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Walk-Forward Efficiency, parameter-stability scoring, and WFO window generation.
About
Walk-Forward Efficiency, parameter-stability scoring, and WFO window generation.
Remote endpoints: streamable-http: https://walkforward-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 (18/18 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-walkforward-validator-mcp": {
"url": "https://walkforward-validator-mcp.mcpize.run/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
walkforward-validator-mcp
An MCP server for walk-forward analysis of trading strategies — Walk-Forward Efficiency ratio, parameter-stability scoring, lock-after-optimization audits, and WFO window generation.
The problem this solves
A strategy optimized on one historical window and never re-validated on a fresh, unseen window is a curve fit until proven otherwise. Walk-forward analysis is the standard fix, but building the rolling windows correctly and scoring whether a parameter surface is a robust plateau or a fragile spike is easy to get subtly wrong by hand.
Tools
walk_forward_efficiency
Computes the Walk-Forward Efficiency ratio — out-of-sample performance as a fraction of in-sample performance — the core signal for whether an optimization generalizes.
parameter_stability_score
Scores a parameter surface for fragile curve-fit spikes vs. robust plateaus, flagging optimizations that only work at one exact parameter value.
lock_after_wfo_check
Audits whether parameters were genuinely locked after the walk-forward optimization step, or quietly re-tuned against the "out-of-sample" data — the mistake that silently invalidates a WFO result.
walk_forward_window_generator
Generates correctly non-overlapping rolling in-sample/out-of-sample windows for a given date range and step size.
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, montecarlo-validator-mcp, overfitting-audit-mcp, pinescript-audit-mcp, backtest-cost-sensitivity-mcp, pinescript-mcp.
License
MIT
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