Monte Carlo Backtest Validator

io.github.tylerscomic-labv1.0.0Updated Oct 2, 2026

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

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Overview

AI-generated overview

Statistically validates trading backtests with bootstrap Monte Carlo methods and simulates prop-firm challenge pass probability.

What it does
Provides tools that apply bootstrap resampling and reshuffling Monte Carlo methods to a backtest's trade results. It computes a 90% confidence interval on per-trade expected value and flags when that interval includes zero, derives drawdown-path percentiles, calculates per-trade expected value from win rate, average win and average loss, and simulates prop-firm challenge pass probability from win-rate, risk-reward, target and drawdown-limit inputs. A comparator ranks several win-rate/risk-reward geometries by simulated pass rate.
When to use it
Useful for algo traders and quant developers who want to check whether a backtest edge is likely real rather than a single lucky equity curve, and for anyone preparing for a prop-firm challenge who wants pass-probability estimates across different risk geometries.
Requirements
Runs as a hosted remote MCP endpoint over streamable HTTP; no authentication, environment variables or headers are declared. The README also describes a self-hosted option via npm install and node server.js, which would require Node.js. The hosted tier is free with a paid Pro tier for higher limits.
Before you install
The README mentions a free tier and a paid Pro tier for higher limits, so hosted use may involve a paid plan. The tools are analytical and appear to compute statistics from inputs rather than modify data, but results are simulations and should not be treated as guarantees of future trading performance.

Installation

In SourceWeft

  1. Open Monte Carlo Backtest Validator in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "montecarlo-validator-mcp": {
      "type": "http",
      "url": "https://montecarlo-validator-mcp.mcpize.run/mcp"
    }
  }
}

README

montecarlo-validator-mcp

[License: MIT] [Live on MCPize]

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:

bash
npm installnode 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

Source: README.md at commit 3a0e98c

Tools

0
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Version history

1
  1. v1.0.0LatestOct 2, 2026