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Quant Orchestrator (Free)

v1.0.0

Multi-Agent AI Quant System with factor mining, strategy generation, and automated backtesting

0· 390· 1 versions· 2 current· 2 all-time· Updated 12h ago· MIT-0

Install

openclaw skills install quant-orchestrator-free

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

Version tags

aivk97dqdt6nn23wjx50vdnxzxd9d82h3apfreevk97dqdt6nn23wjx50vdnxzxd9d82h3aplatestvk97dqdt6nn23wjx50vdnxzxd9d82h3apquantvk97dqdt6nn23wjx50vdnxzxd9d82h3aptradingvk97dqdt6nn23wjx50vdnxzxd9d82h3ap

Runtime requirements

📊 Clawdis
OSmacOS · Linux · Windows
Binspython, pip