An open-source framework that deploys specialized AI agents — analysts, researchers, traders, and risk managers — to collaboratively analyze markets and generate investment recommendations through structured debate.
Getting Started • Web Dashboard • Python API • Architecture • Contributing • hjlabs.in
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Multi-Agent Collaboration — Specialized AI agents (Technical, Fundamental, Sentiment, Risk) work together, each bringing domain expertise to stock analysis. Structured Debate System — Bull and bear researchers debate findings, challenge assumptions, and reach consensus through reasoned discussion. |
Real-Time Web Dashboard — Production-grade React frontend with live analysis pipeline visualization, backtesting, and portfolio simulation. Configurable & Extensible — Swap LLM providers (OpenAI, Anthropic Claude), adjust debate rounds, configure data sources, and extend with custom agents. |
Dashboard — AI Recommendations at a Glance
History — Backtesting & Portfolio Simulation
Stock Detail — Deep Analysis View
Analysis Pipeline — 12-Step AI Decision Process
Investment Debates — Bull vs Bear AI Agents
Historical Date View — Ranked Stock Lists
How It Works — Multi-Agent AI System
Settings — Configurable AI Models
Dark Mode
TradingAgents mirrors the structure of real-world trading firms by decomposing complex trading tasks into specialized roles:
+-------------------------------------+
| Data Collection |
| (Market, News, Social, Financials) |
+-----------------+-------------------+
|
+-----------------v-------------------+
| Analyst Team |
| Technical | Fundamental | Sentiment |
| | News | |
+-----------------+-------------------+
|
+-----------------v-------------------+
| Researcher Team |
| Bull Researcher vs Bear Researcher |
| (Structured AI Debate) |
+-----------------+-------------------+
|
+-----------------v-------------------+
| Trader Agent |
| Synthesizes reports -> Decision |
+-----------------+-------------------+
|
+-----------------v-------------------+
| Risk Management & Portfolio Mgr |
| Evaluates risk -> Approves/Rejects |
+-------------------------------------+
Agent Details
| Agent | Role | Key Capabilities |
|---|---|---|
| Technical Analyst | Chart & indicator analysis | RSI, MACD, Bollinger Bands, moving averages, volume patterns |
| Fundamental Analyst | Financial evaluation | P/E ratios, earnings, debt analysis, intrinsic value |
| Sentiment Analyst | Market mood assessment | Social media trends, analyst ratings, market psychology |
| News Analyst | Event impact analysis | Macro indicators, breaking news, sector developments |
| Bull Researcher | Bullish case builder | Identifies growth catalysts, upside potential |
| Bear Researcher | Risk challenger | Highlights risks, valuation concerns, downside scenarios |
| Trader Agent | Decision synthesis | Combines all reports into actionable BUY/SELL/HOLD |
| Risk Manager | Portfolio protection | Volatility assessment, position sizing, drawdown limits |
- Python 3.13+
- Node.js 18+ (for web dashboard)
- API keys: OpenAI or Anthropic Claude, Alpha Vantage (free)
# Clone the repository
git clone https://github.com/hemangjoshi37a/TradingAgents.git
cd TradingAgents
# Create virtual environment
conda create -n tradingagents python=3.13
conda activate tradingagents
# Install dependencies
pip install -r requirements.txtexport OPENAI_API_KEY=your_openai_key
export ALPHA_VANTAGE_API_KEY=your_alpha_vantage_keyOr create a .env file from the template:
cp .env.example .envNote: Alpha Vantage provides a free API key with 60 requests/minute for TradingAgents-sourced requests. For offline experimentation, a local data vendor option is also available.
python -m cli.mainSelect your tickers, date, LLMs, and research depth from the interactive interface.
A production-grade web dashboard built for Indian Nifty 50 stocks with full transparency into the multi-agent decision process.
