aitrados/finance-trading-ai-agents-mcp

pip install finance-trading-ai-agents-mcp

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

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

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

from finance_trading_ai_agents_mcp import mcp_run
from finance_trading_ai_agents_mcp.api.apiinterface import api_interface
from finance_trading_ai_agents_mcp.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(name, data, **kwargs):
    print("Multi-timeframe data received:", name, data)


def event_handle_callback(client, data_list):
    print("Event data received:", data_list)


def news_handle_callback(client, data_list):
    print("News data received:", data_list)


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


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


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


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


def ohlc_handle_callback(client, data_list):
    print("OHLC handle message received:", data_list)


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

    api_interface.callback_manage.add_custom_ohlc_chart_flow_streaming_callback(ohlc_chart_flow_streaming_callback)
    api_interface.callback_manage.add_custom_ohlc_handle_msg(ohlc_handle_callback)

    mcp_run()

# --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"}'

# 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 finance_trading_ai_agents_mcp/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"}

##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


##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=150

#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

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