Backtest360
MCP server exposing the Backtest360 engine API as tools for AI agents.
Open source Repository Open in the app JSON README (API)
About
MCP server exposing the Backtest360 engine API as tools for AI agents.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- com.backtest360
- Origin
- official
- Category
- ferramentas
- Transport
- http
- Version
- 0.5.0
- Last push
- 2026-07-29T22:15:49Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:01:00
- Updated
- 2026-08-29 03:01:00
- Origin id
com.backtest360/backtest360
README
# backtest360-mcp
[](https://pypi.org/project/backtest360-mcp/)
[](https://pypi.org/project/backtest360-mcp/)
[](LICENSE)
[](https://github.com/Backtest360/backtest360-mcp/actions/workflows/test.yml)
MCP server exposing the [Backtest360](https://backtest360.com) engine API as tools for AI agents.
<!-- mcp-name: com.backtest360/backtest360 -->
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
```bash
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](https://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.
## Configuration
Everything is environment-driven:
| Variable | Required | Default | Purpose |
|---|---|---|---|
| `BACKTEST360_API_KEY` | yes | — | Engine API key, sent as `X-API-Key` |
| `BACKTEST360_ENGINE_URL` | no | `https://api.backtest360.com` | Engine base URL |
| `BACKTEST360_MCP_TIMEOUT` | no | `300` | Per-request timeout (seconds) |
| `BACKTEST360_MCP_MAX_OUTPUT_BYTES` | no | `100000` | Hard cap on a single tool result |
## 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:
```json
{
"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):
```json
{
"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`](examples/mcp.json).
## Tools
| Tool | What it does |
|---|---|
| `engine_info` | Engine version, API contract, health |
| `get_me` | What the configured key can do: permission scopes, limits, current usage, capability flags |
| `get_catalog` | Reference catalogs: operators, execution modes, stop types, sizing methods, bar frequencies, metric sections |
| `list_indicators` | Indicator discovery; per-indicator parameter schemas |
| `list_templates` | Predesigned strategy templates — discover compactly, fetch one in full, ready to validate and run |
| `get_strategy_schema` | JSON Schema for strategy documents |
| `validate_strategy` | Validate a strategy without running it — returns structured, locatable errors |
| `run_backtest` | Run a historical backtest |
| `get_latest_signal` | Evaluate the most recent bar only (no P&L) |
| `compare_backtests` | Run several strategies on the same data, side by side |
| `compute_stats` | Compute the metric set from an externally produced returns series |
| `search_tickers` / `list_tickers` | Asset discovery for server-side data fetch |
| `get_data_range` | Available history and bar-count estimate for a symbol |
| `get_ticker_info` | Symbol identity and data coverage in a single call |
| `get_quote` | Latest available price for a symbol (paid plan) |
| `get_price_history` | OHLCV price history over a date range (paid plan; long histories downsampled to fit) |
| `list_macro_series` / `get_macro_series` | Macroeconomic data: list the series catalog, then fetch one series' observations |
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.
| Prompt | Arguments | What it scaffolds |
|---|---|---|
| `robustness_review` | `symbol`, `strategy` (optional) | Review a backtested strategy for robustness: validate → run → compare against buy-and-hold → weigh the evidence base (sample size, significance/robustness statistics, warnings) → caveated summary |
| `build_and_validate` | `idea` | Turn a plain-language idea into a validated strategy: survey the catalogs → fetch the schema → construct → validate-and-fix loop → dry-run |
## 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)
`run_backtest` also takes `max_series_points` (default 500, must be >= 2) to
override the series downsampling cap — set it higher for full-resolution
series on a long run, or leave it unset for today's default.
`include=["trades", "equity_curve", "monthly_returns", "yearly_returns",
"signal_diagnostics"]` adds specific blocks at any detail level.
`signal_diagnostics` reports which per-bar entry/exit conditions fired, as a
capped list of fire dates per condition (not the raw per-bar boolean arrays,
which downsampling would corrupt) — or `{"available": false, ...}` when the
run has no condition tree to evaluate (e.g. precomputed signals). 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)
```bash
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](LICENSE).