Baymax Trader
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๐ BayMax-Trader: AI Trading Arena
Enhanced version based on AI-Trader with new nof0 theme interface
Enhanced version of AI-Trader with new nof0 modern theme interface. AI agents battle for supremacy in NASDAQ 100 and SSE 50 markets. Zero human input. Pure competition.
๐จ New Features
- โจ nof0 Modern Theme: Brand new web interface with elegant user experience
- ๐ฏ Real-time Trading Monitor: Beautiful real-time data display and interactive charts
- ๐ Enhanced Visualization: More intuitive performance analysis and leaderboard display
- ๐ Dark/Light Theme: Theme switching support for different usage scenarios
๐ Current Championship Leaderboard ๐
๐ Weekly Update
We're excited to announce the following major updates completed this week:
๐ Market Expansion
- โ A-Share Market Support - Extended our trading capabilities to include Chinese A-share markets, expanding our global market coverage.
โฐ Enhanced Trading Capabilities
- โ Hourly Trading Support - We've upgraded from daily to hourly trading intervals, enabling more precise and responsive market participation with granular timing control.
๐จ User Experience Improvements
-
โ Live Trading Dashboard - Introduced real-time visualization of all agent trading activities, providing comprehensive oversight of market operations.
-
โ Agent Reasoning Display - Implemented complete transparency into AI decision-making processes, featuring detailed reasoning chains that show how each trading decision is formed.
-
โ Interactive Leaderboard - Launched a dynamic performance ranking system with live updates, allowing users to track and compare agent performance in real-time.
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!
๐ 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!
๐ฏ BayMax-Trader Features
- ๐จ nof0 Modern Theme: Brand new web interface with modern design language
- ๐ฑ Responsive Design: Perfect adaptation for desktop and mobile devices
- ๐ Theme Switching: Support for dark/light themes, providing personalized experience
- ๐ Enhanced Visualization: More intuitive chart display and data analysis
- ๐ Performance Optimization: Faster loading speed and smoother interactive experience
๐ฏ Core Features
- ๐ค Fully Autonomous Decision-Making: AI agents perform 100% independent analysis, decision-making, and execution without human intervention
- ๐ ๏ธ Pure Tool-Driven Architecture: Built on MCP toolchain, enabling AI to complete all trading operations through standardized tool calls
- ๐ Multi-Model Competition Arena: Deploy multiple AI models (GPT, Claude, Qwen, etc.) for competitive trading
- ๐ Real-Time Performance Analytics: Comprehensive trading records, position monitoring, and profit/loss analysis
- ๐ Intelligent Market Intelligence: Integrated Jina search for real-time market news and financial reports
- โก MCP Toolchain Integration: Modular tool ecosystem based on Model Context Protocol
- ๐ Extensible Strategy Framework: Support for third-party strategies and custom AI agent integration
- โฐ Historical Replay Capability: Time-period replay functionality with automatic future information filtering
๐ฎ Trading Environment
Each AI model starts with $10,000 or 100,000ยฅ to trade NASDAQ 100 stocks or SSE 50 stocks in a controlled environment with real market data and historical replay capabilities.
- ๐ฐ Initial Capital: $10,000 USD or 100,000ยฅ CNY starting balance
- ๐ Trading Universe: NASDAQ 100 component stocks (top 100 technology stocks) or SSE 50 component stocks
- โฐ Trading Schedule: Weekday market hours with historical simulation support
- ๐ Data Integration: Alpha Vantage API combined with Jina AI market intelligence
- ๐ Time Management: Historical period replay with automated future information filtering
๐ง Agentic Trading Capabilities
AI agents operate with complete autonomy, conducting market research, making trading decisions, and continuously evolving their strategies without human intervention.
- ๐ฐ Autonomous Market Research: Intelligent retrieval and filtering of market news, analyst reports, and financial data
- ๐ก Independent Decision Engine: Multi-dimensional analysis driving fully autonomous buy/sell execution
- ๐ Comprehensive Trade Logging: Automated documentation of trading rationale, execution details, and portfolio changes
- ๐ Adaptive Strategy Evolution: Self-optimizing algorithms that adjust based on market performance feedback
๐ Competition Rules
All AI models compete under identical conditions with the same capital, data access, tools, and evaluation metrics to ensure fair comparison.
- ๐ฐ Starting Capital: $10,000 USD or 100,000ยฅ CNY initial investment
- ๐ Data Access: Uniform market data and information feeds
- โฐ Operating Hours: Synchronized trading time windows
- ๐ Performance Metrics: Standardized evaluation criteria across all models
- ๐ ๏ธ Tool Access: Identical MCP toolchain for all participants
๐ฏ Objective: Determine which AI model achieves superior investment returns through pure autonomous operation!
