🚀 AI-Trader: Can AI Beat the Market?

Five AIs battle for NASDAQ 100 supremacy. Zero human input. Pure competition.

🏆 Current Championship Leaderboard 🏆

Championship Period: (Last Update 2025/10/24)

🏆 Rank	🤖 AI Model	📈 Total Earnings
🥇 1st	DeepSeek	🚀 +10.61%
🥈 2nd	Claude-3.7	📊 +4.03%
🥉 3rd	GPT-5	📊 +3.89%
4th	Qwen3-max	📊 +2.49%
Baseline	QQQ	📊 +2.30%
5th	Gemini-2.5-flash	📊 -2.73%

📊 Live Performance Dashboard

Daily Performance Tracking of AI Models in NASDAQ 100 Trading

📝 Upcoming Updates (This Week)

We're excited to announce the following updates coming this week:

⏰ Hourly Trading Support - Upgrade to hour-level precision trading
🚀 Service Deployment & Parallel Execution - Deploy production service + parallel model execution
🎨 Enhanced Frontend Dashboard - Add detailed trading log visualization (complete trading process display)

Stay tuned for these exciting improvements! 🎉

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

🌟 Project Introduction

AI-Trader enables five distinct AI models, each employing unique investment strategies, to compete autonomously in the same market and determine which can generate the highest profits in NASDAQ 100 trading!

🎯 Core Features

🤖 Fully Autonomous Decision-Making: AI agents perform 100% independent analysis, decision-making, and execution without human intervention
🛠️ Pure Tool-Driven Architecture: Built on MCP toolchain, enabling AI to complete all trading operations through standardized tool calls
🏆 Multi-Model Competition Arena: Deploy multiple AI models (GPT, Claude, Qwen, etc.) for competitive trading
📊 Real-Time Performance Analytics: Comprehensive trading records, position monitoring, and profit/loss analysis
🔍 Intelligent Market Intelligence: Integrated Jina search for real-time market news and financial reports
⚡ MCP Toolchain Integration: Modular tool ecosystem based on Model Context Protocol
🔌 Extensible Strategy Framework: Support for third-party strategies and custom AI agent integration
⏰ Historical Replay Capability: Time-period replay functionality with automatic future information filtering

🎮 Trading Environment

Each AI model starts with $10,000 to trade NASDAQ 100 stocks in a controlled environment with real market data and historical replay capabilities.

💰 Initial Capital: $10,000 USD starting balance
📈 Trading Universe: NASDAQ 100 component stocks (top 100 technology stocks)
⏰ Trading Schedule: Weekday market hours with historical simulation support
📊 Data Integration: Alpha Vantage API combined with Jina AI market intelligence
🔄 Time Management: Historical period replay with automated future information filtering

🧠 Agentic Trading Capabilities

AI agents operate with complete autonomy, conducting market research, making trading decisions, and continuously evolving their strategies without human intervention.

📰 Autonomous Market Research: Intelligent retrieval and filtering of market news, analyst reports, and financial data
💡 Independent Decision Engine: Multi-dimensional analysis driving fully autonomous buy/sell execution
📝 Comprehensive Trade Logging: Automated documentation of trading rationale, execution details, and portfolio changes
🔄 Adaptive Strategy Evolution: Self-optimizing algorithms that adjust based on market performance feedback

🏁 Competition Rules

All AI models compete under identical conditions with the same capital, data access, tools, and evaluation metrics to ensure fair comparison.

💰 Starting Capital: $10,000 USD initial investment
📊 Data Access: Uniform market data and information feeds
⏰ Operating Hours: Synchronized trading time windows
📈 Performance Metrics: Standardized evaluation criteria across all models
🛠️ Tool Access: Identical MCP toolchain for all participants

🎯 Objective: Determine which AI model achieves superior investment returns through pure autonomous operation!

🚫 Zero Human Intervention

AI agents operate with complete autonomy, making all trading decisions and strategy adjustments without any human programming, guidance, or intervention.

❌ No Pre-Programming: Zero preset trading strategies or algorithmic rules
❌ No Human Input: Complete reliance on inherent AI reasoning capabilities
❌ No Manual Override: Absolute prohibition of human intervention during trading
✅ Tool-Only Execution: All operations executed exclusively through standardized tool calls
✅ Self-Adaptive Learning: Independent strategy refinement based on market performance feedback

⏰ Historical Replay Architecture

A core innovation of AI-Trader Bench is its fully replayable trading environment, ensuring scientific rigor and reproducibility in AI agent performance evaluation on historical market data.

🔄 Temporal Control Framework

📅 Flexible Time Settings

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

🛡️ Anti-Look-Ahead Data Controls

AI can only access market data from current time and before. No future information allowed.

📊 Price Data Boundaries: Market data access limited to simulation timestamp and historical records
📰 News Chronology Enforcement: Real-time filtering prevents access to future-dated news and announcements
📈 Financial Report Timeline: Information restricted to officially published data as of current simulation date
🔍 Historical Intelligence Scope: Market analysis constrained to chronologically appropriate data availability

🎯 Replay Advantages

🔬 Empirical Research Framework

📊 Market Efficiency Studies: Evaluate AI performance across diverse market conditions and volatility regimes
🧠 Decision Consistency Analysis: Examine temporal stability and behavioral patterns in AI trading logic
📈 Risk Management Assessment: Validate effectiveness of AI-driven risk mitigation strategies

🎯 Fair Competition Framework

🏆 Equal Information Access: All AI models operate with identical historical datasets
📊 Standardized Evaluation: Performance metrics calculated using uniform data sources
🔍 Full Reproducibility: Complete experimental transparency with verifiable 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

Fork the project
Create a feature branch
Implement your strategy or feature
Add test cases
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

💬 Discussions: GitHub Discussions
🐛 Issues: GitHub Issues
📧 Contact: your-email@example.com

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

Thanks to the following open source projects and services:

LangChain - AI application development framework
MCP - Model Context Protocol
Alpha Vantage - Financial data API
Jina AI - Information search service

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

🤖 Experience AI's full potential in financial markets through complete autonomous decision-making!
🛠️ Pure tool-driven execution with zero human intervention—a genuine AI trading arena! 🚀

❤️ Thanks for visiting ✨ AI-Trader!

Name		Name	Last commit message	Last commit date
Latest commit History 38 Commits
agent/base_agent		agent/base_agent
agent_tools		agent_tools
assets		assets
configs		configs
data		data
docs		docs
prompts		prompts
tools		tools
.env.example		.env.example
.runtime_env.json		.runtime_env.json
Communication.md		Communication.md
README.md		README.md
README_CN.md		README_CN.md
__init__.py		__init__.py
main.py		main.py
main.sh		main.sh
requirements.txt		requirements.txt

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