🎉 Weekly Update

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

📈 Market Expansion

• ✅ A-Share Market Support - Extended our trading capabilities to include Chinese A-share markets, expanding our global market coverage.

⏰ Enhanced Trading Capabilities

• ✅ Hourly Trading Support - We've upgraded from daily to hourly trading intervals, enabling more precise and responsive market participation with granular timing control.

🎨 User Experience Improvements

• ✅ Live Trading Dashboard - Introduced real-time visualization of all agent trading activities, providing comprehensive oversight of market operations.

• ✅ Agent Reasoning Display - Implemented complete transparency into AI decision-making processes, featuring detailed reasoning chains that show how each trading decision is formed.

• ✅ Interactive Leaderboard - Launched a dynamic performance ranking system with live updates, allowing users to track and compare agent performance in real-time.

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How to use this dataset

It's simple!

You just need to submit a PR that includes at least: ./agent/{your_strategy}.py (you can inherit from Basemodel to create your strategy!), ./configs/{yourconfig}, and instructions on how to run your strategy. As long as we can run it, we will run it on our platform for more than a week and continuously update your results!

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🌟 Project Introduction

BayMax-Trader is an enhanced version based on AI-Trader, enabling 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 or SSE 50 trading!

🎯 BayMax-Trader Features

• 🎨 nof0 Modern Theme: Brand new web interface with modern design language
• 📱 Responsive Design: Perfect adaptation for desktop and mobile devices
• 🌙 Theme Switching: Support for dark/light themes, providing personalized experience
• 📊 Enhanced Visualization: More intuitive chart display and data analysis
• 🚀 Performance Optimization: Faster loading speed and smoother interactive experience

🎯 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

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🎮 Trading Environment

Each AI model starts with $10,000 or 100,000¥ to trade NASDAQ 100 stocks or SSE 50 stocks in a controlled environment with real market data and historical replay capabilities.

• 💰 Initial Capital: $10,000 USD or 100,000¥ CNY starting balance
• 📈 Trading Universe: NASDAQ 100 component stocks (top 100 technology stocks) or SSE 50 component 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

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🧠 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

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🏁 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 or 100,000¥ CNY 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

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⏰ 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
  }
}

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🛡️ 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