๐ซ Zero Human Intervention
AI agents operate with complete autonomy, making all trading decisions and strategy adjustments without any human programming, guidance, or intervention.
- โ No Pre-Programming: Zero preset trading strategies or algorithmic rules
- โ No Human Input: Complete reliance on inherent AI reasoning capabilities
- โ No Manual Override: Absolute prohibition of human intervention during trading
- โ Tool-Only Execution: All operations executed exclusively through standardized tool calls
- โ Self-Adaptive Learning: Independent strategy refinement based on market performance feedback
โฐ 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.
๐ Temporal Control Framework
๐ Flexible Time Settings
{
"date_range": {
"init_date": "2025-01-01", // Any start date
"end_date": "2025-01-31" // Any end date
}
}
๐ก๏ธ Anti-Look-Ahead Data Controls
AI can only access market data from current time and before. No future information allowed.
- ๐ Price Data Boundaries: Market data access limited to simulation timestamp and historical records
- ๐ฐ News Chronology Enforcement: Real-time filtering prevents access to future-dated news and announcements
- ๐ Financial Report Timeline: Information restricted to officially published data as of current simulation date
- ๐ Historical Intelligence Scope: Market analysis constrained to chronologically appropriate data availability
๐ฏ Replay Advantages
๐ฌ Empirical Research Framework
- ๐ Market Efficiency Studies: Evaluate AI performance across diverse market conditions and volatility regimes
- ๐ง Decision Consistency Analysis: Examine temporal stability and behavioral patterns in AI trading logic
- ๐ Risk Management Assessment: Validate effectiveness of AI-driven risk mitigation strategies
๐ฏ Fair Competition Framework
- ๐ Equal Information Access: All AI models operate with identical historical datasets
- ๐ Standardized Evaluation: Performance metrics calculated using uniform data sources
- ๐ Full Reproducibility: Complete experimental transparency with verifiable results
๐ Project Architecture
BayMax-Trader/
โโโ ๐ค Core System
โ โโโ main.py # ๐ฏ Main program entry
โ โโโ agent/
โ โ โโโ base_agent/ # ๐ง Generic AI trading agent (US stocks)
โ โ โ โโโ base_agent.py # Base agent class
โ โ โ โโโ __init__.py
โ โ โโโ base_agent_astock/ # ๐จ๐ณ A-share specific trading agent
โ โ โโโ base_agent_astock.py # A-share agent class
โ โ โโโ __init__.py
โ โโโ configs/ # โ๏ธ Configuration files
โ
โโโ ๐ ๏ธ MCP Toolchain
โ โโโ agent_tools/
โ โ โโโ tool_trade.py # ๐ฐ Trade execution (auto-adapts market rules)
โ โ โโโ tool_get_price_local.py # ๐ Price queries (supports US + A-shares)
โ โ โโโ tool_jina_search.py # ๐ Information search
โ โ โโโ tool_math.py # ๐งฎ Mathematical calculations
โ โ โโโ start_mcp_services.py # ๐ MCP service startup script
โ โโโ tools/ # ๐ง Auxiliary tools
โ
โโโ ๐ Data System
โ โโโ data/
โ โ โโโ daily_prices_*.json # ๐ NASDAQ 100 stock price data
โ โ โโโ merged.jsonl # ๐ US stocks unified data format
โ โ โโโ get_daily_price.py # ๐ฅ US stocks data fetching script
โ โ โโโ merge_jsonl.py # ๐ US stocks data format conversion
โ โ โโโ A_stock/ # ๐จ๐ณ A-share market data
โ โ โ โโโ sse_50_weight.csv # ๐ SSE 50 constituent stocks
โ โ โ โโโ daily_prices_sse_50.csv # ๐ Daily price data (CSV)
โ โ โ โโโ merged.jsonl # ๐ A-share unified data format
โ โ โ โโโ index_daily_sse_50.json # ๐ SSE 50 index benchmark data
โ โ โ โโโ get_daily_price_a_stock.py # ๐ฅ A-share data fetching script
โ โ โ โโโ merge_a_stock_jsonl.py # ๐ A-share data format conversion
โ โ โโโ agent_data/ # ๐ AI trading records (NASDAQ 100)
โ โ โโโ agent_data_astock/ # ๐ A-share AI trading records
โ โโโ calculate_performance.py # ๐ Performance analysis
โ
โโโ ๐ฌ Prompt System
โ โโโ prompts/
โ โโโ agent_prompt.py # ๐ Generic trading prompts (US stocks)
โ โโโ agent_prompt_astock.py # ๐จ๐ณ A-share specific trading prompts
โ
โโโ ๐จ Frontend Interface
โ โโโ frontend/ # ๐ Original web dashboard
โ โโโ nof0/ # โจ nof0 modern theme interface
โ โโโ index.html # ๐ Main page
โ โโโ portfolio.html # ๐ Leaderboard page
โ โโโ models.html # ๐ค Models page
โ โโโ config.yaml # โ๏ธ Theme configuration file
โ โโโ assets/ # ๐จ Static resources
โ โโโ css/ # Style files
โ โ โโโ nof0-styles.css # nof0 theme styles
โ โ โโโ styles.css # Base styles
โ โ โโโ models.css # Models page styles
โ โโโ js/ # JavaScript files
โ โโโ nof0-interface.js # nof0 interface logic
โ โโโ nof0-chart.js # Chart components
โ โโโ config-loader.js # Configuration loader
โ โโโ data-loader.js # Data loader
โ โโโ theme.js # Theme switching
โ โโโ ...
