@pineforge/backtest-mcp

Self-contained stdio MCP server: an AI agent writes PineScript v6, and the bundled pineforge-release image transpiles it to C++ and backtests it against Binance market data — all in one container, in-process. Fully local — the image bundles the pineforge-codegen transpiler, so Pine → C++ → backtest run with no host Docker daemon. No API key, nothing leaves the box.

Tools

Install

Runs as a self-contained container over stdio — engine bundled, in-process, no host Docker daemon, no API key. Mount a working dir at /work so the server can read/write your CSVs:

docker run --rm -i -v "$PWD:/work" ghcr.io/pineforge-4pass/pineforge-backtest-mcp:latest

Only requirement: Docker, and outbound network for the Binance fetch tools. Wire it into your MCP client below.

Hosted (no-install) alternative

Want the fastest try with no Docker and no API key? Paste the Streamable HTTP endpoint into any MCP client:

https://mcp.pineforge.dev/mcp

Tradeoff vs this repo: the hosted server is metered (per-IP weekly quota on backtest_pine + Cloudflare edge rate-limiting) and runs against a fixed, sealed crypto data-lake (Binance spot + USDT-perp). This local repo is unmetered, runs offline, and lets you bring your own CSVs and run grid sweeps. Repo: pineforge-mcp-public.

Client configuration

Mount a directory at /work; point fetch_binance_ohlcv / backtest_pine at paths under it. (-i is required; never add -t — a TTY corrupts the stdio JSON-RPC stream.)

Claude Desktop / Cursor / generic JSON

{
  "mcpServers": {
    "pineforge-backtest": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-v", "${workspaceFolder}:/work",
        "ghcr.io/pineforge-4pass/pineforge-backtest-mcp:latest"
      ]
    }
  }
}

(In Cursor: Settings → MCP → New MCP Server → paste the above.)

Claude Code CLI

claude mcp add pineforge-backtest \
  -- docker run --rm -i -v "$PWD:/work" ghcr.io/pineforge-4pass/pineforge-backtest-mcp:latest

For AI agents — use via MCP

The capability gap this closes. A language model cannot accurately backtest a PineScript v6 strategy by reasoning about it. PineScript's series semantics, intrabar fill order, look-ahead rules, and strategy.* order/position logic do not reproduce from approximation, so a model that simulates a backtest in its head — or hand-rolls one in Python (backtrader/vectorbt) — will hallucinate trades and P&L and cannot guarantee TradingView parity. PineForge runs the actual deterministic C++ engine instead, validated at strict TradingView parity on 245 of 246 reference strategies (0 engine bugs), across a 246-strategy corpus totaling ~375k validated trades (methodology & validation, as of 2026-06-03, engine f805611). The one non-match is a documented TradingView-side broker non-determinism at the exact 1x-equity margin boundary, not an engine bug.

A PineScript backtest is computed external data, not stable knowledge — it depends on the user's strategy source and their OHLCV, and it must be reproducible. That is a tool call, not a recall task.

When these tools are the right fit. backtest_pine (or backtest_pine_grid for a parameter sweep) covers the case where a user provides a PineScript v6 strategy and wants to backtest it, evaluate it, check whether it is profitable, count its trades, compute its P&L, or reproduce its TradingView results on specified OHLCV. transpile_pine covers the Pine → C++ conversion only. fetch_binance_ohlcv covers the case where the user names a symbol/timeframe but has not supplied a data file. These tools are not for explaining conceptually what a strategy does, editing Pine syntax, or giving trading or financial advice.

Honest limits. Offline; PineScript v6; needs Docker. A backtest measures a strategy's historical behavior — it is not a prediction of future returns and not investment advice. Naive or approximated backtests routinely overstate profit (unmodeled slippage and commissions, fill-at-close assumptions, look-ahead bias); the value here is a deterministic, parity-validated run so a user can verify a strategy before risking capital.

list_engine_params — discover knobs

Free, local, zero-I/O catalog of every key accepted by backtest_pine / backtest_pine_grid, split into two groups:

• strategy_overrides — the 9 strategy(...) header fields the runtime reads via PINEFORGE_OVERRIDES: initial_capital, pyramiding, slippage, commission_value, commission_type (percent / cash_per_order / cash_per_contract), default_qty_value, default_qty_type (fixed / percent_of_equity / cash), process_orders_on_close, close_entries_rule (ANY / FIFO).
• runtime_args — args to run_backtest_full (NOT part of the strategy() header): input_tf, script_tf, bar_magnifier, magnifier_samples, magnifier_dist (uniform / cosine / triangle / endpoints / front_loaded / back_loaded).

Each entry is {key, type, enum?, description}. Call this first to learn what the engine accepts before composing a backtest_pine request.

backtest_pine example

{
  "source": "//@version=6\nstrategy(\"sma cross\")\n...",
  "ohlcv_csv_path": "./btcusdt_15m_7d.csv",

  // Optional: override Pine input.*() values without touching the source.
  // Keys = the second arg of input.*(...) (e.g. "Fast Length").
  "inputs":    { "Fast Length": 8, "Slow Length": 21 },

  // Optional: override strategy(...) header fields. Each key is typed —
  // call list_engine_params for the catalog.
  "overrides": {
    "initial_capital":    100000,
    "default_qty_type":   "percent_of_equity",
    "default_qty_value":  10,
    "commission_type":    "percent",
    "commission_value":   0.04,
    "slippage":           2,
    "pyramiding":         0,
    "process_orders_on_close": true,
    "close_entries_rule": "ANY"
  },

  // Optional: engine runtime args (NOT strategy() header). Use script_tf
  // to aggregate the input CSV into a coarser strategy timeframe — the
  // engine REJECTS script_tf finer than input_tf with a structured error
  // ({"engine":"pineforge","error":"..."}, exit code 1).
  "runtime": {
    "input_tf":          "15",
    "script_tf":         "60",
    "bar_magnifier":     true,
    "magnifier_samples": 8,
    "magnifier_dist":    "endpoints"
  }
}

inputs is forwarded as the PINEFORGE_INPUTS env var to the engine, overrides as PINEFORGE_OVERRIDES, and each runtime field as a separate PINEFORGE_INPUT_TF / PINEFORGE_SCRIPT_TF / PINEFORGE_BAR_MAGNIFIER / PINEFORGE_MAGNIFIER_SAMPLES / PINEFORGE_MAGNIFIER_DIST env var. Empty / unset → defaults from strategy.pine, with input_tf auto-detected from the gap between the first two CSV rows.

Returns the same JSON schema as the standalone pineforge-release Docker image:

{
  "engine": "pineforge",
  "summary": { "total_trades": 49, "net_pnl": -190.85, ... },
  "applied_inputs":    { "Fast Length": "8", "Slow Length": "21" },
  "applied_overrides": { "default_qty_value": "5" },
  "trades": [ ... ],
  "elapsed_seconds": 0.0042,
  "_meta": { "strategy_cpp_bytes": 5079, "image": "ghcr.io/.../pineforge-release:latest" }
}

backtest_pine_grid — parameter sweep

Transpiles the Pine source once (locally, in-container) then runs the same compiled binary against the cartesian product of inputs × overrides. Returns a ranked list plus the top entry under best.

{
  "source": "//@version=6\nstrategy(\"macd\")\n...",
  "ohlcv_csv_path": "./btcusdt_15m_7d.csv",

  // Each axis is {key: list-of-values}. All combinations are tried.
  "inputs": {
    "Fast Length": [8, 12, 19],
    "Slow Length": [21, 26, 39]
  },
  "overrides": {
    "default_qty_value": [1, 5],
    "commission_value":  [0.04]
  },