MCP

OpenAlgo - Model Context Protocol

OpenAlgo MCP - AI Trading Assistant

An AI-powered trading assistant platform for OpenAlgo, leveraging Model Context Protocol (MCP) and Large Language Models to provide intelligent trading capabilities.

Overview

OpenAlgo MCP integrates the powerful OpenAlgo trading platform with advanced AI capabilities through:

An MCP server that exposes OpenAlgo API functions as tools for AI interaction
An intelligent client application providing a conversational interface for trading

This bridge between OpenAlgo's trading capabilities and AI allows for a natural language interface to complex trading operations, making algorithmic trading more accessible to users of all technical backgrounds.

Key Features

Comprehensive Trading Capabilities

Order Management: Place, modify, and cancel orders with support for various order types (market, limit, stop-loss)
Advanced Order Types: Basket orders, split orders, and smart orders with position sizing
Market Data Access: Real-time quotes, market depth, and historical data
Portfolio Management: Track holdings, positions, order books, and trade history
Account Information: Monitor funds, margins, and trading limits

Intelligent Symbol Format Handling

Smart parsing and formatting of instrument symbols across exchanges
Support for equity, futures, and options symbology
Built-in knowledge of common indices and exchange-specific formats

AI-Powered Trading Assistant

Natural language interface for all trading operations
Contextual understanding of trading terminology and concepts
Guided assistance for complex trading operations
Real-time data presentation in human-readable formats

Project Structure

openalgo-mcp/
├── .env                 # Common environment configuration
├── .env.example         # Example configuration template
├── requirements.txt     # Common dependencies for both client and server
├── LICENSE              # MIT License
├── server/              # MCP Server implementation
│   ├── server.py        # OpenAlgo MCP server code
└── client/              # Client implementation
    ├── trading_agent.py # AI assistant client code

Installation Guide

Prerequisites

Python 3.9+ installed
OpenAlgo platform installed and configured
OpenAI API key (for the client component)

Step 1: Clone the Repository

git clone https://github.com/marketcalls/openalgo-mcp.git
cd openalgo-mcp/mcpserver

Step 2: Set Up Environment

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Step 3: Configure Environment Variables

# Copy example environment file
cp .env.example .env

# Edit the .env file with your API keys and settings
# vim .env or use any text editor

Usage

Starting the MCP Server

cd server
python server.py

The server supports the following options:

--api-key: OpenAlgo API key (alternative to setting in .env)
--host: OpenAlgo API host URL (default: http://127.0.0.1:5000)
--port: Server port (default: 8001)
--mode: Server transport mode - 'stdio' or 'sse' (default: sse)

Starting the Trading Assistant Client

cd client
python trading_agent.py

The client supports these options:

--host: MCP server host (default: from .env)
--port: MCP server port (default: from .env)
--model: OpenAI model to use (default: from .env)

Configuration

The project uses a unified configuration approach with environment variables:

Common configuration is stored in the root .env file
Component-specific configuration can be set in server/.env or client/.env
Common settings will be loaded first, then possibly overridden by component-specific settings

Required API Keys

OpenAlgo API Key - Set in .env as OPENALGO_API_KEY
- Required for accessing the OpenAlgo trading platform
- Obtain from your OpenAlgo account dashboard
OpenAI API Key - Set in .env as OPENAI_API_KEY (for the client only)
- Required for the AI assistant capabilities
- Obtain from OpenAI Platform

Technical Capabilities

The OpenAlgo MCP implementation provides comprehensive API coverage including:

Order Management:
- place_order: Standard order placement
- modify_order: Order modification with parameter validation
- cancel_order: Order cancellation by ID
Advanced Order Types:
- place_basket_order: Place multiple orders simultaneously
- place_split_order: Split large orders into smaller chunks
- place_smart_order: Position-aware order placement
Market Data:
- get_quote: Latest market quotes
- get_depth: Order book depth data
- get_history: Historical price data with various timeframes
Account Information:
- get_funds: Available funds and margin
- get_holdings: Portfolio holdings
- get_position_book, get_order_book, get_trade_book: Trading records
Symbol Information:
- get_symbol_metadata: Detailed symbol information
- get_all_tickers: Available trading symbols
- get_intervals: Supported timeframes for historical data

The implementation uses FastMCP with SSE (Server-Sent Events) transport for real-time communication and includes proper error handling, logging, and parameter validation.

