montecarlo-validator-mcp

An MCP server that statistically validates whether a backtest's edge is real, using bootstrap-resampling and reshuffling Monte Carlo methodology, plus prop-firm-specific pass-probability simulation.

The problem this solves

A single backtest equity curve tells you what happened on one path through history — it doesn't tell you how likely that result was to happen by chance, or what the range of plausible outcomes looks like on the next set of trades. This wraps the actual statistical validation (bootstrap confidence intervals, drawdown-path percentiles, challenge pass-probability simulation) instead of eyeballing one curve.

Tools

monte_carlo_validate

Bootstrap 90% confidence interval on per-trade expected value (flags when the interval includes zero), plus drawdown-path percentiles via reshuffling.

expected_value_calculator

Per-trade EV from win rate, average win, and average loss.

prop_firm_pass_probability

Simulates challenge pass probability from win-rate/risk-reward/target/drawdown-limit inputs.

risk_geometry_comparator

Ranks multiple win-rate/risk-reward geometries by simulated pass rate — surfaces that tight, high-win-rate setups often out-pass high-RR/low-win-rate setups on a fixed-target challenge, independent of raw expected value.

Use it

Hosted (recommended): MCPize — free tier, paid Pro tier for higher limits.

Self-host:

npm install
node server.js

Part of the AlgoForge suite

Prop-firm and quant-validation tools for algo traders: prop-rules-mcp, trade-journal-mcp, payout-calc-mcp, econ-calendar-mcp, overfitting-audit-mcp, walkforward-validator-mcp, pinescript-audit-mcp, backtest-cost-sensitivity-mcp, pinescript-mcp.

License

MIT