Building a Real-Time Trading Intelligence Skill with OpenClaw

Introduction

Automating market intelligence is one of the most powerful use cases for a personal agent fleet. While most traders rely on manual dashboards or brittle notification scripts, an OpenClaw-powered trading skill can synthesize live price action, news sentiment, and technical indicators into actionable executive summaries.

In this guide, I'll show you how to build a production-grade Trading Intelligence Skill. We'll use the Finnhub API for real-time data, wrap it in a custom MCP server, and deploy it as a native OpenClaw skill that any agent in your fleet can use.

1. Setting Up the Finnhub MCP Server

To get real-time data, we need an MCP server that speaks to a market data provider. We'll use Finnhub because of its generous free tier and clean JSON responses.

Once configured, you can verify the connection using the MCP CLI to ensure the tools are surfacing correctly.

2. Architecting the Trading Intelligence Skill

An OpenClaw skill is more than just a tool definition; it's a set of instructions that tells the agent how to use those tools effectively. We'll create a new skill directory in your workspace.

The SKILL.md is the "soul" of the skill. It defines the persona and the logic the agent should follow when performing market research.

3. Writing the SKILL.md

The secret to a great trading skill is multi-step reasoning. Don't just fetch the price; ask the agent to check the news sentiment and the moving averages in parallel.

# Trading Intelligence Skill

Use this skill to perform technical and fundamental analysis on stocks and crypto.

## Workflow
1. **Price Check**: Use `finnhub_quote` to get current price and daily change.
2. **Sentiment Analysis**: Use `finnhub_news` for the last 48 hours.
3. **Synthesis**: Compare price action against news volume.
4. **Report**: Output a structured summary with a 'Vibe' score (1-10).

## Rules
- Never give financial advice.
- Always include the timestamp of the last data point.
- If data is missing for a ticker, report the specific error.

For more on writing effective skill definitions, check out our guide on Building Custom Skills.

4. Implementation and Usage

Now that the skill is defined, you can invoke it from your main OpenClaw session or schedule it as a cron job for daily updates.

To automate this, add a Cron Job that runs every morning at 6:00 AM PST (market open).

5. Advanced Tips: Multi-Model Verification

Trading data is sensitive. I recommend using multi-model routing to cross-verify signals. Have a "Thinker" model (like Claude 3.5 Sonnet) analyze the data, and a "Validator" model (like GPT-4o) check the logic for hallucinations.

• Rate Limiting: Finnhub has limits. If you're tracking 50+ tickers, implement a queue in your skill logic.
• Context Window: Market news can be long. Use a summarization step before the final synthesis to save tokens.
• Webhooks: Connect the output to a Slack or Discord channel using the message tool.