backtester-mcp

# backtester-mcp Local-first backtesting engine with built-in overfitting detection. Asset-class agnostic. MCP-native.    ## The Problem QuantConnect requires Docker + C#, supports 9 hardcoded asset classes, and ships zero statistical robustness tools. Solo quants and AI agents need something that's `pip install`, works on any price series (equities, crypto, prediction markets) and tells you if your strategy is overfit before you risk real money. backtester-mcp is a validation layer for AI-generated trading strategies. Vectorized execution on NumPy + Numba, automatic overfitting detection via PBO, walk-forward validation, execution scenario analysis, and a native MCP server so AI agents can validate strategies directly. ## Quick Start ```bash pip install backtester-mcp ``` Runs on synthetic data out of the box, no datasets to download: ```python 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 | Feature | QuantConnect | backtester-mcp | |---|---|---| | Setup | Docker + .NET | `pip install` | | Engine | C# (Python wrapper) | Pure Python + NumPy + Numba | | Asset classes | 9 hardcoded | Any price series | | Overfitting detection | None | …