OpenFinClaw CLI

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Your quant research team, in one prompt.

Research · strategy · backtest · paper trade — ship a complete quant workflow from a single natural-language prompt, inside Claude Code, Cursor, and 20+ AI agents.

🚀 Try it in 60 seconds — zero install

Run a full research → strategy → backtest loop in your browser. No install, no API key, real market data.

Quick Start · Example Prompts · Community · Platforms · vs. other tools

What you get

Example Prompts

Copy-paste any of these into openfinclaw deepagent research "…" (or drop them straight into your AI agent). Each one runs the full research → strategy → backtest loop.

📈 Technical analysis

• Find RSI divergence signals on NVDA in the last 6 months, then backtest them.

• Compare a Bollinger Bands strategy on TSLA vs AAPL over 1 year — which wins?

• Screen the S&P 500 for golden-cross signals this month.

📊 Fundamentals & macro

• Pull Apple's last 8 quarters of revenue, margins, and guidance. Summarize the trend.

• What's driving the NVDA move this quarter — earnings, guidance, or narrative?

• Compare AMD / INTC / NVDA on growth, margin, and valuation.

🎯 Strategy generation

• Design a momentum strategy on US mega-cap tech. Backtest 2y. Tell me where it breaks.

• Write a mean-reversion strategy on BTC and show drawdown behavior through 2022.

• A-shares 沪深 300 日内轮动策略，年化目标 15%，最大回撤 < 10%。

🧪 Backtest & stress-test

• Backtest a 50/200 SMA crossover on SPY from 2015. Include costs and slippage.

• Stress-test my forked strategy against the 2020 and 2022 crashes.

Want a ready-made one? Run openfinclaw leaderboard to browse the community's highest-ranked strategies, then fork any of them.

Quick Start

💡 Want to see it in action before installing? Try DeepAgent in your browser first.

60-second onboarding

npx @openfinclaw/cli@latest install               # wizard + MCP configs + Skill registration + doctor
openfinclaw deepagent +research "盘点 BTC 周线"     # streaming research / strategy / backtest

install runs the interactive wizard, writes MCP configs to every detected AI agent, persists your fch_ key to ~/.openfinclaw/config.json (chmod 600 on Unix), drops a SKILL.md under ~/.claude/skills/openfinclaw/ so Claude Code / Cursor auto-trigger on keywords like quant / backtest / 量化, and finishes with a connectivity check.

Non-interactive / CI:

npx @openfinclaw/cli@latest install --yes \
  --platforms cursor,claude-code --tool-groups deepagent,strategy \
  --api-key fch_xxx --register-skill

Just the wizard, no SKILL.md registration, no doctor: npx @openfinclaw/cli init.

CLI quick reference

A single fch_ key drives both DeepAgent and the strategy group. Resolution order: --api-key → OPENFINCLAW_API_KEY → ~/.openfinclaw/config.json.

+verb (e.g. deepagent +research) is the human-friendly streaming path; the bare verbs and MCP-only atomic triplet research_submit / research_poll / research_finalize are for agents/scripts. Run openfinclaw --help for the full surface.

Sample DeepAgent output — one prompt → strategy definition + backtest metrics + per-trade P&L + improvement notes:

Community: leaderboard → fork → publish

OpenFinClaw ships with a community strategy exchange. Browse what others are running, copy any strategy locally, tweak it, and publish back — think of it as a Hugging Face for quant strategies.

openfinclaw leaderboard --limit 20          # Browse top-ranked strategies
openfinclaw strategy-info <id>              # See how a strategy performs
openfinclaw fork <id>                       # Copy to ./strategies/<slug>
# ... edit strategy.py, tweak fep.yaml ...
openfinclaw validate ./strategies/<slug>    # Pre-flight FEP v2.0 check
openfinclaw publish ./my-strategy.zip       # Ship to the leaderboard
openfinclaw publish-verify --submission-id <id>   # Track backtest progress

Every published strategy is backtested server-side and ranked by live-market-equivalent returns — no self-reported numbers.

Supported Platforms

OpenFinClaw works with any MCP-compatible agent platform:

Platform Config Examples

{
  "mcpServers": {
    "openfinclaw": {
      "command": "npx",
      "args": ["@openfinclaw/cli", "serve", "--tools=deepagent,strategy"],
      "env": {
        "OPENFINCLAW_API_KEY": "fch_xxx"
      }
    }
  }
}

{
  "mcpServers": {
    "openfinclaw": {
      "command": "npx",
      "args": ["@openfinclaw/cli", "serve", "--tools=deepagent,strategy"],
      "env": {
        "OPENFINCLAW_API_KEY": "fch_xxx"
      }
    }
  }
}

For other platforms (VS Code, Hermes, Windsurf, Zed, OpenClaw, Junie, Trae, …), see configs/ for ready-to-copy templates. The shape is the same — only the host key (servers vs mcpServers vs context_servers) and config path differ.

Tool Groups & Context Optimization

Load only what you need to save tokens: serve --tools=deepagent (~1,400 tk) or serve --tools=strategy (~1,000 tk), or omit --tools for both.

Environment Variables

Only one is required:

Advanced overrides (rarely needed): OPENFINCLAW_CONFIG_PATH, HUB_API_URL, DEEPAGENT_API_URL, REQUEST_TIMEOUT_MS, DEEPAGENT_SSE_TIMEOUT_MS — see packages/core/src/config.ts.

Development

git clone https://github.com/mirror29/openfinclaw-cli.git && cd openfinclaw-cli && pnpm install && pnpm build
OPENFINCLAW_API_KEY=<fch_...> node packages/cli/dist/index.js doctor   # smoke test

Monorepo: @openfinclaw/core (zero-dep business logic) + @openfinclaw/cli (MCP server + terminal CLI + install wizard).

License

MIT