backtester-mcp

Local-first backtesting engine with built-in overfitting detection. Asset-class agnostic. MCP-native.

Why this exists

I'm a current student and incoming quant trading intern, and throughout past projects I've kept running into the limits of existing backtesting systems. In particular, existing tools make it possible to run backtests, but difficult to tell whether the results are real or due to something like overfitting.

As a human with intuition, I was usually able to work around this and verify the results myself, but the same isn't true for AI agents tasked with trading. So, I decided to build a tool that can be used by any agent to quickly and reliably validate strategies before switching to real money.

The Problem

If you're a solo quant or an AI agent iterating on strategies, the existing tooling assumes you want a full platform. Installing the engines is a project in itself (Docker images, compiled runtimes, asset-model scaffolding), and the specific thing you most need while iterating, a fast answer to "is this strategy actually overfit or did I just get lucky," isn't something they ship out of the box.

backtester-mcp is the opposite trade-off: pip install, any numpy price array works, and the statistical robustness toolkit (PBO, deflated Sharpe, bootstrap CI, walk-forward) is the core feature, not an add-on. A native MCP server means AI agents can call the validation pipeline directly.

Quick Start

pip install backtester-mcp

Runs on synthetic data out of the box, no datasets to download:

import numpy as np
from backtester_mcp import backtest

# synthetic price series, reproducible
rng = np.random.default_rng(0)
prices = np.cumprod(1 + rng.normal(0, 0.01, 2000))

# moving average crossover signal
fast = np.convolve(prices, np.ones(10) / 10, mode="full")[:len(prices)]
slow = np.convolve(prices, np.ones(50) / 50, mode="full")[:len(prices)]
signals = np.where(fast > slow, 1.0, -1.0)
signals[:50] = 0

result = backtest(prices, signals)
print(result.metrics)

To run against a real dataset, clone the repo (git clone https://github.com/bcosm/backtester-mcp) and point load() at anything in datasets/ or your own CSV/Parquet file.

Key Features

Validation Pipeline

backtester-mcp runs a full validation pipeline that tells you whether to trust a strategy:

1. Backtest with realistic estimated fills

2. Bootstrap Sharpe CI: is the Sharpe distinguishable from zero?

3. Deflated Sharpe: does it survive correction for multiple testing?

4. PBO (perturbation): are the exact parameters robust, or did you get lucky?

5. Walk-forward validation: does the strategy hold up out-of-sample?

6. Execution scenarios: optimistic, base, and conservative cost assumptions

backtester-mcp backtest -s strategies/momentum.py -d datasets/spy_daily.parquet \
  --robustness --execution-scenarios --walk-forward

CLI Usage

# Basic backtest
backtester-mcp backtest -s strategies/momentum.py -d datasets/spy_daily.parquet

# Full validation: robustness + execution scenarios
backtester-mcp backtest -s strategies/momentum.py -d datasets/spy_daily.parquet \
  --robustness --execution-scenarios

# Override strategy parameters
backtester-mcp backtest -s strategies/momentum.py -d datasets/spy_daily.parquet \
  --set fast_period=20 --set slow_period=100

# Estimated fills from market data
backtester-mcp backtest -s strategies/momentum.py -d datasets/spy_daily.parquet \
  --realistic-fills

# Optimize with Bayesian search + PBO check
backtester-mcp optimize -s strategies/momentum.py -d datasets/spy_daily.parquet \
  -p fast_period:5:50 -p slow_period:20:200

# Generate HTML validation report
backtester-mcp report -s strategies/momentum.py -d datasets/spy_daily.parquet \
  --robustness --execution-scenarios -o report.html

# Persist and compare runs
backtester-mcp backtest -s strategies/momentum.py -d datasets/spy_daily.parquet --save-run
backtester-mcp list-runs
backtester-mcp show-run <run-id>
backtester-mcp compare-runs <run-id-1> <run-id-2>

MCP Server

backtester-mcp exposes 13 tools via the Model Context Protocol for AI agents:

{
  "mcpServers": {
    "backtester-mcp": {
      "command": "backtester-mcp",
      "args": ["serve", "--transport", "stdio"]
    }
  }
}

If your MCP client (e.g. Claude Desktop) can't find the backtester-mcp binary because the venv isn't on its PATH, use the Python-module form instead:

{
  "mcpServers": {
    "backtester-mcp": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["-m", "backtester_mcp", "serve", "--transport", "stdio"]
    }
  }
}

Available tools:

The validate_strategy tool runs the full pipeline in one call and returns a structured verdict:

{
  "verdict": "caution",
  "reasons": ["bootstrap CI includes zero", "conservative scenario negative return"],
  "metrics": {"sharpe": 0.28, ...},
  "pbo": {"pbo": 0.36, ...},
  "scenarios": {"optimistic": {...}, "base": {...}, "conservative": {...}}
}

Performance

Benchmarks on a 2024 thin-and-light laptop CPU (Intel Core Ultra 7 256V, Lunar Lake generation; performance is in the same ballpark as an Apple M3 MacBook Air or Ryzen 7 7840U for single-threaded NumPy/Numba workloads). Python 3.13, numba 0.65, min of 5 runs after JIT warmup:

The first call in a process pays for numba JIT compilation (roughly 1 to 3 seconds depending on the function). Subsequent calls hit numba's on-disk cache and run at the numbers above.

How PBO Works

Probability of Backtest Overfitting (PBO) answers: "if I pick the best strategy from an in-sample optimization, what's the probability it underperforms out-of-sample?"

It works by splitting your backtest data into S sub-periods, then forming all combinations of S/2 sub-periods as in-sample and the rest as out-of-sample (combinatorially symmetric cross-validation). For each combination, it checks whether the strategy that ranked best in-sample still performs above median out-of-sample. The PBO score is the fraction of combinations where it doesn't. A score above 0.5 means your strategy is more likely overfit than not.

For single strategies, backtester-mcp uses perturbation PBO: it generates variants by jittering parameters within +/-20%, runs each variant, then computes PBO over the resulting returns matrix. This answers "would nearby parameters work just as well, or did you get lucky with these exact numbers?"

From Lopez de Prado (2018), "The Probability of Backtest Overfitting," Journal of Computational Finance.

Architecture

Data (CSV/Parquet/DuckDB) -> Engine (NumPy + Numba) -> Metrics
                                                    -> Robustness (PBO, Bootstrap, DSR, Walk-Forward)
                                                    -> Sensitivity (Execution Scenarios, Stress Test)
                                                    -> Report (HTML)
                                                    -> Manifest (JSON audit trail)
                                                    -> Store (DuckDB persistence)

Strategies are plain functions: f(prices, **params) -> signals. No class hierarchies, no inheritance, no plugin system.

Stochastic Order Book

Most backtesting engines either ignore fill simulation or require hand-tuned models per asset class. backtester-mcp automatically estimates fill characteristics from your price data:

• Spread estimation: Corwin-Schultz (2012) from OHLC data, Roll (1984) from close prices

• Market impact: square-root model calibrated to observed volatility

• Three execution modes: optimistic (zero cost), base (estimated), conservative (2x spread)

This works on any price series without configuration.

Contributing

See [CONTRIBUTI