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🤖 AI-Trader Bench

Let AI Show Its Power in Financial Markets

Python License

AI stock trading agent system that lets multiple LLMs compete autonomously in the NASDAQ 100 stock pool!

click me to see the leaderboard

🏆 Current Championship Leaderboard

🥇 Last Update: 2025/10/22

🏆 Rank 🤖 AI Model 📈 Total Earnings
🥇 1st DeepSeek 🚀 +9.03%
🥈 2nd Claude-3.7 📊 +2.34%
🥉 3rd GPT-5 📊 +1.22%

📊 Live Performance Dashboard

rank

Daily tracking of AI models' performance in NASDAQ 100

🚀 Quick Start • 📈 Performance Analysis • 🛠️ Configuration Guide • 中文文档

🌟 Project Introduction

Imagine: 5 different AI models, each with unique investment strategies, competing autonomously in the same market, seeing who can make the most profit in NASDAQ 100!

🎯 Core Features

  • 🤖 Fully Autonomous Decision-Making: AI agents make 100% autonomous analysis, decisions, and execution with zero human intervention
  • 🛠️ Pure Tool-Driven: Based on MCP toolchain, AI completes all trading operations through tool calls
  • 🏆 Multi-Model Arena: Run GPT, Claude, Qwen and other AI models for trading
  • 📊 Real-time Performance Tracking: Complete trading records, position changes and profit analysis
  • 🔍 Intelligent Information Retrieval: Integrated Jina search for latest market news and financial reports
  • ⚡ MCP Toolchain: Modular tool system based on Model Context Protocol
  • 🔌 Pluggable Strategies: Support for third-party strategies and custom AI agent integration
  • ⏰ Replay Design: Support for replaying any time period with automatic future information filtering

🚀 Project Overview

AI-Trader Bench is an innovative AI trading agent system that allows multiple large language models to compete in real stock trading environments. Each AI agent has:

🎮 Trading Environment

  • 💰 Initial Capital: $10,000 USD
  • 📈 Trading Targets: NASDAQ 100 component stocks (100 top tech stocks)
  • ⏰ Trading Hours: Weekday trading with historical replay support
  • 📊 Data Sources: Alpha Vantage API + Jina AI search
  • 🔄 Time Control: Support for historical replay of any time period and future information filtering

🧠 AI Agent Capabilities

  • 📰 Intelligent Information Retrieval: Automatically search market news, analyst reports, and autonomously filter information
  • 💡 Pure AI Decision-Making: Based on multi-dimensional analysis, AI makes buy/sell decisions completely autonomously
  • 📝 Automatic Recording: System automatically records detailed logs and position changes for each trade
  • 🔄 Continuous Learning: AI autonomously adjusts strategies based on market feedback

🏁 Competition Rules

Each AI model runs completely independently, using the same:

  • 💰 Initial Capital: $10,000 USD starting capital
  • 📊 Market Data: Same price data and information sources
  • ⏰ Trading Hours: Same trading time windows
  • 📈 Evaluation Criteria: Unified performance evaluation metrics
  • 🛠️ Tool Set: Same MCP toolchain

🎯 Goal: See which AI model can achieve the highest investment return under complete autonomy!

🚫 Zero Human Intervention

  • ❌ No Preset Strategies: No preset trading strategies or rules provided
  • ❌ No Human Guidance: AI relies completely on its own reasoning abilities for decisions
  • ❌ No Manual Intervention: No human intervention allowed during trading process
  • ✅ Pure Tool-Driven: AI completes all operations through tool calls
  • ✅ Autonomous Learning: AI autonomously adjusts behavior based on market feedback

⏰ Replay Design

One of the core features of AI-Trader Bench is the fully replayable trading environment, ensuring that AI agent performance on historical data is scientific and reproducible.

🔄 Time Control Mechanism

📅 Flexible Time Settings

{
  "date_range": {
    "init_date": "2025-01-01",  // Any start date
    "end_date": "2025-01-31"    // Any end date
  }
}

🛡️ Future Information Filtering

  • 📊 Price Data: Only provides price information for current date and earlier
  • 📰 News Search: Automatically filters news and announcements from future dates
  • 📈 Financial Reports: Only includes published financial data
  • 🔍 Market Analysis: Limited to information available at specified time points

🎯 Replay Advantages

🔬 Scientific Research

  • 📊 Market Efficiency Research: Test AI performance under different market conditions
  • 🧠 Cognitive Bias Analysis: Study temporal consistency of AI decisions
  • 📈 Risk Model Validation: Verify effectiveness of risk management strategies

🎯 Competition Fairness

  • 🏆 Fair Competition: All AI models use the same historical information
  • 📊 Objective Evaluation: Performance comparison based on same dataset
  • 🔍 Transparency: Completely reproducible experimental results

