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
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zh_cn.md 简体中文
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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