🤖 AI Quant Fund — Multi-Agent Live Trading Analysis

12 AI Agents. 1 Ticker. Real-Time Debate. Consensus Decision.

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What is this?

AI Quant Fund is a multi-agent trading analysis system where 12 specialized AI agents debate and analyze stocks in real-time — just like a Wall Street trading floor, but powered entirely by AI.

You enter a ticker → 12 AI agents activate → They debate → You get a consensus

The 12 Agents

Phase	Agent	Role
📊 Intelligence	Market Analyst	Technical analysis, price action, volume patterns
📊 Intelligence	Social Analyst	Social media sentiment, retail trader mood
📊 Intelligence	News Analyst	Breaking news, SEC filings, macro events
📊 Intelligence	Fundamentals Analyst	Revenue, P/E, balance sheet, cash flow
🔥 Debate	Bull Researcher	Makes the bullish case with evidence
🔥 Debate	Bear Researcher	Makes the bearish case with evidence
🔥 Debate	Aggressive Debater	Pushes for high-conviction trades
🔥 Debate	Conservative Debater	Argues for caution and risk management
🔥 Debate	Neutral Debater	Weighs both sides objectively
⚡ Decision	Trader	Proposes the trade (buy/hold/sell, size, timing)
🛡️ Risk	Risk Manager	VETO POWER — can block any trade
🎯 Final	Investment Judge	Final verdict after all debates

How It Works

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌──────────────┐
│ INTELLIGENCE │ →  │    DEBATE    │ →  │   TRADING   │ →  │   DECISION   │
│              │    │              │    │             │    │              │
│ 4 Analysts   │    │ Bull vs Bear │    │ Trader      │    │ Risk Manager │
│ gather data  │    │ 3 Debaters   │    │ proposes    │    │ Judge rules  │
└─────────────┘    └──────────────┘    └─────────────┘    └──────────────┘

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Quick Start

Install

pip install ai-quant-agents

Usage — 3 Lines of Code

from ai_quant_agents import QuantClient

client = QuantClient()  # connects to wss://dream.hmyk.ai/ws/live

# Analyze any US stock or China A-share
result = client.analyze("NVDA")
print(result.decision)    # "BUY" / "HOLD" / "SELL"
print(result.confidence)  # 0.85
print(result.consensus)   # {"buy": 8, "hold": 3, "sell": 1}

Live Streaming — Watch Agents Debate

from ai_quant_agents import QuantClient

client = QuantClient()

# Stream real-time agent messages
for msg in client.stream("TSLA"):
    print(f"[{msg.speaker}] {msg.message}")
    # [Market Analyst] TSLA showing bullish divergence on RSI...
    # [Bear Researcher] However, delivery numbers missed by 12%...
    # [Risk Manager] Position size should not exceed 3% of portfolio...

China A-Share Support 🇨🇳

result = client.analyze("600519")          # 贵州茅台
result = client.analyze("000001")          # 平安银行
result = client.analyze("比亚迪")           # Chinese name supported

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Example Output

{
  "ticker": "NVDA",
  "decision": "BUY",
  "confidence": 0.85,
  "consensus": {"buy": 8, "hold": 3, "sell": 1},
  "risk_approved": true,
  "key_reasons": [
    "Strong data center revenue growth +120% YoY",
    "Blackwell GPU demand exceeding supply",
    "Institutional accumulation detected"
  ],
  "risk_warnings": [
    "Valuation stretched at 45x forward P/E",
    "China export restrictions uncertainty"
  ],
  "suggested_action": {
    "entry": "\$142-145",
    "stop_loss": "\$132",
    "target": "\$165",
    "position_size": "2-3% of portfolio"
  }
}

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🔴 Try the Full Live Experience

The demo SDK gives you the analysis result. But the real magic is watching 12 agents debate in real-time in our cyberpunk trading room:

👆 Click to enter the Live Trading Room — Free to try

What you get in the full version:

• 🔴 Real-time streaming debate (watch agents think out loud)
• 📊 Live market data ticker (US + China A-shares)
• 🎯 Historical analysis archive
• 📱 Mobile responsive UI
• 🌐 English + Chinese bilingual

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Architecture

Built on TradingAgents (Apache 2.0) by Tauric Research.

Extended with:

• China A-share market support (Tushare data)
• Real-time WebSocket live streaming
• Hybrid LLM (GPT + Gemini) for speed + depth
• Cyberpunk UI trading room

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Disclaimer

⚠️ NFA — Not Financial Advice. This tool is for research and entertainment purposes only. AI analysis should not be used as the sole basis for investment decisions. Always do your own research. Past performance does not guarantee future results.

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License

Apache License 2.0 — See LICENSE for details.

Built with ❤️ by DEMAND AI