Finance Trading AI Agents MCP

A free, professional, open-source financial analysis and quantitative trading MCP server. It lets you deploy a local financial MCP service with one command, using a departmental architecture that simulates a real financial firm's operations. It supports traditional indicators, price action analysis, economic calendar, fundamentals, and news integration—providing seamless interaction with LLMs and algorithmic trading.

💬 Community & Support

Join our community for discussions, support, and updates!

✨ Features

• 🚀 One-click deployment: Quickly spin up a local financial MCP service
• 🏢 Departmental architecture: Simulates real-world financial company departments
• 📊 Comprehensive analysis: Traditional technical indicators + price action analysis
• 📅 Real-time data: Economic calendar, fundamentals,broker/securities information ,and news integration
• 🤖 AI integration: Interfaces optimized for LLMs
• ⚡ High performance: Real-time streaming OHLC data processing
• 🔧 Extensible: Support for custom MCP services
• MCP can call the broker to query account information, place orders, and cancel orders.
• 🪩 Support local RPC/PubSub service.you can cross process/software/code languages communication.

Fastest experience

• MCP FOR AI (langchain-trading-agents) for starters

DOCS

• Full Documentation: https://docs.aitrados.com/en/docs/finance-trading-ai-agents-mcp/quickstart/

Free secret_key (AITRADOS_SECRET_KEY) for finance data

Get it here: https://www.aitrados.com/

Basic call tools prompt words embedded in your agent system prompt

##### de.md Deutsch ##### en.md English ##### fr.md Français ##### ja.md 日本語 ##### kr.md 한국어 ##### es.md Español ##### ru.md Русский ##### zh_cn.md 简体中文 ##### zh_tw.md 繁体中文

Examples

##### examples/addition_custom_mcp_examples ##### examples/mcp_clients ##### examples/run_mcp_examples ##### examples/env_example.py

📦 Installation

From PyPI (recommended)

pip install finance-trading-ai-agents-mcp

From source

git clone https://github.com/aitrados/finance-trading-ai-agents-mcp.git
cd finance-trading-ai-agents-mcp
pip install -r requirements.txt
#pip install -e .

🚀 Quick Start

Basic usage

from finance_trading_ai_agents_mcp import mcp_run
if __name__ == "__main__":
    #from examples.env_example import get_example_env
    #get_example_env()
    mcp_run()

or easy command

# auto finding .env file and config.toml
finance-trading-ai-agents-mcp #or python -m finance-trading-ai-agents-mcp

#Specify .env file path
finance-trading-ai-agents-mcp --env-file .env

env_example and config_example.toml

Intermediate usage

###### Custom MCP server and custom MCP functions

from finance_trading_ai_agents_mcp import mcp_run
from examples.env_example import get_example_env

if __name__ == "__main__":
    get_example_env()
    from examples.addition_custom_mcp_examples.addition_custom_mcp_example import
        AdditionCustomMcpExample

    AdditionCustomMcpExample()
    mcp_run()
    # or
    # mcp_run(addition_custom_mcp_py_file="./addition_custom_mcp_example.py")

Advanced usage

###### Real-time WebSocket Data Integration with MCP Server

from aitrados_api.universal_interface.callback_manage import CallbackManage
from finance_trading_ai_agents_mcp import mcp_run
from examples.env_example import get_example_env

"""
Real-time WebSocket Data Integration with MCP Server

This script demonstrates how to run an MCP (Model Context Protocol) server while simultaneously 
receiving real-time WebSocket data streams, achieving data reusability across your project.

Key Benefits:

🔄 **Data Reusability**: 
   - The same real-time data feed serves both MCP clients (like Claude Desktop) and your custom application logic
   - Eliminates duplicate API calls and reduces bandwidth usage
   - Centralizes data management in one location

⚡ **Real-time Integration**:
   - Multi-timeframe OHLC data: Real-time price feeds for trading analysis
   - Event data: Market events, earnings, splits, etc.
   - News data: Financial news updates as they happen
   - Authentication: Connection status and auth events
   - General messages: System notifications and other data

🏗️ **Architecture Advantages**:
   - MCP server handles AI/LLM requests with structured financial data
   - Custom callbacks process the same data for your trading algorithms
   - WebSocket connection is shared, ensuring data consistency
   - Thread-safe data management with proper synchronization

📊 **Use Cases**:
   - AI-powered trading assistants with real-time market data
   - Automated trading systems with LLM decision support
   - Real-time portfolio monitoring with AI analysis
   - Market research tools combining AI insights with live data
   - Risk management systems with instant alert capabilities

🎯 **Practical Example**:
   When Claude Desktop requests "Show me AAPL's current price", the MCP server provides real-time data.
   Simultaneously, your custom callback can execute trading logic based on the same price update.
   This eliminates the need for separate data feeds and ensures perfect synchronization.

