backtest360-mcp

MCP server exposing the Backtest360 engine API as tools for AI agents.

Connect any MCP-capable AI client and drive real backtests conversationally: discover indicators, build and validate strategies, run backtests, and read the results — all against the deterministic Backtest360 engine. The server contains no AI and computes no numbers of its own; it is a thin, faithful adapter over the engine HTTP API. Your engine API key and its plan govern everything (permissions, rate limits, data access).

Two transports: a hosted HTTP endpoint at https://mcp.backtest360.com/mcp (send your key as an X-API-Key header) and local stdio (self-host — see below).

Install

pip install backtest360-mcp        # or, from a clone: pip install -e .

Requires Python 3.10+ and a Backtest360 API key. Get one free, instantly at backtest360.com/api-access — submit your email and a key (format b360_…) is issued on the spot and emailed to you; no approval needed. Authentication is API-key only. The free tier runs backtests on data you upload; fetching historical price data from the engine server-side is a paid capability.

Related MCP server: Jesse MCP Server

Configuration

Everything is environment-driven:

Connect an MCP client

Hosted (recommended)

Point your MCP client at the hosted endpoint over HTTP and send your key as an X-API-Key header:

{
  "mcpServers": {
    "backtest360": {
      "type": "streamable-http",
      "url": "https://mcp.backtest360.com/mcp",
      "headers": {
        "X-API-Key": "b360_..."
      }
    }
  }
}

Local (stdio)

Run the server yourself and let your client launch it over stdio (the common mcpServers shape):

{
  "mcpServers": {
    "backtest360": {
      "command": "backtest360-mcp",
      "env": {
        "BACKTEST360_API_KEY": "b360_..."
      }
    }
  }
}

Prefer not to put the key in a config file? Point command at a small wrapper script that exports the key from your secrets manager and then runs backtest360-mcp. A minimal example config is in examples/mcp.json.

Tools

The cheap static catalogs are also published as MCP resources (backtest360://catalog/{name}, backtest360://schema/strategy) for clients that support resource attachment.

Prompts

Two workflow prompts scaffold the common multi-tool flows for a connected AI: each names which tools to call, in what order, and what to look at in the results. They carry no interpretation and compute nothing — the connected AI does the reasoning.

Response shaping

A full backtest result is megabytes; an agent's context is not. run_backtest and compare_backtests take response_detail:

• summary (default) — headline metrics, warnings, counts, equity endpoints

• stats — every metric the plan allows

• full — plus series (downsampled, endpoints preserved) and trades (paginated)

include=["trades", "equity_curve", "monthly_returns", "yearly_returns"] adds specific blocks at the lighter levels. Results exceeding the output cap are reduced further and explicitly marked truncated_by_mcp — never silently cut. Shaping only ever selects and thins what the engine returned; no value is computed or altered.

Error semantics

Designed for agents:

• Fixable by changing the request → returned as a normal result: failed validations arrive as {"valid": false, "errors": [...]} with machine codes and document locations; engine rejections arrive as {"accepted": false, "error": ...} with a hint.

• Not fixable that way → a tool error with explicit guidance: rate limits carry the Retry-After value; engine-busy says retry with backoff; a compute timeout says do not retry and reduce scope instead; permission problems name the missing capability. Engine request ids are included for support.

Running the tests (self-host)

pip install -e ".[dev]"
pytest   # unit suite vs a mock engine — no network

Questions / feedback

Questions or feedback? hello@backtest360.com — we read everything. backtest360-mcp is in active development, so help shape it.

Bug reports and feature requests: open an issue on GitHub.

License

MIT — see LICENSE.