MCP Hybrid Forecasting Server

Professional algorithmic trading system combining ARIMA, ARIMA-GARCH, and XGBoost models with MCP server integration for intelligent stock forecasting

python

1

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "j1c4b-mcp-hybrid-forecasting": {
      "command": "python",
      "args": [
        "mcp_trading_server.py"
      ]
    }
  }
}

You can run and connect to the MCP Hybrid Forecasting Server to generate stock trading signals with integrated risk management, volatility analysis, and real-time market insights. This server exposes a Model Context Protocol (MCP) endpoint so you can query portfolios, compare strategies, and obtain market summaries through a client that speaks MCP. It is designed for production use with robust error handling and flexible output options.

How to use

You will run the MCP server locally and connect to it from your MCP client or integration layer. The server exposes a standard set of MCP endpoints for analyzing portfolios, comparing strategies, and fetching market summaries. Start the server from your development environment and then issue commands from your client to load a portfolio, request signals, or run natural language queries to discover trading opportunities. Use the provided port and endpoint to integrate with your existing trading workflow, dashboards, or automation scripts.

How to install

Prerequisites:

Python 3.8+ installed on your machine
Access to a compatible Python environment (virtual environments recommended)
Network access to your preferred data sources (e.g., yfinance or configured data fetchers) if you plan to run live data queries

Step-by-step setup:

Clone the project repository to your development machine
Create and activate a Python virtual environment
Install the required Python dependencies
Start the MCP server process that exposes the trading MCP endpoints

# Step 1: Acquire the server source
# Clone the repository (adjust the URL to your environment)
# git clone <repository-url>
# cd mcp-hybrid-forecasting

# Step 2: Create and activate a virtual environment
python3 -m venv mcp_env
source mcp_env/bin/activate  # On Windows: mcp_env\Scripts\activate

# Step 3: Install dependencies
pip install -r requirements.txt

# Step 4: Start the MCP server
python mcp_trading_server.py

Configuration and usage notes

Configure trading behavior and portfolios through the existing configuration files. You can customize tickers, thresholds, and risk parameters to tailor signal generation to your investment approach. The MCP server will handle portfolio analysis, signal generation, and market summaries according to the defined configuration.

Troubleshooting

If you encounter issues starting the server, ensure your Python environment is active and dependencies are installed. Check that any required data sources are accessible and that there are no port conflicts when the MCP service starts.

Notes

This server focuses on providing MCP endpoints for portfolio analysis, strategy comparison, market summaries, and natural language queries. It emphasizes robust error handling, multi-stock support, configurable portfolios, and flexible output options like CSV exports and charts.

Available tools

analyze_portfolio

Analyze a specific portfolio within the MCP server to understand signal distribution, returns, and risk metrics.

compare_portfolios

Compare multiple portfolios side by side to evaluate different strategies and risk profiles.

find_opportunities

Query the market using natural language to discover trading opportunities based on signals and thresholds.

get_market_summary

Fetch a real-time market analysis, including signal distribution and key indicators across portfolios.

refresh_data

Clear cached data and refresh market information to ensure up-to-date analysis.