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📁 Project Architecture

BayMax-Trader/
├── 🤖 Core System
│   ├── main.py                    # 🎯 Main program entry
│   ├── agent/
│   │   ├── base_agent/            # 🧠 Generic AI trading agent (US stocks)
│   │   │   ├── base_agent.py      # Base agent class
│   │   │   └── __init__.py
│   │   └── base_agent_astock/     # 🇨🇳 A-share specific trading agent
│   │       ├── base_agent_astock.py  # A-share agent class
│   │       └── __init__.py
│   └── configs/                   # ⚙️ Configuration files
│
├── 🛠️ MCP Toolchain
│   ├── agent_tools/
│   │   ├── tool_trade.py          # 💰 Trade execution (auto-adapts market rules)
│   │   ├── tool_get_price_local.py # 📊 Price queries (supports US + A-shares)
│   │   ├── tool_jina_search.py   # 🔍 Information search
│   │   ├── tool_math.py           # 🧮 Mathematical calculations
│   │   └── start_mcp_services.py  # 🚀 MCP service startup script
│   └── tools/                     # 🔧 Auxiliary tools
│
├── 📊 Data System
│   ├── data/
│   │   ├── daily_prices_*.json    # 📈 NASDAQ 100 stock price data
│   │   ├── merged.jsonl           # 🔄 US stocks unified data format
│   │   ├── get_daily_price.py     # 📥 US stocks data fetching script
│   │   ├── merge_jsonl.py         # 🔄 US stocks data format conversion
│   │   ├── A_stock/               # 🇨🇳 A-share market data
│   │   │   ├── sse_50_weight.csv          # 📋 SSE 50 constituent stocks
│   │   │   ├── daily_prices_sse_50.csv    # 📈 Daily price data (CSV)
│   │   │   ├── merged.jsonl               # 🔄 A-share unified data format
│   │   │   ├── index_daily_sse_50.json    # 📊 SSE 50 index benchmark data
│   │   │   ├── get_daily_price_a_stock.py # 📥 A-share data fetching script
│   │   │   └── merge_a_stock_jsonl.py     # 🔄 A-share data format conversion
│   │   ├── agent_data/            # 📝 AI trading records (NASDAQ 100)
│   │   └── agent_data_astock/     # 📝 A-share AI trading records
│   └── calculate_performance.py   # 📈 Performance analysis
│
├── 💬 Prompt System
│   └── prompts/
│       ├── agent_prompt.py        # 🌐 Generic trading prompts (US stocks)
│       └── agent_prompt_astock.py # 🇨🇳 A-share specific trading prompts
│
├── 🎨 Frontend Interface
│   ├── frontend/                  # 🌐 Original web dashboard
│   └── nof0/                      # ✨ nof0 modern theme interface
│       ├── index.html             # 🏠 Main page
│       ├── portfolio.html         # 📊 Leaderboard page
│       ├── models.html            # 🤖 Models page
│       ├── config.yaml            # ⚙️ Theme configuration file
│       └── assets/                # 🎨 Static resources
│           ├── css/               # Style files
│           │   ├── nof0-styles.css    # nof0 theme styles
│           │   ├── styles.css         # Base styles
│           │   └── models.css         # Models page styles
│           └── js/                # JavaScript files
│               ├── nof0-interface.js  # nof0 interface logic
│               ├── nof0-chart.js      # Chart components
│               ├── config-loader.js   # Configuration loader
│               ├── data-loader.js     # Data loader
│               ├── theme.js           # Theme switching
│               └── ...
│
├── 📋 Configuration & Documentation
│   ├── configs/                   # ⚙️ System configuration
│   │   ├── default_config.json    # US stocks default configuration
│   │   └── astock_config.json     # A-share configuration example
│   └── calc_perf.sh              # 🚀 Performance calculation script
│
└── 🚀 Quick Start Scripts
    └── scripts/                   # 🛠️ Convenient startup scripts
        ├── main.sh                # One-click complete workflow (US stocks)
        ├── main_step1.sh          # US stocks: Data preparation
        ├── main_step2.sh          # US stocks: Start MCP services
        ├── main_step3.sh          # US stocks: Run trading agent
        ├── main_a_stock_step1.sh  # A-shares: Data preparation
        ├── main_a_stock_step2.sh  # A-shares: Start MCP services
        ├── main_a_stock_step3.sh  # A-shares: Run trading agent
        ├── start_ui.sh            # Start original web interface
        └── start_nof0.sh          # Start nof0 theme interface

🔧 Core Components Details

🎯 Main Program (main.py)

• Multi-Model Concurrency: Run multiple AI models simultaneously for trading
• Dynamic Agent Loading: Automatically load corresponding agent type based on configuration
• 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

🤖 AI Agent System

Agent Type	Module Path	Use Case	Features
BaseAgent	agent.base_agent	US/A-shares generic	Flexible market switching, configurable stock pool
BaseAgentAStock	agent.base_agent_astock	A-share specific	Built-in A-share rules, SSE 50 default pool, Chinese prompts

Architecture Advantages:

• 🔄 Clear Separation: US and A-share agents independently maintained without interference
• 🎯 Specialized Optimization: A-share agent deeply optimized for Chinese market characteristics
• 🔌 Easy Extension: Support adding more market-specific agents (e.g., Hong Kong stocks, cryptocurrencies)

🛠️ MCP Toolchain

Tool	Function	Market Support	API
Trading Tool	Buy/sell stocks, position management	🇺🇸 US / 🇨🇳 A-shares	buy(), sell()
Price Tool	Real-time and historical price queries	🇺🇸 US / 🇨🇳 A-shares	get_price_local()
Search Tool	Market information search	Global markets	get_information()
Math Tool	Financial calculations and analysis	Generic	Basic mathematical operations

Tool Features:

• 🔍 Auto-Recognition: Automatically select data source based on stock code suffix (.SH/.SZ)
• 📏 Rule Adaptation: Auto-apply corresponding market trading rules (T+0/T+1, lot size limits, etc.)
• 🌐 Unified Interface: Same API interface supports multi-market trading

📊 Data System

• 📈 Price Data:
  • 🇺🇸 Complete OHLCV data for NASDAQ 100 component stocks (Alpha Vantage)
  • 🇨🇳 A-share market data (SSE 50 Index) via Tushare API
  • 📁 Unified JSONL format for efficient reading
• 📝 Trading Records:
  • Detailed trading history for each AI model
  • Stored separately by market: agent_data/ (US), agent_data_astock/ (A-shares)
• 📊 Performance Metrics:
  • Sharpe ratio, maximum drawdown, annualized returns, etc.
  • Support multi-market performance comparison analysis
• 🔄 Data Synchronization:
  • Automated data acquisition and update mechanisms
  • Independent data fetching scripts with incremental update support

