name: quant-orchestrator description: Multi-Agent AI Quant System with factor mining, strategy generation, and automated backtesting metadata: clawdbot: emoji: "📊" homepage: "https://clawhub.com/quant-orchestrator" os: ["darwin", "linux", "win32"] requires: bins: ["python", "pip"] env: [] install: - type: "script" run: "pip install lightgbm pandas numpy requests" description: "Install dependencies"

Quant Orchestrator Skill

📊 Description

Multi-Agent AI Quant System for automated strategy research and backtesting.

💰 Pricing

• 1 USDC per call
• 1 USDT = 1000 tokens
• Min deposit: 8 USDT
• Platform fee: 5%

🚀 Features

• AI Factor Mining - Generate trading factors automatically
• AI Strategy Generation - Create strategies from factors
• Automated Backtesting - One-click backtest with metrics
• Strategy Evaluation - Sharpe, MaxDrawdown, WinRate, IC, IR

Architecture

Agentic Quant Platform
├── DataAgent (market data)
├── FactorAgent (AI factor mining)
├── StrategyAgent (AI strategy generation)
├── BacktestAgent (automated backtesting)
└── EvaluationAgent (strategy evaluation)

Usage

from skill_with_billing import QuantOrchestrator

# Run with user billing
orchestrator = QuantOrchestrator()
result = orchestrator.run_pipeline(
    task="研究BTC动量策略", 
    user_id="USER_ID"
)

Output

• Sharpe Ratio
• Max Drawdown
• Win Rate
• IC (Information Coefficient)
• IR (Information Ratio)
• Strategy code
• Backtest results

Quick Start

# Install dependencies
pip install lightgbm pandas numpy requests

# Run basic pipeline
python skill.py "研究BTC动量策略"

# Run with billing
python skill_with_billing.py "研究BTC策略" user123

Files

• skill.py - Basic skill
• skill_with_billing.py - Skill with payment
• billing.py - Payment SDK
• btc_predictor_optimized.py - BTC prediction