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NoFxAiOS/nofx

An open-source terminal that lets AI trade your money

NOFX wires LLMs directly into crypto and stock exchanges, complete with referral links.

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What it does NOFX is a self-hosted trading dashboard (Go backend, React frontend) that connects to nine crypto exchanges—Binance, Bybit, Hyperliquid, and others—plus claims support for US stocks, commodities, and forex. It bundles research, strategy building, execution, and portfolio tracking into one local web UI at localhost:3000. A Telegram bot lets you monitor and bark orders from chat.

The interesting bit The project outsources all AI inference to a third-party service called Claw402, which it accesses through a “pay-as-you-go” discounted channel. Users never touch an API key. The README is unusually candid about its business model: every exchange link is a referral code, and the project is “backed by vergex.trade.” This is less a pure open-source tool and more a lead-generation frontend with an AGPL license.

Key highlights

  • One-liner install via curl | bash or Docker Compose; Railway deploy button included
  • “Strategy Studio” for assembling indicators, risk controls, and logic without leaving the UI
  • “Model competition” leaderboard that pits AI-driven traders against each other
  • Multi-language READMEs (Chinese, Japanese, Korean, Russian, Ukrainian, Vietnamese)
  • AGPL-3.0 license

Caveats

  • The AI layer is entirely opaque: you cannot swap in your own OpenAI, Anthropic, or local model; you are locked into Claw402
  • The architecture diagram in the README is truncated mid-stream, suggesting the docs may be incomplete
  • No performance metrics, backtest results, or risk disclosures are shown in the README
  • Heavy reliance on referral links raises questions about whose interests the “AI” serves

Verdict Worth a look if you want a quick local dashboard for crypto execution and do not mind routing your strategy logic through an undisclosed third-party AI broker. Avoid if you need transparency into model choice, custom LLM support, or serious quantitative rigor.

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