APEX Research MCP Server
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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 |
| Report which market datasets + live forward-test logs are available |
| Backtest the opening-range-breakout rule on M15 data; returns expectancy (R), win rate, profit factor, max drawdown |
| Summarise a live forward-test log (n, expectancy, win rate) |
| Search already-rejected hypotheses so an agent doesn't re-test a dead idea |
Related MCP server: Vintage
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 toolConnect 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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
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