# Or locally
git clone https://github.com/olaxbt/ai-market-maker.git
cd ai-market-maker
claw skill install ./openclaw

Verification

# Check dependencies
./openclaw/scripts/verify_installation.sh

# Or
python3 openclaw/scripts/claw_runner.py --verify

Usage

# Run backtest with default settings
python3 openclaw/scripts/claw_runner.py --backtest

# Paper trading
python3 openclaw/scripts/claw_runner.py --paper --ticker BTC/USDT

# Custom backtest
python3 openclaw/scripts/claw_runner.py --backtest --symbols "BTC/USDT,ETH/USDT" --steps 150

Default Configuration

The default settings use multiple symbols and conservative risk parameters:

Trade count: 17
Total return: 14.95%
Excess return vs BTC buy & hold: +30.25%
Sharpe ratio: 1.79
Maximum drawdown: 11.84%
Win rate: 62.5%

These results are based on 100 days of historical data across BTC, ETH, and SOL, with full benchmark comparison and risk event logging.

🔧 OpenClaw-Specific Features

Automatic Environment Configuration

• Python path setup for OpenClaw environments
• Nexus API key management (demo key included)
• TA-Lib dependency detection and guidance
• Error recovery and logging optimized for Claw

Pre-configured Commands

# Paper trading with custom ticker
claw run ai-market-maker --paper --ticker ETH/USDT

# Backtesting with multiple symbols
claw run ai-market-maker --backtest --symbols "BTC/USDT,ETH/USDT" --steps 150

# Installation verification
claw run ai-market-maker --verify

Common Issues & Fixes

1. TA-Lib Installation

Problem: ModuleNotFoundError: No module named 'talib'
Solution:

# Recommended for environments without sudo
conda install -y ta-lib -c conda-forge

# Alternative: source compilation
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=$HOME/.local
make
make install
export LD_LIBRARY_PATH=$HOME/.local/lib:$LD_LIBRARY_PATH
pip install ta-lib

2. Module Import Errors

Problem: ModuleNotFoundError: No module named 'agents.market_scanner'
Solution:

# Install in development mode
pip install -e .

# Or set Python path
export PYTHONPATH=/path/to/ai-market-maker/src:$PYTHONPATH

3. Nexus API Rate Limits

Problem: 429 Too Many Requests
Solution:

• Demo key included (rate-limited)
• Set your own Nexus API key in .env for production use
• Implement request caching

4. Environment Configuration

Problem: Environment variables not set
Solution:

# Automatic configuration via claw_runner.py
# Manual override:
export NEXUS_API_KEY=your_key
export AIMM_DESK_STRATEGY_PRESET=default

📊 Flow API (for external tools)

The repo exposes a lightweight, mostly read-only HTTP API:

• GET /runs/latest → Latest run data
• GET /runs/{run_id}/payload → Full payload of a run
• GET /runs/{run_id}/events → Events and traces
• GET /pm/portfolio-health → Portfolio summary
• GET /backtests → List backtest runs

Security

• If AIMM_API_KEY is not set → API is open (intended for local development only).
• If AIMM_API_KEY is set → All non-local requests require x-api-key header.
• In production, always put the Flow API behind a reverse proxy and configure AIMM_CORS_ORIGINS appropriately.

Key Files for OpenClaw Integration

Area	Location	Purpose
OpenClaw Runner	openclaw/scripts/claw_runner.py	Main entry point
Installation Verifier	openclaw/scripts/verify_installation.sh	Dependency checker
Skill Manifest	openclaw/manifest.json	OpenClaw skill definition
Usage Examples	openclaw/examples/claw_usage.md	Usage guides
Main Workflow	src/main.py	Core trading logic
Agent System	src/agents/	7 trading desks
Web Dashboard	web/	Next.js monitoring UI

Performance Tips

1. Enable Caching: Reduce API calls with local OHLCV cache
2. Adjust Intervals: Increase STRATEGY_INTERVAL_SEC for lower resource usage
3. Use Paper Mode: Test strategies without real funds
4. Monitor Resources: Check memory/CPU usage

Contributing

We welcome contributions! Please read the main CONTRIBUTING.md first.

Priority Areas:

1. Error handling improvements
2. Installation simplification
3. Performance optimizations
4. Documentation enhancements

How to Contribute:

# 1. Fork the repository
# 2. Create a feature branch
git checkout -b feature/improvement

# 3. Make your changes
# 4. Test with verification script
./openclaw/scripts/verify_installation.sh

# 5. Submit Pull Request

Documentation

Available Guides

• Quick Start: README.md
• OpenClaw Usage: openclaw/examples/claw_usage.md
• Korean Guide: openclaw/examples/korean_guide.md
• Technical Docs: docs/ directory

Support

• GitHub Issues: https://github.com/olaxbt/ai-market-maker/issues
• Documentation: docs/ directory and openclaw/examples/

---

Version: 1.0.0
Last Updated: 2026-04-17