ai-trading-crew
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
Datos de origen
- Repositorio
- mahmoud20138/Tradecraft
- Última actividad en el origen
- 23 de abril de 2026 a las 08:40
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 15
- Forks
- 4
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- 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
```bash
git clone https://github.com/omer475/ai-trading-crew
cd agents
pip install -r requirements.txt
cp .env.example .env
```
**`.env` keys required:**
```bash
OPENAI_API_KEY="sk-..."
ALPACA_API_KEY="..."
ALPACA_SECRET_KEY="..."
POLYGON_API_KEY="..."
```
---
## Usage
```bash
# 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
```python
# 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
```python
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
```python
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 |
---
Ver en GitHub