research-strategy

What This Skill Does

The research-strategy skill is a sophisticated autonomous workflow engine designed to automate the lifecycle of quantitative trading strategy development for OpenClaw. It orchestrates a complete pipeline—from market research and strategy implementation to backtesting, evaluation, and organizational documentation. By leveraging a dedicated, persistent sub-agent, this skill removes the manual labor involved in iterating through trading hypotheses. It strictly follows a predefined protocol that interacts with the market-intel-assistant for data gathering, generates boilerplate Python code compliant with existing strategy architectures, and utilizes a background processing loop to manage log monitoring and automated decision-making based on defined performance thresholds such as win rates and drawdown limits.

Installation

To integrate this research framework into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/earnabitmore365/research-strategy

Ensure your file system permissions allow for the creation of directories within core/strategy/ and execution permissions for the primary research workflow script.

Use Cases

• Automated Alpha Generation: Continuously discover and validate new trading strategies without constant developer intervention.
• Rapid Backtesting Loops: Streamline the creation of test files and the subsequent triggering of Python-based backtest suites.
• Performance Benchmarking: Automatically filter out sub-optimal strategies based on predefined KPIs (e.g., trading volume > 50).
• Systematic Strategy Migration: Automatically move successful experiments into the production strategy directory, ensuring a clean and organized code base.

Example Prompts

1. "Start the research-strategy workflow and keep running until you find three profitable strategies."
2. "Research new mean-reversion strategies using the automated flow and report the status of the current batch."
3. "Execute the research-strategy loop to validate the performance of momentum-based crypto trading algorithms."

Tips & Limitations

• Tip: Always verify the memory.md file format before running, as the implementation step strictly follows this interface.
• Tip: You can use tail -f logs/research_workflow.log to watch the decision-making process in real-time.
• Limitation: The skill relies on local file system operations; ensure you have enough disk space for logs and test files.
• Limitation: Performance is gated by the accuracy of the market-intel-assistant; ensure that skill is configured correctly to receive relevant market data.