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openfinclaw-ai-quant-research

Runs quantitative research, strategy backtesting, and paper trading via MCP tools.

4|Updated May 16, 2026
Search Tags:#MCP#technical analysis#backtesting#trading strategy#quantitative research#paper trading#openfinclaw
One-click install
npx skills add https://github.com/reason-machines/devtools-skills --skill openfinclaw-ai-quant-research-reason-machines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openfinclaw-ai-quant-research
Source: https://github.com/reason-machines/devtools-skills/tree/main/skills/openfinclaw-ai-quant-research
Command: npx skills add https://github.com/reason-machines/devtools-skills --skill openfinclaw-ai-quant-research-reason-machines

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @openfinclaw/cli, @openfinclaw/core.

What problem does it solve? Quantitative research and strategy backtesting normally require juggling data feeds, backtesting frameworks, and broker APIs across multiple tools. This Skill exposes the OpenFinClaw platform as MCP tools so AI agents can run research, generate strategies, backtest them, and manage paper trading from natural language prompts. ## Core Features & Use Cases - DeepAgent Research: 60+ built-in analysis skills covering technical, fundamental, sentiment, risk, timing, and factor analysis across US equities, A-shares, HK stocks, crypto, and forex. - Strategy Lifecycle Management: Browse a community leaderboard, fork strategies locally, validate FEP v2.0 compliance, and publish back to the leaderboard. - End-to-End Workflow: A single prompt can drive research, strategy generation, backtesting, metrics reporting, and paper trading. - Use Case: Ask your AI agent to "design a momentum strategy on US mega-cap tech and backtest 2 years" and receive the strategy code, annualized return, max drawdown, and Sharpe ratio in one streamed session. ## Quick Start Ask your AI agent to backtest a Bollinger Bands strategy on TSLA over the past year and show the performance metrics.

Frequently Asked Questions about openfinclaw-ai-quant-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I backtest a trading strategy with an AI agent?▼

Install the OpenFinClaw MCP server, then prompt your agent with a request like "backtest a mean-reversion strategy on BTC for the last year." The agent submits the query via fin_deepagent_research_submit, polls for completion, and returns strategy code with backtest metrics.

What markets does OpenFinClaw support for quantitative research?▼

OpenFinClaw covers US equities, A-shares, Hong Kong stocks, crypto, and forex markets. Its DeepAgent provides 60+ built-in analysis skills spanning technical, fundamental, sentiment, risk, timing, and factor analysis.

Does OpenFinClaw work with Claude Code and Cursor?▼

Yes, OpenFinClaw runs as an MCP server compatible with Claude Code, Cursor, VS Code, and 20+ AI agents. The install wizard writes MCP configs to detected agents automatically, or you can add the server entry manually.

How do I get an OpenFinClaw API key?▼

Obtain an fch_ API key from hub.openfinclaw.ai. Provide it via the --api-key flag, the OPENFINCLAW_API_KEY environment variable, or the ~/.openfinclaw/config.json file created by the install wizard.

Why is the OpenFinClaw MCP server not starting?▼

Run openfinclaw doctor to check connectivity, then test the server manually with npx @openfinclaw/cli serve --tools=deepagent,strategy. Verify the MCP config path in your agent settings and restart the agent after installation.

Why does strategy publishing fail validation?▼

Publishing fails when the strategy lacks fep.yaml or strategy.py, violates the FEP v2.0 schema, or misses required fields like name, description, or entry/exit logic. Run openfinclaw validate locally on the strategy directory before publishing.

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