TradingAgents

Multi-Agent LLM Financial Trading Framework

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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Highlights

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.

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Screenshots

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

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Architecture

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

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Getting Started

Prerequisites

• Python 3.13+
• Node.js 18+ (for web dashboard)
• API keys: OpenAI or Anthropic Claude, Alpha Vantage (free)

Installation

# 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.txt

API Keys

export OPENAI_API_KEY=your_openai_key
export ALPHA_VANTAGE_API_KEY=your_alpha_vantage_key

Or create a .env file from the template:

cp .env.example .env

Note: 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.

CLI Usage

python -m cli.main

Select your tickers, date, LLMs, and research depth from the interactive interface.

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Nifty50 AI Web Dashboard

A production-grade web dashboard built for Indian Nifty 50 stocks with full transparency into the multi-agent decision process.

Quick Start

# 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

Features

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

Tech Stack

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

Project Structure

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

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Python API

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)

Custom Configuration

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.

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Contributing

We welcome contributions! Whether it's fixing a bug, improving documentation, or suggesting a new feature — your input helps make this project better.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes
4. Push to the branch and open a Pull Request

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Disclaimer

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.

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Citation

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},
}

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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!