⚙️ **Callback System**:
   Each callback type handles specific data streams:
   - multi_timeframe_callback: OHLC candlestick data for technical analysis
   - event_handle_callback: Corporate actions and market events
   - news_handle_callback: Breaking financial news
   - auth_handle_callback: Connection and authentication status
   - general_handle_callback: System messages and notifications
   - show_subscribe_handle_callback: Subscription management events

This dual-purpose architecture maximizes the value of your real-time data subscription while 
providing both AI capabilities and custom application logic in a single, efficient system.
"""


def multi_timeframe_callback(*args, **kwargs):
    print("Multi-timeframe data received:", args, kwargs)


def event_handle_callback(client, *args, **kwargs):
    print("Event data received:", args, kwargs)


def news_handle_callback(client, *args, **kwargs):
    print("News data received:", args, kwargs)


def auth_handle_callback(client, *args, **kwargs):
    print("Auth message received:", args, kwargs)


def general_handle_callback(client, *args, **kwargs):
    print("General message received:", args, kwargs)


def show_subscribe_handle_callback(client, *args, **kwargs):
    print("Subscribe handle message received:", args, kwargs)


def ohlc_chart_flow_streaming_callback(*args, **kwargs):
    print("OHLC chart flow streaming data received:", args, kwargs)


def ohlc_handle_callback(client, *args, **kwargs):
    print("OHLC handle message received:", args, kwargs)


def error_handle_callback(client, *args, **kwargs):
    print("Error handle message received:", args, kwargs)


if __name__ == "__main__":
    get_example_env()
    # Register all custom callbacks
    CallbackManage.add_custom_multi_timeframe_callback(multi_timeframe_callback)
    CallbackManage.add_custom_event_handle_msg(event_handle_callback)
    CallbackManage.add_custom_news_handle_msg(news_handle_callback)
    CallbackManage.add_custom_auth_handle_msg(auth_handle_callback)
    CallbackManage.add_custom_handle_msg(general_handle_callback)
    CallbackManage.add_custom_show_subscribe_handle_msg(show_subscribe_handle_callback)

    CallbackManage.add_custom_ohlc_chart_flow_streaming_callback(ohlc_chart_flow_streaming_callback)
    CallbackManage.add_custom_ohlc_handle_msg(ohlc_handle_callback)
    CallbackManage.add_custom_error_msgs(error_handle_callback)

    mcp_run()

Command Line Interface (CLI)

# auto finding .env file
python -m finance-trading-ai-agents-mcp #or finance-trading-ai-agents-mcp

#Specify .env file path
finance-trading-ai-agents-mcp --env-file .env

# --env-config
--env-config '{"DEBUG":"1","AITRADOS_SECRET_KEY":"YOUR_SECRET_KEY","OHLC_LIMIT_FOR_LLM":"20","RENAME_COLUMN_NAME_MAPPING_FOR_LLM":"interval:timeframe,","OHLC_COLUMN_NAMES_FOR_LLM":"timeframe,close_datetime,open,high,low,close,volume","LIVE_STREAMING_OHLC_LIMIT":"150","ENABLE_RPC_PUBSUB_SERVICE":"1"}'

# Show help
finance-trading-ai-agents-mcp --help

# Start the service
finance-trading-ai-agents-mcp --env-config '{"DEBUG":"1","AITRADOS_SECRET_KEY":"YOUR_SECRET_KEY"}'


# Run with custom MCP server and custom MCP functions from Python file
python -m finance_trading_ai_agents_mcp -c examples/addition_custom_mcp_examples/addition_custom_mcp_example.py --env-config '{"DEBUG":"1","AITRADOS_SECRET_KEY":"YOUR_SECRET_KEY"}'

# Specify port
python -m finance_trading_ai_agents_mcp -p 9000 --env-config '{"DEBUG":"1","AITRADOS_SECRET_KEY":"YOUR_SECRET_KEY"}'