📁 Project Architecture

AI-Trader Bench/
├── 🤖 Core System
│   ├── main.py    # 🎯 Main program entry
│   ├── agent/base_agent/          # 🧠 AI agent core
│   └── configs/                   # ⚙️ Configuration files
│
├── 🛠️ MCP Toolchain
│   ├── agent_tools/
│   │   ├── tool_trade.py          # 💰 Trade execution
│   │   ├── tool_get_price_local.py # 📊 Price queries
│   │   ├── tool_jina_search.py   # 🔍 Information search
│   │   └── tool_math.py           # 🧮 Mathematical calculations
│   └── tools/                     # 🔧 Auxiliary tools
│
├── 📊 Data System
│   ├── data/
│   │   ├── daily_prices_*.json    # 📈 Stock price data
│   │   ├── merged.jsonl           # 🔄 Unified data format
│   │   └── agent_data/            # 📝 AI trading records
│   └── calculate_performance.py   # 📈 Performance analysis
│
├── 🎨 Frontend Interface
│   └── frontend/                  # 🌐 Web dashboard
│
└── 📋 Configuration & Documentation
    ├── configs/                   # ⚙️ System configuration
    ├── prompts/                   # 💬 AI prompts
    └── calc_perf.sh              # 🚀 Performance calculation script

🔧 Core Components Details

🎯 Main Program (main.py)

  • Multi-Model Concurrency: Run multiple AI models simultaneously for trading
  • Configuration Management: Support for JSON configuration files and environment variables
  • Date Management: Flexible trading calendar and date range settings
  • Error Handling: Comprehensive exception handling and retry mechanisms

🛠️ MCP Toolchain

Tool Function API
Trading Tool Buy/sell stocks, position management buy(), sell()
Price Tool Real-time and historical price queries get_price_local()
Search Tool Market information search get_information()
Math Tool Financial calculations and analysis Basic mathematical operations

📊 Data System

  • 📈 Price Data: Complete OHLCV data for NASDAQ 100 component stocks
  • 📝 Trading Records: Detailed trading history for each AI model
  • 📊 Performance Metrics: Sharpe ratio, maximum drawdown, annualized returns, etc.
  • 🔄 Data Synchronization: Automated data acquisition and update mechanisms

🚀 Quick Start

📋 Prerequisites

  • Python 3.8+
  • API Keys: OpenAI, Alpha Vantage, Jina AI

⚡ One-Click Installation

# 1. Clone project
git clone https://github.com/HKUDS/AI-Trader.git
cd AI-Trader

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure environment variables
cp .env.example .env
# Edit .env file and fill in your API keys

🔑 Environment Configuration

Create .env file and configure the following variables:

# 🤖 AI Model API Configuration
OPENAI_API_BASE=https://your-openai-proxy.com/v1
OPENAI_API_KEY=your_openai_key

# 📊 Data Source Configuration
ALPHAADVANTAGE_API_KEY=your_alpha_vantage_key
JINA_API_KEY=your_jina_api_key

# ⚙️ System Configuration
RUNTIME_ENV_PATH=./runtime_env.json # Recommended to use absolute path

# 🌐 Service Port Configuration
MATH_HTTP_PORT=8000
SEARCH_HTTP_PORT=8001
TRADE_HTTP_PORT=8002
GETPRICE_HTTP_PORT=8003
# 🧠 AI Agent Configuration
AGENT_MAX_STEP=30             # Maximum reasoning steps

📦 Dependencies

# Install production dependencies
pip install -r requirements.txt

# Or manually install core dependencies
pip install langchain langchain-openai langchain-mcp-adapters fastmcp python-dotenv requests numpy pandas

🎮 Running Guide

📊 Step 1: Data Preparation (./fresh_data.sh)

# 📈 Get NASDAQ 100 stock data
cd data
python get_daily_price.py

# 🔄 Merge data into unified format
python merge_jsonl.py

🛠️ Step 2: Start MCP Services

cd ./agent_tools
python start_mcp_services.py

🚀 Step 3: Start AI Arena

# 🎯 Run main program - let AIs start trading!
python main.py

# 🎯 Or use custom configuration
python main.py configs/my_config.json

⏰ Time Settings Example

📅 Create Custom Time Configuration

{
  "agent_type": "BaseAgent",
  "date_range": {
    "init_date": "2024-01-01",  // Backtest start date
    "end_date": "2024-03-31"     // Backtest end date
  },
  "models": [
    {
      "name": "claude-3.7-sonnet",
      "basemodel": "anthropic/claude-3.7-sonnet",
      "signature": "claude-3.7-sonnet",
      "enabled": true
    }
  ]
}

📈 Start Web Interface

cd docs
python3 -m http.server 8000
# Visit http://localhost:8000

📈 Performance Analysis

🏆 Competition Rules

Rule Item Setting Description
💰 Initial Capital $10,000 Starting capital for each AI model
📈 Trading Targets NASDAQ 100 100 top tech stocks
⏰ Trading Hours Weekdays Monday to Friday
💲 Price Benchmark Opening Price Trade using daily opening price
📝 Recording Method JSONL Format Complete trading history records

⚙️ Configuration Guide

📋 Configuration File Structure

{
  "agent_type": "BaseAgent",
  "date_range": {
    "init_date": "2025-01-01",
    "end_date": "2025-01-31"
  },
  "models": [
    {
      "name": "claude-3.7-sonnet",
      "basemodel": "anthropic/claude-3.7-sonnet",
      "signature": "claude-3.7-sonnet",
      "enabled": true
    }
  ],
  "agent_config": {
    "max_steps": 30,
    "max_retries": 3,
    "base_delay": 1.0,
    "initial_cash": 10000.0
  },
  "log_config": {
    "log_path": "./data/agent_data"
  }
}

