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tylerscomic-lab

overfitting-audit-mcp

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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables statistical validation of trading strategy equity curves via MCP, providing probabilistic and deflated Sharpe ratios, minimum track record length, regime-conditional performance attribution, and self-attack controls.
      49 npm
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Checks whether a trading backtest survives its own statistics: deflated Sharpe, multiple-testing correction against a best-of-N-noise benchmark, minimum track record length, and fill realism. Takes no market data and no API keys, and cannot recommend a trade — it only reports that a result is weaker than claimed or not yet provable.
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables statistical validation of whether a trading backtest’s edge is real using bootstrap-resampling and reshuffling Monte Carlo methods, including confidence intervals, drawdown-path percentiles, expected value calculations, and prop-firm challenge pass-probability simulation. It also compares multiple win-rate/risk-reward geometries by simulated pass rate.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Local-first backtesting engine with built-in overfitting detection (PBO, deflated Sharpe, bootstrap CI, walk-forward) and a native MCP server for AI agents to validate trading strategies.
      4
      Apache 2.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Provides tools to research crypto trading strategies via backtesting, walk-forward validation, and paper trading, with a deflated-Sharpe overfitting check. Enables natural-language-driven analysis and interpretation of strategy performance.
      3
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables walk-forward analysis of trading strategies by computing Walk-Forward Efficiency ratios, scoring parameter surfaces for fragile curve-fit spikes versus robust plateaus, auditing whether parameters were genuinely locked after optimization, and generating correctly non-overlapping rolling in-sample/out-of-sample windows.
      MIT