Server Implementation Details

The OpenAlgo MCP Server is built using the FastMCP library and exposes OpenAlgo trading functionality through a comprehensive set of tools. It uses Server-Sent Events (SSE) as the primary transport mechanism for real-time communication.

Server Architecture

Framework: Uses FastMCP with Starlette for the web server
Transport: Server-Sent Events (SSE) for real-time bidirectional communication
API Client: Wraps the OpenAlgo API with appropriate error handling and logging
Configuration: Uses environment variables with command-line override capabilities

Available API Tools

The server exposes over 15 trading-related tools, including:

Order Management: place_order, modify_order, cancel_order, get_order_status
Advanced Orders: place_basket_order, place_split_order, place_smart_order
Market Data: get_quote, get_depth, get_history, get_intervals
Account Information: get_funds, get_holdings, get_position_book, get_order_book, get_trade_book
Symbol Information: get_symbol_metadata, get_all_tickers

Client Implementation Details

The Trading Assistant client provides a user-friendly interface to interact with the OpenAlgo platform through natural language. It uses OpenAI's language models to interpret user commands and invoke the appropriate trading functions.

Client Architecture

Framework: Uses Agno agent framework with OpenAI Chat models
UI: Rich console interface with custom styling for an enhanced terminal experience
Symbol Helper: Built-in utilities for correct symbol formatting across exchanges
Error Handling: Comprehensive exception handling with user-friendly feedback

Trading Assistant Capabilities

Natural Language Interface: Understands trading terminology and concepts
Symbol Format Assistance: Helps construct proper symbol formats for equities, futures, and options
Data Presentation: Formats market data in clean, readable formats
Contextual Awareness: Maintains conversation history to provide contextual responses

Troubleshooting Guide

Common Issues

Connection Issues

If you're having trouble connecting to the MCP server:

Verify the server is running:
```
cd server
python server.py
```
You should see output indicating the server is running on the configured port.
Check environment variables:
- Ensure MCP_HOST and MCP_PORT in .env match the server's configuration
- Verify that SERVER_PORT is the same as MCP_PORT
Test local connectivity:
- Try accessing http://localhost:8001/sse in your browser (replace 8001 with your configured port)
- You should see a message indicating the endpoint is for SSE connections

API Authentication Issues

If you see 403 Forbidden or authentication errors:

Check your API key:
- Verify your OpenAlgo API key in the .env file is correct and active
- Ensure the API key has the necessary permissions for the operations you're trying to perform
Verify API host:
- Make sure OPENALGO_API_HOST points to the correct endpoint
- For testing, the default value http://127.0.0.1:5000 should work if you're running OpenAlgo locally

Client Issues

Silent failures in the client:
- The client uses a SilentFilter for logging to provide a clean interface
- If you suspect issues, temporarily modify the logging configuration in trading_agent.py
- Check that the OpenAI API key is valid if you experience model generation failures

Acknowledgements and Credits

This project is made possible by the following open-source projects and tools:

Core Technologies

OpenAlgo: The powerful trading platform that powers all trading operations in this project
Model Context Protocol (MCP): The communication protocol that enables AI agents to use tools and APIs
Agno: The agent framework used for building the trading assistant client

Inspiration

This project was inspired by Zerodha MCP, which pioneered the use of Model Context Protocol for trading applications. The OpenAlgo MCP project adapts and extends this concept for the OpenAlgo trading platform, with a focus on enhanced symbol handling, comprehensive trading operations, and a more user-friendly interface.

Symbol Formatting Issues

If your symbol-related requests are failing:

Follow format guidelines:
- Equity symbols: Simple uppercase symbol (e.g., INFY, SBIN)
- Futures: [BaseSymbol][Year][Month][Date]FUT (e.g., BANKNIFTY24APR24FUT)
- Options: [BaseSymbol][Date][Month][Year][Strike][OptionType] (e.g., NIFTY28MAR2420800CE)
Use the SymbolHelper class:
- The client includes formatting assistance methods that can help construct proper symbols

Debugging Mode

For more detailed logging, enable debugging in the .env file:

SERVER_DEBUG=true

This will output additional information to help diagnose connection and API issues.

License

This project is licensed under the Apache-2.0 license - see the LICENSE file for details.

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Last updated 2 months ago