ai-trading-crew

name	ai-trading-crew
description	AI Trading Crew — 50-agent AutoGen system for US stock analysis. 8 specialized teams (Technical, Fundamental, Macro, Sentiment, Quant, Risk, Execution, Strategy) reporting to a Head Coach supervisor. Risk team has veto power. Devil's Advocate agent f
kind	agent
category	trading/ai-agents
status	active
tags	["ai-agents","crew","risk-and-portfolio","sentiment","trading"]
related_skills	["autohedge-swarm","freqtrade-bot","openalice-trading-agent","polymarket-prediction-agents","ritmex-crypto-agent"]

AI Trading Crew

USE FOR:

• "50-agent trading crew simulation"
• "AutoGen multi-agent stock analysis"
• "devil's advocate + risk veto trading system"
• "US stock consensus trading agent"
• "multi-team agent debate for trading decisions"
• "ChromaDB RAG for trading knowledge" tags: [AutoGen, multi-agent, trading, US-stocks, Alpaca, Polygon, ChromaDB, RAG, risk-veto, paper-trading] kind: framework category: quant-ml-trading

---

What Is AI Trading Crew?

50-agent AutoGen system simulating a collaborative trading firm for US equities.

• Repo: https://github.com/omer475/ai-trading-crew
• Framework: AutoGen (Microsoft multi-agent)
• LLM: OpenAI
• Broker: Alpaca (paper trading)
• Data: Polygon.io real-time market data
• Memory: ChromaDB RAG knowledge base

---

Agent Architecture: 8 Teams + Head Coach

                    Head Coach (Supervisor)
                          ↑ synthesis
    ┌──────────┬──────────┼──────────┬──────────┐
    │          │          │          │          │
Technical  Fundamental  Macro    Sentiment   Quant
(7 agents) (7 agents) (6 agents) (6 agents) (6 agents)
    │          │          │          │          │
    └──────────┴──────────┼──────────┴──────────┘
                          │
                    Risk Management (5 agents) ← VETO POWER
                          │ approved?
                    Execution & Ops (5 agents)
                    Strategy & Special (8 agents)
                          │
                    Devil's Advocate ← contrarian challenge
                          │
                    Final Decision + Order

---

Team Responsibilities

Team	Agents	Specialty
Technical Analysis	7	Chart patterns, indicators, price action
Fundamental Analysis	7	Earnings, P/E, balance sheet, moat
Macro & Economics	6	Fed policy, rates, sectors, macro
Sentiment & News	6	News NLP, social sentiment, analyst ratings
Quantitative	6	Statistical models, factor analysis, signals
Risk Management	5	VETO authority over all trades
Execution & Ops	5	Order routing, timing, slippage management
Strategy & Special	8	Special situations, M&A, catalysts

---

Trading Workflow

1. Input: python main.py --symbol AAPL

2. Teams debate internally via AutoGen GroupChat
   → Each team reaches internal consensus

3. Team leaders report to Head Coach
   → Cross-team synthesis

4. Risk Management review
   → Can VETO any trade (overrides Head Coach)

5. Devil's Advocate challenges recommendation
   → Forces bull/bear stress test

6. Head Coach final decision

7. Human approval gate (configurable)

8. Execution team submits order to Alpaca

---

Installation

git clone https://github.com/omer475/ai-trading-crew
cd agents
pip install -r requirements.txt
cp .env.example .env

.env keys required:

OPENAI_API_KEY="sk-..."
ALPACA_API_KEY="..."
ALPACA_SECRET_KEY="..."
POLYGON_API_KEY="..."

---

Usage

# Full 50-agent analysis
python main.py --symbol AAPL

# Quick 5-agent test mode
python main.py --symbol NVDA --test

# Output: consensus decision + rationale + risk assessment + order

---

Key Design Patterns

AutoGen GroupChat per Team

# Each team runs internal debate
groupchat = autogen.GroupChat(
    agents=[tech_agent_1, tech_agent_2, ..., tech_agent_7],
    messages=[],
    max_round=5
)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

Risk Veto Pattern

class RiskManager(autogen.AssistantAgent):
    def check_veto(self, proposal: dict) -> bool:
        if proposal["position_size"] > self.max_risk:
            return True   # VETO
        if proposal["volatility"] > self.vol_threshold:
            return True   # VETO
        return False      # Approved

ChromaDB RAG Knowledge Base

import chromadb
client = chromadb.Client()
collection = client.get_or_create_collection("trading_knowledge")

# Query before analysis
results = collection.query(
    query_texts=["AAPL earnings history semiconductor cycle"],
    n_results=5
)

---

Unique Features vs Other Trading Agent Frameworks

Feature	AI Trading Crew	TradingAgents	AutoHedge
Agent count	50 agents	~8 agents	~4 agents
Veto mechanism	Risk team veto	Risk approval	Risk gate
Contrarian agent	Devil's Advocate	Bearish researcher	No
Framework	AutoGen	LangGraph	Swarms
Knowledge base	ChromaDB RAG	None	None
Markets	US stocks only	US stocks	Solana crypto

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