🔧 Configuration Parameters

Parameter Description Default Value
agent_type AI agent type "BaseAgent"
max_steps Maximum reasoning steps 30
max_retries Maximum retry attempts 3
base_delay Operation delay (seconds) 1.0
initial_cash Initial capital $10,000

📊 Data Format

💰 Position Records (position.jsonl)

{
  "date": "2025-01-20",
  "id": 1,
  "this_action": {
    "action": "buy",
    "symbol": "AAPL", 
    "amount": 10
  },
  "positions": {
    "AAPL": 10,
    "MSFT": 0,
    "CASH": 9737.6
  }
}

📈 Price Data (merged.jsonl)

{
  "Meta Data": {
    "2. Symbol": "AAPL",
    "3. Last Refreshed": "2025-01-20"
  },
  "Time Series (Daily)": {
    "2025-01-20": {
      "1. buy price": "255.8850",
      "2. high": "264.3750", 
      "3. low": "255.6300",
      "4. sell price": "262.2400",
      "5. volume": "90483029"
    }
  }
}

📁 File Structure

data/agent_data/
├── claude-3.7-sonnet/
│   ├── position/
│   │   └── position.jsonl      # 📝 Position records
│   └── log/
│       └── 2025-01-20/
│           └── log.jsonl       # 📊 Trading logs
├── gpt-4o/
│   └── ...
└── qwen3-max/
    └── ...

🔌 Third-Party Strategy Integration

AI-Trader Bench adopts a modular design, supporting easy integration of third-party strategies and custom AI agents.

🛠️ Integration Methods

1. Custom AI Agent

# Create new AI agent class
class CustomAgent(BaseAgent):
    def __init__(self, model_name, **kwargs):
        super().__init__(model_name, **kwargs)
        # Add custom logic

2. Register New Agent

# Register in main.py
AGENT_REGISTRY = {
    "BaseAgent": {
        "module": "agent.base_agent.base_agent",
        "class": "BaseAgent"
    },
    "CustomAgent": {  # New addition
        "module": "agent.custom.custom_agent",
        "class": "CustomAgent"
    },
}

3. Configuration File Settings

{
  "agent_type": "CustomAgent",
  "models": [
    {
      "name": "your-custom-model",
      "basemodel": "your/model/path",
      "signature": "custom-signature",
      "enabled": true
    }
  ]
}

🔧 Extending Toolchain

Adding Custom Tools

# Create new MCP tool
@mcp.tools()
class CustomTool:
    def __init__(self):
        self.name = "custom_tool"
    
    def execute(self, params):
        # Implement custom tool logic
        return result

🚀 Roadmap

🌟 Future Plans

  • 🇨🇳 A-Share Support - Extend to Chinese stock market
  • 📊 Post-Market Statistics - Automatic profit analysis
  • 🔌 Strategy Marketplace - Add third-party strategy sharing platform
  • 🎨 Cool Frontend Interface - Modern web dashboard
  • ₿ Cryptocurrency - Support digital currency trading
  • 📈 More Strategies - Technical analysis, quantitative strategies
  • ⏰ Advanced Replay - Support minute-level time precision and real-time replay
  • 🔍 Smart Filtering - More precise future information detection and filtering

🤝 Contributing Guide

We welcome contributions of all kinds! Especially AI trading strategies and agent implementations.

🧠 AI Strategy Contributions

  • 🎯 Trading Strategies: Contribute your AI trading strategy implementations
  • 🤖 Custom Agents: Implement new AI agent types
  • 📊 Analysis Tools: Add new market analysis tools
  • 🔍 Data Sources: Integrate new data sources and APIs

🐛 Issue Reporting

  • Use GitHub Issues to report bugs
  • Provide detailed reproduction steps
  • Include system environment information

💡 Feature Suggestions

  • Propose new feature ideas in Issues
  • Describe use cases in detail
  • Discuss implementation approaches

🔧 Code Contributions

  1. Fork the project
  2. Create a feature branch
  3. Implement your strategy or feature
  4. Add test cases
  5. Create a Pull Request

📚 Documentation Improvements

  • Improve README documentation
  • Add code comments
  • Write usage tutorials
  • Contribute strategy documentation

🏆 Strategy Sharing

  • 📈 Technical Analysis Strategies: AI strategies based on technical indicators
  • 📊 Quantitative Strategies: Multi-factor models and quantitative analysis
  • 🔍 Fundamental Strategies: Analysis strategies based on financial data
  • 🌐 Macro Strategies: Strategies based on macroeconomic data

📞 Support & Community

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

Thanks to the following open source projects and services:

🌟 If this project helps you, please give us a Star!

GitHub stars GitHub forks

🤖 Let AI show its power in financial markets with complete autonomous decision-making!
🛠️ Pure tool-driven, zero human intervention, a true AI trading arena! 🚀

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