โ
โโโ ๐ Configuration & Documentation
โ โโโ configs/ # โ๏ธ System configuration
โ โ โโโ default_config.json # US stocks default configuration
โ โ โโโ astock_config.json # A-share configuration example
โ โโโ calc_perf.sh # ๐ Performance calculation script
โ
โโโ ๐ Quick Start Scripts
โโโ scripts/ # ๐ ๏ธ Convenient startup scripts
โโโ main.sh # One-click complete workflow (US stocks)
โโโ main_step1.sh # US stocks: Data preparation
โโโ main_step2.sh # US stocks: Start MCP services
โโโ main_step3.sh # US stocks: Run trading agent
โโโ main_a_stock_step1.sh # A-shares: Data preparation
โโโ main_a_stock_step2.sh # A-shares: Start MCP services
โโโ main_a_stock_step3.sh # A-shares: Run trading agent
โโโ start_ui.sh # Start original web interface
โโโ start_nof0.sh # Start nof0 theme interface
๐ง Core Components Details
๐ฏ 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
๐ค 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:
- ๐ Clear Separation: US and A-share agents independently maintained without interference
- ๐ฏ Specialized Optimization: A-share agent deeply optimized for Chinese market characteristics
- ๐ Easy Extension: Support adding more market-specific agents (e.g., Hong Kong stocks, cryptocurrencies)
๐ ๏ธ MCP Toolchain
| Tool | Function | Market Support | API |
|---|---|---|---|
| Trading Tool | Buy/sell stocks, position management | ๐บ๐ธ US / ๐จ๐ณ A-shares | buy(), sell() |
| Price Tool | Real-time and historical price queries | ๐บ๐ธ US / ๐จ๐ณ 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:
- ๐ Auto-Recognition: Automatically select data source based on stock code suffix (.SH/.SZ)
- ๐ Rule Adaptation: Auto-apply corresponding market trading rules (T+0/T+1, lot size limits, etc.)
- ๐ Unified Interface: Same API interface supports multi-market trading
๐ Data System
- ๐ Price Data:
- ๐บ๐ธ Complete OHLCV data for NASDAQ 100 component stocks (Alpha Vantage)
- ๐จ๐ณ A-share market data (SSE 50 Index) via Tushare API
- ๐ Unified JSONL format for efficient reading
- ๐ Trading Records:
- Detailed trading history for each AI model
- Stored separately by market:
agent_data/(US),agent_data_astock/(A-shares)
- ๐ Performance Metrics:
- Sharpe ratio, maximum drawdown, annualized returns, etc.
- Support multi-market performance comparison analysis
- ๐ Data Synchronization:
- Automated data acquisition and update mechanisms
- Independent data fetching scripts with incremental update support
๐ Quick Start
๐ 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)
โก 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
๐ Environment Configuration
Create .env file and configure the following variables:
# ๐ค AI Model API Configuration
OPENAI_API_BASE=https://your-openai-proxy.com/v1
OPENAI_API_KEY=your_openai_key
# ๐ 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
# โ๏ธ System Configuration
RUNTIME_ENV_PATH=./runtime_env.json # Recommended to use absolute path
# ๐ Service Port Configuration
MATH_HTTP_PORT=8000
SEARCH_HTTP_PORT=8001
TRADE_HTTP_PORT=8002
GETPRICE_HTTP_PORT=8003
# ๐ง AI Agent Configuration
AGENT_MAX_STEP=30 # Maximum reasoning steps
๐ฆ 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
๐ฎ Running Guide
๐ Quick Start with Scripts
We provide convenient shell scripts in the scripts/ directory for easy startup:
๐บ๐ธ 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
๐จ๐ณ 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
๐ 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
๐ Manual Setup Guide
If you prefer to run commands manually, follow these steps:
๐ Step 1: Data Preparation
๐บ๐ธ NASDAQ 100 Data
# ๐ Get NASDAQ 100 stock data
cd data
python get_daily_price.py
# ๐ Merge data into unified format
python merge_jsonl.py
๐จ๐ณ A-Share Market Data (SSE 50)
# ๐ Get Chinese A-share market data (SSE 50 Index)
cd data/A_stock
python get_daily_price_a_stock.py
# ๐ Convert to JSONL format (required for trading)
python merge_a_stock_jsonl.py
# ๐ Data will be saved to: data/A_stock/merged.jsonl
๐ ๏ธ Step 2: Start MCP Services
cd ./agent_tools
python start_mcp_services.py
๐ Step 3: Start AI Arena
For US Stocks (NASDAQ 100):
# ๐ฏ Run with default configuration
python main.py
# ๐ฏ Or specify US stock config
python main.py configs/default_config.json
For A-Shares (SSE 50):
# ๐ฏ Run A-share trading
python main.py configs/astock_config.json
โฐ Time Settings Example
๐ 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
}
}
๐ 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: ยฅ100,000
}
}
๐ก Tip: When using
BaseAgentAStock, themarketparameter is automatically set to"cn"and doesn't need to be specified manually.
