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Bootstrap Monte Carlo backtest validation and prop-firm challenge pass-probability simulation.
About
Bootstrap Monte Carlo backtest validation and prop-firm challenge pass-probability simulation.
Remote endpoints: streamable-http: https://montecarlo-validator-mcp.mcpize.run/mcp
Security Report
Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. Trust signals: trusted author (9/9 approved).
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-tylerscomic-lab-montecarlo-validator-mcp": {
"url": "https://montecarlo-validator-mcp.mcpize.run/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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
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