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Probability of Backtest Overfitting (CSCV), Deflated Sharpe Ratio, and purged CV splits.
About
Probability of Backtest Overfitting (CSCV), Deflated Sharpe Ratio, and purged CV splits.
Remote endpoints: streamable-http: https://overfitting-audit-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 Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
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-overfitting-audit-mcp": {
"url": "https://overfitting-audit-mcp.mcpize.run/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
overfitting-audit-mcp
An MCP server that answers "is this edge real, or a testing-hundreds-of-variants artifact?" — implementing the Probability of Backtest Overfitting (CSCV method), Deflated Sharpe Ratio, Minimum Backtest Length, and purged/embargoed cross-validation splits.
The problem this solves
Testing enough parameter combinations against the same historical data will eventually produce a great-looking backtest by chance alone. Standard backtest metrics (Sharpe, win rate, profit factor) don't distinguish a genuine edge from the best-looking result out of hundreds of near-identical variants. This audits for that specific failure mode directly, rather than trusting a single strong-looking curve.
Tools
probability_of_backtest_overfitting
Combinatorially Symmetric Cross-Validation (CSCV) method — estimates the probability that a strategy's in-sample performance rank won't hold out-of-sample.
deflated_sharpe_ratio
Adjusts a Sharpe ratio for the number of trials run and the non-normality of returns, so it can't be inflated just by testing more variants.
minimum_backtest_length
The minimum number of independent trials/observations needed before a given Sharpe ratio is statistically meaningful at all.
purged_cv_split
Generates purged and embargoed cross-validation splits for time-series backtests, preventing the lookahead leakage that ordinary k-fold CV introduces on financial data.
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, walkforward-validator-mcp, pinescript-audit-mcp, backtest-cost-sensitivity-mcp, pinescript-mcp.
License
MIT
Reviews
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