About Monte Carlo Backtest Validator
Monte Carlo Backtest Validator is an MCP server for traders assessing backtest reliability and prop-firm challenge outcomes. It uses bootstrap resampling and reshuffling to estimate confidence intervals, drawdown paths, and pass probabilities.
Key features
- 01Estimate bootstrap confidence intervals for trade EV
- 02Calculate drawdown-path percentiles by reshuffling
- 03Calculate per-trade expected value
- 04Simulate prop-firm challenge pass probability
- 05Compare risk-reward setups by simulated pass rate
Use cases
- 01Assess whether a backtest's edge may be due to chance
- 02Explore plausible drawdown paths across trade sequences
- 03Estimate pass chances for a prop-firm challenge
- 04Compare trading setups for a fixed-target challenge
MCP support
Exposes statistical backtest validation, expected-value calculation, challenge pass-probability simulation, and risk-geometry comparison tools to MCP clients.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://montecarlo-validator-mcp.mcpize.run/mcpclaude mcp add --transport http tylerscomic-lab-montecarlo-validator https://montecarlo-validator-mcp.mcpize.run/mcp{"mcpServers":{"tylerscomic-lab-montecarlo-validator":{"type":"http","url":"https://montecarlo-validator-mcp.mcpize.run/mcp"}}}Open in Cursor{"servers":{"tylerscomic-lab-montecarlo-validator":{"type":"http","url":"https://montecarlo-validator-mcp.mcpize.run/mcp"}}}Open in VS Code{"mcpServers":{"tylerscomic-lab-montecarlo-validator":{"type":"http","url":"https://montecarlo-validator-mcp.mcpize.run/mcp"}}}{"mcpServers":{"tylerscomic-lab-montecarlo-validator":{"serverUrl":"https://montecarlo-validator-mcp.mcpize.run/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEmontecarlo-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
Comments
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