# TradingAgents ### Multi-Agent LLM Financial Trading Framework [![arXiv](https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv)](https://arxiv.org/abs/2412.20138) [![Python 3.13+](https://img.shields.io/badge/Python-3.13+-3776AB?logo=python&logoColor=white)](https://www.python.org/) [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE) [![React](https://img.shields.io/badge/React_18-TypeScript-61DAFB?logo=react&logoColor=white)](https://react.dev/) [![FastAPI](https://img.shields.io/badge/FastAPI-Backend-009688?logo=fastapi&logoColor=white)](https://fastapi.tiangolo.com/) [![Tailwind CSS](https://img.shields.io/badge/Tailwind_CSS-4.0-06B6D4?logo=tailwindcss&logoColor=white)](https://tailwindcss.com/)
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](#getting-started) • [Web Dashboard](#nifty50-ai-web-dashboard) • [Python API](#python-api) • [Architecture](#architecture) • [Contributing](#contributing)

--- ## 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.

--- ## Screenshots

Dashboard — AI Recommendations at a Glance

TradingAgents Dashboard showing all 50 Nifty stocks with AI-powered BUY, SELL, HOLD recommendations, rank badges, confidence levels, and decision filters

History — Backtesting & Portfolio Simulation

Historical backtesting page with prediction accuracy tracking, Sharpe ratio, max drawdown, win rate, portfolio simulator with Zerodha brokerage, and AI vs Nifty50 index comparison chart

Stock Detail — Deep Analysis View

Individual stock analysis page showing AI recommendation with confidence level, risk assessment, recommendation history timeline, and detailed analysis summary

Analysis Pipeline — 12-Step AI Decision Process

12-step analysis pipeline visualization showing data collection, technical analysis, fundamental analysis, sentiment analysis, bull vs bear debate, and final trading decision

Investment Debates — Bull vs Bear AI Agents

AI-powered investment debate between bull and bear researcher agents with research manager synthesis and final judgment

Historical Date View — Ranked Stock Lists

History page with date cards showing buy/sell/hold breakdown, return percentages, and expanded ranked stock list with hold periods

How It Works — Multi-Agent AI System

Educational page explaining the multi-agent AI system architecture with agent role cards and structured debate process flow

Settings — Configurable AI Models

Settings panel for configuring LLM provider selection between Claude and OpenAI, model tier selection, API key management, and debate round configuration

Dark Mode

Dashboard in dark mode with glassmorphic card design, premium styling, and automatic system theme detection

--- ## 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 |

--- ## Getting Started ### Prerequisites - Python 3.13+ - Node.js 18+ (for web dashboard) - API keys: OpenAI or Anthropic Claude, [Alpha Vantage](https://www.alphavantage.co/support/#api-key) (free) ### Installation ```bash # 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 ```bash export OPENAI_API_KEY=your_openai_key export ALPHA_VANTAGE_API_KEY=your_alpha_vantage_key ``` Or create a `.env` file from the template: ```bash 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 ```bash python -m cli.main ``` Select your tickers, date, LLMs, and research depth from the interactive interface.

TradingAgents CLI interface showing ticker selection, date picker, and LLM configuration

--- ## 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 ```bash # 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 ``` --- ## Python API Use TradingAgents programmatically in your own projects: ```python 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 ```python 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. --- ## 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 --- ## 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. --- ## Citation If you find TradingAgents useful in your research, please cite: ```bibtex @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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