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Monte Carlo Backtest Validator

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Bootstrap Monte Carlo backtest validation and prop-firm challenge pass-probability simulation.

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

Remote (streamable-http)
Claude Code 
claude mcp add --transport http monte-carlo-backtest-validator 'https://montecarlo-validator-mcp.mcpize.run/mcp'
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "monte-carlo-backtest-validator": {
            "type": "http",
            "url": "https://montecarlo-validator-mcp.mcpize.run/mcp"
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. montecarlo-validator-mcp
  2. The problem this solves
  3. Tools
  4. Use it
  5. Part of the AlgoForge suite
  6. License

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

Frequently asked questions

What is Monte Carlo Backtest Validator?

Bootstrap Monte Carlo backtest validation and prop-firm challenge pass-probability simulation.

How do I install Monte Carlo Backtest Validator?

It runs as a remote server. In Claude Code: claude mcp add --transport http monte-carlo-backtest-validator 'https://montecarlo-validator-mcp.mcpize.run/mcp'. For other clients, paste the JSON configuration from this page into their MCP settings.

Does Monte Carlo Backtest Validator need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is Monte Carlo Backtest Validator open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains Monte Carlo Backtest Validator?

It is published by tylerscomic-lab. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.