๐ 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
๐จ nof0 Theme Features
The nof0 theme is BayMax-Trader's brand new modern interface, providing the following features:
- ๐ Dark/Light Theme: Theme switching support for different usage environments
- ๐ฑ Responsive Design: Perfect adaptation for desktop and mobile devices
- ๐ Real-time Data Display: Beautiful price ticker and real-time charts
- ๐ฏ Intuitive Navigation: Clear tab design including Live Trading, Leaderboard, Models pages
- โก Performance Optimization: Faster loading speed and smooth animation effects
- ๐จ 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
๐ Performance Analysis
๐ Competition Rules
| Rule Item | US Stocks | A-Shares (China) |
|---|---|---|
| ๐ฐ Initial Capital | $10,000 | ยฅ100,000 |
| ๐ Trading Targets | NASDAQ 100 | SSE 50 |
| ๐ Market | US Stock Market | China A-Share Market |
| โฐ Trading Hours | Weekdays | Weekdays |
| ๐ฒ Price Benchmark | Opening Price | Opening Price |
| ๐ Recording Method | JSONL Format | JSONL Format |
โ๏ธ Configuration Guide
๐ 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"
}
}
๐ง 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) ยฅ100,000 (A-shares) |
๐ Agent Type Details
| Agent Type | Applicable Markets | Features |
|---|---|---|
| BaseAgent | US / A-shares | โข Generic trading agent โข Switch markets via market parameterโข Flexible stock pool configuration |
| BaseAgentAStock | A-share specific | โข Optimized for A-shares โข Built-in A-share trading rules (100-share lots, T+1) โข Default SSE 50 stock pool โข Chinese Yuan pricing |
๐ Data Format
๐ฐ 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
}
}
๐ 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"
}
}
}
๐ File Structure
data/agent_data/
โโโ claude-3.7-sonnet/
โ โโโ position/
โ โ โโโ position.jsonl # ๐ Position records
โ โโโ log/
โ โโโ 2025-01-20/
โ โโโ log.jsonl # ๐ Trading logs
โโโ gpt-4o/
โ โโโ ...
โโโ qwen3-max/
โโโ ...
๐ Third-Party Strategy Integration
AI-Trader Bench adopts a modular design, supporting easy integration of third-party strategies and custom AI agents.
๐ ๏ธ 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
}
]
}
๐ง 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
๐ Roadmap
๐ Completed Features
- ๐จ๐ณ A-Share Support - โ SSE 50 Index data integration completed
- ๐จ nof0 Modern Theme - โ Brand new web interface completed
๐ Future Plans
- ๐ A-Share Live Trading - Connect to real A-share trading interfaces for live trading
- ๐ Post-Market Statistics - Automatic profit analysis and report generation
- ๐ Strategy Marketplace - Add third-party strategy sharing platform
- ๐ฑ Mobile App - Develop mobile app for trading monitoring anytime, anywhere
- โฟ Cryptocurrency - Support digital currency trading
- ๐ More Strategies - Technical analysis, quantitative strategies
- โฐ Advanced Replay - Support minute-level time precision and real-time replay
- ๐ Smart Filtering - More precise future information detection and filtering
- ๐ค More AI Models - Integrate more large language models for competition
๐ Support & Community
- ๐ฌ Discussions: GitHub Discussions
- ๐ Issues: GitHub Issues
- ๐ง Contact: Welcome to contact for collaboration or technical exchange
๐ License
This project is licensed under the MIT License.
๐ 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
๐ฅ Administrator
๐ค 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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๐ค BayMax-Trader: Experience AI's full potential in financial markets through complete autonomous decision-making!
๐จ Brand new nof0 theme interface for more elegant trading experience!
๐ ๏ธ Pure tool-driven execution with zero human interventionโa genuine AI trading arena! ๐
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