APEX Research MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@APEX Research MCP Serverbacktest ORB on AAPL with 10 bps cost"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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: OpenFinClaw CLI
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.
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