A comprehensive automation skill for discovering, implementing, and backtesting new trading strategies through an autonomous loop.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-strategy
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install research-strategy using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Research Strategy?
Research Strategy is a specialized framework designed for the Openclaw Skills ecosystem to automate the entire lifecycle of trading strategy development. It empowers AI agents to search for novel market insights using the market-intel-assistant, translate those ideas into executable Python code, and perform rigorous backtesting without human intervention.
By utilizing a continuous loop of search, implementation, and evaluation, this skill ensures that only high-performing strategies meeting specific volume and yield criteria are promoted to production. It bridges the gap between theoretical market research and technical execution, providing a robust pipeline for quantitative developers.
Research Strategy Use Cases
- Autonomous discovery of new cryptocurrency or stock trading strategies.
- Automated backtesting of technical indicators like VWAP or moving averages.
- Continuous optimization and refinement of trading logic based on historical performance.
- Managing a high-volume pipeline of strategy experiments in a sandboxed environment.
How Research Strategy Works
- Discovery: The agent uses market-intel-assistant to search for potential trading strategies online.
- Implementation: It creates a new strategy file in the test directory, referencing existing standards like vwap.py.
- Backtesting: A dedicated backtest script is executed to simulate the strategy against historical data.
- Automated Evaluation: The system analyzes logs to check if the strategy meets performance thresholds (e.g., volume ≥ 50, profit > 0).
- Decision & Promotion: Based on results, the strategy is either moved to the production folder, revised, or discarded.
- Iteration: The workflow automatically loops back to the discovery phase to find the next strategy.
Research Strategy Setup
To deploy this skill within your Openclaw Skills environment, ensure your workspace is prepared and run the following commands:
# Navigate to the skill directory
cd /Users/allenbot/.openclaw/skills/research-strategy
# Initialize the testing environment
mkdir -p core/strategy/test
touch core/strategy/test/__init__.py
# Launch the research workflow manager
python3 research_workflow.py &
Research Strategy Data Schema & Taxonomy
The skill maintains a structured directory for managing the transition from experimental to verified code:
| Path | Purpose | Status |
|---|---|---|
core/strategy/ |
Production-ready strategies | Verified |
core/strategy/test/ |
Experimental strategy files | In-Progress |
logs/research.log |
Detailed execution and backtest logs | System |
MEMORY.md |
Interface specifications and agent memory | Documentation |
Research Strategy Advanced Features
- Sub-Agent Spawning: Specifically designed to work with sub-agents for parallel strategy processing.
- Autonomous Decision Logic: Hardcoded rules for moving files between test and production based on backtest metrics.
- Integrated Intel Support: Mandatory integration with market-intel-assistant for high-quality data sourcing.
- Silent Execution: Optimized for non-interactive environments where agents proceed through steps without requiring manual confirmation.
SKILL.md
Loading
Related Openclaw Skills
FarmOS Marketing
An advanced AI-driven marketing module for tracking grain positions, contract fulfillment, and revenue across multiple farm entities.
Xiaohongshu Automation Suite
A comprehensive automation toolkit for publishing long-form content and managing community interactions on Xiaohongshu.
Social Trend Report
An automated monitoring system that tracks trends across Reddit, Twitter/X, and YouTube to generate data-driven content strategy reports.
English Homework Grader (Grades 3-6)
An intelligent grading assistant designed to provide warm, structured, and age-appropriate feedback on elementary English written assignments.
Qwen3-TTS Skill
A high-performance text-to-speech engine for Openclaw Skills featuring emotional control, voice cloning, and direct messaging integration.
Thought Mining
A conversational framework designed to extract, refine, and structure fragmented ideas into professional writing outlines.
Featured*