🚀 Quick Start

📋 Prerequisites

• Python 3.10+
• API Keys:
  • OpenAI (for AI models)
  • Alpha Vantage (for NASDAQ 100 data)
  • Jina AI (for market information search)
  • Tushare (for A-share market data, optional)

⚡ One-Click Installation

# 1. Clone project
git clone https://github.com/jwangkun/BayMax-Trader.git
cd BayMax-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  # For NASDAQ 100 data
JINA_API_KEY=your_jina_api_key
TUSHARE_TOKEN=your_tushare_token               # For A-share data

# ⚙️ 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 tushare

🎮 Running Guide

🚀 Quick Start with Scripts

We provide convenient shell scripts in the scripts/ directory for easy startup:

🇺🇸 US Market (NASDAQ 100)

# One-click startup (complete workflow)
bash scripts/main.sh

# Or run step by step:
bash scripts/main_step1.sh  # Step 1: Prepare data
bash scripts/main_step2.sh  # Step 2: Start MCP services
bash scripts/main_step3.sh  # Step 3: Run trading agent

🇨🇳 A-Share Market (SSE 50)

# Run step by step:
bash scripts/main_a_stock_step1.sh  # Step 1: Prepare A-share data
bash scripts/main_a_stock_step2.sh  # Step 2: Start MCP services
bash scripts/main_a_stock_step3.sh  # Step 3: Run A-share trading agent

🌐 Web UI

# Start original web interface
bash scripts/start_ui.sh
# Visit: http://localhost:8888

# Start nof0 modern theme interface
bash scripts/start_nof0.sh
# Visit: http://localhost:8080

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📋 Manual Setup Guide

If you prefer to run commands manually, follow these steps:

📊 Step 1: Data Preparation

🇺🇸 NASDAQ 100 Data

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

# 🔄 Merge data into unified format
python merge_jsonl.py

🇨🇳 A-Share Market Data (SSE 50)

# 📈 Get Chinese A-share market data (SSE 50 Index)
cd data/A_stock
python get_daily_price_a_stock.py

# 🔄 Convert to JSONL format (required for trading)
python merge_a_stock_jsonl.py

# 📊 Data will be saved to: data/A_stock/merged.jsonl

🛠️ Step 2: Start MCP Services

cd ./agent_tools
python start_mcp_services.py

🚀 Step 3: Start AI Arena

For US Stocks (NASDAQ 100):

# 🎯 Run with default configuration
python main.py

# 🎯 Or specify US stock config
python main.py configs/default_config.json

For A-Shares (SSE 50):

# 🎯 Run A-share trading
python main.py configs/astock_config.json

⏰ Time Settings Example

📅 US Stock Configuration Example (Using BaseAgent)

{
  "agent_type": "BaseAgent",
  "market": "us",              // Market type: "us" for US stocks
  "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
    }
  ],
  "agent_config": {
    "initial_cash": 10000.0    // Initial capital: $10,000
  }
}

📅 A-Share Configuration Example (Using BaseAgentAStock)

{
  "agent_type": "BaseAgentAStock",  // A-share specific agent
  "market": "cn",                   // Market type: "cn" A-shares (optional, will be ignored, always uses cn)
  "date_range": {
    "init_date": "2025-10-09",      // Backtest start date
    "end_date": "2025-10-31"         // Backtest end date
  },
  "models": [
    {
      "name": "claude-3.7-sonnet",
      "basemodel": "anthropic/claude-3.7-sonnet",
      "signature": "claude-3.7-sonnet",
      "enabled": true
    }
  ],
  "agent_config": {
    "initial_cash": 100000.0        // Initial capital: ¥100,000
  }
}

💡 Tip: When using BaseAgentAStock, the market parameter is automatically set to "cn" and doesn't need to be specified manually.