API usage examples

Departmental Service Architecture

finance_trading_ai_agents_mcp/
├── api/                    # API interface layer
├── mcp_services/           # Core MCP services
│   ├── traditional_indicator_service.py
│   ├── news_service.py
│   └── global_instance.py
├── live_streaming_ohlc_operations/  # Real-time data streaming
├── parameter_validator/    # Parameter validation
├── mcp_result_control/     # Result control
├── addition_custom_mcp/    # Custom extensions
└── examples/               # Usage examples

Main Service Modules

• Traditional Indicator Service: Calculations for traditional technical indicators
• News Service: Fetching and analyzing financial news
• Live Streaming Operations: Real-time OHLC data stream processing
• API Instance: RESTful API
• MCP Manager: Service management and coordination

📊 Functional Modules

Technical Indicator Analysis

• Moving Averages (SMA, EMA, WMA)
• Relative Strength Index (RSI)
• Bollinger Bands
• MACD
• Stochastic Oscillator
• More traditional indicators...

Price Action Analysis

• Support and resistance identification
• Chart pattern recognition
• Trend analysis
• Volume analysis

Fundamental Analysis

• Financial data acquisition
• Economic indicator analysis
• Company fundamentals evaluation

News and Sentiment Analysis

• Real-time financial news
• Sentiment analysis
• Event-driven analysis

🛠️ Configuration

Environment variables

Create a .env file:

##debug
DEBUG=true


##Free Register at AiTrados website https://www.aitrados.com/ to get your API secret key (Free).
AITRADOS_SECRET_KEY=YOUR_SECRET_KEY

##Enable RPC/PubSub Service.you can cross process/software/code language communication.easily call api /websocks/other service
## see https://docs.aitrados.com/en/docs/api/trade_middleware/overview/
ENABLE_RPC_PUBSUB_SERVICE=0

##LIVE_STREAMING_OHLC_LIMIT:Real-time OHLC data stream length,default 150
##Prevent the strategy result from not being obtained due to insufficient ohlc length. For example, the value of MA200 can only be calculated when the length of ohlc is greater than 200.
LIVE_STREAMING_OHLC_LIMIT=149

#MCP LLM Setting

##OHLC_LIMIT_FOR_LLM :Due to the window context size limitations of the Large Language Model (LLM), please set a reasonable number of OHLC rows. This setting will only affect the output to the LLM and will not influence strategy calculations
##If it is a multi-period chart analysis, the OHLC_LIMIT_FOR_LLM adjustment is smaller
OHLC_LIMIT_FOR_LLM=30
##You can modify the ohlc column names to suit your trading system. Mapping example:name1:myname1,name2:myname2
RENAME_COLUMN_NAME_MAPPING_FOR_LLM=interval:timeframe,
##OHLC_COLUMN_NAMES_FOR_LLM:Filter out redundant column names for LLM input. The column names should be separated by commas.
OHLC_COLUMN_NAMES_FOR_LLM=timeframe,close_datetime,open,high,low,close,volume

📚 Documentation & Examples

API Docs

After starting the service, visit: http://127.0.0.1:11999/

Examples

See the examples/ directory for more examples:

• examples/basic_usage.py - Basic usage
• examples/advanced_analysis.py - Advanced analysis
• examples/custom_indicators.py - Custom indicators

🤝 Contributing

We welcome all forms of contribution!

1. Fork the repo
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development setup

git clone https://github.com/aitrados/finance-trading-ai-agents-mcp.git
cd finance-trading-ai-agents-mcp
pip install -e ".[dev]"

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Links

• GitHub: https://github.com/aitrados/finance-trading-ai-agents-mcp
• PyPI: https://pypi.org/project/finance-trading-ai-agents-mcp/
• Wiki Docs: https://github.com/aitrados/finance-trading-ai-agents-mcp/wiki
• Issue Tracker: https://github.com/aitrados/finance-trading-ai-agents-mcp/issues

📞 Support

If you encounter any issues or have suggestions, please contact us:

• Email: [email protected]
• Issues: https://github.com/aitrados/finance-trading-ai-agents-mcp/issues

🙏 Acknowledgements

Thanks to all developers and users who have contributed to this project.

⭐ If this project helps you, please consider giving it a star!