Probability of Backtest Overfitting (CSCV), Deflated Sharpe Ratio, and purged CV splits for quant strategies
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git clone https://github.com/tylerscomic-lab/overfitting-audit-mcp{
"mcpServers": {
"overfitting-audit-mcp": {
"command": "node",
"args": ["/path/to/overfitting-audit-mcp/dist/index.js"]
}
}
}MCP Servers overview
# overfitting-audit-mcp [](LICENSE) [](https://mcpize.com/mcp/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](https://mcpize.com/mcp/overfitting-audit-mcp) — free tier, paid Pro tier for higher limits. **Self-host:** ```bash npm install node server.js ``` ## Part of the AlgoForge suite Prop-firm and quant-validation tools for algo traders: [prop-rules-mcp](https://github.com/tylerscomic-lab/prop-rules-mcp), [trade-journal-mcp](https://github.com/tylerscomic-lab/trade-journal-mcp), [payout-calc-mcp](https://github.com/tylerscomic-lab/payout-calc-mcp), [econ-calendar-mcp](https://github.com/tylerscomic-lab/econ-calendar-mcp), [montecarlo-validator-mcp](https://github.com/tylerscomic-lab/montecarlo-validator-mcp), [walkforward-validator-mcp](https://github.com/tylerscomic-lab/walkforward-validator-mcp), [pinescript-audit-mcp](https://github.com/tylerscomic-lab/pinescript-audit-mcp), [backtest-cost-sensitivity-mcp](https://github.com/tylerscomic-lab/backtest-cost-sensitivity-mcp), [pinescript-mcp](https://github.com/tylerscomic-lab/pinescript-mcp). ## License MIT
What people ask about overfitting-audit-mcp
What is tylerscomic-lab/overfitting-audit-mcp?
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tylerscomic-lab/overfitting-audit-mcp is mcp servers for the Claude AI ecosystem. Probability of Backtest Overfitting (CSCV), Deflated Sharpe Ratio, and purged CV splits for quant strategies It has 0 GitHub stars and its last recorded update is dated 2026-07-27.
How do I install overfitting-audit-mcp?
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You can install overfitting-audit-mcp by cloning the repository (https://github.com/tylerscomic-lab/overfitting-audit-mcp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is tylerscomic-lab/overfitting-audit-mcp safe to use?
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Our security agent has analyzed tylerscomic-lab/overfitting-audit-mcp and assigned a Trust Score of 90/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains tylerscomic-lab/overfitting-audit-mcp?
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tylerscomic-lab/overfitting-audit-mcp is maintained by tylerscomic-lab. The last recorded GitHub activity is dated 2026-07-27, with 0 open issues.
Are there alternatives to overfitting-audit-mcp?
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Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
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