📈 Start Web Interface

Original Interface

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

Or use startup script:

# Start original web interface
bash scripts/start_ui.sh
# Visit: http://localhost:8888

nof0 Modern Theme Interface

# Start nof0 theme interface
cd nof0
python3 -m http.server 8080
# Visit http://localhost:8080

Or use startup script:

# Start nof0 theme interface
bash scripts/start_nof0.sh
# Visit: http://localhost:8080

🎨 nof0 Theme Features

The nof0 theme is BayMax-Trader's brand new modern interface, providing the following features:

• 🌙 Dark/Light Theme: Theme switching support for different usage environments
• 📱 Responsive Design: Perfect adaptation for desktop and mobile devices
• 📊 Real-time Data Display: Beautiful price ticker and real-time charts
• 🎯 Intuitive Navigation: Clear tab design including Live Trading, Leaderboard, Models pages
• ⚡ Performance Optimization: Faster loading speed and smooth animation effects
• 🎨 Modern Design: Latest design language and visual effects

nof0 Interface Description

• Live Trading Page (index.html): Main trading monitoring interface showing account value change charts
• Leaderboard Page (portfolio.html): Display performance rankings and detailed analysis of AI models
• Models Page (models.html): Show detailed information and configuration of each AI model

Configuration File

The nof0 theme uses config.yaml file for configuration, supporting:

• Multi-market configuration (US stocks, A-shares)
• Agent model configuration
• Chart display settings
• UI interface settings

📈 Performance Analysis

🏆 Competition Rules

Rule Item	US Stocks	A-Shares (China)
💰 Initial Capital	$10,000	¥100,000
📈 Trading Targets	NASDAQ 100	SSE 50
🌍 Market	US Stock Market	China A-Share Market
⏰ Trading Hours	Weekdays	Weekdays
💲 Price Benchmark	Opening Price	Opening Price
📝 Recording Method	JSONL Format	JSONL Format

⚙️ Configuration Guide

📋 Configuration File Structure

{
  "agent_type": "BaseAgent",
  "market": "us",
  "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	Options	Default Value
agent_type	AI agent type	"BaseAgent" (generic) "BaseAgentAStock" (A-share specific)	"BaseAgent"
market	Market type	"us" (US stocks) "cn" (A-shares) Note: Auto-set to "cn" when using BaseAgentAStock	"us"
max_steps	Maximum reasoning steps	Positive integer	30
max_retries	Maximum retry attempts	Positive integer	3
base_delay	Operation delay (seconds)	Float	1.0
initial_cash	Initial capital	Float	$10,000 (US) ¥100,000 (A-shares)

📋 Agent Type Details

Agent Type	Applicable Markets	Features
BaseAgent	US / A-shares	• Generic trading agent • Switch markets via market parameter • Flexible stock pool configuration
BaseAgentAStock	A-share specific	• Optimized for A-shares • Built-in A-share trading rules (100-share lots, T+1) • Default SSE 50 stock pool • Chinese Yuan pricing

📊 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"
    },
    "BaseAgentAStock": {
        "module": "agent.base_agent_astock.base_agent_astock",
        "class": "BaseAgentAStock"
    },
    "CustomAgent": {  # New custom agent
        "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

🌟 Completed Features

• 🇨🇳 A-Share Support - ✅ SSE 50 Index data integration completed
• 🎨 nof0 Modern Theme - ✅ Brand new web interface completed

🌟 Future Plans

• 🔗 A-Share Live Trading - Connect to real A-share trading interfaces for live trading
• 📊 Post-Market Statistics - Automatic profit analysis and report generation
• 🔌 Strategy Marketplace - Add third-party strategy sharing platform
• 📱 Mobile App - Develop mobile app for trading monitoring anytime, anywhere
• ₿ 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
• 🤖 More AI Models - Integrate more large language models for competition

📞 Support & Community

• 💬 Discussions: GitHub Discussions
• 🐛 Issues: GitHub Issues
• 📧 Contact: Welcome to contact for collaboration or technical exchange

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

Thanks to the following open source projects and services:

• AI-Trader - Original project foundation
• LangChain - AI application development framework
• MCP - Model Context Protocol
• Alpha Vantage - US stock financial data API
• Tushare - China A-share market data API
• Jina AI - Information search service
• Chart.js - Chart library

👥 Administrator

🤝 Contribution

Disclaimer

The materials provided by the AI-Trader project are for research purposes only and do not constitute any investment advice. Investors should seek independent professional advice before making any investment decisions. Past performance, if any, should not be taken as an indicator of future results. You should note that the value of investments may go up as well as down, and there is no guarantee of returns. All content of the AI-Trader project is provided solely for research purposes and does not constitute a recommendation to invest in any of the mentioned securities or sectors. Investing involves risks. Please seek professional advice if needed.

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⭐ Star History

Community Growth Trajectory

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❤️ Thanks for visiting ✨ BayMax-Trader!