# Terminal 1: Start the backend
cd frontend/backend
pip install -r requirements.txt
python server.py # http://localhost:8001
# Terminal 2: Start the frontend
cd frontend
npm install
npm run dev # http://localhost:5173| Feature | Description |
|---|---|
| AI Recommendations | BUY/SELL/HOLD decisions for all 50 Nifty stocks with confidence levels and risk ratings |
| Stock Ranking (1-50) | Composite scoring algorithm ranks stocks from best to worst investment opportunity |
| Analysis Pipeline | 12-step visualization showing data collection, agent analysis, debate, and decision |
| Investment Debates | Full bull vs bear debate transcripts with research manager synthesis |
| Backtesting | Prediction accuracy tracking, risk metrics (Sharpe, drawdown), win/loss ratios, date backtest runner with cancel support |
| Portfolio Simulator | Paper trading simulation with Zerodha-accurate brokerage charges and Nifty50 benchmarking |
| Settings Panel | Configure LLM provider (Claude/OpenAI), model tiers, debate rounds, parallel workers |
| Dark Mode | Automatic system theme detection with manual toggle |
| Layer | Technology |
|---|---|
| Frontend | React 18 + TypeScript, Vite, Tailwind CSS 4 |
| Charts | Recharts |
| Icons | Lucide React |
| Backend | FastAPI (Python) |
| Database | SQLite |
| Fonts | DM Sans + Plus Jakarta Sans |
frontend/
├── src/
│ ├── components/ # Reusable UI components
│ │ ├── pipeline/ # Analysis pipeline visualization
│ │ ├── StockCard.tsx # Stock cards with rank badges
│ │ ├── TopPicks.tsx # Top picks & stocks to avoid
│ │ └── Header.tsx # Navigation header
│ ├── contexts/ # React contexts (Settings, Theme)
│ ├── pages/
│ │ ├── Dashboard.tsx # Main stock grid with filters
│ │ ├── StockDetail.tsx # Individual stock analysis
│ │ ├── History.tsx # Backtesting & portfolio sim
│ │ └── About.tsx # How it works
│ ├── services/api.ts # API client
│ └── types/index.ts # TypeScript type definitions
├── backend/
│ ├── server.py # FastAPI server
│ ├── database.py # SQLite operations & ranking
│ └── backtest_service.py # Backtesting engine
└── docs/screenshots/ # Documentation screenshots
Use TradingAgents programmatically in your own projects:
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
# Analyze a stock
_, decision = ta.propagate("NVDA", "2024-05-10")
print(decision)from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-4.1-nano"
config["quick_think_llm"] = "gpt-4.1-nano"
config["max_debate_rounds"] = 3
config["data_vendors"] = {
"core_stock_apis": "yfinance",
"technical_indicators": "yfinance",
"fundamental_data": "alpha_vantage",
"news_data": "alpha_vantage",
}
ta = TradingAgentsGraph(debug=True, config=config)
_, decision = ta.propagate("NVDA", "2024-05-10")See tradingagents/default_config.py for the full list of configuration options.
We welcome contributions! Whether it's fixing a bug, improving documentation, or suggesting a new feature — your input helps make this project better.
- Fork the repository
- Create a feature branch (
git checkout -b feature/your-feature) - Commit your changes
- Push to the branch and open a Pull Request
TradingAgents is designed for research and educational purposes only. Trading performance varies based on LLM selection, model temperature, data quality, and other non-deterministic factors. This software is not financial, investment, or trading advice. Always do your own research and consult a qualified financial advisor.
If you find TradingAgents useful in your research, please cite:
@misc{xiao2025tradingagentsmultiagentsllmfinancial,
title={TradingAgents: Multi-Agents LLM Financial Trading Framework},
author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
year={2025},
eprint={2412.20138},
archivePrefix={arXiv},
primaryClass={q-fin.TR},
url={https://arxiv.org/abs/2412.20138},
}Built and maintained by hjlabs.in
Made with AI agents that actually debate before deciding.
If you find this project useful, please consider giving it a star on GitHub!