A sophisticated multi-agent AI system for automated trading factor mining, strategy generation, and industrial-grade backtesting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-orchestrator-free
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 quant-orchestrator-free 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 Quant Orchestrator?
Quant Orchestrator is a comprehensive multi-agent framework designed to automate the entire quantitative research lifecycle. As a featured tool within the Openclaw Skills ecosystem, it leverages specialized agents to handle market data, identify alpha factors, and generate executable trading strategies. This skill streamlines the complex process of financial modeling, allowing developers and traders to move from a research hypothesis to a fully evaluated backtest in minutes.
By integrating this skill, users gain access to a modular architecture where each agent focuses on a specific part of the quant pipeline. Whether you are looking for new predictors or need to validate existing ideas with rigorous metrics like Sharpe Ratio and Information Ratio, Quant Orchestrator provides the technical infrastructure to execute these tasks efficiently.
Quant Orchestrator Use Cases
- Automating the discovery of novel alpha factors for crypto and equity markets.
- Rapidly prototyping and generating Python-based trading strategies using AI.
- Performing one-click backtesting to validate strategy performance against historical data.
- Implementing an automated quant research desk within larger financial applications using Openclaw Skills.
How Quant Orchestrator Works
- The DataAgent retrieves the necessary historical market data based on the provided research task.
- The FactorAgent utilizes AI mining techniques to identify significant trading factors and predictors.
- The StrategyAgent synthesizes the mined factors into a coherent, executable trading strategy code.
- The BacktestAgent executes the generated strategy against historical datasets to simulate real-world performance.
- The EvaluationAgent analyzes the results to produce a comprehensive report including Sharpe Ratio, Max Drawdown, and Win Rate.
Quant Orchestrator Setup
To get started with this skill, ensure you have Python and pip installed, then install the required dependencies:
pip install lightgbm pandas numpy requests
You can then run a basic research pipeline using the following command:
python skill.py "Research BTC momentum strategy"
For production environments requiring usage tracking, the Openclaw Skills framework supports integrated billing through the skill_with_billing.py implementation.
Quant Orchestrator Data Schema & Taxonomy
The Quant Orchestrator organizes its output into structured performance metrics and source files:
| Component | Details |
|---|---|
| Metrics | Sharpe Ratio, Max Drawdown, Win Rate, IC (Information Coefficient), IR (Information Ratio) |
| Code Assets | btc_predictor_optimized.py and other generated strategy scripts |
| Results | Automated backtest logs and historical simulation data |
| Infrastructure | billing.py for payment SDK integration and skill.py for core logic |
Quant Orchestrator Advanced Features
- Multi-Agent Agentic Quant Platform involving five specialized AI agents.
- Automated factor mining capabilities to discover hidden patterns in market data.
- Integrated billing and payment SDK supporting USDC/USDT for commercial API usage.
- Support for advanced financial evaluation metrics including Information Ratio and Information Coefficient.
- Cross-platform compatibility for Darwin, Linux, and Win32 environments within the Openclaw Skills ecosystem.
SKILL.md
Loading
Related Openclaw Skills
Taiwan Calendar
A precision tool for querying official Taiwan administrative calendar data to identify holidays, working days, and make-up shifts.
Blinko
A powerful CLI tool to manage your Blinko notes and short-form thoughts directly from the terminal.
Bitaxe Monitor
A powerful monitoring tool to track real-time performance and health metrics of Bitaxe Gamma Bitcoin miners.
Gemini Collaborator 01
A specialized AI agent focused on deep, authentic human-like interaction and creative coding assistance.
Quant System 5-Steps
An end-to-end quantitative trading pipeline that automates data collection, machine learning modeling, and strategy backtesting.
Quant Trading System
An automated quantitative trading engine utilizing a four-strategy consensus mechanism for high-conviction cryptocurrency execution.
Featured*
