MCP Servers

APEX Research MCP Server

Enables quantitative trading research by providing tools to backtest strategies, list market datasets, review forward-test logs, and search previously rejected hypotheses, all through an MCP interface.

README

APEX Research — MCP Server (FastMCP)

A Model Context Protocol server that exposes a quantitative trading-research toolkit as tools an LLM/agent can call. Built in Python with FastMCP.

Each tool is a plain typed Python function; FastMCP turns its type hints + docstring into the JSON schema the model sees. Tools run real computation over real market datasets.

Tools

Tool	What it does
`list_datasets`	Report which market datasets + live forward-test logs are available
`run_orb_backtest(instrument, cost_bps)`	Backtest the opening-range-breakout rule on M15 data; returns expectancy (R), win rate, profit factor, max drawdown
`forward_test_report(market)`	Summarise a live forward-test log (n, expectancy, win rate)
`search_graveyard(query)`	Search already-rejected hypotheses so an agent doesn't re-test a dead idea

Run it

pip install fastmcp pandas numpy
python mcp_server/apex_mcp.py        # starts the MCP server (stdio transport)

Test it (no external client needed)

python mcp_server/test_apex_mcp.py   # in-memory FastMCP Client calls every tool

Connect it to an MCP client (e.g. Claude Desktop)

Add to the client's MCP config:

{
  "mcpServers": {
    "apex-research": {
      "command": "python",
      "args": ["C:\\Users\\user\\Desktop\\Apex\\mcp_server\\apex_mcp.py"]
    }
  }
}

Then the model can call, e.g. "backtest the ORB rule on DAX" and it invokes run_orb_backtest.

What this demonstrates

Building MCP servers in Python with FastMCP (typed tools, auto-generated schemas)
Clean tool design: clear contracts, input validation, graceful errors
Local testing of an MCP server with an in-memory client
Real backend/data work (pandas/numpy over multi-year market data)

Built by M. Junaid Shahid.

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