# Add to your Claude Code skills
git clone https://github.com/netzpirat/openclaw-trading-agentGuides for using ai agents skills like openclaw-trading-agent.
Last scanned: 5/19/2026
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"status": "PASSED",
"scannedAt": "2026-05-19T07:46:30.454Z",
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}openclaw-trading-agent is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by netzpirat. trading agent. It has 447 GitHub stars.
Yes. openclaw-trading-agent passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/netzpirat/openclaw-trading-agent" and add it to your Claude Code skills directory (see the Installation section above).
openclaw-trading-agent is primarily written in Python. It is open-source under netzpirat on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh openclaw-trading-agent against similar tools.
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OpenClaw is an AI-powered trading agent that uses LLMs to analyze real-time market data, generate trading decisions, and execute trades on the Hyperliquid decentralized exchange.
Built for automation, low-latency execution, and strategy experimentation, OpenClaw continuously monitors markets, evaluates technical indicators, and manages positions with risk controls.
This project is experimental and unaudited. There is no guarantee of profitability. Use at your own risk.
See full documentation:
→ docs/ARCHITECTURE.md
Architecture diagram:
https://github.com/user-attachments/assets/d8f5110a-6401-42bd-b5f5-b154c7b0a418
(Replace with your updated endpoints if needed)
OpenClaw is designed to integrate with a high-performance stack including trading bots, MEV tools, analytics platforms, and low-latency infrastructure.
Using the right stack improves:
Axiom Trade
Odin Bot
Bloom (Telegram Bot)
GMGN
Padre
Polymarket
Low-Latency VPS (New York Recommended)
→ Get VPS
src/
├── main.py # Entry point / trading loop
├── agent/
│ └── decision_maker.py # LLM decision engine
├── indicators/
│ └── taapi_client.py # TAAPI integration
├── trading/
│ └── hyperliquid_api.py # Trade execution
└── config_loader.py # Env config loader
Create .env (see .env.example):
TAAPI_API_KEY=
HYPERLIQUID_PRIVATE_KEY=
OPENROUTER_API_KEY=
LLM_MODEL=
Optional:
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
API_PORT=3000
poetry run python src/main.py --assets BTC ETH --interval 1h
/diary → trade history/logs → runtime logsSupports dynamic indicator fetching via TAAPI:
Run inside a TEE (Trusted Execution Environment) for secure key handling.
curl -fsSL https://eigenx-scripts.s3.us-east-1.amazonaws.com/install-eigenx.sh | bash
eigenx app deploy
eigenx app logs --watch